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}
}