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.

other : Series or DataFrame

The other Series or DataFrame to be compared with the first.


True if all elements are the same in both objects, False otherwise.

See also

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.
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.
Return True if left and right Series are equal, False otherwise.
Return True if left and right DataFrames are equal, False otherwise.
Return True if two arrays have the same shape and elements, False otherwise.


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.


>>> 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)

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)

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)
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