I. Seaborn
Seaborn is built on top of the matplotlib library. It has many built-in functions using which beautiful plots can be created with simple lines of code. It provides a variety of advanced visual plots and simple syntax, such as box plots, violin plots, distance plots, joint plots, pairwise plots, heat maps, etc.
mounting
ip install seaborn
Key Features:
- can be used to determine the relationship between two variables.
- Distinctions are made when analyzing univariate or bivariate distributions.
- Plot a linear regression model for the dependent variable.
- Provides multi-grid plotting
Draw beautiful shapes with just a few lines of simple code
official document
/
II. Plotly
Plotly is an advanced Python analytics library that helps build interactive dashboards. Graphs built with Plotly are interactive, which means you can easily find the value of any particular point or session of the graph.Plotly makes it very easy to generate dashboards and deploy them on the server. It supports the Python, R, and Julia programming languages.
Plotly code for making simple scatterplots:
official document
/
III. Geoplotlib
Geoplotlib is a Python toolkit for visualizing geographic data and making maps. You can create a variety of maps using this library. Some sample maps you can create with it include heat maps, point density maps, geographic maps, and more.
mounting
pip install geoplotlib
github documentation
/andrea-cuttone/geoplotlib/wiki/User-Guide
IV. Gleam
Gleam is inspired by R's Shiny package. It allows you to turn graphics into great web applications using only Python code. This is helpful for people who don't know HTML and CSS. It is not really a visualization library, but works with any visualization library.
github documentation
/dgrtwo/gleam
V. ggplot
ggplot works differently than matplotlib. It allows you to add multiple components as layers to create a complete graph or plot at the end. For example, at the beginning you can add an axis, then add points and other components such as trend lines.
%matplotlib inline from ggplot import * ggplot(diamonds, aes(x='price', fill='clarity')) + geom_histogram()
github documentation
/tidyverse/ggplot2
VI. Bokeh
The Bokeh library was created by Continuum Analytics to generate visualizations that are friendly to web interfaces and browsers.The visualizations that Bokeh generates are inherently interactive, allowing you to convey more information.
# Bokeh Libraries
from import output_file
from import figure, show
# The figure will be rendered in a static HTML file called output_file_test.html
output_file('output_file_test.html',
title='Empty Bokeh Figure')
# Set up a generic figure() object
fig = figure()
# See what it looks like
show(fig)
official document
/en/latest/
VII. Missingo
Data science is all about finding useful information from given data and making it visible to all. The best way to do this is to visualize the data. For all the data scientist enthusiasts, this package could be a craze. It helps you find all the missing values and display them in a nice graphical way in a real-world dataset without headaches and with a single line of code. It supports graphical representations such as bar charts, graphs, heatmaps, tree diagrams, etc.
# Importing Necessary Libraries
import pandas as pd
import missingno as mi
# Reading the Titanic dataset (From Local Env)
data = pd.read_csv("")
# Checking missing values Using ()
print(().sum()) ## It will display a table with all the missing values
### The best practice is to visualize this so that everyone even a non-tech person
### can understand and find the missing values, Let's use the `missingno` package
#Visualizing using missingno
print("Visualizing missing value using bar graph")
print((data, figsize = (10,5)))
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