Famous Python Libraries for Data Visualization

1. Matplotlib

Chart's Type: Static Inspired by Matlab’s plotting functionality. Very commonly used by researchers to create high-quality publication charts. We can link charts to "ipywidgets" widgets (dropdown, checkbox, etc.) in Jupyter Notebook to add interactivity. Supports animation.

2. Bokeh

Chart's Type: Interactive Bokeh is a javascript-based library that let us create interactive charts in Jupyter Notebooks. It supports addition of custom Javascript to support specialized cases. Bokeh also provides various widgets and dashboarding functionalities. It is good at handling streaming data.

3. Plotly

Chart's Type: Interactive Supports a very vast majority of charts covering many different areas. Its plotly express version let us create charts with few lines of code. It also provides us with dashboarding functionality through "dash" library. Dash enterprise version supports creation of heavy AI/ML application dashboards.

4. Bqplot

Chart's Type: Interactive Bqplot is 2D visualization system for Jupyter Notebooks. It is built on top of "d3.js" and "ipywidgets" by Bloomberg developers. Individual components of chart are widgets internally. Supports matplotlib-like API (Pyplot API) as well as grammar of Graphics-based API (Object Model API) to create a chart.

5. Plotnine

Chart's Type: Static Library based on grammar of Graphics concept. The grammar of graphics allows us to compose chart components (Axes, titles, etc) individually and combine them to create a chart. Built on top of "Matplotlib" . Inspired by famous data visualization library "ggplot2" of R Programming.

6. Altair

Chart's Type: Interactive Altair is an interactive data visualization library based on Vega and Vega-Lite (Declarative Languages specifying Grammar of Interactive Charts). It supports many statistical visualizations. Internally all visualizations are represented using Vega /Vega-lite JSON schema.

7. Chartify

Chart's Type: Interactive Chartify is a small data visualization library built on the top of "Bokeh" . It is developed by Spotify . The main aim of library is to make charting easier so that more time can be spent on data analysis. It has an intuitive API and a default style for charts.

8. Seaborn

Chart's Type: Static Seaborn is a statistical data visualization library built on top of "matplotlib" . It let us better explore relationships between our data variables with few lines of code. Stat visualizations like hist plot, box/violin plot, hex plot, KDE plot, heatmap, swarm plot, etc.

9. Holoviews

Chart's Type: Static & Interactive Open-source visualization library from Anaconda. Creates charts using either "matplotlib" , "bokeh" or "plotly" as a backend. We need to provide chart details with few lines of holoviews code and it’ll create a chart using the specified backend. Let us specify chart details using Notebook magic commands .

10. Pandas Viz

Chart's Type: Static Pandas is most preferred tool to maintain/manipulate structured data in Python. It provides basic visualization functionality using "plot()" method of dataframe. We cap specify chart type using "kind" parameter. Supports a few statistical charts through "plotting" sub-module. Creates charts using "matplotlib" behind the scene.

11. Cufflinks

Chart's Type: Interactive Cufflinks is another visualization library designed on top of "plotly" . Let us create charts from Pandas dataframe (using just one line of code) by calling "iplot()" or "figure()" method. Works exactly like pandas "plot()" method but creates interactive charts.

12. Hvplot

Chart's Type: Interactive Hvplot let us create interactive charts directly from pandas dataframe. Designed on top of "holoviews" (uses "matplotlib" , "bokeh" and "plotly" as backend). We just need to import "hvplot.pandas" and all further calls to "plot()" method on dataframe will generate interactive charts.

13. Pandas-Bokeh

Chart's Type: Interactive Pandas bokeh let us create interactive visualizations from pandas dataframe with just one line of code. We need to set the plotting backend as "pandas_bokeh" first. Then, we can call "plot_bokeh()" method on the pandas dataframe to create charts.

Few Other Libraries

Pygal (Interactive Charts) Toyplot (Interactive Charts) Lets-plot (Interactive Charts) Autoviz (Interactive Charts) Proplot (Static Charts) Biggles - Scientific Charts (Static Charts) Chart (Static Charts) Visit PyViz for a comprehensive list of Python data visualization libraries.

Feel free to visit CoderzColumn Data Science section to learn about these libraries through simple tutorials.