In [7]:

    

from pandas import DataFrame, read_csv

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

pd.set_option('display.line_width', 5000) 
pd.set_option('display.max_columns', 60) 

%matplotlib inline


    

In [9]:

    

# Read in the data
Location = "data/Campaign_Finance_-_FPPC_Form_460_-_Schedule_A_-_Monetary_Contributions.csv"
df = read_csv(Location)

# Convert dollars contributed to a float
df['Tran_Amt1'] = df['Tran_Amt1'].str[1:].astype(float)

# Remove all non-mayor race contributions
mayor_ids = ['1357609', '1354678', '1362261', '1359017']
df = df[df['Filer_ID'].isin(mayor_ids)]


    

In [10]:

    

# Sum by recipient and contributor
totals = df.groupby(['Filer_NamL', 'Tran_Emp']).sum()

# Sort each recipient group
sort_amt = lambda x: x.sort('Tran_Amt1', ascending=False)
candidate_max = totals.groupby(level=0).apply(sort_amt)

#candidate_max.groupby(level=0).apply(lambda x: x[:10]).reset_index([0,1], drop=True)['Tran_Amt1']


    

In [11]:

    

def aggregate(x):
    Dollars = x['Tran_Amt1'].sum()
    Contributors = x['Tran_Amt1'].count()
    return pd.Series([Dollars, Contributors], index=['Dollars', 'Contributors'])
    

# Sum by recipient and contributor
totals = df.groupby(['Filer_NamL', 'Tran_Emp']).apply(aggregate)
#agg({'Tran_Amt1' : np.sum,
#Tran_Amt2' : np.count_nonzero})

# Sort each recipient group
sort_amt = lambda x: x.sort('Dollars', ascending=False)
candidate_max = totals.groupby(level=0).apply(sort_amt)

candidate_max.groupby(level=0).apply(lambda x: x[:10]).reset_index([0,1], drop=True)


    

    
Out[11]:






  

    

      

      

      
Dollars
      
Contributors
    
    

      
Filer_NamL
      
Tran_Emp
      

      

    
  
  

    

      
Joe Tuman for Mayor 2014
      
Retired
      
 28585
      
 114
    
    

      
Self employed
      
 15850
      
  53
    
    

      
A B & I Foundary
      
  5600
      
   8
    
    

      
Mechanics Bank
      
  3250
      
   4
    
    

      
SKC Communication Products
      
  2100
      
   2
    
    

      
Mills College
      
  1900
      
   3
    
    

      
Self-employed
      
  1900
      
   3
    
    

      
Reed Smith
      
  1700
      
   2
    
    

      
Starr Finley
      
  1400
      
   2
    
    

      
Piedmont USD
      
  1400
      
   1
    
    

      
Libby Schaaf for Oakland Mayor 2014
      
Retired
      
  6350
      
  15
    
    

      
none
      
  5000
      
   8
    
    

      
retired
      
  3550
      
  10
    
    

      
None
      
  2100
      
   3
    
    

      
SF Regional Center
      
  2100
      
   3
    
    

      
Terra Tech
      
  1400
      
   2
    
    

      
Mark Borsuk  Inc.
      
  1400
      
   2
    
    

      
n/a
      
  1400
      
   2
    
    

      
City and County of San Francisco
      
  1400
      
   2
    
    

      
San Francisco Regional Center
      
  1400
      
   2
    
    

      
Parker for Oakland Mayor 2014
      
Vaquero Capital
      
  2100
      
   3
    
    

      
DaVita
      
  1900
      
   7
    
    

      
IBM
      
  1500
      
   4
    
    

      
The Lapham Company Inc.
      
  1400
      
   3
    
    

      
Reed Smith
      
  1400
      
   3
    
    

      
American City Pest & Termite
      
  1400
      
   2
    
    

      
Fred Thompson Executive Search
      
  1400
      
   2
    
    

      
Wendel Rosen Black & Dean
      
  1400
      
   2
    
    

      
AE3 Partners
      
  1400
      
   2
    
    

      
Braddock & Logan
      
  1400
      
   2
    
    

      
Re-Elect Mayor Quan 2014
      
retired
      
 13495
      
  66
    
    

      
Lawrence Berkeley National Laboratory
      
  1799
      
   4
    
    

      
Old Republic Title Company
      
  1400
      
   2
    
    

      
not employed
      
  1400
      
   2
    
    

      
Shorenstein Properties LLC
      
  1050
      
   3
    
    

      
City of Oakland
      
  1000
      
   7
    
    

      
United Health Group
      
   800
      
   2
    
    

      
Pacific Life Insurance Company
      
   700
      
   1
    
    

      
Store Development
      
   700
      
   1
    
    

      
Neural ID
      
   700
      
   1
    
  

40 rows × 2 columns

In [11]:

    




    

In [ ]: