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

## Challenge - Find the most popular item

In this task, you need to do the following things:

- Load the `raw_data.csv` to the DataFrame object.
- Group data based on the `Item ID` column.
- Get the most popular item based on the count.
- Return the `Item ID` and the corresponding count of the most popular item.

Both of them should be an integer.

# Challenge - Find the most popular item

In this task, you need to do the following things:

- Load the `raw_data.csv` to the DataFrame object.
- Group data based on the `Item ID` column.
- Get the most popular item based on the count.
- Return the `Item ID` and the corresponding count of the most popular item.

Both of them should be an integer.

In this challenge, you need to solve a few problems, such as the apply() function.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Challenge - Find the Most Popular Item

Challenge - Find the most popular item