text: #
  # Copyright (C) 2019 Databricks, Inc.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  """
  Base and utility classes for Koalas objects.
  """
  from abc import ABCMeta, abstractmethod
  from collections import OrderedDict
  from functools import wraps, partial
  from typing import Union, Callable, Any
  import warnings
  
  import numpy as np
  import pandas as pd
  from pandas.api.types import is_list_like
  from pandas.core.accessor import CachedAccessor
  from pyspark.sql import functions as F, Window, Column
  from pyspark.sql.types import DateType, DoubleType, FloatType, LongType, StringType, TimestampType
  
  from databricks import koalas as ks  # For running doctests and reference resolution in PyCharm.
  from databricks.koalas import numpy_compat
  from databricks.koalas.internal import (
      InternalFrame,
      NATURAL_ORDER_COLUMN_NAME,
      SPARK_DEFAULT_INDEX_NAME,
  )
  from databricks.koalas.spark.accessors import SparkIndexOpsMethods
  from databricks.koalas.typedef import spark_type_to_pandas_dtype
  from databricks.koalas.utils import align_diff_series, same_anchor, scol_for, validate_axis
  from databricks.koalas.frame import DataFrame
  
  
  def booleanize_null(left_scol, scol, f):
      """
      Booleanize Null in Spark Column
      """
      comp_ops = [
          getattr(Column, "__{}__".format(comp_op))
          for comp_op in ["eq", "ne", "lt", "le", "ge", "gt"]
      ]
  
      if f in comp_ops:
          # if `f` is "!=", fill null with True otherwise False
          filler = f == Column.__ne__
          scol = F.when(scol.isNull(), filler).otherwise(scol)
  
      elif f == Column.__or__:
          scol = F.when(left_scol.isNull() | scol.isNull(), False).otherwise(scol)
  
      elif f == Column.__and__:
          scol = F.when(scol.isNull(), False).otherwise(scol)
  
      return scol
  
  
  def column_op(f):
      """
      A decorator that wraps APIs taking/returning Spark Column so that Koalas Series can be
      supported too. If this decorator is used for the `f` function that takes Spark Column and
      returns Spark Column, decorated `f` takes Koalas Series as well and returns Koalas
      Series.
  
      :param f: a function that takes Spark Column and returns Spark Column.
      :param self: Koalas Series
      :param args: arguments that the function `f` takes.
      """
  
      @wraps(f)
      def wrapper(self, *args):
          # It is possible for the function `f` takes other arguments than Spark Column.
          # To cover this case, explicitly check if the argument is Koalas Series and
          # extract Spark Column. For other arguments, they are used as are.
          cols = [arg for arg in args if isinstance(arg, IndexOpsMixin)]
          if all(same_anchor(self, col) for col in cols):
              # Same DataFrame anchors
              args = [arg.spark.column if isinstance(arg, IndexOpsMixin) else arg for arg in args]
              scol = f(self.spark.column, *args)
              scol = booleanize_null(self.spark.column, scol, f)
  
              return self._with_new_scol(scol)
          else:
              # Different DataFrame anchors
              def apply_func(this_column, *that_columns):
                  scol = f(this_column, *that_columns)
                  return booleanize_null(this_column, scol, f)
  
              return align_diff_series(apply_func, self, *args, how="full")
  
      return wrapper
  
  
  def numpy_column_op(f):
      @wraps(f)
      def wrapper(self, *args):
          # PySpark does not support NumPy type out of the box. For now, we convert NumPy types
          # into some primitive types understandable in PySpark.
          new_args = []
          for arg in args:
              # TODO: This is a quick hack to support NumPy type. We should revisit this.
              if isinstance(self.spark.data_type, LongType) and isinstance(arg, np.timedelta64):
                  new_args.append(float(arg / np.timedelta64(1, "s")))
              else:
                  new_args.append(arg)
          return column_op(f)(self, *new_args)
  
      return wrapper
  
  
  class IndexOpsMixin(object, metaclass=ABCMeta):
      """common ops mixin to support a unified interface / docs for Series / Index
  
      Assuming there are following attributes or properties and function.
  
      :ivar _anchor: Parent's Koalas DataFrame
      :type _anchor: ks.DataFrame
      """
  
      def __init__(self, anchor: DataFrame):
          assert anchor is not None
          self._anchor = anchor
  
      @property
      @abstractmethod
      def _internal(self) -> InternalFrame:
          pass
  
      @property
      def _kdf(self) -> DataFrame:
          return self._anchor
  
      spark = CachedAccessor("spark", SparkIndexOpsMethods)
  
      @property
      def spark_column(self):
          warnings.warn(
              "Series.spark_column is deprecated as of Series.spark.column. "
              "Please use the API instead.",
              FutureWarning,
          )
          return self.spark.column
  
      spark_column.__doc__ = SparkIndexOpsMethods.column.__doc__
  
      # arithmetic operators
      __neg__ = column_op(Column.__neg__)
  
      def __add__(self, other):
          if isinstance(self.spark.data_type, StringType):
              # Concatenate string columns
              if isinstance(other, IndexOpsMixin) and isinstance(other.spark.data_type, StringType):
                  return column_op(F.concat)(self, other)
              # Handle df['col'] + 'literal'
              elif isinstance(other, str):
                  return column_op(F.concat)(self, F.lit(other))
              else:
                  raise TypeError("string addition can only be applied to string series or literals.")
          else:
              return column_op(Column.__add__)(self, other)
  
      def __sub__(self, other):
          # Note that timestamp subtraction casts arguments to integer. This is to mimic pandas's
          # behaviors. pandas returns 'timedelta64[ns]' from 'datetime64[ns]'s subtraction.
          if isinstance(other, IndexOpsMixin) and isinstance(self.spark.data_type, TimestampType):
              if not isinstance(other.spark.data_type, TimestampType):
                  raise TypeError("datetime subtraction can only be applied to datetime series.")
              return self.astype("bigint") - other.astype("bigint")
          elif isinstance(other, IndexOpsMixin) and isinstance(self.spark.data_type, DateType):
              if not isinstance(other.spark.data_type, DateType):
                  raise TypeError("date subtraction can only be applied to date series.")
              return column_op(F.datediff)(self, other)
          else:
              return column_op(Column.__sub__)(self, other)
  
      __mul__ = column_op(Column.__mul__)
  
      def __truediv__(self, other):
          """
          __truediv__ has different behaviour between pandas and PySpark for several cases.
          1. When divide np.inf by zero, PySpark returns null whereas pandas returns np.inf
          2. When divide positive number by zero, PySpark returns null whereas pandas returns np.inf
          3. When divide -np.inf by zero, PySpark returns null whereas pandas returns -np.inf
          4. When divide negative number by zero, PySpark returns null whereas pandas returns -np.inf
  
          +-------------------------------------------+
          | dividend (divisor: 0) | PySpark |  pandas |
          |-----------------------|---------|---------|
          |         np.inf        |   null  |  np.inf |
          |        -np.inf        |   null  | -np.inf |
          |           10          |   null  |  np.inf |
          |          -10          |   null  | -np.inf |
          +-----------------------|---------|---------+
          """
  
          def truediv(left, right):
              return F.when(F.lit(right != 0) | F.lit(right).isNull(), left.__div__(right)).otherwise(
                  F.when(F.lit(left == np.inf) | F.lit(left == -np.inf), left).otherwise(
                      F.lit(np.inf).__div__(left)
                  )
              )
  
          return numpy_column_op(truediv)(self, other)
  
      def __mod__(self, other):
          def mod(left, right):
              return ((left % right) + right) % right
  
          return column_op(mod)(self, other)
  
      def __radd__(self, other):
          # Handle 'literal' + df['col']
          if isinstance(self.spark.data_type, StringType) and isinstance(other, str):
              return self._with_new_scol(F.concat(F.lit(other), self.spark.column))
          else:
              return column_op(Column.__radd__)(self, other)
  
      __rsub__ = column_op(Column.__rsub__)
      __rmul__ = column_op(Column.__rmul__)
  
      def __rtruediv__(self, other):
          def rtruediv(left, right):
              return F.when(left == 0, F.lit(np.inf).__div__(right)).otherwise(
                  F.lit(right).__truediv__(left)
              )
  
          return numpy_column_op(rtruediv)(self, other)
  
      def __floordiv__(self, other):
          """
          __floordiv__ has different behaviour between pandas and PySpark for several cases.
          1. When divide np.inf by zero, PySpark returns null whereas pandas returns np.inf
          2. When divide positive number by zero, PySpark returns null whereas pandas returns np.inf
          3. When divide -np.inf by zero, PySpark returns null whereas pandas returns -np.inf
          4. When divide negative number by zero, PySpark returns null whereas pandas returns -np.inf
  
          +-------------------------------------------+
          | dividend (divisor: 0) | PySpark |  pandas |
          |-----------------------|---------|---------|
          |         np.inf        |   null  |  np.inf |
          |        -np.inf        |   null  | -np.inf |
          |           10          |   null  |  np.inf |
          |          -10          |   null  | -np.inf |
          +-----------------------|---------|---------+
          """
  
          def floordiv(left, right):
              return F.when(F.lit(right is np.nan), np.nan).otherwise(
                  F.when(
                      F.lit(right != 0) | F.lit(right).isNull(), F.floor(left.__div__(right))
                  ).otherwise(
                      F.when(F.lit(left == np.inf) | F.lit(left == -np.inf), left).otherwise(
                          F.lit(np.inf).__div__(left)
                      )
                  )
              )
  
          return numpy_column_op(floordiv)(self, other)
  
      def __rfloordiv__(self, other):
          def rfloordiv(left, right):
              return F.when(F.lit(left == 0), F.lit(np.inf).__div__(right)).otherwise(
                  F.when(F.lit(left) == np.nan, np.nan).otherwise(F.floor(F.lit(right).__div__(left)))
              )
  
          return numpy_column_op(rfloordiv)(self, other)
  
      def __rmod__(self, other):
          def rmod(left, right):
              return ((right % left) + left) % left
  
          return column_op(rmod)(self, other)
  
      __pow__ = column_op(Column.__pow__)
      __rpow__ = column_op(Column.__rpow__)
      __abs__ = column_op(F.abs)
  
      # comparison operators
      __eq__ = column_op(Column.__eq__)
      __ne__ = column_op(Column.__ne__)
      __lt__ = column_op(Column.__lt__)
      __le__ = column_op(Column.__le__)
      __ge__ = column_op(Column.__ge__)
      __gt__ = column_op(Column.__gt__)
  
      # `and`, `or`, `not` cannot be overloaded in Python,
      # so use bitwise operators as boolean operators
      __and__ = column_op(Column.__and__)
      __or__ = column_op(Column.__or__)
      __invert__ = column_op(Column.__invert__)
      __rand__ = column_op(Column.__rand__)
      __ror__ = column_op(Column.__ror__)
  
      # NDArray Compat
      def __array_ufunc__(self, ufunc: Callable, method: str, *inputs: Any, **kwargs: Any):
          # Try dunder methods first.
          result = numpy_compat.maybe_dispatch_ufunc_to_dunder_op(
              self, ufunc, method, *inputs, **kwargs
          )
  
          # After that, we try with PySpark APIs.
          if result is NotImplemented:
              result = numpy_compat.maybe_dispatch_ufunc_to_spark_func(
                  self, ufunc, method, *inputs, **kwargs
              )
  
          if result is not NotImplemented:
              return result
          else:
              # TODO: support more APIs?
              raise NotImplementedError("Koalas objects currently do not support %s." % ufunc)
  
      @property
      def dtype(self):
          """Return the dtype object of the underlying data.
  
          Examples
          --------
          >>> s = ks.Series([1, 2, 3])
          >>> s.dtype
          dtype('int64')
  
          >>> s = ks.Series(list('abc'))
          >>> s.dtype
          dtype('O')
  
          >>> s = ks.Series(pd.date_range('20130101', periods=3))
          >>> s.dtype
          dtype('<M8[ns]')
  
          >>> s.rename("a").to_frame().set_index("a").index.dtype
          dtype('<M8[ns]')
          """
          return spark_type_to_pandas_dtype(self.spark.data_type)
  
      @property
      def empty(self):
          """
          Returns true if the current object is empty. Otherwise, returns false.
  
          >>> ks.range(10).id.empty
          False
  
          >>> ks.range(0).id.empty
          True
  
          >>> ks.DataFrame({}, index=list('abc')).index.empty
          False
          """
          return self._internal.resolved_copy.spark_frame.rdd.isEmpty()
  
      @property
      def hasnans(self):
          """
          Return True if it has any missing values. Otherwise, it returns False.
  
          >>> ks.DataFrame({}, index=list('abc')).index.hasnans
          False
  
          >>> ks.Series(['a', None]).hasnans
          True
  
          >>> ks.Series([1.0, 2.0, np.nan]).hasnans
          True
  
          >>> ks.Series([1, 2, 3]).hasnans
          False
  
          >>> (ks.Series([1.0, 2.0, np.nan]) + 1).hasnans
          True
  
          >>> ks.Series([1, 2, 3]).rename("a").to_frame().set_index("a").index.hasnans
          False
          """
          sdf = self._internal.spark_frame
          scol = self.spark.column
  
          return sdf.select(F.max(scol.isNull() | F.isnan(scol))).collect()[0][0]
  
      @property
      def is_monotonic(self):
          """
          Return boolean if values in the object are monotonically increasing.
  
          .. note:: the current implementation of is_monotonic requires to shuffle
              and aggregate multiple times to check the order locally and globally,
              which is potentially expensive. In case of multi-index, all data are
              transferred to single node which can easily cause out-of-memory error currently.
  
          Returns
          -------
          is_monotonic : boolean
  
          Examples
          --------
          >>> ser = ks.Series(['1/1/2018', '3/1/2018', '4/1/2018'])
          >>> ser.is_monotonic
          True
  
          >>> df = ks.DataFrame({'dates': [None, '1/1/2018', '2/1/2018', '3/1/2018']})
          >>> df.dates.is_monotonic
          False
  
          >>> df.index.is_monotonic
          True
  
          >>> ser = ks.Series([1])
          >>> ser.is_monotonic
          True
  
          >>> ser = ks.Series([])
          >>> ser.is_monotonic
          True
  
          >>> ser.rename("a").to_frame().set_index("a").index.is_monotonic
          True
  
          >>> ser = ks.Series([5, 4, 3, 2, 1], index=[1, 2, 3, 4, 5])
          >>> ser.is_monotonic
          False
  
          >>> ser.index.is_monotonic
          True
  
          Support for MultiIndex
  
          >>> midx = ks.MultiIndex.from_tuples(
          ... [('x', 'a'), ('x', 'b'), ('y', 'c'), ('y', 'd'), ('z', 'e')])
          >>> midx  # doctest: +SKIP
          MultiIndex([('x', 'a'),
                      ('x', 'b'),
                      ('y', 'c'),
                      ('y', 'd'),
                      ('z', 'e')],
                     )
          >>> midx.is_monotonic
          True
  
          >>> midx = ks.MultiIndex.from_tuples(
          ... [('z', 'a'), ('z', 'b'), ('y', 'c'), ('y', 'd'), ('x', 'e')])
          >>> midx  # doctest: +SKIP
          MultiIndex([('z', 'a'),
                      ('z', 'b'),
                      ('y', 'c'),
                      ('y', 'd'),
                      ('x', 'e')],
                     )
          >>> midx.is_monotonic
          False
          """
          return self._is_monotonic("increasing")
  
      is_monotonic_increasing = is_monotonic
  
      @property
      def is_monotonic_decreasing(self):
          """
          Return boolean if values in the object are monotonically decreasing.
  
          .. note:: the current implementation of is_monotonic_decreasing requires to shuffle
              and aggregate multiple times to check the order locally and globally,
              which is potentially expensive. In case of multi-index, all data are transferred
              to single node which can easily cause out-of-memory error currently.
  
          Returns
          -------
          is_monotonic : boolean
  
          Examples
          --------
          >>> ser = ks.Series(['4/1/2018', '3/1/2018', '1/1/2018'])
          >>> ser.is_monotonic_decreasing
          True
  
          >>> df = ks.DataFrame({'dates': [None, '3/1/2018', '2/1/2018', '1/1/2018']})
          >>> df.dates.is_monotonic_decreasing
          False
  
          >>> df.index.is_monotonic_decreasing
          False
  
          >>> ser = ks.Series([1])
          >>> ser.is_monotonic_decreasing
          True
  
          >>> ser = ks.Series([])
          >>> ser.is_monotonic_decreasing
          True
  
          >>> ser.rename("a").to_frame().set_index("a").index.is_monotonic_decreasing
          True
  
          >>> ser = ks.Series([5, 4, 3, 2, 1], index=[1, 2, 3, 4, 5])
          >>> ser.is_monotonic_decreasing
          True
  
          >>> ser.index.is_monotonic_decreasing
          False
  
          Support for MultiIndex
  
          >>> midx = ks.MultiIndex.from_tuples(
          ... [('x', 'a'), ('x', 'b'), ('y', 'c'), ('y', 'd'), ('z', 'e')])
          >>> midx  # doctest: +SKIP
          MultiIndex([('x', 'a'),
                      ('x', 'b'),
                      ('y', 'c'),
                      ('y', 'd'),
                      ('z', 'e')],
                     )
          >>> midx.is_monotonic_decreasing
          False
  
          >>> midx = ks.MultiIndex.from_tuples(
          ... [('z', 'e'), ('z', 'd'), ('y', 'c'), ('y', 'b'), ('x', 'a')])
          >>> midx  # doctest: +SKIP
          MultiIndex([('z', 'a'),
                      ('z', 'b'),
                      ('y', 'c'),
                      ('y', 'd'),
                      ('x', 'e')],
                     )
          >>> midx.is_monotonic_decreasing
          True
          """
          return self._is_monotonic("decreasing")
  
      def _is_locally_monotonic_spark_column(self, order):
          window = (
              Window.partitionBy(F.col("__partition_id"))
              .orderBy(NATURAL_ORDER_COLUMN_NAME)
              .rowsBetween(-1, -1)
          )
  
          if order == "increasing":
              return (F.col("__origin") >= F.lag(F.col("__origin"), 1).over(window)) & F.col(
                  "__origin"
              ).isNotNull()
          else:
              return (F.col("__origin") <= F.lag(F.col("__origin"), 1).over(window)) & F.col(
                  "__origin"
              ).isNotNull()
  
      def _is_monotonic(self, order):
          assert order in ("increasing", "decreasing")
  
          sdf = self._internal.spark_frame
  
          sdf = (
              sdf.select(
                  F.spark_partition_id().alias(
                      "__partition_id"
                  ),  # Make sure we use the same partition id in the whole job.
                  F.col(NATURAL_ORDER_COLUMN_NAME),
                  self.spark.column.alias("__origin"),
              )
              .select(
                  F.col("__partition_id"),
                  F.col("__origin"),
                  self._is_locally_monotonic_spark_column(order).alias(
                      "__comparison_within_partition"
                  ),
              )
              .groupby(F.col("__partition_id"))
              .agg(
                  F.min(F.col("__origin")).alias("__partition_min"),
                  F.max(F.col("__origin")).alias("__partition_max"),
                  F.min(F.coalesce(F.col("__comparison_within_partition"), F.lit(True))).alias(
                      "__comparison_within_partition"
                  ),
              )
          )
  
          # Now we're windowing the aggregation results without partition specification.
          # The number of rows here will be as the same of partitions, which is expected
          # to be small.
          window = Window.orderBy(F.col("__partition_id")).rowsBetween(-1, -1)
          if order == "increasing":
              comparison_col = F.col("__partition_min") >= F.lag(F.col("__partition_max"), 1).over(
                  window
              )
          else:
              comparison_col = F.col("__partition_min") <= F.lag(F.col("__partition_max"), 1).over(
                  window
              )
  
          sdf = sdf.select(
              comparison_col.alias("__comparison_between_partitions"),
              F.col("__comparison_within_partition"),
          )
  
          ret = sdf.select(
              F.min(F.coalesce(F.col("__comparison_between_partitions"), F.lit(True)))
              & F.min(F.coalesce(F.col("__comparison_within_partition"), F.lit(True)))
          ).collect()[0][0]
          if ret is None:
              return True
          else:
              return ret
  
      @property
      def ndim(self):
          """
          Return an int representing the number of array dimensions.
  
          Return 1 for Series / Index / MultiIndex.
  
          Examples
          --------
  
          For Series
  
          >>> s = ks.Series([None, 1, 2, 3, 4], index=[4, 5, 2, 1, 8])
          >>> s.ndim
          1
  
          For Index
  
          >>> s.index.ndim
          1
  
          For MultiIndex
  
          >>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
          ...                       ['speed', 'weight', 'length']],
          ...                      [[0, 0, 0, 1, 1, 1, 2, 2, 2],
          ...                       [1, 1, 1, 1, 1, 2, 1, 2, 2]])
          >>> s = ks.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], index=midx)
          >>> s.index.ndim
          1
          """
          return 1
  
      def astype(self, dtype):
          """
          Cast a Koalas object to a specified dtype ``dtype``.
  
          Parameters
          ----------
          dtype : data type
              Use a numpy.dtype or Python type to cast entire pandas object to
              the same type.
  
          Returns
          -------
          casted : same type as caller
  
          See Also
          --------
          to_datetime : Convert argument to datetime.
  
          Examples
          --------
          >>> ser = ks.Series([1, 2], dtype='int32')
          >>> ser
          0    1
          1    2
          Name: 0, dtype: int32
  
          >>> ser.astype('int64')
          0    1
          1    2
          Name: 0, dtype: int64
  
          >>> ser.rename("a").to_frame().set_index("a").index.astype('int64')
          Int64Index([1, 2], dtype='int64', name='a')
          """
          from databricks.koalas.typedef import as_spark_type
  
          spark_type = as_spark_type(dtype)
          if not spark_type:
              raise ValueError("Type {} not understood".format(dtype))
          return self._with_new_scol(self.spark.column.cast(spark_type))
  
      def isin(self, values):
          """
          Check whether `values` are contained in Series.
  
          Return a boolean Series showing whether each element in the Series
          matches an element in the passed sequence of `values` exactly.
  
          Parameters
          ----------
          values : list or set
              The sequence of values to test.
  
          Returns
          -------
          isin : Series (bool dtype)
  
          Examples
          --------
          >>> s = ks.Series(['lama', 'cow', 'lama', 'beetle', 'lama',
          ...                'hippo'], name='animal')
          >>> s.isin(['cow', 'lama'])
          0     True
          1     True
          2     True
          3    False
          4     True
          5    False
          Name: animal, dtype: bool
  
          Passing a single string as ``s.isin('lama')`` will raise an error. Use
          a list of one element instead:
  
          >>> s.isin(['lama'])
          0     True
          1    False
          2     True
          3    False
          4     True
          5    False
          Name: animal, dtype: bool
  
          >>> s.rename("a").to_frame().set_index("a").index.isin(['lama'])
          Index([True, False, True, False, True, False], dtype='object', name='a')
          """
          if not is_list_like(values):
              raise TypeError(
                  "only list-like objects are allowed to be passed"
                  " to isin(), you passed a [{values_type}]".format(values_type=type(values).__name__)
              )
  
          return self._with_new_scol(self.spark.column.isin(list(values))).rename(self.name)
  
      def isnull(self):
          """
          Detect existing (non-missing) values.
  
          Return a boolean same-sized object indicating if the values are NA.
          NA values, such as None or numpy.NaN, gets mapped to True values.
          Everything else gets mapped to False values. Characters such as empty strings '' or
          numpy.inf are not considered NA values
          (unless you set pandas.options.mode.use_inf_as_na = True).
  
          Returns
          -------
          Series : Mask of bool values for each element in Series
              that indicates whether an element is not an NA value.
  
          Examples
          --------
          >>> ser = ks.Series([5, 6, np.NaN])
          >>> ser.isna()  # doctest: +NORMALIZE_WHITESPACE
          0    False
          1    False
          2     True
          Name: 0, dtype: bool
  
          >>> ser.rename("a").to_frame().set_index("a").index.isna()
          Index([False, False, True], dtype='object', name='a')
          """
          from databricks.koalas.indexes import MultiIndex
  
          if isinstance(self, MultiIndex):
              raise NotImplementedError("isna is not defined for MultiIndex")
          if isinstance(self.spark.data_type, (FloatType, DoubleType)):
              return self._with_new_scol(
                  self.spark.column.isNull() | F.isnan(self.spark.column)
              ).rename(self.name)
          else:
              return self._with_new_scol(self.spark.column.isNull()).rename(self.name)
  
      isna = isnull
  
      def notnull(self):
          """
          Detect existing (non-missing) values.
          Return a boolean same-sized object indicating if the values are not NA.
          Non-missing values get mapped to True.
          Characters such as empty strings '' or numpy.inf are not considered NA values
          (unless you set pandas.options.mode.use_inf_as_na = True).
          NA values, such as None or numpy.NaN, get mapped to False values.
  
          Returns
          -------
          Series : Mask of bool values for each element in Series
              that indicates whether an element is not an NA value.
  
          Examples
          --------
          Show which entries in a Series are not NA.
  
          >>> ser = ks.Series([5, 6, np.NaN])
          >>> ser
          0    5.0
          1    6.0
          2    NaN
          Name: 0, dtype: float64
  
          >>> ser.notna()
          0     True
          1     True
          2    False
          Name: 0, dtype: bool
  
          >>> ser.rename("a").to_frame().set_index("a").index.notna()
          Index([True, True, False], dtype='object', name='a')
          """
          from databricks.koalas.indexes import MultiIndex
  
          if isinstance(self, MultiIndex):
              raise NotImplementedError("notna is not defined for MultiIndex")
          return (~self.isnull()).rename(self.name)
  
      notna = notnull
  
      # TODO: axis, skipna, and many arguments should be implemented.
      def all(self, axis: Union[int, str] = 0) -> bool:
          """
          Return whether all elements are True.
  
          Returns True unless there at least one element within a series that is
          False or equivalent (e.g. zero or empty)
  
          Parameters
          ----------
          axis : {0 or 'index'}, default 0
              Indicate which axis or axes should be reduced.
  
              * 0 / 'index' : reduce the index, return a Series whose index is the
                original column labels.
  
          Examples
          --------
          >>> ks.Series([True, True]).all()
          True
  
          >>> ks.Series([True, False]).all()
          False
  
          >>> ks.Series([0, 1]).all()
          False
  
          >>> ks.Series([1, 2, 3]).all()
          True
  
          >>> ks.Series([True, True, None]).all()
          True
  
          >>> ks.Series([True, False, None]).all()
          False
  
          >>> ks.Series([]).all()
          True
  
          >>> ks.Series([np.nan]).all()
          True
  
          >>> df = ks.Series([True, False, None]).rename("a").to_frame()
          >>> df.set_index("a").index.all()
          False
          """
          axis = validate_axis(axis)
          if axis != 0:
              raise NotImplementedError('axis should be either 0 or "index" currently.')
  
          sdf = self._internal.spark_frame.select(self.spark.column)
          col = scol_for(sdf, sdf.columns[0])
  
          # Note that we're ignoring `None`s here for now.
          # any and every was added as of Spark 3.0
          # ret = sdf.select(F.expr("every(CAST(`%s` AS BOOLEAN))" % sdf.columns[0])).collect()[0][0]
          # Here we use min as its alternative:
          ret = sdf.select(F.min(F.coalesce(col.cast("boolean"), F.lit(True)))).collect()[0][0]
          if ret is None:
              return True
          else:
              return ret
  
      # TODO: axis, skipna, and many arguments should be implemented.
      def any(self, axis: Union[int, str] = 0) -> bool:
          """
          Return whether any element is True.
  
          Returns False unless there at least one element within a series that is
          True or equivalent (e.g. non-zero or non-empty).
  
          Parameters
          ----------
          axis : {0 or 'index'}, default 0
              Indicate which axis or axes should be reduced.
  
              * 0 / 'index' : reduce the index, return a Series whose index is the
                original column labels.
  
          Examples
          --------
          >>> ks.Series([False, False]).any()
          False
  
          >>> ks.Series([True, False]).any()
          True
  
          >>> ks.Series([0, 0]).any()
          False
  
          >>> ks.Series([0, 1, 2]).any()
          True
  
          >>> ks.Series([False, False, None]).any()
          False
  
          >>> ks.Series([True, False, None]).any()
          True
  
          >>> ks.Series([]).any()
          False
  
          >>> ks.Series([np.nan]).any()
          False
  
          >>> df = ks.Series([True, False, None]).rename("a").to_frame()
          >>> df.set_index("a").index.any()
          True
          """
          axis = validate_axis(axis)
          if axis != 0:
              raise NotImplementedError('axis should be either 0 or "index" currently.')
  
          sdf = self._internal.spark_frame.select(self.spark.column)
          col = scol_for(sdf, sdf.columns[0])
  
          # Note that we're ignoring `None`s here for now.
          # any and every was added as of Spark 3.0
          # ret = sdf.select(F.expr("any(CAST(`%s` AS BOOLEAN))" % sdf.columns[0])).collect()[0][0]
          # Here we use max as its alternative:
          ret = sdf.select(F.max(F.coalesce(col.cast("boolean"), F.lit(False)))).collect()[0][0]
          if ret is None:
              return False
          else:
              return ret
  
      # TODO: add frep and axis parameter
      def shift(self, periods=1, fill_value=None):
          """
          Shift Series/Index by desired number of periods.
  
          .. note:: the current implementation of shift uses Spark's Window without
              specifying partition specification. This leads to move all data into
              single partition in single machine and could cause serious
              performance degradation. Avoid this method against very large dataset.
  
          Parameters
          ----------
          periods : int
              Number of periods to shift. Can be positive or negative.
          fill_value : object, optional
              The scalar value to use for newly introduced missing values.
              The default depends on the dtype of self. For numeric data, np.nan is used.
  
          Returns
          -------
          Copy of input Series/Index, shifted.
  
          Examples
          --------
          >>> df = ks.DataFrame({'Col1': [10, 20, 15, 30, 45],
          ...                    'Col2': [13, 23, 18, 33, 48],
          ...                    'Col3': [17, 27, 22, 37, 52]},
          ...                   columns=['Col1', 'Col2', 'Col3'])
  
          >>> df.Col1.shift(periods=3)
          0     NaN
          1     NaN
          2     NaN
          3    10.0
          4    20.0
          Name: Col1, dtype: float64
  
          >>> df.Col2.shift(periods=3, fill_value=0)
          0     0
          1     0
          2     0
          3    13
          4    23
          Name: Col2, dtype: int64
  
          >>> df.index.shift(periods=3, fill_value=0)
          Int64Index([0, 0, 0, 0, 1], dtype='int64')
          """
          return self._shift(periods, fill_value)
  
      def _shift(self, periods, fill_value, part_cols=()):
          if not isinstance(periods, int):
              raise ValueError("periods should be an int; however, got [%s]" % type(periods))
  
          col = self.spark.column
          window = (
              Window.partitionBy(*part_cols)
              .orderBy(NATURAL_ORDER_COLUMN_NAME)
              .rowsBetween(-periods, -periods)
          )
          lag_col = F.lag(col, periods).over(window)
          col = F.when(lag_col.isNull() | F.isnan(lag_col), fill_value).otherwise(lag_col)
          return self._with_new_scol(col).rename(self.name)
  
      # TODO: Update Documentation for Bins Parameter when its supported
      def value_counts(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True):
          """
          Return a Series containing counts of unique values.
          The resulting object will be in descending order so that the
          first element is the most frequently-occurring element.
          Excludes NA values by default.
  
          Parameters
          ----------
          normalize : boolean, default False
              If True then the object returned will contain the relative
              frequencies of the unique values.
          sort : boolean, default True
              Sort by values.
          ascending : boolean, default False
              Sort in ascending order.
          bins : Not Yet Supported
          dropna : boolean, default True
              Don't include counts of NaN.
  
          Returns
          -------
          counts : Series
  
          See Also
          --------
          Series.count: Number of non-NA elements in a Series.
  
          Examples
          --------
          For Series
  
          >>> df = ks.DataFrame({'x':[0, 0, 1, 1, 1, np.nan]})
          >>> df.x.value_counts()  # doctest: +NORMALIZE_WHITESPACE
          1.0    3
          0.0    2
          Name: x, dtype: int64
  
          With `normalize` set to `True`, returns the relative frequency by
          dividing all values by the sum of values.
  
          >>> df.x.value_counts(normalize=True)  # doctest: +NORMALIZE_WHITESPACE
          1.0    0.6
          0.0    0.4
          Name: x, dtype: float64
  
          **dropna**
          With `dropna` set to `False` we can also see NaN index values.
  
          >>> df.x.value_counts(dropna=False)  # doctest: +NORMALIZE_WHITESPACE
          1.0    3
          0.0    2
          NaN    1
          Name: x, dtype: int64
  
          For Index
  
          >>> from databricks.koalas.indexes import Index
          >>> idx = Index([3, 1, 2, 3, 4, np.nan])
          >>> idx
          Float64Index([3.0, 1.0, 2.0, 3.0, 4.0, nan], dtype='float64')
  
          >>> idx.value_counts().sort_index()
          1.0    1
          2.0    1
          3.0    2
          4.0    1
          Name: count, dtype: int64
  
          **sort**
  
          With `sort` set to `False`, the result wouldn't be sorted by number of count.
  
          >>> idx.value_counts(sort=True).sort_index()
          1.0    1
          2.0    1
          3.0    2
          4.0    1
          Name: count, dtype: int64
  
          **normalize**
  
          With `normalize` set to `True`, returns the relative frequency by
          dividing all values by the sum of values.
  
          >>> idx.value_counts(normalize=True).sort_index()
          1.0    0.2
          2.0    0.2
          3.0    0.4
          4.0    0.2
          Name: count, dtype: float64
  
          **dropna**
  
          With `dropna` set to `False` we can also see NaN index values.
  
          >>> idx.value_counts(dropna=False).sort_index()  # doctest: +SKIP
          1.0    1
          2.0    1
          3.0    2
          4.0    1
          NaN    1
          Name: count, dtype: int64
  
          For MultiIndex.
  
          >>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
          ...                       ['speed', 'weight', 'length']],
          ...                      [[0, 0, 0, 1, 1, 1, 2, 2, 2],
          ...                       [1, 1, 1, 1, 1, 2, 1, 2, 2]])
          >>> s = ks.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], index=midx)
          >>> s.index  # doctest: +SKIP
          MultiIndex([(  'lama', 'weight'),
                      (  'lama', 'weight'),
                      (  'lama', 'weight'),
                      (   'cow', 'weight'),
                      (   'cow', 'weight'),
                      (   'cow', 'length'),
                      ('falcon', 'weight'),
                      ('falcon', 'length'),
                      ('falcon', 'length')],
                     )
  
          >>> s.index.value_counts().sort_index()
          (cow, length)       1
          (cow, weight)       2
          (falcon, length)    2
          (falcon, weight)    1
          (lama, weight)      3
          Name: count, dtype: int64
  
          >>> s.index.value_counts(normalize=True).sort_index()
          (cow, length)       0.111111
          (cow, weight)       0.222222
          (falcon, length)    0.222222
          (falcon, weight)    0.111111
          (lama, weight)      0.333333
          Name: count, dtype: float64
  
          If Index has name, keep the name up.
  
          >>> idx = Index([0, 0, 0, 1, 1, 2, 3], name='koalas')
          >>> idx.value_counts().sort_index()
          0    3
          1    2
          2    1
          3    1
          Name: koalas, dtype: int64
          """
          from databricks.koalas.series import first_series
  
          if bins is not None:
              raise NotImplementedError("value_counts currently does not support bins")
  
          if dropna:
              sdf_dropna = self._internal.spark_frame.select(self.spark.column).dropna()
          else:
              sdf_dropna = self._internal.spark_frame.select(self.spark.column)
          index_name = SPARK_DEFAULT_INDEX_NAME
          column_name = self._internal.data_spark_column_names[0]
          sdf = sdf_dropna.groupby(scol_for(sdf_dropna, column_name).alias(index_name)).count()
          if sort:
              if ascending:
                  sdf = sdf.orderBy(F.col("count"))
              else:
                  sdf = sdf.orderBy(F.col("count").desc())
  
          if normalize:
              sum = sdf_dropna.count()
              sdf = sdf.withColumn("count", F.col("count") / F.lit(sum))
  
          column_labels = self._internal.column_labels
          if (column_labels[0] is None) or (None in column_labels[0]):
              internal = InternalFrame(
                  spark_frame=sdf,
                  index_map=OrderedDict({index_name: None}),
                  data_spark_columns=[scol_for(sdf, "count")],
              )
          else:
              internal = InternalFrame(
                  spark_frame=sdf,
                  index_map=OrderedDict({index_name: None}),
                  column_labels=column_labels,
                  data_spark_columns=[scol_for(sdf, "count")],
                  column_label_names=self._internal.column_label_names,
              )
  
          return first_series(DataFrame(internal))
  
      def nunique(self, dropna: bool = True, approx: bool = False, rsd: float = 0.05) -> int:
          """
          Return number of unique elements in the object.
          Excludes NA values by default.
  
          Parameters
          ----------
          dropna : bool, default True
              Don’t include NaN in the count.
          approx: bool, default False
              If False, will use the exact algorithm and return the exact number of unique.
              If True, it uses the HyperLogLog approximate algorithm, which is significantly faster
              for large amount of data.
              Note: This parameter is specific to Koalas and is not found in pandas.
          rsd: float, default 0.05
              Maximum estimation error allowed in the HyperLogLog algorithm.
              Note: Just like ``approx`` this parameter is specific to Koalas.
  
          Returns
          -------
          int
  
          See Also
          --------
          DataFrame.nunique: Method nunique for DataFrame.
          Series.count: Count non-NA/null observations in the Series.
  
          Examples
          --------
          >>> ks.Series([1, 2, 3, np.nan]).nunique()
          3
  
          >>> ks.Series([1, 2, 3, np.nan]).nunique(dropna=False)
          4
  
          On big data, we recommend using the approximate algorithm to speed up this function.
          The result will be very close to the exact unique count.
  
          >>> ks.Series([1, 2, 3, np.nan]).nunique(approx=True)
          3
  
          >>> idx = ks.Index([1, 1, 2, None])
          >>> idx
          Float64Index([1.0, 1.0, 2.0, nan], dtype='float64')
  
          >>> idx.nunique()
          2
  
          >>> idx.nunique(dropna=False)
          3
          """
          res = self._internal.spark_frame.select([self._nunique(dropna, approx, rsd)])
          return res.collect()[0][0]
  
      def _nunique(self, dropna=True, approx=False, rsd=0.05):
          colname = self._internal.data_spark_column_names[0]
          count_fn = partial(F.approx_count_distinct, rsd=rsd) if approx else F.countDistinct
          if dropna:
              return count_fn(self.spark.column).alias(colname)
          else:
              return (
                  count_fn(self.spark.column)
                  + F.when(
                      F.count(F.when(self.spark.column.isNull(), 1).otherwise(None)) >= 1, 1
                  ).otherwise(0)
              ).alias(colname)
  
      def take(self, indices):
          """
          Return the elements in the given *positional* indices along an axis.
  
          This means that we are not indexing according to actual values in
          the index attribute of the object. We are indexing according to the
          actual position of the element in the object.
  
          Parameters
          ----------
          indices : array-like
              An array of ints indicating which positions to take.
  
          Returns
          -------
          taken : same type as caller
              An array-like containing the elements taken from the object.
  
          See Also
          --------
          DataFrame.loc : Select a subset of a DataFrame by labels.
          DataFrame.iloc : Select a subset of a DataFrame by positions.
          numpy.take : Take elements from an array along an axis.
  
          Examples
          --------
  
          Series
  
          >>> kser = ks.Series([100, 200, 300, 400, 500])
          >>> kser
          0    100
          1    200
          2    300
          3    400
          4    500
          Name: 0, dtype: int64
  
          >>> kser.take([0, 2, 4]).sort_index()
          0    100
          2    300
          4    500
          Name: 0, dtype: int64
  
          Index
  
          >>> kidx = ks.Index([100, 200, 300, 400, 500])
          >>> kidx
          Int64Index([100, 200, 300, 400, 500], dtype='int64')
  
          >>> kidx.take([0, 2, 4]).sort_values()
          Int64Index([100, 300, 500], dtype='int64')
  
          MultiIndex
  
          >>> kmidx = ks.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("x", "c")])
          >>> kmidx  # doctest: +SKIP
          MultiIndex([('x', 'a'),
                      ('x', 'b'),
                      ('x', 'c')],
                     )
  
          >>> kmidx.take([0, 2])  # doctest: +SKIP
          MultiIndex([('x', 'a'),
                      ('x', 'c')],
                     )
          """
          if not is_list_like(indices) or isinstance(indices, (dict, set)):
              raise ValueError("`indices` must be a list-like except dict or set")
          if isinstance(self, ks.Series):
              result = self.iloc[indices]
          elif isinstance(self, ks.Index):
              result = self._kdf.iloc[indices].index
          return result
  