Advanced Visualization for Data Scientists with Matplotlib

import  matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv("property_tax_report.csv")

#Removing the null values
df = df[(df[('PROPERTY_POSTAL_CODE')].notnull())]
df = df[['PID', 'YEAR_BUILT']].groupby('YEAR_BUILT', as_index = False).count().astype('int').rename(columns = {'PID':'No_of_properties_built'})

df = df[(df['YEAR_BUILT']>=1900)&(df['YEAR_BUILT']<=2018)]

x = df['YEAR_BUILT']
y = df['No_of_properties_built']

plt.figure(figsize=(17,6))
plt.plot(x,y,'dodgerblue',label = 'Number of properties built',linewidth = 1)
plt.xlabel('YEAR',fontsize = 16)

plt.title('Number of houses built between\n1990 and 2018', fontsize = 16)
plt.grid(False)
plt.legend()
plt.savefig('Line_plot.png',dpi = 400,qulity = 100)

plt.show()

plt.clf()

# Bar plot
import  matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv("property_tax_report.csv")

df = df[(df['PROPERTY_POSTAL_CODE'].notnull())]
df = df[['PID', 'YEAR_BUILT']].groupby('YEAR_BUILT', as_index = False).count().astype('int').rename(columns = {'PID':'No_of_properties_built'})
df = df[(df['YEAR_BUILT'] >= 1900) & (df['YEAR_BUILT'] <= 2018)]
x = df['YEAR_BUILT']
y = df['No_of_properties_built']
plt.figure(figsize=(17,6))
plt.bar(x,y,label='Number of properties built',color = 'dodgerblue',width = 1,align='center')
plt.xlabel('YEAR',fontsize = 16)
plt.ylabel('Number of properties built',fontsize = 16)
plt.title('Number of houses built between\n1900 and 2018',fontsize = 16)

plt.grid(axis='y')
plt.legend()

plt.savefig('Bar_plot.png',dpi = 400,quality = 100)
plt.show()
plt.clf()