Return groupby columns as new dataframe in Python Pandas

Question

Input: CSV with 5 columns.

Expected Output: Unique combinations of 'col1', 'col2', 'col3'.

Sample Input:

   col1 col2 col3 col4 col5 

0   A    B    C    11   30

1   A    B    C    52   10

2   B    C    A    15   14 

3   B    C    A     1   91

Sample Expected Output:

col1 col2 col3

A     B     C

B     C     A

Just expecting this as output. I don't need col4 and col5 in output. And also don't need any sum, count, mean etc. Tried using pandas to achieve this but no luck.

My code:

input_df = pd.read_csv("input.csv");

output_df = input_df.groupby(['col1', 'col2', 'col3'])

This code is returning 'pandas.core.groupby.DataFrameGroupBy object at 0x0000000009134278'. But I need dataframe like above. Any help much appreciated.

Mr.F · Accepted Answer · 2016-05-24 00:19:35Z

3

df[['col1', 'col2', 'col3']].drop_duplicates()

answered May 24, 2016 at 0:19

Mr.F

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Comments

Joe T. Boka · Accepted Answer · 2016-05-24 00:53:06Z

0

First you can use .drop() to delete col4 and col5 as you said you don't need them.

df = df.drop(['col4', 'col5'], axis=1)

Then, you can use .drop_duplicates() to delete the duplicate rows in col1, col2 and col3.

df = df.drop_duplicates(['col1', 'col2', 'col3'])
df

The output:

col1    col2    col3
0   A   B   C
2   B   C   A

You noticed that in the output the index is 0, 2 instead of 0,1. To fix that you can do this:

df.index = range(len(df))
df

The output:

col1    col2    col3
0   A   B   C
1   B   C   A

edited May 24, 2016 at 0:53

answered May 24, 2016 at 0:47

Joe T. Boka

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