In [1]:

    

from pandas import *
import matplotlib as mpl
mpl.rc('figure', figsize=(12, 6))
set_printoptions(max_columns=18)
months = {'JAN' : 1, 'FEB' : 2, 'MAR' : 3, 'APR' : 4, 'MAY' : 5, 'JUN' : 6,
          'JUL' : 7, 'AUG' : 8, 'SEP' : 9, 'OCT': 10, 'NOV': 11, 'DEC' : 12}
of_interest = ['Obama, Barack', 'Romney, Mitt', 'Santorum, Rick', 
               'Paul, Ron', 'Gingrich, Newt']
parties = {
  'Bachmann, Michelle': 'Republican',
  'Romney, Mitt': 'Republican',
  'Obama, Barack': 'Democrat',
  "Roemer, Charles E. 'Buddy' III": 'Reform',
  'Pawlenty, Timothy': 'Republican',
  'Johnson, Gary Earl': 'Libertarian',
  'Paul, Ron': 'Republican',
  'Santorum, Rick': 'Republican',
  'Cain, Herman': 'Republican',
  'Gingrich, Newt': 'Republican',
  'McCotter, Thaddeus G': 'Republican',
  'Huntsman, Jon': 'Republican',
  'Perry, Rick': 'Republican'           
 }


    

In [2]:

    

fec = read_csv('P00000001-ALL.txt')


    

In [3]:

    

fec


    

    
Out[3]:





<class 'pandas.core.frame.DataFrame'>
Int64Index: 536041 entries, 0 to 536040
Data columns:
cmte_id              536041  non-null values
cand_id              536041  non-null values
cand_nm              536041  non-null values
contbr_nm            536041  non-null values
contbr_city          536026  non-null values
contbr_st            536040  non-null values
contbr_zip           535973  non-null values
contbr_employer      531417  non-null values
contbr_occupation    531448  non-null values
contb_receipt_amt    536041  non-null values
contb_receipt_dt     536041  non-null values
receipt_desc         8479  non-null values
memo_cd              49718  non-null values
memo_text            52740  non-null values
form_tp              536041  non-null values
file_num             536041  non-null values
dtypes: float64(1), int64(1), object(14)

In [4]:

    

parties    # dictionary


    

    
Out[4]:





{'Bachmann, Michelle': 'Republican',
 'Cain, Herman': 'Republican',
 'Gingrich, Newt': 'Republican',
 'Huntsman, Jon': 'Republican',
 'Johnson, Gary Earl': 'Libertarian',
 'McCotter, Thaddeus G': 'Republican',
 'Obama, Barack': 'Democrat',
 'Paul, Ron': 'Republican',
 'Pawlenty, Timothy': 'Republican',
 'Perry, Rick': 'Republican',
 "Roemer, Charles E. 'Buddy' III": 'Reform',
 'Romney, Mitt': 'Republican',
 'Santorum, Rick': 'Republican'}

In [5]:

    

fec.cand_nm


    

    
Out[5]:





0     Bachmann, Michelle
1     Bachmann, Michelle
2     Bachmann, Michelle
3     Bachmann, Michelle
4     Bachmann, Michelle
5     Bachmann, Michelle
6     Bachmann, Michelle
7     Bachmann, Michelle
8     Bachmann, Michelle
9     Bachmann, Michelle
10    Bachmann, Michelle
11    Bachmann, Michelle
12    Bachmann, Michelle
13    Bachmann, Michelle
14    Bachmann, Michelle
...
536026    Perry, Rick
536027    Perry, Rick
536028    Perry, Rick
536029    Perry, Rick
536030    Perry, Rick
536031    Perry, Rick
536032    Perry, Rick
536033    Perry, Rick
536034    Perry, Rick
536035    Perry, Rick
536036    Perry, Rick
536037    Perry, Rick
536038    Perry, Rick
536039    Perry, Rick
536040    Perry, Rick
Name: cand_nm, Length: 536041

In [6]:

    

fec['party'] = fec.cand_nm.map(parties)  # map can take dictionary, series... 
                                         # Alternatively, use list comprehension: [parties[x] for x in fec.cand_nm]


    

In [8]:

    

fec.ix[0]    # A single record.


    

    
Out[8]:





cmte_id                       C00410118
cand_id                       P20002978
cand_nm              Bachmann, Michelle
contbr_nm               HARVEY, WILLIAM
contbr_city                      MOBILE
contbr_st                            AL
contbr_zip                    366010290
contbr_employer                 RETIRED
contbr_occupation               RETIRED
contb_receipt_amt                   250
contb_receipt_dt              20-JUN-11
receipt_desc                        NaN
memo_cd                             NaN
memo_text                           NaN
form_tp                           SA17A
file_num                         736166
party                        Republican
Name: 0

In [9]:

    

fec.party.value_counts()   # Doing it high level way using histogram.


    

    
Out[9]:





Democrat       292400
Republican     237575
Reform           5364
Libertarian       702

In [10]:

    

np.unique(fec.party)


    

    
Out[10]:





134010       Democrat
361727    Libertarian
356462         Reform
6067       Republican
Name: party

In [12]:

    

fec.groupby('party')['contb_receipt_amt'].sum()


    

    
Out[12]:





party
Democrat       8.105758e+07
Libertarian    4.132769e+05
Reform         3.390338e+05
Republican     1.192255e+08
Name: contb_receipt_amt

In [13]:

    

fec.groupby(['party', 'contb_receipt_dt'])['contb_receipt_amt'].sum()   # Not a python date object.


    

    
Out[13]:





party     contb_receipt_dt
Democrat  01-AUG-11           175281.00
          01-DEC-11           651532.82
          01-JAN-12            58098.80
          01-JUL-11           165961.00
          01-JUN-11           145459.00
          01-MAY-11            82644.00
          01-NOV-11           122529.87
          01-OCT-11           148977.00
          01-SEP-11           403297.62
          02-AUG-11           164510.11
          02-DEC-11           216056.96
          02-JAN-12            89743.60
          02-JUL-11            17105.00
          02-JUN-11           422453.00
          02-MAY-11           396675.00
...
Republican  30-JUL-11             12249.04
            30-JUN-11           2744932.63
            30-MAR-11             50240.00
            30-MAY-11             17803.60
            30-NOV-11            809014.83
            30-OCT-11             43913.16
            30-SEP-11           4886331.76
            31-AUG-11           1017735.02
            31-DEC-11           1094376.72
            31-JAN-11              6000.00
            31-JAN-12            869890.41
            31-JUL-11             12781.02
            31-MAR-11             62475.00
            31-MAY-11            301339.80
            31-OCT-11            734601.83
Name: contb_receipt_amt, Length: 1183

In [14]:

    

fec.contb_receipt_dt[0]


    

    
Out[14]:





'20-JUN-11'

In [15]:

    

print months


    

    




{'FEB': 2, 'AUG': 8, 'JAN': 1, 'DEC': 12, 'OCT': 10, 'MAR': 3, 'SEP': 9, 'MAY': 5, 'JUN': 6, 'JUL': 7, 'APR': 4, 'NOV': 11}

In [16]:

    

def convert_date(val):    # function to convert date to python's date object.
    d, m, y = val.split('-')
    m = months[m]
    return datetime(int ('20' + y), m, int(d))

fec.contb_receipt_dt   # output a list of date (not yet in python objects).
fec['contb_receipt_dt'] = fec.contb_receipt_dt.map(convert_date)


    

In [17]:

    

fec.contb_receipt_dt[0]


    

    
Out[17]:





datetime.datetime(2011, 6, 20, 0, 0)

In [18]:

    

fec.groupby(['party', 'contb_receipt_dt'])['contb_receipt_amt'].sum() # Same as above, however, date converted to python object.


    

    
Out[18]:





party     contb_receipt_dt
Democrat  2011-04-04          640235.12
          2011-04-05          307641.00
          2011-04-06          247542.59
          2011-04-07          252336.00
          2011-04-08          295452.00
          2011-04-09          105879.00
          2011-04-10          100373.00
          2011-04-11          360728.00
          2011-04-12          405160.46
          2011-04-13          382836.33
          2011-04-14          306643.50
          2011-04-15          408377.00
          2011-04-16           82658.00
          2011-04-17           70802.00
          2011-04-18          501971.37
...
Republican  2012-01-17           625365.77
            2012-01-18           888681.17
            2012-01-19          1066250.23
            2012-01-20           401298.03
            2012-01-21           374261.81
            2012-01-22           507168.71
            2012-01-23           645477.15
            2012-01-24           462233.66
            2012-01-25           416931.39
            2012-01-26           256406.86
            2012-01-27           368441.82
            2012-01-28            82775.80
            2012-01-29            75220.02
            2012-01-30           255204.80
            2012-01-31           869890.41
Name: contb_receipt_amt, Length: 1183

In [19]:

    

by_date = fec.groupby(['party', 'contb_receipt_dt'])['contb_receipt_amt'].sum()


    

In [22]:

    

by_date.unstack('party')   # Indexes are dates. Columns are political parties.


    

    
Out[22]:





<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 376 entries, 2011-01-01 00:00:00 to 2012-01-31 00:00:00
Data columns:
Democrat       303  non-null values
Libertarian    190  non-null values
Reform         314  non-null values
Republican     376  non-null values
dtypes: float64(4)

In [23]:

    

by_date.unstack('party').cumsum().plot()


    

    
Out[23]:





<matplotlib.axes.AxesSubplot at 0x106e6ef90>






    

In [24]:

    

s = by_date.unstack('party')[-5:].stack()


    

In [25]:

    

s    # Note the inner level indexes. Not every date has 3 parties. Top level indexes are dates. stack() makes 1 dimensional.
     # Labels propagated down for all 3 observations: political parties.


    

    
Out[25]:





contb_receipt_dt  party     
2012-01-27        Democrat       305785.47
                  Reform           3176.37
                  Republican     368441.82
2012-01-28        Democrat       235492.85
                  Reform            175.00
                  Republican      82775.80
2012-01-29        Democrat        93177.00
                  Reform            200.00
                  Republican      75220.02
2012-01-30        Democrat       435921.72
                  Reform            130.00
                  Republican     255204.80
2012-01-31        Democrat      1418410.31
                  Reform            150.00
                  Republican     869890.41

In [30]:

    

set_printoptions(notebook_repr_html=False)  # turn off html, i.e., without table.
s.unstack()    # unstack() 2 dimensional.


    

    
Out[30]:





party               Democrat   Reform  Republican
contb_receipt_dt                                 
2012-01-27         305785.47  3176.37   368441.82
2012-01-28         235492.85   175.00    82775.80
2012-01-29          93177.00   200.00    75220.02
2012-01-30         435921.72   130.00   255204.80
2012-01-31        1418410.31   150.00   869890.41

In [31]:

    

s.unstack().stack()  # 2 dimensional to 1 dimensional


    

    
Out[31]:





contb_receipt_dt  party     
2012-01-27        Democrat       305785.47
                  Reform           3176.37
                  Republican     368441.82
2012-01-28        Democrat       235492.85
                  Reform            175.00
                  Republican      82775.80
2012-01-29        Democrat        93177.00
                  Reform            200.00
                  Republican      75220.02
2012-01-30        Democrat       435921.72
                  Reform            130.00
                  Republican     255204.80
2012-01-31        Democrat      1418410.31
                  Reform            150.00
                  Republican     869890.41

In [34]:

    

df = s.unstack('party')   # data frame


    

In [33]:

    

s.unstack('contb_receipt_dt')


    

    
Out[33]:





contb_receipt_dt  2012-01-27  2012-01-28  2012-01-29  2012-01-30  2012-01-31
party                                                                       
Democrat           305785.47   235492.85    93177.00   435921.72  1418410.31
Reform               3176.37      175.00      200.00      130.00      150.00
Republican         368441.82    82775.80    75220.02   255204.80   869890.41

In [35]:

    

concat([df, df], axis=1, keys=['A', 'B'])  # join 2 copies of data frame.
                                           # standard hierarchical indexing stuff.


    

    
Out[35]:





                           A                                B                     
party               Democrat   Reform  Republican    Democrat   Reform  Republican
contb_receipt_dt                                                                  
2012-01-27         305785.47  3176.37   368441.82   305785.47  3176.37   368441.82
2012-01-28         235492.85   175.00    82775.80   235492.85   175.00    82775.80
2012-01-29          93177.00   200.00    75220.02    93177.00   200.00    75220.02
2012-01-30         435921.72   130.00   255204.80   435921.72   130.00   255204.80
2012-01-31        1418410.31   150.00   869890.41  1418410.31   150.00   869890.41

In [36]:

    

concat([df, df], axis=1, keys=['A', 'B'])['A']   # select out columns by the first key 'A'. Or select out the 'B' group.conc


    

    
Out[36]:





party               Democrat   Reform  Republican
contb_receipt_dt                                 
2012-01-27         305785.47  3176.37   368441.82
2012-01-28         235492.85   175.00    82775.80
2012-01-29          93177.00   200.00    75220.02
2012-01-30         435921.72   130.00   255204.80
2012-01-31        1418410.31   150.00   869890.41

In [37]:

    

concat([df, df], axis=1, keys=['A', 'B']).stack('party')


    

    
Out[37]:





                                      A           B
contb_receipt_dt party                             
2012-01-27       Democrat     305785.47   305785.47
                 Reform         3176.37     3176.37
                 Republican   368441.82   368441.82
2012-01-28       Democrat     235492.85   235492.85
                 Reform          175.00      175.00
                 Republican    82775.80    82775.80
2012-01-29       Democrat      93177.00    93177.00
                 Reform          200.00      200.00
                 Republican    75220.02    75220.02
2012-01-30       Democrat     435921.72   435921.72
                 Reform          130.00      130.00
                 Republican   255204.80   255204.80
2012-01-31       Democrat    1418410.31  1418410.31
                 Reform          150.00      150.00
                 Republican   869890.41   869890.41

In [38]:

    

concat([df, df], axis=1, keys=['A', 'B']).stack(0)  # The group indexes have no labels.


    

    
Out[38]:





party                 Democrat   Reform  Republican
contb_receipt_dt                                   
2012-01-27       A   305785.47  3176.37   368441.82
                 B   305785.47  3176.37   368441.82
2012-01-28       A   235492.85   175.00    82775.80
                 B   235492.85   175.00    82775.80
2012-01-29       A    93177.00   200.00    75220.02
                 B    93177.00   200.00    75220.02
2012-01-30       A   435921.72   130.00   255204.80
                 B   435921.72   130.00   255204.80
2012-01-31       A  1418410.31   150.00   869890.41
                 B  1418410.31   150.00   869890.41

In [40]:

    

result = fec.groupby(['cand_nm', 'contbr_occupation'])['contb_receipt_amt'].sum()   # Top contributors to candidates by occupation.


    

In [41]:

    

result  # is a data frame instead of series.


    

    
Out[41]:





cand_nm             contbr_occupation             
Bachmann, Michelle  100% DISABLED VETERAN              1869.50
                    100% DISABLED VIETNAM VETERAN       236.25
                    A/C AND HEAT                        250.00
                    ACADEMIC EMPLOYMENT SPECIALIST       25.00
                    ACCOUNT                              50.00
                    ACCOUNT ANALYST                     200.00
                    ACCOUNT EXECUTIVE                   100.00
                    ACCOUNT MANAGER                     230.00
                    ACCOUNTANT                        12979.00
                    ACCOUNTANT TRAINER                  250.00
                    ACCOUNTING                          350.00
                    ACCOUNTING MANAGER                   50.00
                    ACCOUNTING SPECIALIST               250.00
                    ACCOUNTING SUPERVISOR               125.00
                    ACCOUNTING/AUDITING                 700.00
...
Santorum, Rick  WILDLIFE REFUGE MANAGER       200.00
                WIND FARM DEVELOPER           400.00
                WINDOW CLEANER                 85.00
                WINERY                        300.00
                WIRELESS NETWORK ENGINEER     220.15
                WITHHELD                     1000.00
                WNG                           700.00
                WORKER II                     300.00
                WRITER                       4894.55
                WRITER ACTIVIST              1112.00
                WRITER EDITOR                 250.00
                WRITER/ACCOUNT EXEC           500.00
                WRITER/JOURNALIST             250.00
                WRITER/LAWYER                 300.00
                YOUTH MINISTER                250.00
Name: contb_receipt_amt, Length: 40171

In [42]:

    

result[:, 'ZOMBIE SLAYER']


    

    
Out[42]:





cand_nm
Paul, Ron    1556
Name: contb_receipt_amt

In [43]:

    

result = fec.groupby(['cand_nm', 'contbr_occupation']).sum()  # When doing this aggregation, we don't need to select the columns as above.
# There are columns that are not possible to sum. The code actually tries to sum them all: nuisance columns. Whenever it fails, it just silently drops them out.


    

    
Out[43]:





<class 'pandas.core.frame.DataFrame'>
MultiIndex: 40171 entries, ('Bachmann, Michelle', '100% DISABLED VETERAN') to ('Santorum, Rick', 'YOUTH MINISTER')
Data columns:
contb_receipt_amt    40171  non-null values
file_num             40171  non-null values
dtypes: float64(2)

In [44]:

    

result = result.reset_index()  # opposite of set_index(). Took 'cand_nm' and 'contbr_occupation' and set them as col indexes again, so that the indexes are just simple integer indexes.


    

In [45]:

    

result


    

    
Out[45]:





<class 'pandas.core.frame.DataFrame'>
Int64Index: 40171 entries, 0 to 40170
Data columns:
cand_nm              40171  non-null values
contbr_occupation    40171  non-null values
contb_receipt_amt    40171  non-null values
dtypes: float64(1), object(2)

In [48]:

    

# We have so far aggregated total amount donated to each political party by occupation (the first groupby).
# Additional groupby let us split them up by candidate, sort them in descending order by total amount donated and take the top 5.
set_printoptions(notebook_repr_html=True)
def top5_donors(group):
    return group.sort_index(by='contb_receipt_amt')[-5:]
# top5_donors(result)
result.groupby('cand_nm').apply(top5_donors)   # The retired donated the most to Barack Obama.


    

    
Out[48]:






  

    

      

      

      
cand_nm
      
contbr_occupation
      
contb_receipt_amt
    
    

      
cand_nm
      
None
      

      

      

    
  
  

    

      
Bachmann, Michelle
      
921
      
             Bachmann, Michelle
      
                              PRESIDENT
      
    68781.00
    
    

      
Bachmann, Michelle
      
893
      
             Bachmann, Michelle
      
                              PHYSICIAN
      
    76134.00
    
    

      
Bachmann, Michelle
      
555
      
             Bachmann, Michelle
      
                              HOMEMAKER
      
   102540.00
    
    

      
Bachmann, Michelle
      
594
      
             Bachmann, Michelle
      
                  INFORMATION REQUESTED
      
   147638.89
    
    

      
Bachmann, Michelle
      
1059
      
             Bachmann, Michelle
      
                                RETIRED
      
   824618.05
    
    

      
Cain, Herman
      
1537
      
                   Cain, Herman
      
                                    CEO
      
   125097.00
    
    

      
Cain, Herman
      
2227
      
                   Cain, Herman
      
                              PHYSICIAN
      
   125790.96
    
    

      
Cain, Herman
      
1898
      
                   Cain, Herman
      
                              HOMEMAKER
      
   153964.07
    
    

      
Cain, Herman
      
2391
      
                   Cain, Herman
      
                                RETIRED
      
   779635.51
    
    

      
Cain, Herman
      
1929
      
                   Cain, Herman
      
                  INFORMATION REQUESTED
      
  3838719.32
    
    

      
Gingrich, Newt
      
4068
      
                 Gingrich, Newt
      
                                  OWNER
      
   273310.00
    
    

      
Gingrich, Newt
      
4222
      
                 Gingrich, Newt
      
                              PRESIDENT
      
   332403.00
    
    

      
Gingrich, Newt
      
3561
      
                 Gingrich, Newt
      
                              HOMEMAKER
      
   426118.26
    
    

      
Gingrich, Newt
      
3611
      
                 Gingrich, Newt
      
 INFORMATION REQUESTED PER BEST EFFORTS
      
   910147.00
    
    

      
Gingrich, Newt
      
4484
      
                 Gingrich, Newt
      
                                RETIRED
      
  2279602.27
    
    

      
Huntsman, Jon
      
5470
      
                  Huntsman, Jon
      
                              PRESIDENT
      
    92251.00
    
    

      
Huntsman, Jon
      
4984
      
                  Huntsman, Jon
      
                               ATTORNEY
      
   143532.50
    
    

      
Huntsman, Jon
      
5288
      
                  Huntsman, Jon
      
                              HOMEMAKER
      
   241524.79
    
    

      
Huntsman, Jon
      
5563
      
                  Huntsman, Jon
      
                                RETIRED
      
   352669.00
    
    

      
Huntsman, Jon
      
5301
      
                  Huntsman, Jon
      
 INFORMATION REQUESTED PER BEST EFFORTS
      
   354500.00
    
    

      
Johnson, Gary Earl
      
5773
      
             Johnson, Gary Earl
      
                              HOUSEWIFE
      
    11000.00
    
    

      
Johnson, Gary Earl
      
5855
      
             Johnson, Gary Earl
      
                          SELF-EMPLOYED
      
    14750.00
    
    

      
Johnson, Gary Earl
      
5735
      
             Johnson, Gary Earl
      
                             CONSULTANT
      
    16767.76
    
    

      
Johnson, Gary Earl
      
5853
      
             Johnson, Gary Earl
      
                          SELF EMPLOYED
      
    18781.00
    
    

      
Johnson, Gary Earl
      
5845
      
             Johnson, Gary Earl
      
                                RETIRED
      
    85763.96
    
    

      
McCotter, Thaddeus G
      
5906
      
           McCotter, Thaddeus G
      
                              HOMEMAKER
      
     2400.00
    
    

      
McCotter, Thaddeus G
      
5918
      
           McCotter, Thaddeus G
      
                                RETIRED
      
     2450.00
    
    

      
McCotter, Thaddeus G
      
5904
      
           McCotter, Thaddeus G
      
                             GOVERNMENT
      
     2500.00
    
    

      
McCotter, Thaddeus G
      
5912
      
           McCotter, Thaddeus G
      
                              PHYSICIAN
      
     3650.00
    
    

      
McCotter, Thaddeus G
      
5902
      
           McCotter, Thaddeus G
      
                              EXECUTIVE
      
     3750.00
    
    

      
Obama, Barack
      
16658
      
                  Obama, Barack
      
                              PHYSICIAN
      
  2244445.92
    
    

      
Obama, Barack
      
13527
      
                  Obama, Barack
      
                  INFORMATION REQUESTED
      
  2647247.90
    
    

      
Obama, Barack
      
13113
      
                  Obama, Barack
      
                              HOMEMAKER
      
  2780203.60
    
    

      
Obama, Barack
      
7091
      
                  Obama, Barack
      
                               ATTORNEY
      
  7112343.35
    
    

      
Obama, Barack
      
18862
      
                  Obama, Barack
      
                                RETIRED
      
 15086766.92
    
    

      
Paul, Ron
      
25806
      
                      Paul, Ron
      
                              HOMEMAKER
      
   310823.42
    
    

      
Paul, Ron
      
28349
      
                      Paul, Ron
      
                              REQUESTED
      
   381440.72
    
    

      
Paul, Ron
      
27649
      
                      Paul, Ron
      
                              PHYSICIAN
      
   509300.85
    
    

      
Paul, Ron
      
24957
      
                      Paul, Ron
      
                               ENGINEER
      
   612693.61
    
    

      
Paul, Ron
      
28461
      
                      Paul, Ron
      
                                RETIRED
      
  1799513.76
    
    

      
Pawlenty, Timothy
      
30758
      
              Pawlenty, Timothy
      
 INFORMATION REQUESTED PER BEST EFFORTS
      
   197841.18
    
    

      
Pawlenty, Timothy
      
30902
      
              Pawlenty, Timothy
      
                              PRESIDENT
      
   211708.92
    
    

      
Pawlenty, Timothy
      
30517
      
              Pawlenty, Timothy
      
                               ATTORNEY
      
   238331.10
    
    

      
Pawlenty, Timothy
      
30978
      
              Pawlenty, Timothy
      
                                RETIRED
      
   584195.00
    
    

      
Pawlenty, Timothy
      
30750
      
              Pawlenty, Timothy
      
                              HOMEMAKER
      
   769514.95
    
    

      
Perry, Rick
      
31205
      
                    Perry, Rick
      
                               ATTORNEY
      
   768778.80
    
    

      
Perry, Rick
      
32352
      
                    Perry, Rick
      
                              PRESIDENT
      
   845150.00
    
    

      
Perry, Rick
      
32546
      
                    Perry, Rick
      
                                RETIRED
      
  1091671.00
    
    

      
Perry, Rick
      
31880
      
                    Perry, Rick
      
                              HOMEMAKER
      
  1818443.00
    
    

      
Perry, Rick
      
31916
      
                    Perry, Rick
      
 INFORMATION REQUESTED PER BEST EFFORTS
      
  1975508.76
    
    

      
Roemer, Charles E. 'Buddy' III
      
34273
      
 Roemer, Charles E. 'Buddy' III
      
                                STUDENT
      
     6733.34
    
    

      
Roemer, Charles E. 'Buddy' III
      
33538
      
 Roemer, Charles E. 'Buddy' III
      
                              HOMEMAKER
      
     8195.00
    
    

      
Roemer, Charles E. 'Buddy' III
      
33004
      
 Roemer, Charles E. 'Buddy' III
      
                               ATTORNEY
      
    14186.00
    
    

      
Roemer, Charles E. 'Buddy' III
      
33584
      
 Roemer, Charles E. 'Buddy' III
      
                  INFORMATION REQUESTED
      
    18707.37
    
    

      
Roemer, Charles E. 'Buddy' III
      
34125
      
 Roemer, Charles E. 'Buddy' III
      
                                RETIRED
      
    73000.31
    
    

      
Romney, Mitt
      
37415
      
                   Romney, Mitt
      
                              PRESIDENT
      
  1522751.99
    
    

      
Romney, Mitt
      
34633
      
                   Romney, Mitt
      
                               ATTORNEY
      
  3662610.21
    
    

      
Romney, Mitt
      
36325
      
                   Romney, Mitt
      
                              HOMEMAKER
      
  5442013.48
    
    

      
Romney, Mitt
      
36419
      
                   Romney, Mitt
      
 INFORMATION REQUESTED PER BEST EFFORTS
      
  6394753.06
    
    

      
Romney, Mitt
      
37919
      
                   Romney, Mitt
      
                                RETIRED
      
  6483596.24
    
    

      
Santorum, Rick
      
38865
      
                 Santorum, Rick
      
                               ATTORNEY
      
   107130.00
    
    

      
Santorum, Rick
      
39189
      
                 Santorum, Rick
      
                              EXECUTIVE
      
   131456.55
    
    

      
Santorum, Rick
      
39350
      
                 Santorum, Rick
      
   INFORMATION REQUESTED (BEST EFFORTS)
      
   284718.10
    
    

      
Santorum, Rick
      
39314
      
                 Santorum, Rick
      
                              HOMEMAKER
      
   350597.75
    
    

      
Santorum, Rick
      
39822
      
                 Santorum, Rick
      
                                RETIRED
      
   421952.98
    
  

In [49]:

    

df._data   # Internal data structure. The back data frame. Having hierarchical indexing medicates a lot of the need for higher dimensional objects (because they're very sparse).


    

    
Out[49]:





BlockManager
Items: array([Democrat, Reform, Republican], dtype=object)
Axis 1: <class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-27 00:00:00, ..., 2012-01-31 00:00:00]
Length: 5, Freq: None, Timezone: None
FloatBlock: array([Democrat, Reform, Republican], dtype=object), 3 x 5, dtype float64

In [ ]:

    

# Time Series for financial data will be improved next.