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Alex Rivera
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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.
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