Trying to create a new column from the groupby
calculation. In the code below, I get the correct calculated values for each date (see group below) but when I try to create a new column (df['Data4']
) with it I get NaN. So I am trying to create a new column in the dataframe with the sum of Data3
for the all dates and apply that to each date row. For example, 2015-05-08 is in 2 rows (total is 50+5 = 55) and in this new column I would like to have 55 in both of the rows.
import pandas as pd
import numpy as np
from pandas import DataFrame
df = pd.DataFrame({
'Date' : ['2015-05-08', '2015-05-07', '2015-05-06', '2015-05-05', '2015-05-08', '2015-05-07', '2015-05-06', '2015-05-05'],
'Sym' : ['aapl', 'aapl', 'aapl', 'aapl', 'aaww', 'aaww', 'aaww', 'aaww'],
'Data2': [11, 8, 10, 15, 110, 60, 100, 40],
'Data3': [5, 8, 6, 1, 50, 100, 60, 120]
})
group = df['Data3'].groupby(df['Date']).sum()
df['Data4'] = group
You want to use transform
this will return a Series with the index aligned to the df so you can then add it as a new column:
In [74]:
df = pd.DataFrame({'Date': ['2015-05-08', '2015-05-07', '2015-05-06', '2015-05-05', '2015-05-08', '2015-05-07', '2015-05-06', '2015-05-05'], 'Sym': ['aapl', 'aapl', 'aapl', 'aapl', 'aaww', 'aaww', 'aaww', 'aaww'], 'Data2': [11, 8, 10, 15, 110, 60, 100, 40],'Data3': [5, 8, 6, 1, 50, 100, 60, 120]})
df['Data4'] = df['Data3'].groupby(df['Date']).transform('sum')
df
Out[74]:
Data2 Data3 Date Sym Data4
0 11 5 2015-05-08 aapl 55
1 8 8 2015-05-07 aapl 108
2 10 6 2015-05-06 aapl 66
3 15 1 2015-05-05 aapl 121
4 110 50 2015-05-08 aaww 55
5 60 100 2015-05-07 aaww 108
6 100 60 2015-05-06 aaww 66
7 40 120 2015-05-05 aaww 121