Answers for "groupby mean pandas"

pandas calculate mean by groups

# Basic syntax:
df.groupby('column_name').mean()

# Where this will return the mean of each group with the same values in
#	the column "column_name"

# Example usage:
import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
                   'B': [np.nan, 2, 3, 4, 5],
                   'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])

print(df)
	A	B	C
0	1	NaN	1
1	1	2.0	2
2	2	3.0	1
3	1	4.0	1
4	2	5.0	2

# Calculate the mean of columns B and C grouped by the values in column A
df.groupby('A').mean() # Returns:
	B	C
A		
1	3.0	1.333333
2	4.0	1.500000

# Calculate the mean of column C grouped by the values in columns A and B
df.groupby(['A', 'B']).mean() # Returns:
		C
A	B	
1	2.0	2
	4.0	1
2	3.0	1
	5.0	2

average within group by pandas

In [57]: df.groupby(['cluster', 'org']).mean()
Out[57]:
               time
cluster org
1       a    438886
        c        23
2       d      9874
        h        34
3       w         6

pandas groupby mean

df.groupby(['A', 'B']).mean()