Code python

Pandas DataFrame Group by one Column and Aggregate using MAX, MIN, MEAN and MEDIAN

visibility 635 comment 0 access_time 10 months ago language English

import pandas as pd

categories = []
values = []
for i in range(0,100):
	categories.append(chr(i%10+65))
	values.append(i)
	
df = pd.DataFrame({'category': categories, 'value':values})
print(df)

# Aggregate
df_agg = df.groupby(by=['category']) \
			.aggregate({"value":
			['max', 'min', 'mean', 'median']})

print(df_agg)

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Code description

This code snippet provides one example of grouping a pandas DataFrame by one column and then aggregating on multiple columns using different functions including max, min, mean and median.

We pass in a dictionary for each column that needs to be aggregated: the key is the column name, and the value is a list of aggregation functions supported by pandas DataFrame. The result DataFrame will have multiple levels as following output shows:

Sample output:

value
max min mean median
category
A 90 0 45 45
B 91 1 46 46
C 92 2 47 47
D 93 3 48 48
E 94 4 49 49
F 95 5 50 50
G 96 6 51 51
H 97 7 52 52
I 98 8 53 53
J 99 9 54 54

python pandas