By AllenQ

2013-06-23 23:06:27 8 Comments

Using sample data:

df = pd.DataFrame({'key1' : ['a','a','b','b','a'],
               'key2' : ['one', 'two', 'one', 'two', 'one'],
               'data1' : np.random.randn(5),
               'data2' : np. random.randn(5)})


    data1        data2     key1  key2
0    0.361601    0.375297    a   one
1    0.069889    0.809772    a   two
2    1.468194    0.272929    b   one
3   -1.138458    0.865060    b   two
4   -0.268210    1.250340    a   one

I'm trying to figure out how to group the data by key1 and sum only the data1 values where key2 equals 'one'.

Here's what I've tried

def f(d,a,b):
    d.ix[d[a] == b, 'data1'].sum()

df.groupby(['key1']).apply(f, a = 'key2', b = 'one').reset_index()

But this gives me a dataframe with 'None' values

index   key1    0
0       a       None
1       b       None

Any ideas here? I'm looking for the Pandas equivalent of the following SQL:

SELECT Key1, SUM(CASE WHEN Key2 = 'one' then data1 else 0 end)

FYI - I've seen conditional sums for pandas aggregate but couldn't transform the answer provided there to work with sums rather than counts.

Thanks in advance


@jpp 2019-01-24 11:32:13

You can filter your dataframe before you perform your groupby operation. If this reduces your series index due to all values being out-of-scope, you can use reindex with fillna:

res = df.loc[df['key2'].eq('one')]\


a    3.631610
b    0.978738
c    0.000000
Name: data1, dtype: float64


I have added an additional row for demonstration purposes.


df = pd.DataFrame({'key1': ['a','a','b','b','a','c'],
                   'key2': ['one', 'two', 'one', 'two', 'one', 'two'],
                   'data1': np.random.randn(6),
                   'data2': np.random.randn(6)})

@Diego 2018-06-20 02:49:56

I think that today with pandas 0.23 you can do this:

import numpy as np

 df.assign(result = np.where(df['key2']=='one',df.data1,0))\

The advantage of this is that you can apply it to more than one column of the same dataframe

 result1 = np.where(df['key2']=='one',df.data1,0),
 result2 = np.where(df['key2']=='two',df.data1,0)
  ).groupby('key1').agg({'result1':sum, 'result2':sum})

@Andy Hayden 2013-06-23 23:13:17

First groupby the key1 column:

In [11]: g = df.groupby('key1')

and then for each group take the subDataFrame where key2 equals 'one' and sum the data1 column:

In [12]: g.apply(lambda x: x[x['key2'] == 'one']['data1'].sum())
a       0.093391
b       1.468194
dtype: float64

To explain what's going on let's look at the 'a' group:

In [21]: a = g.get_group('a')

In [22]: a
      data1     data2 key1 key2
0  0.361601  0.375297    a  one
1  0.069889  0.809772    a  two
4 -0.268210  1.250340    a  one

In [23]: a[a['key2'] == 'one']
      data1     data2 key1 key2
0  0.361601  0.375297    a  one
4 -0.268210  1.250340    a  one

In [24]: a[a['key2'] == 'one']['data1']
0    0.361601
4   -0.268210
Name: data1, dtype: float64

In [25]: a[a['key2'] == 'one']['data1'].sum()
Out[25]: 0.093391000000000002

It may be slightly easier/clearer to do this by restricting the dataframe to just those with key2 equals one first:

In [31]: df1 = df[df['key2'] == 'one']

In [32]: df1
      data1     data2 key1 key2
0  0.361601  0.375297    a  one
2  1.468194  0.272929    b  one
4 -0.268210  1.250340    a  one

In [33]: df1.groupby('key1')['data1'].sum()
a       0.093391
b       1.468194
Name: data1, dtype: float64

@AllenQ 2013-06-23 23:39:35

Awesome! I'm trying this out on my actual data (might take awhile) but I think this is what I was looking for. Thanks so much

@AllenQ 2013-06-23 23:50:33

Just searched the documentation and a quick google search...couldn't precisely find what you're referring to by groupby filter...could you point me in the right direction?

@Andy Hayden 2013-06-24 10:04:09

@AllenQ It's a new method in dev (will be in 0.11.1 when it comes out soon).

@ifly6 2019-06-04 21:18:18

Is it possible to avoid using apply in these scenarios

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