Comparing two pandas dataframes for differences

Question

I've got a script updating 5-10 columns worth of data , but sometimes the start csv will be identical to the end csv so instead of writing an identical csvfile I want it to do nothing...

How can I compare two dataframes to check if they're the same or not?

csvdata = pandas.read_csv('csvfile.csv') csvdata_old = csvdata  # ... do stuff with csvdata dataframe  if csvdata_old != csvdata:     csvdata.to_csv('csvfile.csv', index=False) 

Any ideas?

Answer 1

You also need to be careful to create a copy of the DataFrame, otherwise the csvdata_old will be updated with csvdata (since it points to the same object):

csvdata_old = csvdata.copy() 

To check whether they are equal, you can use assert_frame_equal as in this answer:

from pandas.util.testing import assert_frame_equal assert_frame_equal(csvdata, csvdata_old) 

You can wrap this in a function with something like:

try:     assert_frame_equal(csvdata, csvdata_old)     return True except:  # appeantly AssertionError doesn't catch all     return False 

There was discussion of a better way...

Answer 2

Not sure if this is helpful or not, but I whipped together this quick python method for returning just the differences between two dataframes that both have the same columns and shape.

def get_different_rows(source_df, new_df):     """Returns just the rows from the new dataframe that differ from the source dataframe"""     merged_df = source_df.merge(new_df, indicator=True, how='outer')     changed_rows_df = merged_df[merged_df['_merge'] == 'right_only']     return changed_rows_df.drop('_merge', axis=1)