Pandas Groupby Aggregation Multiple Compute Functions

Often you may need to aggregate data such that you compute multiple functions against all columns of data. In this example, I demonstrate how to aggregate data with pandas groupby using multiple compute methods.

import numpy as np
import pandas as pd
from datetime import datetime

To begin, let's read a file with some financial data at the daily level for multiple stocks.

sample_df = pd.read_csv('full_data.csv')
sample_df.head()

	date	Ticker	1. open	2. high	3. low	4. close	5. volume
0	2019-07-03	AAPL	203.603278	203.653537	203.560323	203.606995	53458.197115
1	2019-07-03	AXP	125.587369	125.616468	125.558919	125.587030	14894.287805
2	2019-07-03	BA	353.382682	353.453835	353.301135	353.377761	32912.900474
3	2019-07-03	CAT	135.110770	135.152305	135.065153	135.105175	10665.087379
4	2019-07-03	CSCO	56.063977	56.083697	56.045637	56.065251	61533.024038

Below, we perform multiple aggregations on all price and volume columns to return the mean and var by day.

sample_df.groupby(['date', 'Ticker'], as_index=False).agg([np.mean, np.var]).head()

		1. open		2. high		3. low		4. close		5. volume
		mean	var	mean	var	mean	var	mean	var	mean	var
date	Ticker
2019-07-03	AAPL	203.603278	0.095705	203.653537	0.091135	203.560323	0.104315	203.606995	0.099121	53458.197115	1.364074e+10
	AXP	125.587369	0.073431	125.616468	0.072509	125.558919	0.075973	125.587030	0.074976	14894.287805	1.688696e+08
	BA	353.382682	0.272969	353.453835	0.273478	353.301135	0.272263	353.377761	0.272168	32912.900474	2.697468e+10
	CAT	135.110770	0.169655	135.152305	0.173295	135.065153	0.155551	135.105175	0.161341	10665.087379	7.142522e+07
	CSCO	56.063977	0.091510	56.083697	0.089896	56.045637	0.094455	56.065251	0.092519	61533.024038	2.410866e+10