pandas.Series.equals¶

Series.
equals
(self, other)[source]¶ 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
 otherSeries 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