My data has ages, and also payments per month.
I'm trying to aggregate summing the payments, but without summing the ages (averaging would work).
Is it possible to use different functions for different columns?
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Yes, thank you. -------------------------------------------------- | Date | Recovery | Age of person | ---------------------------------------------- | Jan | 500 | 26 | | Feb | 400 | 26 | | Mar | 1000 | 26 |sapo_cosmico– sapo_cosmico2015-03-18 16:28:09 +00:00Commented Mar 18, 2015 at 16:28
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1 Answer
You can pass a dictionary to agg with column names as keys and the functions you want as values.
import pandas as pd
import numpy as np
# Create some randomised data
N = 20
date_range = pd.date_range('01/01/2015', periods=N, freq='W')
df = pd.DataFrame({'ages':np.arange(N), 'payments':np.arange(N)*10}, index=date_range)
print(df.head())
# ages payments
# 2015-01-04 0 0
# 2015-01-11 1 10
# 2015-01-18 2 20
# 2015-01-25 3 30
# 2015-02-01 4 40
# Apply np.mean to the ages column and np.sum to the payments.
agg_funcs = {'ages':np.mean, 'payments':np.sum}
# Groupby each individual month and then apply the funcs in agg_funcs
grouped = df.groupby(df.index.to_period('M')).agg(agg_funcs)
print(grouped)
# ages payments
# 2015-01 1.5 60
# 2015-02 5.5 220
# 2015-03 10.0 500
# 2015-04 14.5 580
# 2015-05 18.0 540
