Mastering Matplotlib: A Step-by-Step Tutorial for Beginners

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  • Altair

    Declarative statistical visualization library for Python

  • Altair - Declarative statistical visualization library for Python.

  • bokeh

    Interactive Data Visualization in the browser, from Python

  • Bokeh - Interactive Web Plotting for Python.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • bqplot

    Discontinued Plotting library for IPython/Jupyter notebooks [Moved to:] (by bloomberg)

  • bqplot - Interactive Plotting Library for the Jupyter Notebook.

  • Cartopy

    Cartopy - a cartographic python library with matplotlib support

  • Cartopy - A cartographic Python library with matplotlib support.

  • PyQtGraph

    Fast data visualization and GUI tools for scientific / engineering applications

  • PyQtGraph - Interactive and realtime 2D/3D/Image plotting and science/engineering widgets.

  • diagrams

    :art: Diagram as Code for prototyping cloud system architectures

  • diagrams - Diagram as Code.

  • cheatsheets

    Official Matplotlib cheat sheets (by matplotlib)

  • Matplotlib - A Python 2D plotting library.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • plotnine

    A Grammar of Graphics for Python

  • plotnine - A grammar of graphics for Python based on ggplot2.

  • seaborn

    Statistical data visualization in Python

  • Seaborn - Statistical data visualization using Matplotlib.

  • VisPy

    Main repository for Vispy

  • VisPy - High-performance scientific visualization based on OpenGL.

  • Matplotlib

    Matplotlib notes and examples (by mritunjaysri28)

  • ![Matplotlib_25_1]( ## Scatter Graph * It similar to a line graph * Used to show how one variable is related to another * It consists of data points, if it is linear then it is highly correlated * It only marks the data point. * Syntax: plt.scatter(x,y) ### Parameter of Scatter Graph * c: Sets color of markers. * s: Sets the size of markers. * marker: Select a marker. e.g.: circle, triangle, etc * edgecolor: Sets the color of lines on the edges of markers. ```python x=[1,4,6,8,2] y=[10,10,1,5,0] fig = plt.figure(figsize=(8,4)) ax = fig.add_subplot() ax.scatter(x , y , c='red' , s=500 , marker='*' , edgecolor='blue' , label='scatter') ax.set_title('Scatter') ax.set_xlabel('X-Axis') ax.set_ylabel('Y-Axis') plt.legend()

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