Online Course
Data Ingestion with Pandas

Name: Data Ingestion with Pandas Course
Price: 36 USD

Start Your Data Journey and learn everything about ingesting data from various data storage formats like Flat Files, Excel Files, API's, JSON's and Relational Databases.

4.8

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880 students already enrolled

Skill level:

Basic

Duration:

1 hour

Lessons (1 hour)

CPE credits:

2.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Import CSV data using the pandas library.
Read data from flat files and Excel spreadsheets.
Load HTML and JSON datasets with pandas.
Access data from databases using pandas.
Retrieve and load data from APIs.

Topics & tools

Pandas BasicsData EngineeringData AnalysisPythonSql

Your instructor

Samson Afolabi

Data Scientist & Analytics Leader | Team Lead C Analytics at Free2move | Data Engineering & AI

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Mathematics

Ask Samson a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 2.5 Field of Study: Information Technology
Delivery Method: QAS Self Study

Data is everywhere, but before you can analyze it, you need to load it efficiently and correctly. "Data Ingestion with Pandas" is designed to help you master the art of importing data from various sources, including flat files, Excel, databases, APIs, and JSON sources, using Python’s powerful Pandas library.

This course takes you through every aspect of data ingestion, from reading simple CSV's and Excel files to handling large datasets efficiently. You’ll learn how to work with Excel files, including reading multiple sheets, selecting specific columns, and handling large files. As we progress, you'll dive into working with databases, where you'll connect to Relational databases, connect to them and retrieve data using queries, and seamlessly integrate it into Pandas.

In the modern data landscape, APIs and JSON are crucial sources of real-time data. This course will equip you with the skills to extract data from web APIs, parse complex JSON structures, and transform them into clean, structured DataFrames. Along the way, you’ll also learn how to handle common ingestion errors, such as encoding issues, missing values, and memory limitations, ensuring a smooth and efficient data-loading process.

Whether you’re a data analyst, data scientist, or software engineer, mastering data ingestion is an essential skill. This course is very practical, hands-on, and designed for both beginners and those looking to refine their Pandas expertise.

By the end, you’ll have the confidence to import and manage data from multiple sources efficiently, setting the stage for deeper analysis and machine learning workflows. Let’s dive in and unlock the power of data ingestion with Pandas.

Prerequisites

Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
Basic familiarity with Python programming is required.
Familiarity with NumPy is helpful but not mandatory.

Advanced preparation

Introduction to Python

Curriculum

28 lessons 22 exercises 1 exam

1. Introduction

7 min

Start your journey into data ingestion with Pandas! This section introduces the importance of efficient data loading, common data sources, and how Pandas simplifies the process.

You’ll also get an overview of the various chapters of the course.

7 min

Start your journey into data ingestion with Pandas! This section introduces the importance of efficient data loading, common data sources, and how Pandas simplifies the process.

You’ll also get an overview of the various chapters of the course.

Introduction - Data Ingestion With Pandas Free

What You'll Learn and Getting Ready Free

Exercise Free
2. Import Data From Flat Files

16 min

Learn how to read data from Flat Files using pd.read_csv(). You’ll explore handling large datasets efficiently, troubleshooting common errors like encoding issues, and using parameters like usecols and dtype to improve performance.

16 min

Learn how to read data from Flat Files using pd.read_csv(). You’ll explore handling large datasets efficiently, troubleshooting common errors like encoding issues, and using parameters like usecols and dtype to improve performance.

Introduction - Import Data From Flat Files

Flat Files, What About them?

More on Pandas' Read CSV Method

Exercise

Handling Large Files Efficiently

Errors with Loading Flat Files!

Exercise
3. Import Data From Excel Files

16 min

Master working with Excel spreadsheets using pd.read_excel(). This section covers reading multiple sheets, selecting specific columns, writing back to Excel, and ensuring smooth data handling with Pandas. You’ll also learn how to deal with common formatting and date issues.

16 min

Master working with Excel spreadsheets using pd.read_excel(). This section covers reading multiple sheets, selecting specific columns, writing back to Excel, and ensuring smooth data handling with Pandas. You’ll also learn how to deal with common formatting and date issues.

Introduction - Import Data From Excel Files

Ingesting Data From Excel Files

Operations with Excel Files

Exercise

Handling Date and Time Data

Merging & Appending Excel Files

Exercise

Interacting with Google Sheets

Working with Excel Formulas

Exercise
4. Importing Json Data and APIs

19 min

Master working with Excel spreadsheets using pd.read_excel(). This section covers reading multiple sheets, selecting specific columns, and ensuring smooth data handling with Pandas.

You’ll also learn how to deal with common formatting and date issues as well as dealing with Google sheets.

19 min

Master working with Excel spreadsheets using pd.read_excel(). This section covers reading multiple sheets, selecting specific columns, and ensuring smooth data handling with Pandas.

You’ll also learn how to deal with common formatting and date issues as well as dealing with Google sheets.

Introduction - Importing Json Data and APIs

Introduction to JSON

Loading Json Data

Exercise

More Operations with JSON Files

Introduction to APIs

Exercise

Loading Data from APIs

Parsing Responses

Exercise
5. Import Data from Databases

14 min

Go beyond flat files and connect Pandas to Relational databases.

Learn how to execute queries, fetch large datasets efficiently, and integrate SQL data into Pandas for further analysis. This section also covers more about other data ingestion libraries.

14 min

Go beyond flat files and connect Pandas to Relational databases.

Learn how to execute queries, fetch large datasets efficiently, and integrate SQL data into Pandas for further analysis. This section also covers more about other data ingestion libraries.

Introduction - Import Data from Databases

Introduction to Databases

SQL and Pandas

Exercise

More Complex SQL Queries

Other Ingestion Libraries

Connecting with Other Ingestion Libraries

Exercise
6. Conclusion

4 min

Wrap up the course by revisiting key takeaways and best practices for efficient data ingestion. You’ll also get insights on next steps to deepen your Pandas knowledge and integrate your skills into real-world data workflows.

4 min

Wrap up the course by revisiting key takeaways and best practices for efficient data ingestion. You’ll also get insights on next steps to deepen your Pandas knowledge and integrate your skills into real-world data workflows.

Conclusion: Final Recap
7. Course exam

45 min

45 min

Course exam

Free lessons

1.1 Introduction - Data Ingestion With Pandas

6 min

1.2 What You'll Learn and Getting Ready

1 min

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