Campaign Contributions RI - 2016 Presidential Election

SmirkyGraphs - Website - Github
In [1]:
# For data
import pandas as pd
from pandas import Series,DataFrame
import numpy as np

# For visualization
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline

from __future__ import division
import datetime
#use to get info from web
import requests

#stringIO to work with csv
from StringIO import StringIO

Importing Data

In [2]:
df = pd.read_csv("RI_Pres_Election_2016.csv", parse_dates=['contb_receipt_dt'])
In [3]:
df.head()
Out[3]:
cmte_id cand_id cand_nm contbr_nm contbr_city contbr_st contbr_zip contbr_employer contbr_occupation contb_receipt_amt contb_receipt_dt receipt_desc memo_cd memo_text form_tp file_num tran_id election_tp Party County
0 C00458844 P60006723 Rubio, Marco BLOUNT, F. NELSON BARRINGTON RI 28061841.0 BLOUNT FINE FOODS PRESIDENT 250.0 2016-02-02 NaN NaN NaN SA17A 1056862 SA17.969678 P2016 Republican BRISTOL
1 C00458844 P60006723 Rubio, Marco COOPER, DAVID NEWPORT RI 28402004.0 U.S. NAVAL WAR COLLEGE PROFESSOR 100.0 2016-02-21 NaN NaN NaN SA17A 1056862 SA17.1026442 P2016 Republican NEWPORT
2 C00458844 P60006723 Rubio, Marco DEYOE, RONALD A. CAPT. NEWPORT RI 28401528.0 RETIRED RETIRED 50.0 2016-02-18 NaN NaN NaN SA17A 1056862 SA17.1018752 P2016 Republican NEWPORT
3 C00458844 P60006723 Rubio, Marco FOGG, DAVID NORTH KINGSTOWN RI 28525103.0 AVC SEMI-RETIRED 25.0 2016-02-14 NaN NaN NaN SA17A 1056862 SA17.1010821 P2016 Republican WASHINGTON
4 C00458844 P60006723 Rubio, Marco FOGG, DAVID NORTH KINGSTOWN RI 28525103.0 AVC SEMI-RETIRED 25.0 2016-02-18 NaN NaN NaN SA17A 1056862 SA17.1017099 P2016 Republican WASHINGTON
In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 25888 entries, 0 to 25887
Data columns (total 20 columns):
cmte_id              25888 non-null object
cand_id              25888 non-null object
cand_nm              25888 non-null object
contbr_nm            25888 non-null object
contbr_city          25888 non-null object
contbr_st            25888 non-null object
contbr_zip           25884 non-null float64
contbr_employer      22604 non-null object
contbr_occupation    25827 non-null object
contb_receipt_amt    25888 non-null float64
contb_receipt_dt     25888 non-null datetime64[ns]
receipt_desc         6 non-null object
memo_cd              3785 non-null object
memo_text            12315 non-null object
form_tp              25888 non-null object
file_num             25888 non-null int64
tran_id              25888 non-null object
election_tp          25872 non-null object
Party                25888 non-null object
County               25888 non-null object
dtypes: datetime64[ns](1), float64(2), int64(1), object(16)
memory usage: 4.0+ MB

25,888 Donations from Rhode Island

In [5]:
sum_df = df.groupby(['contb_receipt_dt'],as_index=False).sum()
In [6]:
sum_df.head()
Out[6]:
contb_receipt_dt contbr_zip contb_receipt_amt file_num
0 2014-09-25 29162037.0 250.0 1057794
1 2015-03-24 29102034.0 250.0 1004025
2 2015-03-27 28321109.0 500.0 1003942
3 2015-03-29 28403811.0 250.0 1004025
4 2015-04-02 28884929.0 50.0 1024046

Line graph of how much money was donated per day

In [7]:
sum_df.plot('contb_receipt_dt','contb_receipt_amt',figsize=(12,6),marker='',linestyle='-',color='purple',
            xlim=('2016-01-01','2016-11-08'))
Out[7]:
<matplotlib.axes._subplots.AxesSubplot at 0x4278978>

Line graph of how many people donated per day

In [8]:
count_df = df.groupby(['contb_receipt_dt'],as_index=False).count()
In [9]:
count_df.plot('contb_receipt_dt','contb_receipt_amt',figsize=(12,6),marker='',linestyle='-',color='purple',
              xlim=('2016-01-01','2016-11-08'))
Out[9]:
<matplotlib.axes._subplots.AxesSubplot at 0xbda52e8>

Line graph of average donated per day

In [10]:
mean_df = df.groupby(['contb_receipt_dt'],as_index=False).mean()
In [11]:
mean_df.plot('contb_receipt_dt','contb_receipt_amt',figsize=(12,6),marker='',linestyle='-',color='purple',
              xlim=('2016-01-01','2016-11-08'),ylim=(0,1000))
Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0xbdf2eb8>
In [12]:
df.groupby('Party')['contb_receipt_amt'].sum().sort_values().plot(kind='bar')
Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0xebee278>
In [13]:
df['contb_receipt_amt'].describe()
Out[13]:
count    25888.000000
mean       101.918519
std        310.665254
min          0.050000
25%         15.000000
50%         27.000000
75%         52.302500
max       2700.000000
Name: contb_receipt_amt, dtype: float64
In [14]:
df.groupby('cand_nm')['contb_receipt_amt'].count().sort_values(ascending=False)
Out[14]:
cand_nm
Clinton, Hillary Rodham      12538
Sanders, Bernard             10094
Trump, Donald J.              1653
Cruz, Rafael Edward 'Ted'      798
Carson, Benjamin S.            343
Rubio, Marco                   168
Stein, Jill                     59
Kasich, John R.                 46
Fiorina, Carly                  40
Bush, Jeb                       32
O'Malley, Martin Joseph         32
Paul, Rand                      30
Johnson, Gary                   18
Walker, Scott                   10
Lessig, Lawrence                 9
Christie, Christopher J.         7
Graham, Lindsey O.               4
Webb, James Henry Jr.            4
Gilmore, James S III             2
Pataki, George E.                1
Name: contb_receipt_amt, dtype: int64
In [15]:
df['contb_receipt_amt'].value_counts()
Out[15]:
25.00      3660
50.00      2879
10.00      2614
100.00     2325
27.00      1685
15.00      1624
5.00       1539
250.00      842
20.00       514
35.00       468
19.00       445
3.00        395
8.00        389
1000.00     364
200.00      350
500.00      334
40.00       288
38.00       280
28.00       240
2700.00     237
75.00       235
1.00        222
80.00       202
30.00       190
150.00      155
37.00       150
7.00        114
14.00        89
13.50        88
11.00        80
           ...
71.51         1
31.92         1
17.58         1
83.00         1
17.56         1
60.10         1
24.36         1
45.20         1
60.96         1
24.30         1
105.70        1
5.40          1
7.60          1
87.45         1
105.77        1
1125.00       1
93.70         1
31.45         1
42.55         1
68.33         1
1028.00       1
43.26         1
617.32        1
126.61        1
175.61        1
114.00        1
27.75         1
50.50         1
425.00        1
31.80         1
Name: contb_receipt_amt, dtype: int64

As with the full dataset $25 is the most frequent donation

Lets look at the canidates

In [16]:
cand_df = df.groupby("cand_nm").sum().sort_values(by="contb_receipt_amt",ascending=False)
cand_df = cand_df.drop(['contbr_zip','file_num'], axis=1)
In [17]:
cand_df
Out[17]:
contb_receipt_amt
cand_nm
Clinton, Hillary Rodham 1714093.23
Sanders, Bernard 404971.83
Trump, Donald J. 222864.80
Cruz, Rafael Edward 'Ted' 61399.80
Bush, Jeb 59160.00
Kasich, John R. 45200.00
O'Malley, Martin Joseph 31370.00
Carson, Benjamin S. 25441.05
Rubio, Marco 21729.00
Lessig, Lawrence 15050.00
Christie, Christopher J. 7675.00
Stein, Jill 6761.20
Fiorina, Carly 5887.00
Walker, Scott 5200.00
Johnson, Gary 3670.00
Paul, Rand 3283.71
Graham, Lindsey O. 2760.00
Webb, James Henry Jr. 950.00
Gilmore, James S III 750.00
Pataki, George E. 250.00

Obviously Hillary raised the most

However what really surprises me is how much Jill Stein raised, but maybe it was just a few people donating a lot

Lets find out!

In [18]:
count_df = df.groupby("cand_nm").count().sort_values(by="contb_receipt_amt",ascending=False)
count_df = count_df.drop(['contbr_zip','file_num'], axis=1)
count_df['cmte_id']
Out[18]:
cand_nm
Clinton, Hillary Rodham      12538
Sanders, Bernard             10094
Trump, Donald J.              1653
Cruz, Rafael Edward 'Ted'      798
Carson, Benjamin S.            343
Rubio, Marco                   168
Stein, Jill                     59
Kasich, John R.                 46
Fiorina, Carly                  40
Bush, Jeb                       32
O'Malley, Martin Joseph         32
Paul, Rand                      30
Johnson, Gary                   18
Walker, Scott                   10
Lessig, Lawrence                 9
Christie, Christopher J.         7
Graham, Lindsey O.               4
Webb, James Henry Jr.            4
Gilmore, James S III             2
Pataki, George E.                1
Name: cmte_id, dtype: int64

Jill Stein got more donations then Rand Paul and Gary Johnson combined which I found surprising

Total Raised by Canidate

In [19]:
total_donations = df.groupby("cand_nm").sum().sort_values(by="contb_receipt_amt",ascending=True)
In [20]:
total_donations["contb_receipt_amt"].plot(kind="bar",figsize=(12,5))
Out[20]:
<matplotlib.axes._subplots.AxesSubplot at 0xe3c36d8>

Average Ammount Donated to Canidate

In [21]:
avg_donations = df.groupby("cand_nm").mean().sort_values(by="contb_receipt_amt")
avg_donations["contb_receipt_amt"].plot(kind="bar",figsize=(12,5))
Out[21]:
<matplotlib.axes._subplots.AxesSubplot at 0xf1f4f60>

I expected the average donation to Sanders to be low, but not dead last, lets look at lower donations between him and Hillary

In [22]:
# Getting a dataframe of just bernie and Hillary
dem_df = df[df['cand_nm'].isin(['Sanders, Bernard', 'Clinton, Hillary Rodham'])]
In [23]:
# only getting values under 100
don_df_15 = dem_df[dem_df.contb_receipt_amt < 15]
don_df_25 = dem_df[dem_df.contb_receipt_amt < 25]
don_df_35 = dem_df[dem_df.contb_receipt_amt < 35]
don_df_50 = dem_df[dem_df.contb_receipt_amt < 50]
don_df_75 = dem_df[dem_df.contb_receipt_amt < 75]
don_df_100 = dem_df[dem_df.contb_receipt_amt < 100]
In [24]:
# Grouping by canidate and totaling the ammount
don_df_15 = don_df_15.groupby("cand_nm").sum().sort_values(by="contb_receipt_amt")
don_df_25 = don_df_25.groupby("cand_nm").sum().sort_values(by="contb_receipt_amt")
don_df_35 = don_df_35.groupby("cand_nm").sum().sort_values(by="contb_receipt_amt")
don_df_50 = don_df_50.groupby("cand_nm").sum().sort_values(by="contb_receipt_amt")
don_df_75 = don_df_75.groupby("cand_nm").sum().sort_values(by="contb_receipt_amt")
don_df_100 = don_df_100.groupby("cand_nm").sum().sort_values(by="contb_receipt_amt")
In [25]:
# Concatinating the datasets together
frames = [don_df_15, don_df_25, don_df_35, don_df_50, don_df_75, don_df_100]

dem_concat = pd.concat(frames, keys=['15', '25', '35', '50', '75', '100'])

#resetting the index and dropping the columns we don't need
dem_concat = dem_concat.reset_index()
dem_concat = dem_concat.drop('contbr_zip', axis=1)
dem_concat = dem_concat.drop('file_num', axis=1)
In [26]:
dem_concat
Out[26]:
level_0 cand_nm contb_receipt_amt
0 15 Clinton, Hillary Rodham 21390.09
1 15 Sanders, Bernard 22308.33
2 25 Clinton, Hillary Rodham 39354.24
3 25 Sanders, Bernard 49218.35
4 35 Clinton, Hillary Rodham 112791.32
5 35 Sanders, Bernard 116954.85
6 50 Sanders, Bernard 134043.05
7 50 Clinton, Hillary Rodham 136638.72
8 75 Sanders, Bernard 204599.44
9 75 Clinton, Hillary Rodham 211747.62
10 100 Sanders, Bernard 209575.33
11 100 Clinton, Hillary Rodham 235409.69
In [27]:
# Pivoting by the amt ranges
dem_concat = dem_concat.pivot_table('contb_receipt_amt',index='level_0',columns = 'cand_nm',aggfunc='sum')
In [28]:
new_index= ['15', '25', '35', '50', '75', '100']
dem_concat = dem_concat.reindex(new_index)
dem_concat.head()
Out[28]:
cand_nm Clinton, Hillary Rodham Sanders, Bernard
level_0
15 21390.09 22308.33
25 39354.24 49218.35
35 112791.32 116954.85
50 136638.72 134043.05
75 211747.62 204599.44

Bernie vs. Clinton Small Donations

In [29]:
dem_concat[['Clinton, Hillary Rodham','Sanders, Bernard']].plot(kind='bar',figsize=(12,4))
plt.xlabel('Ammount')
locs, labels = plt.xticks()
plt.setp(labels, rotation=360)
plt.title('Bernie vs. Clinton')
Out[29]:
<matplotlib.text.Text at 0x1070be10>

Bernie out earns Hillary till the $50 Mark and larger values where she pushes ahead, though this data continued
on after Bernie dropped out. For a more accurate picture we would only want donations before Bernie dropped out

RI - Cities

In [30]:
df['contbr_city'].value_counts()
Out[30]:
PROVIDENCE          5428
CRANSTON            1916
SOUTH KINGSTOWN     1754
WARWICK             1553
NEWPORT             1286
NORTH KINGSTOWN     1170
BARRINGTON          1003
EAST PROVIDENCE      968
PAWTUCKET            854
EAST GREENWICH       816
NARRAGANSETT         745
BRISTOL              737
WESTERLY             555
CUMBERLAND           553
PORTSMOUTH           509
LINCOLN              485
WEST WARWICK         456
JAMESTOWN            435
COVENTRY             419
MIDDLETOWN           399
NORTH PROVIDENCE     375
CHARLESTOWN          309
WARREN               291
NEW SHOREHAM         288
TIVERTON             286
SCITUATE             279
WOONSOCKET           264
LITTLE COMPTON       261
GLOCESTER            218
JOHNSTON             202
SMITHFIELD           196
NORTH SMITHFIELD     167
BURRILLVILLE         156
HOPKINTON            143
RICHMOND             132
FOSTER               125
WEST GREENWICH        73
EXETER                62
CENTRAL FALLS         20
Name: contbr_city, dtype: int64
In [31]:
city_df = df.pivot_table('contb_receipt_amt',index='contbr_city',columns = 'Party',aggfunc='sum')
city_df["Total"] = city_df.sum(axis=1)
city_df = city_df.sort_values(by="Total",ascending=True)

city_df.head()
Out[31]:
Party 3rd Party Democrat Republican Total
contbr_city
CENTRAL FALLS NaN 900.00 NaN 900.00
NORTH SMITHFIELD NaN 6817.93 1531.93 8349.86
WEST GREENWICH NaN 672.00 8133.66 8805.66
RICHMOND NaN 4588.55 4408.40 8996.95
BURRILLVILLE NaN 7464.01 1796.69 9260.70

Total Ammount Donated by City

In [32]:
city_df[['Democrat','Republican']].plot(kind='barh',figsize=(10,16),cmap='bwr')
Out[32]:
<matplotlib.axes._subplots.AxesSubplot at 0x1064b0f0>

Average Donation by City

In [33]:
city_mean = df.pivot_table('contb_receipt_amt',index='contbr_city',columns = 'Party',aggfunc='mean')
city_mean["Total"] = city_mean.sum(axis=1)
city_mean = city_mean.sort_values(by="Total",ascending=True)

city_mean[['Democrat','Republican']].plot(kind='barh',figsize=(10,16),cmap='bwr')
Out[33]:
<matplotlib.axes._subplots.AxesSubplot at 0xe708710>

Count of Donations by City

In [34]:
city_count = df.pivot_table('contb_receipt_amt',index='contbr_city',columns = 'Party',aggfunc='count')
city_count["Total"] = city_count.sum(axis=1)
city_count = city_count.sort_values(by="Total",ascending=True)

city_count[['Democrat','Republican']].plot(kind='barh',figsize=(10,16),cmap='bwr')
Out[34]:
<matplotlib.axes._subplots.AxesSubplot at 0x10e746a0>

Jobs

In [35]:
occupation_df = df.pivot_table('contb_receipt_amt',index='contbr_occupation',columns = 'Party',aggfunc='sum')
occupation_df["Total"] = occupation_df.sum(axis=1)
occupation_df = occupation_df.sort_values(by="Total",ascending=True)
occupation_df.shape
Out[35]:
(1334, 4)

Well we can't graph 1,000+ Jobs so lets cut it down

In [36]:
occupation_df = occupation_df[occupation_df['Total'] > 10000]
# remove these rows
occupation_df.drop(['INFORMATION REQUESTED', 'RETIRED'],axis=0,inplace=True)

occupation_df.shape
Out[36]:
(31, 4)

Total Ammount Donated by Career

In [37]:
occupation_df[['Democrat','Republican']].plot(kind='barh',figsize=(10,16),cmap='bwr')
Out[37]:
<matplotlib.axes._subplots.AxesSubplot at 0x1150ba58>
In [38]:
occupation_count = df.pivot_table('contb_receipt_amt',index='contbr_occupation',columns = 'Party',aggfunc='count')
occupation_count["Total"] = occupation_count.sum(axis=1)
occupation_count = occupation_count.sort_values(by="Total",ascending=True)

occupation_count.shape
Out[38]:
(1334, 4)
In [39]:
occupation_count = occupation_count[occupation_count['Total'] > 100]
occupation_count.drop(['INFORMATION REQUESTED', 'RETIRED','DISABLED'],axis=0,inplace=True)

occupation_count.loc['REGISTERED NURSE'] = occupation_count.loc['REGISTERED NURSE'] + occupation_count.loc['RN']
occupation_count.drop('RN', inplace=True)
occupation_count.shape
Out[39]:
(24, 4)

How Many Donated From Each Occupation

In [40]:
occupation_count[['Democrat','Republican']].plot(kind='barh',figsize=(10,16),cmap='bwr')
Out[40]:
<matplotlib.axes._subplots.AxesSubplot at 0x12035518>

Looking at Donations by Company

In [41]:
employer_df = df.pivot_table('contb_receipt_amt',index='contbr_employer',columns = 'Party',aggfunc='sum')
employer_df["Total"] = employer_df.sum(axis=1)
employer_df = employer_df[employer_df['Total'] > 5000]

employer_df.shape
Out[41]:
(53, 4)
In [42]:
employer_df.drop(['INFORMATION REQUESTED PER BEST EFFORTS', 'INFORMATION REQUESTED', 'RETIRED'],axis=0,inplace=True)
# combine like rows
employer_df.loc['SELF-EMPLOYED'] = employer_df.loc['SELF-EMPLOYED'] + employer_df.loc['SELF EMPLOYED'] + employer_df.loc['SELF']
employer_df.loc['NOT EMPLOYED'] = employer_df.loc['NOT EMPLOYED'] + employer_df.loc['NONE']
# remove self employed without dash
employer_df.drop('SELF EMPLOYED',inplace=True)
employer_df.drop('SELF',inplace=True)
employer_df.drop('NONE',inplace=True)

# These were far to large to allow us to see anything else
employer_df.drop('HOMEMAKER',inplace=True)
employer_df.drop('NOT EMPLOYED',inplace=True)
employer_df.drop('SELF-EMPLOYED',inplace=True)

employer_df = employer_df.sort_values(by="Total",ascending=True)

Total donations by Company

In [43]:
employer_df[['Democrat','Republican']].plot(kind='barh',figsize=(10,16),cmap='bwr')
Out[43]:
<matplotlib.axes._subplots.AxesSubplot at 0x11aa55f8>
In [44]:
employer_cnt = df.pivot_table('contb_receipt_amt',index='contbr_employer',columns = 'Party',aggfunc='count')
employer_cnt["Total"] = employer_cnt.sum(axis=1)
employer_cnt = employer_cnt[employer_cnt['Total'] > 50]

employer_cnt.shape
Out[44]:
(41, 4)
In [45]:
employer_cnt.drop(['INFORMATION REQUESTED PER BEST EFFORTS', 'INFORMATION REQUESTED', 'RETIRED'],axis=0,inplace=True)
# combine like rows
employer_cnt.loc['SELF-EMPLOYED'] = employer_cnt.loc['SELF-EMPLOYED'] + employer_cnt.loc['SELF EMPLOYED'] + employer_cnt.loc['SELF'] + employer_cnt.loc['SELF EMPLOYED-EMPLOYED']
employer_cnt.loc['NOT EMPLOYED'] = employer_cnt.loc['NOT EMPLOYED'] + employer_cnt.loc['NONE'] + employer_cnt.loc['UNEMPLOYED']
employer_cnt.loc['UNIVERSITY OF RHODE ISLAND'] = employer_cnt.loc['UNIVERSITY OF RHODE ISLAND'] + employer_cnt.loc['UNIVERSITY OF RHODE ISLAND']
# remove self employed without dash
employer_cnt.drop('SELF EMPLOYED',inplace=True)
employer_cnt.drop('SELF EMPLOYED-EMPLOYED',inplace=True)
employer_cnt.drop('SELF',inplace=True)
employer_cnt.drop('NONE',inplace=True)
employer_cnt.drop('UNEMPLOYED',inplace=True)
employer_cnt.drop('UNIVERSITY OF RI',inplace=True)

# These were far to large to allow us to see anything else
employer_cnt.drop('NOT EMPLOYED',inplace=True)
employer_cnt.drop('SELF-EMPLOYED',inplace=True)

employer_cnt = employer_cnt.sort_values(by="Total",ascending=True)

Count of how many donations per Company

In [46]:
employer_cnt[['Democrat','Republican']].plot(kind='barh',figsize=(10,16),cmap='bwr')
Out[46]:
<matplotlib.axes._subplots.AxesSubplot at 0x10480ac8>

Counties

In [47]:
df['County'].value_counts()
Out[47]:
PROVIDENCE    12206
WASHINGTON     5158
KENT           3317
NEWPORT        3176
BRISTOL        2031
Name: County, dtype: int64

Total Ammount Donated by County

In [48]:
county_df = df.pivot_table('contb_receipt_amt',index='County',columns = 'Party',aggfunc='sum')
county_df["Total"] = county_df.sum(axis=1)
county_df = county_df.sort_values(by="Total",ascending=True)

county_df[['Democrat','Republican']].plot(kind='barh',figsize=(12,6),cmap='bwr')
Out[48]:
<matplotlib.axes._subplots.AxesSubplot at 0x12c79160>

Average Donation by County

In [49]:
county_df = df.pivot_table('contb_receipt_amt',index='County',columns = 'Party',aggfunc='mean')
county_df["Total"] = county_df.mean(axis=1)
county_df = county_df.sort_values(by="Total",ascending=True)

county_df[['Democrat','Republican']].plot(kind='barh',figsize=(12,6),cmap='bwr')
Out[49]:
<matplotlib.axes._subplots.AxesSubplot at 0x132eae48>

Count of Donations by County

In [50]:
county_df = df.pivot_table('contb_receipt_amt',index='County',columns = 'Party',aggfunc='count')
county_df["Total"] = county_df.sum(axis=1)
county_df = county_df.sort_values(by="Total",ascending=True)

county_df[['Democrat','Republican']].plot(kind='barh',figsize=(12,6),cmap='bwr')
Out[50]:
<matplotlib.axes._subplots.AxesSubplot at 0x133827b8>