Matplotlib Unlocked
Matplotlib Unlocked
Creating Beautiful Charts in Python
About the Book
Matplotlib Unlocked: Creating beautiful Charts in Python is a comprehensive resource for learning and mastering the Matplotlob library for Data Visualization and Insights in Python. You will learn the tips and best practices for creating charts in Matplotlib.
Whether you are taking your first step in data visualization or are already an advanced user looking for in-depth knowledge of Matplotlib's capabilities, this book has something for everyone.
What sets this book apart is its approach to teaching. Rather than merely walking you through the functions and features of Matplotlib, it empowers you by:
- Offering hands-on tutorials and exercises that teach you how to apply what you learned so that you become more confident in using Matplotlib for your data visualization tasks.
- Instead of using fake or dummy data, you will learn from practical, real-world data and apply those skills to find hidden insights.
- You will learn to create charts incrementally. You will start from scratch, and as you progress through more chapters and exercises, you will learn how to add more layers to your charts for complex visualization.
- You will learn all the skills required to create, modify, and present the data that most impacts your audience.
If you ever felt intimidated by the Matplotlib library, join me on this journey. I will show you how to master it and earn all the praise you deserve for your work.
Happy visualizing! May your data tell compelling stories.
About the Author
Table of Contents
- Chapter 1. Introduction
- What is matplotlib?
- Why you should read this book?
- Prerequisites
- Installation
- Download code
- Jupyter notebook
- Chapter 2. Line charts
- Read data
- Create a line chart
- Title and axis labels
- Multiple line charts
- Legend
- Line style and color
- Style sheets
- Figure size
- Save a figure
- Exercise 2.1
- Summary
- Solution
- Chapter 3. Scatter plots
- Create a scatter plot
- Marker style
- Marker color
- Marker size
- Color bar
- Scatter plot with legend
- Transparent markers
- Exercise 3.1
- Summary
- Solution
- Chapter 4. Bar charts
- Create a bar chart
- Vertical bar chart
- Grid lines
- Width of the bars
- Horizontal bar chart
- Exercise 4.1
- Exercise 4.2
- Stacked bar chart
- Exercise 4.3
- Grouped bar chart
- Exercise 4.4
- Summary
- Solution
- Chapter 5. Histograms
- Create a histogram
- Bin size
- Color
- Multiple histograms
- Histogram types
- Density histogram
- Orientation
- 2D histogram
- Exercise 5.1
- Summary
- Solution
- Chapter 6. Box plot
- Create a box plot
- Multiple box plots
- Colors
- Notched box plot
- Outliers
- Remove outliers
- Horizontal box plot
- Exercise 6.1
- Summary
- Solution
- Chapter 7. Axes and Subplots
- Figure, axes, axis, and subplots
- Figure
- Axes
- Axis
- Subplots
- Matplotlib application interface
- Pyplot Interface
- Object-oriented interface
- plt.subplot() vs plt.subplots()
- Exercise 7.1
- Summary
- Solution
- Chapter 8. Arranging Multiple Axes in a Figure
- Creating grids
- 1x2 grid
- 2x1 grid
- 2x2 grid
- Axes spanning rows or columns in a grid
- Variable widths and heights in a grid
- Nested axes layouts
- Exercise 8.1
- Exercise 8.2
- Exercise 8.3
- Solution
- Chapter 9. Axis Scales and Ticks
- Linear Scale
- Logarithmic Scale
- Symmetrical Logarithmic Scale
- Custom Scale
- Other Scales
- Tick Locations and Labels
- Locators and Formatters
- Styling Ticks
- Chapter 1. Introduction
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