Python Pandas for Beginners: Pandas Specialization for Data Scientist

✍ Scribed by AI Publishing

• Tongue: English
• Leaves: 548
• Category: Library

✦ Synopsis

Python NumPy & Pandas for Beginners

Python Libraries Textbook for Beginners with Codes Folder

Python is doubtless the most versatile programming language.
But are you serious enough about becoming proficient in Python?
If yes, then you need to become a master in the two essential Python libraries—NumPy and Pandas. You simply can’t overlook this truth.
In data science, NumPy and Pandas are by far the most widely used Python libraries. The main features of these libraries are powerful data analysis tools and easy-to-use structures.
Python NumPy Pandas for Beginners presents you with a hands-on, simple approach to learning Python fast. This book is refreshingly different, as there’s a lot for you to do than mere reading. Each theoretical concept you cover is followed by practical examples, making it easier to master the concept.
The step-by-step layout of this book simplifies your learning. The author has gone to great lengths to ensure what you learn sticks. You have short exercises at the end of each one of the 11 chapters to test your knowledge of the theoretical concepts you have learned.
This book presents you with:

&

• A strong foundation in Pandas.
• A deep understanding of fundamental and intermediate topics.
• The essentials of coding in Python.
• Links to reference materials related to the topics you study.
• Quick access to external files to practice and learn advanced concepts of Pandas.
• A Resources folder containing all the datasets used in the book.

The Focus of the Book Is on Learning by Doing

In this learning by doing book, you start with Python installation in the very first chapter. Then there’s a crash course in Python in the second half of the first chapter. In the second chapter, you jump straight to NumPy. Right through the book, you’ll use Jupyter Notebook to write code. You can also get fast access to the datasets used in this book.
The book is loaded with self-explanatory scripts, graphs, and images. They have been meticulously designed to help you understand new concepts easily. Hence, this book is the best choice for self-study, even if you are proficient in Python.
You can tackle new data science problems confidently and develop workable solutions in the real world. Finally, you can rely on this learning by doing book to achieve your Python career goals faster.

This book will help you to quickly master the following topics:

• Environment Setup and Python Crash Course
• Pandas Basics
• Manipulating Pandas Dataframes
• Data Grouping, Aggregation, and Merging with Pandas
• Pandas for Data Visualization
• Handling Time-Series Data with Pandas
• Working with Jupyter Notebook

Hit BUY NOW and start your journey of Python mastery.

✦ Table of Contents

Title Page
Copyright
How to Contact Us
About the Publisher
AI Publishing Is Searching for Authors Like You
Table of Contents
Preface
Book Approach
Who Is This Book For?
How to Use This Book?
About the Author
Get in Touch with Us
Download the PDF version
Warning
Chapter 1: Introduction
1.1. What Is Pandas?
1.2. Environment Setup and Installation
1.2.1. Windows Setup
1.2.2. Mac Setup
1.2.3. Linux Setup
1.2.4. Using Google Colab Cloud Environment
1.2.5. Writing Your First Program
1.3. Python Crash Course
1.3.1. Python Syntax
1.3.2. Python Variables and Data Types
1.3.3. Python Operators
1.3.4. Conditional Statements
1.3.5. Iteration Statements
1.3.6. Functions
1.3.7. Objects and Classes
Exercise 1.1
Exercise 1.2
Chapter 2: Pandas Basics
2.1. Pandas Series
2.1.1. Creating Pandas Series
2.1.2. Useful Operations on Pandas Series
2.2. Pandas Dataframe
2.2.1. Creating a Pandas Dataframe
2.2.2. Basic Operations on Pandas Dataframe
2.3. Importing Data in Pandas
2.3.1. Importing CSV Files
2.3.2. Importing TSV Files.
2.3.3. Importing Data from Databases
2.4. Handling Missing Values in Pandas
2.4.1. Handling Missing Numerical Values
2.4.2. Handling Missing Categorical Values
Exercise 2.1
Exercise 2.2
Chapter 3: Manipulating Pandas Dataframes
3.1. Selecting Data Using Indexing and Slicing
3.1.1. Selecting Data Using Brackets []
3.1.2. Indexing and Slicing Using loc Function
3.1.3. Indexing and Slicing Using iloc Function
3.2. Dropping Rows and Columns with the drop() Method
3.2.1. Dropping Rows
3.2.1. Dropping Columns
3.3. Filtering Rows and Columns with Filter Method
3.3.1. Filtering Rows
3.3.1. Filtering Columns
3.4. Sorting Dataframes
3.5. Pandas Unique and Count Functions
Exercise 3.1
Exercise 3.2
Chapter 4: Data Grouping, Aggregation, and Merging with Pandas
4.1. Grouping Data with GroupBy
4.2. Concatenating and Merging Data
4.2.1. Concatenating Data
4.2.2. Merging Data
4.3. Removing Duplicates
4.3.1. Removing Duplicate Rows
4.3.2. Removing Duplicate Columns
4.4. Pivot and Crosstab
4.5. Discretization and Binning
Exercise 4.1
Exercise 4.2
Chapter 5: Pandas for Data Visualization
5.1. Introduction
5.2. Loading Datasets with Pandas
5.3. Plotting Histograms with Pandas
5.4. Pandas Line Plots
5.5. Pandas Scatter Plots
5.6. Pandas Bar Plots
5.7. Pandas Box Plots
5.8. Pandas Hexagonal Plots
5.9. Pandas Kernel Density Plots
5.10. Pandas Pie Charts
Exercise 5.1
Exercise 5.2
Chapter 6: Handling Time-Series Data with Pandas
6.1. Introduction to Time-Series in Pandas
6.2. Time Resampling and Shifting
6.2.1. Time Sampling with Pandas
6.2.2. Time Shifting with Pandas
6.3. Rolling Window Functions
Exercise 6.1
Exercise 6.2
Appendix: Working with Jupyter Notebook
Exercise Solutions
Exercise 2.1
Exercise 2.2
Exercise 3.1
Exercise 3.2
Exercise 4.1
Exercise 4.2
Exercise 5.1
Exercise 5.2
Exercise 6.1
Exercise 6.2
From the Same Publisher
Back Cover