In [1]:

    

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import statsmodels.formula.api as smf

df = pd.read_excel('2013_NYC_CD_MedianIncome_Recycle.xlsx')


    

In [2]:

    

df.head()


    

    
Out[2]:






  

    

      

      
CD_Name
      
MdHHIncE
      
RecycleRate
    
  
  

    

      
0
      
Battery Park City, Greenwich Village & Soho
      
119596
      
0.286771
    
    

      
1
      
Battery Park City, Greenwich Village & Soho
      
119596
      
0.264074
    
    

      
2
      
Chinatown & Lower East Side
      
40919
      
0.156485
    
    

      
3
      
Chelsea, Clinton & Midtown Business Distric
      
92583
      
0.235125
    
    

      
4
      
Chelsea, Clinton & Midtown Business Distric
      
92583
      
0.246725
    
  

In [3]:

    

df.plot(kind='scatter',x='MdHHIncE',y='RecycleRate')


    

    
Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x9633ac8>






    

In [4]:

    

df.columns = ['location', 'median_income', 'recycle_rate']


    

In [5]:

    

df.corr()['median_income']['recycle_rate']


    

    
Out[5]:





0.8847831827851157

In [6]:

    

lm = smf.ols(formula="recycle_rate~median_income",data=df).fit()
intercept, slope = lm.params
lm.params


    

    
Out[6]:





Intercept        0.074804
median_income    0.000002
dtype: float64

In [7]:

    

df.plot(kind="scatter",x="median_income",y="recycle_rate")
plt.plot(df["median_income"],slope*df["median_income"]+intercept,"-",color="red") 
plt.xlabel('Median Income')
plt.ylabel('Recycle Rate')


    

    
Out[7]:





<matplotlib.text.Text at 0x989da20>






    

In [8]:

    

def predicting_recylerate(income):
    return intercept + float(income) * slope


    

In [9]:

    

x = input('What is the median income of your location? ')

print('Expected recycling rate : ' + str(round(predicting_recylerate(x), 2)))


    

    




What is the median income of your location? 40919
Expected recycling rate : 0.15