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In [1]:

    
import pandas as pd
%matplotlib inline



In [2]:

    
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf



In [3]:

    
df = pd.read_excel("fox_data.xlsx")



In [4]:

    
df









    Out[4]:






  
    
      
      Subject
      Response
      Total
      Dem
      Rep
      Ind
      Men
      Women
      White
      Black
      ...
      No_Degree
      Under_35
      between_35_and_54
      Over_55
      Over_65
      Under_50k
      Over_50k
      Lib
      Cons
      Tea_Party
    
  
  
    
      0
      Obama
      Approve
      0.44
      0.78
      0.10
      0.37
      0.41
      0.47
      0.35
      0.85
      ...
      0.43
      0.56
      0.35
      0.45
      0.43
      0.48
      0.42
      0.69
      0.24
      0.15
    
    
      1
      Obama
      Disapprove
      0.50
      0.17
      0.86
      0.53
      0.53
      0.47
      0.60
      0.07
      ...
      0.52
      0.36
      0.60
      0.50
      0.52
      0.46
      0.52
      0.24
      0.72
      0.82
    
    
      2
      Obama
      Don't know
      0.06
      0.05
      0.04
      0.10
      0.05
      0.06
      0.05
      0.08
      ...
      0.06
      0.08
      0.04
      0.05
      0.05
      0.05
      0.05
      0.06
      0.04
      0.02
    
    
      3
      Iran Negotiations
      Very confident
      0.17
      0.29
      0.04
      0.15
      0.17
      0.17
      0.11
      0.37
      ...
      0.14
      0.18
      0.13
      0.19
      0.18
      0.18
      0.16
      0.24
      0.10
      0.06
    
    
      4
      Iran Negotiations
      Somewhat confident
      0.31
      0.49
      0.13
      0.29
      0.28
      0.35
      0.29
      0.46
      ...
      0.29
      0.39
      0.29
      0.29
      0.26
      0.34
      0.31
      0.48
      0.19
      0.11
    
    
      5
      Iran Negotiations
      Not very confident
      0.19
      0.11
      0.26
      0.22
      0.18
      0.19
      0.21
      0.06
      ...
      0.22
      0.23
      0.20
      0.15
      0.14
      0.16
      0.20
      0.16
      0.22
      0.16
    
    
      6
      Iran Negotiations
      Not at all confident
      0.31
      0.08
      0.57
      0.32
      0.35
      0.27
      0.37
      0.05
      ...
      0.32
      0.17
      0.38
      0.34
      0.40
      0.29
      0.32
      0.11
      0.47
      0.67
    
    
      7
      Iran Negotiations
      Don't know
      0.02
      0.04
      0.01
      0.02
      0.02
      0.02
      0.02
      0.05
      ...
      0.03
      0.03
      NaN
      0.03
      0.02
      0.03
      0.01
      0.01
      0.02
      NaN
    
    
      8
      Iran Deal
      Favor
      0.47
      0.60
      0.34
      0.44
      0.46
      0.47
      0.45
      0.54
      ...
      0.45
      0.51
      0.46
      0.45
      0.41
      0.47
      0.48
      0.61
      0.35
      0.35
    
    
      9
      Iran Deal
      Oppose
      0.43
      0.26
      0.60
      0.49
      0.48
      0.39
      0.46
      0.31
      ...
      0.45
      0.37
      0.47
      0.44
      0.49
      0.45
      0.41
      0.28
      0.56
      0.61
    
    
      10
      Iran Deal
      Don't know
      0.10
      0.14
      0.06
      0.07
      0.06
      0.14
      0.09
      0.15
      ...
      0.10
      0.12
      0.07
      0.11
      0.10
      0.09
      0.11
      0.11
      0.09
      0.04
    
  

11 rows × 21 columns



In [5]:

    
negs_df = df[df['Subject'] == 'Iran Negotiations']



In [6]:

    
negs_df









    Out[6]:






  
    
      
      Subject
      Response
      Total
      Dem
      Rep
      Ind
      Men
      Women
      White
      Black
      ...
      No_Degree
      Under_35
      between_35_and_54
      Over_55
      Over_65
      Under_50k
      Over_50k
      Lib
      Cons
      Tea_Party
    
  
  
    
      3
      Iran Negotiations
      Very confident
      0.17
      0.29
      0.04
      0.15
      0.17
      0.17
      0.11
      0.37
      ...
      0.14
      0.18
      0.13
      0.19
      0.18
      0.18
      0.16
      0.24
      0.10
      0.06
    
    
      4
      Iran Negotiations
      Somewhat confident
      0.31
      0.49
      0.13
      0.29
      0.28
      0.35
      0.29
      0.46
      ...
      0.29
      0.39
      0.29
      0.29
      0.26
      0.34
      0.31
      0.48
      0.19
      0.11
    
    
      5
      Iran Negotiations
      Not very confident
      0.19
      0.11
      0.26
      0.22
      0.18
      0.19
      0.21
      0.06
      ...
      0.22
      0.23
      0.20
      0.15
      0.14
      0.16
      0.20
      0.16
      0.22
      0.16
    
    
      6
      Iran Negotiations
      Not at all confident
      0.31
      0.08
      0.57
      0.32
      0.35
      0.27
      0.37
      0.05
      ...
      0.32
      0.17
      0.38
      0.34
      0.40
      0.29
      0.32
      0.11
      0.47
      0.67
    
    
      7
      Iran Negotiations
      Don't know
      0.02
      0.04
      0.01
      0.02
      0.02
      0.02
      0.02
      0.05
      ...
      0.03
      0.03
      NaN
      0.03
      0.02
      0.03
      0.01
      0.01
      0.02
      NaN
    
  

5 rows × 21 columns



In [7]:

    
very_confident_df = negs_df[negs_df['Response'] == 'Very confident']



In [8]:

    
very_confident_df









    Out[8]:






  
    
      
      Subject
      Response
      Total
      Dem
      Rep
      Ind
      Men
      Women
      White
      Black
      ...
      No_Degree
      Under_35
      between_35_and_54
      Over_55
      Over_65
      Under_50k
      Over_50k
      Lib
      Cons
      Tea_Party
    
  
  
    
      3
      Iran Negotiations
      Very confident
      0.17
      0.29
      0.04
      0.15
      0.17
      0.17
      0.11
      0.37
      ...
      0.14
      0.18
      0.13
      0.19
      0.18
      0.18
      0.16
      0.24
      0.1
      0.06
    
  

1 rows × 21 columns



In [9]:

    
somewhat_confident_df = negs_df[negs_df['Response'] == 'Somewhat confident']



In [10]:

    
somewhat_confident_df









    Out[10]:






  
    
      
      Subject
      Response
      Total
      Dem
      Rep
      Ind
      Men
      Women
      White
      Black
      ...
      No_Degree
      Under_35
      between_35_and_54
      Over_55
      Over_65
      Under_50k
      Over_50k
      Lib
      Cons
      Tea_Party
    
  
  
    
      4
      Iran Negotiations
      Somewhat confident
      0.31
      0.49
      0.13
      0.29
      0.28
      0.35
      0.29
      0.46
      ...
      0.29
      0.39
      0.29
      0.29
      0.26
      0.34
      0.31
      0.48
      0.19
      0.11
    
  

1 rows × 21 columns



In [43]:

    
confident_df = very_confident_df.append(somewhat_confident_df)



In [44]:

    
confident_df









    Out[44]:






  
    
      
      Subject
      Response
      Total
      Dem
      Rep
      Ind
      Men
      Women
      White
      Black
      ...
      No_Degree
      Under_35
      between_35_and_54
      Over_55
      Over_65
      Under_50k
      Over_50k
      Lib
      Cons
      Tea_Party
    
  
  
    
      3
      Iran Negotiations
      Very confident
      0.17
      0.29
      0.04
      0.15
      0.17
      0.17
      0.11
      0.37
      ...
      0.14
      0.18
      0.13
      0.19
      0.18
      0.18
      0.16
      0.24
      0.10
      0.06
    
    
      4
      Iran Negotiations
      Somewhat confident
      0.31
      0.49
      0.13
      0.29
      0.28
      0.35
      0.29
      0.46
      ...
      0.29
      0.39
      0.29
      0.29
      0.26
      0.34
      0.31
      0.48
      0.19
      0.11
    
  

2 rows × 21 columns



In [45]:

    
new_confident_df = confident_df.append(confident_df.sum(numeric_only=True), ignore_index=True)



In [46]:

    
new_confident_df









    Out[46]:






  
    
      
      Subject
      Response
      Total
      Dem
      Rep
      Ind
      Men
      Women
      White
      Black
      ...
      No_Degree
      Under_35
      between_35_and_54
      Over_55
      Over_65
      Under_50k
      Over_50k
      Lib
      Cons
      Tea_Party
    
  
  
    
      0
      Iran Negotiations
      Very confident
      0.17
      0.29
      0.04
      0.15
      0.17
      0.17
      0.11
      0.37
      ...
      0.14
      0.18
      0.13
      0.19
      0.18
      0.18
      0.16
      0.24
      0.10
      0.06
    
    
      1
      Iran Negotiations
      Somewhat confident
      0.31
      0.49
      0.13
      0.29
      0.28
      0.35
      0.29
      0.46
      ...
      0.29
      0.39
      0.29
      0.29
      0.26
      0.34
      0.31
      0.48
      0.19
      0.11
    
    
      2
      NaN
      NaN
      0.48
      0.78
      0.17
      0.44
      0.45
      0.52
      0.40
      0.83
      ...
      0.43
      0.57
      0.42
      0.48
      0.44
      0.52
      0.47
      0.72
      0.29
      0.17
    
  

3 rows × 21 columns



In [47]:

    
final_confident_df = new_confident_df.set_value(2, 'Subject', 'Iran Negotiations')



In [48]:

    
final_confident_df









    Out[48]:






  
    
      
      Subject
      Response
      Total
      Dem
      Rep
      Ind
      Men
      Women
      White
      Black
      ...
      No_Degree
      Under_35
      between_35_and_54
      Over_55
      Over_65
      Under_50k
      Over_50k
      Lib
      Cons
      Tea_Party
    
  
  
    
      0
      Iran Negotiations
      Very confident
      0.17
      0.29
      0.04
      0.15
      0.17
      0.17
      0.11
      0.37
      ...
      0.14
      0.18
      0.13
      0.19
      0.18
      0.18
      0.16
      0.24
      0.10
      0.06
    
    
      1
      Iran Negotiations
      Somewhat confident
      0.31
      0.49
      0.13
      0.29
      0.28
      0.35
      0.29
      0.46
      ...
      0.29
      0.39
      0.29
      0.29
      0.26
      0.34
      0.31
      0.48
      0.19
      0.11
    
    
      2
      Iran Negotiations
      NaN
      0.48
      0.78
      0.17
      0.44
      0.45
      0.52
      0.40
      0.83
      ...
      0.43
      0.57
      0.42
      0.48
      0.44
      0.52
      0.47
      0.72
      0.29
      0.17
    
  

3 rows × 21 columns



In [59]:

    
final_confident_df = new_confident_df.set_value(2, 'Response', 'Total')



In [60]:

    
final_confident_df









    Out[60]:






  
    
      
      Subject
      Response
      Total
      Dem
      Rep
      Ind
      Men
      Women
      White
      Black
      ...
      No_Degree
      Under_35
      between_35_and_54
      Over_55
      Over_65
      Under_50k
      Over_50k
      Lib
      Cons
      Tea_Party
    
  
  
    
      0
      Iran Negotiations
      Very confident
      0.17
      0.29
      0.04
      0.15
      0.17
      0.17
      0.11
      0.37
      ...
      0.14
      0.18
      0.13
      0.19
      0.18
      0.18
      0.16
      0.24
      0.10
      0.06
    
    
      1
      Iran Negotiations
      Somewhat confident
      0.31
      0.49
      0.13
      0.29
      0.28
      0.35
      0.29
      0.46
      ...
      0.29
      0.39
      0.29
      0.29
      0.26
      0.34
      0.31
      0.48
      0.19
      0.11
    
    
      2
      Iran Negotiations
      Total
      0.48
      0.78
      0.17
      0.44
      0.45
      0.52
      0.40
      0.83
      ...
      0.43
      0.57
      0.42
      0.48
      0.44
      0.52
      0.47
      0.72
      0.29
      0.17
    
  

3 rows × 21 columns



In [61]:

    
super_final_confident_df = final_confident_df[final_confident_df['Response'] == 'Total']



In [62]:

    
super_final_confident_df









    Out[62]:






  
    
      
      Subject
      Response
      Total
      Dem
      Rep
      Ind
      Men
      Women
      White
      Black
      ...
      No_Degree
      Under_35
      between_35_and_54
      Over_55
      Over_65
      Under_50k
      Over_50k
      Lib
      Cons
      Tea_Party
    
  
  
    
      2
      Iran Negotiations
      Total
      0.48
      0.78
      0.17
      0.44
      0.45
      0.52
      0.4
      0.83
      ...
      0.43
      0.57
      0.42
      0.48
      0.44
      0.52
      0.47
      0.72
      0.29
      0.17
    
  

1 rows × 21 columns



In [68]:

    
del super_final_confident_df['Subject']



In [69]:

    
del super_final_confident_df['Response']



In [73]:

    
del super_final_confident_df['Total']



In [77]:

    
obama_df = df[df['Subject'] == 'Obama']



In [79]:

    
obama_approve_df = obama_df[obama_df['Response'] == 'Approve']



In [81]:

    
del obama_approve_df['Subject']



In [82]:

    
del obama_approve_df['Response']



In [83]:

    
del obama_approve_df['Total']



In [84]:

    
obama_approve_df









    Out[84]:






  
    
      
      Dem
      Rep
      Ind
      Men
      Women
      White
      Black
      College_Degree
      No_Degree
      Under_35
      between_35_and_54
      Over_55
      Over_65
      Under_50k
      Over_50k
      Lib
      Cons
      Tea_Party
    
  
  
    
      0
      0.78
      0.1
      0.37
      0.41
      0.47
      0.35
      0.85
      0.47
      0.43
      0.56
      0.35
      0.45
      0.43
      0.48
      0.42
      0.69
      0.24
      0.15



In [107]:

    
plottable_df = obama_approve_df.append(super_final_confident_df)



In [108]:

    
plottable_df









    Out[108]:






  
    
      
      Dem
      Rep
      Ind
      Men
      Women
      White
      Black
      College_Degree
      No_Degree
      Under_35
      between_35_and_54
      Over_55
      Over_65
      Under_50k
      Over_50k
      Lib
      Cons
      Tea_Party
    
  
  
    
      0
      0.78
      0.10
      0.37
      0.41
      0.47
      0.35
      0.85
      0.47
      0.43
      0.56
      0.35
      0.45
      0.43
      0.48
      0.42
      0.69
      0.24
      0.15
    
    
      2
      0.78
      0.17
      0.44
      0.45
      0.52
      0.40
      0.83
      0.55
      0.43
      0.57
      0.42
      0.48
      0.44
      0.52
      0.47
      0.72
      0.29
      0.17



In [112]:

    
final_plottable_df = plottable_df.transpose()



In [119]:

    
final_plottable_df.columns = ['Total_Obama_Approve', 'Total_Iran_Negotiations']



In [120]:

    
final_plottable_df









    Out[120]:






  
    
      
      Total_Obama_Approve
      Total_Iran_Negotiations
    
  
  
    
      Dem
      0.78
      0.78
    
    
      Rep
      0.10
      0.17
    
    
      Ind
      0.37
      0.44
    
    
      Men
      0.41
      0.45
    
    
      Women
      0.47
      0.52
    
    
      White
      0.35
      0.40
    
    
      Black
      0.85
      0.83
    
    
      College_Degree
      0.47
      0.55
    
    
      No_Degree
      0.43
      0.43
    
    
      Under_35
      0.56
      0.57
    
    
      between_35_and_54
      0.35
      0.42
    
    
      Over_55
      0.45
      0.48
    
    
      Over_65
      0.43
      0.44
    
    
      Under_50k
      0.48
      0.52
    
    
      Over_50k
      0.42
      0.47
    
    
      Lib
      0.69
      0.72
    
    
      Cons
      0.24
      0.29
    
    
      Tea_Party
      0.15
      0.17



In [125]:

    
lm = smf.ols(formula='Total_Iran_Negotiations ~ Total_Obama_Approve',data=final_plottable_df).fit()
lm.params









    Out[125]:





Intercept              0.074581
Total_Obama_Approve    0.913442
dtype: float64



In [126]:

    
intercept, slope = lm.params



In [127]:

    
ax = final_plottable_df.plot(kind='scatter', x= 'Total_Obama_Approve', y='Total_Iran_Negotiations')
plt.plot(final_plottable_df["Total_Iran_Negotiations"],slope*final_plottable_df["Total_Iran_Negotiations"]+intercept,"-",color="red")
ax.set_title("Obama's Name Polarizes Opinion On Iran Deal")
ax.set_ylabel('Confident in administration\'s Iran negotiations')
ax.set_xlabel("Approve of Obama")









    Out[127]:





<matplotlib.text.Text at 0x10f4c5e80>



In [ ]:

	Subject	Response	Total	Dem	Rep	Ind	Men	Women	White	Black	...	No_Degree	Under_35	between_35_and_54	Over_55	Over_65	Under_50k	Over_50k	Lib	Cons	Tea_Party
0	Obama	Approve	0.44	0.78	0.10	0.37	0.41	0.47	0.35	0.85	...	0.43	0.56	0.35	0.45	0.43	0.48	0.42	0.69	0.24	0.15
1	Obama	Disapprove	0.50	0.17	0.86	0.53	0.53	0.47	0.60	0.07	...	0.52	0.36	0.60	0.50	0.52	0.46	0.52	0.24	0.72	0.82
2	Obama	Don't know	0.06	0.05	0.04	0.10	0.05	0.06	0.05	0.08	...	0.06	0.08	0.04	0.05	0.05	0.05	0.05	0.06	0.04	0.02
3	Iran Negotiations	Very confident	0.17	0.29	0.04	0.15	0.17	0.17	0.11	0.37	...	0.14	0.18	0.13	0.19	0.18	0.18	0.16	0.24	0.10	0.06
4	Iran Negotiations	Somewhat confident	0.31	0.49	0.13	0.29	0.28	0.35	0.29	0.46	...	0.29	0.39	0.29	0.29	0.26	0.34	0.31	0.48	0.19	0.11
5	Iran Negotiations	Not very confident	0.19	0.11	0.26	0.22	0.18	0.19	0.21	0.06	...	0.22	0.23	0.20	0.15	0.14	0.16	0.20	0.16	0.22	0.16
6	Iran Negotiations	Not at all confident	0.31	0.08	0.57	0.32	0.35	0.27	0.37	0.05	...	0.32	0.17	0.38	0.34	0.40	0.29	0.32	0.11	0.47	0.67
7	Iran Negotiations	Don't know	0.02	0.04	0.01	0.02	0.02	0.02	0.02	0.05	...	0.03	0.03	NaN	0.03	0.02	0.03	0.01	0.01	0.02	NaN
8	Iran Deal	Favor	0.47	0.60	0.34	0.44	0.46	0.47	0.45	0.54	...	0.45	0.51	0.46	0.45	0.41	0.47	0.48	0.61	0.35	0.35
9	Iran Deal	Oppose	0.43	0.26	0.60	0.49	0.48	0.39	0.46	0.31	...	0.45	0.37	0.47	0.44	0.49	0.45	0.41	0.28	0.56	0.61
10	Iran Deal	Don't know	0.10	0.14	0.06	0.07	0.06	0.14	0.09	0.15	...	0.10	0.12	0.07	0.11	0.10	0.09	0.11	0.11	0.09	0.04