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)})
df
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)
FROM df
GROUP BY key1
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
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())
Out[12]:
key1
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
Out[22]:
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']
Out[23]:
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']
Out[24]:
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
Out[32]:
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()
Out[33]:
key1
a 0.093391
b 1.468194
Name: data1, dtype: float64