pandas groupby multiple aggregation on different columns code example

Example 1: 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)

Example 2: Aggregate on the entire DataFrame without group

# Aggregate on the entire DataFrame without group

df.agg({"age": "max"}).collect()
# [Row(max(age)=5)]
from pyspark.sql import functions as F
df.agg(F.min(df.age)).collect()
# [Row(min(age)=2)]

Example 3: 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