What is the groupby command in pandas?

Python allows many different libraries that enable data manipulation. One such library, pandas, has a command used to group the dataset by the selected column. It can be used to group large datasets and apply operations on them.

The default implementation of groupby is:

dataframe.groupby( by= None, axis= 0, level= None, as_index: bool = True, sort:bool = True, group_key:bool = True, squeeze: bool = False, observed:bool = False )

Parameters

• by: mapping, function, label, list of labels* - This is used to define the groups for groupby. These can be functions, labels, or several labels (in order of group).

• level: int, level name, sequence - You can group the axis in levels if the axis is a MultiIndex(hierarchical).

• axis: 0 or 1 - Split along rows(0) or columns(1).

• as_index: bool - Return objects with group labels as the index.

• sort: bool - Sort group keys.

• group-key: bool - Add group key to an index to identify pieces.

• squeeze: bool - Reduce dimensionality, if possible.

• observed: bool - Only applies if groupers are Categorical.

Code

Let’s look at an example. Import the library and load the dataset in the data frame. Here, the dataset includes the zip codes for different cities in the ​US.

Use groupby to group zip codes according to the ​city.

Groupby can be used to group data into multiple levels.

Note: grouping is done according to the array passed, with the first element being the first condition.

The official documentation can be found here.