In [1]:

    

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
import statistics
from decimal import Decimal


    

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['MdHHIncE'].describe()


    

    
Out[4]:





count        59.000000
mean      53895.932203
std       24371.741796
min       21318.000000
25%       37950.000000
50%       48252.000000
75%       61967.000000
max      119596.000000
Name: MdHHIncE, dtype: float64

In [5]:

    

df.plot(kind='scatter', x='MdHHIncE', y='RecycleRate')
plt.xlabel('Median Income')
plt.ylabel('Recycling Rate')


    

    
Out[5]:





<matplotlib.text.Text at 0x6c95590>






    

In [6]:

    

df.corr(method='pearson', min_periods=1)


    

    
Out[6]:






  

    

      

      
MdHHIncE
      
RecycleRate
    
  
  

    

      
MdHHIncE
      
1.000000
      
0.884783
    
    

      
RecycleRate
      
0.884783
      
1.000000
    
  

In [7]:

    

plt.matshow(df.corr())


    

    
Out[7]:





<matplotlib.image.AxesImage at 0x7ed92f0>






    

In [8]:

    

df['MdHHIncE'].median()


    

    
Out[8]:





48252.0

In [9]:

    

df['MdHHIncE'].mode()


    

    
Out[9]:





0     21318
1     22343
2     51251
3     92583
4    119596
dtype: int64

In [10]:

    

df['MdHHIncE'].quantile(q=0.25) #1st Quartile


    

    
Out[10]:





37950.0

In [11]:

    

df['MdHHIncE'].quantile(q=0.5) #2nd Quartile (Median)


    

    
Out[11]:





48252.0

In [12]:

    

df['MdHHIncE'].quantile(q=0.75) #3rd Quartile


    

    
Out[12]:





61967.0

In [13]:

    

IQR = df['MdHHIncE'].quantile(q=0.75) - df['MdHHIncE'].quantile(q=0.25)
IQR


    

    
Out[13]:





24017.0

In [14]:

    

1.5 * IQR


    

    
Out[14]:





36025.5

In [15]:

    

lower_outliers = (df['MdHHIncE'].quantile(q=0.25))- (IQR * 1.5)


    

In [16]:

    

upper_outliers = (df['MdHHIncE'].quantile(q=0.75)) + (IQR * 1.5)


    

In [21]:

    

lower_outliers


    

    
Out[21]:





1924.5

In [18]:

    

upper_outliers


    

    
Out[18]:





97992.5

In [19]:

    

df[(df['MdHHIncE'] > upper_outliers)]


    

    
Out[19]:






  

    

      

      
CD_Name
      
MdHHIncE
      
RecycleRate
    
  
  

    

      
0
      
Battery Park City, Greenwich Village & Soho
      
119596
      
0.286771
    
    

      
1
      
Battery Park City, Greenwich Village & Soho
      
119596
      
0.264074
    
    

      
5
      
Murray Hill, Gramercy & Stuyvesant Town
      
101769
      
0.222046
    
    

      
7
      
Upper East Side
      
104602
      
0.253719
    
  

In [22]:

    

df[(df['MdHHIncE'] < lower_outliers)]


    

    
Out[22]:






  

    

      

      
CD_Name
      
MdHHIncE
      
RecycleRate
    
  
  

  

In [ ]: