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
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 structureThe course is divided into 6 chapters:IntroductionData exploration and manipulationData filteringCombining DataFramesData analysis and visualizationUse casesMore learningsEach 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.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Course structure and installation
Lecture 3 Reading data
Lecture 4 Exploring a DataFrame
Section 2: Data exploration and manipulation
Lecture 5 Data types
Lecture 6 Column operations
Lecture 7 Date manipulation
Lecture 8 String manipulation
Lecture 9 Categorical data
Section 3: Data filtering
Lecture 10 Missing values
Lecture 11 loc and iloc methods
Lecture 12 Filtering DataFrames
Section 4: Combining DataFrames
Lecture 13 Combining DataFrames
Lecture 14 Merging DataFrames
Lecture 15 Reshaping DataFrames
Section 5: Data analysis and visualization
Lecture 16 Data analysis
Lecture 17 Data visualization
Lecture 18 Time series analysis with Pandas
Section 6: Use cases
Lecture 19 Data cleaning and analysis 1 (obesity dataset)
Lecture 20 Data cleaning and analysis 2 (customer churn dataset)
Section 7: More learnings
Lecture 21 Python dictionaries with Pandas
Lecture 22 Pandas pipelines
Lecture 23 Styling DataFrames
Lecture 24 Functions not to forget
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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