code: """
  DataFrame
  ---------
  An efficient 2D container for potentially mixed-type time series or other
  labeled data series.
  
  Similar to its R counterpart, data.frame, except providing automatic data
  alignment and a host of useful data manipulation methods having to do with the
  labeling information
  """
  from __future__ import annotations
  
  import collections
  from collections import abc
  import datetime
  from io import StringIO
  import itertools
  import mmap
  from textwrap import dedent
  from typing import (
      IO,
      TYPE_CHECKING,
      Any,
      AnyStr,
      Dict,
      FrozenSet,
      Hashable,
      Iterable,
      Iterator,
      List,
      Optional,
      Sequence,
      Set,
      Tuple,
      Type,
      Union,
      cast,
      overload,
  )
  import warnings
  
  import numpy as np
  import numpy.ma as ma
  
  from pandas._config import get_option
  
  from pandas._libs import algos as libalgos, lib, properties
  from pandas._libs.lib import no_default
  from pandas._typing import (
      AggFuncType,
      ArrayLike,
      Axes,
      Axis,
      CompressionOptions,
      Dtype,
      FilePathOrBuffer,
      FrameOrSeriesUnion,
      IndexKeyFunc,
      Label,
      Level,
      Renamer,
      StorageOptions,
      ValueKeyFunc,
  )
  from pandas.compat._optional import import_optional_dependency
  from pandas.compat.numpy import function as nv
  from pandas.util._decorators import (
      Appender,
      Substitution,
      deprecate_kwarg,
      doc,
      rewrite_axis_style_signature,
  )
  from pandas.util._validators import (
      validate_axis_style_args,
      validate_bool_kwarg,
      validate_percentile,
  )
  
  from pandas.core.dtypes.cast import (
      cast_scalar_to_array,
      coerce_to_dtypes,
      construct_1d_arraylike_from_scalar,
      find_common_type,
      infer_dtype_from_scalar,
      invalidate_string_dtypes,
      maybe_box_datetimelike,
      maybe_cast_to_datetime,
      maybe_casted_values,
      maybe_convert_platform,
      maybe_downcast_to_dtype,
      maybe_infer_to_datetimelike,
      maybe_upcast,
      validate_numeric_casting,
  )
  from pandas.core.dtypes.common import (
      ensure_int64,
      ensure_platform_int,
      infer_dtype_from_object,
      is_bool_dtype,
      is_dataclass,
      is_datetime64_any_dtype,
      is_dict_like,
      is_dtype_equal,
      is_extension_array_dtype,
      is_float,
      is_float_dtype,
      is_hashable,
      is_integer,
      is_integer_dtype,
      is_iterator,
      is_list_like,
      is_named_tuple,
      is_object_dtype,
      is_scalar,
      is_sequence,
      pandas_dtype,
  )
  from pandas.core.dtypes.missing import isna, notna
  
  from pandas.core import algorithms, common as com, generic, nanops, ops
  from pandas.core.accessor import CachedAccessor
  from pandas.core.aggregation import (
      aggregate,
      reconstruct_func,
      relabel_result,
      transform,
  )
  from pandas.core.arraylike import OpsMixin
  from pandas.core.arrays import Categorical, ExtensionArray
  from pandas.core.arrays.sparse import SparseFrameAccessor
  from pandas.core.construction import extract_array
  from pandas.core.generic import NDFrame, _shared_docs
  from pandas.core.indexes import base as ibase
  from pandas.core.indexes.api import (
      DatetimeIndex,
      Index,
      PeriodIndex,
      ensure_index,
      ensure_index_from_sequences,
  )
  from pandas.core.indexes.multi import MultiIndex, maybe_droplevels
  from pandas.core.indexing import check_bool_indexer, convert_to_index_sliceable
  from pandas.core.internals import BlockManager
  from pandas.core.internals.construction import (
      arrays_to_mgr,
      dataclasses_to_dicts,
      get_names_from_index,
      init_dict,
      init_ndarray,
      masked_rec_array_to_mgr,
      reorder_arrays,
      sanitize_index,
      to_arrays,
  )
  from pandas.core.reshape.melt import melt
  from pandas.core.series import Series
  from pandas.core.sorting import get_group_index, lexsort_indexer, nargsort
  
  from pandas.io.common import get_handle
  from pandas.io.formats import console, format as fmt
  from pandas.io.formats.info import BaseInfo, DataFrameInfo
  import pandas.plotting
  
  if TYPE_CHECKING:
      from typing import Literal
  
      from pandas.core.groupby.generic import DataFrameGroupBy
  
      from pandas.io.formats.style import Styler
  
  # ---------------------------------------------------------------------
  # Docstring templates
  
  _shared_doc_kwargs = {
      "axes": "index, columns",
      "klass": "DataFrame",
      "axes_single_arg": "{0 or 'index', 1 or 'columns'}",
      "axis": """axis : {0 or 'index', 1 or 'columns'}, default 0
          If 0 or 'index': apply function to each column.
          If 1 or 'columns': apply function to each row.""",
      "optional_by": """
          by : str or list of str
              Name or list of names to sort by.
  
              - if `axis` is 0 or `'index'` then `by` may contain index
                levels and/or column labels.
              - if `axis` is 1 or `'columns'` then `by` may contain column
                levels and/or index labels.""",
      "optional_labels": """labels : array-like, optional
              New labels / index to conform the axis specified by 'axis' to.""",
      "optional_axis": """axis : int or str, optional
              Axis to target. Can be either the axis name ('index', 'columns')
              or number (0, 1).""",
  }
  
  _numeric_only_doc = """numeric_only : boolean, default None
      Include only float, int, boolean data. If None, will attempt to use
      everything, then use only numeric data
  """
  
  _merge_doc = """
  Merge DataFrame or named Series objects with a database-style join.
  
  The join is done on columns or indexes. If joining columns on
  columns, the DataFrame indexes *will be ignored*. Otherwise if joining indexes
  on indexes or indexes on a column or columns, the index will be passed on.
  When performing a cross merge, no column specifications to merge on are
  allowed.
  
  Parameters
  ----------%s
  right : DataFrame or named Series
      Object to merge with.
  how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'inner'
      Type of merge to be performed.
  
      * left: use only keys from left frame, similar to a SQL left outer join;
        preserve key order.
      * right: use only keys from right frame, similar to a SQL right outer join;
        preserve key order.
      * outer: use union of keys from both frames, similar to a SQL full outer
        join; sort keys lexicographically.
      * inner: use intersection of keys from both frames, similar to a SQL inner
        join; preserve the order of the left keys.
      * cross: creates the cartesian product from both frames, preserves the order
        of the left keys.
  
        .. versionadded:: 1.2.0
  
  on : label or list
      Column or index level names to join on. These must be found in both
      DataFrames. If `on` is None and not merging on indexes then this defaults
      to the intersection of the columns in both DataFrames.
  left_on : label or list, or array-like
      Column or index level names to join on in the left DataFrame. Can also
      be an array or list of arrays of the length of the left DataFrame.
      These arrays are treated as if they are columns.
  right_on : label or list, or array-like
      Column or index level names to join on in the right DataFrame. Can also
      be an array or list of arrays of the length of the right DataFrame.
      These arrays are treated as if they are columns.
  left_index : bool, default False
      Use the index from the left DataFrame as the join key(s). If it is a
      MultiIndex, the number of keys in the other DataFrame (either the index
      or a number of columns) must match the number of levels.
  right_index : bool, default False
      Use the index from the right DataFrame as the join key. Same caveats as
      left_index.
  sort : bool, default False
      Sort the join keys lexicographically in the result DataFrame. If False,
      the order of the join keys depends on the join type (how keyword).
  suffixes : list-like, default is ("_x", "_y")
      A length-2 sequence where each element is optionally a string
      indicating the suffix to add to overlapping column names in
      `left` and `right` respectively. Pass a value of `None` instead
      of a string to indicate that the column name from `left` or
      `right` should be left as-is, with no suffix. At least one of the
      values must not be None.
  copy : bool, default True
      If False, avoid copy if possible.
  indicator : bool or str, default False
      If True, adds a column to the output DataFrame called "_merge" with
      information on the source of each row. The column can be given a different
      name by providing a string argument. The column will have a Categorical
      type with the value of "left_only" for observations whose merge key only
      appears in the left DataFrame, "right_only" for observations
      whose merge key only appears in the right DataFrame, and "both"
      if the observation's merge key is found in both DataFrames.
  
  validate : str, optional
      If specified, checks if merge is of specified type.
  
      * "one_to_one" or "1:1": check if merge keys are unique in both
        left and right datasets.
      * "one_to_many" or "1:m": check if merge keys are unique in left
        dataset.
      * "many_to_one" or "m:1": check if merge keys are unique in right
        dataset.
      * "many_to_many" or "m:m": allowed, but does not result in checks.
  
  Returns
  -------
  DataFrame
      A DataFrame of the two merged objects.
  
  See Also
  --------
  merge_ordered : Merge with optional filling/interpolation.
  merge_asof : Merge on nearest keys.
  DataFrame.join : Similar method using indices.
  
  Notes
  -----
  Support for specifying index levels as the `on`, `left_on`, and
  `right_on` parameters was added in version 0.23.0
  Support for merging named Series objects was added in version 0.24.0
  
  Examples
  --------
  >>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
  ...                     'value': [1, 2, 3, 5]})
  >>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
  ...                     'value': [5, 6, 7, 8]})
  >>> df1
      lkey value
  0   foo      1
  1   bar      2
  2   baz      3
  3   foo      5
  >>> df2
      rkey value
  0   foo      5
  1   bar      6
  2   baz      7
  3   foo      8
  
  Merge df1 and df2 on the lkey and rkey columns. The value columns have
  the default suffixes, _x and _y, appended.
  
  >>> df1.merge(df2, left_on='lkey', right_on='rkey')
    lkey  value_x rkey  value_y
  0  foo        1  foo        5
  1  foo        1  foo        8
  2  foo        5  foo        5
  3  foo        5  foo        8
  4  bar        2  bar        6
  5  baz        3  baz        7
  
  Merge DataFrames df1 and df2 with specified left and right suffixes
  appended to any overlapping columns.
  
  >>> df1.merge(df2, left_on='lkey', right_on='rkey',
  ...           suffixes=('_left', '_right'))
    lkey  value_left rkey  value_right
  0  foo           1  foo            5
  1  foo           1  foo            8
  2  foo           5  foo            5
  3  foo           5  foo            8
  4  bar           2  bar            6
  5  baz           3  baz            7
  
  Merge DataFrames df1 and df2, but raise an exception if the DataFrames have
  any overlapping columns.
  
  >>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
  Traceback (most recent call last):
  ...
  ValueError: columns overlap but no suffix specified:
      Index(['value'], dtype='object')
  
  >>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
  >>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
  >>> df1
        a  b
  0   foo  1
  1   bar  2
  >>> df2
        a  c
  0   foo  3
  1   baz  4
  
  >>> df1.merge(df2, how='inner', on='a')
        a  b  c
  0   foo  1  3
  
  >>> df1.merge(df2, how='left', on='a')
        a  b  c
  0   foo  1  3.0
  1   bar  2  NaN
  
  >>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
  >>> df2 = pd.DataFrame({'right': [7, 8]})
  >>> df1
      left
  0   foo
  1   bar
  >>> df2
      right
  0   7
  1   8
  
  >>> df1.merge(df2, how='cross')
     left  right
  0   foo      7
  1   foo      8
  2   bar      7
  3   bar      8
  """
  
  
  # -----------------------------------------------------------------------
  # DataFrame class
  
  
  class DataFrame(NDFrame, OpsMixin):
      """
      Two-dimensional, size-mutable, potentially heterogeneous tabular data.
  
      Data structure also contains labeled axes (rows and columns).
      Arithmetic operations align on both row and column labels. Can be
      thought of as a dict-like container for Series objects. The primary
      pandas data structure.
  
      Parameters
      ----------
      data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame
          Dict can contain Series, arrays, constants, dataclass or list-like objects. If
          data is a dict, column order follows insertion-order.
  
          .. versionchanged:: 0.25.0
             If data is a list of dicts, column order follows insertion-order.
  
      index : Index or array-like
          Index to use for resulting frame. Will default to RangeIndex if
          no indexing information part of input data and no index provided.
      columns : Index or array-like
          Column labels to use for resulting frame. Will default to
          RangeIndex (0, 1, 2, ..., n) if no column labels are provided.
      dtype : dtype, default None
          Data type to force. Only a single dtype is allowed. If None, infer.
      copy : bool, default False
          Copy data from inputs. Only affects DataFrame / 2d ndarray input.
  
      See Also
      --------
      DataFrame.from_records : Constructor from tuples, also record arrays.
      DataFrame.from_dict : From dicts of Series, arrays, or dicts.
      read_csv : Read a comma-separated values (csv) file into DataFrame.
      read_table : Read general delimited file into DataFrame.
      read_clipboard : Read text from clipboard into DataFrame.
  
      Examples
      --------
      Constructing DataFrame from a dictionary.
  
      >>> d = {'col1': [1, 2], 'col2': [3, 4]}
      >>> df = pd.DataFrame(data=d)
      >>> df
         col1  col2
      0     1     3
      1     2     4
  
      Notice that the inferred dtype is int64.
  
      >>> df.dtypes
      col1    int64
      col2    int64
      dtype: object
  
      To enforce a single dtype:
  
      >>> df = pd.DataFrame(data=d, dtype=np.int8)
      >>> df.dtypes
      col1    int8
      col2    int8
      dtype: object
  
      Constructing DataFrame from numpy ndarray:
  
      >>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
      ...                    columns=['a', 'b', 'c'])
      >>> df2
         a  b  c
      0  1  2  3
      1  4  5  6
      2  7  8  9
  
      Constructing DataFrame from dataclass:
  
      >>> from dataclasses import make_dataclass
      >>> Point = make_dataclass("Point", [("x", int), ("y", int)])
      >>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
          x  y
      0  0  0
      1  0  3
      2  2  3
      """
  
      _internal_names_set = {"columns", "index"} | NDFrame._internal_names_set
      _typ = "dataframe"
      _HANDLED_TYPES = (Series, Index, ExtensionArray, np.ndarray)
  
      @property
      def _constructor(self) -> Type[DataFrame]:
          return DataFrame
  
      _constructor_sliced: Type[Series] = Series
      _hidden_attrs: FrozenSet[str] = NDFrame._hidden_attrs | frozenset([])
      _accessors: Set[str] = {"sparse"}
  
      @property
      def _constructor_expanddim(self):
          # GH#31549 raising NotImplementedError on a property causes trouble
          #  for `inspect`
          def constructor(*args, **kwargs):
              raise NotImplementedError("Not supported for DataFrames!")
  
          return constructor
  
      # ----------------------------------------------------------------------
      # Constructors
  
      def __init__(
          self,
          data=None,
          index: Optional[Axes] = None,
          columns: Optional[Axes] = None,
          dtype: Optional[Dtype] = None,
          copy: bool = False,
      ):
          if data is None:
              data = {}
          if dtype is not None:
              dtype = self._validate_dtype(dtype)
  
          if isinstance(data, DataFrame):
              data = data._mgr
  
          if isinstance(data, BlockManager):
              if index is None and columns is None and dtype is None and copy is False:
                  # GH#33357 fastpath
                  NDFrame.__init__(self, data)
                  return
  
              mgr = self._init_mgr(
                  data, axes={"index": index, "columns": columns}, dtype=dtype, copy=copy
              )
  
          elif isinstance(data, dict):
              mgr = init_dict(data, index, columns, dtype=dtype)
          elif isinstance(data, ma.MaskedArray):
              import numpy.ma.mrecords as mrecords
  
              # masked recarray
              if isinstance(data, mrecords.MaskedRecords):
                  mgr = masked_rec_array_to_mgr(data, index, columns, dtype, copy)
  
              # a masked array
              else:
                  mask = ma.getmaskarray(data)
                  if mask.any():
                      data, fill_value = maybe_upcast(data, copy=True)
                      data.soften_mask()  # set hardmask False if it was True
                      data[mask] = fill_value
                  else:
                      data = data.copy()
                  mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
  
          elif isinstance(data, (np.ndarray, Series, Index)):
              if data.dtype.names:
                  data_columns = list(data.dtype.names)
                  data = {k: data[k] for k in data_columns}
                  if columns is None:
                      columns = data_columns
                  mgr = init_dict(data, index, columns, dtype=dtype)
              elif getattr(data, "name", None) is not None:
                  mgr = init_dict({data.name: data}, index, columns, dtype=dtype)
              else:
                  mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
  
          # For data is list-like, or Iterable (will consume into list)
          elif isinstance(data, abc.Iterable) and not isinstance(data, (str, bytes)):
              if not isinstance(data, (abc.Sequence, ExtensionArray)):
                  data = list(data)
              if len(data) > 0:
                  if is_dataclass(data[0]):
                      data = dataclasses_to_dicts(data)
                  if is_list_like(data[0]) and getattr(data[0], "ndim", 1) == 1:
                      if is_named_tuple(data[0]) and columns is None:
                          columns = data[0]._fields
                      arrays, columns = to_arrays(data, columns, dtype=dtype)
                      columns = ensure_index(columns)
  
                      # set the index
                      if index is None:
                          if isinstance(data[0], Series):
                              index = get_names_from_index(data)
                          elif isinstance(data[0], Categorical):
                              index = ibase.default_index(len(data[0]))
                          else:
                              index = ibase.default_index(len(data))
  
                      mgr = arrays_to_mgr(arrays, columns, index, columns, dtype=dtype)
                  else:
                      mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
              else:
                  mgr = init_dict({}, index, columns, dtype=dtype)
          # For data is scalar
          else:
              if index is None or columns is None:
                  raise ValueError("DataFrame constructor not properly called!")
  
              if not dtype:
                  dtype, _ = infer_dtype_from_scalar(data, pandas_dtype=True)
  
              # For data is a scalar extension dtype
              if is_extension_array_dtype(dtype):
  
                  values = [
                      construct_1d_arraylike_from_scalar(data, len(index), dtype)
                      for _ in range(len(columns))
                  ]
                  mgr = arrays_to_mgr(values, columns, index, columns, dtype=None)
              else:
                  # Attempt to coerce to a numpy array
                  try:
                      arr = np.array(data, dtype=dtype, copy=copy)
                  except (ValueError, TypeError) as err:
                      exc = TypeError(
                          "DataFrame constructor called with "
                          f"incompatible data and dtype: {err}"
                      )
                      raise exc from err
  
                  if arr.ndim != 0:
                      raise ValueError("DataFrame constructor not properly called!")
  
                  values = cast_scalar_to_array(
                      (len(index), len(columns)), data, dtype=dtype
                  )
  
                  mgr = init_ndarray(
                      values, index, columns, dtype=values.dtype, copy=False
                  )
  
          NDFrame.__init__(self, mgr)
  
      # ----------------------------------------------------------------------
  
      @property
      def axes(self) -> List[Index]:
          """
          Return a list representing the axes of the DataFrame.
  
          It has the row axis labels and column axis labels as the only members.
          They are returned in that order.
  
          Examples
          --------
          >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
          >>> df.axes
          [RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'],
          dtype='object')]
          """
          return [self.index, self.columns]
  
      @property
      def shape(self) -> Tuple[int, int]:
          """
          Return a tuple representing the dimensionality of the DataFrame.
  
          See Also
          --------
          ndarray.shape : Tuple of array dimensions.
  
          Examples
          --------
          >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
          >>> df.shape
          (2, 2)
  
          >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4],
          ...                    'col3': [5, 6]})
          >>> df.shape
          (2, 3)
          """
          return len(self.index), len(self.columns)
  
      @property
      def _is_homogeneous_type(self) -> bool:
          """
          Whether all the columns in a DataFrame have the same type.
  
          Returns
          -------
          bool
  
          See Also
          --------
          Index._is_homogeneous_type : Whether the object has a single
              dtype.
          MultiIndex._is_homogeneous_type : Whether all the levels of a
              MultiIndex have the same dtype.
  
          Examples
          --------
          >>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
          True
          >>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous_type
          False
  
          Items with the same type but different sizes are considered
          different types.
  
          >>> DataFrame({
          ...    "A": np.array([1, 2], dtype=np.int32),
          ...    "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous_type
          False
          """
          if self._mgr.any_extension_types:
              return len({block.dtype for block in self._mgr.blocks}) == 1
          else:
              return not self._is_mixed_type
  
      @property
      def _can_fast_transpose(self) -> bool:
          """
          Can we transpose this DataFrame without creating any new array objects.
          """
          if self._mgr.any_extension_types:
              # TODO(EA2D) special case would be unnecessary with 2D EAs
              return False
          return len(self._mgr.blocks) == 1
  
      # ----------------------------------------------------------------------
      # Rendering Methods
  
      def _repr_fits_vertical_(self) -> bool:
          """
          Check length against max_rows.
          """
          max_rows = get_option("display.max_rows")
          return len(self) <= max_rows
  
      def _repr_fits_horizontal_(self, ignore_width: bool = False) -> bool:
          """
          Check if full repr fits in horizontal boundaries imposed by the display
          options width and max_columns.
  
          In case of non-interactive session, no boundaries apply.
  
          `ignore_width` is here so ipynb+HTML output can behave the way
          users expect. display.max_columns remains in effect.
          GH3541, GH3573
          """
          width, height = console.get_console_size()
          max_columns = get_option("display.max_columns")
          nb_columns = len(self.columns)
  
          # exceed max columns
          if (max_columns and nb_columns > max_columns) or (
              (not ignore_width) and width and nb_columns > (width // 2)
          ):
              return False
  
          # used by repr_html under IPython notebook or scripts ignore terminal
          # dims
          if ignore_width or not console.in_interactive_session():
              return True
  
          if get_option("display.width") is not None or console.in_ipython_frontend():
              # check at least the column row for excessive width
              max_rows = 1
          else:
              max_rows = get_option("display.max_rows")
  
          # when auto-detecting, so width=None and not in ipython front end
          # check whether repr fits horizontal by actually checking
          # the width of the rendered repr
          buf = StringIO()
  
          # only care about the stuff we'll actually print out
          # and to_string on entire frame may be expensive
          d = self
  
          if not (max_rows is None):  # unlimited rows
              # min of two, where one may be None
              d = d.iloc[: min(max_rows, len(d))]
          else:
              return True
  
          d.to_string(buf=buf)
          value = buf.getvalue()
          repr_width = max(len(line) for line in value.split("\n"))
  
          return repr_width < width
  
      def _info_repr(self) -> bool:
          """
          True if the repr should show the info view.
          """
          info_repr_option = get_option("display.large_repr") == "info"
          return info_repr_option and not (
              self._repr_fits_horizontal_() and self._repr_fits_vertical_()
          )
  
      def __repr__(self) -> str:
          """
          Return a string representation for a particular DataFrame.
          """
          buf = StringIO("")
          if self._info_repr():
              self.info(buf=buf)
              return buf.getvalue()
  
          max_rows = get_option("display.max_rows")
          min_rows = get_option("display.min_rows")
          max_cols = get_option("display.max_columns")
          max_colwidth = get_option("display.max_colwidth")
          show_dimensions = get_option("display.show_dimensions")
          if get_option("display.expand_frame_repr"):
              width, _ = console.get_console_size()
          else:
              width = None
          self.to_string(
              buf=buf,
              max_rows=max_rows,
              min_rows=min_rows,
              max_cols=max_cols,
              line_width=width,
              max_colwidth=max_colwidth,
              show_dimensions=show_dimensions,
          )
  
          return buf.getvalue()
  
      def _repr_html_(self) -> Optional[str]:
          """
          Return a html representation for a particular DataFrame.
  
          Mainly for IPython notebook.
          """
          if self._info_repr():
              buf = StringIO("")
              self.info(buf=buf)
              # need to escape the <class>, should be the first line.
              val = buf.getvalue().replace("<", r"&lt;", 1)
              val = val.replace(">", r"&gt;", 1)
              return "<pre>" + val + "</pre>"
  
          if get_option("display.notebook_repr_html"):
              max_rows = get_option("display.max_rows")
              min_rows = get_option("display.min_rows")
              max_cols = get_option("display.max_columns")
              show_dimensions = get_option("display.show_dimensions")
  
              formatter = fmt.DataFrameFormatter(
                  self,
                  columns=None,
                  col_space=None,
                  na_rep="NaN",
                  formatters=None,
                  float_format=None,
                  sparsify=None,
                  justify=None,
                  index_names=True,
                  header=True,
                  index=True,
                  bold_rows=True,
                  escape=True,
                  max_rows=max_rows,
                  min_rows=min_rows,
                  max_cols=max_cols,
                  show_dimensions=show_dimensions,
                  decimal=".",
              )
              return fmt.DataFrameRenderer(formatter).to_html(notebook=True)
          else:
              return None
  
      @Substitution(
          header_type="bool or sequence",
          header="Write out the column names. If a list of strings "
          "is given, it is assumed to be aliases for the "
          "column names",
          col_space_type="int, list or dict of int",
          col_space="The minimum width of each column",
      )
      @Substitution(shared_params=fmt.common_docstring, returns=fmt.return_docstring)
      def to_string(
          self,
          buf: Optional[FilePathOrBuffer[str]] = None,
          columns: Optional[Sequence[str]] = None,
          col_space: Optional[int] = None,
          header: Union[bool, Sequence[str]] = True,
          index: bool = True,
          na_rep: str = "NaN",
          formatters: Optional[fmt.FormattersType] = None,
          float_format: Optional[fmt.FloatFormatType] = None,
          sparsify: Optional[bool] = None,
          index_names: bool = True,
          justify: Optional[str] = None,
          max_rows: Optional[int] = None,
          min_rows: Optional[int] = None,
          max_cols: Optional[int] = None,
          show_dimensions: bool = False,
          decimal: str = ".",
          line_width: Optional[int] = None,
          max_colwidth: Optional[int] = None,
          encoding: Optional[str] = None,
      ) -> Optional[str]:
          """
          Render a DataFrame to a console-friendly tabular output.
          %(shared_params)s
          line_width : int, optional
              Width to wrap a line in characters.
          max_colwidth : int, optional
              Max width to truncate each column in characters. By default, no limit.
  
              .. versionadded:: 1.0.0
          encoding : str, default "utf-8"
              Set character encoding.
  
              .. versionadded:: 1.0
          %(returns)s
          See Also
          --------
          to_html : Convert DataFrame to HTML.
  
          Examples
          --------
          >>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
          >>> df = pd.DataFrame(d)
          >>> print(df.to_string())
             col1  col2
          0     1     4
          1     2     5
          2     3     6
          """
          from pandas import option_context
  
          with option_context("display.max_colwidth", max_colwidth):
              formatter = fmt.DataFrameFormatter(
                  self,
                  columns=columns,
                  col_space=col_space,
                  na_rep=na_rep,
                  formatters=formatters,
                  float_format=float_format,
                  sparsify=sparsify,
                  justify=justify,
                  index_names=index_names,
                  header=header,
                  index=index,
                  min_rows=min_rows,
                  max_rows=max_rows,
                  max_cols=max_cols,
                  show_dimensions=show_dimensions,
                  decimal=decimal,
              )
              return fmt.DataFrameRenderer(formatter).to_string(
                  buf=buf,
                  encoding=encoding,
                  line_width=line_width,
              )
  
      # ----------------------------------------------------------------------
  
      @property
      def style(self) -> Styler:
          """
          Returns a Styler object.
  
          Contains methods for building a styled HTML representation of the DataFrame.
  
          See Also
          --------
          io.formats.style.Styler : Helps style a DataFrame or Series according to the
              data with HTML and CSS.
          """
          from pandas.io.formats.style import Styler
  
          return Styler(self)
  
      _shared_docs[
          "items"
      ] = r"""
          Iterate over (column name, Series) pairs.
  
          Iterates over the DataFrame columns, returning a tuple with
          the column name and the content as a Series.
  
          Yields
          ------
          label : object
              The column names for the DataFrame being iterated over.
          content : Series
              The column entries belonging to each label, as a Series.
  
          See Also
          --------
          DataFrame.iterrows : Iterate over DataFrame rows as
              (index, Series) pairs.
          DataFrame.itertuples : Iterate over DataFrame rows as namedtuples
              of the values.
  
          Examples
          --------
          >>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
          ...                   'population': [1864, 22000, 80000]},
          ...                   index=['panda', 'polar', 'koala'])
          >>> df
                  species   population
          panda   bear      1864
          polar   bear      22000
          koala   marsupial 80000
          >>> for label, content in df.items():
          ...     print(f'label: {label}')
          ...     print(f'content: {content}', sep='\n')
          ...
          label: species
          content:
          panda         bear
          polar         bear
          koala    marsupial
          Name: species, dtype: object
          label: population
          content:
          panda     1864
          polar    22000
          koala    80000
          Name: population, dtype: int64
          """
  
      @Appender(_shared_docs["items"])
      def items(self) -> Iterable[Tuple[Label, Series]]:
          if self.columns.is_unique and hasattr(self, "_item_cache"):
              for k in self.columns:
                  yield k, self._get_item_cache(k)
          else:
              for i, k in enumerate(self.columns):
                  yield k, self._ixs(i, axis=1)
  
      @Appender(_shared_docs["items"])
      def iteritems(self) -> Iterable[Tuple[Label, Series]]:
          yield from self.items()
  
      def iterrows(self) -> Iterable[Tuple[Label, Series]]:
          """
          Iterate over DataFrame rows as (index, Series) pairs.
  
          Yields
          ------
          index : label or tuple of label
              The index of the row. A tuple for a `MultiIndex`.
          data : Series
              The data of the row as a Series.
  
          See Also
          --------
          DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.
          DataFrame.items : Iterate over (column name, Series) pairs.
  
          Notes
          -----
          1. Because ``iterrows`` returns a Series for each row,
             it does **not** preserve dtypes across the rows (dtypes are
             preserved across columns for DataFrames). For example,
  
             >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
             >>> row = next(df.iterrows())[1]
             >>> row
             int      1.0
             float    1.5
             Name: 0, dtype: float64
             >>> print(row['int'].dtype)
             float64
             >>> print(df['int'].dtype)
             int64
  
             To preserve dtypes while iterating over the rows, it is better
             to use :meth:`itertuples` which returns namedtuples of the values
             and which is generally faster than ``iterrows``.
  
          2. You should **never modify** something you are iterating over.
             This is not guaranteed to work in all cases. Depending on the
             data types, the iterator returns a copy and not a view, and writing
             to it will have no effect.
          """
          columns = self.columns
          klass = self._constructor_sliced
          for k, v in zip(self.index, self.values):
              s = klass(v, index=columns, name=k)
              yield k, s
  
      def itertuples(self, index: bool = True, name: Optional[str] = "Pandas"):
          """
          Iterate over DataFrame rows as namedtuples.
  
          Parameters
          ----------
          index : bool, default True
              If True, return the index as the first element of the tuple.
          name : str or None, default "Pandas"
              The name of the returned namedtuples or None to return regular
              tuples.
  
          Returns
          -------
          iterator
              An object to iterate over namedtuples for each row in the
              DataFrame with the first field possibly being the index and
              following fields being the column values.
  
          See Also
          --------
          DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)
              pairs.
          DataFrame.items : Iterate over (column name, Series) pairs.
  
          Notes
          -----
          The column names will be renamed to positional names if they are
          invalid Python identifiers, repeated, or start with an underscore.
          On python versions < 3.7 regular tuples are returned for DataFrames
          with a large number of columns (>254).
  
          Examples
          --------
          >>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
          ...                   index=['dog', 'hawk'])
          >>> df
                num_legs  num_wings
          dog          4          0
          hawk         2          2
          >>> for row in df.itertuples():
          ...     print(row)
          ...
          Pandas(Index='dog', num_legs=4, num_wings=0)
          Pandas(Index='hawk', num_legs=2, num_wings=2)
  
          By setting the `index` parameter to False we can remove the index
          as the first element of the tuple:
  
          >>> for row in df.itertuples(index=False):
          ...     print(row)
          ...
          Pandas(num_legs=4, num_wings=0)
          Pandas(num_legs=2, num_wings=2)
  
          With the `name` parameter set we set a custom name for the yielded
          namedtuples:
  
          >>> for row in df.itertuples(name='Animal'):
          ...     print(row)
          ...
          Animal(Index='dog', num_legs=4, num_wings=0)
          Animal(Index='hawk', num_legs=2, num_wings=2)
          """
          arrays = []
          fields = list(self.columns)
          if index:
              arrays.append(self.index)
              fields.insert(0, "Index")
  
          # use integer indexing because of possible duplicate column names
          arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))
  
          if name is not None:
              # https://github.com/python/mypy/issues/9046
              # error: namedtuple() expects a string literal as the first argument
              itertuple = collections.namedtuple(  # type: ignore[misc]
                  name, fields, rename=True
              )
              return map(itertuple._make, zip(*arrays))
  
          # fallback to regular tuples
          return zip(*arrays)
  
      def __len__(self) -> int:
          """
          Returns length of info axis, but here we use the index.
          """
          return len(self.index)
  
      def dot(self, other):
          """
          Compute the matrix multiplication between the DataFrame and other.
  
          This method computes the matrix product between the DataFrame and the
          values of an other Series, DataFrame or a numpy array.
  
          It can also be called using ``self @ other`` in Python >= 3.5.
  
          Parameters
          ----------
          other : Series, DataFrame or array-like
              The other object to compute the matrix product with.
  
          Returns
          -------
          Series or DataFrame
              If other is a Series, return the matrix product between self and
              other as a Series. If other is a DataFrame or a numpy.array, return
              the matrix product of self and other in a DataFrame of a np.array.
  
          See Also
          --------
          Series.dot: Similar method for Series.
  
          Notes
          -----
          The dimensions of DataFrame and other must be compatible in order to
          compute the matrix multiplication. In addition, the column names of
          DataFrame and the index of other must contain the same values, as they
          will be aligned prior to the multiplication.
  
          The dot method for Series computes the inner product, instead of the
          matrix product here.
  
          Examples
          --------
          Here we multiply a DataFrame with a Series.
  
          >>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
          >>> s = pd.Series([1, 1, 2, 1])
          >>> df.dot(s)
          0    -4
          1     5
          dtype: int64
  
          Here we multiply a DataFrame with another DataFrame.
  
          >>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
          >>> df.dot(other)
              0   1
          0   1   4
          1   2   2
  
          Note that the dot method give the same result as @
  
          >>> df @ other
              0   1
          0   1   4
          1   2   2
  
          The dot method works also if other is an np.array.
  
          >>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
          >>> df.dot(arr)
              0   1
          0   1   4
          1   2   2
  
          Note how shuffling of the objects does not change the result.
  
          >>> s2 = s.reindex([1, 0, 2, 3])
          >>> df.dot(s2)
          0    -4
          1     5
          dtype: int64
          """
          if isinstance(other, (Series, DataFrame)):
              common = self.columns.union(other.index)
              if len(common) > len(self.columns) or len(common) > len(other.index):
                  raise ValueError("matrices are not aligned")
  
              left = self.reindex(columns=common, copy=False)
              right = other.reindex(index=common, copy=False)
              lvals = left.values
              rvals = right._values
          else:
              left = self
              lvals = self.values
              rvals = np.asarray(other)
              if lvals.shape[1] != rvals.shape[0]:
                  raise ValueError(
                      f"Dot product shape mismatch, {lvals.shape} vs {rvals.shape}"
                  )
  
          if isinstance(other, DataFrame):
              return self._constructor(
                  np.dot(lvals, rvals), index=left.index, columns=other.columns
              )
          elif isinstance(other, Series):
              return self._constructor_sliced(np.dot(lvals, rvals), index=left.index)
          elif isinstance(rvals, (np.ndarray, Index)):
              result = np.dot(lvals, rvals)
              if result.ndim == 2:
                  return self._constructor(result, index=left.index)
              else:
                  return self._constructor_sliced(result, index=left.index)
          else:  # pragma: no cover
              raise TypeError(f"unsupported type: {type(other)}")
  
      def __matmul__(self, other):
          """
          Matrix multiplication using binary `@` operator in Python>=3.5.
          """
          return self.dot(other)
  
      def __rmatmul__(self, other):
          """
          Matrix multiplication using binary `@` operator in Python>=3.5.
          """
          try:
              return self.T.dot(np.transpose(other)).T
          except ValueError as err:
              if "shape mismatch" not in str(err):
                  raise
              # GH#21581 give exception message for original shapes
              msg = f"shapes {np.shape(other)} and {self.shape} not aligned"
              raise ValueError(msg) from err
  
      # ----------------------------------------------------------------------
      # IO methods (to / from other formats)
  
      @classmethod
      def from_dict(cls, data, orient="columns", dtype=None, columns=None) -> DataFrame:
          """
          Construct DataFrame from dict of array-like or dicts.
  
          Creates DataFrame object from dictionary by columns or by index
          allowing dtype specification.
  
          Parameters
          ----------
          data : dict
              Of the form {field : array-like} or {field : dict}.
          orient : {'columns', 'index'}, default 'columns'
              The "orientation" of the data. If the keys of the passed dict
              should be the columns of the resulting DataFrame, pass 'columns'
              (default). Otherwise if the keys should be rows, pass 'index'.
          dtype : dtype, default None
              Data type to force, otherwise infer.
          columns : list, default None
              Column labels to use when ``orient='index'``. Raises a ValueError
              if used with ``orient='columns'``.
  
          Returns
          -------
          DataFrame
  
          See Also
          --------
          DataFrame.from_records : DataFrame from structured ndarray, sequence
              of tuples or dicts, or DataFrame.
          DataFrame : DataFrame object creation using constructor.
  
          Examples
          --------
          By default the keys of the dict become the DataFrame columns:
  
          >>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
          >>> pd.DataFrame.from_dict(data)
             col_1 col_2
          0      3     a
          1      2     b
          2      1     c
          3      0     d
  
          Specify ``orient='index'`` to create the DataFrame using dictionary
          keys as rows:
  
          >>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
          >>> pd.DataFrame.from_dict(data, orient='index')
                 0  1  2  3
          row_1  3  2  1  0
          row_2  a  b  c  d
  
          When using the 'index' orientation, the column names can be
          specified manually:
  
          >>> pd.DataFrame.from_dict(data, orient='index',
          ...                        columns=['A', 'B', 'C', 'D'])
                 A  B  C  D
          row_1  3  2  1  0
          row_2  a  b  c  d
          """
          index = None
          orient = orient.lower()
          if orient == "index":
              if len(data) > 0:
                  # TODO speed up Series case
                  if isinstance(list(data.values())[0], (Series, dict)):
                      data = _from_nested_dict(data)
                  else:
                      data, index = list(data.values()), list(data.keys())
          elif orient == "columns":
              if columns is not None:
                  raise ValueError("cannot use columns parameter with orient='columns'")
          else:  # pragma: no cover
              raise ValueError("only recognize index or columns for orient")
  
          return cls(data, index=index, columns=columns, dtype=dtype)
  
      def to_numpy(
          self, dtype=None, copy: bool = False, na_value=lib.no_default
      ) -> np.ndarray:
          """
          Convert the DataFrame to a NumPy array.
  
          .. versionadded:: 0.24.0
  
          By default, the dtype of the returned array will be the common NumPy
          dtype of all types in the DataFrame. For example, if the dtypes are
          ``float16`` and ``float32``, the results dtype will be ``float32``.
          This may require copying data and coercing values, which may be
          expensive.
  
          Parameters
          ----------
          dtype : str or numpy.dtype, optional
              The dtype to pass to :meth:`numpy.asarray`.
          copy : bool, default False
              Whether to ensure that the returned value is not a view on
              another array. Note that ``copy=False`` does not *ensure* that
              ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
              a copy is made, even if not strictly necessary.
          na_value : Any, optional
              The value to use for missing values. The default value depends
              on `dtype` and the dtypes of the DataFrame columns.
  
              .. versionadded:: 1.1.0
  
          Returns
          -------
          numpy.ndarray
  
          See Also
          --------
          Series.to_numpy : Similar method for Series.
  
          Examples
          --------
          >>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
          array([[1, 3],
                 [2, 4]])
  
          With heterogeneous data, the lowest common type will have to
          be used.
  
          >>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
          >>> df.to_numpy()
          array([[1. , 3. ],
                 [2. , 4.5]])
  
          For a mix of numeric and non-numeric types, the output array will
          have object dtype.
  
          >>> df['C'] = pd.date_range('2000', periods=2)
          >>> df.to_numpy()
          array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
                 [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
          """
          self._consolidate_inplace()
          result = self._mgr.as_array(
              transpose=self._AXIS_REVERSED, dtype=dtype, copy=copy, na_value=na_value
          )
          if result.dtype is not dtype:
              result = np.array(result, dtype=dtype, copy=False)
  
          return result
  
      def to_dict(self, orient="dict", into=dict):
          """
          Convert the DataFrame to a dictionary.
  
          The type of the key-value pairs can be customized with the parameters
          (see below).
  
          Parameters
          ----------
          orient : str {'dict', 'list', 'series', 'split', 'records', 'index'}
              Determines the type of the values of the dictionary.
  
              - 'dict' (default) : dict like {column -> {index -> value}}
              - 'list' : dict like {column -> [values]}
              - 'series' : dict like {column -> Series(values)}
              - 'split' : dict like
                {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
              - 'records' : list like
                [{column -> value}, ... , {column -> value}]
              - 'index' : dict like {index -> {column -> value}}
  
              Abbreviations are allowed. `s` indicates `series` and `sp`
              indicates `split`.
  
          into : class, default dict
              The collections.abc.Mapping subclass used for all Mappings
              in the return value.  Can be the actual class or an empty
              instance of the mapping type you want.  If you want a
              collections.defaultdict, you must pass it initialized.
  
          Returns
          -------
          dict, list or collections.abc.Mapping
              Return a collections.abc.Mapping object representing the DataFrame.
              The resulting transformation depends on the `orient` parameter.
  
          See Also
          --------
          DataFrame.from_dict: Create a DataFrame from a dictionary.
          DataFrame.to_json: Convert a DataFrame to JSON format.
  
          Examples
          --------
          >>> df = pd.DataFrame({'col1': [1, 2],
          ...                    'col2': [0.5, 0.75]},
          ...                   index=['row1', 'row2'])
          >>> df
                col1  col2
          row1     1  0.50
          row2     2  0.75
          >>> df.to_dict()
          {'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
  
          You can specify the return orientation.
  
          >>> df.to_dict('series')
          {'col1': row1    1
                   row2    2
          Name: col1, dtype: int64,
          'col2': row1    0.50
                  row2    0.75
          Name: col2, dtype: float64}
  
          >>> df.to_dict('split')
          {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
           'data': [[1, 0.5], [2, 0.75]]}
  
          >>> df.to_dict('records')
          [{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
  
          >>> df.to_dict('index')
          {'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
  
          You can also specify the mapping type.
  
          >>> from collections import OrderedDict, defaultdict
          >>> df.to_dict(into=OrderedDict)
          OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
                       ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
  
          If you want a `defaultdict`, you need to initialize it:
  
          >>> dd = defaultdict(list)
          >>> df.to_dict('records', into=dd)
          [defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
           defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
          """
          if not self.columns.is_unique:
              warnings.warn(
                  "DataFrame columns are not unique, some columns will be omitted.",
                  UserWarning,
                  stacklevel=2,
              )
          # GH16122
          into_c = com.standardize_mapping(into)
  
          orient = orient.lower()
          # GH32515
          if orient.startswith(("d", "l", "s", "r", "i")) and orient not in {
              "dict",
              "list",
              "series",
              "split",
              "records",
              "index",
          }:
              warnings.warn(
                  "Using short name for 'orient' is deprecated. Only the "
                  "options: ('dict', list, 'series', 'split', 'records', 'index') "
                  "will be used in a future version. Use one of the above "
                  "to silence this warning.",
                  FutureWarning,
              )
  
              if orient.startswith("d"):
                  orient = "dict"
              elif orient.startswith("l"):
                  orient = "list"
              elif orient.startswith("sp"):
                  orient = "split"
              elif orient.startswith("s"):
                  orient = "series"
              elif orient.startswith("r"):
                  orient = "records"
              elif orient.startswith("i"):
                  orient = "index"
  
          if orient == "dict":
              return into_c((k, v.to_dict(into)) for k, v in self.items())
  
          elif orient == "list":
              return into_c((k, v.tolist()) for k, v in self.items())
  
          elif orient == "split":
              return into_c(
                  (
                      ("index", self.index.tolist()),
                      ("columns", self.columns.tolist()),
                      (
                          "data",
                          [
                              list(map(maybe_box_datetimelike, t))
                              for t in self.itertuples(index=False, name=None)
                          ],
                      ),
                  )
              )
  
          elif orient == "series":
              return into_c((k, maybe_box_datetimelike(v)) for k, v in self.items())
  
          elif orient == "records":
              columns = self.columns.tolist()
              rows = (
                  dict(zip(columns, row))
                  for row in self.itertuples(index=False, name=None)
              )
              return [
                  into_c((k, maybe_box_datetimelike(v)) for k, v in row.items())
                  for row in rows
              ]
  
          elif orient == "index":
              if not self.index.is_unique:
                  raise ValueError("DataFrame index must be unique for orient='index'.")
              return into_c(
                  (t[0], dict(zip(self.columns, t[1:])))
                  for t in self.itertuples(name=None)
              )
  
          else:
              raise ValueError(f"orient '{orient}' not understood")
  
      def to_gbq(
          self,
          destination_table,
          project_id=None,
          chunksize=None,
          reauth=False,
          if_exists="fail",
          auth_local_webserver=False,
          table_schema=None,
          location=None,
          progress_bar=True,
          credentials=None,
      ) -> None:
          """
          Write a DataFrame to a Google BigQuery table.
  
          This function requires the `pandas-gbq package
          <https://pandas-gbq.readthedocs.io>`__.
  
          See the `How to authenticate with Google BigQuery
          <https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
          guide for authentication instructions.
  
          Parameters
          ----------
          destination_table : str
              Name of table to be written, in the form ``dataset.tablename``.
          project_id : str, optional
              Google BigQuery Account project ID. Optional when available from
              the environment.
          chunksize : int, optional
              Number of rows to be inserted in each chunk from the dataframe.
              Set to ``None`` to load the whole dataframe at once.
          reauth : bool, default False
              Force Google BigQuery to re-authenticate the user. This is useful
              if multiple accounts are used.
          if_exists : str, default 'fail'
              Behavior when the destination table exists. Value can be one of:
  
              ``'fail'``
                  If table exists raise pandas_gbq.gbq.TableCreationError.
              ``'replace'``
                  If table exists, drop it, recreate it, and insert data.
              ``'append'``
                  If table exists, insert data. Create if does not exist.
          auth_local_webserver : bool, default False
              Use the `local webserver flow`_ instead of the `console flow`_
              when getting user credentials.
  
              .. _local webserver flow:
                  https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
              .. _console flow:
                  https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
  
              *New in version 0.2.0 of pandas-gbq*.
          table_schema : list of dicts, optional
              List of BigQuery table fields to which according DataFrame
              columns conform to, e.g. ``[{'name': 'col1', 'type':
              'STRING'},...]``. If schema is not provided, it will be
              generated according to dtypes of DataFrame columns. See
              BigQuery API documentation on available names of a field.
  
              *New in version 0.3.1 of pandas-gbq*.
          location : str, optional
              Location where the load job should run. See the `BigQuery locations
              documentation
              <https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
              list of available locations. The location must match that of the
              target dataset.
  
              *New in version 0.5.0 of pandas-gbq*.
          progress_bar : bool, default True
              Use the library `tqdm` to show the progress bar for the upload,
              chunk by chunk.
  
              *New in version 0.5.0 of pandas-gbq*.
          credentials : google.auth.credentials.Credentials, optional
              Credentials for accessing Google APIs. Use this parameter to
              override default credentials, such as to use Compute Engine
              :class:`google.auth.compute_engine.Credentials` or Service
              Account :class:`google.oauth2.service_account.Credentials`
              directly.
  
              *New in version 0.8.0 of pandas-gbq*.
  
              .. versionadded:: 0.24.0
  
          See Also
          --------
          pandas_gbq.to_gbq : This function in the pandas-gbq library.
          read_gbq : Read a DataFrame from Google BigQuery.
          """
          from pandas.io import gbq
  
          gbq.to_gbq(
              self,
              destination_table,
              project_id=project_id,
              chunksize=chunksize,
              reauth=reauth,
              if_exists=if_exists,
              auth_local_webserver=auth_local_webserver,
              table_schema=table_schema,
              location=location,
              progress_bar=progress_bar,
              credentials=credentials,
          )
  
      @classmethod
      def from_records(
          cls,
          data,
          index=None,
          exclude=None,
          columns=None,
          coerce_float=False,
          nrows=None,
      ) -> DataFrame:
          """
          Convert structured or record ndarray to DataFrame.
  
          Creates a DataFrame object from a structured ndarray, sequence of
          tuples or dicts, or DataFrame.
  
          Parameters
          ----------
          data : structured ndarray, sequence of tuples or dicts, or DataFrame
              Structured input data.
          index : str, list of fields, array-like
              Field of array to use as the index, alternately a specific set of
              input labels to use.
          exclude : sequence, default None
              Columns or fields to exclude.
          columns : sequence, default None
              Column names to use. If the passed data do not have names
              associated with them, this argument provides names for the
              columns. Otherwise this argument indicates the order of the columns
              in the result (any names not found in the data will become all-NA
              columns).
          coerce_float : bool, default False
              Attempt to convert values of non-string, non-numeric objects (like
              decimal.Decimal) to floating point, useful for SQL result sets.
          nrows : int, default None
              Number of rows to read if data is an iterator.
  
          Returns
          -------
          DataFrame
  
          See Also
          --------
          DataFrame.from_dict : DataFrame from dict of array-like or dicts.
          DataFrame : DataFrame object creation using constructor.
  
          Examples
          --------
          Data can be provided as a structured ndarray:
  
          >>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
          ...                 dtype=[('col_1', 'i4'), ('col_2', 'U1')])
          >>> pd.DataFrame.from_records(data)
             col_1 col_2
          0      3     a
          1      2     b
          2      1     c
          3      0     d
  
          Data can be provided as a list of dicts:
  
          >>> data = [{'col_1': 3, 'col_2': 'a'},
          ...         {'col_1': 2, 'col_2': 'b'},
          ...         {'col_1': 1, 'col_2': 'c'},
          ...         {'col_1': 0, 'col_2': 'd'}]
          >>> pd.DataFrame.from_records(data)
             col_1 col_2
          0      3     a
          1      2     b
          2      1     c
          3      0     d
  
          Data can be provided as a list of tuples with corresponding columns:
  
          >>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
          >>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
             col_1 col_2
          0      3     a
          1      2     b
          2      1     c
          3      0     d
          """
          # Make a copy of the input columns so we can modify it
          if columns is not None:
              columns = ensure_index(columns)
  
          if is_iterator(data):
              if nrows == 0:
                  return cls()
  
              try:
                  first_row = next(data)
              except StopIteration:
                  return cls(index=index, columns=columns)
  
              dtype = None
              if hasattr(first_row, "dtype") and first_row.dtype.names:
                  dtype = first_row.dtype
  
              values = [first_row]
  
              if nrows is None:
                  values += data
              else:
                  values.extend(itertools.islice(data, nrows - 1))
  
              if dtype is not None:
                  data = np.array(values, dtype=dtype)
              else:
                  data = values
  
          if isinstance(data, dict):
              if columns is None:
                  columns = arr_columns = ensure_index(sorted(data))
                  arrays = [data[k] for k in columns]
              else:
                  arrays = []
                  arr_columns_list = []
                  for k, v in data.items():
                      if k in columns:
                          arr_columns_list.append(k)
                          arrays.append(v)
  
                  arrays, arr_columns = reorder_arrays(arrays, arr_columns_list, columns)
  
          elif isinstance(data, (np.ndarray, DataFrame)):
              arrays, columns = to_arrays(data, columns)
              if columns is not None:
                  columns = ensure_index(columns)
              arr_columns = columns
          else:
              arrays, arr_columns = to_arrays(data, columns, coerce_float=coerce_float)
  
              arr_columns = ensure_index(arr_columns)
              if columns is not None:
                  columns = ensure_index(columns)
              else:
                  columns = arr_columns
  
          if exclude is None:
              exclude = set()
          else:
              exclude = set(exclude)
  
          result_index = None
          if index is not None:
              if isinstance(index, str) or not hasattr(index, "__iter__"):
                  i = columns.get_loc(index)
                  exclude.add(index)
                  if len(arrays) > 0:
                      result_index = Index(arrays[i], name=index)
                  else:
                      result_index = Index([], name=index)
              else:
                  try:
                      index_data = [arrays[arr_columns.get_loc(field)] for field in index]
                  except (KeyError, TypeError):
                      # raised by get_loc, see GH#29258
                      result_index = index
                  else:
                      result_index = ensure_index_from_sequences(index_data, names=index)
                      exclude.update(index)
  
          if any(exclude):
              arr_exclude = [x for x in exclude if x in arr_columns]
              to_remove = [arr_columns.get_loc(col) for col in arr_exclude]
              arrays = [v for i, v in enumerate(arrays) if i not in to_remove]
  
              arr_columns = arr_columns.drop(arr_exclude)
              columns = columns.drop(exclude)
  
          mgr = arrays_to_mgr(arrays, arr_columns, result_index, columns)
  
          return cls(mgr)
  
      def to_records(
          self, index=True, column_dtypes=None, index_dtypes=None
      ) -> np.recarray:
          """
          Convert DataFrame to a NumPy record array.
  
          Index will be included as the first field of the record array if
          requested.
  
          Parameters
          ----------
          index : bool, default True
              Include index in resulting record array, stored in 'index'
              field or using the index label, if set.
          column_dtypes : str, type, dict, default None
              .. versionadded:: 0.24.0
  
              If a string or type, the data type to store all columns. If
              a dictionary, a mapping of column names and indices (zero-indexed)
              to specific data types.
          index_dtypes : str, type, dict, default None
              .. versionadded:: 0.24.0
  
              If a string or type, the data type to store all index levels. If
              a dictionary, a mapping of index level names and indices
              (zero-indexed) to specific data types.
  
              This mapping is applied only if `index=True`.
  
          Returns
          -------
          numpy.recarray
              NumPy ndarray with the DataFrame labels as fields and each row
              of the DataFrame as entries.
  
          See Also
          --------
          DataFrame.from_records: Convert structured or record ndarray
              to DataFrame.
          numpy.recarray: An ndarray that allows field access using
              attributes, analogous to typed columns in a
              spreadsheet.
  
          Examples
          --------
          >>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
          ...                   index=['a', 'b'])
          >>> df
             A     B
          a  1  0.50
          b  2  0.75
          >>> df.to_records()
          rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
                    dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])
  
          If the DataFrame index has no label then the recarray field name
          is set to 'index'. If the index has a label then this is used as the
          field name:
  
          >>> df.index = df.index.rename("I")
          >>> df.to_records()
          rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
                    dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])
  
          The index can be excluded from the record array:
  
          >>> df.to_records(index=False)
          rec.array([(1, 0.5 ), (2, 0.75)],
                    dtype=[('A', '<i8'), ('B', '<f8')])
  
          Data types can be specified for the columns:
  
          >>> df.to_records(column_dtypes={"A": "int32"})
          rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
                    dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])
  
          As well as for the index:
  
          >>> df.to_records(index_dtypes="<S2")
          rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
                    dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
  
          >>> index_dtypes = f"<S{df.index.str.len().max()}"
          >>> df.to_records(index_dtypes=index_dtypes)
          rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
                    dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
          """
          if index:
              if isinstance(self.index, MultiIndex):
                  # array of tuples to numpy cols. copy copy copy
                  ix_vals = list(map(np.array, zip(*self.index._values)))
              else:
                  ix_vals = [self.index.values]
  
              arrays = ix_vals + [
                  np.asarray(self.iloc[:, i]) for i in range(len(self.columns))
              ]
  
              count = 0
              index_names = list(self.index.names)
  
              if isinstance(self.index, MultiIndex):
                  for i, n in enumerate(index_names):
                      if n is None:
                          index_names[i] = f"level_{count}"
                          count += 1
              elif index_names[0] is None:
                  index_names = ["index"]
  
              names = [str(name) for name in itertools.chain(index_names, self.columns)]
          else:
              arrays = [np.asarray(self.iloc[:, i]) for i in range(len(self.columns))]
              names = [str(c) for c in self.columns]
              index_names = []
  
          index_len = len(index_names)
          formats = []
  
          for i, v in enumerate(arrays):
              index = i
  
              # When the names and arrays are collected, we
              # first collect those in the DataFrame's index,
              # followed by those in its columns.
              #
              # Thus, the total length of the array is:
              # len(index_names) + len(DataFrame.columns).
              #
              # This check allows us to see whether we are
              # handling a name / array in the index or column.
              if index < index_len:
                  dtype_mapping = index_dtypes
                  name = index_names[index]
              else:
                  index -= index_len
                  dtype_mapping = column_dtypes
                  name = self.columns[index]
  
              # We have a dictionary, so we get the data type
              # associated with the index or column (which can
              # be denoted by its name in the DataFrame or its
              # position in DataFrame's array of indices or
              # columns, whichever is applicable.
              if is_dict_like(dtype_mapping):
                  if name in dtype_mapping:
                      dtype_mapping = dtype_mapping[name]
                  elif index in dtype_mapping:
                      dtype_mapping = dtype_mapping[index]
                  else:
                      dtype_mapping = None
  
              # If no mapping can be found, use the array's
              # dtype attribute for formatting.
              #
              # A valid dtype must either be a type or
              # string naming a type.
              if dtype_mapping is None:
                  formats.append(v.dtype)
              elif isinstance(dtype_mapping, (type, np.dtype, str)):
                  formats.append(dtype_mapping)
              else:
                  element = "row" if i < index_len else "column"
                  msg = f"Invalid dtype {dtype_mapping} specified for {element} {name}"
                  raise ValueError(msg)
  
          return np.rec.fromarrays(arrays, dtype={"names": names, "formats": formats})
  
      @classmethod
      def _from_arrays(
          cls,
          arrays,
          columns,
          index,
          dtype: Optional[Dtype] = None,
          verify_integrity: bool = True,
      ) -> DataFrame:
          """
          Create DataFrame from a list of arrays corresponding to the columns.
  
          Parameters
          ----------
          arrays : list-like of arrays
              Each array in the list corresponds to one column, in order.
          columns : list-like, Index
              The column names for the resulting DataFrame.
          index : list-like, Index
              The rows labels for the resulting DataFrame.
          dtype : dtype, optional
              Optional dtype to enforce for all arrays.
          verify_integrity : bool, default True
              Validate and homogenize all input. If set to False, it is assumed
              that all elements of `arrays` are actual arrays how they will be
              stored in a block (numpy ndarray or ExtensionArray), have the same
              length as and are aligned with the index, and that `columns` and
              `index` are ensured to be an Index object.
  
          Returns
          -------
          DataFrame
          """
          if dtype is not None:
              dtype = pandas_dtype(dtype)
  
          mgr = arrays_to_mgr(
              arrays,
              columns,
              index,
              columns,
              dtype=dtype,
              verify_integrity=verify_integrity,
          )
          return cls(mgr)
  
      @doc(storage_options=generic._shared_docs["storage_options"])
      @deprecate_kwarg(old_arg_name="fname", new_arg_name="path")
      def to_stata(
          self,
          path: FilePathOrBuffer,
          convert_dates: Optional[Dict[Label, str]] = None,
          write_index: bool = True,
          byteorder: Optional[str] = None,
          time_stamp: Optional[datetime.datetime] = None,
          data_label: Optional[str] = None,
          variable_labels: Optional[Dict[Label, str]] = None,
          version: Optional[int] = 114,
          convert_strl: Optional[Sequence[Label]] = None,
          compression: CompressionOptions = "infer",
          storage_options: StorageOptions = None,
      ) -> None:
          """
          Export DataFrame object to Stata dta format.
  
          Writes the DataFrame to a Stata dataset file.
          "dta" files contain a Stata dataset.
  
          Parameters
          ----------
          path : str, buffer or path object
              String, path object (pathlib.Path or py._path.local.LocalPath) or
              object implementing a binary write() function. If using a buffer
              then the buffer will not be automatically closed after the file
              data has been written.
  
              .. versionchanged:: 1.0.0
  
              Previously this was "fname"
  
          convert_dates : dict
              Dictionary mapping columns containing datetime types to stata
              internal format to use when writing the dates. Options are 'tc',
              'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer
              or a name. Datetime columns that do not have a conversion type
              specified will be converted to 'tc'. Raises NotImplementedError if
              a datetime column has timezone information.
          write_index : bool
              Write the index to Stata dataset.
          byteorder : str
              Can be ">", "<", "little", or "big". default is `sys.byteorder`.
          time_stamp : datetime
              A datetime to use as file creation date.  Default is the current
              time.
          data_label : str, optional
              A label for the data set.  Must be 80 characters or smaller.
          variable_labels : dict
              Dictionary containing columns as keys and variable labels as
              values. Each label must be 80 characters or smaller.
          version : {{114, 117, 118, 119, None}}, default 114
              Version to use in the output dta file. Set to None to let pandas
              decide between 118 or 119 formats depending on the number of
              columns in the frame. Version 114 can be read by Stata 10 and
              later. Version 117 can be read by Stata 13 or later. Version 118
              is supported in Stata 14 and later. Version 119 is supported in
              Stata 15 and later. Version 114 limits string variables to 244
              characters or fewer while versions 117 and later allow strings
              with lengths up to 2,000,000 characters. Versions 118 and 119
              support Unicode characters, and version 119 supports more than
              32,767 variables.
  
              Version 119 should usually only be used when the number of
              variables exceeds the capacity of dta format 118. Exporting
              smaller datasets in format 119 may have unintended consequences,
              and, as of November 2020, Stata SE cannot read version 119 files.
  
              .. versionchanged:: 1.0.0
  
                  Added support for formats 118 and 119.
  
          convert_strl : list, optional
              List of column names to convert to string columns to Stata StrL
              format. Only available if version is 117.  Storing strings in the
              StrL format can produce smaller dta files if strings have more than
              8 characters and values are repeated.
          compression : str or dict, default 'infer'
              For on-the-fly compression of the output dta. If string, specifies
              compression mode. If dict, value at key 'method' specifies
              compression mode. Compression mode must be one of {{'infer', 'gzip',
              'bz2', 'zip', 'xz', None}}. If compression mode is 'infer' and
              `fname` is path-like, then detect compression from the following
              extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
              compression). If dict and compression mode is one of {{'zip',
              'gzip', 'bz2'}}, or inferred as one of the above, other entries
              passed as additional compression options.
  
              .. versionadded:: 1.1.0
  
          {storage_options}
  
              .. versionadded:: 1.2.0
  
          Raises
          ------
          NotImplementedError
              * If datetimes contain timezone information
              * Column dtype is not representable in Stata
          ValueError
              * Columns listed in convert_dates are neither datetime64[ns]
                or datetime.datetime
              * Column listed in convert_dates is not in DataFrame
              * Categorical label contains more than 32,000 characters
  
          See Also
          --------
          read_stata : Import Stata data files.
          io.stata.StataWriter : Low-level writer for Stata data files.
          io.stata.StataWriter117 : Low-level writer for version 117 files.
  
          Examples
          --------
          >>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon',
          ...                               'parrot'],
          ...                    'speed': [350, 18, 361, 15]}})
          >>> df.to_stata('animals.dta')  # doctest: +SKIP
          """
          if version not in (114, 117, 118, 119, None):
              raise ValueError("Only formats 114, 117, 118 and 119 are supported.")
          if version == 114:
              if convert_strl is not None:
                  raise ValueError("strl is not supported in format 114")
              from pandas.io.stata import StataWriter as statawriter
          elif version == 117:
              # mypy: Name 'statawriter' already defined (possibly by an import)
              from pandas.io.stata import (  # type: ignore[no-redef]
                  StataWriter117 as statawriter,
              )
          else:  # versions 118 and 119
              # mypy: Name 'statawriter' already defined (possibly by an import)
              from pandas.io.stata import (  # type: ignore[no-redef]
                  StataWriterUTF8 as statawriter,
              )
  
          kwargs: Dict[str, Any] = {}
          if version is None or version >= 117:
              # strl conversion is only supported >= 117
              kwargs["convert_strl"] = convert_strl
          if version is None or version >= 118:
              # Specifying the version is only supported for UTF8 (118 or 119)
              kwargs["version"] = version
  
          # mypy: Too many arguments for "StataWriter"
          writer = statawriter(  # type: ignore[call-arg]
              path,
              self,
              convert_dates=convert_dates,
              byteorder=byteorder,
              time_stamp=time_stamp,
              data_label=data_label,
              write_index=write_index,
              variable_labels=variable_labels,
              compression=compression,
              storage_options=storage_options,
              **kwargs,
          )
          writer.write_file()
  
      @deprecate_kwarg(old_arg_name="fname", new_arg_name="path")
      def to_feather(self, path: FilePathOrBuffer[AnyStr], **kwargs) -> None:
          """
          Write a DataFrame to the binary Feather format.
  
          Parameters
          ----------
          path : str or file-like object
              If a string, it will be used as Root Directory path.
          **kwargs :
              Additional keywords passed to :func:`pyarrow.feather.write_feather`.
              Starting with pyarrow 0.17, this includes the `compression`,
              `compression_level`, `chunksize` and `version` keywords.
  
              .. versionadded:: 1.1.0
          """
          from pandas.io.feather_format import to_feather
  
          to_feather(self, path, **kwargs)
  
      @doc(
          Series.to_markdown,
          klass=_shared_doc_kwargs["klass"],
          storage_options=_shared_docs["storage_options"],
          examples="""Examples
          --------
          >>> df = pd.DataFrame(
          ...     data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}
          ... )
          >>> print(df.to_markdown())
          |    | animal_1   | animal_2   |
          |---:|:-----------|:-----------|
          |  0 | elk        | dog        |
          |  1 | pig        | quetzal    |
  
          Output markdown with a tabulate option.
  
          >>> print(df.to_markdown(tablefmt="grid"))
          +----+------------+------------+
          |    | animal_1   | animal_2   |
          +====+============+============+
          |  0 | elk        | dog        |
          +----+------------+------------+
          |  1 | pig        | quetzal    |
          +----+------------+------------+
          """,
      )
      def to_markdown(
          self,
          buf: Optional[Union[IO[str], str]] = None,
          mode: str = "wt",
          index: bool = True,
          storage_options: StorageOptions = None,
          **kwargs,
      ) -> Optional[str]:
          if "showindex" in kwargs:
              warnings.warn(
                  "'showindex' is deprecated. Only 'index' will be used "
                  "in a future version. Use 'index' to silence this warning.",
                  FutureWarning,
                  stacklevel=2,
              )
  
          kwargs.setdefault("headers", "keys")
          kwargs.setdefault("tablefmt", "pipe")
          kwargs.setdefault("showindex", index)
          tabulate = import_optional_dependency("tabulate")
          result = tabulate.tabulate(self, **kwargs)
          if buf is None:
              return result
  
          with get_handle(buf, mode, storage_options=storage_options) as handles:
              assert not isinstance(handles.handle, (str, mmap.mmap))
              handles.handle.writelines(result)
          return None
  
      @doc(storage_options=generic._shared_docs["storage_options"])
      @deprecate_kwarg(old_arg_name="fname", new_arg_name="path")
      def to_parquet(
          self,
          path: Optional[FilePathOrBuffer] = None,
          engine: str = "auto",
          compression: Optional[str] = "snappy",
          index: Optional[bool] = None,
          partition_cols: Optional[List[str]] = None,
          storage_options: StorageOptions = None,
          **kwargs,
      ) -> Optional[bytes]:
          """
          Write a DataFrame to the binary parquet format.
  
          This function writes the dataframe as a `parquet file
          <https://parquet.apache.org/>`_. You can choose different parquet
          backends, and have the option of compression. See
          :ref:`the user guide <io.parquet>` for more details.
  
          Parameters
          ----------
          path : str or file-like object, default None
              If a string, it will be used as Root Directory path
              when writing a partitioned dataset. By file-like object,
              we refer to objects with a write() method, such as a file handle
              (e.g. via builtin open function) or io.BytesIO. The engine
              fastparquet does not accept file-like objects. If path is None,
              a bytes object is returned.
  
              .. versionchanged:: 1.2.0
  
              Previously this was "fname"
  
          engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
              Parquet library to use. If 'auto', then the option
              ``io.parquet.engine`` is used. The default ``io.parquet.engine``
              behavior is to try 'pyarrow', falling back to 'fastparquet' if
              'pyarrow' is unavailable.
          compression : {{'snappy', 'gzip', 'brotli', None}}, default 'snappy'
              Name of the compression to use. Use ``None`` for no compression.
          index : bool, default None
              If ``True``, include the dataframe's index(es) in the file output.
              If ``False``, they will not be written to the file.
              If ``None``, similar to ``True`` the dataframe's index(es)
              will be saved. However, instead of being saved as values,
              the RangeIndex will be stored as a range in the metadata so it
              doesn't require much space and is faster. Other indexes will
              be included as columns in the file output.
  
              .. versionadded:: 0.24.0
  
          partition_cols : list, optional, default None
              Column names by which to partition the dataset.
              Columns are partitioned in the order they are given.
              Must be None if path is not a string.
  
              .. versionadded:: 0.24.0
  
          {storage_options}
  
              .. versionadded:: 1.2.0
  
          **kwargs
              Additional arguments passed to the parquet library. See
              :ref:`pandas io <io.parquet>` for more details.
  
          Returns
          -------
          bytes if no path argument is provided else None
  
          See Also
          --------
          read_parquet : Read a parquet file.
          DataFrame.to_csv : Write a csv file.
          DataFrame.to_sql : Write to a sql table.
          DataFrame.to_hdf : Write to hdf.
  
          Notes
          -----
          This function requires either the `fastparquet
          <https://pypi.org/project/fastparquet>`_ or `pyarrow
          <https://arrow.apache.org/docs/python/>`_ library.
  
          Examples
          --------
          >>> df = pd.DataFrame(data={{'col1': [1, 2], 'col2': [3, 4]}})
          >>> df.to_parquet('df.parquet.gzip',
          ...               compression='gzip')  # doctest: +SKIP
          >>> pd.read_parquet('df.parquet.gzip')  # doctest: +SKIP
             col1  col2
          0     1     3
          1     2     4
  
          If you want to get a buffer to the parquet content you can use a io.BytesIO
          object, as long as you don't use partition_cols, which creates multiple files.
  
          >>> import io
          >>> f = io.BytesIO()
          >>> df.to_parquet(f)
          >>> f.seek(0)
          0
          >>> content = f.read()
          """
          from pandas.io.parquet import to_parquet
  
          return to_parquet(
              self,
              path,
              engine,
              compression=compression,
              index=index,
              partition_cols=partition_cols,
              storage_options=storage_options,
              **kwargs,
          )
  
      @Substitution(
          header_type="bool",
          header="Whether to print column labels, default True",
          col_space_type="str or int, list or dict of int or str",
          col_space="The minimum width of each column in CSS length "
          "units.  An int is assumed to be px units.\n\n"
          "            .. versionadded:: 0.25.0\n"
          "                Ability to use str",
      )
      @Substitution(shared_params=fmt.common_docstring, returns=fmt.return_docstring)
      def to_html(
          self,
          buf=None,
          columns=None,
          col_space=None,
          header=True,
          index=True,
          na_rep="NaN",
          formatters=None,
          float_format=None,
          sparsify=None,
          index_names=True,
          justify=None,
          max_rows=None,
          max_cols=None,
          show_dimensions=False,
          decimal=".",
          bold_rows=True,
          classes=None,
          escape=True,
          notebook=False,
          border=None,
          table_id=None,
          render_links=False,
          encoding=None,
      ):
          """
          Render a DataFrame as an HTML table.
          %(shared_params)s
          bold_rows : bool, default True
              Make the row labels bold in the output.
          classes : str or list or tuple, default None
              CSS class(es) to apply to the resulting html table.
          escape : bool, default True
              Convert the characters <, >, and & to HTML-safe sequences.
          notebook : {True, False}, default False
              Whether the generated HTML is for IPython Notebook.
          border : int
              A ``border=border`` attribute is included in the opening
              `<table>` tag. Default ``pd.options.display.html.border``.
          encoding : str, default "utf-8"
              Set character encoding.
  
              .. versionadded:: 1.0
  
          table_id : str, optional
              A css id is included in the opening `<table>` tag if specified.
          render_links : bool, default False
              Convert URLs to HTML links.
  
              .. versionadded:: 0.24.0
          %(returns)s
          See Also
          --------
          to_string : Convert DataFrame to a string.
          """
          if justify is not None and justify not in fmt._VALID_JUSTIFY_PARAMETERS:
              raise ValueError("Invalid value for justify parameter")
  
          formatter = fmt.DataFrameFormatter(
              self,
              columns=columns,
              col_space=col_space,
              na_rep=na_rep,
              header=header,
              index=index,
              formatters=formatters,
              float_format=float_format,
              bold_rows=bold_rows,
              sparsify=sparsify,
              justify=justify,
              index_names=index_names,
              escape=escape,
              decimal=decimal,
              max_rows=max_rows,
              max_cols=max_cols,
              show_dimensions=show_dimensions,
          )
          # TODO: a generic formatter wld b in DataFrameFormatter
          return fmt.DataFrameRenderer(formatter).to_html(
              buf=buf,
              classes=classes,
              notebook=notebook,
              border=border,
              encoding=encoding,
              table_id=table_id,
              render_links=render_links,
          )
  
      # ----------------------------------------------------------------------
      @Substitution(
          klass="DataFrame",
          type_sub=" and columns",
          max_cols_sub=dedent(
              """\
              max_cols : int, optional
                  When to switch from the verbose to the truncated output. If the
                  DataFrame has more than `max_cols` columns, the truncated output
                  is used. By default, the setting in
                  ``pandas.options.display.max_info_columns`` is used."""
          ),
          show_counts_sub=dedent(
              """\
              show_counts : bool, optional
                  Whether to show the non-null counts. By default, this is shown
                  only if the DataFrame is smaller than
                  ``pandas.options.display.max_info_rows`` and
                  ``pandas.options.display.max_info_columns``. A value of True always
                  shows the counts, and False never shows the counts.
              null_counts : bool, optional
                  .. deprecated:: 1.2.0
                      Use show_counts instead."""
          ),
          examples_sub=dedent(
              """\
              >>> int_values = [1, 2, 3, 4, 5]
              >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
              >>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
              >>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
              ...                   "float_col": float_values})
              >>> df
                  int_col text_col  float_col
              0        1    alpha       0.00
              1        2     beta       0.25
              2        3    gamma       0.50
              3        4    delta       0.75
              4        5  epsilon       1.00
  
              Prints information of all columns:
  
              >>> df.info(verbose=True)
              <class 'pandas.core.frame.DataFrame'>
              RangeIndex: 5 entries, 0 to 4
              Data columns (total 3 columns):
               #   Column     Non-Null Count  Dtype
              ---  ------     --------------  -----
               0   int_col    5 non-null      int64
               1   text_col   5 non-null      object
               2   float_col  5 non-null      float64
              dtypes: float64(1), int64(1), object(1)
              memory usage: 248.0+ bytes
  
              Prints a summary of columns count and its dtypes but not per column
              information:
  
              >>> df.info(verbose=False)
              <class 'pandas.core.frame.DataFrame'>
              RangeIndex: 5 entries, 0 to 4
              Columns: 3 entries, int_col to float_col
              dtypes: float64(1), int64(1), object(1)
              memory usage: 248.0+ bytes
  
              Pipe output of DataFrame.info to buffer instead of sys.stdout, get
              buffer content and writes to a text file:
  
              >>> import io
              >>> buffer = io.StringIO()
              >>> df.info(buf=buffer)
              >>> s = buffer.getvalue()
              >>> with open("df_info.txt", "w",
              ...           encoding="utf-8") as f:  # doctest: +SKIP
              ...     f.write(s)
              260
  
              The `memory_usage` parameter allows deep introspection mode, specially
              useful for big DataFrames and fine-tune memory optimization:
  
              >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
              >>> df = pd.DataFrame({
              ...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
              ...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
              ...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
              ... })
              >>> df.info()
              <class 'pandas.core.frame.DataFrame'>
              RangeIndex: 1000000 entries, 0 to 999999
              Data columns (total 3 columns):
               #   Column    Non-Null Count    Dtype
              ---  ------    --------------    -----
               0   column_1  1000000 non-null  object
               1   column_2  1000000 non-null  object
               2   column_3  1000000 non-null  object
              dtypes: object(3)
              memory usage: 22.9+ MB
  
              >>> df.info(memory_usage='deep')
              <class 'pandas.core.frame.DataFrame'>
              RangeIndex: 1000000 entries, 0 to 999999
              Data columns (total 3 columns):
               #   Column    Non-Null Count    Dtype
              ---  ------    --------------    -----
               0   column_1  1000000 non-null  object
               1   column_2  1000000 non-null  object
               2   column_3  1000000 non-null  object
              dtypes: object(3)
              memory usage: 165.9 MB"""
          ),
          see_also_sub=dedent(
              """\
              DataFrame.describe: Generate descriptive statistics of DataFrame
                  columns.
              DataFrame.memory_usage: Memory usage of DataFrame columns."""
          ),
          version_added_sub="",
      )
      @doc(BaseInfo.render)
      def info(
          self,
          verbose: Optional[bool] = None,
          buf: Optional[IO[str]] = None,
          max_cols: Optional[int] = None,
          memory_usage: Optional[Union[bool, str]] = None,
          show_counts: Optional[bool] = None,
          null_counts: Optional[bool] = None,
      ) -> None:
          if null_counts is not None:
              if show_counts is not None:
                  raise ValueError("null_counts used with show_counts. Use show_counts.")
              warnings.warn(
                  "null_counts is deprecated. Use show_counts instead",
                  FutureWarning,
                  stacklevel=2,
              )
              show_counts = null_counts
          info = DataFrameInfo(
              data=self,
              memory_usage=memory_usage,
          )
          info.render(
              buf=buf,
              max_cols=max_cols,
              verbose=verbose,
              show_counts=show_counts,
          )
  
      def memory_usage(self, index=True, deep=False) -> Series:
          """
          Return the memory usage of each column in bytes.
  
          The memory usage can optionally include the contribution of
          the index and elements of `object` dtype.
  
          This value is displayed in `DataFrame.info` by default. This can be
          suppressed by setting ``pandas.options.display.memory_usage`` to False.
  
          Parameters
          ----------
          index : bool, default True
              Specifies whether to include the memory usage of the DataFrame's
              index in returned Series. If ``index=True``, the memory usage of
              the index is the first item in the output.
          deep : bool, default False
              If True, introspect the data deeply by interrogating
              `object` dtypes for system-level memory consumption, and include
              it in the returned values.
  
          Returns
          -------
          Series
              A Series whose index is the original column names and whose values
              is the memory usage of each column in bytes.
  
          See Also
          --------
          numpy.ndarray.nbytes : Total bytes consumed by the elements of an
              ndarray.
          Series.memory_usage : Bytes consumed by a Series.
          Categorical : Memory-efficient array for string values with
              many repeated values.
          DataFrame.info : Concise summary of a DataFrame.
  
          Examples
          --------
          >>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
          >>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
          ...              for t in dtypes])
          >>> df = pd.DataFrame(data)
          >>> df.head()
             int64  float64            complex128  object  bool
          0      1      1.0              1.0+0.0j       1  True
          1      1      1.0              1.0+0.0j       1  True
          2      1      1.0              1.0+0.0j       1  True
          3      1      1.0              1.0+0.0j       1  True
          4      1      1.0              1.0+0.0j       1  True
  
          >>> df.memory_usage()
          Index           128
          int64         40000
          float64       40000
          complex128    80000
          object        40000
          bool           5000
          dtype: int64
  
          >>> df.memory_usage(index=False)
          int64         40000
          float64       40000
          complex128    80000
          object        40000
          bool           5000
          dtype: int64
  
          The memory footprint of `object` dtype columns is ignored by default:
  
          >>> df.memory_usage(deep=True)
          Index            128
          int64          40000
          float64        40000
          complex128     80000
          object        180000
          bool            5000
          dtype: int64
  
          Use a Categorical for efficient storage of an object-dtype column with
          many repeated values.
  
          >>> df['object'].astype('category').memory_usage(deep=True)
          5244
          """
          result = self._constructor_sliced(
              [c.memory_usage(index=False, deep=deep) for col, c in self.items()],
              index=self.columns,
          )
          if index:
              result = self._constructor_sliced(
                  self.index.memory_usage(deep=deep), index=["Index"]
              ).append(result)
          return result
  
      def transpose(self, *args, copy: bool = False) -> DataFrame:
          """
          Transpose index and columns.
  
          Reflect the DataFrame over its main diagonal by writing rows as columns
          and vice-versa. The property :attr:`.T` is an accessor to the method
          :meth:`transpose`.
  
          Parameters
          ----------
          *args : tuple, optional
              Accepted for compatibility with NumPy.
          copy : bool, default False
              Whether to copy the data after transposing, even for DataFrames
              with a single dtype.
  
              Note that a copy is always required for mixed dtype DataFrames,
              or for DataFrames with any extension types.
  
          Returns
          -------
          DataFrame
              The transposed DataFrame.
  
          See Also
          --------
          numpy.transpose : Permute the dimensions of a given array.
  
          Notes
          -----
          Transposing a DataFrame with mixed dtypes will result in a homogeneous
          DataFrame with the `object` dtype. In such a case, a copy of the data
          is always made.
  
          Examples
          --------
          **Square DataFrame with homogeneous dtype**
  
          >>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
          >>> df1 = pd.DataFrame(data=d1)
          >>> df1
             col1  col2
          0     1     3
          1     2     4
  
          >>> df1_transposed = df1.T # or df1.transpose()
          >>> df1_transposed
                0  1
          col1  1  2
          col2  3  4
  
          When the dtype is homogeneous in the original DataFrame, we get a
          transposed DataFrame with the same dtype:
  
          >>> df1.dtypes
          col1    int64
          col2    int64
          dtype: object
          >>> df1_transposed.dtypes
          0    int64
          1    int64
          dtype: object
  
          **Non-square DataFrame with mixed dtypes**
  
          >>> d2 = {'name': ['Alice', 'Bob'],
          ...       'score': [9.5, 8],
          ...       'employed': [False, True],
          ...       'kids': [0, 0]}
          >>> df2 = pd.DataFrame(data=d2)
          >>> df2
              name  score  employed  kids
          0  Alice    9.5     False     0
          1    Bob    8.0      True     0
  
          >>> df2_transposed = df2.T # or df2.transpose()
          >>> df2_transposed
                        0     1
          name      Alice   Bob
          score       9.5   8.0
          employed  False  True
          kids          0     0
  
          When the DataFrame has mixed dtypes, we get a transposed DataFrame with
          the `object` dtype:
  
          >>> df2.dtypes
          name         object
          score       float64
          employed       bool
          kids          int64
          dtype: object
          >>> df2_transposed.dtypes
          0    object
          1    object
          dtype: object
          """
          nv.validate_transpose(args, {})
          # construct the args
  
          dtypes = list(self.dtypes)
          if self._is_homogeneous_type and dtypes and is_extension_array_dtype(dtypes[0]):
              # We have EAs with the same dtype. We can preserve that dtype in transpose.
              dtype = dtypes[0]
              arr_type = dtype.construct_array_type()
              values = self.values
  
              new_values = [arr_type._from_sequence(row, dtype=dtype) for row in values]
              result = self._constructor(
                  dict(zip(self.index, new_values)), index=self.columns
              )
  
          else:
              new_values = self.values.T
              if copy:
                  new_values = new_values.copy()
              result = self._constructor(
                  new_values, index=self.columns, columns=self.index
              )
  
          return result.__finalize__(self, method="transpose")
  
      @property
      def T(self) -> DataFrame:
          return self.transpose()
  
      # ----------------------------------------------------------------------
      # Indexing Methods
  
      def _ixs(self, i: int, axis: int = 0):
          """
          Parameters
          ----------
          i : int
          axis : int
  
          Notes
          -----
          If slice passed, the resulting data will be a view.
          """
          # irow
          if axis == 0:
              new_values = self._mgr.fast_xs(i)
  
              # if we are a copy, mark as such
              copy = isinstance(new_values, np.ndarray) and new_values.base is None
              result = self._constructor_sliced(
                  new_values,
                  index=self.columns,
                  name=self.index[i],
                  dtype=new_values.dtype,
              )
              result._set_is_copy(self, copy=copy)
              return result
  
          # icol
          else:
              label = self.columns[i]
  
              values = self._mgr.iget(i)
              result = self._box_col_values(values, i)
  
              # this is a cached value, mark it so
              result._set_as_cached(label, self)
  
              return result
  
      def _get_column_array(self, i: int) -> ArrayLike:
          """
          Get the values of the i'th column (ndarray or ExtensionArray, as stored
          in the Block)
          """
          return self._mgr.iget_values(i)
  
      def _iter_column_arrays(self) -> Iterator[ArrayLike]:
          """
          Iterate over the arrays of all columns in order.
          This returns the values as stored in the Block (ndarray or ExtensionArray).
          """
          for i in range(len(self.columns)):
              yield self._get_column_array(i)
  
      def __getitem__(self, key):
          key = lib.item_from_zerodim(key)
          key = com.apply_if_callable(key, self)
  
          if is_hashable(key):
              # shortcut if the key is in columns
              if self.columns.is_unique and key in self.columns:
                  if isinstance(self.columns, MultiIndex):
                      return self._getitem_multilevel(key)
                  return self._get_item_cache(key)
  
          # Do we have a slicer (on rows)?
          indexer = convert_to_index_sliceable(self, key)
          if indexer is not None:
              if isinstance(indexer, np.ndarray):
                  indexer = lib.maybe_indices_to_slice(
                      indexer.astype(np.intp, copy=False), len(self)
                  )
              # either we have a slice or we have a string that can be converted
              #  to a slice for partial-string date indexing
              return self._slice(indexer, axis=0)
  
          # Do we have a (boolean) DataFrame?
          if isinstance(key, DataFrame):
              return self.where(key)
  
          # Do we have a (boolean) 1d indexer?
          if com.is_bool_indexer(key):
              return self._getitem_bool_array(key)
  
          # We are left with two options: a single key, and a collection of keys,
          # We interpret tuples as collections only for non-MultiIndex
          is_single_key = isinstance(key, tuple) or not is_list_like(key)
  
          if is_single_key:
              if self.columns.nlevels > 1:
                  return self._getitem_multilevel(key)
              indexer = self.columns.get_loc(key)
              if is_integer(indexer):
                  indexer = [indexer]
          else:
              if is_iterator(key):
                  key = list(key)
              indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
  
          # take() does not accept boolean indexers
          if getattr(indexer, "dtype", None) == bool:
              indexer = np.where(indexer)[0]
  
          data = self._take_with_is_copy(indexer, axis=1)
  
          if is_single_key:
              # What does looking for a single key in a non-unique index return?
              # The behavior is inconsistent. It returns a Series, except when
              # - the key itself is repeated (test on data.shape, #9519), or
              # - we have a MultiIndex on columns (test on self.columns, #21309)
              if data.shape[1] == 1 and not isinstance(self.columns, MultiIndex):
                  # GH#26490 using data[key] can cause RecursionError
                  data = data._get_item_cache(key)
  
          return data
  
      def _getitem_bool_array(self, key):
          # also raises Exception if object array with NA values
          # warning here just in case -- previously __setitem__ was
          # reindexing but __getitem__ was not; it seems more reasonable to
          # go with the __setitem__ behavior since that is more consistent
          # with all other indexing behavior
          if isinstance(key, Series) and not key.index.equals(self.index):
              warnings.warn(
                  "Boolean Series key will be reindexed to match DataFrame index.",
                  UserWarning,
                  stacklevel=3,
              )
          elif len(key) != len(self.index):
              raise ValueError(
                  f"Item wrong length {len(key)} instead of {len(self.index)}."
              )
  
          # check_bool_indexer will throw exception if Series key cannot
          # be reindexed to match DataFrame rows
          key = check_bool_indexer(self.index, key)
          indexer = key.nonzero()[0]
          return self._take_with_is_copy(indexer, axis=0)
  
      def _getitem_multilevel(self, key):
          # self.columns is a MultiIndex
          loc = self.columns.get_loc(key)
          if isinstance(loc, (slice, np.ndarray)):
              new_columns = self.columns[loc]
              result_columns = maybe_droplevels(new_columns, key)
              if self._is_mixed_type:
                  result = self.reindex(columns=new_columns)
                  result.columns = result_columns
              else:
                  new_values = self.values[:, loc]
                  result = self._constructor(
                      new_values, index=self.index, columns=result_columns
                  )
                  result = result.__finalize__(self)
  
              # If there is only one column being returned, and its name is
              # either an empty string, or a tuple with an empty string as its
              # first element, then treat the empty string as a placeholder
              # and return the column as if the user had provided that empty
              # string in the key. If the result is a Series, exclude the
              # implied empty string from its name.
              if len(result.columns) == 1:
                  top = result.columns[0]
                  if isinstance(top, tuple):
                      top = top[0]
                  if top == "":
                      result = result[""]
                      if isinstance(result, Series):
                          result = self._constructor_sliced(
                              result, index=self.index, name=key
                          )
  
              result._set_is_copy(self)
              return result
          else:
              # loc is neither a slice nor ndarray, so must be an int
              return self._ixs(loc, axis=1)
  
      def _get_value(self, index, col, takeable: bool = False):
          """
          Quickly retrieve single value at passed column and index.
  
          Parameters
          ----------
          index : row label
          col : column label
          takeable : interpret the index/col as indexers, default False
  
          Returns
          -------
          scalar
          """
          if takeable:
              series = self._ixs(col, axis=1)
              return series._values[index]
  
          series = self._get_item_cache(col)
          engine = self.index._engine
  
          try:
              loc = engine.get_loc(index)
              return series._values[loc]
          except KeyError:
              # GH 20629
              if self.index.nlevels > 1:
                  # partial indexing forbidden
                  raise
  
          # we cannot handle direct indexing
          # use positional
          col = self.columns.get_loc(col)
          index = self.index.get_loc(index)
          return self._get_value(index, col, takeable=True)
  
      def __setitem__(self, key, value):
          key = com.apply_if_callable(key, self)
  
          # see if we can slice the rows
          indexer = convert_to_index_sliceable(self, key)
          if indexer is not None:
              # either we have a slice or we have a string that can be converted
              #  to a slice for partial-string date indexing
              return self._setitem_slice(indexer, value)
  
          if isinstance(key, DataFrame) or getattr(key, "ndim", None) == 2:
              self._setitem_frame(key, value)
          elif isinstance(key, (Series, np.ndarray, list, Index)):
              self._setitem_array(key, value)
          else:
              # set column
              self._set_item(key, value)
  
      def _setitem_slice(self, key: slice, value):
          # NB: we can't just use self.loc[key] = value because that
          #  operates on labels and we need to operate positional for
          #  backwards-compat, xref GH#31469
          self._check_setitem_copy()
          self.iloc._setitem_with_indexer(key, value)
  
      def _setitem_array(self, key, value):
          # also raises Exception if object array with NA values
          if com.is_bool_indexer(key):
              if len(key) != len(self.index):
                  raise ValueError(
                      f"Item wrong length {len(key)} instead of {len(self.index)}!"
                  )
              key = check_bool_indexer(self.index, key)
              indexer = key.nonzero()[0]
              self._check_setitem_copy()
              self.iloc._setitem_with_indexer(indexer, value)
          else:
              if isinstance(value, DataFrame):
                  if len(value.columns) != len(key):
                      raise ValueError("Columns must be same length as key")
                  for k1, k2 in zip(key, value.columns):
                      self[k1] = value[k2]
              else:
                  self.loc._ensure_listlike_indexer(key, axis=1, value=value)
                  indexer = self.loc._get_listlike_indexer(
                      key, axis=1, raise_missing=False
                  )[1]
                  self._check_setitem_copy()
                  self.iloc._setitem_with_indexer((slice(None), indexer), value)
  
      def _setitem_frame(self, key, value):
          # support boolean setting with DataFrame input, e.g.
          # df[df > df2] = 0
          if isinstance(key, np.ndarray):
              if key.shape != self.shape:
                  raise ValueError("Array conditional must be same shape as self")
              key = self._constructor(key, **self._construct_axes_dict())
  
          if key.size and not is_bool_dtype(key.values):
              raise TypeError(
                  "Must pass DataFrame or 2-d ndarray with boolean values only"
              )
  
          self._check_inplace_setting(value)
          self._check_setitem_copy()
          self._where(-key, value, inplace=True)
  
      def _iset_item(self, loc: int, value):
          self._ensure_valid_index(value)
  
          # technically _sanitize_column expects a label, not a position,
          #  but the behavior is the same as long as we pass broadcast=False
          value = self._sanitize_column(loc, value, broadcast=False)
          NDFrame._iset_item(self, loc, value)
  
          # check if we are modifying a copy
          # try to set first as we want an invalid
          # value exception to occur first
          if len(self):
              self._check_setitem_copy()
  
      def _set_item(self, key, value):
          """
          Add series to DataFrame in specified column.
  
          If series is a numpy-array (not a Series/TimeSeries), it must be the
          same length as the DataFrames index or an error will be thrown.
  
          Series/TimeSeries will be conformed to the DataFrames index to
          ensure homogeneity.
          """
          self._ensure_valid_index(value)
          value = self._sanitize_column(key, value)
          NDFrame._set_item(self, key, value)
  
          # check if we are modifying a copy
          # try to set first as we want an invalid
          # value exception to occur first
          if len(self):
              self._check_setitem_copy()
  
      def _set_value(self, index, col, value, takeable: bool = False):
          """
          Put single value at passed column and index.
  
          Parameters
          ----------
          index : row label
          col : column label
          value : scalar
          takeable : interpret the index/col as indexers, default False
          """
          try:
              if takeable is True:
                  series = self._ixs(col, axis=1)
                  series._set_value(index, value, takeable=True)
                  return
  
              series = self._get_item_cache(col)
              engine = self.index._engine
              loc = engine.get_loc(index)
              validate_numeric_casting(series.dtype, value)
  
              series._values[loc] = value
              # Note: trying to use series._set_value breaks tests in
              #  tests.frame.indexing.test_indexing and tests.indexing.test_partial
          except (KeyError, TypeError):
              # set using a non-recursive method & reset the cache
              if takeable:
                  self.iloc[index, col] = value
              else:
                  self.loc[index, col] = value
              self._item_cache.pop(col, None)
  
      def _ensure_valid_index(self, value):
          """
          Ensure that if we don't have an index, that we can create one from the
          passed value.
          """
          # GH5632, make sure that we are a Series convertible
          if not len(self.index) and is_list_like(value) and len(value):
              try:
                  value = Series(value)
              except (ValueError, NotImplementedError, TypeError) as err:
                  raise ValueError(
                      "Cannot set a frame with no defined index "
                      "and a value that cannot be converted to a Series"
                  ) from err
  
              # GH31368 preserve name of index
              index_copy = value.index.copy()
              if self.index.name is not None:
                  index_copy.name = self.index.name
  
              self._mgr = self._mgr.reindex_axis(index_copy, axis=1, fill_value=np.nan)
  
      def _box_col_values(self, values, loc: int) -> Series:
          """
          Provide boxed values for a column.
          """
          # Lookup in columns so that if e.g. a str datetime was passed
          #  we attach the Timestamp object as the name.
          name = self.columns[loc]
          klass = self._constructor_sliced
          return klass(values, index=self.index, name=name, fastpath=True)
  
      # ----------------------------------------------------------------------
      # Unsorted
  
      def query(self, expr, inplace=False, **kwargs):
          """
          Query the columns of a DataFrame with a boolean expression.
  
          Parameters
          ----------
          expr : str
              The query string to evaluate.
  
              You can refer to variables
              in the environment by prefixing them with an '@' character like
              ``@a + b``.
  
              You can refer to column names that are not valid Python variable names
              by surrounding them in backticks. Thus, column names containing spaces
              or punctuations (besides underscores) or starting with digits must be
              surrounded by backticks. (For example, a column named "Area (cm^2) would
              be referenced as `Area (cm^2)`). Column names which are Python keywords
              (like "list", "for", "import", etc) cannot be used.
  
              For example, if one of your columns is called ``a a`` and you want
              to sum it with ``b``, your query should be ```a a` + b``.
  
              .. versionadded:: 0.25.0
                  Backtick quoting introduced.
  
              .. versionadded:: 1.0.0
                  Expanding functionality of backtick quoting for more than only spaces.
  
          inplace : bool
              Whether the query should modify the data in place or return
              a modified copy.
          **kwargs
              See the documentation for :func:`eval` for complete details
              on the keyword arguments accepted by :meth:`DataFrame.query`.
  
          Returns
          -------
          DataFrame or None
              DataFrame resulting from the provided query expression or
              None if ``inplace=True``.
  
          See Also
          --------
          eval : Evaluate a string describing operations on
              DataFrame columns.
          DataFrame.eval : Evaluate a string describing operations on
              DataFrame columns.
  
          Notes
          -----
          The result of the evaluation of this expression is first passed to
          :attr:`DataFrame.loc` and if that fails because of a
          multidimensional key (e.g., a DataFrame) then the result will be passed
          to :meth:`DataFrame.__getitem__`.
  
          This method uses the top-level :func:`eval` function to
          evaluate the passed query.
  
          The :meth:`~pandas.DataFrame.query` method uses a slightly
          modified Python syntax by default. For example, the ``&`` and ``|``
          (bitwise) operators have the precedence of their boolean cousins,
          :keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,
          however the semantics are different.
  
          You can change the semantics of the expression by passing the keyword
          argument ``parser='python'``. This enforces the same semantics as
          evaluation in Python space. Likewise, you can pass ``engine='python'``
          to evaluate an expression using Python itself as a backend. This is not
          recommended as it is inefficient compared to using ``numexpr`` as the
          engine.
  
          The :attr:`DataFrame.index` and
          :attr:`DataFrame.columns` attributes of the
          :class:`~pandas.DataFrame` instance are placed in the query namespace
          by default, which allows you to treat both the index and columns of the
          frame as a column in the frame.
          The identifier ``index`` is used for the frame index; you can also
          use the name of the index to identify it in a query. Please note that
          Python keywords may not be used as identifiers.
  
          For further details and examples see the ``query`` documentation in
          :ref:`indexing <indexing.query>`.
  
          *Backtick quoted variables*
  
          Backtick quoted variables are parsed as literal Python code and
          are converted internally to a Python valid identifier.
          This can lead to the following problems.
  
          During parsing a number of disallowed characters inside the backtick
          quoted string are replaced by strings that are allowed as a Python identifier.
          These characters include all operators in Python, the space character, the
          question mark, the exclamation mark, the dollar sign, and the euro sign.
          For other characters that fall outside the ASCII range (U+0001..U+007F)
          and those that are not further specified in PEP 3131,
          the query parser will raise an error.
          This excludes whitespace different than the space character,
          but also the hashtag (as it is used for comments) and the backtick
          itself (backtick can also not be escaped).
  
          In a special case, quotes that make a pair around a backtick can
          confuse the parser.
          For example, ```it's` > `that's``` will raise an error,
          as it forms a quoted string (``'s > `that'``) with a backtick inside.
  
          See also the Python documentation about lexical analysis
          (https://docs.python.org/3/reference/lexical_analysis.html)
          in combination with the source code in :mod:`pandas.core.computation.parsing`.
  
          Examples
          --------
          >>> df = pd.DataFrame({'A': range(1, 6),
          ...                    'B': range(10, 0, -2),
          ...                    'C C': range(10, 5, -1)})
          >>> df
             A   B  C C
          0  1  10   10
          1  2   8    9
          2  3   6    8
          3  4   4    7
          4  5   2    6
          >>> df.query('A > B')
             A  B  C C
          4  5  2    6
  
          The previous expression is equivalent to
  
          >>> df[df.A > df.B]
             A  B  C C
          4  5  2    6
  
          For columns with spaces in their name, you can use backtick quoting.
  
          >>> df.query('B == `C C`')
             A   B  C C
          0  1  10   10
  
          The previous expression is equivalent to
  
          >>> df[df.B == df['C C']]
             A   B  C C
          0  1  10   10
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
          if not isinstance(expr, str):
              msg = f"expr must be a string to be evaluated, {type(expr)} given"
              raise ValueError(msg)
          kwargs["level"] = kwargs.pop("level", 0) + 1
          kwargs["target"] = None
          res = self.eval(expr, **kwargs)
  
          try:
              result = self.loc[res]
          except ValueError:
              # when res is multi-dimensional loc raises, but this is sometimes a
              # valid query
              result = self[res]
  
          if inplace:
              self._update_inplace(result)
          else:
              return result
  
      def eval(self, expr, inplace=False, **kwargs):
          """
          Evaluate a string describing operations on DataFrame columns.
  
          Operates on columns only, not specific rows or elements.  This allows
          `eval` to run arbitrary code, which can make you vulnerable to code
          injection if you pass user input to this function.
  
          Parameters
          ----------
          expr : str
              The expression string to evaluate.
          inplace : bool, default False
              If the expression contains an assignment, whether to perform the
              operation inplace and mutate the existing DataFrame. Otherwise,
              a new DataFrame is returned.
          **kwargs
              See the documentation for :func:`eval` for complete details
              on the keyword arguments accepted by
              :meth:`~pandas.DataFrame.query`.
  
          Returns
          -------
          ndarray, scalar, pandas object, or None
              The result of the evaluation or None if ``inplace=True``.
  
          See Also
          --------
          DataFrame.query : Evaluates a boolean expression to query the columns
              of a frame.
          DataFrame.assign : Can evaluate an expression or function to create new
              values for a column.
          eval : Evaluate a Python expression as a string using various
              backends.
  
          Notes
          -----
          For more details see the API documentation for :func:`~eval`.
          For detailed examples see :ref:`enhancing performance with eval
          <enhancingperf.eval>`.
  
          Examples
          --------
          >>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
          >>> df
             A   B
          0  1  10
          1  2   8
          2  3   6
          3  4   4
          4  5   2
          >>> df.eval('A + B')
          0    11
          1    10
          2     9
          3     8
          4     7
          dtype: int64
  
          Assignment is allowed though by default the original DataFrame is not
          modified.
  
          >>> df.eval('C = A + B')
             A   B   C
          0  1  10  11
          1  2   8  10
          2  3   6   9
          3  4   4   8
          4  5   2   7
          >>> df
             A   B
          0  1  10
          1  2   8
          2  3   6
          3  4   4
          4  5   2
  
          Use ``inplace=True`` to modify the original DataFrame.
  
          >>> df.eval('C = A + B', inplace=True)
          >>> df
             A   B   C
          0  1  10  11
          1  2   8  10
          2  3   6   9
          3  4   4   8
          4  5   2   7
  
          Multiple columns can be assigned to using multi-line expressions:
  
          >>> df.eval(
          ...     '''
          ... C = A + B
          ... D = A - B
          ... '''
          ... )
             A   B   C  D
          0  1  10  11 -9
          1  2   8  10 -6
          2  3   6   9 -3
          3  4   4   8  0
          4  5   2   7  3
          """
          from pandas.core.computation.eval import eval as _eval
  
          inplace = validate_bool_kwarg(inplace, "inplace")
          resolvers = kwargs.pop("resolvers", None)
          kwargs["level"] = kwargs.pop("level", 0) + 1
          if resolvers is None:
              index_resolvers = self._get_index_resolvers()
              column_resolvers = self._get_cleaned_column_resolvers()
              resolvers = column_resolvers, index_resolvers
          if "target" not in kwargs:
              kwargs["target"] = self
          kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers)
  
          return _eval(expr, inplace=inplace, **kwargs)
  
      def select_dtypes(self, include=None, exclude=None) -> DataFrame:
          """
          Return a subset of the DataFrame's columns based on the column dtypes.
  
          Parameters
          ----------
          include, exclude : scalar or list-like
              A selection of dtypes or strings to be included/excluded. At least
              one of these parameters must be supplied.
  
          Returns
          -------
          DataFrame
              The subset of the frame including the dtypes in ``include`` and
              excluding the dtypes in ``exclude``.
  
          Raises
          ------
          ValueError
              * If both of ``include`` and ``exclude`` are empty
              * If ``include`` and ``exclude`` have overlapping elements
              * If any kind of string dtype is passed in.
  
          See Also
          --------
          DataFrame.dtypes: Return Series with the data type of each column.
  
          Notes
          -----
          * To select all *numeric* types, use ``np.number`` or ``'number'``
          * To select strings you must use the ``object`` dtype, but note that
            this will return *all* object dtype columns
          * See the `numpy dtype hierarchy
            <https://numpy.org/doc/stable/reference/arrays.scalars.html>`__
          * To select datetimes, use ``np.datetime64``, ``'datetime'`` or
            ``'datetime64'``
          * To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
            ``'timedelta64'``
          * To select Pandas categorical dtypes, use ``'category'``
          * To select Pandas datetimetz dtypes, use ``'datetimetz'`` (new in
            0.20.0) or ``'datetime64[ns, tz]'``
  
          Examples
          --------
          >>> df = pd.DataFrame({'a': [1, 2] * 3,
          ...                    'b': [True, False] * 3,
          ...                    'c': [1.0, 2.0] * 3})
          >>> df
                  a      b  c
          0       1   True  1.0
          1       2  False  2.0
          2       1   True  1.0
          3       2  False  2.0
          4       1   True  1.0
          5       2  False  2.0
  
          >>> df.select_dtypes(include='bool')
             b
          0  True
          1  False
          2  True
          3  False
          4  True
          5  False
  
          >>> df.select_dtypes(include=['float64'])
             c
          0  1.0
          1  2.0
          2  1.0
          3  2.0
          4  1.0
          5  2.0
  
          >>> df.select_dtypes(exclude=['int64'])
                 b    c
          0   True  1.0
          1  False  2.0
          2   True  1.0
          3  False  2.0
          4   True  1.0
          5  False  2.0
          """
          if not is_list_like(include):
              include = (include,) if include is not None else ()
          if not is_list_like(exclude):
              exclude = (exclude,) if exclude is not None else ()
  
          selection = (frozenset(include), frozenset(exclude))
  
          if not any(selection):
              raise ValueError("at least one of include or exclude must be nonempty")
  
          # convert the myriad valid dtypes object to a single representation
          include = frozenset(infer_dtype_from_object(x) for x in include)
          exclude = frozenset(infer_dtype_from_object(x) for x in exclude)
          for dtypes in (include, exclude):
              invalidate_string_dtypes(dtypes)
  
          # can't both include AND exclude!
          if not include.isdisjoint(exclude):
              raise ValueError(f"include and exclude overlap on {(include & exclude)}")
  
          # We raise when both include and exclude are empty
          # Hence, we can just shrink the columns we want to keep
          keep_these = np.full(self.shape[1], True)
  
          def extract_unique_dtypes_from_dtypes_set(
              dtypes_set: FrozenSet[Dtype], unique_dtypes: np.ndarray
          ) -> List[Dtype]:
              extracted_dtypes = [
                  unique_dtype
                  for unique_dtype in unique_dtypes
                  # error: Argument 1 to "tuple" has incompatible type
                  # "FrozenSet[Union[ExtensionDtype, str, Any, Type[str],
                  # Type[float], Type[int], Type[complex], Type[bool]]]";
                  # expected "Iterable[Union[type, Tuple[Any, ...]]]"
                  if issubclass(
                      unique_dtype.type, tuple(dtypes_set)  # type: ignore[arg-type]
                  )
              ]
              return extracted_dtypes
  
          unique_dtypes = self.dtypes.unique()
  
          if include:
              included_dtypes = extract_unique_dtypes_from_dtypes_set(
                  include, unique_dtypes
              )
              keep_these &= self.dtypes.isin(included_dtypes)
  
          if exclude:
              excluded_dtypes = extract_unique_dtypes_from_dtypes_set(
                  exclude, unique_dtypes
              )
              keep_these &= ~self.dtypes.isin(excluded_dtypes)
  
          return self.iloc[:, keep_these.values]
  
      def insert(self, loc, column, value, allow_duplicates=False) -> None:
          """
          Insert column into DataFrame at specified location.
  
          Raises a ValueError if `column` is already contained in the DataFrame,
          unless `allow_duplicates` is set to True.
  
          Parameters
          ----------
          loc : int
              Insertion index. Must verify 0 <= loc <= len(columns).
          column : str, number, or hashable object
              Label of the inserted column.
          value : int, Series, or array-like
          allow_duplicates : bool, optional
          """
          if allow_duplicates and not self.flags.allows_duplicate_labels:
              raise ValueError(
                  "Cannot specify 'allow_duplicates=True' when "
                  "'self.flags.allows_duplicate_labels' is False."
              )
          self._ensure_valid_index(value)
          value = self._sanitize_column(column, value, broadcast=False)
          self._mgr.insert(loc, column, value, allow_duplicates=allow_duplicates)
  
      def assign(self, **kwargs) -> DataFrame:
          r"""
          Assign new columns to a DataFrame.
  
          Returns a new object with all original columns in addition to new ones.
          Existing columns that are re-assigned will be overwritten.
  
          Parameters
          ----------
          **kwargs : dict of {str: callable or Series}
              The column names are keywords. If the values are
              callable, they are computed on the DataFrame and
              assigned to the new columns. The callable must not
              change input DataFrame (though pandas doesn't check it).
              If the values are not callable, (e.g. a Series, scalar, or array),
              they are simply assigned.
  
          Returns
          -------
          DataFrame
              A new DataFrame with the new columns in addition to
              all the existing columns.
  
          Notes
          -----
          Assigning multiple columns within the same ``assign`` is possible.
          Later items in '\*\*kwargs' may refer to newly created or modified
          columns in 'df'; items are computed and assigned into 'df' in order.
  
          Examples
          --------
          >>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
          ...                   index=['Portland', 'Berkeley'])
          >>> df
                    temp_c
          Portland    17.0
          Berkeley    25.0
  
          Where the value is a callable, evaluated on `df`:
  
          >>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
                    temp_c  temp_f
          Portland    17.0    62.6
          Berkeley    25.0    77.0
  
          Alternatively, the same behavior can be achieved by directly
          referencing an existing Series or sequence:
  
          >>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
                    temp_c  temp_f
          Portland    17.0    62.6
          Berkeley    25.0    77.0
  
          You can create multiple columns within the same assign where one
          of the columns depends on another one defined within the same assign:
  
          >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
          ...           temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)
                    temp_c  temp_f  temp_k
          Portland    17.0    62.6  290.15
          Berkeley    25.0    77.0  298.15
          """
          data = self.copy()
  
          for k, v in kwargs.items():
              data[k] = com.apply_if_callable(v, data)
          return data
  
      def _sanitize_column(self, key, value, broadcast=True):
          """
          Ensures new columns (which go into the BlockManager as new blocks) are
          always copied and converted into an array.
  
          Parameters
          ----------
          key : object
          value : scalar, Series, or array-like
          broadcast : bool, default True
              If ``key`` matches multiple duplicate column names in the
              DataFrame, this parameter indicates whether ``value`` should be
              tiled so that the returned array contains a (duplicated) column for
              each occurrence of the key. If False, ``value`` will not be tiled.
  
          Returns
          -------
          numpy.ndarray
          """
  
          def reindexer(value):
              # reindex if necessary
  
              if value.index.equals(self.index) or not len(self.index):
                  value = value._values.copy()
              else:
  
                  # GH 4107
                  try:
                      value = value.reindex(self.index)._values
                  except ValueError as err:
                      # raised in MultiIndex.from_tuples, see test_insert_error_msmgs
                      if not value.index.is_unique:
                          # duplicate axis
                          raise err
  
                      # other
                      raise TypeError(
                          "incompatible index of inserted column with frame index"
                      ) from err
              return value
  
          if isinstance(value, Series):
              value = reindexer(value)
  
          elif isinstance(value, DataFrame):
              # align right-hand-side columns if self.columns
              # is multi-index and self[key] is a sub-frame
              if isinstance(self.columns, MultiIndex) and key in self.columns:
                  loc = self.columns.get_loc(key)
                  if isinstance(loc, (slice, Series, np.ndarray, Index)):
                      cols = maybe_droplevels(self.columns[loc], key)
                      if len(cols) and not cols.equals(value.columns):
                          value = value.reindex(cols, axis=1)
              # now align rows
              value = reindexer(value).T
  
          elif isinstance(value, ExtensionArray):
              # Explicitly copy here, instead of in sanitize_index,
              # as sanitize_index won't copy an EA, even with copy=True
              value = value.copy()
              value = sanitize_index(value, self.index)
  
          elif isinstance(value, Index) or is_sequence(value):
  
              # turn me into an ndarray
              value = sanitize_index(value, self.index)
              if not isinstance(value, (np.ndarray, Index)):
                  if isinstance(value, list) and len(value) > 0:
                      value = maybe_convert_platform(value)
                  else:
                      value = com.asarray_tuplesafe(value)
              elif value.ndim == 2:
                  value = value.copy().T
              elif isinstance(value, Index):
                  value = value.copy(deep=True)
              else:
                  value = value.copy()
  
              # possibly infer to datetimelike
              if is_object_dtype(value.dtype):
                  value = maybe_infer_to_datetimelike(value)
  
          else:
              # cast ignores pandas dtypes. so save the dtype first
              infer_dtype, _ = infer_dtype_from_scalar(value, pandas_dtype=True)
  
              # upcast
              if is_extension_array_dtype(infer_dtype):
                  value = construct_1d_arraylike_from_scalar(
                      value, len(self.index), infer_dtype
                  )
              else:
                  # pandas\core\frame.py:3827: error: Argument 1 to
                  # "cast_scalar_to_array" has incompatible type "int"; expected
                  # "Tuple[Any, ...]"  [arg-type]
                  value = cast_scalar_to_array(
                      len(self.index), value  # type: ignore[arg-type]
                  )
  
              value = maybe_cast_to_datetime(value, infer_dtype)
  
          # return internal types directly
          if is_extension_array_dtype(value):
              return value
  
          # broadcast across multiple columns if necessary
          if broadcast and key in self.columns and value.ndim == 1:
              if not self.columns.is_unique or isinstance(self.columns, MultiIndex):
                  existing_piece = self[key]
                  if isinstance(existing_piece, DataFrame):
                      value = np.tile(value, (len(existing_piece.columns), 1))
  
          return np.atleast_2d(np.asarray(value))
  
      @property
      def _series(self):
          return {
              item: Series(
                  self._mgr.iget(idx), index=self.index, name=item, fastpath=True
              )
              for idx, item in enumerate(self.columns)
          }
  
      def lookup(self, row_labels, col_labels) -> np.ndarray:
          """
          Label-based "fancy indexing" function for DataFrame.
          Given equal-length arrays of row and column labels, return an
          array of the values corresponding to each (row, col) pair.
  
          .. deprecated:: 1.2.0
              DataFrame.lookup is deprecated,
              use DataFrame.melt and DataFrame.loc instead.
              For an example see :meth:`~pandas.DataFrame.lookup`
              in the user guide.
  
          Parameters
          ----------
          row_labels : sequence
              The row labels to use for lookup.
          col_labels : sequence
              The column labels to use for lookup.
  
          Returns
          -------
          numpy.ndarray
              The found values.
          """
          msg = (
              "The 'lookup' method is deprecated and will be"
              "removed in a future version."
              "You can use DataFrame.melt and DataFrame.loc"
              "as a substitute."
          )
          warnings.warn(msg, FutureWarning, stacklevel=2)
  
          n = len(row_labels)
          if n != len(col_labels):
              raise ValueError("Row labels must have same size as column labels")
          if not (self.index.is_unique and self.columns.is_unique):
              # GH#33041
              raise ValueError("DataFrame.lookup requires unique index and columns")
  
          thresh = 1000
          if not self._is_mixed_type or n > thresh:
              values = self.values
              ridx = self.index.get_indexer(row_labels)
              cidx = self.columns.get_indexer(col_labels)
              if (ridx == -1).any():
                  raise KeyError("One or more row labels was not found")
              if (cidx == -1).any():
                  raise KeyError("One or more column labels was not found")
              flat_index = ridx * len(self.columns) + cidx
              result = values.flat[flat_index]
          else:
              result = np.empty(n, dtype="O")
              for i, (r, c) in enumerate(zip(row_labels, col_labels)):
                  result[i] = self._get_value(r, c)
  
          if is_object_dtype(result):
              result = lib.maybe_convert_objects(result)
  
          return result
  
      # ----------------------------------------------------------------------
      # Reindexing and alignment
  
      def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy):
          frame = self
  
          columns = axes["columns"]
          if columns is not None:
              frame = frame._reindex_columns(
                  columns, method, copy, level, fill_value, limit, tolerance
              )
  
          index = axes["index"]
          if index is not None:
              frame = frame._reindex_index(
                  index, method, copy, level, fill_value, limit, tolerance
              )
  
          return frame
  
      def _reindex_index(
          self,
          new_index,
          method,
          copy,
          level,
          fill_value=np.nan,
          limit=None,
          tolerance=None,
      ):
          new_index, indexer = self.index.reindex(
              new_index, method=method, level=level, limit=limit, tolerance=tolerance
          )
          return self._reindex_with_indexers(
              {0: [new_index, indexer]},
              copy=copy,
              fill_value=fill_value,
              allow_dups=False,
          )
  
      def _reindex_columns(
          self,
          new_columns,
          method,
          copy,
          level,
          fill_value=None,
          limit=None,
          tolerance=None,
      ):
          new_columns, indexer = self.columns.reindex(
              new_columns, method=method, level=level, limit=limit, tolerance=tolerance
          )
          return self._reindex_with_indexers(
              {1: [new_columns, indexer]},
              copy=copy,
              fill_value=fill_value,
              allow_dups=False,
          )
  
      def _reindex_multi(self, axes, copy, fill_value) -> DataFrame:
          """
          We are guaranteed non-Nones in the axes.
          """
          new_index, row_indexer = self.index.reindex(axes["index"])
          new_columns, col_indexer = self.columns.reindex(axes["columns"])
  
          if row_indexer is not None and col_indexer is not None:
              indexer = row_indexer, col_indexer
              new_values = algorithms.take_2d_multi(
                  self.values, indexer, fill_value=fill_value
              )
              return self._constructor(new_values, index=new_index, columns=new_columns)
          else:
              return self._reindex_with_indexers(
                  {0: [new_index, row_indexer], 1: [new_columns, col_indexer]},
                  copy=copy,
                  fill_value=fill_value,
              )
  
      @doc(NDFrame.align, **_shared_doc_kwargs)
      def align(
          self,
          other,
          join="outer",
          axis=None,
          level=None,
          copy=True,
          fill_value=None,
          method=None,
          limit=None,
          fill_axis=0,
          broadcast_axis=None,
      ) -> DataFrame:
          return super().align(
              other,
              join=join,
              axis=axis,
              level=level,
              copy=copy,
              fill_value=fill_value,
              method=method,
              limit=limit,
              fill_axis=fill_axis,
              broadcast_axis=broadcast_axis,
          )
  
      @Appender(
          """
          Examples
          --------
          >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
  
          Change the row labels.
  
          >>> df.set_axis(['a', 'b', 'c'], axis='index')
             A  B
          a  1  4
          b  2  5
          c  3  6
  
          Change the column labels.
  
          >>> df.set_axis(['I', 'II'], axis='columns')
             I  II
          0  1   4
          1  2   5
          2  3   6
  
          Now, update the labels inplace.
  
          >>> df.set_axis(['i', 'ii'], axis='columns', inplace=True)
          >>> df
             i  ii
          0  1   4
          1  2   5
          2  3   6
          """
      )
      @Substitution(
          **_shared_doc_kwargs,
          extended_summary_sub=" column or",
          axis_description_sub=", and 1 identifies the columns",
          see_also_sub=" or columns",
      )
      @Appender(NDFrame.set_axis.__doc__)
      def set_axis(self, labels, axis: Axis = 0, inplace: bool = False):
          return super().set_axis(labels, axis=axis, inplace=inplace)
  
      @Substitution(**_shared_doc_kwargs)
      @Appender(NDFrame.reindex.__doc__)
      @rewrite_axis_style_signature(
          "labels",
          [
              ("method", None),
              ("copy", True),
              ("level", None),
              ("fill_value", np.nan),
              ("limit", None),
              ("tolerance", None),
          ],
      )
      def reindex(self, *args, **kwargs) -> DataFrame:
          axes = validate_axis_style_args(self, args, kwargs, "labels", "reindex")
          kwargs.update(axes)
          # Pop these, since the values are in `kwargs` under different names
          kwargs.pop("axis", None)
          kwargs.pop("labels", None)
          return super().reindex(**kwargs)
  
      def drop(
          self,
          labels=None,
          axis=0,
          index=None,
          columns=None,
          level=None,
          inplace=False,
          errors="raise",
      ):
          """
          Drop specified labels from rows or columns.
  
          Remove rows or columns by specifying label names and corresponding
          axis, or by specifying directly index or column names. When using a
          multi-index, labels on different levels can be removed by specifying
          the level.
  
          Parameters
          ----------
          labels : single label or list-like
              Index or column labels to drop.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              Whether to drop labels from the index (0 or 'index') or
              columns (1 or 'columns').
          index : single label or list-like
              Alternative to specifying axis (``labels, axis=0``
              is equivalent to ``index=labels``).
          columns : single label or list-like
              Alternative to specifying axis (``labels, axis=1``
              is equivalent to ``columns=labels``).
          level : int or level name, optional
              For MultiIndex, level from which the labels will be removed.
          inplace : bool, default False
              If False, return a copy. Otherwise, do operation
              inplace and return None.
          errors : {'ignore', 'raise'}, default 'raise'
              If 'ignore', suppress error and only existing labels are
              dropped.
  
          Returns
          -------
          DataFrame or None
              DataFrame without the removed index or column labels or
              None if ``inplace=True``.
  
          Raises
          ------
          KeyError
              If any of the labels is not found in the selected axis.
  
          See Also
          --------
          DataFrame.loc : Label-location based indexer for selection by label.
          DataFrame.dropna : Return DataFrame with labels on given axis omitted
              where (all or any) data are missing.
          DataFrame.drop_duplicates : Return DataFrame with duplicate rows
              removed, optionally only considering certain columns.
          Series.drop : Return Series with specified index labels removed.
  
          Examples
          --------
          >>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
          ...                   columns=['A', 'B', 'C', 'D'])
          >>> df
             A  B   C   D
          0  0  1   2   3
          1  4  5   6   7
          2  8  9  10  11
  
          Drop columns
  
          >>> df.drop(['B', 'C'], axis=1)
             A   D
          0  0   3
          1  4   7
          2  8  11
  
          >>> df.drop(columns=['B', 'C'])
             A   D
          0  0   3
          1  4   7
          2  8  11
  
          Drop a row by index
  
          >>> df.drop([0, 1])
             A  B   C   D
          2  8  9  10  11
  
          Drop columns and/or rows of MultiIndex DataFrame
  
          >>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
          ...                              ['speed', 'weight', 'length']],
          ...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
          ...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
          >>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
          ...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
          ...                         [250, 150], [1.5, 0.8], [320, 250],
          ...                         [1, 0.8], [0.3, 0.2]])
          >>> df
                          big     small
          lama    speed   45.0    30.0
                  weight  200.0   100.0
                  length  1.5     1.0
          cow     speed   30.0    20.0
                  weight  250.0   150.0
                  length  1.5     0.8
          falcon  speed   320.0   250.0
                  weight  1.0     0.8
                  length  0.3     0.2
  
          >>> df.drop(index='cow', columns='small')
                          big
          lama    speed   45.0
                  weight  200.0
                  length  1.5
          falcon  speed   320.0
                  weight  1.0
                  length  0.3
  
          >>> df.drop(index='length', level=1)
                          big     small
          lama    speed   45.0    30.0
                  weight  200.0   100.0
          cow     speed   30.0    20.0
                  weight  250.0   150.0
          falcon  speed   320.0   250.0
                  weight  1.0     0.8
          """
          return super().drop(
              labels=labels,
              axis=axis,
              index=index,
              columns=columns,
              level=level,
              inplace=inplace,
              errors=errors,
          )
  
      @rewrite_axis_style_signature(
          "mapper",
          [("copy", True), ("inplace", False), ("level", None), ("errors", "ignore")],
      )
      def rename(
          self,
          mapper: Optional[Renamer] = None,
          *,
          index: Optional[Renamer] = None,
          columns: Optional[Renamer] = None,
          axis: Optional[Axis] = None,
          copy: bool = True,
          inplace: bool = False,
          level: Optional[Level] = None,
          errors: str = "ignore",
      ) -> Optional[DataFrame]:
          """
          Alter axes labels.
  
          Function / dict values must be unique (1-to-1). Labels not contained in
          a dict / Series will be left as-is. Extra labels listed don't throw an
          error.
  
          See the :ref:`user guide <basics.rename>` for more.
  
          Parameters
          ----------
          mapper : dict-like or function
              Dict-like or function transformations to apply to
              that axis' values. Use either ``mapper`` and ``axis`` to
              specify the axis to target with ``mapper``, or ``index`` and
              ``columns``.
          index : dict-like or function
              Alternative to specifying axis (``mapper, axis=0``
              is equivalent to ``index=mapper``).
          columns : dict-like or function
              Alternative to specifying axis (``mapper, axis=1``
              is equivalent to ``columns=mapper``).
          axis : {0 or 'index', 1 or 'columns'}, default 0
              Axis to target with ``mapper``. Can be either the axis name
              ('index', 'columns') or number (0, 1). The default is 'index'.
          copy : bool, default True
              Also copy underlying data.
          inplace : bool, default False
              Whether to return a new DataFrame. If True then value of copy is
              ignored.
          level : int or level name, default None
              In case of a MultiIndex, only rename labels in the specified
              level.
          errors : {'ignore', 'raise'}, default 'ignore'
              If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
              or `columns` contains labels that are not present in the Index
              being transformed.
              If 'ignore', existing keys will be renamed and extra keys will be
              ignored.
  
          Returns
          -------
          DataFrame or None
              DataFrame with the renamed axis labels or None if ``inplace=True``.
  
          Raises
          ------
          KeyError
              If any of the labels is not found in the selected axis and
              "errors='raise'".
  
          See Also
          --------
          DataFrame.rename_axis : Set the name of the axis.
  
          Examples
          --------
          ``DataFrame.rename`` supports two calling conventions
  
          * ``(index=index_mapper, columns=columns_mapper, ...)``
          * ``(mapper, axis={'index', 'columns'}, ...)``
  
          We *highly* recommend using keyword arguments to clarify your
          intent.
  
          Rename columns using a mapping:
  
          >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
          >>> df.rename(columns={"A": "a", "B": "c"})
             a  c
          0  1  4
          1  2  5
          2  3  6
  
          Rename index using a mapping:
  
          >>> df.rename(index={0: "x", 1: "y", 2: "z"})
             A  B
          x  1  4
          y  2  5
          z  3  6
  
          Cast index labels to a different type:
  
          >>> df.index
          RangeIndex(start=0, stop=3, step=1)
          >>> df.rename(index=str).index
          Index(['0', '1', '2'], dtype='object')
  
          >>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
          Traceback (most recent call last):
          KeyError: ['C'] not found in axis
  
          Using axis-style parameters:
  
          >>> df.rename(str.lower, axis='columns')
             a  b
          0  1  4
          1  2  5
          2  3  6
  
          >>> df.rename({1: 2, 2: 4}, axis='index')
             A  B
          0  1  4
          2  2  5
          4  3  6
          """
          return super().rename(
              mapper=mapper,
              index=index,
              columns=columns,
              axis=axis,
              copy=copy,
              inplace=inplace,
              level=level,
              errors=errors,
          )
  
      @doc(NDFrame.fillna, **_shared_doc_kwargs)
      def fillna(
          self,
          value=None,
          method=None,
          axis=None,
          inplace=False,
          limit=None,
          downcast=None,
      ) -> Optional[DataFrame]:
          return super().fillna(
              value=value,
              method=method,
              axis=axis,
              inplace=inplace,
              limit=limit,
              downcast=downcast,
          )
  
      def pop(self, item: Label) -> Series:
          """
          Return item and drop from frame. Raise KeyError if not found.
  
          Parameters
          ----------
          item : label
              Label of column to be popped.
  
          Returns
          -------
          Series
  
          Examples
          --------
          >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
          ...                    ('parrot', 'bird', 24.0),
          ...                    ('lion', 'mammal', 80.5),
          ...                    ('monkey', 'mammal', np.nan)],
          ...                   columns=('name', 'class', 'max_speed'))
          >>> df
               name   class  max_speed
          0  falcon    bird      389.0
          1  parrot    bird       24.0
          2    lion  mammal       80.5
          3  monkey  mammal        NaN
  
          >>> df.pop('class')
          0      bird
          1      bird
          2    mammal
          3    mammal
          Name: class, dtype: object
  
          >>> df
               name  max_speed
          0  falcon      389.0
          1  parrot       24.0
          2    lion       80.5
          3  monkey        NaN
          """
          return super().pop(item=item)
  
      @doc(NDFrame.replace, **_shared_doc_kwargs)
      def replace(
          self,
          to_replace=None,
          value=None,
          inplace=False,
          limit=None,
          regex=False,
          method="pad",
      ):
          return super().replace(
              to_replace=to_replace,
              value=value,
              inplace=inplace,
              limit=limit,
              regex=regex,
              method=method,
          )
  
      def _replace_columnwise(
          self, mapping: Dict[Label, Tuple[Any, Any]], inplace: bool, regex
      ):
          """
          Dispatch to Series.replace column-wise.
  
  
          Parameters
          ----------
          mapping : dict
              of the form {col: (target, value)}
          inplace : bool
          regex : bool or same types as `to_replace` in DataFrame.replace
  
          Returns
          -------
          DataFrame or None
          """
          # Operate column-wise
          res = self if inplace else self.copy()
          ax = self.columns
  
          for i in range(len(ax)):
              if ax[i] in mapping:
                  ser = self.iloc[:, i]
  
                  target, value = mapping[ax[i]]
                  newobj = ser.replace(target, value, regex=regex)
  
                  res.iloc[:, i] = newobj
  
          if inplace:
              return
          return res.__finalize__(self)
  
      @doc(NDFrame.shift, klass=_shared_doc_kwargs["klass"])
      def shift(
          self, periods=1, freq=None, axis=0, fill_value=lib.no_default
      ) -> DataFrame:
          axis = self._get_axis_number(axis)
  
          ncols = len(self.columns)
          if axis == 1 and periods != 0 and fill_value is lib.no_default and ncols > 0:
              # We will infer fill_value to match the closest column
  
              if periods > 0:
                  result = self.iloc[:, :-periods]
                  for col in range(min(ncols, abs(periods))):
                      # TODO(EA2D): doing this in a loop unnecessary with 2D EAs
                      # Define filler inside loop so we get a copy
                      filler = self.iloc[:, 0].shift(len(self))
                      result.insert(0, col, filler, allow_duplicates=True)
              else:
                  result = self.iloc[:, -periods:]
                  for col in range(min(ncols, abs(periods))):
                      # Define filler inside loop so we get a copy
                      filler = self.iloc[:, -1].shift(len(self))
                      result.insert(
                          len(result.columns), col, filler, allow_duplicates=True
                      )
  
              result.columns = self.columns.copy()
              return result
  
          return super().shift(
              periods=periods, freq=freq, axis=axis, fill_value=fill_value
          )
  
      def set_index(
          self, keys, drop=True, append=False, inplace=False, verify_integrity=False
      ):
          """
          Set the DataFrame index using existing columns.
  
          Set the DataFrame index (row labels) using one or more existing
          columns or arrays (of the correct length). The index can replace the
          existing index or expand on it.
  
          Parameters
          ----------
          keys : label or array-like or list of labels/arrays
              This parameter can be either a single column key, a single array of
              the same length as the calling DataFrame, or a list containing an
              arbitrary combination of column keys and arrays. Here, "array"
              encompasses :class:`Series`, :class:`Index`, ``np.ndarray``, and
              instances of :class:`~collections.abc.Iterator`.
          drop : bool, default True
              Delete columns to be used as the new index.
          append : bool, default False
              Whether to append columns to existing index.
          inplace : bool, default False
              If True, modifies the DataFrame in place (do not create a new object).
          verify_integrity : bool, default False
              Check the new index for duplicates. Otherwise defer the check until
              necessary. Setting to False will improve the performance of this
              method.
  
          Returns
          -------
          DataFrame or None
              Changed row labels or None if ``inplace=True``.
  
          See Also
          --------
          DataFrame.reset_index : Opposite of set_index.
          DataFrame.reindex : Change to new indices or expand indices.
          DataFrame.reindex_like : Change to same indices as other DataFrame.
  
          Examples
          --------
          >>> df = pd.DataFrame({'month': [1, 4, 7, 10],
          ...                    'year': [2012, 2014, 2013, 2014],
          ...                    'sale': [55, 40, 84, 31]})
          >>> df
             month  year  sale
          0      1  2012    55
          1      4  2014    40
          2      7  2013    84
          3     10  2014    31
  
          Set the index to become the 'month' column:
  
          >>> df.set_index('month')
                 year  sale
          month
          1      2012    55
          4      2014    40
          7      2013    84
          10     2014    31
  
          Create a MultiIndex using columns 'year' and 'month':
  
          >>> df.set_index(['year', 'month'])
                      sale
          year  month
          2012  1     55
          2014  4     40
          2013  7     84
          2014  10    31
  
          Create a MultiIndex using an Index and a column:
  
          >>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
                   month  sale
             year
          1  2012  1      55
          2  2014  4      40
          3  2013  7      84
          4  2014  10     31
  
          Create a MultiIndex using two Series:
  
          >>> s = pd.Series([1, 2, 3, 4])
          >>> df.set_index([s, s**2])
                month  year  sale
          1 1       1  2012    55
          2 4       4  2014    40
          3 9       7  2013    84
          4 16     10  2014    31
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
          self._check_inplace_and_allows_duplicate_labels(inplace)
          if not isinstance(keys, list):
              keys = [keys]
  
          err_msg = (
              'The parameter "keys" may be a column key, one-dimensional '
              "array, or a list containing only valid column keys and "
              "one-dimensional arrays."
          )
  
          missing: List[Label] = []
          for col in keys:
              if isinstance(col, (Index, Series, np.ndarray, list, abc.Iterator)):
                  # arrays are fine as long as they are one-dimensional
                  # iterators get converted to list below
                  if getattr(col, "ndim", 1) != 1:
                      raise ValueError(err_msg)
              else:
                  # everything else gets tried as a key; see GH 24969
                  try:
                      found = col in self.columns
                  except TypeError as err:
                      raise TypeError(
                          f"{err_msg}. Received column of type {type(col)}"
                      ) from err
                  else:
                      if not found:
                          missing.append(col)
  
          if missing:
              raise KeyError(f"None of {missing} are in the columns")
  
          if inplace:
              frame = self
          else:
              frame = self.copy()
  
          arrays = []
          names: List[Label] = []
          if append:
              names = list(self.index.names)
              if isinstance(self.index, MultiIndex):
                  for i in range(self.index.nlevels):
                      arrays.append(self.index._get_level_values(i))
              else:
                  arrays.append(self.index)
  
          to_remove: List[Label] = []
          for col in keys:
              if isinstance(col, MultiIndex):
                  for n in range(col.nlevels):
                      arrays.append(col._get_level_values(n))
                  names.extend(col.names)
              elif isinstance(col, (Index, Series)):
                  # if Index then not MultiIndex (treated above)
                  arrays.append(col)
                  names.append(col.name)
              elif isinstance(col, (list, np.ndarray)):
                  arrays.append(col)
                  names.append(None)
              elif isinstance(col, abc.Iterator):
                  arrays.append(list(col))
                  names.append(None)
              # from here, col can only be a column label
              else:
                  arrays.append(frame[col]._values)
                  names.append(col)
                  if drop:
                      to_remove.append(col)
  
              if len(arrays[-1]) != len(self):
                  # check newest element against length of calling frame, since
                  # ensure_index_from_sequences would not raise for append=False.
                  raise ValueError(
                      f"Length mismatch: Expected {len(self)} rows, "
                      f"received array of length {len(arrays[-1])}"
                  )
  
          index = ensure_index_from_sequences(arrays, names)
  
          if verify_integrity and not index.is_unique:
              duplicates = index[index.duplicated()].unique()
              raise ValueError(f"Index has duplicate keys: {duplicates}")
  
          # use set to handle duplicate column names gracefully in case of drop
          for c in set(to_remove):
              del frame[c]
  
          # clear up memory usage
          index._cleanup()
  
          frame.index = index
  
          if not inplace:
              return frame
  
      @overload
      # https://github.com/python/mypy/issues/6580
      # Overloaded function signatures 1 and 2 overlap with incompatible return types
      def reset_index(  # type: ignore[misc]
          self,
          level: Optional[Union[Hashable, Sequence[Hashable]]] = ...,
          drop: bool = ...,
          inplace: Literal[False] = ...,
          col_level: Hashable = ...,
          col_fill: Label = ...,
      ) -> DataFrame:
          ...
  
      @overload
      def reset_index(
          self,
          level: Optional[Union[Hashable, Sequence[Hashable]]] = ...,
          drop: bool = ...,
          inplace: Literal[True] = ...,
          col_level: Hashable = ...,
          col_fill: Label = ...,
      ) -> None:
          ...
  
      def reset_index(
          self,
          level: Optional[Union[Hashable, Sequence[Hashable]]] = None,
          drop: bool = False,
          inplace: bool = False,
          col_level: Hashable = 0,
          col_fill: Label = "",
      ) -> Optional[DataFrame]:
          """
          Reset the index, or a level of it.
  
          Reset the index of the DataFrame, and use the default one instead.
          If the DataFrame has a MultiIndex, this method can remove one or more
          levels.
  
          Parameters
          ----------
          level : int, str, tuple, or list, default None
              Only remove the given levels from the index. Removes all levels by
              default.
          drop : bool, default False
              Do not try to insert index into dataframe columns. This resets
              the index to the default integer index.
          inplace : bool, default False
              Modify the DataFrame in place (do not create a new object).
          col_level : int or str, default 0
              If the columns have multiple levels, determines which level the
              labels are inserted into. By default it is inserted into the first
              level.
          col_fill : object, default ''
              If the columns have multiple levels, determines how the other
              levels are named. If None then the index name is repeated.
  
          Returns
          -------
          DataFrame or None
              DataFrame with the new index or None if ``inplace=True``.
  
          See Also
          --------
          DataFrame.set_index : Opposite of reset_index.
          DataFrame.reindex : Change to new indices or expand indices.
          DataFrame.reindex_like : Change to same indices as other DataFrame.
  
          Examples
          --------
          >>> df = pd.DataFrame([('bird', 389.0),
          ...                    ('bird', 24.0),
          ...                    ('mammal', 80.5),
          ...                    ('mammal', np.nan)],
          ...                   index=['falcon', 'parrot', 'lion', 'monkey'],
          ...                   columns=('class', 'max_speed'))
          >>> df
                   class  max_speed
          falcon    bird      389.0
          parrot    bird       24.0
          lion    mammal       80.5
          monkey  mammal        NaN
  
          When we reset the index, the old index is added as a column, and a
          new sequential index is used:
  
          >>> df.reset_index()
              index   class  max_speed
          0  falcon    bird      389.0
          1  parrot    bird       24.0
          2    lion  mammal       80.5
          3  monkey  mammal        NaN
  
          We can use the `drop` parameter to avoid the old index being added as
          a column:
  
          >>> df.reset_index(drop=True)
              class  max_speed
          0    bird      389.0
          1    bird       24.0
          2  mammal       80.5
          3  mammal        NaN
  
          You can also use `reset_index` with `MultiIndex`.
  
          >>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
          ...                                    ('bird', 'parrot'),
          ...                                    ('mammal', 'lion'),
          ...                                    ('mammal', 'monkey')],
          ...                                   names=['class', 'name'])
          >>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
          ...                                      ('species', 'type')])
          >>> df = pd.DataFrame([(389.0, 'fly'),
          ...                    ( 24.0, 'fly'),
          ...                    ( 80.5, 'run'),
          ...                    (np.nan, 'jump')],
          ...                   index=index,
          ...                   columns=columns)
          >>> df
                         speed species
                           max    type
          class  name
          bird   falcon  389.0     fly
                 parrot   24.0     fly
          mammal lion     80.5     run
                 monkey    NaN    jump
  
          If the index has multiple levels, we can reset a subset of them:
  
          >>> df.reset_index(level='class')
                   class  speed species
                            max    type
          name
          falcon    bird  389.0     fly
          parrot    bird   24.0     fly
          lion    mammal   80.5     run
          monkey  mammal    NaN    jump
  
          If we are not dropping the index, by default, it is placed in the top
          level. We can place it in another level:
  
          >>> df.reset_index(level='class', col_level=1)
                          speed species
                   class    max    type
          name
          falcon    bird  389.0     fly
          parrot    bird   24.0     fly
          lion    mammal   80.5     run
          monkey  mammal    NaN    jump
  
          When the index is inserted under another level, we can specify under
          which one with the parameter `col_fill`:
  
          >>> df.reset_index(level='class', col_level=1, col_fill='species')
                        species  speed species
                          class    max    type
          name
          falcon           bird  389.0     fly
          parrot           bird   24.0     fly
          lion           mammal   80.5     run
          monkey         mammal    NaN    jump
  
          If we specify a nonexistent level for `col_fill`, it is created:
  
          >>> df.reset_index(level='class', col_level=1, col_fill='genus')
                          genus  speed species
                          class    max    type
          name
          falcon           bird  389.0     fly
          parrot           bird   24.0     fly
          lion           mammal   80.5     run
          monkey         mammal    NaN    jump
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
          self._check_inplace_and_allows_duplicate_labels(inplace)
          if inplace:
              new_obj = self
          else:
              new_obj = self.copy()
  
          new_index = ibase.default_index(len(new_obj))
          if level is not None:
              if not isinstance(level, (tuple, list)):
                  level = [level]
              level = [self.index._get_level_number(lev) for lev in level]
              if len(level) < self.index.nlevels:
                  new_index = self.index.droplevel(level)
  
          if not drop:
              to_insert: Iterable[Tuple[Any, Optional[Any]]]
              if isinstance(self.index, MultiIndex):
                  names = [
                      (n if n is not None else f"level_{i}")
                      for i, n in enumerate(self.index.names)
                  ]
                  to_insert = zip(self.index.levels, self.index.codes)
              else:
                  default = "index" if "index" not in self else "level_0"
                  names = [default] if self.index.name is None else [self.index.name]
                  to_insert = ((self.index, None),)
  
              multi_col = isinstance(self.columns, MultiIndex)
              for i, (lev, lab) in reversed(list(enumerate(to_insert))):
                  if not (level is None or i in level):
                      continue
                  name = names[i]
                  if multi_col:
                      col_name = list(name) if isinstance(name, tuple) else [name]
                      if col_fill is None:
                          if len(col_name) not in (1, self.columns.nlevels):
                              raise ValueError(
                                  "col_fill=None is incompatible "
                                  f"with incomplete column name {name}"
                              )
                          col_fill = col_name[0]
  
                      lev_num = self.columns._get_level_number(col_level)
                      name_lst = [col_fill] * lev_num + col_name
                      missing = self.columns.nlevels - len(name_lst)
                      name_lst += [col_fill] * missing
                      name = tuple(name_lst)
                  # to ndarray and maybe infer different dtype
                  level_values = maybe_casted_values(lev, lab)
                  new_obj.insert(0, name, level_values)
  
          new_obj.index = new_index
          if not inplace:
              return new_obj
  
          return None
  
      # ----------------------------------------------------------------------
      # Reindex-based selection methods
  
      @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
      def isna(self) -> DataFrame:
          result = self._constructor(self._mgr.isna(func=isna))
          return result.__finalize__(self, method="isna")
  
      @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
      def isnull(self) -> DataFrame:
          return self.isna()
  
      @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
      def notna(self) -> DataFrame:
          return ~self.isna()
  
      @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
      def notnull(self) -> DataFrame:
          return ~self.isna()
  
      def dropna(self, axis=0, how="any", thresh=None, subset=None, inplace=False):
          """
          Remove missing values.
  
          See the :ref:`User Guide <missing_data>` for more on which values are
          considered missing, and how to work with missing data.
  
          Parameters
          ----------
          axis : {0 or 'index', 1 or 'columns'}, default 0
              Determine if rows or columns which contain missing values are
              removed.
  
              * 0, or 'index' : Drop rows which contain missing values.
              * 1, or 'columns' : Drop columns which contain missing value.
  
              .. versionchanged:: 1.0.0
  
                 Pass tuple or list to drop on multiple axes.
                 Only a single axis is allowed.
  
          how : {'any', 'all'}, default 'any'
              Determine if row or column is removed from DataFrame, when we have
              at least one NA or all NA.
  
              * 'any' : If any NA values are present, drop that row or column.
              * 'all' : If all values are NA, drop that row or column.
  
          thresh : int, optional
              Require that many non-NA values.
          subset : array-like, optional
              Labels along other axis to consider, e.g. if you are dropping rows
              these would be a list of columns to include.
          inplace : bool, default False
              If True, do operation inplace and return None.
  
          Returns
          -------
          DataFrame or None
              DataFrame with NA entries dropped from it or None if ``inplace=True``.
  
          See Also
          --------
          DataFrame.isna: Indicate missing values.
          DataFrame.notna : Indicate existing (non-missing) values.
          DataFrame.fillna : Replace missing values.
          Series.dropna : Drop missing values.
          Index.dropna : Drop missing indices.
  
          Examples
          --------
          >>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
          ...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
          ...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
          ...                             pd.NaT]})
          >>> df
                 name        toy       born
          0    Alfred        NaN        NaT
          1    Batman  Batmobile 1940-04-25
          2  Catwoman   Bullwhip        NaT
  
          Drop the rows where at least one element is missing.
  
          >>> df.dropna()
               name        toy       born
          1  Batman  Batmobile 1940-04-25
  
          Drop the columns where at least one element is missing.
  
          >>> df.dropna(axis='columns')
                 name
          0    Alfred
          1    Batman
          2  Catwoman
  
          Drop the rows where all elements are missing.
  
          >>> df.dropna(how='all')
                 name        toy       born
          0    Alfred        NaN        NaT
          1    Batman  Batmobile 1940-04-25
          2  Catwoman   Bullwhip        NaT
  
          Keep only the rows with at least 2 non-NA values.
  
          >>> df.dropna(thresh=2)
                 name        toy       born
          1    Batman  Batmobile 1940-04-25
          2  Catwoman   Bullwhip        NaT
  
          Define in which columns to look for missing values.
  
          >>> df.dropna(subset=['name', 'toy'])
                 name        toy       born
          1    Batman  Batmobile 1940-04-25
          2  Catwoman   Bullwhip        NaT
  
          Keep the DataFrame with valid entries in the same variable.
  
          >>> df.dropna(inplace=True)
          >>> df
               name        toy       born
          1  Batman  Batmobile 1940-04-25
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
          if isinstance(axis, (tuple, list)):
              # GH20987
              raise TypeError("supplying multiple axes to axis is no longer supported.")
  
          axis = self._get_axis_number(axis)
          agg_axis = 1 - axis
  
          agg_obj = self
          if subset is not None:
              ax = self._get_axis(agg_axis)
              indices = ax.get_indexer_for(subset)
              check = indices == -1
              if check.any():
                  raise KeyError(list(np.compress(check, subset)))
              agg_obj = self.take(indices, axis=agg_axis)
  
          count = agg_obj.count(axis=agg_axis)
  
          if thresh is not None:
              mask = count >= thresh
          elif how == "any":
              mask = count == len(agg_obj._get_axis(agg_axis))
          elif how == "all":
              mask = count > 0
          else:
              if how is not None:
                  raise ValueError(f"invalid how option: {how}")
              else:
                  raise TypeError("must specify how or thresh")
  
          result = self.loc(axis=axis)[mask]
  
          if inplace:
              self._update_inplace(result)
          else:
              return result
  
      def drop_duplicates(
          self,
          subset: Optional[Union[Hashable, Sequence[Hashable]]] = None,
          keep: Union[str, bool] = "first",
          inplace: bool = False,
          ignore_index: bool = False,
      ) -> Optional[DataFrame]:
          """
          Return DataFrame with duplicate rows removed.
  
          Considering certain columns is optional. Indexes, including time indexes
          are ignored.
  
          Parameters
          ----------
          subset : column label or sequence of labels, optional
              Only consider certain columns for identifying duplicates, by
              default use all of the columns.
          keep : {'first', 'last', False}, default 'first'
              Determines which duplicates (if any) to keep.
              - ``first`` : Drop duplicates except for the first occurrence.
              - ``last`` : Drop duplicates except for the last occurrence.
              - False : Drop all duplicates.
          inplace : bool, default False
              Whether to drop duplicates in place or to return a copy.
          ignore_index : bool, default False
              If True, the resulting axis will be labeled 0, 1, …, n - 1.
  
              .. versionadded:: 1.0.0
  
          Returns
          -------
          DataFrame or None
              DataFrame with duplicates removed or None if ``inplace=True``.
  
          See Also
          --------
          DataFrame.value_counts: Count unique combinations of columns.
  
          Examples
          --------
          Consider dataset containing ramen rating.
  
          >>> df = pd.DataFrame({
          ...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
          ...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
          ...     'rating': [4, 4, 3.5, 15, 5]
          ... })
          >>> df
              brand style  rating
          0  Yum Yum   cup     4.0
          1  Yum Yum   cup     4.0
          2  Indomie   cup     3.5
          3  Indomie  pack    15.0
          4  Indomie  pack     5.0
  
          By default, it removes duplicate rows based on all columns.
  
          >>> df.drop_duplicates()
              brand style  rating
          0  Yum Yum   cup     4.0
          2  Indomie   cup     3.5
          3  Indomie  pack    15.0
          4  Indomie  pack     5.0
  
          To remove duplicates on specific column(s), use ``subset``.
  
          >>> df.drop_duplicates(subset=['brand'])
              brand style  rating
          0  Yum Yum   cup     4.0
          2  Indomie   cup     3.5
  
          To remove duplicates and keep last occurrences, use ``keep``.
  
          >>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
              brand style  rating
          1  Yum Yum   cup     4.0
          2  Indomie   cup     3.5
          4  Indomie  pack     5.0
          """
          if self.empty:
              return self.copy()
  
          inplace = validate_bool_kwarg(inplace, "inplace")
          ignore_index = validate_bool_kwarg(ignore_index, "ignore_index")
          duplicated = self.duplicated(subset, keep=keep)
  
          result = self[-duplicated]
          if ignore_index:
              result.index = ibase.default_index(len(result))
  
          if inplace:
              self._update_inplace(result)
              return None
          else:
              return result
  
      def duplicated(
          self,
          subset: Optional[Union[Hashable, Sequence[Hashable]]] = None,
          keep: Union[str, bool] = "first",
      ) -> Series:
          """
          Return boolean Series denoting duplicate rows.
  
          Considering certain columns is optional.
  
          Parameters
          ----------
          subset : column label or sequence of labels, optional
              Only consider certain columns for identifying duplicates, by
              default use all of the columns.
          keep : {'first', 'last', False}, default 'first'
              Determines which duplicates (if any) to mark.
  
              - ``first`` : Mark duplicates as ``True`` except for the first occurrence.
              - ``last`` : Mark duplicates as ``True`` except for the last occurrence.
              - False : Mark all duplicates as ``True``.
  
          Returns
          -------
          Series
              Boolean series for each duplicated rows.
  
          See Also
          --------
          Index.duplicated : Equivalent method on index.
          Series.duplicated : Equivalent method on Series.
          Series.drop_duplicates : Remove duplicate values from Series.
          DataFrame.drop_duplicates : Remove duplicate values from DataFrame.
  
          Examples
          --------
          Consider dataset containing ramen rating.
  
          >>> df = pd.DataFrame({
          ...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
          ...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
          ...     'rating': [4, 4, 3.5, 15, 5]
          ... })
          >>> df
              brand style  rating
          0  Yum Yum   cup     4.0
          1  Yum Yum   cup     4.0
          2  Indomie   cup     3.5
          3  Indomie  pack    15.0
          4  Indomie  pack     5.0
  
          By default, for each set of duplicated values, the first occurrence
          is set on False and all others on True.
  
          >>> df.duplicated()
          0    False
          1     True
          2    False
          3    False
          4    False
          dtype: bool
  
          By using 'last', the last occurrence of each set of duplicated values
          is set on False and all others on True.
  
          >>> df.duplicated(keep='last')
          0     True
          1    False
          2    False
          3    False
          4    False
          dtype: bool
  
          By setting ``keep`` on False, all duplicates are True.
  
          >>> df.duplicated(keep=False)
          0     True
          1     True
          2    False
          3    False
          4    False
          dtype: bool
  
          To find duplicates on specific column(s), use ``subset``.
  
          >>> df.duplicated(subset=['brand'])
          0    False
          1     True
          2    False
          3     True
          4     True
          dtype: bool
          """
          from pandas._libs.hashtable import SIZE_HINT_LIMIT, duplicated_int64
  
          if self.empty:
              return self._constructor_sliced(dtype=bool)
  
          def f(vals):
              labels, shape = algorithms.factorize(
                  vals, size_hint=min(len(self), SIZE_HINT_LIMIT)
              )
              return labels.astype("i8", copy=False), len(shape)
  
          if subset is None:
              subset = self.columns
          elif (
              not np.iterable(subset)
              or isinstance(subset, str)
              or isinstance(subset, tuple)
              and subset in self.columns
          ):
              subset = (subset,)
  
          #  needed for mypy since can't narrow types using np.iterable
          subset = cast(Iterable, subset)
  
          # Verify all columns in subset exist in the queried dataframe
          # Otherwise, raise a KeyError, same as if you try to __getitem__ with a
          # key that doesn't exist.
          diff = Index(subset).difference(self.columns)
          if not diff.empty:
              raise KeyError(diff)
  
          vals = (col.values for name, col in self.items() if name in subset)
          labels, shape = map(list, zip(*map(f, vals)))
  
          ids = get_group_index(labels, shape, sort=False, xnull=False)
          result = self._constructor_sliced(duplicated_int64(ids, keep), index=self.index)
          return result.__finalize__(self, method="duplicated")
  
      # ----------------------------------------------------------------------
      # Sorting
      # TODO: Just move the sort_values doc here.
      @Substitution(**_shared_doc_kwargs)
      @Appender(NDFrame.sort_values.__doc__)
      # error: Signature of "sort_values" incompatible with supertype "NDFrame"
      def sort_values(  # type: ignore[override]
          self,
          by,
          axis=0,
          ascending=True,
          inplace=False,
          kind="quicksort",
          na_position="last",
          ignore_index=False,
          key: ValueKeyFunc = None,
      ):
          inplace = validate_bool_kwarg(inplace, "inplace")
          axis = self._get_axis_number(axis)
  
          if not isinstance(by, list):
              by = [by]
          if is_sequence(ascending) and len(by) != len(ascending):
              raise ValueError(
                  f"Length of ascending ({len(ascending)}) != length of by ({len(by)})"
              )
          if len(by) > 1:
  
              keys = [self._get_label_or_level_values(x, axis=axis) for x in by]
  
              # need to rewrap columns in Series to apply key function
              if key is not None:
                  keys = [Series(k, name=name) for (k, name) in zip(keys, by)]
  
              indexer = lexsort_indexer(
                  keys, orders=ascending, na_position=na_position, key=key
              )
              indexer = ensure_platform_int(indexer)
          else:
  
              by = by[0]
              k = self._get_label_or_level_values(by, axis=axis)
  
              # need to rewrap column in Series to apply key function
              if key is not None:
                  k = Series(k, name=by)
  
              if isinstance(ascending, (tuple, list)):
                  ascending = ascending[0]
  
              indexer = nargsort(
                  k, kind=kind, ascending=ascending, na_position=na_position, key=key
              )
  
          new_data = self._mgr.take(
              indexer, axis=self._get_block_manager_axis(axis), verify=False
          )
  
          if ignore_index:
              new_data.axes[1] = ibase.default_index(len(indexer))
  
          result = self._constructor(new_data)
          if inplace:
              return self._update_inplace(result)
          else:
              return result.__finalize__(self, method="sort_values")
  
      def sort_index(
          self,
          axis=0,
          level=None,
          ascending: bool = True,
          inplace: bool = False,
          kind: str = "quicksort",
          na_position: str = "last",
          sort_remaining: bool = True,
          ignore_index: bool = False,
          key: IndexKeyFunc = None,
      ):
          """
          Sort object by labels (along an axis).
  
          Returns a new DataFrame sorted by label if `inplace` argument is
          ``False``, otherwise updates the original DataFrame and returns None.
  
          Parameters
          ----------
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis along which to sort.  The value 0 identifies the rows,
              and 1 identifies the columns.
          level : int or level name or list of ints or list of level names
              If not None, sort on values in specified index level(s).
          ascending : bool or list of bools, default True
              Sort ascending vs. descending. When the index is a MultiIndex the
              sort direction can be controlled for each level individually.
          inplace : bool, default False
              If True, perform operation in-place.
          kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
              Choice of sorting algorithm. See also ndarray.np.sort for more
              information.  `mergesort` is the only stable algorithm. For
              DataFrames, this option is only applied when sorting on a single
              column or label.
          na_position : {'first', 'last'}, default 'last'
              Puts NaNs at the beginning if `first`; `last` puts NaNs at the end.
              Not implemented for MultiIndex.
          sort_remaining : bool, default True
              If True and sorting by level and index is multilevel, sort by other
              levels too (in order) after sorting by specified level.
          ignore_index : bool, default False
              If True, the resulting axis will be labeled 0, 1, …, n - 1.
  
              .. versionadded:: 1.0.0
  
          key : callable, optional
              If not None, apply the key function to the index values
              before sorting. This is similar to the `key` argument in the
              builtin :meth:`sorted` function, with the notable difference that
              this `key` function should be *vectorized*. It should expect an
              ``Index`` and return an ``Index`` of the same shape. For MultiIndex
              inputs, the key is applied *per level*.
  
              .. versionadded:: 1.1.0
  
          Returns
          -------
          DataFrame or None
              The original DataFrame sorted by the labels or None if ``inplace=True``.
  
          See Also
          --------
          Series.sort_index : Sort Series by the index.
          DataFrame.sort_values : Sort DataFrame by the value.
          Series.sort_values : Sort Series by the value.
  
          Examples
          --------
          >>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],
          ...                   columns=['A'])
          >>> df.sort_index()
               A
          1    4
          29   2
          100  1
          150  5
          234  3
  
          By default, it sorts in ascending order, to sort in descending order,
          use ``ascending=False``
  
          >>> df.sort_index(ascending=False)
               A
          234  3
          150  5
          100  1
          29   2
          1    4
  
          A key function can be specified which is applied to the index before
          sorting. For a ``MultiIndex`` this is applied to each level separately.
  
          >>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
          >>> df.sort_index(key=lambda x: x.str.lower())
             a
          A  1
          b  2
          C  3
          d  4
          """
          return super().sort_index(
              axis,
              level,
              ascending,
              inplace,
              kind,
              na_position,
              sort_remaining,
              ignore_index,
              key,
          )
  
      def value_counts(
          self,
          subset: Optional[Sequence[Label]] = None,
          normalize: bool = False,
          sort: bool = True,
          ascending: bool = False,
      ):
          """
          Return a Series containing counts of unique rows in the DataFrame.
  
          .. versionadded:: 1.1.0
  
          Parameters
          ----------
          subset : list-like, optional
              Columns to use when counting unique combinations.
          normalize : bool, default False
              Return proportions rather than frequencies.
          sort : bool, default True
              Sort by frequencies.
          ascending : bool, default False
              Sort in ascending order.
  
          Returns
          -------
          Series
  
          See Also
          --------
          Series.value_counts: Equivalent method on Series.
  
          Notes
          -----
          The returned Series will have a MultiIndex with one level per input
          column. By default, rows that contain any NA values are omitted from
          the result. By default, the resulting Series will be in descending
          order so that the first element is the most frequently-occurring row.
  
          Examples
          --------
          >>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],
          ...                    'num_wings': [2, 0, 0, 0]},
          ...                   index=['falcon', 'dog', 'cat', 'ant'])
          >>> df
                  num_legs  num_wings
          falcon         2          2
          dog            4          0
          cat            4          0
          ant            6          0
  
          >>> df.value_counts()
          num_legs  num_wings
          4         0            2
          2         2            1
          6         0            1
          dtype: int64
  
          >>> df.value_counts(sort=False)
          num_legs  num_wings
          2         2            1
          4         0            2
          6         0            1
          dtype: int64
  
          >>> df.value_counts(ascending=True)
          num_legs  num_wings
          2         2            1
          6         0            1
          4         0            2
          dtype: int64
  
          >>> df.value_counts(normalize=True)
          num_legs  num_wings
          4         0            0.50
          2         2            0.25
          6         0            0.25
          dtype: float64
          """
          if subset is None:
              subset = self.columns.tolist()
  
          counts = self.groupby(subset).grouper.size()
  
          if sort:
              counts = counts.sort_values(ascending=ascending)
          if normalize:
              counts /= counts.sum()
  
          # Force MultiIndex for single column
          if len(subset) == 1:
              counts.index = MultiIndex.from_arrays(
                  [counts.index], names=[counts.index.name]
              )
  
          return counts
  
      def nlargest(self, n, columns, keep="first") -> DataFrame:
          """
          Return the first `n` rows ordered by `columns` in descending order.
  
          Return the first `n` rows with the largest values in `columns`, in
          descending order. The columns that are not specified are returned as
          well, but not used for ordering.
  
          This method is equivalent to
          ``df.sort_values(columns, ascending=False).head(n)``, but more
          performant.
  
          Parameters
          ----------
          n : int
              Number of rows to return.
          columns : label or list of labels
              Column label(s) to order by.
          keep : {'first', 'last', 'all'}, default 'first'
              Where there are duplicate values:
  
              - `first` : prioritize the first occurrence(s)
              - `last` : prioritize the last occurrence(s)
              - ``all`` : do not drop any duplicates, even it means
                          selecting more than `n` items.
  
              .. versionadded:: 0.24.0
  
          Returns
          -------
          DataFrame
              The first `n` rows ordered by the given columns in descending
              order.
  
          See Also
          --------
          DataFrame.nsmallest : Return the first `n` rows ordered by `columns` in
              ascending order.
          DataFrame.sort_values : Sort DataFrame by the values.
          DataFrame.head : Return the first `n` rows without re-ordering.
  
          Notes
          -----
          This function cannot be used with all column types. For example, when
          specifying columns with `object` or `category` dtypes, ``TypeError`` is
          raised.
  
          Examples
          --------
          >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
          ...                                   434000, 434000, 337000, 11300,
          ...                                   11300, 11300],
          ...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
          ...                            17036, 182, 38, 311],
          ...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
          ...                                "IS", "NR", "TV", "AI"]},
          ...                   index=["Italy", "France", "Malta",
          ...                          "Maldives", "Brunei", "Iceland",
          ...                          "Nauru", "Tuvalu", "Anguilla"])
          >>> df
                    population      GDP alpha-2
          Italy       59000000  1937894      IT
          France      65000000  2583560      FR
          Malta         434000    12011      MT
          Maldives      434000     4520      MV
          Brunei        434000    12128      BN
          Iceland       337000    17036      IS
          Nauru          11300      182      NR
          Tuvalu         11300       38      TV
          Anguilla       11300      311      AI
  
          In the following example, we will use ``nlargest`` to select the three
          rows having the largest values in column "population".
  
          >>> df.nlargest(3, 'population')
                  population      GDP alpha-2
          France    65000000  2583560      FR
          Italy     59000000  1937894      IT
          Malta       434000    12011      MT
  
          When using ``keep='last'``, ties are resolved in reverse order:
  
          >>> df.nlargest(3, 'population', keep='last')
                  population      GDP alpha-2
          France    65000000  2583560      FR
          Italy     59000000  1937894      IT
          Brunei      434000    12128      BN
  
          When using ``keep='all'``, all duplicate items are maintained:
  
          >>> df.nlargest(3, 'population', keep='all')
                    population      GDP alpha-2
          France      65000000  2583560      FR
          Italy       59000000  1937894      IT
          Malta         434000    12011      MT
          Maldives      434000     4520      MV
          Brunei        434000    12128      BN
  
          To order by the largest values in column "population" and then "GDP",
          we can specify multiple columns like in the next example.
  
          >>> df.nlargest(3, ['population', 'GDP'])
                  population      GDP alpha-2
          France    65000000  2583560      FR
          Italy     59000000  1937894      IT
          Brunei      434000    12128      BN
          """
          return algorithms.SelectNFrame(self, n=n, keep=keep, columns=columns).nlargest()
  
      def nsmallest(self, n, columns, keep="first") -> DataFrame:
          """
          Return the first `n` rows ordered by `columns` in ascending order.
  
          Return the first `n` rows with the smallest values in `columns`, in
          ascending order. The columns that are not specified are returned as
          well, but not used for ordering.
  
          This method is equivalent to
          ``df.sort_values(columns, ascending=True).head(n)``, but more
          performant.
  
          Parameters
          ----------
          n : int
              Number of items to retrieve.
          columns : list or str
              Column name or names to order by.
          keep : {'first', 'last', 'all'}, default 'first'
              Where there are duplicate values:
  
              - ``first`` : take the first occurrence.
              - ``last`` : take the last occurrence.
              - ``all`` : do not drop any duplicates, even it means
                selecting more than `n` items.
  
              .. versionadded:: 0.24.0
  
          Returns
          -------
          DataFrame
  
          See Also
          --------
          DataFrame.nlargest : Return the first `n` rows ordered by `columns` in
              descending order.
          DataFrame.sort_values : Sort DataFrame by the values.
          DataFrame.head : Return the first `n` rows without re-ordering.
  
          Examples
          --------
          >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
          ...                                   434000, 434000, 337000, 337000,
          ...                                   11300, 11300],
          ...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
          ...                            17036, 182, 38, 311],
          ...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
          ...                                "IS", "NR", "TV", "AI"]},
          ...                   index=["Italy", "France", "Malta",
          ...                          "Maldives", "Brunei", "Iceland",
          ...                          "Nauru", "Tuvalu", "Anguilla"])
          >>> df
                    population      GDP alpha-2
          Italy       59000000  1937894      IT
          France      65000000  2583560      FR
          Malta         434000    12011      MT
          Maldives      434000     4520      MV
          Brunei        434000    12128      BN
          Iceland       337000    17036      IS
          Nauru         337000      182      NR
          Tuvalu         11300       38      TV
          Anguilla       11300      311      AI
  
          In the following example, we will use ``nsmallest`` to select the
          three rows having the smallest values in column "population".
  
          >>> df.nsmallest(3, 'population')
                    population    GDP alpha-2
          Tuvalu         11300     38      TV
          Anguilla       11300    311      AI
          Iceland       337000  17036      IS
  
          When using ``keep='last'``, ties are resolved in reverse order:
  
          >>> df.nsmallest(3, 'population', keep='last')
                    population  GDP alpha-2
          Anguilla       11300  311      AI
          Tuvalu         11300   38      TV
          Nauru         337000  182      NR
  
          When using ``keep='all'``, all duplicate items are maintained:
  
          >>> df.nsmallest(3, 'population', keep='all')
                    population    GDP alpha-2
          Tuvalu         11300     38      TV
          Anguilla       11300    311      AI
          Iceland       337000  17036      IS
          Nauru         337000    182      NR
  
          To order by the smallest values in column "population" and then "GDP", we can
          specify multiple columns like in the next example.
  
          >>> df.nsmallest(3, ['population', 'GDP'])
                    population  GDP alpha-2
          Tuvalu         11300   38      TV
          Anguilla       11300  311      AI
          Nauru         337000  182      NR
          """
          return algorithms.SelectNFrame(
              self, n=n, keep=keep, columns=columns
          ).nsmallest()
  
      def swaplevel(self, i=-2, j=-1, axis=0) -> DataFrame:
          """
          Swap levels i and j in a MultiIndex on a particular axis.
  
          Parameters
          ----------
          i, j : int or str
              Levels of the indices to be swapped. Can pass level name as string.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to swap levels on. 0 or 'index' for row-wise, 1 or
              'columns' for column-wise.
  
          Returns
          -------
          DataFrame
          """
          result = self.copy()
  
          axis = self._get_axis_number(axis)
  
          if not isinstance(result._get_axis(axis), MultiIndex):  # pragma: no cover
              raise TypeError("Can only swap levels on a hierarchical axis.")
  
          if axis == 0:
              assert isinstance(result.index, MultiIndex)
              result.index = result.index.swaplevel(i, j)
          else:
              assert isinstance(result.columns, MultiIndex)
              result.columns = result.columns.swaplevel(i, j)
          return result
  
      def reorder_levels(self, order, axis=0) -> DataFrame:
          """
          Rearrange index levels using input order. May not drop or duplicate levels.
  
          Parameters
          ----------
          order : list of int or list of str
              List representing new level order. Reference level by number
              (position) or by key (label).
          axis : {0 or 'index', 1 or 'columns'}, default 0
              Where to reorder levels.
  
          Returns
          -------
          DataFrame
          """
          axis = self._get_axis_number(axis)
          if not isinstance(self._get_axis(axis), MultiIndex):  # pragma: no cover
              raise TypeError("Can only reorder levels on a hierarchical axis.")
  
          result = self.copy()
  
          if axis == 0:
              assert isinstance(result.index, MultiIndex)
              result.index = result.index.reorder_levels(order)
          else:
              assert isinstance(result.columns, MultiIndex)
              result.columns = result.columns.reorder_levels(order)
          return result
  
      # ----------------------------------------------------------------------
      # Arithmetic Methods
  
      def _cmp_method(self, other, op):
          axis = 1  # only relevant for Series other case
  
          self, other = ops.align_method_FRAME(self, other, axis, flex=False, level=None)
  
          # See GH#4537 for discussion of scalar op behavior
          new_data = self._dispatch_frame_op(other, op, axis=axis)
          return self._construct_result(new_data)
  
      def _arith_method(self, other, op):
          if ops.should_reindex_frame_op(self, other, op, 1, 1, None, None):
              return ops.frame_arith_method_with_reindex(self, other, op)
  
          axis = 1  # only relevant for Series other case
  
          self, other = ops.align_method_FRAME(self, other, axis, flex=True, level=None)
  
          new_data = self._dispatch_frame_op(other, op, axis=axis)
          return self._construct_result(new_data)
  
      _logical_method = _arith_method
  
      def _dispatch_frame_op(self, right, func, axis: Optional[int] = None):
          """
          Evaluate the frame operation func(left, right) by evaluating
          column-by-column, dispatching to the Series implementation.
  
          Parameters
          ----------
          right : scalar, Series, or DataFrame
          func : arithmetic or comparison operator
          axis : {None, 0, 1}
  
          Returns
          -------
          DataFrame
          """
          # Get the appropriate array-op to apply to each column/block's values.
          array_op = ops.get_array_op(func)
  
          right = lib.item_from_zerodim(right)
          if not is_list_like(right):
              # i.e. scalar, faster than checking np.ndim(right) == 0
              bm = self._mgr.apply(array_op, right=right)
              return type(self)(bm)
  
          elif isinstance(right, DataFrame):
              assert self.index.equals(right.index)
              assert self.columns.equals(right.columns)
              # TODO: The previous assertion `assert right._indexed_same(self)`
              #  fails in cases with empty columns reached via
              #  _frame_arith_method_with_reindex
  
              bm = self._mgr.operate_blockwise(right._mgr, array_op)
              return type(self)(bm)
  
          elif isinstance(right, Series) and axis == 1:
              # axis=1 means we want to operate row-by-row
              assert right.index.equals(self.columns)
  
              right = right._values
              # maybe_align_as_frame ensures we do not have an ndarray here
              assert not isinstance(right, np.ndarray)
  
              arrays = [
                  array_op(_left, _right)
                  for _left, _right in zip(self._iter_column_arrays(), right)
              ]
  
          elif isinstance(right, Series):
              assert right.index.equals(self.index)  # Handle other cases later
              right = right._values
  
              arrays = [array_op(left, right) for left in self._iter_column_arrays()]
  
          else:
              # Remaining cases have less-obvious dispatch rules
              raise NotImplementedError(right)
  
          return type(self)._from_arrays(
              arrays, self.columns, self.index, verify_integrity=False
          )
  
      def _combine_frame(self, other: DataFrame, func, fill_value=None):
          # at this point we have `self._indexed_same(other)`
  
          if fill_value is None:
              # since _arith_op may be called in a loop, avoid function call
              #  overhead if possible by doing this check once
              _arith_op = func
  
          else:
  
              def _arith_op(left, right):
                  # for the mixed_type case where we iterate over columns,
                  # _arith_op(left, right) is equivalent to
                  # left._binop(right, func, fill_value=fill_value)
                  left, right = ops.fill_binop(left, right, fill_value)
                  return func(left, right)
  
          new_data = self._dispatch_frame_op(other, _arith_op)
          return new_data
  
      def _construct_result(self, result) -> DataFrame:
          """
          Wrap the result of an arithmetic, comparison, or logical operation.
  
          Parameters
          ----------
          result : DataFrame
  
          Returns
          -------
          DataFrame
          """
          out = self._constructor(result, copy=False)
          # Pin columns instead of passing to constructor for compat with
          #  non-unique columns case
          out.columns = self.columns
          out.index = self.index
          return out
  
      def __divmod__(self, other) -> Tuple[DataFrame, DataFrame]:
          # Naive implementation, room for optimization
          div = self // other
          mod = self - div * other
          return div, mod
  
      def __rdivmod__(self, other) -> Tuple[DataFrame, DataFrame]:
          # Naive implementation, room for optimization
          div = other // self
          mod = other - div * self
          return div, mod
  
      # ----------------------------------------------------------------------
      # Combination-Related
  
      @doc(
          _shared_docs["compare"],
          """
  Returns
  -------
  DataFrame
      DataFrame that shows the differences stacked side by side.
  
      The resulting index will be a MultiIndex with 'self' and 'other'
      stacked alternately at the inner level.
  
  Raises
  ------
  ValueError
      When the two DataFrames don't have identical labels or shape.
  
  See Also
  --------
  Series.compare : Compare with another Series and show differences.
  DataFrame.equals : Test whether two objects contain the same elements.
  
  Notes
  -----
  Matching NaNs will not appear as a difference.
  
  Can only compare identically-labeled
  (i.e. same shape, identical row and column labels) DataFrames
  
  Examples
  --------
  >>> df = pd.DataFrame(
  ...     {{
  ...         "col1": ["a", "a", "b", "b", "a"],
  ...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
  ...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
  ...     }},
  ...     columns=["col1", "col2", "col3"],
  ... )
  >>> df
    col1  col2  col3
  0    a   1.0   1.0
  1    a   2.0   2.0
  2    b   3.0   3.0
  3    b   NaN   4.0
  4    a   5.0   5.0
  
  >>> df2 = df.copy()
  >>> df2.loc[0, 'col1'] = 'c'
  >>> df2.loc[2, 'col3'] = 4.0
  >>> df2
    col1  col2  col3
  0    c   1.0   1.0
  1    a   2.0   2.0
  2    b   3.0   4.0
  3    b   NaN   4.0
  4    a   5.0   5.0
  
  Align the differences on columns
  
  >>> df.compare(df2)
    col1       col3
    self other self other
  0    a     c  NaN   NaN
  2  NaN   NaN  3.0   4.0
  
  Stack the differences on rows
  
  >>> df.compare(df2, align_axis=0)
          col1  col3
  0 self     a   NaN
    other    c   NaN
  2 self   NaN   3.0
    other  NaN   4.0
  
  Keep the equal values
  
  >>> df.compare(df2, keep_equal=True)
    col1       col3
    self other self other
  0    a     c  1.0   1.0
  2    b     b  3.0   4.0
  
  Keep all original rows and columns
  
  >>> df.compare(df2, keep_shape=True)
    col1       col2       col3
    self other self other self other
  0    a     c  NaN   NaN  NaN   NaN
  1  NaN   NaN  NaN   NaN  NaN   NaN
  2  NaN   NaN  NaN   NaN  3.0   4.0
  3  NaN   NaN  NaN   NaN  NaN   NaN
  4  NaN   NaN  NaN   NaN  NaN   NaN
  
  Keep all original rows and columns and also all original values
  
  >>> df.compare(df2, keep_shape=True, keep_equal=True)
    col1       col2       col3
    self other self other self other
  0    a     c  1.0   1.0  1.0   1.0
  1    a     a  2.0   2.0  2.0   2.0
  2    b     b  3.0   3.0  3.0   4.0
  3    b     b  NaN   NaN  4.0   4.0
  4    a     a  5.0   5.0  5.0   5.0
  """,
          klass=_shared_doc_kwargs["klass"],
      )
      def compare(
          self,
          other: DataFrame,
          align_axis: Axis = 1,
          keep_shape: bool = False,
          keep_equal: bool = False,
      ) -> DataFrame:
          return super().compare(
              other=other,
              align_axis=align_axis,
              keep_shape=keep_shape,
              keep_equal=keep_equal,
          )
  
      def combine(
          self, other: DataFrame, func, fill_value=None, overwrite=True
      ) -> DataFrame:
          """
          Perform column-wise combine with another DataFrame.
  
          Combines a DataFrame with `other` DataFrame using `func`
          to element-wise combine columns. The row and column indexes of the
          resulting DataFrame will be the union of the two.
  
          Parameters
          ----------
          other : DataFrame
              The DataFrame to merge column-wise.
          func : function
              Function that takes two series as inputs and return a Series or a
              scalar. Used to merge the two dataframes column by columns.
          fill_value : scalar value, default None
              The value to fill NaNs with prior to passing any column to the
              merge func.
          overwrite : bool, default True
              If True, columns in `self` that do not exist in `other` will be
              overwritten with NaNs.
  
          Returns
          -------
          DataFrame
              Combination of the provided DataFrames.
  
          See Also
          --------
          DataFrame.combine_first : Combine two DataFrame objects and default to
              non-null values in frame calling the method.
  
          Examples
          --------
          Combine using a simple function that chooses the smaller column.
  
          >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
          >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
          >>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
          >>> df1.combine(df2, take_smaller)
             A  B
          0  0  3
          1  0  3
  
          Example using a true element-wise combine function.
  
          >>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
          >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
          >>> df1.combine(df2, np.minimum)
             A  B
          0  1  2
          1  0  3
  
          Using `fill_value` fills Nones prior to passing the column to the
          merge function.
  
          >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
          >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
          >>> df1.combine(df2, take_smaller, fill_value=-5)
             A    B
          0  0 -5.0
          1  0  4.0
  
          However, if the same element in both dataframes is None, that None
          is preserved
  
          >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
          >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
          >>> df1.combine(df2, take_smaller, fill_value=-5)
              A    B
          0  0 -5.0
          1  0  3.0
  
          Example that demonstrates the use of `overwrite` and behavior when
          the axis differ between the dataframes.
  
          >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
          >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
          >>> df1.combine(df2, take_smaller)
               A    B     C
          0  NaN  NaN   NaN
          1  NaN  3.0 -10.0
          2  NaN  3.0   1.0
  
          >>> df1.combine(df2, take_smaller, overwrite=False)
               A    B     C
          0  0.0  NaN   NaN
          1  0.0  3.0 -10.0
          2  NaN  3.0   1.0
  
          Demonstrating the preference of the passed in dataframe.
  
          >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
          >>> df2.combine(df1, take_smaller)
             A    B   C
          0  0.0  NaN NaN
          1  0.0  3.0 NaN
          2  NaN  3.0 NaN
  
          >>> df2.combine(df1, take_smaller, overwrite=False)
               A    B   C
          0  0.0  NaN NaN
          1  0.0  3.0 1.0
          2  NaN  3.0 1.0
          """
          other_idxlen = len(other.index)  # save for compare
  
          this, other = self.align(other, copy=False)
          new_index = this.index
  
          if other.empty and len(new_index) == len(self.index):
              return self.copy()
  
          if self.empty and len(other) == other_idxlen:
              return other.copy()
  
          # sorts if possible
          new_columns = this.columns.union(other.columns)
          do_fill = fill_value is not None
          result = {}
          for col in new_columns:
              series = this[col]
              otherSeries = other[col]
  
              this_dtype = series.dtype
              other_dtype = otherSeries.dtype
  
              this_mask = isna(series)
              other_mask = isna(otherSeries)
  
              # don't overwrite columns unnecessarily
              # DO propagate if this column is not in the intersection
              if not overwrite and other_mask.all():
                  result[col] = this[col].copy()
                  continue
  
              if do_fill:
                  series = series.copy()
                  otherSeries = otherSeries.copy()
                  series[this_mask] = fill_value
                  otherSeries[other_mask] = fill_value
  
              if col not in self.columns:
                  # If self DataFrame does not have col in other DataFrame,
                  # try to promote series, which is all NaN, as other_dtype.
                  new_dtype = other_dtype
                  try:
                      series = series.astype(new_dtype, copy=False)
                  except ValueError:
                      # e.g. new_dtype is integer types
                      pass
              else:
                  # if we have different dtypes, possibly promote
                  new_dtype = find_common_type([this_dtype, other_dtype])
                  if not is_dtype_equal(this_dtype, new_dtype):
                      series = series.astype(new_dtype)
                  if not is_dtype_equal(other_dtype, new_dtype):
                      otherSeries = otherSeries.astype(new_dtype)
  
              arr = func(series, otherSeries)
              arr = maybe_downcast_to_dtype(arr, new_dtype)
  
              result[col] = arr
  
          # convert_objects just in case
          return self._constructor(result, index=new_index, columns=new_columns)
  
      def combine_first(self, other: DataFrame) -> DataFrame:
          """
          Update null elements with value in the same location in `other`.
  
          Combine two DataFrame objects by filling null values in one DataFrame
          with non-null values from other DataFrame. The row and column indexes
          of the resulting DataFrame will be the union of the two.
  
          Parameters
          ----------
          other : DataFrame
              Provided DataFrame to use to fill null values.
  
          Returns
          -------
          DataFrame
  
          See Also
          --------
          DataFrame.combine : Perform series-wise operation on two DataFrames
              using a given function.
  
          Examples
          --------
          >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
          >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
          >>> df1.combine_first(df2)
               A    B
          0  1.0  3.0
          1  0.0  4.0
  
          Null values still persist if the location of that null value
          does not exist in `other`
  
          >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
          >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
          >>> df1.combine_first(df2)
               A    B    C
          0  NaN  4.0  NaN
          1  0.0  3.0  1.0
          2  NaN  3.0  1.0
          """
          import pandas.core.computation.expressions as expressions
  
          def combiner(x, y):
              mask = extract_array(isna(x))
  
              x_values = extract_array(x, extract_numpy=True)
              y_values = extract_array(y, extract_numpy=True)
  
              # If the column y in other DataFrame is not in first DataFrame,
              # just return y_values.
              if y.name not in self.columns:
                  return y_values
  
              return expressions.where(mask, y_values, x_values)
  
          return self.combine(other, combiner, overwrite=False)
  
      def update(
          self, other, join="left", overwrite=True, filter_func=None, errors="ignore"
      ) -> None:
          """
          Modify in place using non-NA values from another DataFrame.
  
          Aligns on indices. There is no return value.
  
          Parameters
          ----------
          other : DataFrame, or object coercible into a DataFrame
              Should have at least one matching index/column label
              with the original DataFrame. If a Series is passed,
              its name attribute must be set, and that will be
              used as the column name to align with the original DataFrame.
          join : {'left'}, default 'left'
              Only left join is implemented, keeping the index and columns of the
              original object.
          overwrite : bool, default True
              How to handle non-NA values for overlapping keys:
  
              * True: overwrite original DataFrame's values
                with values from `other`.
              * False: only update values that are NA in
                the original DataFrame.
  
          filter_func : callable(1d-array) -> bool 1d-array, optional
              Can choose to replace values other than NA. Return True for values
              that should be updated.
          errors : {'raise', 'ignore'}, default 'ignore'
              If 'raise', will raise a ValueError if the DataFrame and `other`
              both contain non-NA data in the same place.
  
              .. versionchanged:: 0.24.0
                 Changed from `raise_conflict=False|True`
                 to `errors='ignore'|'raise'`.
  
          Returns
          -------
          None : method directly changes calling object
  
          Raises
          ------
          ValueError
              * When `errors='raise'` and there's overlapping non-NA data.
              * When `errors` is not either `'ignore'` or `'raise'`
          NotImplementedError
              * If `join != 'left'`
  
          See Also
          --------
          dict.update : Similar method for dictionaries.
          DataFrame.merge : For column(s)-on-column(s) operations.
  
          Examples
          --------
          >>> df = pd.DataFrame({'A': [1, 2, 3],
          ...                    'B': [400, 500, 600]})
          >>> new_df = pd.DataFrame({'B': [4, 5, 6],
          ...                        'C': [7, 8, 9]})
          >>> df.update(new_df)
          >>> df
             A  B
          0  1  4
          1  2  5
          2  3  6
  
          The DataFrame's length does not increase as a result of the update,
          only values at matching index/column labels are updated.
  
          >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
          ...                    'B': ['x', 'y', 'z']})
          >>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
          >>> df.update(new_df)
          >>> df
             A  B
          0  a  d
          1  b  e
          2  c  f
  
          For Series, its name attribute must be set.
  
          >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
          ...                    'B': ['x', 'y', 'z']})
          >>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])
          >>> df.update(new_column)
          >>> df
             A  B
          0  a  d
          1  b  y
          2  c  e
          >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
          ...                    'B': ['x', 'y', 'z']})
          >>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])
          >>> df.update(new_df)
          >>> df
             A  B
          0  a  x
          1  b  d
          2  c  e
  
          If `other` contains NaNs the corresponding values are not updated
          in the original dataframe.
  
          >>> df = pd.DataFrame({'A': [1, 2, 3],
          ...                    'B': [400, 500, 600]})
          >>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
          >>> df.update(new_df)
          >>> df
             A      B
          0  1    4.0
          1  2  500.0
          2  3    6.0
          """
          import pandas.core.computation.expressions as expressions
  
          # TODO: Support other joins
          if join != "left":  # pragma: no cover
              raise NotImplementedError("Only left join is supported")
          if errors not in ["ignore", "raise"]:
              raise ValueError("The parameter errors must be either 'ignore' or 'raise'")
  
          if not isinstance(other, DataFrame):
              other = DataFrame(other)
  
          other = other.reindex_like(self)
  
          for col in self.columns:
              this = self[col]._values
              that = other[col]._values
              if filter_func is not None:
                  with np.errstate(all="ignore"):
                      mask = ~filter_func(this) | isna(that)
              else:
                  if errors == "raise":
                      mask_this = notna(that)
                      mask_that = notna(this)
                      if any(mask_this & mask_that):
                          raise ValueError("Data overlaps.")
  
                  if overwrite:
                      mask = isna(that)
                  else:
                      mask = notna(this)
  
              # don't overwrite columns unnecessarily
              if mask.all():
                  continue
  
              self[col] = expressions.where(mask, this, that)
  
      # ----------------------------------------------------------------------
      # Data reshaping
      @Appender(
          """
  Examples
  --------
  >>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
  ...                               'Parrot', 'Parrot'],
  ...                    'Max Speed': [380., 370., 24., 26.]})
  >>> df
     Animal  Max Speed
  0  Falcon      380.0
  1  Falcon      370.0
  2  Parrot       24.0
  3  Parrot       26.0
  >>> df.groupby(['Animal']).mean()
          Max Speed
  Animal
  Falcon      375.0
  Parrot       25.0
  
  **Hierarchical Indexes**
  
  We can groupby different levels of a hierarchical index
  using the `level` parameter:
  
  >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
  ...           ['Captive', 'Wild', 'Captive', 'Wild']]
  >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
  >>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
  ...                   index=index)
  >>> df
                  Max Speed
  Animal Type
  Falcon Captive      390.0
         Wild         350.0
  Parrot Captive       30.0
         Wild          20.0
  >>> df.groupby(level=0).mean()
          Max Speed
  Animal
  Falcon      370.0
  Parrot       25.0
  >>> df.groupby(level="Type").mean()
           Max Speed
  Type
  Captive      210.0
  Wild         185.0
  
  We can also choose to include NA in group keys or not by setting
  `dropna` parameter, the default setting is `True`:
  
  >>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
  >>> df = pd.DataFrame(l, columns=["a", "b", "c"])
  
  >>> df.groupby(by=["b"]).sum()
      a   c
  b
  1.0 2   3
  2.0 2   5
  
  >>> df.groupby(by=["b"], dropna=False).sum()
      a   c
  b
  1.0 2   3
  2.0 2   5
  NaN 1   4
  
  >>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
  >>> df = pd.DataFrame(l, columns=["a", "b", "c"])
  
  >>> df.groupby(by="a").sum()
      b     c
  a
  a   13.0   13.0
  b   12.3  123.0
  
  >>> df.groupby(by="a", dropna=False).sum()
      b     c
  a
  a   13.0   13.0
  b   12.3  123.0
  NaN 12.3   33.0
  """
      )
      @Appender(_shared_docs["groupby"] % _shared_doc_kwargs)
      def groupby(
          self,
          by=None,
          axis=0,
          level=None,
          as_index: bool = True,
          sort: bool = True,
          group_keys: bool = True,
          squeeze: bool = no_default,
          observed: bool = False,
          dropna: bool = True,
      ) -> DataFrameGroupBy:
          from pandas.core.groupby.generic import DataFrameGroupBy
  
          if squeeze is not no_default:
              warnings.warn(
                  (
                      "The `squeeze` parameter is deprecated and "
                      "will be removed in a future version."
                  ),
                  FutureWarning,
                  stacklevel=2,
              )
          else:
              squeeze = False
  
          if level is None and by is None:
              raise TypeError("You have to supply one of 'by' and 'level'")
          axis = self._get_axis_number(axis)
  
          return DataFrameGroupBy(
              obj=self,
              keys=by,
              axis=axis,
              level=level,
              as_index=as_index,
              sort=sort,
              group_keys=group_keys,
              squeeze=squeeze,
              observed=observed,
              dropna=dropna,
          )
  
      _shared_docs[
          "pivot"
      ] = """
          Return reshaped DataFrame organized by given index / column values.
  
          Reshape data (produce a "pivot" table) based on column values. Uses
          unique values from specified `index` / `columns` to form axes of the
          resulting DataFrame. This function does not support data
          aggregation, multiple values will result in a MultiIndex in the
          columns. See the :ref:`User Guide <reshaping>` for more on reshaping.
  
          Parameters
          ----------%s
          index : str or object or a list of str, optional
              Column to use to make new frame's index. If None, uses
              existing index.
  
              .. versionchanged:: 1.1.0
                 Also accept list of index names.
  
          columns : str or object or a list of str
              Column to use to make new frame's columns.
  
              .. versionchanged:: 1.1.0
                 Also accept list of columns names.
  
          values : str, object or a list of the previous, optional
              Column(s) to use for populating new frame's values. If not
              specified, all remaining columns will be used and the result will
              have hierarchically indexed columns.
  
          Returns
          -------
          DataFrame
              Returns reshaped DataFrame.
  
          Raises
          ------
          ValueError:
              When there are any `index`, `columns` combinations with multiple
              values. `DataFrame.pivot_table` when you need to aggregate.
  
          See Also
          --------
          DataFrame.pivot_table : Generalization of pivot that can handle
              duplicate values for one index/column pair.
          DataFrame.unstack : Pivot based on the index values instead of a
              column.
          wide_to_long : Wide panel to long format. Less flexible but more
              user-friendly than melt.
  
          Notes
          -----
          For finer-tuned control, see hierarchical indexing documentation along
          with the related stack/unstack methods.
  
          Examples
          --------
          >>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
          ...                            'two'],
          ...                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
          ...                    'baz': [1, 2, 3, 4, 5, 6],
          ...                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
          >>> df
              foo   bar  baz  zoo
          0   one   A    1    x
          1   one   B    2    y
          2   one   C    3    z
          3   two   A    4    q
          4   two   B    5    w
          5   two   C    6    t
  
          >>> df.pivot(index='foo', columns='bar', values='baz')
          bar  A   B   C
          foo
          one  1   2   3
          two  4   5   6
  
          >>> df.pivot(index='foo', columns='bar')['baz']
          bar  A   B   C
          foo
          one  1   2   3
          two  4   5   6
  
          >>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
                baz       zoo
          bar   A  B  C   A  B  C
          foo
          one   1  2  3   x  y  z
          two   4  5  6   q  w  t
  
          You could also assign a list of column names or a list of index names.
  
          >>> df = pd.DataFrame({
          ...        "lev1": [1, 1, 1, 2, 2, 2],
          ...        "lev2": [1, 1, 2, 1, 1, 2],
          ...        "lev3": [1, 2, 1, 2, 1, 2],
          ...        "lev4": [1, 2, 3, 4, 5, 6],
          ...        "values": [0, 1, 2, 3, 4, 5]})
          >>> df
              lev1 lev2 lev3 lev4 values
          0   1    1    1    1    0
          1   1    1    2    2    1
          2   1    2    1    3    2
          3   2    1    2    4    3
          4   2    1    1    5    4
          5   2    2    2    6    5
  
          >>> df.pivot(index="lev1", columns=["lev2", "lev3"],values="values")
          lev2    1         2
          lev3    1    2    1    2
          lev1
          1     0.0  1.0  2.0  NaN
          2     4.0  3.0  NaN  5.0
  
          >>> df.pivot(index=["lev1", "lev2"], columns=["lev3"],values="values")
                lev3    1    2
          lev1  lev2
             1     1  0.0  1.0
                   2  2.0  NaN
             2     1  4.0  3.0
                   2  NaN  5.0
  
          A ValueError is raised if there are any duplicates.
  
          >>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
          ...                    "bar": ['A', 'A', 'B', 'C'],
          ...                    "baz": [1, 2, 3, 4]})
          >>> df
             foo bar  baz
          0  one   A    1
          1  one   A    2
          2  two   B    3
          3  two   C    4
  
          Notice that the first two rows are the same for our `index`
          and `columns` arguments.
  
          >>> df.pivot(index='foo', columns='bar', values='baz')
          Traceback (most recent call last):
             ...
          ValueError: Index contains duplicate entries, cannot reshape
          """
  
      @Substitution("")
      @Appender(_shared_docs["pivot"])
      def pivot(self, index=None, columns=None, values=None) -> DataFrame:
          from pandas.core.reshape.pivot import pivot
  
          return pivot(self, index=index, columns=columns, values=values)
  
      _shared_docs[
          "pivot_table"
      ] = """
          Create a spreadsheet-style pivot table as a DataFrame.
  
          The levels in the pivot table will be stored in MultiIndex objects
          (hierarchical indexes) on the index and columns of the result DataFrame.
  
          Parameters
          ----------%s
          values : column to aggregate, optional
          index : column, Grouper, array, or list of the previous
              If an array is passed, it must be the same length as the data. The
              list can contain any of the other types (except list).
              Keys to group by on the pivot table index.  If an array is passed,
              it is being used as the same manner as column values.
          columns : column, Grouper, array, or list of the previous
              If an array is passed, it must be the same length as the data. The
              list can contain any of the other types (except list).
              Keys to group by on the pivot table column.  If an array is passed,
              it is being used as the same manner as column values.
          aggfunc : function, list of functions, dict, default numpy.mean
              If list of functions passed, the resulting pivot table will have
              hierarchical columns whose top level are the function names
              (inferred from the function objects themselves)
              If dict is passed, the key is column to aggregate and value
              is function or list of functions.
          fill_value : scalar, default None
              Value to replace missing values with (in the resulting pivot table,
              after aggregation).
          margins : bool, default False
              Add all row / columns (e.g. for subtotal / grand totals).
          dropna : bool, default True
              Do not include columns whose entries are all NaN.
          margins_name : str, default 'All'
              Name of the row / column that will contain the totals
              when margins is True.
          observed : bool, default False
              This only applies if any of the groupers are Categoricals.
              If True: only show observed values for categorical groupers.
              If False: show all values for categorical groupers.
  
              .. versionchanged:: 0.25.0
  
          Returns
          -------
          DataFrame
              An Excel style pivot table.
  
          See Also
          --------
          DataFrame.pivot : Pivot without aggregation that can handle
              non-numeric data.
          DataFrame.melt: Unpivot a DataFrame from wide to long format,
              optionally leaving identifiers set.
          wide_to_long : Wide panel to long format. Less flexible but more
              user-friendly than melt.
  
          Examples
          --------
          >>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
          ...                          "bar", "bar", "bar", "bar"],
          ...                    "B": ["one", "one", "one", "two", "two",
          ...                          "one", "one", "two", "two"],
          ...                    "C": ["small", "large", "large", "small",
          ...                          "small", "large", "small", "small",
          ...                          "large"],
          ...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
          ...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
          >>> df
               A    B      C  D  E
          0  foo  one  small  1  2
          1  foo  one  large  2  4
          2  foo  one  large  2  5
          3  foo  two  small  3  5
          4  foo  two  small  3  6
          5  bar  one  large  4  6
          6  bar  one  small  5  8
          7  bar  two  small  6  9
          8  bar  two  large  7  9
  
          This first example aggregates values by taking the sum.
  
          >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
          ...                     columns=['C'], aggfunc=np.sum)
          >>> table
          C        large  small
          A   B
          bar one    4.0    5.0
              two    7.0    6.0
          foo one    4.0    1.0
              two    NaN    6.0
  
          We can also fill missing values using the `fill_value` parameter.
  
          >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
          ...                     columns=['C'], aggfunc=np.sum, fill_value=0)
          >>> table
          C        large  small
          A   B
          bar one      4      5
              two      7      6
          foo one      4      1
              two      0      6
  
          The next example aggregates by taking the mean across multiple columns.
  
          >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
          ...                     aggfunc={'D': np.mean,
          ...                              'E': np.mean})
          >>> table
                          D         E
          A   C
          bar large  5.500000  7.500000
              small  5.500000  8.500000
          foo large  2.000000  4.500000
              small  2.333333  4.333333
  
          We can also calculate multiple types of aggregations for any given
          value column.
  
          >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
          ...                     aggfunc={'D': np.mean,
          ...                              'E': [min, max, np.mean]})
          >>> table
                          D    E
                      mean  max      mean  min
          A   C
          bar large  5.500000  9.0  7.500000  6.0
              small  5.500000  9.0  8.500000  8.0
          foo large  2.000000  5.0  4.500000  4.0
              small  2.333333  6.0  4.333333  2.0
          """
  
      @Substitution("")
      @Appender(_shared_docs["pivot_table"])
      def pivot_table(
          self,
          values=None,
          index=None,
          columns=None,
          aggfunc="mean",
          fill_value=None,
          margins=False,
          dropna=True,
          margins_name="All",
          observed=False,
      ) -> DataFrame:
          from pandas.core.reshape.pivot import pivot_table
  
          return pivot_table(
              self,
              values=values,
              index=index,
              columns=columns,
              aggfunc=aggfunc,
              fill_value=fill_value,
              margins=margins,
              dropna=dropna,
              margins_name=margins_name,
              observed=observed,
          )
  
      def stack(self, level=-1, dropna=True):
          """
          Stack the prescribed level(s) from columns to index.
  
          Return a reshaped DataFrame or Series having a multi-level
          index with one or more new inner-most levels compared to the current
          DataFrame. The new inner-most levels are created by pivoting the
          columns of the current dataframe:
  
            - if the columns have a single level, the output is a Series;
            - if the columns have multiple levels, the new index
              level(s) is (are) taken from the prescribed level(s) and
              the output is a DataFrame.
  
          Parameters
          ----------
          level : int, str, list, default -1
              Level(s) to stack from the column axis onto the index
              axis, defined as one index or label, or a list of indices
              or labels.
          dropna : bool, default True
              Whether to drop rows in the resulting Frame/Series with
              missing values. Stacking a column level onto the index
              axis can create combinations of index and column values
              that are missing from the original dataframe. See Examples
              section.
  
          Returns
          -------
          DataFrame or Series
              Stacked dataframe or series.
  
          See Also
          --------
          DataFrame.unstack : Unstack prescribed level(s) from index axis
               onto column axis.
          DataFrame.pivot : Reshape dataframe from long format to wide
               format.
          DataFrame.pivot_table : Create a spreadsheet-style pivot table
               as a DataFrame.
  
          Notes
          -----
          The function is named by analogy with a collection of books
          being reorganized from being side by side on a horizontal
          position (the columns of the dataframe) to being stacked
          vertically on top of each other (in the index of the
          dataframe).
  
          Examples
          --------
          **Single level columns**
  
          >>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
          ...                                     index=['cat', 'dog'],
          ...                                     columns=['weight', 'height'])
  
          Stacking a dataframe with a single level column axis returns a Series:
  
          >>> df_single_level_cols
               weight height
          cat       0      1
          dog       2      3
          >>> df_single_level_cols.stack()
          cat  weight    0
               height    1
          dog  weight    2
               height    3
          dtype: int64
  
          **Multi level columns: simple case**
  
          >>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
          ...                                        ('weight', 'pounds')])
          >>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
          ...                                     index=['cat', 'dog'],
          ...                                     columns=multicol1)
  
          Stacking a dataframe with a multi-level column axis:
  
          >>> df_multi_level_cols1
               weight
                   kg    pounds
          cat       1        2
          dog       2        4
          >>> df_multi_level_cols1.stack()
                      weight
          cat kg           1
              pounds       2
          dog kg           2
              pounds       4
  
          **Missing values**
  
          >>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
          ...                                        ('height', 'm')])
          >>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
          ...                                     index=['cat', 'dog'],
          ...                                     columns=multicol2)
  
          It is common to have missing values when stacking a dataframe
          with multi-level columns, as the stacked dataframe typically
          has more values than the original dataframe. Missing values
          are filled with NaNs:
  
          >>> df_multi_level_cols2
              weight height
                  kg      m
          cat    1.0    2.0
          dog    3.0    4.0
          >>> df_multi_level_cols2.stack()
                  height  weight
          cat kg     NaN     1.0
              m      2.0     NaN
          dog kg     NaN     3.0
              m      4.0     NaN
  
          **Prescribing the level(s) to be stacked**
  
          The first parameter controls which level or levels are stacked:
  
          >>> df_multi_level_cols2.stack(0)
                       kg    m
          cat height  NaN  2.0
              weight  1.0  NaN
          dog height  NaN  4.0
              weight  3.0  NaN
          >>> df_multi_level_cols2.stack([0, 1])
          cat  height  m     2.0
               weight  kg    1.0
          dog  height  m     4.0
               weight  kg    3.0
          dtype: float64
  
          **Dropping missing values**
  
          >>> df_multi_level_cols3 = pd.DataFrame([[None, 1.0], [2.0, 3.0]],
          ...                                     index=['cat', 'dog'],
          ...                                     columns=multicol2)
  
          Note that rows where all values are missing are dropped by
          default but this behaviour can be controlled via the dropna
          keyword parameter:
  
          >>> df_multi_level_cols3
              weight height
                  kg      m
          cat    NaN    1.0
          dog    2.0    3.0
          >>> df_multi_level_cols3.stack(dropna=False)
                  height  weight
          cat kg     NaN     NaN
              m      1.0     NaN
          dog kg     NaN     2.0
              m      3.0     NaN
          >>> df_multi_level_cols3.stack(dropna=True)
                  height  weight
          cat m      1.0     NaN
          dog kg     NaN     2.0
              m      3.0     NaN
          """
          from pandas.core.reshape.reshape import stack, stack_multiple
  
          if isinstance(level, (tuple, list)):
              result = stack_multiple(self, level, dropna=dropna)
          else:
              result = stack(self, level, dropna=dropna)
  
          return result.__finalize__(self, method="stack")
  
      def explode(
          self, column: Union[str, Tuple], ignore_index: bool = False
      ) -> DataFrame:
          """
          Transform each element of a list-like to a row, replicating index values.
  
          .. versionadded:: 0.25.0
  
          Parameters
          ----------
          column : str or tuple
              Column to explode.
          ignore_index : bool, default False
              If True, the resulting index will be labeled 0, 1, …, n - 1.
  
              .. versionadded:: 1.1.0
  
          Returns
          -------
          DataFrame
              Exploded lists to rows of the subset columns;
              index will be duplicated for these rows.
  
          Raises
          ------
          ValueError :
              if columns of the frame are not unique.
  
          See Also
          --------
          DataFrame.unstack : Pivot a level of the (necessarily hierarchical)
              index labels.
          DataFrame.melt : Unpivot a DataFrame from wide format to long format.
          Series.explode : Explode a DataFrame from list-like columns to long format.
  
          Notes
          -----
          This routine will explode list-likes including lists, tuples, sets,
          Series, and np.ndarray. The result dtype of the subset rows will
          be object. Scalars will be returned unchanged, and empty list-likes will
          result in a np.nan for that row. In addition, the ordering of rows in the
          output will be non-deterministic when exploding sets.
  
          Examples
          --------
          >>> df = pd.DataFrame({'A': [[1, 2, 3], 'foo', [], [3, 4]], 'B': 1})
          >>> df
                     A  B
          0  [1, 2, 3]  1
          1        foo  1
          2         []  1
          3     [3, 4]  1
  
          >>> df.explode('A')
               A  B
          0    1  1
          0    2  1
          0    3  1
          1  foo  1
          2  NaN  1
          3    3  1
          3    4  1
          """
          if not (is_scalar(column) or isinstance(column, tuple)):
              raise ValueError("column must be a scalar")
          if not self.columns.is_unique:
              raise ValueError("columns must be unique")
  
          df = self.reset_index(drop=True)
          result = df[column].explode()
          result = df.drop([column], axis=1).join(result)
          if ignore_index:
              result.index = ibase.default_index(len(result))
          else:
              result.index = self.index.take(result.index)
          result = result.reindex(columns=self.columns, copy=False)
  
          return result
  
      def unstack(self, level=-1, fill_value=None):
          """
          Pivot a level of the (necessarily hierarchical) index labels.
  
          Returns a DataFrame having a new level of column labels whose inner-most level
          consists of the pivoted index labels.
  
          If the index is not a MultiIndex, the output will be a Series
          (the analogue of stack when the columns are not a MultiIndex).
  
          Parameters
          ----------
          level : int, str, or list of these, default -1 (last level)
              Level(s) of index to unstack, can pass level name.
          fill_value : int, str or dict
              Replace NaN with this value if the unstack produces missing values.
  
          Returns
          -------
          Series or DataFrame
  
          See Also
          --------
          DataFrame.pivot : Pivot a table based on column values.
          DataFrame.stack : Pivot a level of the column labels (inverse operation
              from `unstack`).
  
          Examples
          --------
          >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
          ...                                    ('two', 'a'), ('two', 'b')])
          >>> s = pd.Series(np.arange(1.0, 5.0), index=index)
          >>> s
          one  a   1.0
               b   2.0
          two  a   3.0
               b   4.0
          dtype: float64
  
          >>> s.unstack(level=-1)
               a   b
          one  1.0  2.0
          two  3.0  4.0
  
          >>> s.unstack(level=0)
             one  two
          a  1.0   3.0
          b  2.0   4.0
  
          >>> df = s.unstack(level=0)
          >>> df.unstack()
          one  a  1.0
               b  2.0
          two  a  3.0
               b  4.0
          dtype: float64
          """
          from pandas.core.reshape.reshape import unstack
  
          result = unstack(self, level, fill_value)
  
          return result.__finalize__(self, method="unstack")
  
      @Appender(_shared_docs["melt"] % {"caller": "df.melt(", "other": "melt"})
      def melt(
          self,
          id_vars=None,
          value_vars=None,
          var_name=None,
          value_name="value",
          col_level=None,
          ignore_index=True,
      ) -> DataFrame:
  
          return melt(
              self,
              id_vars=id_vars,
              value_vars=value_vars,
              var_name=var_name,
              value_name=value_name,
              col_level=col_level,
              ignore_index=ignore_index,
          )
  
      # ----------------------------------------------------------------------
      # Time series-related
  
      @doc(
          Series.diff,
          klass="Dataframe",
          extra_params="axis : {0 or 'index', 1 or 'columns'}, default 0\n    "
          "Take difference over rows (0) or columns (1).\n",
          other_klass="Series",
          examples=dedent(
              """
          Difference with previous row
  
          >>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],
          ...                    'b': [1, 1, 2, 3, 5, 8],
          ...                    'c': [1, 4, 9, 16, 25, 36]})
          >>> df
             a  b   c
          0  1  1   1
          1  2  1   4
          2  3  2   9
          3  4  3  16
          4  5  5  25
          5  6  8  36
  
          >>> df.diff()
               a    b     c
          0  NaN  NaN   NaN
          1  1.0  0.0   3.0
          2  1.0  1.0   5.0
          3  1.0  1.0   7.0
          4  1.0  2.0   9.0
          5  1.0  3.0  11.0
  
          Difference with previous column
  
          >>> df.diff(axis=1)
              a  b   c
          0 NaN  0   0
          1 NaN -1   3
          2 NaN -1   7
          3 NaN -1  13
          4 NaN  0  20
          5 NaN  2  28
  
          Difference with 3rd previous row
  
          >>> df.diff(periods=3)
               a    b     c
          0  NaN  NaN   NaN
          1  NaN  NaN   NaN
          2  NaN  NaN   NaN
          3  3.0  2.0  15.0
          4  3.0  4.0  21.0
          5  3.0  6.0  27.0
  
          Difference with following row
  
          >>> df.diff(periods=-1)
               a    b     c
          0 -1.0  0.0  -3.0
          1 -1.0 -1.0  -5.0
          2 -1.0 -1.0  -7.0
          3 -1.0 -2.0  -9.0
          4 -1.0 -3.0 -11.0
          5  NaN  NaN   NaN
  
          Overflow in input dtype
  
          >>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8)
          >>> df.diff()
                 a
          0    NaN
          1  255.0"""
          ),
      )
      def diff(self, periods: int = 1, axis: Axis = 0) -> DataFrame:
          if not isinstance(periods, int):
              if not (is_float(periods) and periods.is_integer()):
                  raise ValueError("periods must be an integer")
              periods = int(periods)
  
          bm_axis = self._get_block_manager_axis(axis)
  
          if bm_axis == 0 and periods != 0:
              return self - self.shift(periods, axis=axis)
  
          new_data = self._mgr.diff(n=periods, axis=bm_axis)
          return self._constructor(new_data).__finalize__(self, "diff")
  
      # ----------------------------------------------------------------------
      # Function application
  
      def _gotitem(
          self,
          key: Union[Label, List[Label]],
          ndim: int,
          subset: Optional[FrameOrSeriesUnion] = None,
      ) -> FrameOrSeriesUnion:
          """
          Sub-classes to define. Return a sliced object.
  
          Parameters
          ----------
          key : string / list of selections
          ndim : 1,2
              requested ndim of result
          subset : object, default None
              subset to act on
          """
          if subset is None:
              subset = self
          elif subset.ndim == 1:  # is Series
              return subset
  
          # TODO: _shallow_copy(subset)?
          return subset[key]
  
      _agg_summary_and_see_also_doc = dedent(
          """
      The aggregation operations are always performed over an axis, either the
      index (default) or the column axis. This behavior is different from
      `numpy` aggregation functions (`mean`, `median`, `prod`, `sum`, `std`,
      `var`), where the default is to compute the aggregation of the flattened
      array, e.g., ``numpy.mean(arr_2d)`` as opposed to
      ``numpy.mean(arr_2d, axis=0)``.
  
      `agg` is an alias for `aggregate`. Use the alias.
  
      See Also
      --------
      DataFrame.apply : Perform any type of operations.
      DataFrame.transform : Perform transformation type operations.
      core.groupby.GroupBy : Perform operations over groups.
      core.resample.Resampler : Perform operations over resampled bins.
      core.window.Rolling : Perform operations over rolling window.
      core.window.Expanding : Perform operations over expanding window.
      core.window.ExponentialMovingWindow : Perform operation over exponential weighted
          window.
      """
      )
  
      _agg_examples_doc = dedent(
          """
      Examples
      --------
      >>> df = pd.DataFrame([[1, 2, 3],
      ...                    [4, 5, 6],
      ...                    [7, 8, 9],
      ...                    [np.nan, np.nan, np.nan]],
      ...                   columns=['A', 'B', 'C'])
  
      Aggregate these functions over the rows.
  
      >>> df.agg(['sum', 'min'])
              A     B     C
      sum  12.0  15.0  18.0
      min   1.0   2.0   3.0
  
      Different aggregations per column.
  
      >>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
              A    B
      sum  12.0  NaN
      min   1.0  2.0
      max   NaN  8.0
  
      Aggregate different functions over the columns and rename the index of the resulting
      DataFrame.
  
      >>> df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean))
           A    B    C
      x  7.0  NaN  NaN
      y  NaN  2.0  NaN
      z  NaN  NaN  6.0
  
      Aggregate over the columns.
  
      >>> df.agg("mean", axis="columns")
      0    2.0
      1    5.0
      2    8.0
      3    NaN
      dtype: float64
      """
      )
  
      @doc(
          _shared_docs["aggregate"],
          klass=_shared_doc_kwargs["klass"],
          axis=_shared_doc_kwargs["axis"],
          see_also=_agg_summary_and_see_also_doc,
          examples=_agg_examples_doc,
      )
      def aggregate(self, func=None, axis=0, *args, **kwargs):
          axis = self._get_axis_number(axis)
  
          relabeling, func, columns, order = reconstruct_func(func, **kwargs)
  
          result = None
          try:
              result, how = self._aggregate(func, axis, *args, **kwargs)
          except TypeError as err:
              exc = TypeError(
                  "DataFrame constructor called with "
                  f"incompatible data and dtype: {err}"
              )
              raise exc from err
          if result is None:
              return self.apply(func, axis=axis, args=args, **kwargs)
  
          if relabeling:
              # This is to keep the order to columns occurrence unchanged, and also
              # keep the order of new columns occurrence unchanged
  
              # For the return values of reconstruct_func, if relabeling is
              # False, columns and order will be None.
              assert columns is not None
              assert order is not None
  
              result_in_dict = relabel_result(result, func, columns, order)
              result = DataFrame(result_in_dict, index=columns)
  
          return result
  
      def _aggregate(self, arg, axis=0, *args, **kwargs):
          if axis == 1:
              # NDFrame.aggregate returns a tuple, and we need to transpose
              # only result
              result, how = aggregate(self.T, arg, *args, **kwargs)
              result = result.T if result is not None else result
              return result, how
          return aggregate(self, arg, *args, **kwargs)
  
      agg = aggregate
  
      @doc(
          _shared_docs["transform"],
          klass=_shared_doc_kwargs["klass"],
          axis=_shared_doc_kwargs["axis"],
      )
      def transform(
          self, func: AggFuncType, axis: Axis = 0, *args, **kwargs
      ) -> DataFrame:
          result = transform(self, func, axis, *args, **kwargs)
          assert isinstance(result, DataFrame)
          return result
  
      def apply(self, func, axis=0, raw=False, result_type=None, args=(), **kwds):
          """
          Apply a function along an axis of the DataFrame.
  
          Objects passed to the function are Series objects whose index is
          either the DataFrame's index (``axis=0``) or the DataFrame's columns
          (``axis=1``). By default (``result_type=None``), the final return type
          is inferred from the return type of the applied function. Otherwise,
          it depends on the `result_type` argument.
  
          Parameters
          ----------
          func : function
              Function to apply to each column or row.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              Axis along which the function is applied:
  
              * 0 or 'index': apply function to each column.
              * 1 or 'columns': apply function to each row.
  
          raw : bool, default False
              Determines if row or column is passed as a Series or ndarray object:
  
              * ``False`` : passes each row or column as a Series to the
                function.
              * ``True`` : the passed function will receive ndarray objects
                instead.
                If you are just applying a NumPy reduction function this will
                achieve much better performance.
  
          result_type : {'expand', 'reduce', 'broadcast', None}, default None
              These only act when ``axis=1`` (columns):
  
              * 'expand' : list-like results will be turned into columns.
              * 'reduce' : returns a Series if possible rather than expanding
                list-like results. This is the opposite of 'expand'.
              * 'broadcast' : results will be broadcast to the original shape
                of the DataFrame, the original index and columns will be
                retained.
  
              The default behaviour (None) depends on the return value of the
              applied function: list-like results will be returned as a Series
              of those. However if the apply function returns a Series these
              are expanded to columns.
          args : tuple
              Positional arguments to pass to `func` in addition to the
              array/series.
          **kwds
              Additional keyword arguments to pass as keywords arguments to
              `func`.
  
          Returns
          -------
          Series or DataFrame
              Result of applying ``func`` along the given axis of the
              DataFrame.
  
          See Also
          --------
          DataFrame.applymap: For elementwise operations.
          DataFrame.aggregate: Only perform aggregating type operations.
          DataFrame.transform: Only perform transforming type operations.
  
          Examples
          --------
          >>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
          >>> df
             A  B
          0  4  9
          1  4  9
          2  4  9
  
          Using a numpy universal function (in this case the same as
          ``np.sqrt(df)``):
  
          >>> df.apply(np.sqrt)
               A    B
          0  2.0  3.0
          1  2.0  3.0
          2  2.0  3.0
  
          Using a reducing function on either axis
  
          >>> df.apply(np.sum, axis=0)
          A    12
          B    27
          dtype: int64
  
          >>> df.apply(np.sum, axis=1)
          0    13
          1    13
          2    13
          dtype: int64
  
          Returning a list-like will result in a Series
  
          >>> df.apply(lambda x: [1, 2], axis=1)
          0    [1, 2]
          1    [1, 2]
          2    [1, 2]
          dtype: object
  
          Passing ``result_type='expand'`` will expand list-like results
          to columns of a Dataframe
  
          >>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
             0  1
          0  1  2
          1  1  2
          2  1  2
  
          Returning a Series inside the function is similar to passing
          ``result_type='expand'``. The resulting column names
          will be the Series index.
  
          >>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
             foo  bar
          0    1    2
          1    1    2
          2    1    2
  
          Passing ``result_type='broadcast'`` will ensure the same shape
          result, whether list-like or scalar is returned by the function,
          and broadcast it along the axis. The resulting column names will
          be the originals.
  
          >>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
             A  B
          0  1  2
          1  1  2
          2  1  2
          """
          from pandas.core.apply import frame_apply
  
          op = frame_apply(
              self,
              func=func,
              axis=axis,
              raw=raw,
              result_type=result_type,
              args=args,
              kwds=kwds,
          )
          return op.get_result()
  
      def applymap(self, func, na_action: Optional[str] = None) -> DataFrame:
          """
          Apply a function to a Dataframe elementwise.
  
          This method applies a function that accepts and returns a scalar
          to every element of a DataFrame.
  
          Parameters
          ----------
          func : callable
              Python function, returns a single value from a single value.
          na_action : {None, 'ignore'}, default None
              If ‘ignore’, propagate NaN values, without passing them to func.
  
              .. versionadded:: 1.2
  
          Returns
          -------
          DataFrame
              Transformed DataFrame.
  
          See Also
          --------
          DataFrame.apply : Apply a function along input axis of DataFrame.
  
          Examples
          --------
          >>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
          >>> df
                 0      1
          0  1.000  2.120
          1  3.356  4.567
  
          >>> df.applymap(lambda x: len(str(x)))
             0  1
          0  3  4
          1  5  5
  
          Like Series.map, NA values can be ignored:
  
          >>> df_copy = df.copy()
          >>> df_copy.iloc[0, 0] = pd.NA
          >>> df_copy.applymap(lambda x: len(str(x)), na_action='ignore')
                0  1
          0  <NA>  4
          1     5  5
  
          Note that a vectorized version of `func` often exists, which will
          be much faster. You could square each number elementwise.
  
          >>> df.applymap(lambda x: x**2)
                     0          1
          0   1.000000   4.494400
          1  11.262736  20.857489
  
          But it's better to avoid applymap in that case.
  
          >>> df ** 2
                     0          1
          0   1.000000   4.494400
          1  11.262736  20.857489
          """
          if na_action not in {"ignore", None}:
              raise ValueError(
                  f"na_action must be 'ignore' or None. Got {repr(na_action)}"
              )
          ignore_na = na_action == "ignore"
  
          # if we have a dtype == 'M8[ns]', provide boxed values
          def infer(x):
              if x.empty:
                  return lib.map_infer(x, func, ignore_na=ignore_na)
              return lib.map_infer(x.astype(object)._values, func, ignore_na=ignore_na)
  
          return self.apply(infer).__finalize__(self, "applymap")
  
      # ----------------------------------------------------------------------
      # Merging / joining methods
  
      def append(
          self, other, ignore_index=False, verify_integrity=False, sort=False
      ) -> DataFrame:
          """
          Append rows of `other` to the end of caller, returning a new object.
  
          Columns in `other` that are not in the caller are added as new columns.
  
          Parameters
          ----------
          other : DataFrame or Series/dict-like object, or list of these
              The data to append.
          ignore_index : bool, default False
              If True, the resulting axis will be labeled 0, 1, …, n - 1.
          verify_integrity : bool, default False
              If True, raise ValueError on creating index with duplicates.
          sort : bool, default False
              Sort columns if the columns of `self` and `other` are not aligned.
  
              .. versionchanged:: 1.0.0
  
                  Changed to not sort by default.
  
          Returns
          -------
          DataFrame
  
          See Also
          --------
          concat : General function to concatenate DataFrame or Series objects.
  
          Notes
          -----
          If a list of dict/series is passed and the keys are all contained in
          the DataFrame's index, the order of the columns in the resulting
          DataFrame will be unchanged.
  
          Iteratively appending rows to a DataFrame can be more computationally
          intensive than a single concatenate. A better solution is to append
          those rows to a list and then concatenate the list with the original
          DataFrame all at once.
  
          Examples
          --------
          >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
          >>> df
             A  B
          0  1  2
          1  3  4
          >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
          >>> df.append(df2)
             A  B
          0  1  2
          1  3  4
          0  5  6
          1  7  8
  
          With `ignore_index` set to True:
  
          >>> df.append(df2, ignore_index=True)
             A  B
          0  1  2
          1  3  4
          2  5  6
          3  7  8
  
          The following, while not recommended methods for generating DataFrames,
          show two ways to generate a DataFrame from multiple data sources.
  
          Less efficient:
  
          >>> df = pd.DataFrame(columns=['A'])
          >>> for i in range(5):
          ...     df = df.append({'A': i}, ignore_index=True)
          >>> df
             A
          0  0
          1  1
          2  2
          3  3
          4  4
  
          More efficient:
  
          >>> pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],
          ...           ignore_index=True)
             A
          0  0
          1  1
          2  2
          3  3
          4  4
          """
          if isinstance(other, (Series, dict)):
              if isinstance(other, dict):
                  if not ignore_index:
                      raise TypeError("Can only append a dict if ignore_index=True")
                  other = Series(other)
              if other.name is None and not ignore_index:
                  raise TypeError(
                      "Can only append a Series if ignore_index=True "
                      "or if the Series has a name"
                  )
  
              index = Index([other.name], name=self.index.name)
              idx_diff = other.index.difference(self.columns)
              try:
                  combined_columns = self.columns.append(idx_diff)
              except TypeError:
                  combined_columns = self.columns.astype(object).append(idx_diff)
              other = (
                  other.reindex(combined_columns, copy=False)
                  .to_frame()
                  .T.infer_objects()
                  .rename_axis(index.names, copy=False)
              )
              if not self.columns.equals(combined_columns):
                  self = self.reindex(columns=combined_columns)
          elif isinstance(other, list):
              if not other:
                  pass
              elif not isinstance(other[0], DataFrame):
                  other = DataFrame(other)
                  if (self.columns.get_indexer(other.columns) >= 0).all():
                      other = other.reindex(columns=self.columns)
  
          from pandas.core.reshape.concat import concat
  
          if isinstance(other, (list, tuple)):
              to_concat = [self, *other]
          else:
              to_concat = [self, other]
          return (
              concat(
                  to_concat,
                  ignore_index=ignore_index,
                  verify_integrity=verify_integrity,
                  sort=sort,
              )
          ).__finalize__(self, method="append")
  
      def join(
          self, other, on=None, how="left", lsuffix="", rsuffix="", sort=False
      ) -> DataFrame:
          """
          Join columns of another DataFrame.
  
          Join columns with `other` DataFrame either on index or on a key
          column. Efficiently join multiple DataFrame objects by index at once by
          passing a list.
  
          Parameters
          ----------
          other : DataFrame, Series, or list of DataFrame
              Index should be similar to one of the columns in this one. If a
              Series is passed, its name attribute must be set, and that will be
              used as the column name in the resulting joined DataFrame.
          on : str, list of str, or array-like, optional
              Column or index level name(s) in the caller to join on the index
              in `other`, otherwise joins index-on-index. If multiple
              values given, the `other` DataFrame must have a MultiIndex. Can
              pass an array as the join key if it is not already contained in
              the calling DataFrame. Like an Excel VLOOKUP operation.
          how : {'left', 'right', 'outer', 'inner'}, default 'left'
              How to handle the operation of the two objects.
  
              * left: use calling frame's index (or column if on is specified)
              * right: use `other`'s index.
              * outer: form union of calling frame's index (or column if on is
                specified) with `other`'s index, and sort it.
                lexicographically.
              * inner: form intersection of calling frame's index (or column if
                on is specified) with `other`'s index, preserving the order
                of the calling's one.
          lsuffix : str, default ''
              Suffix to use from left frame's overlapping columns.
          rsuffix : str, default ''
              Suffix to use from right frame's overlapping columns.
          sort : bool, default False
              Order result DataFrame lexicographically by the join key. If False,
              the order of the join key depends on the join type (how keyword).
  
          Returns
          -------
          DataFrame
              A dataframe containing columns from both the caller and `other`.
  
          See Also
          --------
          DataFrame.merge : For column(s)-on-column(s) operations.
  
          Notes
          -----
          Parameters `on`, `lsuffix`, and `rsuffix` are not supported when
          passing a list of `DataFrame` objects.
  
          Support for specifying index levels as the `on` parameter was added
          in version 0.23.0.
  
          Examples
          --------
          >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
          ...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
  
          >>> df
            key   A
          0  K0  A0
          1  K1  A1
          2  K2  A2
          3  K3  A3
          4  K4  A4
          5  K5  A5
  
          >>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
          ...                       'B': ['B0', 'B1', 'B2']})
  
          >>> other
            key   B
          0  K0  B0
          1  K1  B1
          2  K2  B2
  
          Join DataFrames using their indexes.
  
          >>> df.join(other, lsuffix='_caller', rsuffix='_other')
            key_caller   A key_other    B
          0         K0  A0        K0   B0
          1         K1  A1        K1   B1
          2         K2  A2        K2   B2
          3         K3  A3       NaN  NaN
          4         K4  A4       NaN  NaN
          5         K5  A5       NaN  NaN
  
          If we want to join using the key columns, we need to set key to be
          the index in both `df` and `other`. The joined DataFrame will have
          key as its index.
  
          >>> df.set_index('key').join(other.set_index('key'))
                A    B
          key
          K0   A0   B0
          K1   A1   B1
          K2   A2   B2
          K3   A3  NaN
          K4   A4  NaN
          K5   A5  NaN
  
          Another option to join using the key columns is to use the `on`
          parameter. DataFrame.join always uses `other`'s index but we can use
          any column in `df`. This method preserves the original DataFrame's
          index in the result.
  
          >>> df.join(other.set_index('key'), on='key')
            key   A    B
          0  K0  A0   B0
          1  K1  A1   B1
          2  K2  A2   B2
          3  K3  A3  NaN
          4  K4  A4  NaN
          5  K5  A5  NaN
          """
          return self._join_compat(
              other, on=on, how=how, lsuffix=lsuffix, rsuffix=rsuffix, sort=sort
          )
  
      def _join_compat(
          self, other, on=None, how="left", lsuffix="", rsuffix="", sort=False
      ):
          from pandas.core.reshape.concat import concat
          from pandas.core.reshape.merge import merge
  
          if isinstance(other, Series):
              if other.name is None:
                  raise ValueError("Other Series must have a name")
              other = DataFrame({other.name: other})
  
          if isinstance(other, DataFrame):
              if how == "cross":
                  return merge(
                      self,
                      other,
                      how=how,
                      on=on,
                      suffixes=(lsuffix, rsuffix),
                      sort=sort,
                  )
              return merge(
                  self,
                  other,
                  left_on=on,
                  how=how,
                  left_index=on is None,
                  right_index=True,
                  suffixes=(lsuffix, rsuffix),
                  sort=sort,
              )
          else:
              if on is not None:
                  raise ValueError(
                      "Joining multiple DataFrames only supported for joining on index"
                  )
  
              frames = [self] + list(other)
  
              can_concat = all(df.index.is_unique for df in frames)
  
              # join indexes only using concat
              if can_concat:
                  if how == "left":
                      res = concat(
                          frames, axis=1, join="outer", verify_integrity=True, sort=sort
                      )
                      return res.reindex(self.index, copy=False)
                  else:
                      return concat(
                          frames, axis=1, join=how, verify_integrity=True, sort=sort
                      )
  
              joined = frames[0]
  
              for frame in frames[1:]:
                  joined = merge(
                      joined, frame, how=how, left_index=True, right_index=True
                  )
  
              return joined
  
      @Substitution("")
      @Appender(_merge_doc, indents=2)
      def merge(
          self,
          right,
          how="inner",
          on=None,
          left_on=None,
          right_on=None,
          left_index=False,
          right_index=False,
          sort=False,
          suffixes=("_x", "_y"),
          copy=True,
          indicator=False,
          validate=None,
      ) -> DataFrame:
          from pandas.core.reshape.merge import merge
  
          return merge(
              self,
              right,
              how=how,
              on=on,
              left_on=left_on,
              right_on=right_on,
              left_index=left_index,
              right_index=right_index,
              sort=sort,
              suffixes=suffixes,
              copy=copy,
              indicator=indicator,
              validate=validate,
          )
  
      def round(self, decimals=0, *args, **kwargs) -> DataFrame:
          """
          Round a DataFrame to a variable number of decimal places.
  
          Parameters
          ----------
          decimals : int, dict, Series
              Number of decimal places to round each column to. If an int is
              given, round each column to the same number of places.
              Otherwise dict and Series round to variable numbers of places.
              Column names should be in the keys if `decimals` is a
              dict-like, or in the index if `decimals` is a Series. Any
              columns not included in `decimals` will be left as is. Elements
              of `decimals` which are not columns of the input will be
              ignored.
          *args
              Additional keywords have no effect but might be accepted for
              compatibility with numpy.
          **kwargs
              Additional keywords have no effect but might be accepted for
              compatibility with numpy.
  
          Returns
          -------
          DataFrame
              A DataFrame with the affected columns rounded to the specified
              number of decimal places.
  
          See Also
          --------
          numpy.around : Round a numpy array to the given number of decimals.
          Series.round : Round a Series to the given number of decimals.
  
          Examples
          --------
          >>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
          ...                   columns=['dogs', 'cats'])
          >>> df
              dogs  cats
          0  0.21  0.32
          1  0.01  0.67
          2  0.66  0.03
          3  0.21  0.18
  
          By providing an integer each column is rounded to the same number
          of decimal places
  
          >>> df.round(1)
              dogs  cats
          0   0.2   0.3
          1   0.0   0.7
          2   0.7   0.0
          3   0.2   0.2
  
          With a dict, the number of places for specific columns can be
          specified with the column names as key and the number of decimal
          places as value
  
          >>> df.round({'dogs': 1, 'cats': 0})
              dogs  cats
          0   0.2   0.0
          1   0.0   1.0
          2   0.7   0.0
          3   0.2   0.0
  
          Using a Series, the number of places for specific columns can be
          specified with the column names as index and the number of
          decimal places as value
  
          >>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
          >>> df.round(decimals)
              dogs  cats
          0   0.2   0.0
          1   0.0   1.0
          2   0.7   0.0
          3   0.2   0.0
          """
          from pandas.core.reshape.concat import concat
  
          def _dict_round(df, decimals):
              for col, vals in df.items():
                  try:
                      yield _series_round(vals, decimals[col])
                  except KeyError:
                      yield vals
  
          def _series_round(s, decimals):
              if is_integer_dtype(s) or is_float_dtype(s):
                  return s.round(decimals)
              return s
  
          nv.validate_round(args, kwargs)
  
          if isinstance(decimals, (dict, Series)):
              if isinstance(decimals, Series):
                  if not decimals.index.is_unique:
                      raise ValueError("Index of decimals must be unique")
              new_cols = list(_dict_round(self, decimals))
          elif is_integer(decimals):
              # Dispatch to Series.round
              new_cols = [_series_round(v, decimals) for _, v in self.items()]
          else:
              raise TypeError("decimals must be an integer, a dict-like or a Series")
  
          if len(new_cols) > 0:
              return self._constructor(
                  concat(new_cols, axis=1), index=self.index, columns=self.columns
              )
          else:
              return self
  
      # ----------------------------------------------------------------------
      # Statistical methods, etc.
  
      def corr(self, method="pearson", min_periods=1) -> DataFrame:
          """
          Compute pairwise correlation of columns, excluding NA/null values.
  
          Parameters
          ----------
          method : {'pearson', 'kendall', 'spearman'} or callable
              Method of correlation:
  
              * pearson : standard correlation coefficient
              * kendall : Kendall Tau correlation coefficient
              * spearman : Spearman rank correlation
              * callable: callable with input two 1d ndarrays
                  and returning a float. Note that the returned matrix from corr
                  will have 1 along the diagonals and will be symmetric
                  regardless of the callable's behavior.
  
                  .. versionadded:: 0.24.0
  
          min_periods : int, optional
              Minimum number of observations required per pair of columns
              to have a valid result. Currently only available for Pearson
              and Spearman correlation.
  
          Returns
          -------
          DataFrame
              Correlation matrix.
  
          See Also
          --------
          DataFrame.corrwith : Compute pairwise correlation with another
              DataFrame or Series.
          Series.corr : Compute the correlation between two Series.
  
          Examples
          --------
          >>> def histogram_intersection(a, b):
          ...     v = np.minimum(a, b).sum().round(decimals=1)
          ...     return v
          >>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
          ...                   columns=['dogs', 'cats'])
          >>> df.corr(method=histogram_intersection)
                dogs  cats
          dogs   1.0   0.3
          cats   0.3   1.0
          """
          numeric_df = self._get_numeric_data()
          cols = numeric_df.columns
          idx = cols.copy()
          mat = numeric_df.to_numpy(dtype=float, na_value=np.nan, copy=False)
  
          if method == "pearson":
              correl = libalgos.nancorr(mat, minp=min_periods)
          elif method == "spearman":
              correl = libalgos.nancorr_spearman(mat, minp=min_periods)
          elif method == "kendall" or callable(method):
              if min_periods is None:
                  min_periods = 1
              mat = mat.T
              corrf = nanops.get_corr_func(method)
              K = len(cols)
              correl = np.empty((K, K), dtype=float)
              mask = np.isfinite(mat)
              for i, ac in enumerate(mat):
                  for j, bc in enumerate(mat):
                      if i > j:
                          continue
  
                      valid = mask[i] & mask[j]
                      if valid.sum() < min_periods:
                          c = np.nan
                      elif i == j:
                          c = 1.0
                      elif not valid.all():
                          c = corrf(ac[valid], bc[valid])
                      else:
                          c = corrf(ac, bc)
                      correl[i, j] = c
                      correl[j, i] = c
          else:
              raise ValueError(
                  "method must be either 'pearson', "
                  "'spearman', 'kendall', or a callable, "
                  f"'{method}' was supplied"
              )
  
          return self._constructor(correl, index=idx, columns=cols)
  
      def cov(
          self, min_periods: Optional[int] = None, ddof: Optional[int] = 1
      ) -> DataFrame:
          """
          Compute pairwise covariance of columns, excluding NA/null values.
  
          Compute the pairwise covariance among the series of a DataFrame.
          The returned data frame is the `covariance matrix
          <https://en.wikipedia.org/wiki/Covariance_matrix>`__ of the columns
          of the DataFrame.
  
          Both NA and null values are automatically excluded from the
          calculation. (See the note below about bias from missing values.)
          A threshold can be set for the minimum number of
          observations for each value created. Comparisons with observations
          below this threshold will be returned as ``NaN``.
  
          This method is generally used for the analysis of time series data to
          understand the relationship between different measures
          across time.
  
          Parameters
          ----------
          min_periods : int, optional
              Minimum number of observations required per pair of columns
              to have a valid result.
  
          ddof : int, default 1
              Delta degrees of freedom.  The divisor used in calculations
              is ``N - ddof``, where ``N`` represents the number of elements.
  
              .. versionadded:: 1.1.0
  
          Returns
          -------
          DataFrame
              The covariance matrix of the series of the DataFrame.
  
          See Also
          --------
          Series.cov : Compute covariance with another Series.
          core.window.ExponentialMovingWindow.cov: Exponential weighted sample covariance.
          core.window.Expanding.cov : Expanding sample covariance.
          core.window.Rolling.cov : Rolling sample covariance.
  
          Notes
          -----
          Returns the covariance matrix of the DataFrame's time series.
          The covariance is normalized by N-ddof.
  
          For DataFrames that have Series that are missing data (assuming that
          data is `missing at random
          <https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>`__)
          the returned covariance matrix will be an unbiased estimate
          of the variance and covariance between the member Series.
  
          However, for many applications this estimate may not be acceptable
          because the estimate covariance matrix is not guaranteed to be positive
          semi-definite. This could lead to estimate correlations having
          absolute values which are greater than one, and/or a non-invertible
          covariance matrix. See `Estimation of covariance matrices
          <https://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_
          matrices>`__ for more details.
  
          Examples
          --------
          >>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
          ...                   columns=['dogs', 'cats'])
          >>> df.cov()
                    dogs      cats
          dogs  0.666667 -1.000000
          cats -1.000000  1.666667
  
          >>> np.random.seed(42)
          >>> df = pd.DataFrame(np.random.randn(1000, 5),
          ...                   columns=['a', 'b', 'c', 'd', 'e'])
          >>> df.cov()
                    a         b         c         d         e
          a  0.998438 -0.020161  0.059277 -0.008943  0.014144
          b -0.020161  1.059352 -0.008543 -0.024738  0.009826
          c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
          d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
          e  0.014144  0.009826 -0.000271 -0.013692  0.977795
  
          **Minimum number of periods**
  
          This method also supports an optional ``min_periods`` keyword
          that specifies the required minimum number of non-NA observations for
          each column pair in order to have a valid result:
  
          >>> np.random.seed(42)
          >>> df = pd.DataFrame(np.random.randn(20, 3),
          ...                   columns=['a', 'b', 'c'])
          >>> df.loc[df.index[:5], 'a'] = np.nan
          >>> df.loc[df.index[5:10], 'b'] = np.nan
          >>> df.cov(min_periods=12)
                    a         b         c
          a  0.316741       NaN -0.150812
          b       NaN  1.248003  0.191417
          c -0.150812  0.191417  0.895202
          """
          numeric_df = self._get_numeric_data()
          cols = numeric_df.columns
          idx = cols.copy()
          mat = numeric_df.to_numpy(dtype=float, na_value=np.nan, copy=False)
  
          if notna(mat).all():
              if min_periods is not None and min_periods > len(mat):
                  base_cov = np.empty((mat.shape[1], mat.shape[1]))
                  base_cov.fill(np.nan)
              else:
                  base_cov = np.cov(mat.T, ddof=ddof)
              base_cov = base_cov.reshape((len(cols), len(cols)))
          else:
              base_cov = libalgos.nancorr(mat, cov=True, minp=min_periods)
  
          return self._constructor(base_cov, index=idx, columns=cols)
  
      def corrwith(self, other, axis=0, drop=False, method="pearson") -> Series:
          """
          Compute pairwise correlation.
  
          Pairwise correlation is computed between rows or columns of
          DataFrame with rows or columns of Series or DataFrame. DataFrames
          are first aligned along both axes before computing the
          correlations.
  
          Parameters
          ----------
          other : DataFrame, Series
              Object with which to compute correlations.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to use. 0 or 'index' to compute column-wise, 1 or 'columns' for
              row-wise.
          drop : bool, default False
              Drop missing indices from result.
          method : {'pearson', 'kendall', 'spearman'} or callable
              Method of correlation:
  
              * pearson : standard correlation coefficient
              * kendall : Kendall Tau correlation coefficient
              * spearman : Spearman rank correlation
              * callable: callable with input two 1d ndarrays
                  and returning a float.
  
              .. versionadded:: 0.24.0
  
          Returns
          -------
          Series
              Pairwise correlations.
  
          See Also
          --------
          DataFrame.corr : Compute pairwise correlation of columns.
          """
          axis = self._get_axis_number(axis)
          this = self._get_numeric_data()
  
          if isinstance(other, Series):
              return this.apply(lambda x: other.corr(x, method=method), axis=axis)
  
          other = other._get_numeric_data()
          left, right = this.align(other, join="inner", copy=False)
  
          if axis == 1:
              left = left.T
              right = right.T
  
          if method == "pearson":
              # mask missing values
              left = left + right * 0
              right = right + left * 0
  
              # demeaned data
              ldem = left - left.mean()
              rdem = right - right.mean()
  
              num = (ldem * rdem).sum()
              dom = (left.count() - 1) * left.std() * right.std()
  
              correl = num / dom
  
          elif method in ["kendall", "spearman"] or callable(method):
  
              def c(x):
                  return nanops.nancorr(x[0], x[1], method=method)
  
              correl = self._constructor_sliced(
                  map(c, zip(left.values.T, right.values.T)), index=left.columns
              )
  
          else:
              raise ValueError(
                  f"Invalid method {method} was passed, "
                  "valid methods are: 'pearson', 'kendall', "
                  "'spearman', or callable"
              )
  
          if not drop:
              # Find non-matching labels along the given axis
              # and append missing correlations (GH 22375)
              raxis = 1 if axis == 0 else 0
              result_index = this._get_axis(raxis).union(other._get_axis(raxis))
              idx_diff = result_index.difference(correl.index)
  
              if len(idx_diff) > 0:
                  correl = correl.append(Series([np.nan] * len(idx_diff), index=idx_diff))
  
          return correl
  
      # ----------------------------------------------------------------------
      # ndarray-like stats methods
  
      def count(self, axis=0, level=None, numeric_only=False):
          """
          Count non-NA cells for each column or row.
  
          The values `None`, `NaN`, `NaT`, and optionally `numpy.inf` (depending
          on `pandas.options.mode.use_inf_as_na`) are considered NA.
  
          Parameters
          ----------
          axis : {0 or 'index', 1 or 'columns'}, default 0
              If 0 or 'index' counts are generated for each column.
              If 1 or 'columns' counts are generated for each row.
          level : int or str, optional
              If the axis is a `MultiIndex` (hierarchical), count along a
              particular `level`, collapsing into a `DataFrame`.
              A `str` specifies the level name.
          numeric_only : bool, default False
              Include only `float`, `int` or `boolean` data.
  
          Returns
          -------
          Series or DataFrame
              For each column/row the number of non-NA/null entries.
              If `level` is specified returns a `DataFrame`.
  
          See Also
          --------
          Series.count: Number of non-NA elements in a Series.
          DataFrame.value_counts: Count unique combinations of columns.
          DataFrame.shape: Number of DataFrame rows and columns (including NA
              elements).
          DataFrame.isna: Boolean same-sized DataFrame showing places of NA
              elements.
  
          Examples
          --------
          Constructing DataFrame from a dictionary:
  
          >>> df = pd.DataFrame({"Person":
          ...                    ["John", "Myla", "Lewis", "John", "Myla"],
          ...                    "Age": [24., np.nan, 21., 33, 26],
          ...                    "Single": [False, True, True, True, False]})
          >>> df
             Person   Age  Single
          0    John  24.0   False
          1    Myla   NaN    True
          2   Lewis  21.0    True
          3    John  33.0    True
          4    Myla  26.0   False
  
          Notice the uncounted NA values:
  
          >>> df.count()
          Person    5
          Age       4
          Single    5
          dtype: int64
  
          Counts for each **row**:
  
          >>> df.count(axis='columns')
          0    3
          1    2
          2    3
          3    3
          4    3
          dtype: int64
  
          Counts for one level of a `MultiIndex`:
  
          >>> df.set_index(["Person", "Single"]).count(level="Person")
                  Age
          Person
          John      2
          Lewis     1
          Myla      1
          """
          axis = self._get_axis_number(axis)
          if level is not None:
              return self._count_level(level, axis=axis, numeric_only=numeric_only)
  
          if numeric_only:
              frame = self._get_numeric_data()
          else:
              frame = self
  
          # GH #423
          if len(frame._get_axis(axis)) == 0:
              result = self._constructor_sliced(0, index=frame._get_agg_axis(axis))
          else:
              if frame._is_mixed_type or frame._mgr.any_extension_types:
                  # the or any_extension_types is really only hit for single-
                  # column frames with an extension array
                  result = notna(frame).sum(axis=axis)
              else:
                  # GH13407
                  series_counts = notna(frame).sum(axis=axis)
                  counts = series_counts.values
                  result = self._constructor_sliced(
                      counts, index=frame._get_agg_axis(axis)
                  )
  
          return result.astype("int64")
  
      def _count_level(self, level, axis=0, numeric_only=False):
          if numeric_only:
              frame = self._get_numeric_data()
          else:
              frame = self
  
          count_axis = frame._get_axis(axis)
          agg_axis = frame._get_agg_axis(axis)
  
          if not isinstance(count_axis, MultiIndex):
              raise TypeError(
                  f"Can only count levels on hierarchical {self._get_axis_name(axis)}."
              )
  
          # Mask NaNs: Mask rows or columns where the index level is NaN, and all
          # values in the DataFrame that are NaN
          if frame._is_mixed_type:
              # Since we have mixed types, calling notna(frame.values) might
              # upcast everything to object
              values_mask = notna(frame).values
          else:
              # But use the speedup when we have homogeneous dtypes
              values_mask = notna(frame.values)
  
          index_mask = notna(count_axis.get_level_values(level=level))
          if axis == 1:
              mask = index_mask & values_mask
          else:
              mask = index_mask.reshape(-1, 1) & values_mask
  
          if isinstance(level, str):
              level = count_axis._get_level_number(level)
  
          level_name = count_axis._names[level]
          level_index = count_axis.levels[level]._shallow_copy(name=level_name)
          level_codes = ensure_int64(count_axis.codes[level])
          counts = lib.count_level_2d(mask, level_codes, len(level_index), axis=axis)
  
          if axis == 1:
              result = self._constructor(counts, index=agg_axis, columns=level_index)
          else:
              result = self._constructor(counts, index=level_index, columns=agg_axis)
  
          return result
  
      def _reduce(
          self,
          op,
          name: str,
          *,
          axis=0,
          skipna=True,
          numeric_only=None,
          filter_type=None,
          **kwds,
      ):
  
          assert filter_type is None or filter_type == "bool", filter_type
          out_dtype = "bool" if filter_type == "bool" else None
  
          own_dtypes = [arr.dtype for arr in self._iter_column_arrays()]
  
          dtype_is_dt = np.array(
              [is_datetime64_any_dtype(dtype) for dtype in own_dtypes],
              dtype=bool,
          )
          if numeric_only is None and name in ["mean", "median"] and dtype_is_dt.any():
              warnings.warn(
                  "DataFrame.mean and DataFrame.median with numeric_only=None "
                  "will include datetime64 and datetime64tz columns in a "
                  "future version.",
                  FutureWarning,
                  stacklevel=5,
              )
              cols = self.columns[~dtype_is_dt]
              self = self[cols]
  
          # TODO: Make other agg func handle axis=None properly GH#21597
          axis = self._get_axis_number(axis)
          labels = self._get_agg_axis(axis)
          assert axis in [0, 1]
  
          def func(values):
              if is_extension_array_dtype(values.dtype):
                  return extract_array(values)._reduce(name, skipna=skipna, **kwds)
              else:
                  return op(values, axis=axis, skipna=skipna, **kwds)
  
          def blk_func(values):
              if isinstance(values, ExtensionArray):
                  return values._reduce(name, skipna=skipna, **kwds)
              else:
                  return op(values, axis=1, skipna=skipna, **kwds)
  
          def _get_data() -> DataFrame:
              if filter_type is None:
                  data = self._get_numeric_data()
              else:
                  # GH#25101, GH#24434
                  assert filter_type == "bool"
                  data = self._get_bool_data()
              return data
  
          if numeric_only is not None or axis == 0:
              # For numeric_only non-None and axis non-None, we know
              #  which blocks to use and no try/except is needed.
              #  For numeric_only=None only the case with axis==0 and no object
              #  dtypes are unambiguous can be handled with BlockManager.reduce
              # Case with EAs see GH#35881
              df = self
              if numeric_only is True:
                  df = _get_data()
              if axis == 1:
                  df = df.T
                  axis = 0
  
              ignore_failures = numeric_only is None
  
              # After possibly _get_data and transposing, we are now in the
              #  simple case where we can use BlockManager.reduce
              res, indexer = df._mgr.reduce(blk_func, ignore_failures=ignore_failures)
              out = df._constructor(res).iloc[0]
              if out_dtype is not None:
                  out = out.astype(out_dtype)
              if axis == 0 and is_object_dtype(out.dtype):
                  # GH#35865 careful to cast explicitly to object
                  nvs = coerce_to_dtypes(out.values, df.dtypes.iloc[np.sort(indexer)])
                  out[:] = np.array(nvs, dtype=object)
              if axis == 0 and len(self) == 0 and name in ["sum", "prod"]:
                  # Even if we are object dtype, follow numpy and return
                  #  float64, see test_apply_funcs_over_empty
                  out = out.astype(np.float64)
              return out
  
          assert numeric_only is None
  
          data = self
          values = data.values
  
          try:
              result = func(values)
  
          except TypeError:
              # e.g. in nanops trying to convert strs to float
  
              data = _get_data()
              labels = data._get_agg_axis(axis)
  
              values = data.values
              with np.errstate(all="ignore"):
                  result = func(values)
  
          if filter_type == "bool" and notna(result).all():
              result = result.astype(np.bool_)
          elif filter_type is None and is_object_dtype(result.dtype):
              try:
                  result = result.astype(np.float64)
              except (ValueError, TypeError):
                  # try to coerce to the original dtypes item by item if we can
                  if axis == 0:
                      result = coerce_to_dtypes(result, data.dtypes)
  
          result = self._constructor_sliced(result, index=labels)
          return result
  
      def nunique(self, axis=0, dropna=True) -> Series:
          """
          Count distinct observations over requested axis.
  
          Return Series with number of distinct observations. Can ignore NaN
          values.
  
          Parameters
          ----------
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
              column-wise.
          dropna : bool, default True
              Don't include NaN in the counts.
  
          Returns
          -------
          Series
  
          See Also
          --------
          Series.nunique: Method nunique for Series.
          DataFrame.count: Count non-NA cells for each column or row.
  
          Examples
          --------
          >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 1, 1]})
          >>> df.nunique()
          A    3
          B    1
          dtype: int64
  
          >>> df.nunique(axis=1)
          0    1
          1    2
          2    2
          dtype: int64
          """
          return self.apply(Series.nunique, axis=axis, dropna=dropna)
  
      def idxmin(self, axis=0, skipna=True) -> Series:
          """
          Return index of first occurrence of minimum over requested axis.
  
          NA/null values are excluded.
  
          Parameters
          ----------
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
          skipna : bool, default True
              Exclude NA/null values. If an entire row/column is NA, the result
              will be NA.
  
          Returns
          -------
          Series
              Indexes of minima along the specified axis.
  
          Raises
          ------
          ValueError
              * If the row/column is empty
  
          See Also
          --------
          Series.idxmin : Return index of the minimum element.
  
          Notes
          -----
          This method is the DataFrame version of ``ndarray.argmin``.
  
          Examples
          --------
          Consider a dataset containing food consumption in Argentina.
  
          >>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
          ...                    'co2_emissions': [37.2, 19.66, 1712]},
          ...                    index=['Pork', 'Wheat Products', 'Beef'])
  
          >>> df
                          consumption  co2_emissions
          Pork                  10.51         37.20
          Wheat Products       103.11         19.66
          Beef                  55.48       1712.00
  
          By default, it returns the index for the minimum value in each column.
  
          >>> df.idxmin()
          consumption                Pork
          co2_emissions    Wheat Products
          dtype: object
  
          To return the index for the minimum value in each row, use ``axis="columns"``.
  
          >>> df.idxmin(axis="columns")
          Pork                consumption
          Wheat Products    co2_emissions
          Beef                consumption
          dtype: object
          """
          axis = self._get_axis_number(axis)
  
          res = self._reduce(
              nanops.nanargmin, "argmin", axis=axis, skipna=skipna, numeric_only=False
          )
          indices = res._values
  
          # indices will always be np.ndarray since axis is not None and
          # values is a 2d array for DataFrame
          # error: Item "int" of "Union[int, Any]" has no attribute "__iter__"
          assert isinstance(indices, np.ndarray)  # for mypy
  
          index = self._get_axis(axis)
          result = [index[i] if i >= 0 else np.nan for i in indices]
          return self._constructor_sliced(result, index=self._get_agg_axis(axis))
  
      def idxmax(self, axis=0, skipna=True) -> Series:
          """
          Return index of first occurrence of maximum over requested axis.
  
          NA/null values are excluded.
  
          Parameters
          ----------
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
          skipna : bool, default True
              Exclude NA/null values. If an entire row/column is NA, the result
              will be NA.
  
          Returns
          -------
          Series
              Indexes of maxima along the specified axis.
  
          Raises
          ------
          ValueError
              * If the row/column is empty
  
          See Also
          --------
          Series.idxmax : Return index of the maximum element.
  
          Notes
          -----
          This method is the DataFrame version of ``ndarray.argmax``.
  
          Examples
          --------
          Consider a dataset containing food consumption in Argentina.
  
          >>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
          ...                    'co2_emissions': [37.2, 19.66, 1712]},
          ...                    index=['Pork', 'Wheat Products', 'Beef'])
  
          >>> df
                          consumption  co2_emissions
          Pork                  10.51         37.20
          Wheat Products       103.11         19.66
          Beef                  55.48       1712.00
  
          By default, it returns the index for the maximum value in each column.
  
          >>> df.idxmax()
          consumption     Wheat Products
          co2_emissions             Beef
          dtype: object
  
          To return the index for the maximum value in each row, use ``axis="columns"``.
  
          >>> df.idxmax(axis="columns")
          Pork              co2_emissions
          Wheat Products     consumption
          Beef              co2_emissions
          dtype: object
          """
          axis = self._get_axis_number(axis)
  
          res = self._reduce(
              nanops.nanargmax, "argmax", axis=axis, skipna=skipna, numeric_only=False
          )
          indices = res._values
  
          # indices will always be np.ndarray since axis is not None and
          # values is a 2d array for DataFrame
          # error: Item "int" of "Union[int, Any]" has no attribute "__iter__"
          assert isinstance(indices, np.ndarray)  # for mypy
  
          index = self._get_axis(axis)
          result = [index[i] if i >= 0 else np.nan for i in indices]
          return self._constructor_sliced(result, index=self._get_agg_axis(axis))
  
      def _get_agg_axis(self, axis_num: int) -> Index:
          """
          Let's be explicit about this.
          """
          if axis_num == 0:
              return self.columns
          elif axis_num == 1:
              return self.index
          else:
              raise ValueError(f"Axis must be 0 or 1 (got {repr(axis_num)})")
  
      def mode(self, axis=0, numeric_only=False, dropna=True) -> DataFrame:
          """
          Get the mode(s) of each element along the selected axis.
  
          The mode of a set of values is the value that appears most often.
          It can be multiple values.
  
          Parameters
          ----------
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to iterate over while searching for the mode:
  
              * 0 or 'index' : get mode of each column
              * 1 or 'columns' : get mode of each row.
  
          numeric_only : bool, default False
              If True, only apply to numeric columns.
          dropna : bool, default True
              Don't consider counts of NaN/NaT.
  
              .. versionadded:: 0.24.0
  
          Returns
          -------
          DataFrame
              The modes of each column or row.
  
          See Also
          --------
          Series.mode : Return the highest frequency value in a Series.
          Series.value_counts : Return the counts of values in a Series.
  
          Examples
          --------
          >>> df = pd.DataFrame([('bird', 2, 2),
          ...                    ('mammal', 4, np.nan),
          ...                    ('arthropod', 8, 0),
          ...                    ('bird', 2, np.nan)],
          ...                   index=('falcon', 'horse', 'spider', 'ostrich'),
          ...                   columns=('species', 'legs', 'wings'))
          >>> df
                     species  legs  wings
          falcon        bird     2    2.0
          horse       mammal     4    NaN
          spider   arthropod     8    0.0
          ostrich       bird     2    NaN
  
          By default, missing values are not considered, and the mode of wings
          are both 0 and 2. Because the resulting DataFrame has two rows,
          the second row of ``species`` and ``legs`` contains ``NaN``.
  
          >>> df.mode()
            species  legs  wings
          0    bird   2.0    0.0
          1     NaN   NaN    2.0
  
          Setting ``dropna=False`` ``NaN`` values are considered and they can be
          the mode (like for wings).
  
          >>> df.mode(dropna=False)
            species  legs  wings
          0    bird     2    NaN
  
          Setting ``numeric_only=True``, only the mode of numeric columns is
          computed, and columns of other types are ignored.
  
          >>> df.mode(numeric_only=True)
             legs  wings
          0   2.0    0.0
          1   NaN    2.0
  
          To compute the mode over columns and not rows, use the axis parameter:
  
          >>> df.mode(axis='columns', numeric_only=True)
                     0    1
          falcon   2.0  NaN
          horse    4.0  NaN
          spider   0.0  8.0
          ostrich  2.0  NaN
          """
          data = self if not numeric_only else self._get_numeric_data()
  
          def f(s):
              return s.mode(dropna=dropna)
  
          return data.apply(f, axis=axis)
  
      def quantile(self, q=0.5, axis=0, numeric_only=True, interpolation="linear"):
          """
          Return values at the given quantile over requested axis.
  
          Parameters
          ----------
          q : float or array-like, default 0.5 (50% quantile)
              Value between 0 <= q <= 1, the quantile(s) to compute.
          axis : {0, 1, 'index', 'columns'}, default 0
              Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
          numeric_only : bool, default True
              If False, the quantile of datetime and timedelta data will be
              computed as well.
          interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
              This optional parameter specifies the interpolation method to use,
              when the desired quantile lies between two data points `i` and `j`:
  
              * linear: `i + (j - i) * fraction`, where `fraction` is the
                fractional part of the index surrounded by `i` and `j`.
              * lower: `i`.
              * higher: `j`.
              * nearest: `i` or `j` whichever is nearest.
              * midpoint: (`i` + `j`) / 2.
  
          Returns
          -------
          Series or DataFrame
  
              If ``q`` is an array, a DataFrame will be returned where the
                index is ``q``, the columns are the columns of self, and the
                values are the quantiles.
              If ``q`` is a float, a Series will be returned where the
                index is the columns of self and the values are the quantiles.
  
          See Also
          --------
          core.window.Rolling.quantile: Rolling quantile.
          numpy.percentile: Numpy function to compute the percentile.
  
          Examples
          --------
          >>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
          ...                   columns=['a', 'b'])
          >>> df.quantile(.1)
          a    1.3
          b    3.7
          Name: 0.1, dtype: float64
          >>> df.quantile([.1, .5])
                 a     b
          0.1  1.3   3.7
          0.5  2.5  55.0
  
          Specifying `numeric_only=False` will also compute the quantile of
          datetime and timedelta data.
  
          >>> df = pd.DataFrame({'A': [1, 2],
          ...                    'B': [pd.Timestamp('2010'),
          ...                          pd.Timestamp('2011')],
          ...                    'C': [pd.Timedelta('1 days'),
          ...                          pd.Timedelta('2 days')]})
          >>> df.quantile(0.5, numeric_only=False)
          A                    1.5
          B    2010-07-02 12:00:00
          C        1 days 12:00:00
          Name: 0.5, dtype: object
          """
          validate_percentile(q)
  
          data = self._get_numeric_data() if numeric_only else self
          axis = self._get_axis_number(axis)
          is_transposed = axis == 1
  
          if is_transposed:
              data = data.T
  
          if len(data.columns) == 0:
              # GH#23925 _get_numeric_data may have dropped all columns
              cols = Index([], name=self.columns.name)
              if is_list_like(q):
                  return self._constructor([], index=q, columns=cols)
              return self._constructor_sliced([], index=cols, name=q, dtype=np.float64)
  
          result = data._mgr.quantile(
              qs=q, axis=1, interpolation=interpolation, transposed=is_transposed
          )
  
          if result.ndim == 2:
              result = self._constructor(result)
          else:
              result = self._constructor_sliced(result, name=q)
  
          if is_transposed:
              result = result.T
  
          return result
  
      def to_timestamp(
          self, freq=None, how: str = "start", axis: Axis = 0, copy: bool = True
      ) -> DataFrame:
          """
          Cast to DatetimeIndex of timestamps, at *beginning* of period.
  
          Parameters
          ----------
          freq : str, default frequency of PeriodIndex
              Desired frequency.
          how : {'s', 'e', 'start', 'end'}
              Convention for converting period to timestamp; start of period
              vs. end.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to convert (the index by default).
          copy : bool, default True
              If False then underlying input data is not copied.
  
          Returns
          -------
          DataFrame with DatetimeIndex
          """
          new_obj = self.copy(deep=copy)
  
          axis_name = self._get_axis_name(axis)
          old_ax = getattr(self, axis_name)
          if not isinstance(old_ax, PeriodIndex):
              raise TypeError(f"unsupported Type {type(old_ax).__name__}")
  
          new_ax = old_ax.to_timestamp(freq=freq, how=how)
  
          setattr(new_obj, axis_name, new_ax)
          return new_obj
  
      def to_period(self, freq=None, axis: Axis = 0, copy: bool = True) -> DataFrame:
          """
          Convert DataFrame from DatetimeIndex to PeriodIndex.
  
          Convert DataFrame from DatetimeIndex to PeriodIndex with desired
          frequency (inferred from index if not passed).
  
          Parameters
          ----------
          freq : str, default
              Frequency of the PeriodIndex.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to convert (the index by default).
          copy : bool, default True
              If False then underlying input data is not copied.
  
          Returns
          -------
          DataFrame with PeriodIndex
          """
          new_obj = self.copy(deep=copy)
  
          axis_name = self._get_axis_name(axis)
          old_ax = getattr(self, axis_name)
          if not isinstance(old_ax, DatetimeIndex):
              raise TypeError(f"unsupported Type {type(old_ax).__name__}")
  
          new_ax = old_ax.to_period(freq=freq)
  
          setattr(new_obj, axis_name, new_ax)
          return new_obj
  
      def isin(self, values) -> DataFrame:
          """
          Whether each element in the DataFrame is contained in values.
  
          Parameters
          ----------
          values : iterable, Series, DataFrame or dict
              The result will only be true at a location if all the
              labels match. If `values` is a Series, that's the index. If
              `values` is a dict, the keys must be the column names,
              which must match. If `values` is a DataFrame,
              then both the index and column labels must match.
  
          Returns
          -------
          DataFrame
              DataFrame of booleans showing whether each element in the DataFrame
              is contained in values.
  
          See Also
          --------
          DataFrame.eq: Equality test for DataFrame.
          Series.isin: Equivalent method on Series.
          Series.str.contains: Test if pattern or regex is contained within a
              string of a Series or Index.
  
          Examples
          --------
          >>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
          ...                   index=['falcon', 'dog'])
          >>> df
                  num_legs  num_wings
          falcon         2          2
          dog            4          0
  
          When ``values`` is a list check whether every value in the DataFrame
          is present in the list (which animals have 0 or 2 legs or wings)
  
          >>> df.isin([0, 2])
                  num_legs  num_wings
          falcon      True       True
          dog        False       True
  
          When ``values`` is a dict, we can pass values to check for each
          column separately:
  
          >>> df.isin({'num_wings': [0, 3]})
                  num_legs  num_wings
          falcon     False      False
          dog        False       True
  
          When ``values`` is a Series or DataFrame the index and column must
          match. Note that 'falcon' does not match based on the number of legs
          in df2.
  
          >>> other = pd.DataFrame({'num_legs': [8, 2], 'num_wings': [0, 2]},
          ...                      index=['spider', 'falcon'])
          >>> df.isin(other)
                  num_legs  num_wings
          falcon      True       True
          dog        False      False
          """
          if isinstance(values, dict):
              from pandas.core.reshape.concat import concat
  
              values = collections.defaultdict(list, values)
              return concat(
                  (
                      self.iloc[:, [i]].isin(values[col])
                      for i, col in enumerate(self.columns)
                  ),
                  axis=1,
              )
          elif isinstance(values, Series):
              if not values.index.is_unique:
                  raise ValueError("cannot compute isin with a duplicate axis.")
              return self.eq(values.reindex_like(self), axis="index")
          elif isinstance(values, DataFrame):
              if not (values.columns.is_unique and values.index.is_unique):
                  raise ValueError("cannot compute isin with a duplicate axis.")
              return self.eq(values.reindex_like(self))
          else:
              if not is_list_like(values):
                  raise TypeError(
                      "only list-like or dict-like objects are allowed "
                      "to be passed to DataFrame.isin(), "
                      f"you passed a '{type(values).__name__}'"
                  )
              return self._constructor(
                  algorithms.isin(self.values.ravel(), values).reshape(self.shape),
                  self.index,
                  self.columns,
              )
  
      # ----------------------------------------------------------------------
      # Add index and columns
      _AXIS_ORDERS = ["index", "columns"]
      _AXIS_TO_AXIS_NUMBER: Dict[Axis, int] = {
          **NDFrame._AXIS_TO_AXIS_NUMBER,
          1: 1,
          "columns": 1,
      }
      _AXIS_REVERSED = True
      _AXIS_LEN = len(_AXIS_ORDERS)
      _info_axis_number = 1
      _info_axis_name = "columns"
  
      index: Index = properties.AxisProperty(
          axis=1, doc="The index (row labels) of the DataFrame."
      )
      columns: Index = properties.AxisProperty(
          axis=0, doc="The column labels of the DataFrame."
      )
  
      @property
      def _AXIS_NUMBERS(self) -> Dict[str, int]:
          """.. deprecated:: 1.1.0"""
          super()._AXIS_NUMBERS
          return {"index": 0, "columns": 1}
  
      @property
      def _AXIS_NAMES(self) -> Dict[int, str]:
          """.. deprecated:: 1.1.0"""
          super()._AXIS_NAMES
          return {0: "index", 1: "columns"}
  
      # ----------------------------------------------------------------------
      # Add plotting methods to DataFrame
      plot = CachedAccessor("plot", pandas.plotting.PlotAccessor)
      hist = pandas.plotting.hist_frame
      boxplot = pandas.plotting.boxplot_frame
      sparse = CachedAccessor("sparse", SparseFrameAccessor)
  
  
  DataFrame._add_numeric_operations()
  
  ops.add_flex_arithmetic_methods(DataFrame)
  
  
  def _from_nested_dict(data) -> collections.defaultdict:
      new_data: collections.defaultdict = collections.defaultdict(dict)
      for index, s in data.items():
          for col, v in s.items():
              new_data[col][index] = v
      return new_data
  
apis: pandas.core.generic.NDFrame._set_item pandas.core.reshape.melt.melt pandas.core.reshape.concat.concat numpy.asarray pandas.core.indexing.convert_to_index_sliceable pandas.core.nanops.nancorr pandas.core.dtypes.cast.maybe_infer_to_datetimelike pandas.core.dtypes.cast.infer_dtype_from_scalar pandas.io.feather_format.to_feather pandas.core.computation.expressions.where numpy.transpose pandas.core.algorithms.SelectNFrame pandas.io.common.get_handle pandas.util._decorators.deprecate_kwarg pandas.core.dtypes.common.is_datetime64_any_dtype pandas.core.ops.align_method_FRAME pandas.core.indexes.api.Index pandas.core.ops.frame_arith_method_with_reindex pandas.core.dtypes.common.is_dataclass pandas.core.generic.NDFrame._iset_item pandas._libs.hashtable.duplicated_int64 pandas.core.dtypes.common.is_named_tuple pandas.io.formats.console.in_ipython_frontend pandas.core.dtypes.cast.find_common_type pandas.core.indexing.check_bool_indexer pandas.core.indexes.multi.MultiIndex.from_arrays pandas.core.dtypes.common.is_scalar pandas.core.dtypes.cast.coerce_to_dtypes pandas.util._decorators.Appender pandas.io.formats.format.DataFrameFormatter numpy.errstate numpy.rec.fromarrays pandas.io.formats.console.get_console_size pandas.core.reshape.reshape.unstack numpy.array numpy.dot pandas.core.dtypes.common.is_sequence pandas.option_context pandas.core.reshape.reshape.stack pandas.core.dtypes.common.ensure_int64 pandas.core.dtypes.common.is_integer pandas.core.aggregation.relabel_result pandas.core.internals.construction.dataclasses_to_dicts numpy.cov pandas.compat.numpy.function.validate_round pandas.core.dtypes.common.is_hashable pandas.core.reshape.reshape.stack_multiple pandas.io.stata.StataWriterUTF8 pandas.core.aggregation.aggregate pandas._libs.properties.AxisProperty pandas.core.aggregation.transform pandas._libs.lib.item_from_zerodim pandas.core.ops.fill_binop pandas.core.dtypes.common.ensure_platform_int pandas.core.dtypes.missing.isna pandas.core.ops.should_reindex_frame_op pandas.util._validators.validate_axis_style_args pandas.core.dtypes.cast.validate_numeric_casting pandas.core.dtypes.common.is_object_dtype pandas.core.internals.construction.arrays_to_mgr pandas.core.reshape.pivot.pivot pandas.core.dtypes.cast.maybe_upcast pandas.core.internals.construction.init_ndarray pandas.core.dtypes.cast.maybe_box_datetimelike pandas.core.dtypes.common.is_integer_dtype pandas.core.dtypes.cast.maybe_casted_values pandas.core.dtypes.common.is_dict_like pandas.core.aggregation.reconstruct_func pandas._libs.algos.nancorr pandas.util._decorators.doc pandas.core.construction.extract_array pandas.core.dtypes.common.is_list_like numpy.ma.getmaskarray pandas.util._decorators.rewrite_axis_style_signature numpy.iterable pandas.core.algorithms.take_2d_multi pandas.io.formats.console.in_interactive_session pandas.io.parquet.to_parquet pandas.core.apply.frame_apply pandas.util._validators.validate_percentile pandas.core.common.asarray_tuplesafe pandas.compat.numpy.function.validate_transpose pandas.core.ops.get_array_op pandas.core.internals.construction.masked_rec_array_to_mgr pandas.core.common.apply_if_callable numpy.sort pandas.core.internals.construction.sanitize_index pandas._libs.algos.nancorr_spearman pandas.core.ops.add_flex_arithmetic_methods pandas.io.formats.style.Styler pandas.core.internals.construction.get_names_from_index pandas.core.sorting.nargsort pandas.core.series.Series pandas.util._validators.validate_bool_kwarg pandas.io.formats.format.DataFrameRenderer pandas.io.formats.info.DataFrameInfo pandas.core.nanops.get_corr_func pandas.core.computation.eval.eval pandas.core.indexes.api.ensure_index_from_sequences pandas.core.accessor.CachedAccessor pandas.core.dtypes.common.is_float_dtype pandas.core.dtypes.cast.maybe_cast_to_datetime pandas.io.gbq.to_gbq pandas.core.sorting.lexsort_indexer pandas.core.dtypes.common.pandas_dtype pandas._libs.lib.maybe_convert_objects numpy.isfinite pandas.core.dtypes.common.is_float pandas._libs.lib.map_infer pandas.core.dtypes.common.infer_dtype_from_object pandas.core.indexes.api.ensure_index pandas.core.reshape.pivot.pivot_table pandas.core.dtypes.common.is_iterator pandas.core.groupby.generic.DataFrameGroupBy pandas.core.dtypes.cast.maybe_downcast_to_dtype pandas.core.dtypes.missing.notna pandas.core.dtypes.cast.invalidate_string_dtypes pandas.core.internals.construction.to_arrays pandas.core.generic.NDFrame.__init__ numpy.where pandas.core.dtypes.common.is_bool_dtype pandas._config.get_option pandas.core.common.is_bool_indexer pandas.core.internals.construction.init_dict pandas.core.reshape.merge.merge pandas.core.dtypes.common.is_extension_array_dtype pandas.core.internals.construction.reorder_arrays pandas.util._decorators.Substitution numpy.compress pandas.core.common.standardize_mapping numpy.empty pandas.core.dtypes.common.is_dtype_equal pandas.compat._optional.import_optional_dependency numpy.shape pandas.core.indexes.multi.maybe_droplevels pandas.core.sorting.get_group_index pandas.core.dtypes.cast.maybe_convert_platform numpy.full