data visualization with python.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd


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from matplotlib import pyplot as plt


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x = [1,4,8]
y = [2,4,9]
z = [2,4,0]
plt.plot(x,y)
plt.plot(y,z)
plt.title("Test plot")
plt.xlabel("x")
plt.ylabel("y and z")
plt.legend(["This is y", "This is z"])
plt.show()


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sample_data = pd.read_csv('sample_data.csv')


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sample_data


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type(sample_data)


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sample_data.column_c


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#retrieving specific  value
sample_data.column_c.iloc[2]


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plt.plot(sample_data.column_a,sample_data.column_c)
plt.plot(sample_data.column_a,sample_data.column_b)
plt.show()


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plt.plot(sample_data.column_a,sample_data.column_c,'o')
plt.plot(sample_data.column_a,sample_data.column_b,'o')
plt.show()


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data=pd.read_csv('countries.csv')


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data


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#compare the population growth in USA and China


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US = data[data.country == 'United States']


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US


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china = data[data.country == 'China']


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china


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plt.plot(US.year, US.population / 10**6)
plt.plot(china.year, china.population / 10**6)
plt.legend(['United States','China'])
plt.xlabel('year')
plt.ylabel('population')
plt.show()


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US.population


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US.population/US.population.iloc[0] * 100


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#Showing population growth
plt.plot(US.year, US.population/US.population.iloc[0] * 100)
plt.plot(china.year, china.population/china.population.iloc[0] * 100)
plt.legend(['United States','China'])
plt.xlabel('year')
plt.ylabel('population growth(first year=100')
plt.show()


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