Pandas for Data Science
Pandas is one of the most important libraries in data engineering and data science with its powerful features and capabilities, you can see that almost any data science or data engineering Notebook that processes data will for sure have Pandas used in it. In this course, we provide to you all what you need to know to be ready to use Pandas and utilize its full features in your projects.
What you will learn?
In this course, you will learn all the powerful features of Pandas and how to use them in your data science or data engineering project, in 22 Notebook you will learn the following:
- What is the usage of Pandas?
- Pandas Objects
- Pandas Series
- Pandas DataFrame
- Reading delimited files
- Reading JSON data
- DataFrame Manipulation (Add, modify, delete data)
- Alter DataFrame structure (Add, Delete Columns)
- Filtering DataFrame
- Join DataFrames
- Handling Missing Values
- DataFrame Aggregations
- How to Style your DataFrame based on Conditions
Introduction
2
Course Data
3
Machine Learning lifecycle
4
Pandas Objects Introduction
5
Pandas Series
6
Pandas DataFrame
7
Exercise #1 – Question
8
Exercise #1 – Solution
Read data from files
1
Read Delimited Files
2
Read JSON files
3
Exercise #2 – Question
4
Exercise #2 – Solution
DataFrame Manipulation
1
DataFrame Manipulation – Part 1a
2
DataFrame Manipulation – Part 1b
3
DataFrame Manipulation – Part 2a
4
DataFrame Manipulation – Part 2b
DataFrame Filtering
1
DataFrame Filtering – Part 1
2
DataFrame Filtering – Part 2
3
Exercise #3 – Question
4
Exercise #3 – Solution
DataFrame Joins
1
DataFrame Joins – Part 1
2
DataFrame Joins – Part 2
3
More on DataFrame Joins
4
Exercise on Joins and Merge
5
Exercise #4 – Question
6
Exercise #4 – Solution
Missing Data Handling
1
Handling Missing Data in a DataFrame
Aggregation
1
DataFrame Aggregation – Part 1
2
DataFrame Aggregation – Part 2
3
Exercise #5 – Question
4
Exercise #5 – Solution
Extra Topics
1
DataFrame Styling
2
Correlation & Covariance
You have to finish the course To review it
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