500 Exercises to Master Python Pandas

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1 - Introduction

1 - Introduction.mp4

01:27

2 - Course structure and installation.mp4

03:28

2 - Lecture notebook.txt

3 - Lecture notebook.txt

3 - Reading data.mp4

11:44

4 - Exploring a DataFrame.mp4

06:28

4 - Lecture notebook.txt

2 - Data exploration and manipulation

5 - Data types.mp4

14:51

5 - Lecture notebook.txt

6 - Column operations.mp4

36:42

6 - Lecture notebook.txt

7 - Date manipulation.mp4

24:05

7 - Lecture notebook.txt

8 - Lecture notebook.txt

8 - String manipulation.mp4

25:42

9 - Categorical data.mp4

12:59

9 - Lecture notebook.txt

3 - Data filtering

10 - Lecture notebook.txt

10 - Missing values.mp4

25:28

11 - Lecture notebook.txt

11 - loc and iloc methods.mp4

22:27

12 - Filtering DataFrames.mp4

36:11

12 - Lecture notebook.txt

4 - Combining DataFrames

13 - Combining DataFrames.mp4

18:28

13 - Lecture notebook.txt

14 - Lecture notebook.txt

14 - Merging DataFrames.mp4

39:53

15 - Lecture notebook.txt

15 - Reshaping DataFrames.mp4

17:24

5 - Data analysis and visualization

16 - Data analysis.mp4

36:29

16 - Lecture notebook.txt

17 - Data visualization.mp4

12:05

17 - Lecture notebook.txt

18 - Lecture notebook.txt

18 - Time series analysis with Pandas.mp4

21:37

6 - Use cases

19 - Data cleaning and analysis 1 obesity dataset.mp4

39:09

19 - Lecture notebook.txt

20 - Data cleaning and analysis 2 customer churn dataset.mp4

21:48

20 - Lecture notebook.txt

7 - More learnings

21 - Lecture notebook.txt

21 - Python dictionaries with Pandas.mp4

14:14

22 - Lecture notebook.txt

22 - Pandas pipelines.mp4

10:14

23 - Lecture notebook.txt

23 - Styling DataFrames.mp4

11:24

24 - Functions not to forget.mp4

12:47

24 - Lecture notebook.txt

Description

Learn Python Pandas by solving exercises on data cleaning, data analysis, data filtering, and more.

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

Who is this for?

Beginner to intermediate level data analysts, data scientist, data engineers.

Students or professionals who want to step into the field of data science.

What You Need to Know?

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)

More details

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:
Introduction
Data exploration and manipulation
Data filtering
Combining DataFrames
Data analysis and visualization
Use cases
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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I'm a data scientist and content creator with extensive experience in Python and SQL. I've been using Pandas for almost 4 years both in and out of work. My content creation experience also focuses on data science. I wrote over 500 articles and tutorials in my blog on Medium. I also created an entire course on Pandas for another e-learning platform.

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