Created a basic visualisation project

age.py

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+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+
+def age():
+	data = pd.read_csv("data/age.csv")
+
+	lables = data['age']
+	title = [1991, 2001, 2011]
+	plt.suptitle('Distribution of ages')
+	for num in range(3):
+		years = data[str(title[num])]/100000
+		plt.subplot(1,3,num+1)
+		plt.bar(lables, years)
+		plt.tick_params(gridOn=True)
+		plt.tick_params(grid_alpha = 190)
+		plt.tick_params(labelsize=6)
+		plt.tick_params(labelrotation=45)
+
+		plt.title(title[num])

data/age.csv

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+age,1991,2001,2011
+0-14,3273482,4492939,4565319
+15-19,914871,1427979,1667375
+20-24,982866,1426860,1764060
+25-29,956788,1358925,1668326
+30-39,1438035,2211006,2753943
+40-49,867731,1432467,2008410
+50-59,504149,759505,1196361
+60,439520,719650,1147445

data/area.csv

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+Year,Rural,Urban,Total
+1991,797.66,685.34,1483
+2001,558.32,924.68,1483
+2011,369.35,1113.65,1483

data/population.csv

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+Year,Population,GrowthRate,Growth
+2019,29399141,0.0325,3533266
+2015,25865875,0.033,3877980
+2010,21987895,0.033,3296565
+2005,18691330,0.0356,2999431
+2000,15691899,0.0527,3553666
+1995,12138233,0.0528,2754024
+1990,9384209,0.0537,2158451
+1985,7225758,0.0528,1638744
+1980,5587014,0.0472,1150629
+1975,4436385,0.0467,905692
+1970,3530693,0.0441,685651
+1965,2845042,0.045,562080
+1960,2282962,0.0508,501338
+1955,1781624,0.054,412255

data/sex_ratio.csv

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+Category,region,2001,2011
+State,Uttarakhand,962,963
+State,Chandigarh,777,818
+State,Rajasthan,921,928
+State,Haryana,861,879
+State,J&K,892,889
+State,Punjab,876,895
+State,UP,898,912
+State,HP,968,972
+Union Territory,Delhi,821,868

population.py

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+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+def population():
+	data = pd.read_csv("data/population.csv")
+	plt.plot(data["Year"], data["Population"]/10000000)
+	plt.title('Population of Delhi (1955 to 2019)')
+	plt.xlabel('Year')
+	plt.ylabel('Population\n(crore)')

sex_ratio.py

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+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+
+
+def sex_ratio():
+	data = pd.read_csv("data/sex_ratio.csv")
+
+	lables = data['region']
+	year2001 = data['2001']
+	year2011 = data['2011']
+
+	index = np.arange(len(lables))
+	width = 0.4
+
+	fig, ax = plt.subplots()
+	rects1 = ax.bar(index - width/2, year2001, width, label='2001')
+	rects2 = ax.bar(index + width/2, year2011, width, label='2011')
+
+	for tick in ax.xaxis.get_major_ticks():
+		tick.label.set_fontsize(8) 
+
+	ax.set_ylabel('No of females per 1000 males')
+	ax.set_title('Sex Ratio of northern states 2001 & 2011')
+	ax.set_xticks(index)
+	ax.set_xticklabels(lables)
+	fig.autofmt_xdate()
+	ax.legend()	

urbanisation.py

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+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+
+
+def urbanisation():
+	data = pd.read_csv("data/area.csv")
+
+	lables = data['Year']
+	urban = data['Urban']
+	rural = data['Rural']
+
+	index = np.arange(len(lables))
+	width = 0.4
+
+	fig, ax = plt.subplots()
+	rects1 = ax.bar(index - width/2, rural, width, label='Rural')
+	rects2 = ax.bar(index + width/2, urban, width, label='Urban')
+
+	ax.set_ylabel('Sq. Km')
+	ax.set_title('Area - Rural and Urban')
+	ax.set_xticks(index)
+	ax.set_xticklabels(lables)
+	ax.legend()

visualisation.py

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+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+import urbanisation
+import sex_ratio
+import population
+import age
+
+
+def show_menu():
+	print()
+	print("1. Rapid Urbanisation")
+	print("2. Population")
+	print("3. Sex Ratio")
+	print("4. Age distribution")
+	print("5. Exit")
+	print()
+
+def show_graph():
+	plt.tick_params(gridOn=True)
+	plt.tick_params(grid_alpha = 190)
+	plt.show()
+
+
+print("Welcome to our project.")
+print("A visualisation of Demographics of Delhi.")
+
+while True:
+	show_menu()
+	choice = int(input("Enter your choice: "))
+
+	if choice == 1:
+		urbanisation.urbanisation()
+	elif choice == 2:
+		population.population()
+	elif choice == 3:
+		sex_ratio.sex_ratio()
+	elif choice == 4:
+		age.age()
+	elif choice == 5:
+		break
+	else:
+		print("Invalid choice.\n")
+
+	show_graph()