pandas dataframe groupby summation

Question

input:

       Date letters numbers mixed         new
0  1/2/2014       a       6    z1  1/2/2014 a
1  1/2/2014       a       3    z1  1/2/2014 a
2  1/3/2014       c       1    x3  1/3/2014 c

I want to groupby new and sum numbers so that the output is:

       Date letters numbers mixed         new
0  1/2/2014       a       9    z1  1/2/2014 a
1  1/3/2014       c       1    x3  1/3/2014 c

I've looked through here: http://pandas.pydata.org/pandas-docs/stable/groupby.html but no luck.

Here is my code:

import pandas
a=[['Date', 'letters', 'numbers', 'mixed'], ['1/2/2014', 'a', '6', 'z1'], ['1/2/2014', 'a', '3', 'z1'], ['1/3/2014', 'c', '1', 'x3']]
df = pandas.DataFrame.from_records(a[1:],columns=a[0])
f=[]
for i in range(0,len(df)):
    f.append(df['Date'][i] + ' ' + df['letters'][i])
df['new']=f

Also, any tricks that will concatenate date and letters without looping thru would also be helpful.

Answer 1

You have to convert your numbers column to int

import pandas as pd
a=[['Date', 'letters', 'numbers', 'mixed'], ['1/2/2014', 'a', '6', 'z1'], ['1/2/2014', 'a', '3', 'z1'], ['1/3/2014', 'c', '1', 'x3']]
df = pd.DataFrame.from_records(a[1:],columns=a[0])
df['new'] = df.Date + " " + df.letters
df.numbers = df.numbers.astype(int)

print df

       Date letters  numbers mixed         new
0  1/2/2014       a        6    z1  1/2/2014 a
1  1/2/2014       a        3    z1  1/2/2014 a
2  1/3/2014       c        1    x3  1/3/2014 c

You can get the dataframe you want to merge with:

df_to_merge = df[df.columns[~df.columns.isin(['numbers'])]].drop_duplicates()

Then you can do your groupby

df_grouped = pd.DataFrame(df.groupby('new').numbers.sum()).reset_index()

To get the result you posted merge

df_result = df_to_merge.merge(df_grouped)
print df_result

       Date letters mixed         new  numbers
0  1/2/2014       a    z1  1/2/2014 a        9
1  1/3/2014       c    x3  1/3/2014 c        1