2D Plotting with Matplotlib

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prerequisites
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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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.

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.

prerequisites

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:

TOOLS
  • 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
TECHNIQUES
  • 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

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