About this course

Learn the basics of data manipulation in Python using Pandas and NumPy, including installation, understanding data structures, and performing data operations.

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Overview

Welcome to the Data Manipulation with Pandas and NumPy course! In this course, you will explore how to handle and analyze data efficiently in Python using the powerful libraries Pandas and NumPy. You’ll learn about different data structures, how to manipulate and process data, and how to import/export data in various formats.

Course Content

This course is divided into the following lessons:

1. Introduction to Pandas and NumPy
2. Installing Pandas and NumPy
3. Understanding Data Frames
4. Basic Operations with Data Frames
5. Importing and Exporting Data
6. Data Cleaning and Preparation
7. Data Analysis and Aggregation
8. Advanced Data Manipulation Techniques
9. Visualization with Pandas and Matplotlib
10. Practical Examples and Case Studies
11. Recap and Review

Key Results

After completing this course, you will be able to:

• Understand what Pandas and NumPy are and their role in data analysis
• Perform basic to advanced data manipulations using Pandas DataFrames
• Import and export data from/to different formats like CSV, Excel, and YAML
• Clean, prepare, and analyze datasets
• Combine datasets and perform complex data operations
• Visualize data using Pandas integration with Matplotlib

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What you’ll need

To follow along with this course, you will need:

• A computer with Python installed
• Familiarity with basic Python syntax
• A text editor or an IDE for writing Python code
• Internet connection to install Pandas and NumPy packages

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How the course works

Each lesson is structured to provide a comprehensive guide on different aspects of data manipulation with Pandas and NumPy. Concepts are explained in a step-by-step manner, accompanied by examples and practical exercises.

New terms and concepts are highlighted and explained in detail, like DataFrame and Series.

Important tips and notes are provided in highlighted sections like below:

Pro Tip

Useful tips and best practices are shared in sections like this

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Caution

Key points to be cautious about are highlighted in different colored sections

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This course is designed to be interactive and hands-on. We encourage you to practice the examples and exercises provided in each lesson. If you have any questions or need assistance, please reach out through the comments section or contact us directly. Let’s dive into the world of data manipulation with Python!

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