Programming, Python

Top 7 Data Visualization Libraries in python for Data Science

A picture is worth thousands of rows and columns.

Every business collects data about their product, customers, services, and whatnot. Only collecting data never helps any business, one has to draw meaning build context around the data. Data scientists are well accustomed to seeing large piles of rows and columns but to communicate with a person who is only interested in the results derived from the data, the rows and columns must be transformed into something which is intuitive and digestible even by a layman.

Converting your data into a visualization such as graphs, charts, animation, etc. adds context to the data and makes it more readable, and accelerates the decision-making process. With visualizations, it becomes easier to recognize the pattern and interpret it cognitively.

Data visualization can help you visualize

  • Correlation and Relationships
  • trends over time
  • Frequency
  • Examining the market
  • Risk and reward
  • Reaction to the market
  • side by side comparison

Data Visualization libraries in python

Altair

Altair is a declarative statistical visualization library for Python. With Altair, you can spend more time understanding your data and its meaning. Altair’s API is simple, friendly and consistent, and built on top of the powerful Vega-Lite JSON specification. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. Altair is developed by Jake Vanderplas and Brian Granger in close collaboration with the UW Interactive Data Lab.

Beside data visualization Altair can make the visualization interactive for example, in the below upon selecting a area in the scatterplot a linked histogram is generated which is filtered based on the selection.

import altair as alt
from vega_datasets import data

source = data.cars()

brush = alt.selection(type='interval')

points = alt.Chart(source).mark_point().encode(
    x='Horsepower',
    y='Miles_per_Gallon',
    color=alt.condition(brush, 'Origin', alt.value('lightgray'))
).add_selection(
    brush
)

bars = alt.Chart(source).mark_bar().encode(
    y='Origin',
    color='Origin',
    x='count(Origin)'
).transform_filter(
    brush
)

points & bars
Altair Interactive

Altair Example Gallery

Altair Github

Bokeh

Bokeh is a modern web browsers based interactive visualization library. It provides elegant, concise construction of versatile graphics, and delivers high-performance interactivity over large or streaming datasets. Bokeh can be used by anyone who want to make interactive plots, dashboards, and data applications.

Bokeh gallery

Bokeh Guide

Bokeh Github

Bqplot

bqplot is a 2-D visualization system for Jupyter, based on the constructs of the Grammar of Graphics.

In bqplot, every component of a plot is an interactive widget. This allows the user to integrate visualizations with other Jupyter interactive widgets to create integrated GUIs with a few lines of Python code.

Features:

  • Provide a unified framework for 2-D visualizations with a pythonic API
  • Provide a sensible API for adding user interactions (panning, zooming, selection, etc)
Wealth of Nations
bqplot example

bqplot documentation

bqplot github

Dash

Dash is a productive Python framework for building web analytic applications.

Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It’s particularly suited for anyone who works with data in Python.

Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build an interactive web-based application. Dash is simple enough that you can bind a user interface around your Python code in an afternoon.

Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready.

It offers a open source as well as enterprise solution.

Financial report
Dash Example

Dash Opensource

Dash Documentation

Dash github

Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.Develop publication quality plots with just a few lines of code. With matplotlib can be use to create interactive figures that can zoom, pan, update .Matplotlib gives you full control over of line styles, font properties, axes properties and other elements of the plot , you can Export, Embed to a number of file formats and interactive environments.

Varying Density, Varying Color, Varying Line Width, Controlling Starting Points, Streamplot with Masking
Matplotlib Example

Matplotlib documentation

Matplotlib github

Matplotlib Gallery

Seaborn

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them

../_images/layered_bivariate_plot.png
Seaborn Example

Seaborn Github

Seaborn Documentation

Pygal

pygal is a dynamic SVG charting library written in python.

Pygal produces SVG graphs which means it create vector graphics which can be scaled infinitely and embedded in a webpage using less space as compared to png and jpeg files.

Pygal Documentation

Pygal Github

Pygal Documentation

Which one do you prefer write in comments.


Thank you for reading, Happy Learning, drop your suggestion in the comments.

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