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Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram or map.

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Data-Visualization-with-Python ✨

Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram or map.

  • Identify trends and outliers
  • Tell a story within the data
  • Reinforce an argument or opinion
  • Highlight an important point in a set of data

Libraries required

Use the package manager pip to install below

pip install matplotlib
pip install seaborn
pip install plotnine
pip install plotly
pip install bokeh

Brief about Libraries:

Matplotlib:

  • Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
No Topics Code Link 🔗
1 Basic Plotting Code
2 Line_and_color_style [Code]
3 Plot_with_line_styles Code
4 Scatter_Plots Code
5 Density_and_Contour_Plots Code
6 Histograms_and_Binnings Code
7 Customizing_legends Code

Seaborn

  • Seaborn harnesses the power of matplotlib to create beautiful charts in a few lines of code. The key difference is Seaborn's default styles and color palettes, which are designed to be more aesthetically pleasing and modern. Since Seaborn is built on top of matplotlib, you'll need to know matplotlib to tweak Seaborn's defaults.
No Topics Code Link 🔗
1 Quick_Intro Code
2 Categorical Code
3 Distribution_plot Code
4 Regression_Plots Code
5 Matrix_Plots Code
6 Multi_Plot Code

Plotnine

  • plotnine is an implementation of a grammar of graphics in Python, it is based on ggplot2. The grammar allows users to compose plots by explicitly mapping data to the visual objects that make up the plot.
No Topics Code Link 🔗
1 Intro Code
2 Stage Code
3 Scale_x_Continuous Code
4 After_Scale Code
5 Facet_grid Code
6 Facet_Wrap Code

Bokeh

  • Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.
No Topics Code Link 🔗
1 Basic_Plotting Code
2 Styling_and_Theming Code
3 Data_sources_and_transformations Code
4 Adding_Annotations Code
5 Presentations_Layout Code
6 Linking_and_Interactions Code

Plotly

  • plotly is an interactive, open-source, and browser-based graphing library for Python Built on top of plotly.js, plotly.py is a high-level, declarative charting library. plotly.js ships with over 30 chart types, including scientific charts, 3D graphs, statistical charts, SVG maps, financial charts, and more.
No Topics Code Link 🔗
1 First_Steps Code
2 Line_Plots Code
3 Bar_Charts Code
4 Pie_Charts Code
5 Sunburst Code
6 Bubble_chart Code

What you will learn

✅ Get an overview of various plots.
✅ Work with different plotting libraries and get to know their strengths and weaknesses.
✅ Learn how to create insightful visualizations.
✅ Understand what makes a good visualization.
✅ Improve your Python data wrangling skills.
✅ Learn the industry standard tools.
✅ Develop your general understanding of data formats and representations.

Samples of Plots

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@misc{Charged Neuron,
    author       = {Roja Achary},
    title        = {Data Visualisation with Python},
    Credits      = {GfG,websites}
    month        = {August},
    year         = {2021}
}

About

Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram or map.

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