Visualize Plot and Subplots using Matplotlib and Python - P1

Published: 16 February 2018
on channel: technologyCult

5,563

Visualize Plot and Subplots using Matplotlib and Python

Topic to be Covered - Data Visualisation

Link for the Dataset - http://www.randalolson.com/2014/06/14...

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('percent-bachelors-degrees-women-usa.csv')

No 1
plt.plot(df['Year'],df['Agriculture'],color='red')
plt.plot(df['Year'],df['Architecture'],color='blue')
plt.plot(df['Year'],df['Art and Performance'],color='green')

plt.plot(df['Year'],df['Physical Sciences'],color='red')
plt.plot(df['Year'],df['Computer Science'],color='blue')

No 2
'''
1 2 3
1 x 3

1,3,1
1,3,2
1,3,3'''

plt.subplot(1,3,1)
plt.plot(df['Year'],df['Architecture'],color='blue')
plt.title('Architecture')

plt.subplot(1,3,2)
plt.plot(df['Year'],df['Computer Science'],color='green')
plt.title('Computer Science')

plt.subplot(1,3,3)
plt.plot(df['Year'],df['Physical Sciences'],color='yellow')
plt.title('Physical Sciences')

plt.show()

No 3

plt.plot(df['Year'], 100 -df['English'],c='blue',label='Men')
plt.plot(df['Year'], df['English'],c='red',label='Women')
plt.title('English Enrollment Comparion between the gender')
plt.xlabel('Year')
plt.ylabel('Enrollment in percentage')

No 4
'''
1 2 3
4 5 6
7 8 9
10 11 12
13 14 15
16 17

6 x 3'''

fig = plt.figure(figsize=(13,5))
ax1 = fig.add_subplot(6,3,1)
ax2 = fig.add_subplot(6,3,2)
ax3 = fig.add_subplot(6,3,3)
ax4 = fig.add_subplot(6,3,4)
ax5 = fig.add_subplot(6,3,5)
ax6 = fig.add_subplot(6,3,6)
ax7 = fig.add_subplot(6,3,7)
ax8 = fig.add_subplot(6,3,8)
ax9 = fig.add_subplot(6,3,9)
ax10 = fig.add_subplot(6,3,10)
ax11 = fig.add_subplot(6,3,11)
ax12 = fig.add_subplot(6,3,12)
ax13 = fig.add_subplot(6,3,13)
ax14 = fig.add_subplot(6,3,14)
ax15 = fig.add_subplot(6,3,15)
ax16 = fig.add_subplot(6,3,16)
ax17 = fig.add_subplot(6,3,17)

categories = ['Agriculture','Architecture','Art and Performance',
'Biology','Business','Communications and Journalism',
'Computer Science','Education','Engineering',
'English','Foreign Languages','Health Professions',
'Math and Statistics','Physical Sciences','Psychology',
'Public Administration','Social Sciences and History']

ax = [ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8,ax9,ax10,ax11,ax12,ax13,ax14,ax15,ax16,ax17]

for i in range(len(categories)):
ax[i].plot(df['Year'],df[categories[i]],c='red',label='Women')
ax[i].plot(df['Year'],100-df[categories[i]],c='blue',label='Women')
ax[i].set_title(categories[i])
ax[i].set_ylim(0,100)

plt.tight_layout()
plt.savefig('categories.jpeg')
plt.show()

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