

In [35]:

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

Assignment 4

Using data from this FiveThirtyEight post, write code to calculate the correlation of the responses from the poll. Respond to the story in your PR. Is this a good example of data journalism? Why or why not?



In [2]:

    
df = pd.read_excel("data/DATA_FOX.xlsx")



In [3]:

    
df.columns = ["NaN", "Demography", "Approve_obama", "Disapprove_obama", "(Don't know)_obama", "Favor", "Oppose","(Don't know)_iran", "NaN","NaN", "NaN","NaN","NaN"]



In [4]:

    
del df['NaN']



In [19]:

    
df1 = df.drop(df.index[[0,10,11,12,13,14,15,16,17,18,19,20,21,22]])



In [6]:

    
df.columns









    Out[6]:





Index(['Demography', 'Approve_obama', 'Disapprove_obama', '(Don't know)_obama',
       'Favor', 'Oppose', '(Don't know)_iran'],
      dtype='object')



In [13]:

    
df2 = df[["Demography", "Approve_obama", "Favor"]]



In [14]:

    
df2.drop(df.index[[0,10,11,12,13,14,15,16,17,18,19,20,21,22]])









    Out[14]:






  
    
      
      Demography
      Approve_obama
      Favor
    
  
  
    
      1
      Dem
      0.78
      0.6
    
    
      2
      Rep
      0.1
      0.34
    
    
      3
      Ind
      0.37
      0.44
    
    
      4
      Men
      0.41
      0.46
    
    
      5
      Women
      0.47
      0.47
    
    
      6
      White
      0.35
      0.45
    
    
      7
      Black
      0.85
      0.54
    
    
      8
      Degree
      0.47
      0.5
    
    
      9
      no Degree
      0.43
      0.45



In [ ]:



In [48]:

    
lm = smf.ols(formula="Favor~Approve_obama",data=df2[0:2]).fit()



In [49]:

    
lm.params









    Out[49]:






  
    
      
      0
      1
    
  
  
    
      Intercept
      1.0
      -1.103220e-16
    
    
      Approve_obama[T.Approve_obama]
      -1.0
      1.000000e+00



In [56]:

    
Intercept, slope = lm.params



In [70]:

    
fig, ax = plt.subplots(figsize=(9,9))
fig.set_facecolor('lightgray')
ax.set_axis_bgcolor('lightgray')


ax.grid(linestyle='-')
ax.spines['left'].set_visible(True)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(True)

plt.tick_params(
    which='major', # both major and minor ticks are affected
    top='off',   # ticks along the top edge are off
    left='on', # ticks along the right edge are off
    right='off', # ticks along the right edge are off
    bottom='on', # ticks along the bottom edge are on
    labelright='off',
    labelleft='on',
    labeltop='off', # top label is on
    labelbottom='on')  # bottom label is on

df1.plot(kind='scatter', x="Approve_obama", y="Favor", color='Black', ax=ax, marker='o')
#Cant get the line!!
#ax.plot(df["Approve_obama"],slope*df["Favor"]+Intercept,"-",color="red")

ax.xaxis.grid(color='darkgrey', linestyle=':', linewidth=0.5)
ax.yaxis.grid(color='darkgrey', linestyle=':', linewidth=0.5)
    
ax.set_ylim((0, 0.8))
ax.set_xlim((0, 0.8))









    Out[70]:





(0, 0.8)



In [ ]:

	Demography	Approve_obama	Favor
1	Dem	0.78	0.6
2	Rep	0.1	0.34
3	Ind	0.37	0.44
4	Men	0.41	0.46
5	Women	0.47	0.47
6	White	0.35	0.45
7	Black	0.85	0.54
8	Degree	0.47	0.5
9	no Degree	0.43	0.45

	0	1
Intercept	1.0	-1.103220e-16
Approve_obama[T.Approve_obama]	-1.0	1.000000e+00