how do I remove rows with duplicate values of columns in pandas data frame?

Question

I have a pandas data frame which looks like this.

  Column1  Column2 Column3 0     cat        1       C 1     dog        1       A 2     cat        1       B 

I want to identify that cat and bat are same values which have been repeated and hence want to remove one record and preserve only the first record. The resulting data frame should only have.

  Column1  Column2 Column3 0     cat        1       C 1     dog        1       A 

Answer 1

Using drop_duplicates with subset with list of columns to check for duplicates on and keep='first' to keep first of duplicates.

If dataframe is:

df = pd.DataFrame({'Column1': ["'cat'", "'toy'", "'cat'"],                    'Column2': ["'bat'", "'flower'", "'bat'"],                    'Column3': ["'xyz'", "'abc'", "'lmn'"]}) print(df) 

Result:

  Column1   Column2 Column3 0   'cat'     'bat'   'xyz' 1   'toy'  'flower'   'abc' 2   'cat'     'bat'   'lmn' 

Then:

result_df = df.drop_duplicates(subset=['Column1', 'Column2'], keep='first') print(result_df) 

Result:

  Column1   Column2 Column3 0   'cat'     'bat'   'xyz' 1   'toy'  'flower'   'abc' 

Answer 2

import pandas as pd  df = pd.DataFrame({"Column1":["cat", "dog", "cat"],                     "Column2":[1,1,1],                     "Column3":["C","A","B"]})  df = df.drop_duplicates(subset=['Column1'], keep='first') print(df)