27

I have a Pandas DataFrame like this:

df = pd.DataFrame({
    'Date': ['2017-1-1', '2017-1-1', '2017-1-2', '2017-1-2', '2017-1-3'],
    'Groups': ['one', 'one', 'one', 'two', 'two'],
    'data': range(1, 6)})

    Date      Groups     data  
0  2017-1-1    one       1
1  2017-1-1    one       2
2  2017-1-2    one       3
3  2017-1-2    two       4
4  2017-1-3    two       5

How can I generate a new DataFrame like this:

    Date       one     two 
0  2017-1-1    3        0
1  2017-1-2    3        4
2  2017-1-3    0        5
рüффп
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Kevin
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3 Answers3

20

pivot_table was made for this:

df.pivot_table(index='Date',columns='Groups',aggfunc=sum)

results in

         data
Groups    one  two
Date
2017-1-1  3.0  NaN
2017-1-2  3.0  4.0
2017-1-3  NaN  5.0

Personally I find this approach much easier to understand, and certainly more pythonic than a convoluted groupby operation. Then if you want the format specified you can just tidy it up:

df.fillna(0,inplace=True)
df.columns = df.columns.droplevel()
df.columns.name = None
df.reset_index(inplace=True)

which gives you

       Date  one  two
0  2017-1-1  3.0  0.0
1  2017-1-2  3.0  4.0
2  2017-1-3  0.0  5.0
tdy
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Josh D.
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9

Pandas black magic:

df = df.groupby(['Date', 'Groups']).sum().sum(
    level=['Date', 'Groups']).unstack('Groups').fillna(0).reset_index()

# Fix the column names
df.columns = ['Date', 'one', 'two']

Resulting df:

       Date  one  two
0  2017-1-1  3.0  0.0
1  2017-1-2  3.0  4.0
2  2017-1-3  0.0  5.0
tuomastik
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0

A (perhaps slightly more idiomatic) alternative to @tuomastik's answer:

df.groupby(['Date', 'Groups']).sum().unstack('Groups', fill_value=0).reset_index()
Olivier Verdier
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