Gain insights into Pandas' powerful tools for data analysis. Learn about Series, DataFrames, and optimize performance for data operations, filtering, and file uploads in data science and machine learning.

Pandas is a very popular Python library that provides powerful, flexible and high-performance tools to process data.

If you’re looking to get into data science, machine learning, or you just want to brush up on your analytics skills, then look no further. In this course, you’ll learn how to process and analyze data with Pandas. 

You’ll start out by covering the basic data structures in Pandas: Series and dataframes. In the latter half of the course, you’ll learn how to perform operations on different data structures, and how to optimize performance for file uploads, data filtering, and more.

Data Analysis & Processing with Pandas

## Aggregate

In the last lesson, we have shown how to use `groupby` to split data and aggregate them in each group. The group is calculated by various native pandas functions. However, pandas also provides a universal interface, `agg()`, which accepts a Numpy function name as a parameter.

In the example below, two different methods are demonstrated. 

# Aggregate

In the last lesson, we have shown how to use `groupby` to split data and aggregate them in each group. The group is calculated by various native pandas functions. However, pandas also provides a universal interface, `agg()`, which accepts a Numpy function name as a parameter.

In the example below, two different methods are demonstrated. 

Let's learn other operations about groups.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Grouping Data __ Part 2

Aggregate