In Pandas, you can employ several criteria in both groupby
and transform
functions in several ways. Some of the ways include:
Group by multiple columns: You can group by multiple columns by passing a list of column names to the groupby
function. For instance, df.groupby(['column1', 'column2']).agg()
will group by column1
and column2
.
Use boolean masks: You can use boolean masks to group data based on conditions. For instance, df.groupby(df['column1'] > 0).agg()
will group df
based on whether column1
is greater than 0
.
Use lambda functions: You can also define a custom function using a lambda function to group data based on more complex conditions.
Apply multiple transform functions: You can apply multiple transform functions to the same column by chaining them, separated by a period. For example, df['column1'].transform(func1).transform(func2)
will apply func1
and func2
to column1
sequentially.
Create custom aggregation functions: You can define your own custom aggregation functions and pass them to the agg
method. For instance, df.groupby('column1').agg({'column2': my_agg_function})
will group by column1
and apply my_agg_function
to column2
.
Asked: 2023-06-24 23:49:57 +0000
Seen: 11 times
Last updated: Jun 25 '23