pandas-groupby

I have the following data frame and want to: Group records by month Sum QTY_SOLD and NET_AMT of each unique UPC_ID (per month) Include the rest of the columns as well in the resulting dataframe The way I thought I can do this is 1st: create a month column to aggregate the D_DATES , then sum QTY_SOLD by UPC_ID . Script: # Convert date to date time object df['D_DATE'] = pd.to_datetime(df['D_DATE']) # Create aggregated months column df['month'] = df['D_DATE'].apply(dt.date.strftime, args=('%Y.%m',)) # Group by month and sum up quantity sold by UPC_ID df = df.groupby(['month', 'UPC_ID'])['QTY_SOLD

I know this must have been answered some where but I just could not find it. Problem : Sample each group after groupby operation. import pandas as pd df = pd.DataFrame({'a': [1,2,3,4,5,6,7], 'b': [1,1,1,0,0,0,0]}) grouped = df.groupby('b') # now sample from each group, e.g., I want 30% of each group Apply a lambda and call sample with param frac : In [2]: df = pd.DataFrame({'a': [1,2,3,4,5,6,7], 'b': [1,1,1,0,0,0,0]}) ​ grouped = df.groupby('b') grouped.apply(lambda x: x.sample(frac=0.3)) Out[2]: a b b 0 6 7 0 1 2 3 1 Sample a fraction of each group You can use GroupBy.apply with sample . You

Assuming I have the following dataframe: a b c Sce1 Sce2 Sce3 Sce4 Sce5 Sc6 Animal Ground Dog 0.0 0.9 0.5 0.0 0.3 0.4 Animal Ground Cat 0.6 0.5 0.3 0.5 1.0 0.2 Animal Air Eagle 1.0 0.1 0.1 0.6 0.9 0.1 Animal Air Owl 0.3 0.1 0.5 0.3 0.5 0.9 Object Metal Car 0.3 0.3 0.8 0.6 0.5 0.6 Object Metal Bike 0.5 0.1 0.4 0.7 0.4 0.2 Object Wood Chair 0.9 0.6 0.1 0.9 0.2 0.8 Object Wood Table 0.9 0.6 0.6 0.1 0.9 0.7 I want to create a MultiIndex, which will contain the sum of each lvl. The output will look like this: a b c Sce1 Sce2 Sce3 Sce4 Sce5 Sce6 Animal 1.9 1.6 1.4 1.3 2.7 1.6 Ground 0.6 1.4 0.8 0.5

I have a large data set in the following format: id, socialmedia 1, facebook 2, facebook 3, google 4, google 5, google 6, twitter 7, google 8, twitter 9, snapchat 10, twitter 11, facebook I want to group by then and assign a group_id column and then ungroup (expand) back to individual records. id, socialmedia, groupId 1, facebook, 1 2, facebook, 1 3, google, 2 4, google, 2 5, google, 2 6, twitter, 3 7, google, 2 8, twitter, 3 9, snapchat, 4 10, twitter, 3 11, facebook, 1 I tried following but end up with 'DataFrameGroupBy' object does not support item assignment. x['grpId'] = x.groupby(

I have a dataframe say like this >>> df = pd.DataFrame({'user_id':['a','a','s','s','s'], 'session':[4,5,4,5,5], 'revenue':[-1,0,1,2,1]}) >>> df revenue session user_id 0 -1 4 a 1 0 5 a 2 1 4 s 3 2 5 s 4 1 5 s And each value of session and revenue represents a kind of type, and I want to count the number of each kind say the number of revenue=-1 and session=4 of user_id=a is 1. And I found simple call count() function afer groupby() can't output the result I want. >>> df.groupby('user_id').count() revenue session user_id a 2 2 s 3 3 How can I do that? You seem to want to group by several