Equality in Pandas DataFrames - Column Order Matters?

Question

As part of a unit test, I need to test two DataFrames for equality. The order of the columns in the DataFrames is not important to me. However, it seems to matter to Pandas:

import pandas df1 = pandas.DataFrame(index = [1,2,3,4]) df2 = pandas.DataFrame(index = [1,2,3,4]) df1['A'] = [1,2,3,4] df1['B'] = [2,3,4,5] df2['B'] = [2,3,4,5] df2['A'] = [1,2,3,4] df1 == df2 

Results in:

Exception: Can only compare identically-labeled DataFrame objects 

I believe the expression df1 == df2 should evaluate to a DataFrame containing all True values. Obviously it's debatable what the correct functionality of == should be in this context. My question is: Is there a Pandas method that does what I want? That is, is there a way to do equality comparison that ignores column order?

Answer 1

The most common intent is handled like this:

def assertFrameEqual(df1, df2, **kwds ):     """ Assert that two dataframes are equal, ignoring ordering of columns"""     from pandas.util.testing import assert_frame_equal     return assert_frame_equal(df1.sort_index(axis=1), df2.sort_index(axis=1), check_names=True, **kwds ) 

Of course see pandas.util.testing.assert_frame_equal for other parameters you can pass

Answer 2

You could sort the columns using sort_index:

df1.sort_index(axis=1) == df2.sort_index(axis=1) 

This will evaluate to a dataframe of all True values.

---

As @osa comments this fails for NaN's and isn't particularly robust either, in practise using something similar to @quant's answer is probably recommended (Note: we want a bool rather than raise if there's an issue):

def my_equal(df1, df2):     from pandas.util.testing import assert_frame_equal     try:         assert_frame_equal(df1.sort_index(axis=1), df2.sort_index(axis=1), check_names=True)         return True     except (AssertionError, ValueError, TypeError):  perhaps something else?         return False