https://github.com/aqueeqazam/matplotlib-for-data-science-analysis-and-statistics
With a nice mix between customization options and ease of use, Matplotlib is a robust Python library that can be used by both novice and seasoned data scientists and machine learning engineer to create a wide range of representations.
https://github.com/aqueeqazam/matplotlib-for-data-science-analysis-and-statistics
data-science data-visualization machine-learning matplotlib pyplot statistics
Last synced: 8 months ago
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With a nice mix between customization options and ease of use, Matplotlib is a robust Python library that can be used by both novice and seasoned data scientists and machine learning engineer to create a wide range of representations.
- Host: GitHub
- URL: https://github.com/aqueeqazam/matplotlib-for-data-science-analysis-and-statistics
- Owner: AqueeqAzam
- Created: 2024-06-09T15:13:22.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-19T20:30:08.000Z (almost 2 years ago)
- Last Synced: 2025-06-04T17:10:32.290Z (about 1 year ago)
- Topics: data-science, data-visualization, machine-learning, matplotlib, pyplot, statistics
- Language: Jupyter Notebook
- Homepage: https://matplotlib.org/
- Size: 194 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
Matplotlib is a powerful Python library for creating all sorts of visualizations. It's a popular choice for both beginners and experienced data scientists because it offers a good balance of ease of use and customization.
Here's a quick rundown of what Matplotlib can do:
Create many common chart types: Line plots, scatter plots, bar charts, histograms, pie charts, and more are all easily created with Matplotlib.
Customization: You have a lot of control over the look and feel of your plots. You can change things like line styles, colors, markers, labels, and even add annotations.
Seamless integration with NumPy: Since data science tasks often involve working with numerical data stored in NumPy arrays, Matplotlib integrates well with NumPy, making it easy to plot data directly from these arrays.