Advanced Grouping Techniques with Pandas
Pandas is a powerful data manipulation library that provides a variety of functionalities to handle and analyze large datasets. One of the most commonly used operations in data analysis is grouping, which allows us to group data based on one or more columns and apply some aggregate functions. In this blog post, we will discuss advanced grouping techniques with Pandas that can help you gain deeper insights from your data.
1- Grouping with multiple columns
In Pandas, you can group data based on multiple columns by passing a list of column names to the groupby() function. For example:
2- Grouping with custom functions
Pandas allows you to use custom functions for grouping by passing them to the groupby() function. For example:
3- Grouping with time series data
Pandas provides powerful time series functionality for grouping data by time intervals. For example:
In this article we have discussed Advance grouping Techniques with Pandas. I hope you liked this article, let me know if you have any question.