Applying functions to groups

After splitting our data into the desired groups, the next step is to independently apply a function to each group of data. This step allows us to implement various techniques in each group to get different outputs based on our data requirements.

Beyond performing the common mean and sum aggregations on grouped data, we can apply various other functions to the groups. These functions can be classified into three categories:

Aggregate: Calculate a summary statistic for each group, e.g., compute mean and standard deviation values.
Transform: Perform group-wise computations and return an output object that retains the same shape, e.g., scale the values in each group based on each group’s min-max values.
Filter: Remove groups based on a group-wise computation that gives a boolean (True/ False) output, e.g., discard groups with a mean value below a certain threshold we define.

We’ll continue working with the credit card dataset for the following examples.

ID	Income	Limit	Rating	Cards	Age	Education	Gender	Student	Married	Ethnicity	Balance
1	14.891	3606	283	2	34	11	Male	No	Yes	Caucasian	333
2	106.025	6645	483	3	82	15	Female	Yes	Yes	Asian	903
3	104.593	7075	514	4	71	11	Male	No	No	Asian	580
4	148.924	9504	681	3	36	11	Female	No	No	Asian	964
5	55.882	4897	357	2	68	16	Male	No	No	Caucasian	331

Before We Begin

Reading Data into pandas

Combining Data

Reshaping and Manipulating Data

Encoding Data Types

Handling Numerical Data

Handling Categorical Data

Handling Text Data

Handling Time Series Data

Handling Sparse Data Structures

Handling Missing Data

Data Analysis and Visualization with sidetable and Bokeh

Leveraging Further Features of pandas

Utilizing Extended Libraries

Wrap Up

Appendix

Time Series Analysis and Visualization Using Python and Plotly

GroupBy—Aggregate

Applying functions to groups

Preview of the Credit Card Dataset