In [67]:
%matplotlib inline

import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = [16, 4]

import pickle
import pandas
import numpy as np
with open("final_project_dataset.pkl", "r") as data_file:
    data_dict = pickle.load(data_file)

In [2]:
data_dict.items()[0]


Out[2]:
('METTS MARK',
 {'bonus': 600000,
  'deferral_payments': 'NaN',
  'deferred_income': 'NaN',
  'director_fees': 'NaN',
  'email_address': 'mark.metts@enron.com',
  'exercised_stock_options': 'NaN',
  'expenses': 94299,
  'from_messages': 29,
  'from_poi_to_this_person': 38,
  'from_this_person_to_poi': 1,
  'loan_advances': 'NaN',
  'long_term_incentive': 'NaN',
  'other': 1740,
  'poi': False,
  'restricted_stock': 585062,
  'restricted_stock_deferred': 'NaN',
  'salary': 365788,
  'shared_receipt_with_poi': 702,
  'to_messages': 807,
  'total_payments': 1061827,
  'total_stock_value': 585062})

In [3]:
data_dict.keys()


Out[3]:
['METTS MARK',
 'BAXTER JOHN C',
 'ELLIOTT STEVEN',
 'CORDES WILLIAM R',
 'HANNON KEVIN P',
 'MORDAUNT KRISTINA M',
 'MEYER ROCKFORD G',
 'MCMAHON JEFFREY',
 'HORTON STANLEY C',
 'PIPER GREGORY F',
 'HUMPHREY GENE E',
 'UMANOFF ADAM S',
 'BLACHMAN JEREMY M',
 'SUNDE MARTIN',
 'GIBBS DANA R',
 'LOWRY CHARLES P',
 'COLWELL WESLEY',
 'MULLER MARK S',
 'JACKSON CHARLENE R',
 'WESTFAHL RICHARD K',
 'WALTERS GARETH W',
 'WALLS JR ROBERT H',
 'KITCHEN LOUISE',
 'CHAN RONNIE',
 'BELFER ROBERT',
 'SHANKMAN JEFFREY A',
 'WODRASKA JOHN',
 'BERGSIEKER RICHARD P',
 'URQUHART JOHN A',
 'BIBI PHILIPPE A',
 'RIEKER PAULA H',
 'WHALEY DAVID A',
 'BECK SALLY W',
 'HAUG DAVID L',
 'ECHOLS JOHN B',
 'MENDELSOHN JOHN',
 'HICKERSON GARY J',
 'CLINE KENNETH W',
 'LEWIS RICHARD',
 'HAYES ROBERT E',
 'MCCARTY DANNY J',
 'KOPPER MICHAEL J',
 'LEFF DANIEL P',
 'LAVORATO JOHN J',
 'BERBERIAN DAVID',
 'DETMERING TIMOTHY J',
 'WAKEHAM JOHN',
 'POWERS WILLIAM',
 'GOLD JOSEPH',
 'BANNANTINE JAMES M',
 'DUNCAN JOHN H',
 'SHAPIRO RICHARD S',
 'SHERRIFF JOHN R',
 'SHELBY REX',
 'LEMAISTRE CHARLES',
 'DEFFNER JOSEPH M',
 'KISHKILL JOSEPH G',
 'WHALLEY LAWRENCE G',
 'MCCONNELL MICHAEL S',
 'PIRO JIM',
 'DELAINEY DAVID W',
 'SULLIVAN-SHAKLOVITZ COLLEEN',
 'WROBEL BRUCE',
 'LINDHOLM TOD A',
 'MEYER JEROME J',
 'LAY KENNETH L',
 'BUTTS ROBERT H',
 'OLSON CINDY K',
 'MCDONALD REBECCA',
 'CUMBERLAND MICHAEL S',
 'GAHN ROBERT S',
 'MCCLELLAN GEORGE',
 'HERMANN ROBERT J',
 'SCRIMSHAW MATTHEW',
 'GATHMANN WILLIAM D',
 'HAEDICKE MARK E',
 'BOWEN JR RAYMOND M',
 'GILLIS JOHN',
 'FITZGERALD JAY L',
 'MORAN MICHAEL P',
 'REDMOND BRIAN L',
 'BAZELIDES PHILIP J',
 'BELDEN TIMOTHY N',
 'DURAN WILLIAM D',
 'THORN TERENCE H',
 'FASTOW ANDREW S',
 'FOY JOE',
 'CALGER CHRISTOPHER F',
 'RICE KENNETH D',
 'KAMINSKI WINCENTY J',
 'LOCKHART EUGENE E',
 'COX DAVID',
 'OVERDYKE JR JERE C',
 'PEREIRA PAULO V. FERRAZ',
 'STABLER FRANK',
 'SKILLING JEFFREY K',
 'BLAKE JR. NORMAN P',
 'SHERRICK JEFFREY B',
 'PRENTICE JAMES',
 'GRAY RODNEY',
 'PICKERING MARK R',
 'THE TRAVEL AGENCY IN THE PARK',
 'NOLES JAMES L',
 'KEAN STEVEN J',
 'TOTAL',
 'FOWLER PEGGY',
 'WASAFF GEORGE',
 'WHITE JR THOMAS E',
 'CHRISTODOULOU DIOMEDES',
 'ALLEN PHILLIP K',
 'SHARP VICTORIA T',
 'JAEDICKE ROBERT',
 'WINOKUR JR. HERBERT S',
 'BROWN MICHAEL',
 'BADUM JAMES P',
 'HUGHES JAMES A',
 'REYNOLDS LAWRENCE',
 'DIMICHELE RICHARD G',
 'BHATNAGAR SANJAY',
 'CARTER REBECCA C',
 'BUCHANAN HAROLD G',
 'YEAP SOON',
 'MURRAY JULIA H',
 'GARLAND C KEVIN',
 'DODSON KEITH',
 'YEAGER F SCOTT',
 'HIRKO JOSEPH',
 'DIETRICH JANET R',
 'DERRICK JR. JAMES V',
 'FREVERT MARK A',
 'PAI LOU L',
 'BAY FRANKLIN R',
 'HAYSLETT RODERICK J',
 'FUGH JOHN L',
 'FALLON JAMES B',
 'KOENIG MARK E',
 'SAVAGE FRANK',
 'IZZO LAWRENCE L',
 'TILNEY ELIZABETH A',
 'MARTIN AMANDA K',
 'BUY RICHARD B',
 'GRAMM WENDY L',
 'CAUSEY RICHARD A',
 'TAYLOR MITCHELL S',
 'DONAHUE JR JEFFREY M',
 'GLISAN JR BEN F']

In [4]:
expenses = map(lambda (x, y): (x,y['expenses']),data_dict.items())
expenses.sort(cmp = lambda (_1,expense1),(_2,expense2): cmp(expense1,expense2))

In [5]:
expenses


Out[5]:
[('MENDELSOHN JOHN', 148),
 ('HAUG DAVID L', 475),
 ('BUCHANAN HAROLD G', 600),
 ('BLAKE JR. NORMAN P', 1279),
 ('WINOKUR JR. HERBERT S', 1413),
 ('MEYER JEROME J', 2151),
 ('DIETRICH JANET R', 3475),
 ('BADUM JAMES P', 3486),
 ('HUMPHREY GENE E', 4994),
 ('KITCHEN LOUISE', 5774),
 ('MARTIN AMANDA K', 8211),
 ('REYNOLDS LAWRENCE', 8409),
 ('BUTTS ROBERT H', 9410),
 ('JACKSON CHARLENE R', 10181),
 ('BAXTER JOHN C', 11200),
 ('BERBERIAN DAVID', 11892),
 ('ALLEN PHILLIP K', 13868),
 ('REDMOND BRIAN L', 14689),
 ('COLWELL WESLEY', 16514),
 ('STABLER FRANK', 16514),
 ('BELDEN TIMOTHY N', 17355),
 ('OVERDYKE JR JERE C', 18834),
 ('ECHOLS JOHN B', 21530),
 ('CUMBERLAND MICHAEL S', 22344),
 ('SHELBY REX', 22884),
 ('FITZGERALD JAY L', 23870),
 ('DURAN WILLIAM D', 25785),
 ('COX DAVID', 27861),
 ('PEREIRA PAULO V. FERRAZ', 27942),
 ('IZZO LAWRENCE L', 28093),
 ('DODSON KEITH', 28164),
 ('SKILLING JEFFREY K', 29336),
 ('CAUSEY RICHARD A', 30674),
 ('PICKERING MARK R', 31653),
 ('PAI LOU L', 32047),
 ('RIEKER PAULA H', 33271),
 ('WALTERS GARETH W', 33785),
 ('HANNON KEVIN P', 34039),
 ('MORDAUNT KRISTINA M', 35018),
 ('DIMICHELE RICHARD G', 35812),
 ('CALGER CHRISTOPHER F', 35818),
 ('BECK SALLY W', 37172),
 ('BIBI PHILIPPE A', 38559),
 ('DEFFNER JOSEPH M', 41626),
 ('KEAN STEVEN J', 41953),
 ('PIPER GREGORY F', 43057),
 ('THORN TERENCE H', 46145),
 ('RICE KENNETH D', 46950),
 ('HERMANN ROBERT J', 48357),
 ('GARLAND C KEVIN', 48405),
 ('BROWN MICHAEL', 49288),
 ('LAVORATO JOHN J', 49537),
 ('GAHN ROBERT S', 50080),
 ('WALLS JR ROBERT H', 50936),
 ('DERRICK JR. JAMES V', 51124),
 ('WESTFAHL RICHARD K', 51870),
 ('DETMERING TIMOTHY J', 52255),
 ('UMANOFF ADAM S', 53122),
 ('YEAGER F SCOTT', 53947),
 ('YEAP SOON', 55097),
 ('FASTOW ANDREW S', 55921),
 ('BANNANTINE JAMES M', 56301),
 ('MURRAY JULIA H', 57580),
 ('LINDHOLM TOD A', 57727),
 ('WHALLEY LAWRENCE G', 57838),
 ('BERGSIEKER RICHARD P', 59175),
 ('OLSON CINDY K', 63791),
 ('BOWEN JR RAYMOND M', 65907),
 ('HAEDICKE MARK E', 76169),
 ('HIRKO JOSEPH', 77978),
 ('ELLIOTT STEVEN', 78552),
 ('WHITE JR THOMAS E', 81353),
 ('MCCONNELL MICHAEL S', 81364),
 ('KAMINSKI WINCENTY J', 83585),
 ('BLACHMAN JEREMY M', 84208),
 ('DELAINEY DAVID W', 86174),
 ('FREVERT MARK A', 86987),
 ('METTS MARK', 94299),
 ('FALLON JAMES B', 95924),
 ('DONAHUE JR JEFFREY M', 96268),
 ('HICKERSON GARY J', 98849),
 ('LAY KENNETH L', 99832),
 ('WAKEHAM JOHN', 103773),
 ('KISHKILL JOSEPH G', 116335),
 ('SHARP VICTORIA T', 116337),
 ('KOPPER MICHAEL J', 118134),
 ('GLISAN JR BEN F', 125978),
 ('KOENIG MARK E', 127017),
 ('BAY FRANKLIN R', 129142),
 ('MCMAHON JEFFREY', 137108),
 ('SHAPIRO RICHARD S', 137767),
 ('SHANKMAN JEFFREY A', 178979),
 ('URQUHART JOHN A', 228656),
 ('MCCLELLAN GEORGE', 228763),
 ('TOTAL', 5235198),
 ('CORDES WILLIAM R', 'NaN'),
 ('MEYER ROCKFORD G', 'NaN'),
 ('HORTON STANLEY C', 'NaN'),
 ('SUNDE MARTIN', 'NaN'),
 ('GIBBS DANA R', 'NaN'),
 ('LOWRY CHARLES P', 'NaN'),
 ('MULLER MARK S', 'NaN'),
 ('CHAN RONNIE', 'NaN'),
 ('BELFER ROBERT', 'NaN'),
 ('WODRASKA JOHN', 'NaN'),
 ('WHALEY DAVID A', 'NaN'),
 ('CLINE KENNETH W', 'NaN'),
 ('LEWIS RICHARD', 'NaN'),
 ('HAYES ROBERT E', 'NaN'),
 ('MCCARTY DANNY J', 'NaN'),
 ('LEFF DANIEL P', 'NaN'),
 ('POWERS WILLIAM', 'NaN'),
 ('GOLD JOSEPH', 'NaN'),
 ('DUNCAN JOHN H', 'NaN'),
 ('SHERRIFF JOHN R', 'NaN'),
 ('LEMAISTRE CHARLES', 'NaN'),
 ('PIRO JIM', 'NaN'),
 ('SULLIVAN-SHAKLOVITZ COLLEEN', 'NaN'),
 ('WROBEL BRUCE', 'NaN'),
 ('MCDONALD REBECCA', 'NaN'),
 ('SCRIMSHAW MATTHEW', 'NaN'),
 ('GATHMANN WILLIAM D', 'NaN'),
 ('GILLIS JOHN', 'NaN'),
 ('MORAN MICHAEL P', 'NaN'),
 ('BAZELIDES PHILIP J', 'NaN'),
 ('FOY JOE', 'NaN'),
 ('LOCKHART EUGENE E', 'NaN'),
 ('SHERRICK JEFFREY B', 'NaN'),
 ('PRENTICE JAMES', 'NaN'),
 ('GRAY RODNEY', 'NaN'),
 ('THE TRAVEL AGENCY IN THE PARK', 'NaN'),
 ('NOLES JAMES L', 'NaN'),
 ('FOWLER PEGGY', 'NaN'),
 ('WASAFF GEORGE', 'NaN'),
 ('CHRISTODOULOU DIOMEDES', 'NaN'),
 ('JAEDICKE ROBERT', 'NaN'),
 ('HUGHES JAMES A', 'NaN'),
 ('BHATNAGAR SANJAY', 'NaN'),
 ('CARTER REBECCA C', 'NaN'),
 ('HAYSLETT RODERICK J', 'NaN'),
 ('FUGH JOHN L', 'NaN'),
 ('SAVAGE FRANK', 'NaN'),
 ('TILNEY ELIZABETH A', 'NaN'),
 ('BUY RICHARD B', 'NaN'),
 ('GRAMM WENDY L', 'NaN'),
 ('TAYLOR MITCHELL S', 'NaN')]

In [6]:
(people, values) = zip(*expenses)
plt.scatter(range(len(values)),values)


Out[6]:
<matplotlib.collections.PathCollection at 0x10d6063d0>

There is an outlier: TOTAL. This certainly a column for totals. Let's remove it before proceeding.


In [7]:
del data_dict['TOTAL']

In [44]:
df = pandas.DataFrame.from_dict(data_dict, orient='index')

In [45]:
df.head()


Out[45]:
salary to_messages deferral_payments total_payments exercised_stock_options bonus restricted_stock shared_receipt_with_poi restricted_stock_deferred total_stock_value ... loan_advances from_messages other from_this_person_to_poi poi director_fees deferred_income long_term_incentive email_address from_poi_to_this_person
ALLEN PHILLIP K 201955 2902 2869717 4484442 1729541 4175000 126027 1407 -126027 1729541 ... NaN 2195 152 65 False NaN -3081055 304805 phillip.allen@enron.com 47
BADUM JAMES P NaN NaN 178980 182466 257817 NaN NaN NaN NaN 257817 ... NaN NaN NaN NaN False NaN NaN NaN NaN NaN
BANNANTINE JAMES M 477 566 NaN 916197 4046157 NaN 1757552 465 -560222 5243487 ... NaN 29 864523 0 False NaN -5104 NaN james.bannantine@enron.com 39
BAXTER JOHN C 267102 NaN 1295738 5634343 6680544 1200000 3942714 NaN NaN 10623258 ... NaN NaN 2660303 NaN False NaN -1386055 1586055 NaN NaN
BAY FRANKLIN R 239671 NaN 260455 827696 NaN 400000 145796 NaN -82782 63014 ... NaN NaN 69 NaN False NaN -201641 NaN frank.bay@enron.com NaN

5 rows × 21 columns


In [13]:
print "Dataset size: %d rows x %d columns"%df.shape


Dataset size: 145 rows x 21 columns

In [52]:
df.dtypes


Out[52]:
salary                       object
to_messages                  object
deferral_payments            object
total_payments               object
exercised_stock_options      object
bonus                        object
restricted_stock             object
shared_receipt_with_poi      object
restricted_stock_deferred    object
total_stock_value            object
expenses                     object
loan_advances                object
from_messages                object
other                        object
from_this_person_to_poi      object
poi                            bool
director_fees                object
deferred_income              object
long_term_incentive          object
email_address                object
from_poi_to_this_person      object
dtype: object

In [56]:
print "Feature | Missing values"
print "---|---"
for column in df.columns:
    if column != 'poi':
        print "%s | %d"%(column,(df[column] == 'NaN').sum())


Feature | Missing values
---|---
salary | 51
to_messages | 59
deferral_payments | 107
total_payments | 21
exercised_stock_options | 44
bonus | 64
restricted_stock | 36
shared_receipt_with_poi | 59
restricted_stock_deferred | 128
total_stock_value | 20
expenses | 51
loan_advances | 142
from_messages | 59
other | 53
from_this_person_to_poi | 59
director_fees | 129
deferred_income | 97
long_term_incentive | 80
email_address | 34
from_poi_to_this_person | 59

In [83]:
df['poi'].hist( figsize=(5,3))
plt.xticks(np.array([0.05,0.95]), ['Non POI', 'POI'], rotation='horizontal')

plt.tick_params(bottom='on',top='off', right='off', left='on')



In [ ]:


In [19]:
df['expenses'] = df['expenses'].astype('float')
df['salary'] = df['salary'].astype('float')
df['from_this_person_to_poi'] = df['from_this_person_to_poi'].astype('float')
df['from_poi_to_this_person'] = df['from_poi_to_this_person'].astype('float')

Expenses Data Distribution


In [20]:
df['expenses'] = df['expenses'].astype('float')
df['expenses'].hist(bins=100)
plt.figure()
df['expenses'].hist(bins=50)
plt.figure()
df['expenses'].hist(bins=20)


Out[20]:
<matplotlib.axes._subplots.AxesSubplot at 0x10dde7cd0>

We can see that there are three employees spend considerably more than others.


In [21]:
df[df['expenses']>150000]


Out[21]:
salary to_messages deferral_payments total_payments exercised_stock_options bonus restricted_stock shared_receipt_with_poi restricted_stock_deferred total_stock_value ... loan_advances from_messages other from_this_person_to_poi poi director_fees deferred_income long_term_incentive email_address from_poi_to_this_person
MCCLELLAN GEORGE 263413 1744 NaN 1318763 506765 900000 441096 1469 NaN 947861 ... NaN 49 51587 0 False NaN -125000 NaN george.mcclellan@enron.com 52
SHANKMAN JEFFREY A 304110 3221 NaN 3038702 1441898 2000000 630137 1730 NaN 2072035 ... NaN 2681 1191 83 False NaN NaN 554422 jeffrey.shankman@enron.com 94
URQUHART JOHN A NaN NaN NaN 228656 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN False 36666 -36666 NaN NaN NaN

3 rows × 21 columns

Salary Data Distribution


In [22]:
df['salary'] = df['salary'].astype('float')
df['salary'].hist(bins=100)
plt.figure()
df['salary'].hist(bins=50)
plt.figure()
df['salary'].hist(bins=20)


Out[22]:
<matplotlib.axes._subplots.AxesSubplot at 0x10e6cad10>

There are four people with a considerably higher salary than other employees (>\$600K).


In [23]:
df[df['salary']>600000]


Out[23]:
salary to_messages deferral_payments total_payments exercised_stock_options bonus restricted_stock shared_receipt_with_poi restricted_stock_deferred total_stock_value ... loan_advances from_messages other from_this_person_to_poi poi director_fees deferred_income long_term_incentive email_address from_poi_to_this_person
FREVERT MARK A 1060932 3275 6426990 17252530 10433518 2000000 4188667 2979 NaN 14622185 ... 2000000 21 7427621 6 False NaN -3367011 1617011 mark.frevert@enron.com 242
LAY KENNETH L 1072321 4273 202911 103559793 34348384 7000000 14761694 2411 NaN 49110078 ... 81525000 36 10359729 16 True NaN -300000 3600000 kenneth.lay@enron.com 123
PICKERING MARK R 655037 898 NaN 1386690 28798 300000 NaN 728 NaN 28798 ... 400000 67 NaN 0 False NaN NaN NaN mark.pickering@enron.com 7
SKILLING JEFFREY K 1111258 3627 NaN 8682716 19250000 5600000 6843672 2042 NaN 26093672 ... NaN 108 22122 30 True NaN NaN 1920000 jeff.skilling@enron.com 88

4 rows × 21 columns

Top salary employees are not the same as top expenses employees.


In [25]:
df[['from_this_person_to_poi','from_poi_to_this_person']].hist(bins=20)


Out[25]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10ee66310>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x10e6feed0>]], dtype=object)

In [26]:
df[df['from_this_person_to_poi']>280]


Out[26]:
salary to_messages deferral_payments total_payments exercised_stock_options bonus restricted_stock shared_receipt_with_poi restricted_stock_deferred total_stock_value ... loan_advances from_messages other from_this_person_to_poi poi director_fees deferred_income long_term_incentive email_address from_poi_to_this_person
BECK SALLY W 231330 7315 NaN 969068 NaN 700000 126027 2639 NaN 126027 ... NaN 4343 566 386 False NaN NaN NaN sally.beck@enron.com 144
DELAINEY DAVID W 365163 3093 NaN 4747979 2291113 3000000 1323148 2097 NaN 3614261 ... NaN 3069 1661 609 True NaN NaN 1294981 david.delainey@enron.com 66
KEAN STEVEN J 404338 12754 NaN 1747522 2022048 1000000 4131594 3639 NaN 6153642 ... NaN 6759 1231 387 False NaN NaN 300000 steven.kean@enron.com 140
LAVORATO JOHN J 339288 7259 NaN 10425757 4158995 8000000 1008149 3962 NaN 5167144 ... NaN 2585 1552 411 False NaN NaN 2035380 john.lavorato@enron.com 528

4 rows × 21 columns


In [27]:
df[df['from_poi_to_this_person']>300]


Out[27]:
salary to_messages deferral_payments total_payments exercised_stock_options bonus restricted_stock shared_receipt_with_poi restricted_stock_deferred total_stock_value ... loan_advances from_messages other from_this_person_to_poi poi director_fees deferred_income long_term_incentive email_address from_poi_to_this_person
DIETRICH JANET R 250100 2572 NaN 1410464 1550019 600000 315068 1902 NaN 1865087 ... NaN 63 473 14 False NaN NaN 556416 janet.dietrich@enron.com 305
LAVORATO JOHN J 339288 7259 NaN 10425757 4158995 8000000 1008149 3962 NaN 5167144 ... NaN 2585 1552 411 False NaN NaN 2035380 john.lavorato@enron.com 528

2 rows × 21 columns


In [28]:
df[['from_this_person_to_poi','from_poi_to_this_person']].sort('from_poi_to_this_person').plot()


/Users/luiscruz/dev/udacity_data_analyst/venv/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
  if __name__ == '__main__':
Out[28]:
<matplotlib.axes._subplots.AxesSubplot at 0x10eac0690>

High 'from_this_person_to_poi' does not necessarily mean high 'from_poi_to_this_person', so both variables should evaluated.


In [33]:
df['bonus'] = df['bonus'].astype('float')

In [37]:
df[['salary','bonus']].plot(kind='scatter', x='salary', y='bonus')


Out[37]:
<matplotlib.axes._subplots.AxesSubplot at 0x10ded8690>

Perhaps makes sense combining salary with bonus. A new feature could be salary+bonus


In [43]:
df['salary+bonus']=df['salary']+df['bonus']

In [44]:
list(df.columns)


Out[44]:
['salary',
 'to_messages',
 'deferral_payments',
 'total_payments',
 'exercised_stock_options',
 'bonus',
 'restricted_stock',
 'shared_receipt_with_poi',
 'restricted_stock_deferred',
 'total_stock_value',
 'expenses',
 'loan_advances',
 'from_messages',
 'other',
 'from_this_person_to_poi',
 'poi',
 'director_fees',
 'deferred_income',
 'long_term_incentive',
 'email_address',
 'from_poi_to_this_person',
 'salary_bonus',
 'salary+bonus']

In [53]:
1+np.nan


Out[53]:
nan

In [76]:
for _,obj in data_dict.items():
    salary_bonus_ratio = np.float(obj['bonus'])/np.float(obj['salary'])
    if np.isnan(salary_bonus_ratio):
        salary_bonus_ratio = -1
    obj['salary_bonus_ratio'] = salary_bonus_ratio

In [77]:
data_dict.items()


Out[77]:
[('METTS MARK',
  {'bonus': 600000,
   'deferral_payments': 'NaN',
   'deferred_income': 'NaN',
   'director_fees': 'NaN',
   'email_address': 'mark.metts@enron.com',
   'exercised_stock_options': 'NaN',
   'expenses': 94299,
   'from_messages': 29,
   'from_poi_to_this_person': 38,
   'from_this_person_to_poi': 1,
   'loan_advances': 'NaN',
   'long_term_incentive': 'NaN',
   'other': 1740,
   'poi': False,
   'restricted_stock': 585062,
   'restricted_stock_deferred': 'NaN',
   'salary': 365788,
   'salary+bonus': 965788.0,
   'salary_bonus_ratio': 1.6402943781644013,
   'shared_receipt_with_poi': 702,
   'to_messages': 807,
   'total_payments': 1061827,
   'total_stock_value': 585062}),
 ('BAXTER JOHN C',
  {'bonus': 1200000,
   'deferral_payments': 1295738,
   'deferred_income': -1386055,
   'director_fees': 'NaN',
   'email_address': 'NaN',
   'exercised_stock_options': 6680544,
   'expenses': 11200,
   'from_messages': 'NaN',
   'from_poi_to_this_person': 'NaN',
   'from_this_person_to_poi': 'NaN',
   'loan_advances': 'NaN',
   'long_term_incentive': 1586055,
   'other': 2660303,
   'poi': False,
   'restricted_stock': 3942714,
   'restricted_stock_deferred': 'NaN',
   'salary': 267102,
   'salary+bonus': 1467102.0,
   'salary_bonus_ratio': 4.492665723206865,
   'shared_receipt_with_poi': 'NaN',
   'to_messages': 'NaN',
   'total_payments': 5634343,
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   'total_payments': 3123383,
   'total_stock_value': 23817930}),
 ('BAY FRANKLIN R',
  {'bonus': 400000,
   'deferral_payments': 260455,
   'deferred_income': -201641,
   'director_fees': 'NaN',
   'email_address': 'frank.bay@enron.com',
   'exercised_stock_options': 'NaN',
   'expenses': 129142,
   'from_messages': 'NaN',
   'from_poi_to_this_person': 'NaN',
   'from_this_person_to_poi': 'NaN',
   'loan_advances': 'NaN',
   'long_term_incentive': 'NaN',
   'other': 69,
   'poi': False,
   'restricted_stock': 145796,
   'restricted_stock_deferred': -82782,
   'salary': 239671,
   'salary+bonus': 639671.0,
   'salary_bonus_ratio': 1.6689545251615756,
   'shared_receipt_with_poi': 'NaN',
   'to_messages': 'NaN',
   'total_payments': 827696,
   'total_stock_value': 63014}),
 ('HAYSLETT RODERICK J',
  {'bonus': 'NaN',
   'deferral_payments': 'NaN',
   'deferred_income': 'NaN',
   'director_fees': 'NaN',
   'email_address': 'rod.hayslett@enron.com',
   'exercised_stock_options': 'NaN',
   'expenses': 'NaN',
   'from_messages': 1061,
   'from_poi_to_this_person': 35,
   'from_this_person_to_poi': 38,
   'loan_advances': 'NaN',
   'long_term_incentive': 'NaN',
   'other': 'NaN',
   'poi': False,
   'restricted_stock': 346663,
   'restricted_stock_deferred': 'NaN',
   'salary': 'NaN',
   'salary+bonus': -1,
   'salary_bonus_ratio': -1,
   'shared_receipt_with_poi': 571,
   'to_messages': 2649,
   'total_payments': 'NaN',
   'total_stock_value': 346663}),
 ('FUGH JOHN L',
  {'bonus': 'NaN',
   'deferral_payments': 50591,
   'deferred_income': 'NaN',
   'director_fees': 'NaN',
   'email_address': 'NaN',
   'exercised_stock_options': 176378,
   'expenses': 'NaN',
   'from_messages': 'NaN',
   'from_poi_to_this_person': 'NaN',
   'from_this_person_to_poi': 'NaN',
   'loan_advances': 'NaN',
   'long_term_incentive': 'NaN',
   'other': 'NaN',
   'poi': False,
   'restricted_stock': 'NaN',
   'restricted_stock_deferred': 'NaN',
   'salary': 'NaN',
   'salary+bonus': -1,
   'salary_bonus_ratio': -1,
   'shared_receipt_with_poi': 'NaN',
   'to_messages': 'NaN',
   'total_payments': 50591,
   'total_stock_value': 176378}),
 ('FALLON JAMES B',
  {'bonus': 2500000,
   'deferral_payments': 'NaN',
   'deferred_income': 'NaN',
   'director_fees': 'NaN',
   'email_address': 'jim.fallon@enron.com',
   'exercised_stock_options': 940257,
   'expenses': 95924,
   'from_messages': 75,
   'from_poi_to_this_person': 42,
   'from_this_person_to_poi': 37,
   'loan_advances': 'NaN',
   'long_term_incentive': 374347,
   'other': 401481,
   'poi': False,
   'restricted_stock': 1392142,
   'restricted_stock_deferred': 'NaN',
   'salary': 304588,
   'salary+bonus': 2804588.0,
   'salary_bonus_ratio': 8.207808580771403,
   'shared_receipt_with_poi': 1604,
   'to_messages': 1755,
   'total_payments': 3676340,
   'total_stock_value': 2332399}),
 ('KOENIG MARK E',
  {'bonus': 700000,
   'deferral_payments': 'NaN',
   'deferred_income': 'NaN',
   'director_fees': 'NaN',
   'email_address': 'mark.koenig@enron.com',
   'exercised_stock_options': 671737,
   'expenses': 127017,
   'from_messages': 61,
   'from_poi_to_this_person': 53,
   'from_this_person_to_poi': 15,
   'loan_advances': 'NaN',
   'long_term_incentive': 300000,
   'other': 150458,
   'poi': True,
   'restricted_stock': 1248318,
   'restricted_stock_deferred': 'NaN',
   'salary': 309946,
   'salary+bonus': 1009946.0,
   'salary_bonus_ratio': 2.2584579249288588,
   'shared_receipt_with_poi': 2271,
   'to_messages': 2374,
   'total_payments': 1587421,
   'total_stock_value': 1920055}),
 ('SAVAGE FRANK',
  {'bonus': 'NaN',
   'deferral_payments': 'NaN',
   'deferred_income': -121284,
   'director_fees': 125034,
   'email_address': 'NaN',
   'exercised_stock_options': 'NaN',
   'expenses': 'NaN',
   'from_messages': 'NaN',
   'from_poi_to_this_person': 'NaN',
   'from_this_person_to_poi': 'NaN',
   'loan_advances': 'NaN',
   'long_term_incentive': 'NaN',
   'other': 'NaN',
   'poi': False,
   'restricted_stock': 'NaN',
   'restricted_stock_deferred': 'NaN',
   'salary': 'NaN',
   'salary+bonus': -1,
   'salary_bonus_ratio': -1,
   'shared_receipt_with_poi': 'NaN',
   'to_messages': 'NaN',
   'total_payments': 3750,
   'total_stock_value': 'NaN'}),
 ('IZZO LAWRENCE L',
  {'bonus': 'NaN',
   'deferral_payments': 'NaN',
   'deferred_income': 'NaN',
   'director_fees': 'NaN',
   'email_address': 'larry.izzo@enron.com',
   'exercised_stock_options': 2165172,
   'expenses': 28093,
   'from_messages': 19,
   'from_poi_to_this_person': 28,
   'from_this_person_to_poi': 5,
   'loan_advances': 'NaN',
   'long_term_incentive': 312500,
   'other': 1553729,
   'poi': False,
   'restricted_stock': 3654808,
   'restricted_stock_deferred': 'NaN',
   'salary': 85274,
   'salary+bonus': -1,
   'salary_bonus_ratio': -1,
   'shared_receipt_with_poi': 437,
   'to_messages': 496,
   'total_payments': 1979596,
   'total_stock_value': 5819980}),
 ('TILNEY ELIZABETH A',
  {'bonus': 300000,
   'deferral_payments': 'NaN',
   'deferred_income': -575000,
   'director_fees': 'NaN',
   'email_address': 'elizabeth.tilney@enron.com',
   'exercised_stock_options': 591250,
   'expenses': 'NaN',
   'from_messages': 19,
   'from_poi_to_this_person': 10,
   'from_this_person_to_poi': 11,
   'loan_advances': 'NaN',
   'long_term_incentive': 275000,
   'other': 152055,
   'poi': False,
   'restricted_stock': 576792,
   'restricted_stock_deferred': 'NaN',
   'salary': 247338,
   'salary+bonus': 547338.0,
   'salary_bonus_ratio': 1.2129151201998885,
   'shared_receipt_with_poi': 379,
   'to_messages': 460,
   'total_payments': 399393,
   'total_stock_value': 1168042}),
 ('MARTIN AMANDA K',
  {'bonus': 'NaN',
   'deferral_payments': 85430,
   'deferred_income': 'NaN',
   'director_fees': 'NaN',
   'email_address': 'a..martin@enron.com',
   'exercised_stock_options': 2070306,
   'expenses': 8211,
   'from_messages': 230,
   'from_poi_to_this_person': 8,
   'from_this_person_to_poi': 0,
   'loan_advances': 'NaN',
   'long_term_incentive': 5145434,
   'other': 2818454,
   'poi': False,
   'restricted_stock': 'NaN',
   'restricted_stock_deferred': 'NaN',
   'salary': 349487,
   'salary+bonus': -1,
   'salary_bonus_ratio': -1,
   'shared_receipt_with_poi': 477,
   'to_messages': 1522,
   'total_payments': 8407016,
   'total_stock_value': 2070306}),
 ('BUY RICHARD B',
  {'bonus': 900000,
   'deferral_payments': 649584,
   'deferred_income': -694862,
   'director_fees': 'NaN',
   'email_address': 'rick.buy@enron.com',
   'exercised_stock_options': 2542813,
   'expenses': 'NaN',
   'from_messages': 1053,
   'from_poi_to_this_person': 156,
   'from_this_person_to_poi': 71,
   'loan_advances': 'NaN',
   'long_term_incentive': 769862,
   'other': 400572,
   'poi': False,
   'restricted_stock': 901657,
   'restricted_stock_deferred': 'NaN',
   'salary': 330546,
   'salary+bonus': 1230546.0,
   'salary_bonus_ratio': 2.722767784211577,
   'shared_receipt_with_poi': 2333,
   'to_messages': 3523,
   'total_payments': 2355702,
   'total_stock_value': 3444470}),
 ('GRAMM WENDY L',
  {'bonus': 'NaN',
   'deferral_payments': 'NaN',
   'deferred_income': 'NaN',
   'director_fees': 119292,
   'email_address': 'NaN',
   'exercised_stock_options': 'NaN',
   'expenses': 'NaN',
   'from_messages': 'NaN',
   'from_poi_to_this_person': 'NaN',
   'from_this_person_to_poi': 'NaN',
   'loan_advances': 'NaN',
   'long_term_incentive': 'NaN',
   'other': 'NaN',
   'poi': False,
   'restricted_stock': 'NaN',
   'restricted_stock_deferred': 'NaN',
   'salary': 'NaN',
   'salary+bonus': -1,
   'salary_bonus_ratio': -1,
   'shared_receipt_with_poi': 'NaN',
   'to_messages': 'NaN',
   'total_payments': 119292,
   'total_stock_value': 'NaN'}),
 ('CAUSEY RICHARD A',
  {'bonus': 1000000,
   'deferral_payments': 'NaN',
   'deferred_income': -235000,
   'director_fees': 'NaN',
   'email_address': 'richard.causey@enron.com',
   'exercised_stock_options': 'NaN',
   'expenses': 30674,
   'from_messages': 49,
   'from_poi_to_this_person': 58,
   'from_this_person_to_poi': 12,
   'loan_advances': 'NaN',
   'long_term_incentive': 350000,
   'other': 307895,
   'poi': True,
   'restricted_stock': 2502063,
   'restricted_stock_deferred': 'NaN',
   'salary': 415189,
   'salary+bonus': 1415189.0,
   'salary_bonus_ratio': 2.4085416521150607,
   'shared_receipt_with_poi': 1585,
   'to_messages': 1892,
   'total_payments': 1868758,
   'total_stock_value': 2502063}),
 ('TAYLOR MITCHELL S',
  {'bonus': 600000,
   'deferral_payments': 227449,
   'deferred_income': 'NaN',
   'director_fees': 'NaN',
   'email_address': 'mitchell.taylor@enron.com',
   'exercised_stock_options': 3181250,
   'expenses': 'NaN',
   'from_messages': 29,
   'from_poi_to_this_person': 0,
   'from_this_person_to_poi': 0,
   'loan_advances': 'NaN',
   'long_term_incentive': 'NaN',
   'other': 'NaN',
   'poi': False,
   'restricted_stock': 563798,
   'restricted_stock_deferred': 'NaN',
   'salary': 265214,
   'salary+bonus': 865214.0,
   'salary_bonus_ratio': 2.2623240100447184,
   'shared_receipt_with_poi': 300,
   'to_messages': 533,
   'total_payments': 1092663,
   'total_stock_value': 3745048}),
 ('DONAHUE JR JEFFREY M',
  {'bonus': 800000,
   'deferral_payments': 'NaN',
   'deferred_income': -300000,
   'director_fees': 'NaN',
   'email_address': 'jeff.donahue@enron.com',
   'exercised_stock_options': 765920,
   'expenses': 96268,
   'from_messages': 22,
   'from_poi_to_this_person': 188,
   'from_this_person_to_poi': 11,
   'loan_advances': 'NaN',
   'long_term_incentive': 'NaN',
   'other': 891,
   'poi': False,
   'restricted_stock': 315068,
   'restricted_stock_deferred': 'NaN',
   'salary': 278601,
   'salary+bonus': 1078601.0,
   'salary_bonus_ratio': 2.8714900520816506,
   'shared_receipt_with_poi': 772,
   'to_messages': 865,
   'total_payments': 875760,
   'total_stock_value': 1080988}),
 ('GLISAN JR BEN F',
  {'bonus': 600000,
   'deferral_payments': 'NaN',
   'deferred_income': 'NaN',
   'director_fees': 'NaN',
   'email_address': 'ben.glisan@enron.com',
   'exercised_stock_options': 384728,
   'expenses': 125978,
   'from_messages': 16,
   'from_poi_to_this_person': 52,
   'from_this_person_to_poi': 6,
   'loan_advances': 'NaN',
   'long_term_incentive': 71023,
   'other': 200308,
   'poi': True,
   'restricted_stock': 393818,
   'restricted_stock_deferred': 'NaN',
   'salary': 274975,
   'salary+bonus': 874975.0,
   'salary_bonus_ratio': 2.1820165469588146,
   'shared_receipt_with_poi': 874,
   'to_messages': 873,
   'total_payments': 1272284,
   'total_stock_value': 778546})]

Feature Selection


In [84]:
import sys
sys.path.append("./tools/")
from feature_format import featureFormat, targetFeatureSplit
import numpy as np
features_all = [
    'poi',
    'salary',
    'to_messages',
    'deferral_payments',
    'total_payments',
    'exercised_stock_options',
    'bonus',
    'restricted_stock',
    'shared_receipt_with_poi',
    'restricted_stock_deferred',
    'total_stock_value',
    'expenses',
    'loan_advances',
    'from_messages',
    'other',
    'from_this_person_to_poi',
    'director_fees',
    'deferred_income',
    'long_term_incentive',
    # 'email_address',
    'from_poi_to_this_person',
    'salary_bonus_ratio',
]
features_list=features_all



data = featureFormat(data_dict, features_list, sort_keys = True)
labels, features = targetFeatureSplit(data)

In [ ]:


In [85]:
from sklearn.feature_selection import SelectKBest, f_classif


# Perform feature selection
selector = SelectKBest(f_classif, k=5)
selector.fit(features, labels)

# Get the raw p-values for each feature, and transform from p-values into scores
scores = -np.log10(selector.pvalues_)
scores= selector.scores_
predictors = features_list[1:]
sort_indices = np.argsort(scores)[::-1]

# Plot the scores.  See how "Pclass", "Sex", "Title", and "Fare" are the best?
X=np.arange(len(predictors))
Y=scores[sort_indices]
X_labels = map(lambda x: x.replace("_"," ").title(),np.array(predictors)[sort_indices])
ax = plt.subplot(111)
ax.bar(X, Y,edgecolor='white',facecolor='#9999ff')
plt.xticks(X+0.4, X_labels, rotation='vertical')
for x,y in zip(X,Y):
    ax.text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom')
pass
plt.tick_params(bottom='off',top='off', right='off', left='off')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)



In [86]:
zip(np.array(predictors)[sort_indices],scores[sort_indices])


Out[86]:
[('exercised_stock_options', 25.380105299760199),
 ('total_stock_value', 24.752523020258508),
 ('bonus', 21.327890413979102),
 ('salary', 18.861795316466416),
 ('salary_bonus_ratio', 12.278007938540965),
 ('deferred_income', 11.732698076065354),
 ('long_term_incentive', 10.222904205832778),
 ('restricted_stock', 9.4807432034789336),
 ('total_payments', 8.9678193476776205),
 ('shared_receipt_with_poi', 8.9038215571655712),
 ('loan_advances', 7.3014066515360359),
 ('expenses', 6.3746144901977475),
 ('from_poi_to_this_person', 5.4466874833253529),
 ('other', 4.2635766381444693),
 ('from_this_person_to_poi', 2.470521222656084),
 ('director_fees', 2.0893098994318806),
 ('to_messages', 1.7516942790340737),
 ('deferral_payments', 0.20970584227026345),
 ('from_messages', 0.1587702392129193),
 ('restricted_stock_deferred', 0.064477028038728595)]

In [87]:
list(np.array(predictors)[sort_indices])


Out[87]:
['exercised_stock_options',
 'total_stock_value',
 'bonus',
 'salary',
 'salary_bonus_ratio',
 'deferred_income',
 'long_term_incentive',
 'restricted_stock',
 'total_payments',
 'shared_receipt_with_poi',
 'loan_advances',
 'expenses',
 'from_poi_to_this_person',
 'other',
 'from_this_person_to_poi',
 'director_fees',
 'to_messages',
 'deferral_payments',
 'from_messages',
 'restricted_stock_deferred']

In [ ]: