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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

1
Course Introduction
1 Minute
2
Course Data
3
Machine Learning lifecycle
2 Minutes
4
Pandas Objects Introduction
2 Minutes
5
Pandas Series
6 Minutes
6
Pandas DataFrame
3 Minutes
7
Exercise #1 – Question
1 Minute
8
Exercise #1 – Solution
2 Minutes

Read data from files

1
Read Delimited Files
3 Minutes
2
Read JSON files
7 Minutes
3
Exercise #2 – Question
1 Minute
4
Exercise #2 – Solution
1 Minute

DataFrame Manipulation

1
DataFrame Manipulation – Part 1a
4 Minutes
2
DataFrame Manipulation – Part 1b
4 Minutes
3
DataFrame Manipulation – Part 2a
3 Minutes
4
DataFrame Manipulation – Part 2b
5 Minutes

DataFrame Filtering

1
DataFrame Filtering – Part 1
7 Minutes
2
DataFrame Filtering – Part 2
7 Minutes
3
Exercise #3 – Question
1 Minute
4
Exercise #3 – Solution
2 Minutes

DataFrame Joins

1
DataFrame Joins – Part 1
7 Minutes
2
DataFrame Joins – Part 2
7 Minutes
3
More on DataFrame Joins
5 Minutes
4
Exercise on Joins and Merge
6 Minutes
5
Exercise #4 – Question
2 Minutes
6
Exercise #4 – Solution
3 Minutes

Missing Data Handling

1
Handling Missing Data in a DataFrame
9 Minutes

Aggregation

1
DataFrame Aggregation – Part 1
8 Minutes
2
DataFrame Aggregation – Part 2
5 Minutes
3
Exercise #5 – Question
2 Minutes
4
Exercise #5 – Solution
4 Minutes

Extra Topics

1
DataFrame Styling
6 Minutes
2
Correlation & Covariance
2 Minutes
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Enrolled: 77 students
Duration: 2 Hours
Lectures: 33
Video: 2 Hours