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500 Exercises to Master Python Pandas
Rating: 4.1 out of 5(7 ratings)
74 students

500 Exercises to Master Python Pandas

Learn Python Pandas by solving exercises on data cleaning, data analysis, data filtering, and more.
Created bySoner Yıldırım
Last updated 6/2023
English

What you'll learn

  • Perform data cleaning and manipulation tasks with Pandas
  • Analyze data and extract insights using Pandas
  • Reshape and manipulate Pandas data structures
  • Learn Python basics

Course content

7 sections • 24 lectures • 7h 57m total length

  • Introduction1:27
  • Course structure and installation3:28

    In this lecture, we'll learn about the course structure and the topics covered in the course. We'll also learn how to install the required packages.

  • Reading data11:44

    In this lecture, we'll learn how to read data from different source formats into a Pandas DataFrame. We'll also learn how to read the data partially.

  • Exploring a DataFrame6:28

    After completing this lecture, you'll be able to use functions and methods to explore a Pandas DataFrame in terms of shape, size, data types, and more.

Requirements

  • Basic experience with the Python programming language
  • Basic knowledge of data types (strings, integers, floating points, booleans)
  • Basic knowledge of Python built-in data structures (list, tuple, dictionary)

Description

Who is this course for?

This course is for those who plan to take a step into the field of data science and beginner to intermediate level data analyst, data scientist, and data engineers.


Most of the exercises are based on my experience of working as a data scientist with real-life datasets so you can benefit from this course even if you are already using Pandas at your job. If you have never used Pandas before or have little experience, you can learn a lot because the exercises are created in a way that is simple and easy-to-understand. All you need is a basic level of Python knowledge.


What is needed to take this course?

Lectures are structured as me going over Jupyter notebooks explaining exercises. Notebooks can be found in the description of each lecture. If you want to download the notebooks and follow along, make sure you also download the relevant datasets available in the data folder in the course repository.


You also need to have Jupyter notebook installed on your computer. You can also Google Colab, which allows for running Jupyter notebooks in your browser for free.


Course structure

The course is divided into 6 chapters:

  1. Introduction

  2. Data exploration and manipulation

  3. Data filtering

  4. Combining DataFrames

  5. Data analysis and visualization

  6. Use cases

  7. More learnings

Each chapter contains multiple lectures with each one focusing on a particular task such as how to filter a DataFrame, how to create pipelines with multiple steps, and how to use Python dictionaries to enhance the power of Pandas functions.


By the time you finish this course, you'll have solved at least 500 exercises and you'll be able to solve most of the tasks related to tabular data.

Who this course is for:

  • Beginner to intermediate level data analysts, data scientist, data engineers.
  • Students or professionals who want to step into the field of data science.

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