pandas-groupby

I have a simple task that I'm wondering if there is a better / more efficient way to do. I have a dataframe that looks like this: Group Score Count 0 A 5 100 1 A 1 50 2 A 3 5 3 B 1 40 4 B 2 20 5 B 1 60 And I want to add a column that holds the value of the group total count: Group Score Count TotalCount 0 A 5 100 155 1 A 1 50 155 2 A 3 5 155 3 B 1 40 120 4 B 2 20 120 5 B 1 60 120 The way I did this was: Grouped=df.groupby('Group')['Count'].sum().reset_index() Grouped=Grouped.rename(columns={'Count':'TotalCount'}) df=pd.merge(df, Grouped, on='Group', how='left') Is there a better / cleaner way

I'm selecting one max row per group and I'm using groupby / agg to return index values and select the rows using loc . For example, to group by "Id" and then select the row with the highest "delta" value: selected_idx = df.groupby("Id").apply(lambda df: df.delta.argmax()) selected_rows = df.loc[selected_idx, :] However, it's so slow this way. Actually, my i7/16G RAM laptop hangs when I'm using this query on 13 million rows. I have two questions for experts: How can I make this query run fast in pandas? What am I doing wrong? Why is this operation so expensive? [Update] Thank you so much for

This should be an easy one, but somehow I couldn't find a solution that works. I have a pandas dataframe which looks like this: index col1 col2 col3 col4 col5 0 a c 1 2 f 1 a c 1 2 f 2 a d 1 2 f 3 b d 1 2 g 4 b e 1 2 g 5 b e 1 2 g I want to group by col1 and col2 and get the sum() of col3 and col4. Col5 can be dropped, since the data can not be aggregated. Here is how the output should look like. I am interested in having both col3 and col4 in the resulting dataframe. It doesn't really matter if col1 and col2 are part of the index or not. index col1 col2 col3 col4 0 a c 2 4 1 a d 1 2 2 b d 1 2

I have a dataframe as follows: user num1 num2 a 1 1 a 2 2 a 3 3 b 4 4 b 5 5 I want a dataframe which has the minimum from num1 for each user, and the maximum of num2 for each user. The output should be like: user num1 num2 a 1 3 b 4 5 I know that if I wanted the max of both columns I could just do: a.groupby('user')['num1', 'num2'].max() Is there some equivalent without having to do something like: series_1 = a.groupby('user')['num1'].min() series_2 = a.groupby('user')['num2'].max() # converting from series to df so I can do a join on user df_1 = pd.DataFrame(np.array([series_1]).transpose(),

Lets say this is my data-frame df = pd.DataFrame({ 'bio' : ['1', '1', '1', '4'], 'center' : ['one', 'one', 'two', 'three'], 'outcome' : ['f','t','f','f'] }) It looks like this ... bio center outcome 0 1 one f 1 1 one t 2 1 two f 3 4 three f I want to drop row 1 because it has the same bio & center as row 0. I want to keep row 2 because it has the same bio but different center then row 0. Something like this won't work based on drop_duplicates input structure but it's what I am trying to do df.drop_duplicates(subset = 'bio' & subset = 'center' ) Any suggestions ? edit : changed df a bit to fit

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