parameters: (self, other)
return_statement: return self._data.equals(other._data)
docstring: Test whether two objects contain the same elements.
  
          This function allows two Series or DataFrames to be compared against
          each other to see if they have the same shape and elements. NaNs in
          the same location are considered equal. The column headers do not
          need to have the same type, but the elements within the columns must
          be the same dtype.
  
          Parameters
          ----------
          other : Series or DataFrame
              The other Series or DataFrame to be compared with the first.
  
          Returns
          -------
          bool
              True if all elements are the same in both objects, False
              otherwise.
  
          See Also
          --------
          Series.eq : Compare two Series objects of the same length
              and return a Series where each element is True if the element
              in each Series is equal, False otherwise.
          DataFrame.eq : Compare two DataFrame objects of the same shape and
              return a DataFrame where each element is True if the respective
              element in each DataFrame is equal, False otherwise.
          testing.assert_series_equal : Raises an AssertionError if left and
              right are not equal. Provides an easy interface to ignore
              inequality in dtypes, indexes and precision among others.
          testing.assert_frame_equal : Like assert_series_equal, but targets
              DataFrames.
          numpy.array_equal : Return True if two arrays have the same shape
              and elements, False otherwise.
  
          Notes
          -----
          This function requires that the elements have the same dtype as their
          respective elements in the other Series or DataFrame. However, the
          column labels do not need to have the same type, as long as they are
          still considered equal.
  
          Examples
          --------
          >>> df = pd.DataFrame({1: [10], 2: [20]})
          >>> df
              1   2
          0  10  20
  
          DataFrames df and exactly_equal have the same types and values for
          their elements and column labels, which will return True.
  
          >>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
          >>> exactly_equal
              1   2
          0  10  20
          >>> df.equals(exactly_equal)
          True
  
          DataFrames df and different_column_type have the same element
          types and values, but have different types for the column labels,
          which will still return True.
  
          >>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
          >>> different_column_type
             1.0  2.0
          0   10   20
          >>> df.equals(different_column_type)
          True
  
          DataFrames df and different_data_type have different types for the
          same values for their elements, and will return False even though
          their column labels are the same values and types.
  
          >>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
          >>> different_data_type
                1     2
          0  10.0  20.0
          >>> df.equals(different_data_type)
          False
docstring_summary: Test whether two objects contain the same elements.
docstring_tokens: Test whether two objects contain the same elements .
function: def equals(self, other):
          """
          Test whether two objects contain the same elements.
  
          This function allows two Series or DataFrames to be compared against
          each other to see if they have the same shape and elements. NaNs in
          the same location are considered equal. The column headers do not
          need to have the same type, but the elements within the columns must
          be the same dtype.
  
          Parameters
          ----------
          other : Series or DataFrame
              The other Series or DataFrame to be compared with the first.
  
          Returns
          -------
          bool
              True if all elements are the same in both objects, False
              otherwise.
  
          See Also
          --------
          Series.eq : Compare two Series objects of the same length
              and return a Series where each element is True if the element
              in each Series is equal, False otherwise.
          DataFrame.eq : Compare two DataFrame objects of the same shape and
              return a DataFrame where each element is True if the respective
              element in each DataFrame is equal, False otherwise.
          testing.assert_series_equal : Raises an AssertionError if left and
              right are not equal. Provides an easy interface to ignore
              inequality in dtypes, indexes and precision among others.
          testing.assert_frame_equal : Like assert_series_equal, but targets
              DataFrames.
          numpy.array_equal : Return True if two arrays have the same shape
              and elements, False otherwise.
  
          Notes
          -----
          This function requires that the elements have the same dtype as their
          respective elements in the other Series or DataFrame. However, the
          column labels do not need to have the same type, as long as they are
          still considered equal.
  
          Examples
          --------
          >>> df = pd.DataFrame({1: [10], 2: [20]})
          >>> df
              1   2
          0  10  20
  
          DataFrames df and exactly_equal have the same types and values for
          their elements and column labels, which will return True.
  
          >>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
          >>> exactly_equal
              1   2
          0  10  20
          >>> df.equals(exactly_equal)
          True
  
          DataFrames df and different_column_type have the same element
          types and values, but have different types for the column labels,
          which will still return True.
  
          >>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
          >>> different_column_type
             1.0  2.0
          0   10   20
          >>> df.equals(different_column_type)
          True
  
          DataFrames df and different_data_type have different types for the
          same values for their elements, and will return False even though
          their column labels are the same values and types.
  
          >>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
          >>> different_data_type
                1     2
          0  10.0  20.0
          >>> df.equals(different_data_type)
          False
          """
          if not isinstance(other, self._constructor):
              return False
          return self._data.equals(other._data)
function_tokens: def equals ( self , other ) : if not isinstance ( other , self . _constructor ) : return False return self . _data . equals ( other . _data )