text: """
  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
  """
  import collections
  from collections import OrderedDict, abc
  from io import StringIO
  import itertools
  import sys
  from textwrap import dedent
  from typing import FrozenSet, List, Optional, Sequence, Set, Tuple, Type, Union
  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
  from pandas.compat import PY36, raise_with_traceback
  from pandas.compat.numpy import function as nv
  from pandas.util._decorators import (
      Appender,
      Substitution,
      deprecate_kwarg,
      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,
      find_common_type,
      infer_dtype_from_scalar,
      invalidate_string_dtypes,
      maybe_cast_to_datetime,
      maybe_convert_platform,
      maybe_downcast_to_dtype,
      maybe_infer_to_datetimelike,
      maybe_upcast,
      maybe_upcast_putmask,
  )
  from pandas.core.dtypes.common import (
      ensure_float64,
      ensure_int64,
      ensure_platform_int,
      infer_dtype_from_object,
      is_bool_dtype,
      is_datetime64_any_dtype,
      is_datetime64tz_dtype,
      is_dict_like,
      is_dtype_equal,
      is_extension_array_dtype,
      is_extension_type,
      is_float_dtype,
      is_hashable,
      is_integer,
      is_integer_dtype,
      is_iterator,
      is_list_like,
      is_named_tuple,
      is_nested_list_like,
      is_object_dtype,
      is_scalar,
      is_sequence,
      needs_i8_conversion,
  )
  from pandas.core.dtypes.generic import (
      ABCDataFrame,
      ABCIndexClass,
      ABCMultiIndex,
      ABCSeries,
  )
  from pandas.core.dtypes.missing import isna, notna
  
  from pandas._typing import Axes, Dtype, FilePathOrBuffer
  from pandas.core import algorithms, common as com, nanops, ops
  from pandas.core.accessor import CachedAccessor
  from pandas.core.arrays import Categorical, ExtensionArray
  from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin as DatetimeLikeArray
  from pandas.core.arrays.sparse import SparseFrameAccessor
  from pandas.core.generic import NDFrame, _shared_docs
  from pandas.core.index import Index, ensure_index, ensure_index_from_sequences
  from pandas.core.indexes import base as ibase
  from pandas.core.indexes.datetimes import DatetimeIndex
  from pandas.core.indexes.multi import maybe_droplevels
  from pandas.core.indexes.period import PeriodIndex
  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,
      get_names_from_index,
      init_dict,
      init_ndarray,
      masked_rec_array_to_mgr,
      reorder_arrays,
      sanitize_index,
      to_arrays,
  )
  from pandas.core.ops.missing import dispatch_fill_zeros
  from pandas.core.series import Series
  
  from pandas.io.formats import console, format as fmt
  from pandas.io.formats.printing import pprint_thing
  import pandas.plotting
  
  # ---------------------------------------------------------------------
  # Docstring templates
  
  _shared_doc_kwargs = dict(
      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
  
              .. versionchanged:: 0.23.0
                 Allow specifying index or column level names.""",
      versionadded_to_excel="",
      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.
  
  Parameters
  ----------%s
  right : DataFrame or named Series
      Object to merge with.
  how : {'left', 'right', 'outer', 'inner'}, 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.
  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 : tuple of (str, str), default ('_x', '_y')
      Suffix to apply to overlapping column names in the left and right
      side, respectively. To raise an exception on overlapping columns use
      (False, False).
  copy : bool, default True
      If False, avoid copy if possible.
  indicator : bool or str, default False
      If True, adds a column to output DataFrame called "_merge" with
      information on the source of each row.
      If string, column with information on source of each row will be added to
      output DataFrame, and column will be named value of string.
      Information column is Categorical-type and takes on a value of "left_only"
      for observations whose merge key only appears in 'left' DataFrame,
      "right_only" for observations whose merge key only appears in 'right'
      DataFrame, and "both" if the observation's merge key is found in both.
  
  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.
  
      .. versionadded:: 0.21.0
  
  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')
  """
  
  
  # -----------------------------------------------------------------------
  # DataFrame class
  
  
  class DataFrame(NDFrame):
      """
      Two-dimensional size-mutable, potentially heterogeneous tabular data
      structure with 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, or list-like objects.
  
          .. versionchanged:: 0.23.0
             If data is a dict, column order follows insertion-order for
             Python 3.6 and later.
  
          .. versionchanged:: 0.25.0
             If data is a list of dicts, column order follows insertion-order
             for Python 3.6 and later.
  
      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.
      DataFrame.from_items : From sequence of (key, value) pairs
          read_csv, pandas.read_table, pandas.read_clipboard.
  
      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
      """
  
      @property
      def _constructor(self) -> Type["DataFrame"]:
          return DataFrame
  
      _constructor_sliced = Series  # type: Type[Series]
      _deprecations = NDFrame._deprecations | frozenset(
          ["from_items"]
      )  # type: FrozenSet[str]
      _accessors = set()  # type: Set[str]
  
      @property
      def _constructor_expanddim(self):
          raise NotImplementedError("Not supported for DataFrames!")
  
      # ----------------------------------------------------------------------
      # 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._data
  
          if isinstance(data, BlockManager):
              mgr = self._init_mgr(
                  data, axes=dict(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):
                  data = list(data)
              if len(data) > 0:
                  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)
          else:
              try:
                  arr = np.array(data, dtype=dtype, copy=copy)
              except (ValueError, TypeError) as e:
                  exc = TypeError(
                      "DataFrame constructor called with "
                      "incompatible data and dtype: {e}".format(e=e)
                  )
                  raise_with_traceback(exc)
  
              if arr.ndim == 0 and index is not None and columns is not None:
                  values = cast_scalar_to_array(
                      (len(index), len(columns)), data, dtype=dtype
                  )
                  mgr = init_ndarray(
                      values, index, columns, dtype=values.dtype, copy=False
                  )
              else:
                  raise ValueError("DataFrame constructor not properly called!")
  
          NDFrame.__init__(self, mgr, fastpath=True)
  
      # ----------------------------------------------------------------------
  
      @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
  
          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._data.any_extension_types:
              return len({block.dtype for block in self._data.blocks}) == 1
          else:
              return not self._data.is_mixed_type
  
      # ----------------------------------------------------------------------
      # 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 off non-interactive session, no boundaries apply.
  
          `ignore_width` is here so ipnb+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(l) for l 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=".",
                  table_id=None,
                  render_links=False,
              )
              return 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",
          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.formatters_type] = None,
          float_format: Optional[fmt.float_format_type] = 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,
      ) -> 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
          %(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,
                  line_width=line_width,
              )
              return formatter.to_string(buf=buf)
  
      # ----------------------------------------------------------------------
  
      @property
      def style(self):
          """
          Property returning a Styler object containing methods for
          building a styled HTML representation fo the DataFrame.
  
          See Also
          --------
          io.formats.style.Styler
          """
          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('label:', label)
          ...     print('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):
          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):
          yield from self.items()
  
      def iterrows(self):
          """
          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.
  
          it : generator
              A generator that iterates over the rows of the frame.
  
          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=True, name="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.
          With a large number of columns (>255), regular tuples are returned.
  
          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)))
  
          # Python 3 supports at most 255 arguments to constructor
          if name is not None and len(self.columns) + index < 256:
              itertuple = collections.namedtuple(name, fields, rename=True)
              return map(itertuple._make, zip(*arrays))
  
          # fallback to regular tuples
          return zip(*arrays)
  
      def __len__(self):
          """
          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 Serie. 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(
                      "Dot product shape mismatch, "
                      "{s} vs {r}".format(s=lvals.shape, r=rvals.shape)
                  )
  
          if isinstance(other, DataFrame):
              return self._constructor(
                  np.dot(lvals, rvals), index=left.index, columns=other.columns
              )
          elif isinstance(other, Series):
              return Series(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 Series(result, index=left.index)
          else:  # pragma: no cover
              raise TypeError("unsupported type: {oth}".format(oth=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.
          """
          return self.T.dot(np.transpose(other)).T
  
      # ----------------------------------------------------------------------
      # IO methods (to / from other formats)
  
      @classmethod
      def from_dict(cls, data, orient="columns", dtype=None, columns=None):
          """
          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'``.
  
              .. versionadded:: 0.23.0
  
          Returns
          -------
          DataFrame
  
          See Also
          --------
          DataFrame.from_records : DataFrame from ndarray (structured
              dtype), list of tuples, dict, 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=False):
          """
          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 a 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.
  
          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 heterogenous 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)
          """
          result = np.array(self.values, dtype=dtype, copy=copy)
          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.
  
              .. versionadded:: 0.21.0
  
          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)
          if orient.lower().startswith("d"):
              return into_c((k, v.to_dict(into)) for k, v in self.items())
          elif orient.lower().startswith("l"):
              return into_c((k, v.tolist()) for k, v in self.items())
          elif orient.lower().startswith("sp"):
              return into_c(
                  (
                      ("index", self.index.tolist()),
                      ("columns", self.columns.tolist()),
                      (
                          "data",
                          [
                              list(map(com.maybe_box_datetimelike, t))
                              for t in self.itertuples(index=False, name=None)
                          ],
                      ),
                  )
              )
          elif orient.lower().startswith("s"):
              return into_c((k, com.maybe_box_datetimelike(v)) for k, v in self.items())
          elif orient.lower().startswith("r"):
              columns = self.columns.tolist()
              rows = (
                  dict(zip(columns, row))
                  for row in self.itertuples(index=False, name=None)
              )
              return [
                  into_c((k, com.maybe_box_datetimelike(v)) for k, v in row.items())
                  for row in rows
              ]
          elif orient.lower().startswith("i"):
              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("orient '{o}' not understood".format(o=orient))
  
      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,
          verbose=None,
          private_key=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, do nothing.
              ``'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:
                  http://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
              .. _console flow:
                  http://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
          verbose : bool, deprecated
              Deprecated in pandas-gbq version 0.4.0. Use the `logging module
              to adjust verbosity instead
              <https://pandas-gbq.readthedocs.io/en/latest/intro.html#logging>`__.
          private_key : str, deprecated
              Deprecated in pandas-gbq version 0.8.0. Use the ``credentials``
              parameter and
              :func:`google.oauth2.service_account.Credentials.from_service_account_info`
              or
              :func:`google.oauth2.service_account.Credentials.from_service_account_file`
              instead.
  
              Service account private key in JSON format. Can be file path
              or string contents. This is useful for remote server
              authentication (eg. Jupyter/IPython notebook on remote host).
  
          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,
              verbose=verbose,
              private_key=private_key,
          )
  
      @classmethod
      def from_records(
          cls,
          data,
          index=None,
          exclude=None,
          columns=None,
          coerce_float=False,
          nrows=None,
      ):
          """
          Convert structured or record ndarray to DataFrame.
  
          Parameters
          ----------
          data : ndarray (structured dtype), list of tuples, dict, or DataFrame
          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
          """
  
          # 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 = []
                  for k, v in data.items():
                      if k in columns:
                          arr_columns.append(k)
                          arrays.append(v)
  
                  arrays, arr_columns = reorder_arrays(arrays, arr_columns, 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]
                      result_index = ensure_index_from_sequences(index_data, names=index)
  
                      exclude.update(index)
                  except Exception:
                      result_index = 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, convert_datetime64=None, column_dtypes=None, index_dtypes=None
      ):
          """
          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.
          convert_datetime64 : bool, default None
              .. deprecated:: 0.23.0
  
              Whether to convert the index to datetime.datetime if it is a
              DatetimeIndex.
          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 = "<S{}".format(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 convert_datetime64 is not None:
              warnings.warn(
                  "The 'convert_datetime64' parameter is "
                  "deprecated and will be removed in a future "
                  "version",
                  FutureWarning,
                  stacklevel=2,
              )
  
          if index:
              if is_datetime64_any_dtype(self.index) and convert_datetime64:
                  ix_vals = [self.index.to_pydatetime()]
              else:
                  if isinstance(self.index, ABCMultiIndex):
                      # 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 + [self[c]._internal_get_values() for c in self.columns]
  
              count = 0
              index_names = list(self.index.names)
  
              if isinstance(self.index, ABCMultiIndex):
                  for i, n in enumerate(index_names):
                      if n is None:
                          index_names[i] = "level_%d" % 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 = [self[c]._internal_get_values() for c in 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 = ("Invalid dtype {dtype} specified for {element} {name}").format(
                      dtype=dtype_mapping, element=element, name=name
                  )
                  raise ValueError(msg)
  
          return np.rec.fromarrays(arrays, dtype={"names": names, "formats": formats})
  
      @classmethod
      def from_items(cls, items, columns=None, orient="columns"):
          """
          Construct a DataFrame from a list of tuples.
  
          .. deprecated:: 0.23.0
            `from_items` is deprecated and will be removed in a future version.
            Use :meth:`DataFrame.from_dict(dict(items)) <DataFrame.from_dict>`
            instead.
            :meth:`DataFrame.from_dict(OrderedDict(items)) <DataFrame.from_dict>`
            may be used to preserve the key order.
  
          Convert (key, value) pairs to DataFrame. The keys will be the axis
          index (usually the columns, but depends on the specified
          orientation). The values should be arrays or Series.
  
          Parameters
          ----------
          items : sequence of (key, value) pairs
              Values should be arrays or Series.
          columns : sequence of column labels, optional
              Must be passed if orient='index'.
          orient : {'columns', 'index'}, default 'columns'
              The "orientation" of the data. If the keys of the
              input correspond to column labels, pass 'columns'
              (default). Otherwise if the keys correspond to the index,
              pass 'index'.
  
          Returns
          -------
          DataFrame
          """
  
          warnings.warn(
              "from_items is deprecated. Please use "
              "DataFrame.from_dict(dict(items), ...) instead. "
              "DataFrame.from_dict(OrderedDict(items)) may be used to "
              "preserve the key order.",
              FutureWarning,
              stacklevel=2,
          )
  
          keys, values = zip(*items)
  
          if orient == "columns":
              if columns is not None:
                  columns = ensure_index(columns)
  
                  idict = dict(items)
                  if len(idict) < len(items):
                      if not columns.equals(ensure_index(keys)):
                          raise ValueError(
                              "With non-unique item names, passed "
                              "columns must be identical"
                          )
                      arrays = values
                  else:
                      arrays = [idict[k] for k in columns if k in idict]
              else:
                  columns = ensure_index(keys)
                  arrays = values
  
              # GH 17312
              # Provide more informative error msg when scalar values passed
              try:
                  return cls._from_arrays(arrays, columns, None)
  
              except ValueError:
                  if not is_nested_list_like(values):
                      raise ValueError(
                          "The value in each (key, value) pair "
                          "must be an array, Series, or dict"
                      )
  
          elif orient == "index":
              if columns is None:
                  raise TypeError("Must pass columns with orient='index'")
  
              keys = ensure_index(keys)
  
              # GH 17312
              # Provide more informative error msg when scalar values passed
              try:
                  arr = np.array(values, dtype=object).T
                  data = [lib.maybe_convert_objects(v) for v in arr]
                  return cls._from_arrays(data, columns, keys)
  
              except TypeError:
                  if not is_nested_list_like(values):
                      raise ValueError(
                          "The value in each (key, value) pair "
                          "must be an array, Series, or dict"
                      )
  
          else:  # pragma: no cover
              raise ValueError("'orient' must be either 'columns' or 'index'")
  
      @classmethod
      def _from_arrays(cls, arrays, columns, index, dtype=None):
          mgr = arrays_to_mgr(arrays, columns, index, columns, dtype=dtype)
          return cls(mgr)
  
      @deprecate_kwarg(old_arg_name="encoding", new_arg_name=None)
      def to_stata(
          self,
          fname,
          convert_dates=None,
          write_index=True,
          encoding="latin-1",
          byteorder=None,
          time_stamp=None,
          data_label=None,
          variable_labels=None,
          version=114,
          convert_strl=None,
      ):
          """
          Export DataFrame object to Stata dta format.
  
          Writes the DataFrame to a Stata dataset file.
          "dta" files contain a Stata dataset.
  
          Parameters
          ----------
          fname : 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.
          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.
          encoding : str
              Default is latin-1. Unicode is not supported.
          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}, default 114
              Version to use in the output dta file.  Version 114 can be used
              read by Stata 10 and later.  Version 117 can be read by Stata 13
              or later. Version 114 limits string variables to 244 characters or
              fewer while 117 allows strings with lengths up to 2,000,000
              characters.
  
              .. versionadded:: 0.23.0
  
          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.
  
              .. versionadded:: 0.23.0
  
          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
          """
          kwargs = {}
          if version not in (114, 117):
              raise ValueError("Only formats 114 and 117 supported.")
          if version == 114:
              if convert_strl is not None:
                  raise ValueError("strl support is only available when using format 117")
              from pandas.io.stata import StataWriter as statawriter
          else:
              from pandas.io.stata import StataWriter117 as statawriter
  
              kwargs["convert_strl"] = convert_strl
  
          writer = statawriter(
              fname,
              self,
              convert_dates=convert_dates,
              byteorder=byteorder,
              time_stamp=time_stamp,
              data_label=data_label,
              write_index=write_index,
              variable_labels=variable_labels,
              **kwargs
          )
          writer.write_file()
  
      def to_feather(self, fname):
          """
          Write out the binary feather-format for DataFrames.
  
          .. versionadded:: 0.20.0
  
          Parameters
          ----------
          fname : str
              String file path.
          """
          from pandas.io.feather_format import to_feather
  
          to_feather(self, fname)
  
      def to_parquet(
          self,
          fname,
          engine="auto",
          compression="snappy",
          index=None,
          partition_cols=None,
          **kwargs
      ):
          """
          Write a DataFrame to the binary parquet format.
  
          .. versionadded:: 0.21.0
  
          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
          ----------
          fname : str
              File path or Root Directory path. Will be used as Root Directory
              path while writing a partitioned dataset.
  
              .. versionchanged:: 0.24.0
  
          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.
  
              .. versionadded:: 0.24.0
  
          **kwargs
              Additional arguments passed to the parquet library. See
              :ref:`pandas io <io.parquet>` for more details.
  
          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
          """
          from pandas.io.parquet import to_parquet
  
          to_parquet(
              self,
              fname,
              engine,
              compression=compression,
              index=index,
              partition_cols=partition_cols,
              **kwargs
          )
  
      @Substitution(
          header_type="bool",
          header="Whether to print column labels, default True",
          col_space_type="str or int",
          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,
      ):
          """
          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``.
          table_id : str, optional
              A css id is included in the opening `<table>` tag if specified.
  
              .. versionadded:: 0.23.0
  
          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,
              formatters=formatters,
              float_format=float_format,
              sparsify=sparsify,
              justify=justify,
              index_names=index_names,
              header=header,
              index=index,
              bold_rows=bold_rows,
              escape=escape,
              max_rows=max_rows,
              max_cols=max_cols,
              show_dimensions=show_dimensions,
              decimal=decimal,
              table_id=table_id,
              render_links=render_links,
          )
          # TODO: a generic formatter wld b in DataFrameFormatter
          return formatter.to_html(
              buf=buf, classes=classes, notebook=notebook, border=border
          )
  
      # ----------------------------------------------------------------------
  
      def info(
          self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None
      ):
          """
          Print a concise summary of a DataFrame.
  
          This method prints information about a DataFrame including
          the index dtype and column dtypes, non-null values and memory usage.
  
          Parameters
          ----------
          verbose : bool, optional
              Whether to print the full summary. By default, the setting in
              ``pandas.options.display.max_info_columns`` is followed.
          buf : writable buffer, defaults to sys.stdout
              Where to send the output. By default, the output is printed to
              sys.stdout. Pass a writable buffer if you need to further process
              the output.
          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.
          memory_usage : bool, str, optional
              Specifies whether total memory usage of the DataFrame
              elements (including the index) should be displayed. By default,
              this follows the ``pandas.options.display.memory_usage`` setting.
  
              True always show memory usage. False never shows memory usage.
              A value of 'deep' is equivalent to "True with deep introspection".
              Memory usage is shown in human-readable units (base-2
              representation). Without deep introspection a memory estimation is
              made based in column dtype and number of rows assuming values
              consume the same memory amount for corresponding dtypes. With deep
              memory introspection, a real memory usage calculation is performed
              at the cost of computational resources.
          null_counts : bool, optional
              Whether to show the non-null counts. By default, this is shown
              only if the frame 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.
  
          Returns
          -------
          None
              This method prints a summary of a DataFrame and returns None.
  
          See Also
          --------
          DataFrame.describe: Generate descriptive statistics of DataFrame
              columns.
          DataFrame.memory_usage: Memory usage of DataFrame columns.
  
          Examples
          --------
          >>> 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):
          int_col      5 non-null int64
          text_col     5 non-null object
          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_1    1000000 non-null object
          column_2    1000000 non-null object
          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_1    1000000 non-null object
          column_2    1000000 non-null object
          column_3    1000000 non-null object
          dtypes: object(3)
          memory usage: 188.8 MB
          """
  
          if buf is None:  # pragma: no cover
              buf = sys.stdout
  
          lines = []
  
          lines.append(str(type(self)))
          lines.append(self.index._summary())
  
          if len(self.columns) == 0:
              lines.append("Empty {name}".format(name=type(self).__name__))
              fmt.buffer_put_lines(buf, lines)
              return
  
          cols = self.columns
  
          # hack
          if max_cols is None:
              max_cols = get_option("display.max_info_columns", len(self.columns) + 1)
  
          max_rows = get_option("display.max_info_rows", len(self) + 1)
  
          if null_counts is None:
              show_counts = (len(self.columns) <= max_cols) and (len(self) < max_rows)
          else:
              show_counts = null_counts
          exceeds_info_cols = len(self.columns) > max_cols
  
          def _verbose_repr():
              lines.append("Data columns (total %d columns):" % len(self.columns))
              space = max(len(pprint_thing(k)) for k in self.columns) + 4
              counts = None
  
              tmpl = "{count}{dtype}"
              if show_counts:
                  counts = self.count()
                  if len(cols) != len(counts):  # pragma: no cover
                      raise AssertionError(
                          "Columns must equal counts "
                          "({cols:d} != {counts:d})".format(
                              cols=len(cols), counts=len(counts)
                          )
                      )
                  tmpl = "{count} non-null {dtype}"
  
              dtypes = self.dtypes
              for i, col in enumerate(self.columns):
                  dtype = dtypes.iloc[i]
                  col = pprint_thing(col)
  
                  count = ""
                  if show_counts:
                      count = counts.iloc[i]
  
                  lines.append(
                      _put_str(col, space) + tmpl.format(count=count, dtype=dtype)
                  )
  
          def _non_verbose_repr():
              lines.append(self.columns._summary(name="Columns"))
  
          def _sizeof_fmt(num, size_qualifier):
              # returns size in human readable format
              for x in ["bytes", "KB", "MB", "GB", "TB"]:
                  if num < 1024.0:
                      return "{num:3.1f}{size_q} {x}".format(
                          num=num, size_q=size_qualifier, x=x
                      )
                  num /= 1024.0
              return "{num:3.1f}{size_q} {pb}".format(
                  num=num, size_q=size_qualifier, pb="PB"
              )
  
          if verbose:
              _verbose_repr()
          elif verbose is False:  # specifically set to False, not nesc None
              _non_verbose_repr()
          else:
              if exceeds_info_cols:
                  _non_verbose_repr()
              else:
                  _verbose_repr()
  
          counts = self._data.get_dtype_counts()
          dtypes = ["{k}({kk:d})".format(k=k[0], kk=k[1]) for k in sorted(counts.items())]
          lines.append("dtypes: {types}".format(types=", ".join(dtypes)))
  
          if memory_usage is None:
              memory_usage = get_option("display.memory_usage")
          if memory_usage:
              # append memory usage of df to display
              size_qualifier = ""
              if memory_usage == "deep":
                  deep = True
              else:
                  # size_qualifier is just a best effort; not guaranteed to catch
                  # all cases (e.g., it misses categorical data even with object
                  # categories)
                  deep = False
                  if "object" in counts or self.index._is_memory_usage_qualified():
                      size_qualifier = "+"
              mem_usage = self.memory_usage(index=True, deep=deep).sum()
              lines.append(
                  "memory usage: {mem}\n".format(
                      mem=_sizeof_fmt(mem_usage, size_qualifier)
                  )
              )
  
          fmt.buffer_put_lines(buf, lines)
  
      def memory_usage(self, index=True, deep=False):
          """
          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).astype(t))
          ...              for t in dtypes])
          >>> df = pd.DataFrame(data)
          >>> df.head()
             int64  float64            complex128  object  bool
          0      1      1.0    1.000000+0.000000j       1  True
          1      1      1.0    1.000000+0.000000j       1  True
          2      1      1.0    1.000000+0.000000j       1  True
          3      1      1.0    1.000000+0.000000j       1  True
          4      1      1.0    1.000000+0.000000j       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        160000
          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)
          5216
          """
          result = Series(
              [c.memory_usage(index=False, deep=deep) for col, c in self.items()],
              index=self.columns,
          )
          if index:
              result = Series(self.index.memory_usage(deep=deep), index=["Index"]).append(
                  result
              )
          return result
  
      def transpose(self, *args, **kwargs):
          """
          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
          ----------
          copy : bool, default False
              If True, the underlying data is copied. Otherwise (default), no
              copy is made if possible.
          *args, **kwargs
              Additional keywords have no effect but might be accepted for
              compatibility with numpy.
  
          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
          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, dict())
          return super().transpose(1, 0, **kwargs)
  
      T = property(transpose)
  
      # ----------------------------------------------------------------------
      # Picklability
  
      # legacy pickle formats
      def _unpickle_frame_compat(self, state):  # pragma: no cover
          if len(state) == 2:  # pragma: no cover
              series, idx = state
              columns = sorted(series)
          else:
              series, cols, idx = state
              columns = com._unpickle_array(cols)
  
          index = com._unpickle_array(idx)
          self._data = self._init_dict(series, index, columns, None)
  
      def _unpickle_matrix_compat(self, state):  # pragma: no cover
          # old unpickling
          (vals, idx, cols), object_state = state
  
          index = com._unpickle_array(idx)
          dm = DataFrame(vals, index=index, columns=com._unpickle_array(cols), copy=False)
  
          if object_state is not None:
              ovals, _, ocols = object_state
              objects = DataFrame(
                  ovals, index=index, columns=com._unpickle_array(ocols), copy=False
              )
  
              dm = dm.join(objects)
  
          self._data = dm._data
  
      # ----------------------------------------------------------------------
      # 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:
              label = self.index[i]
              new_values = self._data.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]
  
              # if the values returned are not the same length
              # as the index (iow a not found value), iget returns
              # a 0-len ndarray. This is effectively catching
              # a numpy error (as numpy should really raise)
              values = self._data.iget(i)
  
              if len(self.index) and not len(values):
                  values = np.array([np.nan] * len(self.index), dtype=object)
              result = self._box_col_values(values, label)
  
              # this is a cached value, mark it so
              result._set_as_cached(label, self)
  
              return result
  
      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 self.columns.nlevels > 1:
                      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:
              # 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(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, ABCMultiIndex):
                  data = data[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(
                  "Item wrong length %d instead of %d." % (len(key), 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(indexer, axis=0)
  
      def _getitem_multilevel(self, key):
          # self.columns is a MultiIndex
          loc = self.columns.get_loc(key)
          if isinstance(loc, (slice, Series, np.ndarray, Index)):
              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:
              return self._get_item_cache(key)
  
      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._iget_item_cache(col)
              return com.maybe_box_datetimelike(series._values[index])
  
          series = self._get_item_cache(col)
          engine = self.index._engine
  
          try:
              return engine.get_value(series._values, index)
          except KeyError:
              # GH 20629
              if self.index.nlevels > 1:
                  # partial indexing forbidden
                  raise
          except (TypeError, ValueError):
              pass
  
          # 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, value):
          self._check_setitem_copy()
          self.loc[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(
                      "Item wrong length %d instead of %d!" % (len(key), len(self.index))
                  )
              key = check_bool_indexer(self.index, key)
              indexer = key.nonzero()[0]
              self._check_setitem_copy()
              self.loc._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:
                  indexer = self.loc._get_listlike_indexer(
                      key, axis=1, raise_missing=False
                  )[1]
                  self._check_setitem_copy()
                  self.loc._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.values.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 _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
  
          Returns
          -------
          DataFrame
              If label pair is contained, will be reference to calling DataFrame,
              otherwise a new object.
          """
          try:
              if takeable is True:
                  series = self._iget_item_cache(col)
                  return series._set_value(index, value, takeable=True)
  
              series = self._get_item_cache(col)
              engine = self.index._engine
              engine.set_value(series._values, index, value)
              return self
          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)
  
              return self
  
      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):
                  raise ValueError(
                      "Cannot set a frame with no defined index "
                      "and a value that cannot be converted to a "
                      "Series"
                  )
  
              self._data = self._data.reindex_axis(
                  value.index.copy(), axis=1, fill_value=np.nan
              )
  
      def _box_item_values(self, key, values):
          items = self.columns[self.columns.get_loc(key)]
          if values.ndim == 2:
              return self._constructor(values.T, columns=items, index=self.index)
          else:
              return self._box_col_values(values, items)
  
      def _box_col_values(self, values, items):
          """
          Provide boxed values for a column.
          """
          klass = self._constructor_sliced
          return klass(values, index=self.index, name=items, 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``.
  
              .. versionadded:: 0.25.0
  
              You can refer to column names that contain spaces by surrounding
              them in backticks.
  
              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``.
  
          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
              DataFrame resulting from the provided query expression.
  
          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>`.
  
          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 = "expr must be a string to be evaluated, {0} given"
              raise ValueError(msg.format(type(expr)))
          kwargs["level"] = kwargs.pop("level", 0) + 1
          kwargs["target"] = None
          res = self.eval(expr, **kwargs)
  
          try:
              new_data = self.loc[res]
          except ValueError:
              # when res is multi-dimensional loc raises, but this is sometimes a
              # valid query
              new_data = self[res]
  
          if inplace:
              self._update_inplace(new_data)
          else:
              return new_data
  
      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 : dict
              See the documentation for :func:`eval` for complete details
              on the keyword arguments accepted by
              :meth:`~pandas.DataFrame.query`.
  
          Returns
          -------
          ndarray, scalar, or pandas object
              The result of the evaluation.
  
          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
          """
          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_space_character_free_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):
          """
          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.
  
          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
            <http://docs.scipy.org/doc/numpy/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=['int'])
                 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(
                  "include and exclude overlap on {inc_ex}".format(
                      inc_ex=(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
                  if issubclass(unique_dtype.type, tuple(dtypes_set))  # type: ignore
              ]
              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):
          """
          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
          """
          self._ensure_valid_index(value)
          value = self._sanitize_column(column, value, broadcast=False)
          self._data.insert(loc, column, value, allow_duplicates=allow_duplicates)
  
      def assign(self, **kwargs):
          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.
          For Python 3.6 and above, later items in '\*\*kwargs' may refer to
          newly created or modified columns in 'df'; items are computed and
          assigned into 'df' in order.  For Python 3.5 and below, the order of
          keyword arguments is not specified, you cannot refer to newly created
          or modified columns. All items are computed first, and then assigned
          in alphabetical order.
  
          .. versionchanged:: 0.23.0
  
             Keyword argument order is maintained for Python 3.6 and later.
  
          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
  
          In Python 3.6+, 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()
  
          # >= 3.6 preserve order of kwargs
          if PY36:
              for k, v in kwargs.items():
                  data[k] = com.apply_if_callable(v, data)
          else:
              # <= 3.5: do all calculations first...
              results = OrderedDict()
              for k, v in kwargs.items():
                  results[k] = com.apply_if_callable(v, data)
  
              # <= 3.5 and earlier
              results = sorted(results.items())
              # ... and then assign
              for k, v in results:
                  data[k] = v
          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 Exception as e:
  
                      # duplicate axis
                      if not value.index.is_unique:
                          raise e
  
                      # other
                      raise TypeError(
                          "incompatible index of inserted column with frame index"
                      )
              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, ABCMultiIndex) 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, copy=False)
  
          elif isinstance(value, Index) or is_sequence(value):
  
              # turn me into an ndarray
              value = sanitize_index(value, self.index, copy=False)
              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
              value = cast_scalar_to_array(len(self.index), value)
              value = maybe_cast_to_datetime(value, infer_dtype)
  
          # return internal types directly
          if is_extension_type(value) or 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, ABCMultiIndex):
                  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._data.iget(idx), index=self.index, name=item)
              for idx, item in enumerate(self.columns)
          }
  
      def lookup(self, row_labels, col_labels):
          """
          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.
  
          Parameters
          ----------
          row_labels : sequence
              The row labels to use for lookup.
          col_labels : sequence
              The column labels to use for lookup.
  
          Returns
          -------
          numpy.ndarray
  
          Notes
          -----
          Akin to::
  
              result = [df.get_value(row, col)
                        for row, col in zip(row_labels, col_labels)]
  
          Examples
          --------
          values : ndarray
              The found values
          """
          n = len(row_labels)
          if n != len(col_labels):
              raise ValueError("Row labels must have same size as column labels")
  
          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):
          """
          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,
              )
  
      @Appender(_shared_docs["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,
      ):
          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,
          )
  
      @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):
          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``).
  
              .. versionadded:: 0.21.0
          columns : single label or list-like
              Alternative to specifying axis (``labels, axis=1``
              is equivalent to ``columns=labels``).
  
              .. versionadded:: 0.21.0
          level : int or level name, optional
              For MultiIndex, level from which the labels will be removed.
          inplace : bool, default False
              If True, do operation inplace and return None.
          errors : {'ignore', 'raise'}, default 'raise'
              If 'ignore', suppress error and only existing labels are
              dropped.
  
          Returns
          -------
          DataFrame
              DataFrame without the removed index or column labels.
  
          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, *args, **kwargs):
          """
          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 functions 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 : int or str
              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
              DataFrame with the renamed axis labels.
  
          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
          """
          axes = validate_axis_style_args(self, args, kwargs, "mapper", "rename")
          kwargs.update(axes)
          # Pop these, since the values are in `kwargs` under different names
          kwargs.pop("axis", None)
          kwargs.pop("mapper", None)
          return super().rename(**kwargs)
  
      @Substitution(**_shared_doc_kwargs)
      @Appender(NDFrame.fillna.__doc__)
      def fillna(
          self,
          value=None,
          method=None,
          axis=None,
          inplace=False,
          limit=None,
          downcast=None,
          **kwargs
      ):
          return super().fillna(
              value=value,
              method=method,
              axis=axis,
              inplace=inplace,
              limit=limit,
              downcast=downcast,
              **kwargs
          )
  
      @Appender(_shared_docs["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,
          )
  
      @Appender(_shared_docs["shift"] % _shared_doc_kwargs)
      def shift(self, periods=1, freq=None, axis=0, fill_value=None):
          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
              Modify 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
              Changed row labels.
  
          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")
          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 = []
          for col in keys:
              if isinstance(
                  col, (ABCIndexClass, ABCSeries, 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:
                      raise TypeError(
                          err_msg + " Received column of type {}".format(type(col))
                      )
                  else:
                      if not found:
                          missing.append(col)
  
          if missing:
              raise KeyError("None of {} are in the columns".format(missing))
  
          if inplace:
              frame = self
          else:
              frame = self.copy()
  
          arrays = []
          names = []
          if append:
              names = [x for x in self.index.names]
              if isinstance(self.index, ABCMultiIndex):
                  for i in range(self.index.nlevels):
                      arrays.append(self.index._get_level_values(i))
              else:
                  arrays.append(self.index)
  
          to_remove = []
          for col in keys:
              if isinstance(col, ABCMultiIndex):
                  for n in range(col.nlevels):
                      arrays.append(col._get_level_values(n))
                  names.extend(col.names)
              elif isinstance(col, (ABCIndexClass, ABCSeries)):
                  # 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(
                      "Length mismatch: Expected {len_self} rows, "
                      "received array of length {len_col}".format(
                          len_self=len(self), len_col=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("Index has duplicate keys: {dup}".format(dup=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
  
      def reset_index(
          self, level=None, drop=False, inplace=False, col_level=0, col_fill=""
      ):
          """
          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
              DataFrame with the new index.
  
          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")
          if inplace:
              new_obj = self
          else:
              new_obj = self.copy()
  
          def _maybe_casted_values(index, labels=None):
              values = index._values
              if not isinstance(index, (PeriodIndex, DatetimeIndex)):
                  if values.dtype == np.object_:
                      values = lib.maybe_convert_objects(values)
  
              # if we have the labels, extract the values with a mask
              if labels is not None:
                  mask = labels == -1
  
                  # we can have situations where the whole mask is -1,
                  # meaning there is nothing found in labels, so make all nan's
                  if mask.all():
                      values = np.empty(len(mask))
                      values.fill(np.nan)
                  else:
                      values = values.take(labels)
  
                      # TODO(https://github.com/pandas-dev/pandas/issues/24206)
                      # Push this into maybe_upcast_putmask?
                      # We can't pass EAs there right now. Looks a bit
                      # complicated.
                      # So we unbox the ndarray_values, op, re-box.
                      values_type = type(values)
                      values_dtype = values.dtype
  
                      if issubclass(values_type, DatetimeLikeArray):
                          values = values._data
  
                      if mask.any():
                          values, changed = maybe_upcast_putmask(values, mask, np.nan)
  
                      if issubclass(values_type, DatetimeLikeArray):
                          values = values_type(values, dtype=values_dtype)
  
              return values
  
          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:
              if isinstance(self.index, ABCMultiIndex):
                  names = [
                      n if n is not None else ("level_%d" % 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, ABCMultiIndex)
              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 "
                                  "with incomplete column name "
                                  "{}".format(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
  
      # ----------------------------------------------------------------------
      # Reindex-based selection methods
  
      @Appender(_shared_docs["isna"] % _shared_doc_kwargs)
      def isna(self):
          return super().isna()
  
      @Appender(_shared_docs["isna"] % _shared_doc_kwargs)
      def isnull(self):
          return super().isnull()
  
      @Appender(_shared_docs["notna"] % _shared_doc_kwargs)
      def notna(self):
          return super().notna()
  
      @Appender(_shared_docs["notna"] % _shared_doc_kwargs)
      def notnull(self):
          return super().notnull()
  
      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.
  
              .. deprecated:: 0.23.0
  
                 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
              DataFrame with NA entries dropped from it.
  
          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', 'born'])
                 name        toy       born
          1    Batman  Batmobile 1940-04-25
  
          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
              msg = (
                  "supplying multiple axes to axis is deprecated and "
                  "will be removed in a future version."
              )
              warnings.warn(msg, FutureWarning, stacklevel=2)
  
              result = self
              for ax in axis:
                  result = result.dropna(how=how, thresh=thresh, subset=subset, axis=ax)
          else:
              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("invalid how option: {h}".format(h=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=None, keep="first", inplace=False):
          """
          Return DataFrame with duplicate rows removed, optionally only
          considering certain columns. 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.
  
          Returns
          -------
          DataFrame
          """
          if self.empty:
              return self.copy()
  
          inplace = validate_bool_kwarg(inplace, "inplace")
          duplicated = self.duplicated(subset, keep=keep)
  
          if inplace:
              inds, = (-duplicated)._ndarray_values.nonzero()
              new_data = self._data.take(inds)
              self._update_inplace(new_data)
          else:
              return self[-duplicated]
  
      def duplicated(self, subset=None, keep="first"):
          """
          Return boolean Series denoting duplicate rows, optionally only
          considering certain columns.
  
          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
          """
          from pandas.core.sorting import get_group_index
          from pandas._libs.hashtable import duplicated_int64, _SIZE_HINT_LIMIT
  
          if self.empty:
              return Series(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,)
  
          # 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)
          return Series(duplicated_int64(ids, keep), index=self.index)
  
      # ----------------------------------------------------------------------
      # Sorting
  
      @Substitution(**_shared_doc_kwargs)
      @Appender(NDFrame.sort_values.__doc__)
      def sort_values(
          self,
          by,
          axis=0,
          ascending=True,
          inplace=False,
          kind="quicksort",
          na_position="last",
      ):
          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(
                  "Length of ascending (%d) != length of by (%d)"
                  % (len(ascending), len(by))
              )
          if len(by) > 1:
              from pandas.core.sorting import lexsort_indexer
  
              keys = [self._get_label_or_level_values(x, axis=axis) for x in by]
              indexer = lexsort_indexer(keys, orders=ascending, na_position=na_position)
              indexer = ensure_platform_int(indexer)
          else:
              from pandas.core.sorting import nargsort
  
              by = by[0]
              k = self._get_label_or_level_values(by, axis=axis)
  
              if isinstance(ascending, (tuple, list)):
                  ascending = ascending[0]
  
              indexer = nargsort(
                  k, kind=kind, ascending=ascending, na_position=na_position
              )
  
          new_data = self._data.take(
              indexer, axis=self._get_block_manager_axis(axis), verify=False
          )
  
          if inplace:
              return self._update_inplace(new_data)
          else:
              return self._constructor(new_data).__finalize__(self)
  
      @Substitution(**_shared_doc_kwargs)
      @Appender(NDFrame.sort_index.__doc__)
      def sort_index(
          self,
          axis=0,
          level=None,
          ascending=True,
          inplace=False,
          kind="quicksort",
          na_position="last",
          sort_remaining=True,
          by=None,
      ):
  
          # TODO: this can be combined with Series.sort_index impl as
          # almost identical
  
          inplace = validate_bool_kwarg(inplace, "inplace")
          # 10726
          if by is not None:
              warnings.warn(
                  "by argument to sort_index is deprecated, "
                  "please use .sort_values(by=...)",
                  FutureWarning,
                  stacklevel=2,
              )
              if level is not None:
                  raise ValueError("unable to simultaneously sort by and level")
              return self.sort_values(by, axis=axis, ascending=ascending, inplace=inplace)
  
          axis = self._get_axis_number(axis)
          labels = self._get_axis(axis)
  
          # make sure that the axis is lexsorted to start
          # if not we need to reconstruct to get the correct indexer
          labels = labels._sort_levels_monotonic()
          if level is not None:
  
              new_axis, indexer = labels.sortlevel(
                  level, ascending=ascending, sort_remaining=sort_remaining
              )
  
          elif isinstance(labels, ABCMultiIndex):
              from pandas.core.sorting import lexsort_indexer
  
              indexer = lexsort_indexer(
                  labels._get_codes_for_sorting(),
                  orders=ascending,
                  na_position=na_position,
              )
          else:
              from pandas.core.sorting import nargsort
  
              # Check monotonic-ness before sort an index
              # GH11080
              if (ascending and labels.is_monotonic_increasing) or (
                  not ascending and labels.is_monotonic_decreasing
              ):
                  if inplace:
                      return
                  else:
                      return self.copy()
  
              indexer = nargsort(
                  labels, kind=kind, ascending=ascending, na_position=na_position
              )
  
          baxis = self._get_block_manager_axis(axis)
          new_data = self._data.take(indexer, axis=baxis, verify=False)
  
          # reconstruct axis if needed
          new_data.axes[baxis] = new_data.axes[baxis]._sort_levels_monotonic()
  
          if inplace:
              return self._update_inplace(new_data)
          else:
              return self._constructor(new_data).__finalize__(self)
  
      def nlargest(self, n, columns, keep="first"):
          """
          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"):
          """
          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, 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 ``nsmallest`` to select the
          three rows having the smallest values in column "a".
  
          >>> df.nsmallest(3, 'population')
                    population  GDP alpha-2
          Nauru          11300  182      NR
          Tuvalu         11300   38      TV
          Anguilla       11300  311      AI
  
          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          11300  182      NR
  
          When using ``keep='all'``, all duplicate items are maintained:
  
          >>> df.nsmallest(3, 'population', keep='all')
                    population  GDP alpha-2
          Nauru          11300  182      NR
          Tuvalu         11300   38      TV
          Anguilla       11300  311      AI
  
          To order by the largest values in column "a" and then "c", we can
          specify multiple columns like in the next example.
  
          >>> df.nsmallest(3, ['population', 'GDP'])
                    population  GDP alpha-2
          Tuvalu         11300   38      TV
          Nauru          11300  182      NR
          Anguilla       11300  311      AI
          """
          return algorithms.SelectNFrame(
              self, n=n, keep=keep, columns=columns
          ).nsmallest()
  
      def swaplevel(self, i=-2, j=-1, axis=0):
          """
          Swap levels i and j in a MultiIndex on a particular axis.
  
          Parameters
          ----------
          i, j : int, str (can be mixed)
              Level of index to be swapped. Can pass level name as string.
  
          Returns
          -------
          DataFrame
          """
          result = self.copy()
  
          axis = self._get_axis_number(axis)
          if axis == 0:
              result.index = result.index.swaplevel(i, j)
          else:
              result.columns = result.columns.swaplevel(i, j)
          return result
  
      def reorder_levels(self, order, axis=0):
          """
          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 : int
              Where to reorder levels.
  
          Returns
          -------
          type of caller (new object)
          """
          axis = self._get_axis_number(axis)
          if not isinstance(self._get_axis(axis), ABCMultiIndex):  # pragma: no cover
              raise TypeError("Can only reorder levels on a hierarchical axis.")
  
          result = self.copy()
  
          if axis == 0:
              result.index = result.index.reorder_levels(order)
          else:
              result.columns = result.columns.reorder_levels(order)
          return result
  
      # ----------------------------------------------------------------------
      # Arithmetic / combination related
  
      def _combine_frame(self, other, func, fill_value=None, level=None):
          this, other = self.align(other, join="outer", level=level, copy=False)
  
          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)
  
          if ops.should_series_dispatch(this, other, func):
              # iterate over columns
              new_data = ops.dispatch_to_series(this, other, _arith_op)
          else:
              with np.errstate(all="ignore"):
                  res_values = _arith_op(this.values, other.values)
              new_data = dispatch_fill_zeros(func, this.values, other.values, res_values)
          return this._construct_result(new_data)
  
      def _combine_match_index(self, other, func):
          # at this point we have `self.index.equals(other.index)`
  
          if ops.should_series_dispatch(self, other, func):
              # operate column-wise; avoid costly object-casting in `.values`
              new_data = ops.dispatch_to_series(self, other, func)
          else:
              # fastpath --> operate directly on values
              with np.errstate(all="ignore"):
                  new_data = func(self.values.T, other.values).T
          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, index=self.index, copy=False)
          # Pin columns instead of passing to constructor for compat with
          #  non-unique columns case
          out.columns = self.columns
          return out
  
      def combine(self, other, func, fill_value=None, overwrite=True):
          """
          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, this_dtype)
  
              result[col] = arr
  
          # convert_objects just in case
          return self._constructor(result, index=new_index, columns=new_columns)
  
      def combine_first(self, other):
          """
          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 extract_values(arr):
              # Does two things:
              # 1. maybe gets the values from the Series / Index
              # 2. convert datelike to i8
              if isinstance(arr, (ABCIndexClass, ABCSeries)):
                  arr = arr._values
  
              if needs_i8_conversion(arr):
                  if is_extension_array_dtype(arr.dtype):
                      arr = arr.asi8
                  else:
                      arr = arr.view("i8")
              return arr
  
          def combiner(x, y):
              mask = isna(x)
              if isinstance(mask, (ABCIndexClass, ABCSeries)):
                  mask = mask._values
  
              x_values = extract_values(x)
              y_values = extract_values(y)
  
              # 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)
  
      @deprecate_kwarg(
          old_arg_name="raise_conflict",
          new_arg_name="errors",
          mapping={False: "ignore", True: "raise"},
      )
      def update(
          self, other, join="left", overwrite=True, filter_func=None, errors="ignore"
      ):
          """
          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-columns(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, it's 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
  
      _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, optional
              Column to use to make new frame's index. If None, uses
              existing index.
          columns : str or object
              Column to use to make new frame's columns.
          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.
  
              .. versionchanged:: 0.23.0
                 Also accept list of column names.
  
          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.
  
          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
  
          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):
          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
          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
  
          See Also
          --------
          DataFrame.pivot : Pivot without aggregation that can handle
              non-numeric data.
  
          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,
      ):
          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.
  
          The new index levels are sorted.
  
          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)):
              return stack_multiple(self, level, dropna=dropna)
          else:
              return stack(self, level, dropna=dropna)
  
      def explode(self, column: Union[str, Tuple]) -> "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.
  
          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,
          Series, and np.ndarray. The result dtype of the subset rows will
          be object. Scalars will be returned unchanged. Empty list-likes will
          result in a np.nan for that row.
  
          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)
          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, returning
          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).
  
          The level involved will automatically get sorted.
  
          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, string 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
  
          return unstack(self, level, fill_value)
  
      _shared_docs[
          "melt"
      ] = """
      Unpivot a DataFrame from wide format to long format, optionally
      leaving identifier variables set.
  
      This function is useful to massage a DataFrame into a format where one
      or more columns are identifier variables (`id_vars`), while all other
      columns, considered measured variables (`value_vars`), are "unpivoted" to
      the row axis, leaving just two non-identifier columns, 'variable' and
      'value'.
      %(versionadded)s
      Parameters
      ----------
      frame : DataFrame
      id_vars : tuple, list, or ndarray, optional
          Column(s) to use as identifier variables.
      value_vars : tuple, list, or ndarray, optional
          Column(s) to unpivot. If not specified, uses all columns that
          are not set as `id_vars`.
      var_name : scalar
          Name to use for the 'variable' column. If None it uses
          ``frame.columns.name`` or 'variable'.
      value_name : scalar, default 'value'
          Name to use for the 'value' column.
      col_level : int or str, optional
          If columns are a MultiIndex then use this level to melt.
  
      Returns
      -------
      DataFrame
          Unpivoted DataFrame.
  
      See Also
      --------
      %(other)s
      pivot_table
      DataFrame.pivot
      Series.explode
  
      Examples
      --------
      >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
      ...                    'B': {0: 1, 1: 3, 2: 5},
      ...                    'C': {0: 2, 1: 4, 2: 6}})
      >>> df
         A  B  C
      0  a  1  2
      1  b  3  4
      2  c  5  6
  
      >>> %(caller)sid_vars=['A'], value_vars=['B'])
         A variable  value
      0  a        B      1
      1  b        B      3
      2  c        B      5
  
      >>> %(caller)sid_vars=['A'], value_vars=['B', 'C'])
         A variable  value
      0  a        B      1
      1  b        B      3
      2  c        B      5
      3  a        C      2
      4  b        C      4
      5  c        C      6
  
      The names of 'variable' and 'value' columns can be customized:
  
      >>> %(caller)sid_vars=['A'], value_vars=['B'],
      ...         var_name='myVarname', value_name='myValname')
         A myVarname  myValname
      0  a         B          1
      1  b         B          3
      2  c         B          5
  
      If you have multi-index columns:
  
      >>> df.columns = [list('ABC'), list('DEF')]
      >>> df
         A  B  C
         D  E  F
      0  a  1  2
      1  b  3  4
      2  c  5  6
  
      >>> %(caller)scol_level=0, id_vars=['A'], value_vars=['B'])
         A variable  value
      0  a        B      1
      1  b        B      3
      2  c        B      5
  
      >>> %(caller)sid_vars=[('A', 'D')], value_vars=[('B', 'E')])
        (A, D) variable_0 variable_1  value
      0      a          B          E      1
      1      b          B          E      3
      2      c          B          E      5
      """
  
      @Appender(
          _shared_docs["melt"]
          % dict(
              caller="df.melt(", versionadded=".. versionadded:: 0.20.0\n", other="melt"
          )
      )
      def melt(
          self,
          id_vars=None,
          value_vars=None,
          var_name=None,
          value_name="value",
          col_level=None,
      ):
          from pandas.core.reshape.melt import melt
  
          return melt(
              self,
              id_vars=id_vars,
              value_vars=value_vars,
              var_name=var_name,
              value_name=value_name,
              col_level=col_level,
          )
  
      # ----------------------------------------------------------------------
      # Time series-related
  
      def diff(self, periods=1, axis=0):
          """
          First discrete difference of element.
  
          Calculates the difference of a DataFrame element compared with another
          element in the DataFrame (default is the element in the same column
          of the previous row).
  
          Parameters
          ----------
          periods : int, default 1
              Periods to shift for calculating difference, accepts negative
              values.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              Take difference over rows (0) or columns (1).
  
              .. versionadded:: 0.16.1.
  
          Returns
          -------
          DataFrame
  
          See Also
          --------
          Series.diff: First discrete difference for a Series.
          DataFrame.pct_change: Percent change over given number of periods.
          DataFrame.shift: Shift index by desired number of periods with an
              optional time freq.
  
          Examples
          --------
          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   0.0
          1 NaN -1.0   3.0
          2 NaN -1.0   7.0
          3 NaN -1.0  13.0
          4 NaN  0.0  20.0
          5 NaN  2.0  28.0
  
          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
          """
          bm_axis = self._get_block_manager_axis(axis)
          new_data = self._data.diff(n=periods, axis=bm_axis)
          return self._constructor(new_data)
  
      # ----------------------------------------------------------------------
      # Function application
  
      def _gotitem(
          self,
          key: Union[str, List[str]],
          ndim: int,
          subset: Optional[Union[Series, ABCDataFrame]] = None,
      ) -> Union[Series, ABCDataFrame]:
          """
          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.EWM : 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
      max   NaN  8.0
      min   1.0  2.0
      sum  12.0  NaN
  
      Aggregate over the columns.
  
      >>> df.agg("mean", axis="columns")
      0    2.0
      1    5.0
      2    8.0
      3    NaN
      dtype: float64
      """
      )
  
      @Substitution(
          see_also=_agg_summary_and_see_also_doc,
          examples=_agg_examples_doc,
          versionadded="\n.. versionadded:: 0.20.0\n",
          **_shared_doc_kwargs
      )
      @Appender(_shared_docs["aggregate"])
      def aggregate(self, func, axis=0, *args, **kwargs):
          axis = self._get_axis_number(axis)
  
          result = None
          try:
              result, how = self._aggregate(func, axis=axis, *args, **kwargs)
          except TypeError:
              pass
          if result is None:
              return self.apply(func, axis=axis, args=args, **kwargs)
          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 = self.T._aggregate(arg, *args, **kwargs)
              result = result.T if result is not None else result
              return result, how
          return super()._aggregate(arg, *args, **kwargs)
  
      agg = aggregate
  
      @Appender(_shared_docs["transform"] % _shared_doc_kwargs)
      def transform(self, func, axis=0, *args, **kwargs):
          axis = self._get_axis_number(axis)
          if axis == 1:
              return self.T.transform(func, *args, **kwargs).T
          return super().transform(func, *args, **kwargs)
  
      def apply(
          self,
          func,
          axis=0,
          broadcast=None,
          raw=False,
          reduce=None,
          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.
          broadcast : bool, optional
              Only relevant for aggregation functions:
  
              * ``False`` or ``None`` : returns a Series whose length is the
                length of the index or the number of columns (based on the
                `axis` parameter)
              * ``True`` : results will be broadcast to the original shape
                of the frame, the original index and columns will be retained.
  
              .. deprecated:: 0.23.0
                 This argument will be removed in a future version, replaced
                 by result_type='broadcast'.
  
          raw : bool, default False
              Determines if row or column is passed as a Series or ndarry 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.
          reduce : bool or None, default None
              Try to apply reduction procedures. If the DataFrame is empty,
              `apply` will use `reduce` to determine whether the result
              should be a Series or a DataFrame. If ``reduce=None`` (the
              default), `apply`'s return value will be guessed by calling
              `func` on an empty Series
              (note: while guessing, exceptions raised by `func` will be
              ignored).
              If ``reduce=True`` a Series will always be returned, and if
              ``reduce=False`` a DataFrame will always be returned.
  
              .. deprecated:: 0.23.0
                 This argument will be removed in a future version, replaced
                 by ``result_type='reduce'``.
  
          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.
  
              .. versionadded:: 0.23.0
  
          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,
              broadcast=broadcast,
              raw=raw,
              reduce=reduce,
              result_type=result_type,
              args=args,
              kwds=kwds,
          )
          return op.get_result()
  
      def applymap(self, func):
          """
          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.
  
          Returns
          -------
          DataFrame
              Transformed DataFrame.
  
          See Also
          --------
          DataFrame.apply : Apply a function along input axis of DataFrame.
  
          Notes
          -----
          In the current implementation applymap calls `func` twice on the
          first column/row to decide whether it can take a fast or slow
          code path. This can lead to unexpected behavior if `func` has
          side-effects, as they will take effect twice for the first
          column/row.
  
          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
  
          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 we have a dtype == 'M8[ns]', provide boxed values
          def infer(x):
              if x.empty:
                  return lib.map_infer(x, func)
              return lib.map_infer(x.astype(object).values, func)
  
          return self.apply(infer)
  
      # ----------------------------------------------------------------------
      # Merging / joining methods
  
      def append(self, other, ignore_index=False, verify_integrity=False, sort=None):
          """
          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, do not use the index labels.
          verify_integrity : bool, default False
              If True, raise ValueError on creating index with duplicates.
          sort : bool, default None
              Sort columns if the columns of `self` and `other` are not aligned.
              The default sorting is deprecated and will change to not-sorting
              in a future version of pandas. Explicitly pass ``sort=True`` to
              silence the warning and sort. Explicitly pass ``sort=False`` to
              silence the warning and not sort.
  
              .. versionadded:: 0.23.0
  
          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):
                  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"
                  )
  
              if other.name is None:
                  index = None
              else:
                  # other must have the same index name as self, otherwise
                  # index name will be reset
                  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)
              other = DataFrame(
                  other.values.reshape((1, len(other))),
                  index=index,
                  columns=combined_columns,
              )
              other = other._convert(datetime=True, timedelta=True)
              if not self.columns.equals(combined_columns):
                  self = self.reindex(columns=combined_columns)
          elif isinstance(other, list) and 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,
          )
  
      def join(self, other, on=None, how="left", lsuffix="", rsuffix="", sort=False):
          """
          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-columns(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.merge import merge
          from pandas.core.reshape.concat import concat
  
          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):
              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,
      ):
          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):
          """
          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 = [col for col in _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):
          """
          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
          Series.corr
  
          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.values
  
          if method == "pearson":
              correl = libalgos.nancorr(ensure_float64(mat), minp=min_periods)
          elif method == "spearman":
              correl = libalgos.nancorr_spearman(ensure_float64(mat), minp=min_periods)
          elif method == "kendall" or callable(method):
              if min_periods is None:
                  min_periods = 1
              mat = ensure_float64(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, "
                  "'{method}' was supplied".format(method=method)
              )
  
          return self._constructor(correl, index=idx, columns=cols)
  
      def cov(self, min_periods=None):
          """
          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.
  
          Returns
          -------
          DataFrame
              The covariance matrix of the series of the DataFrame.
  
          See Also
          --------
          Series.cov : Compute covariance with another Series.
          core.window.EWM.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-1.
  
          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
          <http://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.values
  
          if notna(mat).all():
              if min_periods is not None and min_periods > len(mat):
                  baseCov = np.empty((mat.shape[1], mat.shape[1]))
                  baseCov.fill(np.nan)
              else:
                  baseCov = np.cov(mat.T)
              baseCov = baseCov.reshape((len(cols), len(cols)))
          else:
              baseCov = libalgos.nancorr(ensure_float64(mat), cov=True, minp=min_periods)
  
          return self._constructor(baseCov, index=idx, columns=cols)
  
      def corrwith(self, other, axis=0, drop=False, method="pearson"):
          """
          Compute pairwise correlation 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
          """
          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 = Series(
                  map(c, zip(left.values.T, right.values.T)), index=left.columns
              )
  
          else:
              raise ValueError(
                  "Invalid method {method} was passed, "
                  "valid methods are: 'pearson', 'kendall', "
                  "'spearman', or callable".format(method=method)
              )
  
          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.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 = Series(0, index=frame._get_agg_axis(axis))
          else:
              if frame._is_mixed_type or frame._data.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 = Series(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, ABCMultiIndex):
              raise TypeError(
                  "Can only count levels on hierarchical "
                  "{ax}.".format(ax=self._get_axis_name(axis))
              )
  
          if frame._is_mixed_type:
              # Since we have mixed types, calling notna(frame.values) might
              # upcast everything to object
              mask = notna(frame).values
          else:
              # But use the speedup when we have homogeneous dtypes
              mask = notna(frame.values)
  
          if axis == 1:
              # We're transposing the mask rather than frame to avoid potential
              # upcasts to object, which induces a ~20x slowdown
              mask = mask.T
  
          if isinstance(level, str):
              level = count_axis._get_level_number(level)
  
          level_index = count_axis.levels[level]
          level_codes = ensure_int64(count_axis.codes[level])
          counts = lib.count_level_2d(mask, level_codes, len(level_index), axis=0)
  
          result = DataFrame(counts, index=level_index, columns=agg_axis)
  
          if axis == 1:
              # Undo our earlier transpose
              return result.T
          else:
              return result
  
      def _reduce(
          self, op, name, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds
      ):
          if axis is None and filter_type == "bool":
              labels = None
              constructor = None
          else:
              # TODO: Make other agg func handle axis=None properly
              axis = self._get_axis_number(axis)
              labels = self._get_agg_axis(axis)
              constructor = self._constructor
  
          def f(x):
              return op(x, axis=axis, skipna=skipna, **kwds)
  
          # exclude timedelta/datetime unless we are uniform types
          if (
              axis == 1
              and self._is_datelike_mixed_type
              and (
                  not self._is_homogeneous_type
                  and not is_datetime64tz_dtype(self.dtypes[0])
              )
          ):
              numeric_only = True
  
          if numeric_only is None:
              try:
                  values = self.values
                  result = f(values)
  
                  if filter_type == "bool" and is_object_dtype(values) and axis is None:
                      # work around https://github.com/numpy/numpy/issues/10489
                      # TODO: combine with hasattr(result, 'dtype') further down
                      # hard since we don't have `values` down there.
                      result = np.bool_(result)
              except Exception as e:
  
                  # try by-column first
                  if filter_type is None and axis == 0:
                      try:
  
                          # this can end up with a non-reduction
                          # but not always. if the types are mixed
                          # with datelike then need to make sure a series
  
                          # we only end up here if we have not specified
                          # numeric_only and yet we have tried a
                          # column-by-column reduction, where we have mixed type.
                          # So let's just do what we can
                          from pandas.core.apply import frame_apply
  
                          opa = frame_apply(
                              self, func=f, result_type="expand", ignore_failures=True
                          )
                          result = opa.get_result()
                          if result.ndim == self.ndim:
                              result = result.iloc[0]
                          return result
                      except Exception:
                          pass
  
                  if filter_type is None or filter_type == "numeric":
                      data = self._get_numeric_data()
                  elif filter_type == "bool":
                      data = self._get_bool_data()
                  else:  # pragma: no cover
                      e = NotImplementedError(
                          "Handling exception with filter_type {f} not"
                          "implemented.".format(f=filter_type)
                      )
                      raise_with_traceback(e)
                  with np.errstate(all="ignore"):
                      result = f(data.values)
                  labels = data._get_agg_axis(axis)
          else:
              if numeric_only:
                  if filter_type is None or filter_type == "numeric":
                      data = self._get_numeric_data()
                  elif filter_type == "bool":
                      # GH 25101, # GH 24434
                      data = self._get_bool_data() if axis == 0 else self
                  else:  # pragma: no cover
                      msg = (
                          "Generating numeric_only data with filter_type {f}"
                          "not supported.".format(f=filter_type)
                      )
                      raise NotImplementedError(msg)
                  values = data.values
                  labels = data._get_agg_axis(axis)
              else:
                  values = self.values
              result = f(values)
  
          if hasattr(result, "dtype") and is_object_dtype(result.dtype):
              try:
                  if filter_type is None or filter_type == "numeric":
                      result = result.astype(np.float64)
                  elif filter_type == "bool" and notna(result).all():
                      result = result.astype(np.bool_)
              except (ValueError, TypeError):
  
                  # try to coerce to the original dtypes item by item if we can
                  if axis == 0:
                      result = coerce_to_dtypes(result, self.dtypes)
  
          if constructor is not None:
              result = Series(result, index=labels)
          return result
  
      def nunique(self, axis=0, dropna=True):
          """
          Count distinct observations over requested axis.
  
          Return Series with number of distinct observations. Can ignore NaN
          values.
  
          .. versionadded:: 0.20.0
  
          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):
          """
          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
  
          Notes
          -----
          This method is the DataFrame version of ``ndarray.argmin``.
          """
          axis = self._get_axis_number(axis)
          indices = nanops.nanargmin(self.values, axis=axis, skipna=skipna)
          index = self._get_axis(axis)
          result = [index[i] if i >= 0 else np.nan for i in indices]
          return Series(result, index=self._get_agg_axis(axis))
  
      def idxmax(self, axis=0, skipna=True):
          """
          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
  
          Notes
          -----
          This method is the DataFrame version of ``ndarray.argmax``.
          """
          axis = self._get_axis_number(axis)
          indices = nanops.nanargmax(self.values, axis=axis, skipna=skipna)
          index = self._get_axis(axis)
          result = [index[i] if i >= 0 else np.nan for i in indices]
          return Series(result, index=self._get_agg_axis(axis))
  
      def _get_agg_axis(self, axis_num):
          """
          Let's be explicit about this.
          """
          if axis_num == 0:
              return self.columns
          elif axis_num == 1:
              return self.index
          else:
              raise ValueError("Axis must be 0 or 1 (got %r)" % axis_num)
  
      def mode(self, axis=0, numeric_only=False, dropna=True):
          """
          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. The second row of species and legs contains ``NaN``,
          because they have only one mode, but the DataFrame has two rows.
  
          >>> 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)
  
          result = data._data.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="start", axis=0, copy=True):
          """
          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_data = self._data
          if copy:
              new_data = new_data.copy()
  
          axis = self._get_axis_number(axis)
          if axis == 0:
              new_data.set_axis(1, self.index.to_timestamp(freq=freq, how=how))
          elif axis == 1:
              new_data.set_axis(0, self.columns.to_timestamp(freq=freq, how=how))
          else:  # pragma: no cover
              raise AssertionError("Axis must be 0 or 1. Got {ax!s}".format(ax=axis))
  
          return self._constructor(new_data)
  
      def to_period(self, freq=None, axis=0, copy=True):
          """
          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
          -------
          TimeSeries with PeriodIndex
          """
          new_data = self._data
          if copy:
              new_data = new_data.copy()
  
          axis = self._get_axis_number(axis)
          if axis == 0:
              new_data.set_axis(1, self.index.to_period(freq=freq))
          elif axis == 1:
              new_data.set_axis(0, self.columns.to_period(freq=freq))
          else:  # pragma: no cover
              raise AssertionError("Axis must be 0 or 1. Got {ax!s}".format(ax=axis))
  
          return self._constructor(new_data)
  
      def isin(self, values):
          """
          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(), "
                      "you passed a "
                      "{0!r}".format(type(values).__name__)
                  )
              return DataFrame(
                  algorithms.isin(self.values.ravel(), values).reshape(self.shape),
                  self.index,
                  self.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._setup_axes(
      ["index", "columns"],
      info_axis=1,
      stat_axis=0,
      axes_are_reversed=True,
      aliases={"rows": 0},
      docs={
          "index": "The index (row labels) of the DataFrame.",
          "columns": "The column labels of the DataFrame.",
      },
  )
  DataFrame._add_numeric_operations()
  DataFrame._add_series_or_dataframe_operations()
  
  ops.add_flex_arithmetic_methods(DataFrame)
  ops.add_special_arithmetic_methods(DataFrame)
  
  
  def _from_nested_dict(data):
      # TODO: this should be seriously cythonized
      new_data = OrderedDict()
      for index, s in data.items():
          for col, v in s.items():
              new_data[col] = new_data.get(col, OrderedDict())
              new_data[col][index] = v
      return new_data
  
  
  def _put_str(s, space):
      return "{s}".format(s=s)[:space].ljust(space)
  