I Viz RI
Created by SmirkyGraphs. Code: GitHub. Source: BOE.
Breakdown¶
General Info¶
$525,881.75 was raised from 992 contributions from 29 different states
When Donations Occured¶
- More donations later in the quarter 55% of the people donated in September
- Only 14% of donations were in the first month of the quarter
- Most of the money was raised outside of weekends, Sunday being the lowest and Tuesday being the highest
What Was Donated¶
- 53% (525) were paid by Credit/Debit
- 45% (448) were paid by Check
- 992 donations from 904 doners
- 525881.75 was raised
- Average was 530
- Highest was 1174.25, lowest was 1
- Top 5 most frequent in order were: 1000, 500, 250, 25, 10
Where Donations Came From¶
- 29 diffrent States
- 225 different Cities
- Top 5 States in order by value: RI, NY, MA, CT, CO
- Top 5 Cities in order by value: Providence, New York, Barrington, Jamestown, Denver
Where RI Donations Came From¶
This looks specifically at donations where the person was living in Rhode Island
- 600 donations from RI
- Donations from 36 of the 39 municipalities in RI
- Top 5 cities/towns in order: Providence, Barrington, Jamestown, East Greenwich, Cranston
- Providence made up 29% of the doners from RI
- Providence made up 29% of the total donated from RI
- Counties in order by number of doners: Providence, Washington, Newport, Kent, Bristol
- Counties in order by sum donated: Providence, Newport, Washington, Bristol, Kent
In State vs. Out of State¶
Comparing donations based on whether they live in RI or not
- 60% (600) of donations were from Rhode Island
- 40% (392) of donations were from another state
- 47% (248681.22) of money donated was from Rhode Islanders
- 53% (277200.53) of money donated was from out of state
- Average in state 414
- Average out of state 707
Where People Worked¶
- 489 unique Employers
- 65,309 raised from Retirees
- 38,680 raised from Homemakers
- 16,731 from Self-Employed
- 15,261 from "Info Requested" (Left Empty)
- Top 5 Companies: RI Medical Imaging, Pfizer Inc, General Dynamics, Citizens Bank, Pannone Lopes & Devereaux & West LLC
All Values Included
- 56% (557) Worked In RI
- 44% (435) Worked Outside RI
- 44% (230305.00) Of the Money came from people who work in RI
- 56% (295576.75) Of the Money came from people who work out of state
Extras Removed
- 55% (541) Worked in RI
- 45% (434) Worked Outside RI
Who Donated¶
- 97% of Donations came from Individuals
- 904 Unique Doners
- 8 Interest, 7 PAC, 5 Party Donations
- Top 5 first names: David, Michael, Susan, William, Robert
- Top 5 last names: Ardaya, Kelly, Richardson, Sipprelle, Rogers
Donations 1k and Over¶
In state 37% out of state 63%
Data Importing & Exploring¶
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
sns.set()
import datetime
my_color = sns.color_palette()
In [2]:
# loading the data
df = pd.read_csv("gina.csv", parse_dates=['receipt_dt'])
# removing personal address
df = df.drop(['address'], axis=1)
In [3]:
# Preview
df.head()
Out[3]:
In [4]:
df.info()
In [5]:
df.shape
Out[5]:
In [6]:
df.contb_amt.sum()
Out[6]:
There are 992 Donations in Q3 making a total of $525,881.75
Those missing First/Last name are PAC's/Party donations
In [7]:
date_df = df.groupby(['receipt_dt'],as_index=False).sum()
date_df.plot('receipt_dt','contb_amt',figsize=(12,6),marker='', legend=False,
linestyle='-',color='purple', xlim=('2017-07-01','2017-10-01'))
Out[7]:
In [8]:
# Top 5 Days
date_df.sort_values(by='contb_amt',ascending=False).head()
Out[8]:
In [9]:
count_df = df.groupby(['receipt_dt'],as_index=False).count()
count_df.plot('receipt_dt','contb_amt',figsize=(12,6),marker='',linestyle='-',color='purple', xlim=('2017-07-01','2017-10-01'))
Out[9]:
In [10]:
mean_df = df.groupby(['receipt_dt'],as_index=False).mean()
mean_df.plot('receipt_dt','contb_amt',figsize=(12,6),marker='',linestyle='-',color='purple', xlim=('2017-07-01','2017-10-01'))
Out[10]:
In [11]:
weekday_df = df
weekday_df['weekday'] = pd.Categorical(weekday_df['weekday'],
categories=['Monday','Tuesday','Wednesday','Thursday',
'Friday','Saturday', 'Sunday'], ordered=True)
In [12]:
weekday_df_sum = weekday_df.pivot_table(index=weekday_df['weekday'], values='contb_amt',
aggfunc='sum').plot(kind='bar',rot=0,legend=False, title='Sum of Donations by Weekday')
In [13]:
weekday_df_count = weekday_df.pivot_table(index=weekday_df['weekday'], values='contb_amt',
aggfunc='count').plot(kind='bar',rot=0, legend=False, title='Count of Donations by Weekday')
In [14]:
weekday_df_sum = weekday_df.pivot_table(index=weekday_df['weekday'], values='contb_amt',
aggfunc='mean').plot(kind='bar',rot=0, legend=False, title='Average Donated by Weekday')
In [15]:
df['month'] = df['receipt_dt'].dt.month
In [16]:
df['receipt_dt'].dt.month.value_counts()
Out[16]:
In [17]:
month_sum = df.pivot_table(index=df['month'], values='contb_amt',
aggfunc='sum').plot(kind='bar',rot=0,legend=False, title='Sum of Donations by Month')
In [18]:
month_sum = df.pivot_table(index=df['month'], values='contb_amt',
aggfunc='count').plot(kind='bar',rot=0,legend=False, title='Count of Donations by Month')
In [19]:
df.tran_type.value_counts()
Out[19]:
In [20]:
df.tran_type.value_counts(normalize=True)
Out[20]:
In [21]:
sns.factorplot('tran_type',data=df,kind="count")
Out[21]:
In [22]:
df['contb_amt'].sum()
Out[22]:
In [23]:
df['contb_amt'].mode()
Out[23]:
In [24]:
df['contb_amt'].describe()
Out[24]:
I was surprised to see that the average donation was 530 compared to the presidential race when it was only 100
Lowest donation was 1 and highest was 1174
In [25]:
df['contb_amt'].hist(bins=25)
Out[25]:
In [26]:
df['contb_amt'].value_counts().head()
Out[26]:
Surprisingly 1000 was the most frequent donation
The top most frequent donation values were much higher then those during the presidential race
In [27]:
# 1 State was labeled "Ri" So replace it with RI
df = df.replace(['Ri'],'RI')
In [28]:
df.state.nunique()
Out[28]:
In [29]:
df.state.value_counts()
Out[29]:
In [30]:
where_sum = df.pivot_table(index=df['state'], values='contb_amt', aggfunc='sum').sort_values(
by='contb_amt').plot(kind='barh', rot=0, legend=False, title='Total Donated by State')
In [31]:
where_count = df.pivot_table(index=df['state'], values='contb_amt', aggfunc='count').sort_values(
by='contb_amt').plot(kind='barh', rot=0, legend=False, title='Count of Donations by State')
In [32]:
where_avg = df.pivot_table(index=df['state'], values='contb_amt', aggfunc='mean').sort_values(
by='contb_amt').plot(kind='barh', rot=0, legend=False, title='Avg Donated by State')
In [33]:
df.city.nunique()
Out[33]:
In [34]:
# Top 5 Cities
city_df = df.pivot_table('contb_amt',index='city',aggfunc='sum')
city_df = city_df.sort_values(by="contb_amt",ascending=False)
city_df.head()
Out[34]:
In [35]:
city_sum = df.pivot_table(index=df['city'], values='contb_amt', aggfunc='sum').sort_values(
by='contb_amt').nlargest(5, 'contb_amt').plot(kind='barh', color=my_color, legend=False, title='Total Donated')
In [36]:
city_count = df.pivot_table(index=df['city'], values='contb_amt', aggfunc='count').sort_values(
by='contb_amt').nlargest(5, 'contb_amt').plot(kind='barh', color=my_color, legend=False, title='Num of Donations')
In [37]:
# Just donations from RI
RI_df = df[df.state == 'RI']
In [38]:
RI_df.city.unique()
Out[38]:
In [39]:
# Connect small towns to the City/Town they're part of
RI_df = RI_df.replace(['Pascoag'],'Burrillville')
RI_df = RI_df.replace(['Wakefield','Kingston','Peace Dale'],'South Kingstown')
RI_df = RI_df.replace(['Saunderstown','N Kingstown','North Kingstownq'],'North Kingstown')
RI_df = RI_df.replace(['E Greenwich'],'East Greenwich')
RI_df = RI_df.replace(['PROVIDENCE'],'Providence')
RI_df = RI_df.replace(['Harmony'],'Glocester')
RI_df = RI_df.replace(['Riverside','Rumford'],'East Providence')
RI_df = RI_df.replace(['Block Island'],'New Shoreham')
RI_df = RI_df.replace(['North Scituate'],'Scituate')
RI_df = RI_df.replace(['Albion'],'Lincoln')
In [40]:
RI_df.city.nunique()
Out[40]:
In [41]:
ri_city = df.pivot_table(index=RI_df['city'], values='contb_amt', aggfunc='count').sort_values(ascending=True,
by='contb_amt').plot(kind='barh', figsize=(12,9), legend=False, title='Num of Donations by City')
In [42]:
ri_city = df.pivot_table(index=RI_df['city'], values='contb_amt', aggfunc='sum').sort_values(ascending=True,
by='contb_amt').plot(kind='barh', figsize=(12,9), legend=False, title='Total Donated by City')
In [43]:
RI_df['city'].value_counts().sum()
Out[43]:
In [44]:
RI_df['city'].value_counts().head()
Out[44]:
In [45]:
RI_Sum = RI_df.pivot_table('contb_amt',index='city',aggfunc='sum')
RI_Sum = RI_Sum.sort_values(by='contb_amt', ascending=False)
RI_Sum.sum()
Out[45]:
In [46]:
RI_Sum
Out[46]:
In [47]:
# dictionary of RI Counties
county_map = {'Barrington': 'BRISTOL',
'Bristol': 'BRISTOL',
'Burrillville': 'PROVIDENCE',
'Central Falls': 'PROVIDENCE',
'Charlestown': 'WASHINGTON',
'Coventry': 'KENT',
'Cranston': 'PROVIDENCE',
'Cumberland': 'PROVIDENCE',
'East Greenwich': 'KENT',
'East Providence': 'PROVIDENCE',
'Exeter': 'WASHINGTON',
'Foster': 'PROVIDENCE',
'Glocester': 'PROVIDENCE',
'Hopkinton': 'WASHINGTON',
'Jamestown': 'NEWPORT',
'Johnston': 'PROVIDENCE',
'Lincoln': 'PROVIDENCE',
'Little Compton': 'NEWPORT',
'Middletown': 'NEWPORT',
'Narragansett': 'WASHINGTON',
'Newport': 'NEWPORT',
'New Shoreham': 'WASHINGTON',
'North Kingstown': 'WASHINGTON',
'North Providence': 'PROVIDENCE',
'North Smithfield': 'PROVIDENCE',
'Pawtucket': 'PROVIDENCE',
'Portsmouth': 'NEWPORT',
'Providence': 'PROVIDENCE',
'Richmond': 'WASHINGTON',
'Scituate': 'PROVIDENCE',
'Smithfield': 'PROVIDENCE',
'South Kingstown': 'WASHINGTON',
'Tiverton': 'NEWPORT',
'Warren': 'BRISTOL',
'Warwick': 'KENT',
'Westerly': 'WASHINGTON',
'West Greenwich': 'KENT',
'West Warwick': 'KENT',
'Woonsocket': 'PROVIDENCE'}
# creating a party column and mapping party to canidate
RI_df['County'] = RI_df.city.map(county_map)
In [48]:
RI_df['County'].value_counts()
Out[48]:
In [49]:
ri_city = df.pivot_table(index=RI_df['County'], values='contb_amt', aggfunc='count').sort_values(ascending=True,
by='contb_amt').plot(kind='barh', legend=False, title='Num of Donations by County')
In [50]:
ri_city = df.pivot_table(index=RI_df['County'], values='contb_amt', aggfunc='sum').sort_values(ascending=True,
by='contb_amt').plot(kind='barh', legend=False, title='Total Donated by County')
In [51]:
def in_ri(state):
if state == 'RI':
return 'in state'
else:
return 'out of state'
In [52]:
df['lives'] = df['state'].apply(in_ri)
In [53]:
df_lives = df
In [54]:
df['lives'] = pd.Categorical(df['lives'], categories=['in state','out of state'], ordered=True)
In [55]:
df.head()
Out[55]:
In [56]:
count_df = df.pivot_table(index=df['lives'], values='contb_amt', aggfunc='count').plot(kind='bar',
rot=0, color=my_color, legend=False, title='Count of Donation')
In [57]:
print(df['lives'].value_counts())
(df['lives'].value_counts(normalize=True))
Out[57]:
60% (600) Were from Rhode Island
40% (392) Were from another state
In [58]:
mean_df = df.pivot_table(index=df['lives'], values='contb_amt', aggfunc='mean').plot(kind='bar',
rot=0, color=my_color, legend=False, title='Average Donation')
In [59]:
sum_df = df.pivot_table(index=df['lives'], values='contb_amt', aggfunc='sum').plot(kind='bar',
rot=0, color=my_color, legend=False, title='Total Donated')
In [60]:
percent_df = df.pivot_table(index=df['lives'], values='contb_amt', aggfunc='sum')
In [61]:
total_sum = percent_df.contb_amt.sum()
df['lives'] = df['state'].apply(in_ri)
percent_df['Percent'] = percent_df['contb_amt'] / total_sum
In [62]:
percent_df.head()
Out[62]:
In [63]:
mean_df = df.pivot_table(index=df['lives'], values='contb_amt', aggfunc='mean')
mean_df.head()
Out[63]:
In [64]:
employer_df = df.pivot_table('contb_amt',index='employer',aggfunc='sum')
# Combining Electric Boat & Genral Dynamics
employer_df.loc['General Dynamics'] = employer_df.loc['Electric Boat Corporation'] + employer_df.loc['General Dynamics']
employer_df.drop('Electric Boat Corporation',inplace=True)
employer_df = employer_df.sort_values(by = 'contb_amt',ascending=True)
In [65]:
employer_df.count()
Out[65]:
Donations from people who worked at 489 different companies, lets narrow it down to companies over $1000
In [66]:
# Getting all employer records over $1000
employer_df = employer_df[employer_df['contb_amt'] > 1000]
employer_df.plot(kind='barh',figsize=(10,16))
Out[66]:
In [67]:
# Graphing Only Companies
employer_df.drop('Homemaker',inplace=True)
employer_df.drop('Retired',inplace=True)
employer_df.drop('Self Employed',inplace=True)
employer_df.drop('Info Requested',inplace=True)
employer_df = employer_df.sort_values(by = 'contb_amt',ascending=True)
In [68]:
# Getting all employer records over $1000
employer_df = employer_df[employer_df['contb_amt'] > 1000]
employer_df.plot(kind='barh',figsize=(10,16))
Out[68]:
In [69]:
def in_ri(employ_state):
if employ_state == 'RI':
return 'in state'
else:
return 'out of state'
In [70]:
df['works'] = df['employ_state'].apply(in_ri)
In [71]:
df.head()
Out[71]:
In [72]:
# Including Extras
count_df = df.pivot_table(index=df['works'], values='contb_amt', aggfunc='count').plot(kind='bar',
rot=0, color=my_color, legend=False, title='Count of Donation')
In [73]:
df['works'].value_counts()
Out[73]:
In [74]:
# Including Extras
count_df = df.pivot_table(index=df['works'], values='contb_amt', aggfunc='sum').plot(kind='bar',
rot=0, color=my_color, legend=False, title='Total Donated')
In [75]:
# Including Extras getting % of sum
percent_df = df.pivot_table(index=df['works'], values='contb_amt', aggfunc='sum')
total_sum = df.contb_amt.sum()
percent_df['Percent'] = percent_df['contb_amt'] / total_sum
percent_df.head()
Out[75]:
In [76]:
# Removing Extras
emp_df = df[df.employer != 'Homemaker']
emp_df = df[df.employer != 'Retired']
emp_df = df[df.employer != 'Self Employed']
emp_df = df[df.employer != 'Info Requested']
emp_df = df[df.employer != 'Disabled']
In [77]:
# Extras Removed
count_df = emp_df.pivot_table(index=df['works'], values='contb_amt', aggfunc='count').plot(kind='bar',
rot=0, color=my_color, legend=False, title='Count of Donation')
In [78]:
emp_df['works'].value_counts()
Out[78]:
In [79]:
# Extras Removed
count_df = emp_df.pivot_table(index=df['works'], values='contb_amt', aggfunc='sum').plot(kind='bar',
rot=0, color=my_color, legend=False, title='Total Donated')
In [80]:
df.contbr_nm.nunique()
Out[80]:
In [81]:
df.first_nm.value_counts().head()
Out[81]:
In [82]:
df.last_nm.value_counts().head()
Out[82]:
In [83]:
df.contb_type.value_counts()
Out[83]:
In [84]:
df.contb_type.value_counts(normalize=True)
Out[84]:
In [85]:
don_1k = df[df['contb_amt'] >= 1000]
In [86]:
don_1k = df[(df['contb_amt'] >= 1000)]
In [87]:
don_1k.lives.value_counts(normalize=True)
Out[87]:
In [88]:
sum_df = don_1k.pivot_table(index=df_lives['lives'], values='contb_amt', aggfunc='sum').plot(kind='bar',
rot=0, color=my_color, legend=False, title='Total Donated')
In [89]:
don_df_100 = df[df.contb_amt <= 100]
don_df_250 = df[df.contb_amt <= 250]
don_df_350 = df[df.contb_amt <= 350]
don_df_500 = df[df.contb_amt <= 500]
don_df_750 = df[df.contb_amt <= 750]
don_df_1000 = df[df.contb_amt <= 1000]
In [90]:
# Concatinating the datasets together
frames = [don_df_100, don_df_250, don_df_350, don_df_500, don_df_750, don_df_1000]
don_concat = pd.concat(frames, keys=['100', '250', '350', '500', '750', '1000'])
# resetting the index and dropping the columns we don't need
don_concat = don_concat.reset_index()
In [91]:
# Pivoting by the amt ranges
don_concat = don_concat.pivot_table('contb_amt',index='level_0',columns = 'lives',aggfunc='sum')
In [92]:
new_index= ['100', '250', '350', '500', '750', '1000']
don_concat = don_concat.reindex(new_index)
don_concat.head()
Out[92]:
In [93]:
don_concat[['in state','out of state']].plot(kind='bar',figsize=(12,4))
plt.xlabel('Ammount')
locs, labels = plt.xticks()
plt.setp(labels, rotation=360)
plt.title('In State vs. Out of State')
Out[93]:
In [94]:
# Top 4 Donated Values
don_25 = df[df.contb_amt == 25]
don_250 = df[df.contb_amt == 250]
don_500 = df[df.contb_amt == 500]
don_1000 = df[df.contb_amt == 1000]
In [95]:
# Concatinating the datasets together
frames = [don_25, don_250, don_500, don_1000]
don_concat = pd.concat(frames, keys=['25', '250', '500', '1000'])
#resetting the index and dropping the columns we don't need
don_concat = don_concat.reset_index()
In [96]:
# Pivoting by the amt ranges
don_concat = don_concat.pivot_table('contb_amt',index='level_0',columns = 'lives',aggfunc='sum')
In [97]:
don_concat.head()
Out[97]:
In [98]:
new_index= ['25', '250', '500', '1000']
don_concat = don_concat.reindex(new_index)
don_concat.head()
Out[98]:
In [99]:
don_concat[['in state','out of state']].plot(kind='bar',figsize=(12,4))
plt.xlabel('Ammount')
locs, labels = plt.xticks()
plt.setp(labels, rotation=360)
plt.title('In State vs. Out of State')
Out[99]:
