pandas.DataFrame.equals#

DataFrame.equals(other)[source]#

Test whether two objects contain the same elements.

This function allows two Series or DataFrames to be compared againsteach other to see if they have the same shape and elements. NaNs inthe same location are considered equal.

The row/column index do not need to have the same type, as longas the values are considered equal. Corresponding columns andindex must be of 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, Falseotherwise.

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.

Examples

>>>df=pd.DataFrame({1:[10],2:[20]})>>>df    1   20  10  20

DataFrames df and exactly_equal have the same types and values fortheir elements and column labels, which will return True.

>>>exactly_equal=pd.DataFrame({1:[10],2:[20]})>>>exactly_equal    1   20  10  20>>>df.equals(exactly_equal)True

DataFrames df and different_column_type have the same elementtypes 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  2.00   10   20>>>df.equals(different_column_type)True

DataFrames df and different_data_type have different types for thesame values for their elements, and will return False even thoughtheir column labels are the same values and types.

>>>different_data_type=pd.DataFrame({1:[10.0],2:[20.0]})>>>different_data_type      1     20  10.0  20.0>>>df.equals(different_data_type)False