group by count dataframe
df.groupby(['col1', 'col2']).size().reset_index(name='counts')
Source: stackoverflow.com
pandas new df from groupby
df = pd.DataFrame(old_df.groupby(['groupby_attribute'])['mean_attribute'].mean()) df = df.reset_index() df
groupby as_index=false
When you use as_index=False , you indicate to groupby() that you don't want to set the column ID as the index (duh!). ... Using as_index=True allows you to apply a sum over axis=1 without specifying the names of the columns, then summing the value over axis 0.
pandas group by
data.groupby('amount', as_index=False).agg({"duration": "sum"})
Groups the DataFrame using the specified columns
# Groups the DataFrame using the specified columns df.groupBy().avg().collect() # [Row(avg(age)=3.5)] sorted(df.groupBy('name').agg({'age': 'mean'}).collect()) # [Row(name='Alice', avg(age)=2.0), Row(name='Bob', avg(age)=5.0)] sorted(df.groupBy(df.name).avg().collect()) # [Row(name='Alice', avg(age)=2.0), Row(name='Bob', avg(age)=5.0)] sorted(df.groupBy(['name', df.age]).count().collect()) # [Row(name='Alice', age=2, count=1), Row(name='Bob', age=5, count=1)]
Source: spark.apache.org
group by pandas
#calculate sum of sales grouped by month df.groupby(df.date.dt.month)['sales'].sum() date 1 34 2 44 3 31 Name: sales, dtype: int64
Source: www.statology.org