A Python Pandas crash course to teach you all the essentials to get started with data analytics
This Pandas crash course is designed to be a practical guide with real-life examples about the most common data manipulation tasks. The materials are presented with reusable code examples to allow you to quickly apply what you learn to your data analysis projects.
What you’ll learn
- Describe the Anatomy and main components of Pandas Data Structures. Understand Pandas Data Types and the correct use case for each type..
- Implement several methods to get data into and from Pandas DataFrames. These methods include Python Native Data Structures, Tabular data files, API queries etc.
- Describe any information within a Pandas DataFrame. This will help you to identify data problems such as having missing values or using incorrect data types.
- Perform Data manipulation and cleaning. This part includes fixing data types, handling missing values, removing duplicate records, and many more.
- Merge & Join multiple datasets into Pandas DataFrames.
- Perform Data Summarization & Aggregation within any DataFrame.
- Create different types of Data Visualization.
- Apply all the Pandas knowledge you have learned in this course to a real-world Data Analysis Project to investigate COVID-19 infection, and the consequent lo.
Course Content
- Before We Start –> 1 lecture • 1min.
- Lesson 1 – Getting Started –> 1 lecture • 7min.
- Lesson 2 – Getting Data into and from Pandas –> 5 lectures • 24min.
- Lesson 3 – Exploring Data Objects –> 2 lectures • 9min.
- Lesson 4 – Data Cleaning with Pandas –> 1 lecture • 11min.
- Lesson 5 – Merging & Joining Data –> 3 lectures • 11min.
- Lesson 6 – Data Accessing & Aggregation –> 1 lecture • 11min.
- Lesson 7 – Pandas Data Visualization –> 2 lectures • 9min.
- Lesson 8 – Pandas Analysis Project –> 1 lecture • 10min.
Requirements
This Pandas crash course is designed to be a practical guide with real-life examples about the most common data manipulation tasks. The materials are presented with reusable code examples to allow you to quickly apply what you learn to your data analysis projects.
By the end of this course, you should be able to:
- Describe the Anatomy of Pandas Data Structures. This includes Pandas DataFrames, Series, and Indices.
- Implement several methods to get data into and from Pandas DataFrames. These methods include Python Native Data Structures, Tabular data files, API queries and JSON format, web scraping, and more.
- Describe any information within a Pandas DataFrame. This will help you to identify data problems such as having missing values or using incorrect data types.
- Understand Pandas Data Types and the correct use case for each type.
- Perform Data manipulation and cleaning. This part includes fixing data types, handling missing values, removing duplicate records, and many more.
- Merge & Join multiple datasets into Pandas DataFrames
- Perform Data Summarization & Aggregation within any DataFrame
- Create different types of Data Visualization
- Update Pandas Styling Settings
- Conduct a Data Analysis Project using Pandas library to collect and investigate COVID-19 infection, and the consequent lockdown in different countries.
In addition to the course materials, you will also have free access to the following:- A Jupyter Notebook with all the code examples covered in this course- A free e-book in PDF format