Data Visualization

2D Plotting with Matplotlib you own this product

This project is part of the liveProject series Data Visualization with Matplotlib and Seaborn
basic to intermediate Python 3 programming skills • familiarity with Anaconda Python • familiarity with Jupyter Notebook • basic NumPy • basic pandas
skills learned
use import statement to load required libraries and modules into Jupyter Notebook • use pandas functions to read data from CSV files into a data frame object • use pandas functions to subset required rows and columns for plotting • use Matplotlib API/functions to create, customize, and save plots
Nimrita Koul
1 week · 6-8 hours per week · BEGINNER
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liveProject This project is part of the liveProject series Data Visualization with Matplotlib and Seaborn liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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ABC Bikes Inc. is considering launching a bike rental service in your area. As the company’s data analyst, your task is to provide decision-driving insights on bike rental trends. You’ll extract the relevant data from a publicly available dataset into a pandas data frame. Then, using Python plotting library Matplotlib, you’ll create line plots to visualize changes for bike rentals on a single day as well as over a specific time period, create a grouped bar plot to visualize a comparison of renting data for two years, create subplots with different chart types, and create a violin plot to visualize renting patterns over four seasons. When you’re done, you’ll know how to use Matplotlib to create accurate and informative 2D visualizations.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book and video resources

When you start your liveProject, you get full access to the following books and videos for 90 days.

project author

Nimrita Koul

Nimrita Koul is an assistant professor of Computer Science and Machine Learning from Bangalore, India. She holds a Phd in Computer Science with 16 years of experience in teaching Computer Science courses to University undergraduates. Nimrita is a principal investigator for three funded research projects in machine learning. She has designed and demonstrated about 20 data science and machine learning projects to more than 650 students. Nimrita has spoken at multiple international conferences and events.


This liveProject is for readers interested in enhancing their data analysis capabilities with effective visualizations using Python’s Matplotlib library. To begin these liveProjects you’ll need to be familiar with the following:

  • Intermediate Python 3 Programming (loops, basic debugging, and built-in functions including: zip(), sorted(), and lambda functions)
  • Familiarity with Jupyter Notebook under Anaconda Distribution
  • Basic NumPy and pandas
  • Use module names to call functions
  • Install Anaconda Distribution of Python (preferred)
  • Install/upgrade Jupyter Notebook (if not using Anaconda Distribution)
  • Install required Python Libraries (NumPy, pandas, Matplotlib, seaborn using pip or conda)
  • Add, delete, move, and select cells (code and markdown) in Jupyter Notebook
  • Execute a code cell in Jupyter Notebook

you will learn

In this liveProject, you’ll use Matplotlib to create line, bar, and violin plots to visualize trends.

  • Initialize the required style for plotting
  • Read data from a CSV file into a pandas dataframe
  • Data wrangling: subset specific rows and columns, parse date datatype
  • Group data by specific column
  • Create plots using object-oriented Matplotlib interface
  • Customize various properties of your plots: color, marker size, font size, font name
  • Format x and y axis ticks and labels for your plots
  • Change the color scheme of your plot
  • Annotate your plot with useful information
  • Set the resolution of your plots before saving
  • Export/save your plots as image files and PDF files on your hard drive with the
  • specified resolution
  • Save multiple plots in a single PDF file
  • See available plot styles and change the Matplotlib plot style for your project
  • See available backends and change the backend for Matplotlib


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book resources
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