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# Data-Visualization-with-Python Poster.PNG [Poster.PNG] 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](https://pip.pypa.io/en/stable/) to install below ```bash pip install matplotlib pip install seaborn pip install plotnine pip install plotly pip install bokeh ``` # Brief about Libraries: ## Matplotlib: matplotlib.png [matplotlib.png] - *Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.* |No |Topics |Code Link | |---|-------|--------| |1|Basic Plotting|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Matplotlib_plots/1-Basic%20Plotting.ipynb)| |2|Line_and_color_style|[Code]|(https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Matplotlib_plots/2-Basic_color_and_Line_styles.ipynb)| |3|Plot_with_line_styles|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Matplotlib_plots/3-Simple_Line_Plots_with_Line_Styles.ipynb)| |4|Scatter_Plots|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Matplotlib_plots/4-Simple_Scatter_Plots.ipynb)| |5|Density_and_Contour_Plots|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Matplotlib_plots/5-Density_and_Contour_plots.ipynb)| |6|Histograms_and_Binnings|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Matplotlib_plots/6-Histograms_and_Binnings.ipynb)| |7|Customizing_legends|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Matplotlib_plots/7-Customizing_legends.ipynb)| ## Seaborn Seaborn.png [Seaborn.png] - *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](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Seaborn_Visz/Quick-Into.ipynb)| |2|Categorical|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Seaborn_Visz/2-Categorical.ipynb)| |3|Distribution_plot|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Seaborn_Visz/3-Distribution-plot.ipynb)| |4|Regression_Plots|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Seaborn_Visz/4-Regression-Plots.ipynb)| |5|Matrix_Plots|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Seaborn_Visz/5-Matrix-plot.ipynb)| |6|Multi_Plot|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Seaborn_Visz/6-Multi-plot.ipynb)| ## Plotnine readme-image-5.png [readme-image-5.png] - *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](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotnine/1-Intro.ipynb)| |2|Stage|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotnine/stage.ipynb)| |3|Scale_x_Continuous|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotnine/scale_x_continuous.ipynb)| |4|After_Scale|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotnine/after_scale.ipynb)| |5|Facet_grid|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotnine/facet_grid.ipynb)| |6|Facet_Wrap|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotnine/facet_wrap.ipynb)| ## Bokeh bokeh.png [bokeh.png] - *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](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Bokeh_tuts/01%20-%20Basic%20Plotting.ipynb)| |2|Styling_and_Theming|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Bokeh_tuts/02%20-%20Styling%20and%20Theming.ipynb)| |3|Data_sources_and_transformations|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Bokeh_tuts/03%20-%20Data%20Sources%20and%20Transformations.ipynb)| |4|Adding_Annotations|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Bokeh_tuts/04%20-%20Adding%20Annotations.ipynb)| |5|Presentations_Layout|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Bokeh_tuts/05%20-%20Presentation%20Layouts.ipynb)| |6|Linking_and_Interactions|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Bokeh_tuts/06%20-%20Linking%20and%20Interactions.ipynb)| ## Plotly plotly.PNG [plotly.PNG] - *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](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotly/1-First_Steps.ipynb)| |2|Line_Plots|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotly/2-line_plots.ipynb)| |3|Bar_Charts|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotly/3-BarChart..ipynb)| |4|Pie_Charts|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotly/4-Pie_Charts.ipynb)| |5|Sunburst|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotly/5-Sunburst.ipynb)| |6|Bubble_chart|[Code](https://github.com/rojaAchary/Data-Visualization-with-Python/blob/main/Plotly/6-BubbleChart..ipynb)| ## 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 added_animations.gif [added_animations.gif] ## Want to Stay Updated !! Fork the repository ## Learned Something !! Give a to support me ```bibtex @misc{Charged Neuron, author = {Roja Achary}, title = {Data Visualisation with Python}, Credits = {GfG,websites} month = {August}, year = {2021} } ```
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