Data visualization with different Charts in Python?

Python provides various easy to use libraries for data visualization. Good thing is that these libraries works with small or large datasets.

Some of the most commonly used python libraries for data visualizations are −

• Matplotlib

• Pandas

• Plotly

• Seaborn

Below we are going to plot different types of visualization chart for one fixed data to better analyse that data.

We are going to analyze below data set to visualize through different charts −

Country or Area	Year(s)	Variant	Value
India	2019	Medium	1368737.513
India	2019	High	1378419.072
India	2019	Low	1359043.965
India	2019	Constant fertility	1373707.838
India	2019	Instant replacement	1366687.871
India	2019	Zero migration	1370868.782
India	2019	Constant mortality	1366282.778
India	2019	No change	1371221.64
India	2019	Momentum	1367400.614

Basic Plot 

Let's create some basic plots: Line plots, scatter plots and histograms

Line Plots

Line graphs are plots where a line is drawn to indicate a relationship between a particular set of x and y values.

import matplotlib.pyplot as plt Year = [2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019] India_Population = [1173108018,1189172906,1205073612,1220800359,1266344631,1309053980,1324171354,1339180127,1354051854,1368737513] plt.plot(Year, India_Population) plt.show()

Output

Scatter Plots

Alternatively, you might want to plot quantities with 2 positions as data points.

Consider the same data as for line graph, to create scatter plots we just need to modify one line in the above code −

plt.plot(Year, India_Population,'o')

Output

Histogram

Histograms are very often used in science applications and it's highly likely that you will need to plot them at some point. They are very useful to plot distributions.

import pandas as pd import matplotlib.pyplot as plt data = [ ['India', 2019, 'Medium', 1368737.513], ['India', 2019, 'High', 1378419.072], ['India', 2019, 'Low', 1359043.965], ['India', 2019, 'Constant fertility', 1373707.838], ['India', 2019,'Instant replacement', 1366687.871], ['India', 2019, 'Zero migration', 1370868.782], ['India', 2019,'Constant mortality', 1366282.778], ['India', 2019, 'No change', 1371221.64], ['India', 2019, 'Momentum', 1367400.614],] df = pd.DataFrame(data, columns = ([ 'Country or Area', 'Year(s)', 'Variant', 'Value'])) df.hist() plt.show()

Output

Pie Charts 

import numpy as np import matplotlib.pyplot as plt n = 25 Z = np.ones(n) Z[-1] *= 2.5 plt.axes([0.05, 0.05, 0.95, 0.95]) plt.pie(Z, explode = Z*.05, colors = ['%f' % (i/float(n)) for i in range(n)], wedgeprops = {"linewidth": 1, "edgecolor": "green"}) plt.gca().set_aspect('equal') plt.xticks([]), plt.yticks([]) plt.show()

Output

Polar plot

Code:

import numpy as np import matplotlib.pyplot as plt ax = plt.axes([0.5,0.05,0.95,0.95], polar=True) N = 25 theta = np.arange(0.0, 2.5*np.pi, 2.5*np.pi/N) radii = 10*np.random.rand(N) width = np.pi/4*np.random.rand(N) bars = plt.bar(theta, radii, width=width, bottom=0.0) for r,bar in zip(radii, bars): bar.set_facecolor( plt.cm.jet(r/10.)) bar.set_alpha(0.5) ax.set_xticklabels([]) ax.set_yticklabels([]) plt.show()

Output