Top 11 Data Visualization Libraries in Python.

Data is crucial and understanding it correctly makes more results depended that most certainly can do with visualization with python libraries easily.

Python is rapidly growing and functioning in wide areas of various businesses, technologies, and a variety of industries.

It gives several ways and modules to implements and develops massive areas of business application and analytics support.

Python is utilized for web development, AI application, analytics, prediction and recommendation, data visualization, and many more.

Here we are going to focus on data visualization and reporting application and tools which are extensively used in python.

There are various data visualization and analytics tools and applications that are getting used every day but focusing on the right one is the wise key factor.

Top Data Visualization Libraries in Python as Follows:

Here We are going the explore the python top data visualization and reporting libraries and tools in the following Steps.

1. Matplotlib

It is the graphical visualization, plotting, and charting library in python and as per data scientists, it is the best library for data visualization in Python programming.

Matplotlib supports various graphic user interface toolkits like GTK, Tkinter, QT, etc. for general-purpose API action for plot and charts interfacing and interaction to multiple applications.

Matlab provides the numerical and matrix operation in python with the help of its extension module called NumPy.

Similarly, it gives the Matlab-like interface with the help of the Pyplot module in python for various MATLAB-based operations and visualization.

The first version of matplotlib was 1.2 in python 2 till now it is developed to the 2.0x version in python 3 with several new advancements.

2. Seaborn

In python is the second most popular data visualization and statistical plotting library developed based on matplotlib library.

Seaborn is a more advance, dynamic and interactive python library for various types of data like categorical and numerical, and other.

It uses for basic to advance level of data visualization with several types of dynamic charts and plots including line and multiple forms of bar charts, statistical charts like boxplot or scatterplot. and so on.

Seaborn utilized and integrated with pandas and NumPy and other data analytics libraries for data analysis and visualization in Python.

3. Geoplotlib

The geoplotlib visualization library and API are stimulated from the first and well-known plotting and charting programming model called matplotlib.

In the geoplotlib module visualization process, you can add multiple layers of graphics to empty canvas to create a more interactive canvas and the real rendering occurs when the show method is called.

The geoplotlib library renders several charting built-in operations and deep visualization techniques like shapefiles format creations, geographical shapes, and convex hull rendering, etc.

Similarly, geoplotlib can do multiple plottings including the dot density map generation, customization of on map designs a two-dimensional histogram, building a kernel density graph, and many more.

So at the last, the geoplotlib is the best geological plotting and map building library in python for exact geological data analysis and visualization.

4. Plotly

Plotly.py is the best visualization library for online and offline web analytics application designing and development in python.

plotly.py is a web-based open-source library and tool developed in python based on plotly.js as a backend for interactive browser rendering.

Similar to the plotly.js and R programming Plotly package it supports several types of interactive charts and more than 30 chart types, that can do mathematics and statistical plotting, financial and scientific charting, 3D or SVG graphs and maps, and many more.

It can be used to design, play, and stores the motion charts of a large number of reports and dashboards in a cloud environment for present/future processing and analytics.

5. Bokeh

Bokeh is a Python visualization web efficient tool that allows you to develop multiple statistical charts with simple code.

The benefits of using the Bokeh library for visualization in python can render the output in multiple forms including HTML, notebook, and others.

Similarly, it can easily integrate with various programming modules for java and python includes flask and Django application’s visualization.

Bokeh can help to transform the created visualization code in different libraries like seaborn, matplotlib, and ggplot to the bokeh.

6. Pygal

Pagal is another python visualization library that can serve quality images during data plotting because it provides purpose the SVG form of charts and files as result without losing image quality.

Pagal is the only sexy data visualization tool as the developer said and most certainly proves this because similar to plotly.py it helps for web application visualization and integration benefits.

It supports several types of interactive and rendering styles with multiple customizations using google font, CSS and HTML for web interaction.

7. GGplot

ggplot is the python visualization library that is first developed and use in R programming as ggplot2 then utilized in python and getting more popular now because of its wide range of functionality.

ggplot means the grammar of the graphics plot which is purposely developed to make the statistical visualization and analysis more compelling.

It can serve the multiple layers of visualization functionality with convenient usage of charts and their subparts in the complete visualization process.

It gives multiple ways to customized the charts to make visualization more robust and attractive using high-level API.

Similar to other data visualization libraries in python Ggplot can easily unite with pandas and other data manipulation libraries to make highly reliable analytics.

8. Altair

Altair is based on Vega which is define the visualization grammar and Vega-Lite which defines the high-level grammar for the interactive graphics both make the Altair the best declarative statistical data visualization library in python.

Altair uses vega definitions which is the declarative language operation like creating, maintaining, and distributing interactive visualization designs, describe ing the visual looks and interactive form of plotting in a JSON format by creating web-based designs using SVG or Canvas formats.

Similarly, it uses the Vega-Lite for rendering the graphics based on high-level grammar to generate the declarative JSON syntax and design an artistic range of charting for data analysis and presentation.

It also uses vega-lite JSON specification to makes Altair library and its API more simple, friendly, and consistent.

Altair gives a convenient way to explore and understand your data and its significant application using visualization.

On the other hand, the process of visualization and plotting process is very simple with elegant simplicity designs and powerful visualizations with a small amount of code.

9. Leather

Leather is the Python plotting and visualization library for people who need instant charts without any perfection as per the creator of the library.

It is for rough and instant visualization in python that don’t care about more interactive and dynamic views whereas it makes dirty views with more roughness still look sexy without any plotting accessories.

Leather is good for exploratory analysis or plotting and it Presents more extended scale-independent SVG graphs and designs for the web also using simple and user-friendly API.

However, leather python shows various facts for visualization including you can design the code with Jupyter notebook, iPython, and atom/hydrogen, etc.

It is developed Pure in python without any C dependencies for code compilation and execution whereas it is under MIT licensed and open-source which is free to use for all purposes.

10. Missingno

At the time of data understanding and manipulation, you need the right exploratory analysis libraries and techniques to make the right sense of data.

Python can provide a variant of EDA libraries to make the analysis very easy like the Missingno library which performs the elegant role to shows the missing values from data in visualized ways.

It brings one step forward to present the exact data distribution of NA values from the DataFrame with the informational visualizations.

With the help of Missingno, you can easily find the position of missing data and its exact location in each column.

It gives several types of plots like matrix, heatmap, bar chart, scatter plot, and others to check the correlation, missing values, and variance in data.

Folium

Folium the data visualization python library developed for the integration of maps to web pages using JSON code and geological plotting.

folium library in python delegates the process to visualize data easily which has been manipulated in Python on an interactive leaflet map.

This library has a number of in-build tilesets to plot Mapbox, OpenStreetMap, Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys.

folium visualization library can do other image and video-related tasks like it support different types of overlays like Video, images, GeoJSON, and TopoJSON, etc.

Conclusion

Python is the more vibrant and elegant programming language in the modern era and it is utilized for serval types of data analytics and web development application.

Data visualization and charting play a very important role in python to integrate the plotting and charting functionality with several other python libraries and packages.

The above top library in python is cruciality implements for a variety of data visualization and analytics operation.

Grabbing the right library for data visualization as per your interest and expected data can possible in python only which can make your work more reliable.

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