In [62]:
import pandas as pd
import os
import json
from pprint import pprint
from pandas.io.json import json_normalize
In [64]:
read_address = "../viz-data/"
write_address = "../csv-viz-data/"
file_names = os.listdir(read_address);
In [65]:
# This function was being used for a specific task
def get_single_data(data):
dt_ = []
nodes = data['nodes']
edges = data['edges']
for i in range(0,len(nodes)):
dt_.append([nodes[i]['data']['properties']['name'], nodes[i]['x'], nodes[i]['y']])
return pd.DataFrame(dt_)
# This is to drop the data in csvs
def get_normalized_data(data, addr):
dt_ = []
nodes = data['nodes']
edges = data['edges']
nodes = json_normalize(nodes)
edges = json_normalize(edges)
nodes.to_csv(addr+'-nodes.csv',encoding='utf-8')
edges.to_csv(addr+'-edges.csv',encoding='utf-8')
return nodes, edges
In [67]:
final_data = pd.DataFrame();
for i in range(0,len(file_names)):
file_addr = read_address+file_names[i]
with open(file_addr) as data_file:
data = json.load(data_file)
dt_ = get_single_data(data)
temp_file = file_names[i].split('.')[0]
get_normalized_data(data,write_address+temp_file)
final_data = pd.concat([final_data, dt_])
In [68]:
data
Out[68]:
{u'edges': [{u'active': False,
u'color': u'#8a6960',
u'data': {u'properties': {}, u'type': u'registered'},
u'hover': False,
u'id': 617702,
u'label': u'registered',
u'read_cam0:size': 2,
u'renderer1:size': 12.619146889603865,
u'size': 1,
u'source': 588376,
u'target': 215525},
{u'cc': {u'y': None},
u'color': u'#d48a60',
u'data': {u'properties': {u'date_end': u'',
u'date_start': u'14/12/1999',
u'name': u'Director'},
u'type': u'is officer of'},
u'hover': False,
u'id': 69458,
u'label': u'Director',
u'read_cam0:size': 2,
u'renderer1:size': 12.619146889603865,
u'size': 1,
u'source': 667735,
u'target': 215525,
u'type': u'curvedArrow'},
{u'cc': {u'y': 2},
u'color': u'#d48a60',
u'data': {u'properties': {u'date_end': u'',
u'date_start': u'14/12/1999',
u'name': u'Secretary'},
u'type': u'is officer of'},
u'hover': False,
u'id': 69459,
u'label': u'Secretary',
u'read_cam0:size': 2,
u'renderer1:size': 12.619146889603865,
u'size': 1,
u'source': 667735,
u'target': 215525,
u'type': u'curvedArrow'}],
u'nodes': [{u'active': True,
u'color': u'#420e00',
u'colors': {u'0': u'#420e00'},
u'data': {u'categories': {u'0': u'Company'},
u'properties': {u'file_number': u'18898',
u'inactivationDate': u'04-APR-2003',
u'jurisdiction': u'PMA',
u'name': u'TREI INVESTMENTS CORP.',
u'registrationDate': u'10-DEC-1999',
u'status': u'REN',
u'struck_off_date': u'10-MAR-2001'},
u'statistics': {u'digest': {u'0': {u'edgeType': u'is officer of',
u'edges': 2,
u'nodeCategories': {u'0': u'Officer'},
u'nodes': 1},
u'1': {u'edgeType': u'registered',
u'edges': 1,
u'nodeCategories': {u'0': u'Client'},
u'nodes': 1}},
u'edgeCount': 3,
u'hiddenEdgeCount': 0,
u'visibleEdgeCount': 3}},
u'fa2_x': -0.29408618807792664,
u'fa2_y': 1.4750128984451294,
u'fixed': True,
u'geo': {u'latitudeDiff': 0, u'longitudeDiff': 0},
u'glyphs': {u'0': {u'content': u'0',
u'draw': False,
u'position': u'top-right'},
u'1': {u'content': u'\uf08d',
u'draw': True,
u'font': u'FontAwesome',
u'position': u'bottom-right',
u'textColor': u'#000'}},
u'icon': {u'color': u'#fff',
u'content': u'\uf0f7',
u'font': u'FontAwesome',
u'scale': 1},
u'id': 215525,
u'label': u'TREI INVESTMENTS CORP.',
u'nodelink': {u'x': -7.799, u'y': -6.149},
u'read_cam0:size': 5,
u'read_cam0:x': 7.052376556396484,
u'read_cam0:y': -15.9698992729187,
u'renderer1:size': 31.547867224009664,
u'renderer1:x': 1356.5,
u'renderer1:y': 207,
u'selected': True,
u'size': 1,
u'x': 7.052376556396484,
u'y': -15.9698992729187},
{u'active': False,
u'color': u'#851d00',
u'colors': {u'0': u'#851d00'},
u'data': {u'categories': {u'0': u'Client'},
u'properties': {u'active_since': u'05-SEP-1995',
u'activity': u'STOCK EXCHANGE',
u'city': u'LIMA',
u'classification': u'Special rates',
u'client_name': u'MOSSACK FONSECA & CO. (PERU) CORP.',
u'client_number': u'7879',
u'compliance_classification': u'INTERMEDIARY',
u'country': u'PERU',
u'cross_reference': u'',
u'former_name': u'ARGENTA INTERNATIONAL LIMITED',
u'name': u'MOSSACK FONSECA & CO. (PERU) CORP.',
u'prospect_date': u'',
u'region': u'REPRESENTATIVE CLIENTS',
u'status': u'ACTIVE',
u'subclassification': u'Representative'},
u'statistics': {u'digest': {u'0': {u'edgeType': u'registered',
u'edges': 2055,
u'nodeCategories': {u'0': u'Company'},
u'nodes': 2055}},
u'edgeCount': 2055,
u'hiddenEdgeCount': 2054,
u'visibleEdgeCount': 1}},
u'fa2_x': -11.027679443359375,
u'fa2_y': -8.318906784057617,
u'fixed': True,
u'geo': {u'latitudeDiff': 0, u'longitudeDiff': 0},
u'glyphs': {u'0': {u'content': u'2.054k',
u'draw': True,
u'position': u'top-right'},
u'1': {u'content': u'\uf08d',
u'draw': True,
u'font': u'FontAwesome',
u'position': u'bottom-right',
u'textColor': u'#000'}},
u'icon': {u'color': u'#fff',
u'content': u'\uf19c',
u'font': u'FontAwesome',
u'scale': 1},
u'id': 588376,
u'label': u'MOSSACK FONSECA & CO. (PERU) CORP.',
u'nodelink': {u'x': 11.654, u'y': 9.189},
u'read_cam0:size': 5,
u'read_cam0:x': -21.347623443603517,
u'read_cam0:y': -1.5698992729187022,
u'renderer1:size': 31.547867224009664,
u'renderer1:x': 1072.5,
u'renderer1:y': 351,
u'size': 1,
u'x': -21.347623443603517,
u'y': -1.5698992729187022},
{u'color': u'#be4400',
u'colors': {u'0': u'#be4400'},
u'data': {u'categories': {u'0': u'Officer'},
u'properties': {u'name': u'C\xe9sar Almeyda'},
u'statistics': {u'digest': {u'0': {u'edgeType': u'is officer of',
u'edges': 2,
u'nodeCategories': {u'0': u'Company'},
u'nodes': 1}},
u'edgeCount': 2,
u'hiddenEdgeCount': 0,
u'visibleEdgeCount': 2}},
u'fa2_x': 16.532432556152344,
u'fa2_y': 15.079108238220215,
u'fixed': True,
u'glyphs': {u'0': {u'content': u'0',
u'draw': False,
u'position': u'top-right'},
u'1': {u'content': u'\uf08d',
u'draw': True,
u'font': u'FontAwesome',
u'position': u'bottom-right',
u'textColor': u'#000'}},
u'icon': {u'color': u'#fff',
u'content': u'\uf007',
u'font': u'FontAwesome',
u'scale': 1},
u'id': 667735,
u'label': u'C\xe9sar Almeyda',
u'read_cam0:size': 5,
u'read_cam0:x': 20.15237655639649,
u'read_cam0:y': 14.430100727081296,
u'renderer1:size': 31.547867224009664,
u'renderer1:x': 1487.5,
u'renderer1:y': 511,
u'size': 1,
u'x': 20.15237655639649,
u'y': 14.430100727081296}]}
In [70]:
nodes = data['nodes']
edges = data['edges']
In [71]:
json_normalize(nodes)
Out[71]:
active
color
colors.0
data.categories.0
data.properties.active_since
data.properties.activity
data.properties.city
data.properties.classification
data.properties.client_name
data.properties.client_number
...
read_cam0:size
read_cam0:x
read_cam0:y
renderer1:size
renderer1:x
renderer1:y
selected
size
x
y
0
True
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
7.052377
-15.969899
31.547867
1356.5
207
True
1
7.052377
-15.969899
1
False
#851d00
#851d00
Client
05-SEP-1995
STOCK EXCHANGE
LIMA
Special rates
MOSSACK FONSECA & CO. (PERU) CORP.
7879
...
5
-21.347623
-1.569899
31.547867
1072.5
351
NaN
1
-21.347623
-1.569899
2
NaN
#be4400
#be4400
Officer
NaN
NaN
NaN
NaN
NaN
NaN
...
5
20.152377
14.430101
31.547867
1487.5
511
NaN
1
20.152377
14.430101
3 rows × 66 columns
In [79]:
final_nodes = pd.DataFrame()
final_edges = pd.DataFrame()
for i in range(0,len(file_names)):
file_addr = read_address+file_names[i]
with open(file_addr) as data_file:
data = json.load(data_file)
temp_file = file_names[i].split('.')[0]
nodes, edges = get_normalized_data(data,write_address+temp_file)
final_nodes = pd.concat([final_nodes, nodes])
final_edges = pd.concat([final_edges, edges])
In [74]:
nodes
Out[74]:
active
color
colors.0
data.categories.0
data.properties.active_since
data.properties.activity
data.properties.ceasedmembership
data.properties.certificateNumber
data.properties.citizenship
data.properties.city
...
read_cam0:size
read_cam0:x
read_cam0:y
renderer1:size
renderer1:x
renderer1:y
selected
size
x
y
0
False
#be4400
#be4400
Officer
NaN
NaN
4
NO
NaN
...
5
35.000000
-14.700000
31.547867
1535.145000
389.187099
True
1
35.000000
-14.700000
1
True
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
-16.029000
-14.318000
31.547867
1024.855000
393.007099
NaN
1
-16.029000
-14.318000
2
False
#937c6f
#937c6f
Address
NaN
NaN
NaN
NaN
NaN
NaN
...
5
18.631311
-31.809766
31.547867
1371.458110
218.089444
NaN
1
18.631311
-31.809766
3
NaN
#851d00
#851d00
Client
20-MAY-2004
CORPORATE & BUSINESS SERVICES
NaN
NaN
NaN
HONG KONG
...
5
8.317242
-0.627654
31.547867
1268.317417
529.910556
NaN
1
8.317242
-0.627654
4
NaN
#be4400
#be4400
Officer
NaN
NaN
NaN
NaN
NaN
NaN
...
5
-6.868689
-34.809766
31.547867
1116.458110
188.089444
NaN
1
-6.868689
-34.809766
5 rows × 81 columns
In [81]:
final_nodes
Out[81]:
active
color
colors.0
data.categories.0
data.properties.active_since
data.properties.activity
data.properties.ceasedmembership
data.properties.certificateNumber
data.properties.citizenship
data.properties.city
...
read_cam0:size
read_cam0:x
read_cam0:y
renderer1:size
renderer1:x
renderer1:y
selected
size
x
y
0
False
#be4400
#be4400
Officer
NaN
NaN
4
NO
NaN
...
5
35.000000
-14.700000
31.547867
1535.145000
389.187099
True
1
35.000000
-14.700000
1
True
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
-16.029000
-14.318000
31.547867
1024.855000
393.007099
NaN
1
-16.029000
-14.318000
2
False
#937c6f
#937c6f
Address
NaN
NaN
NaN
NaN
NaN
NaN
...
5
18.631311
-31.809766
31.547867
1371.458110
218.089444
NaN
1
18.631311
-31.809766
3
NaN
#851d00
#851d00
Client
20-MAY-2004
CORPORATE & BUSINESS SERVICES
NaN
NaN
NaN
HONG KONG
...
5
8.317242
-0.627654
31.547867
1268.317417
529.910556
NaN
1
8.317242
-0.627654
4
NaN
#be4400
#be4400
Officer
NaN
NaN
NaN
NaN
NaN
NaN
...
5
-6.868689
-34.809766
31.547867
1116.458110
188.089444
NaN
1
-6.868689
-34.809766
0
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
-8.000000
14.100000
31.547867
1354.630000
547.000000
NaN
1
-8.000000
14.100000
1
False
#be4400
#be4400
Officer
NaN
NaN
1
NO
NaN
...
5
-34.386000
-2.376000
31.547867
1090.770000
382.240000
NaN
1
-34.386000
-2.376000
2
False
#851d00
#851d00
Client
NaN
NaN
NaN
NaN
NaN
NaN
...
5
3.460000
-0.176000
31.547867
1469.230000
404.240000
NaN
1
3.460000
-0.176000
3
False
#be4400
#be4400
Officer
NaN
NaN
NaN
NaN
NaN
NaN
...
5
-4.400000
-23.500000
31.547867
1390.630000
171.000000
NaN
1
-4.400000
-23.500000
0
False
#be4400
#be4400
Officer
NaN
NaN
7
NO
NaN
...
5
3.539448
-4.690944
19.182718
1254.178310
226.225960
NaN
1
3.539448
-4.690944
1
True
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
-13.575000
-7.602000
19.182718
1162.284120
210.595356
True
1
-13.575000
-7.602000
2
False
#937c6f
#937c6f
Address
NaN
NaN
NaN
NaN
NaN
NaN
...
5
72.057000
4.953000
19.182718
1622.075953
278.008081
NaN
1
72.057000
4.953000
3
False
#851d00
#851d00
Client
08-MAR-2012
TAX ADVISOR
NaN
NaN
NaN
WARSAW
...
5
-55.360000
-30.649000
19.182718
937.924047
86.846963
NaN
1
-55.360000
-30.649000
4
False
#be4400
#be4400
Officer
NaN
NaN
30-09-2014
6
NO
NaN
...
5
20.153000
59.855000
19.182718
1343.382996
572.798477
NaN
1
20.153000
59.855000
5
False
#be4400
#be4400
Officer
NaN
NaN
12-02-2014
1
NO
NaN
...
5
19.591000
50.111000
19.182718
1340.365397
520.479115
NaN
1
19.591000
50.111000
6
False
#be4400
#be4400
Officer
NaN
NaN
12-02-2014
2
NO
NaN
...
5
20.153000
70.723000
19.182718
1343.382996
631.153037
NaN
1
20.153000
70.723000
7
False
#be4400
#be4400
Officer
NaN
NaN
12-02-2014
3
NO
NaN
...
5
18.842000
22.754000
19.182718
1336.343722
373.588640
NaN
1
18.842000
22.754000
8
False
#be4400
#be4400
Officer
NaN
NaN
12-02-2014
4
NO
NaN
...
5
19.216000
32.123000
19.182718
1338.351875
423.894480
NaN
1
19.216000
32.123000
9
False
#be4400
#be4400
Officer
NaN
NaN
12-02-2014
5
NO
NaN
...
5
19.404000
40.930000
19.182718
1339.361321
471.182721
NaN
1
19.404000
40.930000
10
False
#be4400
#be4400
Officer
NaN
NaN
NaN
NaN
NO
NaN
...
5
18.654000
13.760000
19.182718
1335.334276
325.296322
NaN
1
18.654000
13.760000
0
False
#be4400
#be4400
Officer
NaN
NaN
7
NO
NaN
...
5
6.909523
-31.677975
28.052787
1355.591950
159.383918
NaN
1
6.909523
-31.677975
1
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
-53.921163
-38.187532
28.052787
830.324811
103.174520
NaN
1
-53.921163
-38.187532
2
False
#937c6f
#937c6f
Address
NaN
NaN
NaN
NaN
NaN
NaN
...
5
55.170031
-32.351378
28.052787
1772.316802
153.569153
NaN
1
55.170031
-32.351378
3
False
#be4400
#be4400
Officer
NaN
NaN
1
NO
NaN
...
5
4.664849
-1.150398
28.052787
1336.209399
422.986615
True
1
4.664849
-1.150398
4
False
#be4400
#be4400
Officer
NaN
NaN
6
NO
NaN
...
5
6.011654
-16.414187
28.052787
1347.838929
291.185267
NaN
1
6.011654
-16.414187
5
False
#be4400
#be4400
Officer
NaN
NaN
10-09-2014
4
NO
NaN
...
5
4.889316
-8.782293
28.052787
1338.147654
357.085941
NaN
1
4.889316
-8.782293
6
False
#be4400
#be4400
Officer
NaN
NaN
10-09-2014
5
NO
NaN
...
5
6.236121
-23.372679
28.052787
1349.777184
231.099358
NaN
1
6.236121
-23.372679
7
False
#851d00
#851d00
Client
16-MAR-2011
CORPORATE & BUSINESS SERVICES
NaN
NaN
NaN
TSIM SHA TSUI
...
5
-61.328590
0.196407
28.052787
766.362392
434.616146
NaN
1
-61.328590
0.196407
8
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
-26.594000
20.967000
28.052787
1066.292243
613.968225
NaN
1
-26.594000
20.967000
9
False
#be4400
#be4400
Officer
NaN
NaN
2
NO
NaN
...
5
20.552548
21.066278
28.052787
1473.398160
614.825480
NaN
1
20.552548
21.066278
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
5
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
14.600000
30.300000
14.895528
1265.525913
521.797019
NaN
1
14.600000
30.300000
6
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
12.400000
-55.700000
14.895528
1256.915380
185.203447
NaN
1
12.400000
-55.700000
7
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
15.600000
37.900000
14.895528
1269.439792
551.542498
NaN
1
15.600000
37.900000
8
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
17.600000
45.300000
14.895528
1277.267550
580.505201
NaN
1
17.600000
45.300000
9
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
15.800000
-9.100000
14.895528
1270.222568
367.590197
NaN
1
15.800000
-9.100000
10
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
15.000000
-17.900000
14.895528
1267.091465
333.148064
NaN
1
15.000000
-17.900000
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False
#420e00
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NaN
NaN
NaN
NaN
NaN
NaN
...
5
12.000000
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14.895528
1255.349829
221.993907
NaN
1
12.000000
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12
False
#420e00
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NaN
NaN
NaN
NaN
NaN
NaN
...
5
18.200000
53.100000
14.895528
1279.615877
611.033455
NaN
1
18.200000
53.100000
13
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
12.800000
-36.100000
14.895528
1258.480932
261.915470
NaN
1
12.800000
-36.100000
14
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
14.600000
6.900000
14.895528
1265.525913
430.212257
NaN
1
14.600000
6.900000
15
False
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
13.400000
-28.100000
14.895528
1260.829259
293.226500
NaN
1
13.400000
-28.100000
16
False
#be4400
#be4400
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NaN
NaN
NaN
NaN
NO
NaN
...
5
99.373031
47.536300
14.895528
1597.317278
589.257808
NaN
1
99.373031
47.536300
17
False
#be4400
#be4400
Officer
NaN
NaN
NaN
NaN
NO
NaN
...
5
-69.582677
51.914570
14.895528
936.045123
606.393824
NaN
1
-69.582677
51.914570
18
False
#be4400
#be4400
Officer
NaN
NaN
16-08-2013
1
NO
NaN
...
5
106.178965
-10.423909
14.895528
1623.954877
362.408576
NaN
1
106.178965
-10.423909
19
False
#be4400
#be4400
Officer
NaN
NaN
23-08-2013
1
NO
NaN
...
5
-50.978848
-82.689608
14.895528
1008.858255
79.569394
NaN
1
-50.978848
-82.689608
0
NaN
#420e00
#420e00
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NaN
NaN
NaN
NaN
NaN
NaN
...
5
19.600000
-8.100000
31.547867
1311.000000
373.000000
NaN
1
19.600000
-8.100000
1
NaN
#be4400
#be4400
Officer
NaN
NaN
1
NO
NaN
...
5
36.200000
9.300000
31.547867
1477.000000
547.000000
NaN
1
36.200000
9.300000
2
NaN
#be4400
#be4400
Officer
NaN
NaN
2
NO
NaN
...
5
38.200000
-28.300000
31.547867
1497.000000
171.000000
NaN
1
38.200000
-28.300000
3
NaN
#851d00
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Client
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NaN
NaN
NaN
MONTEVIDEO
...
5
-2.200000
7.700000
31.547867
1093.000000
531.000000
NaN
1
-2.200000
7.700000
4
NaN
#be4400
#be4400
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NaN
NaN
NaN
NaN
NaN
NaN
...
5
-5.200000
-28.100000
31.547867
1063.000000
173.000000
NaN
1
-5.200000
-28.100000
0
False
#420e00
#420e00
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NaN
NaN
NaN
NaN
NaN
NaN
...
5
-10.200000
-8.900000
31.547867
1048.000000
300.000000
NaN
1
-10.200000
-8.900000
1
False
#be4400
#be4400
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NaN
NaN
9
NO
NaN
...
5
2.400000
13.300000
31.547867
1174.000000
522.000000
NaN
1
2.400000
13.300000
2
False
#937c6f
#937c6f
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NaN
NaN
NaN
NaN
NaN
NaN
...
5
36.200000
-6.700000
31.547867
1512.000000
322.000000
NaN
1
36.200000
-6.700000
3
False
#851d00
#851d00
Client
25-SEP-1992
CO. FORMATION SERVICE
NaN
NaN
NaN
LONDON
...
5
15.600000
-19.300000
31.547867
1306.000000
196.000000
NaN
1
15.600000
-19.300000
0
True
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
4.175474
-6.075677
31.547867
1413.000000
267.000000
True
1
4.175474
-6.075677
1
False
#851d00
#851d00
Client
30-SEP-1993
ATTORNEY
NaN
NaN
NaN
MADRID
...
5
-0.824526
12.324323
31.547867
1363.000000
451.000000
NaN
1
-0.824526
12.324323
2
NaN
#be4400
#be4400
Officer
NaN
NaN
NaN
NaN
NaN
NaN
...
5
-22.424526
5.524323
31.547867
1147.000000
383.000000
NaN
1
-22.424526
5.524323
0
True
#420e00
#420e00
Company
NaN
NaN
NaN
NaN
NaN
NaN
...
5
7.052377
-15.969899
31.547867
1356.500000
207.000000
True
1
7.052377
-15.969899
1
False
#851d00
#851d00
Client
05-SEP-1995
STOCK EXCHANGE
NaN
NaN
NaN
LIMA
...
5
-21.347623
-1.569899
31.547867
1072.500000
351.000000
NaN
1
-21.347623
-1.569899
2
NaN
#be4400
#be4400
Officer
NaN
NaN
NaN
NaN
NaN
NaN
...
5
20.152377
14.430101
31.547867
1487.500000
511.000000
NaN
1
20.152377
14.430101
710 rows × 86 columns
In [ ]:
Content source: amaboura/panama-papers-dataset-2016
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