Pandas groupby multiple columns

Dataframe groupby multiple columns

grouped_multiple = df.groupby(['Team', 'Pos']).agg({'Age': ['mean', 'min', 'max']})
grouped_multiple.columns = ['age_mean', 'age_min', 'age_max']
grouped_multiple = grouped_multiple.reset_index()
print(grouped_multiple)

Source: jamesrledoux.com

Group by 2 columns pandas

In [11]: df.groupby(['col5', 'col2']).size()
Out[11]:
col5  col2
1     A       1
      D       3
2     B       2
3     A       3
      C       1
4     B       1
5     B       2
6     B       1
dtype: int64

Source: stackoverflow.com

Group by, aggregate multiple column -pandas

df[['col1', 'col2', 'col3', 'col4']].groupby(['col1', 'col2']).agg(['mean', 'count'])

Source: stackoverflow.com

Two groupby pandas

In [8]: grouped = df.groupby('A')

In [9]: grouped = df.groupby(['A', 'B'])

Source: pandas.pydata.org

Pandas groupby multiple columns

df['COUNTER'] =1       #initially, set that counter to 1.
group_data = df.groupby(['Alphabet','Words'])['COUNTER'].sum() #sum function
print(group_data)

Source: stackoverflow.com

Pandas sum multiple columns groupby

#UPDATED (June 2020): Introduced in Pandas 0.25.0, 
#Pandas has added new groupby behavior “named aggregation” and tuples, 
#for naming the output columns when applying multiple aggregation functions 
#to specific columns.

df.groupby(
     ['col1','col2']
 ).agg(
     sum_col3 = ('col3','sum'),
     sum_col4     = ('col4','sum'),
 ).reset_index()

Source: stackoverflow.com

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