Introduction to Grouping
Explore how to group DataFrames by single or multiple columns using Pandas. Understand grouping syntax, aggregation functions, and data segmentation strategies applicable in real-world scenarios like travel and medical datasets to better analyze and interpret data.
We'll cover the following...
Concept
The ability to group or segment DataFrames by one or more columns is one of the key features of any data analysis application. Therefore it would most likely show up in a data analysis interview or task.
The idea is to divide a DataFrame into multiple groups to analyze each group separately.
Syntax
As a reminder, the syntax is as simple as
df.groupby(<col_name>) or, in the case of grouping by multiple columns, df.groupby([<col1>, <col2>, ..]).
Operations such as aggregations and apply functions can be applied on DataFrameGroupBy objects which can be reset to a normal DataFrame using reset_index()
Travel dataset
Idea
In your travel dataset, it makes ...