PYTHON

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)

Group multiple columns in pandas

import pandas as pd

# Group multiple columns and get statistics
df = df.groupby(["store", "type"])["weekly_sales"].sum()

# Group multiple columns and multiple statistics
df = df.groupby(["store", "type"])["weekly_sales"].agg([sum, min, max])

# Display DataFrame
print(df)

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

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)

pandas groupby multiple columns

#formatting
candidates_salary_by_month =  candidates_df.groupby('month').agg(num_cand_month = 
                                                                ('num_candidates', 'sum'), 
                                                                avg_sal = ('salary', 'mean')).style.format('{:.0f}')

print(candidates_salary_by_month)

group by multiple columns

Group By X means put all those with the same value for X in the one group.

Group By X, Y means put all those with the same values for both X and Y in the one group.

dataframe how to groupby apply list to multiple columns

df = df.groupby(['a','b']).apply(lambda x: [list(x['c']), list(x['d'])]).apply(pd.Series)
df.columns =['a','b','c','d']

GROUP BY With Multiple Columns

SELECT country, state, MIN(age) as min_age
FROM Persons
GROUP BY country, state;