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i have a pandas frame that looks like this:
Is there a way to add the numbers in the last column without having to iterate through the data frame?
I was playing with the results of Grouping and auto incrementing group id in pandas but haven't made it work for my purposes
Here is the code to produce the dataframe
import pandas as pd
columns = ['Product','SubProd', 'NeedThis']
Index=['4/20/2012','4/27/2012','5/4/2012','5/11/2012','5/18/2012','4/20/2012',
'4/27/2012','5/4/2012','5/11/2012','5/18/2012','5/25/2012','10/31/2014','11/7/2014',
'11/14/2014','11/21/2014','11/28/2014']
datas = {'Product' : ['A','A','A','A','A','A','A','A','A','A','A','B','B','B','B','B'],
'SubProd' : ['BL','BL','BL','BL','BL','lk','lk','lk','lk','lk','lk','po','po','po','po','po']}
df = pd.DataFrame(data=datas, index=Index)
print(df)
Output:
Product SubProd
4/20/2012 A BL
4/27/2012 A BL
5/4/2012 A BL
5/11/2012 A BL
5/18/2012 A BL
4/20/2012 A lk
4/27/2012 A lk
5/4/2012 A lk
5/11/2012 A lk
5/18/2012 A lk
5/25/2012 A lk
10/31/2014 B po
11/7/2014 B po
11/14/2014 B po
11/21/2014 B po
11/28/2014 B po
Thanks
735
pandas GroupBy Multiple Columns Example Most of the time when you are working on a real-time project in pandas DataFrame you are required to do groupby on multiple columns. You can do so by passing a list of column names to DataFrame. groupby() function.
The Hello, World! of pandas GroupBy You call . groupby() and pass the name of the column that you want to group on, which is "state" . Then, you use ["last_name"] to specify the columns on which you want to perform the actual aggregation. You can pass a lot more than just a single column name to .
Use DataFrame. groupby(). sum() to group rows based on one or multiple columns and calculate sum agg function. groupby() function returns a DataFrameGroupBy object which contains an aggregate function sum() to calculate a sum of a given column for each group.
In [10]: df['counter'] = df.groupby(['Product','SubProd']).cumcount()+1
In [11]: df
Out[11]:
Product SubProd counter
4/20/2012 A BL 1
4/27/2012 A BL 2
5/4/2012 A BL 3
5/11/2012 A BL 4
5/18/2012 A BL 5
4/20/2012 A lk 1
4/27/2012 A lk 2
5/4/2012 A lk 3
5/11/2012 A lk 4
5/18/2012 A lk 5
5/25/2012 A lk 6
10/31/2014 B po 1
11/7/2014 B po 2
11/14/2014 B po 3
11/21/2014 B po 4
11/28/2014 B po 5
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