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Using the 2013_NYC_CD_MedianIncome_Recycle.xlsx file, calculate the correlation between the recycling rate and the median income. Discuss your findings in your PR.



In [10]:

    
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import numpy as np

%matplotlib inline
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42



In [2]:

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



In [3]:

    
df.head()









    Out[3]:






  
    
      
      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 [4]:

    
df.columns = ['name', 'median_household_income', 'recycle_rate']



In [5]:

    
df.head()









    Out[5]:






  
    
      
      name
      median_household_income
      recycle_rate
    
  
  
    
      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 [7]:

    
df.corr()









    Out[7]:






  
    
      
      median_household_income
      recycle_rate
    
  
  
    
      median_household_income
      1.000000
      0.884783
    
    
      recycle_rate
      0.884783
      1.000000

It'd be much easier to understand corr() if there's a graph. How to do that?
I guess my biggest question is - how would I explain the relationship between household income and recycle rate? Could it be that neighborhoods with higher recycle rates have more educational materials like posters etc? Could it be levels of education? Could it be more bins? I'd look into that before jumping to conclusions.



In [19]:









    



---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-19-5d9d6485ba54> in <module>()
----> 1 plt.matshow(df.corr.recycle_rate())

AttributeError: 'function' object has no attribute 'recycle_rate'



In [ ]:

	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

	median_household_income	recycle_rate
median_household_income	1.000000	0.884783
recycle_rate	0.884783	1.000000