In [1]:
from __future__ import print_function, division
import pandas as pd
import pylab as pl
import os
import csv
from pandas.tools.plotting import scatter_matrix
import sys
reload(sys)
import zipfile
import geopandas as gpd
sys.setdefaultencoding('utf-8')
%pylab inline
In [2]:
#Unzippping downloaded median income data of 1999 in a dataframe
zipfile.ZipFile(os.path.join("data/DEC_00_SF3_H069.zip")).extractall(r"data/rent2000")
In [3]:
#Reading and saving downloaded median income data of 1999 in a dataframe
data_2000 = pd.read_csv('data/rent2000/DEC_00_SF3_H069_with_ann.csv')
data_2000.head()
Out[3]:
GEO.id
GEO.id2
GEO.display-label
VD01
VD02
VD03
VD04
VD05
VD06
VD07
VD08
VD09
VD10
VD11
0
Id
Id2
Geography
Total:
Less than 10 percent
10 to 14 percent
15 to 19 percent
20 to 24 percent
25 to 29 percent
30 to 34 percent
35 to 39 percent
40 to 49 percent
50 percent or more
Not computed
1
1500000US360470001001
360470001001
Block Group 1, Census Tract 1, Kings County, N...
497
53
106
60
49
27
28
22
67
64
21
2
1500000US360470001002
360470001002
Block Group 2, Census Tract 1, Kings County, N...
550
85
108
78
68
67
62
12
0
59
11
3
1500000US360470001003
360470001003
Block Group 3, Census Tract 1, Kings County, N...
118
14
19
15
9
11
2
4
5
30
9
4
1500000US360470002001
360470002001
Block Group 1, Census Tract 2, Kings County, N...
129
6
11
15
29
6
5
12
16
19
10
In [4]:
#Unzipping downloaded median income data of 2013 in a dataframe
zipfile.ZipFile(os.path.join("data/ACS_13_5YR_B25070.zip")).extractall(r"data/rent2013")
In [5]:
#Reading and saving downloaded median income data of 2013 in a dataframe
data_2013 = pd.read_csv('data/rent2013/ACS_13_5YR_B25070_with_ann.csv')
data_2013.head()
Out[5]:
GEO.id
GEO.id2
GEO.display-label
HD01_VD01
HD02_VD01
HD01_VD02
HD02_VD02
HD01_VD03
HD02_VD03
HD01_VD04
...
HD01_VD07
HD02_VD07
HD01_VD08
HD02_VD08
HD01_VD09
HD02_VD09
HD01_VD10
HD02_VD10
HD01_VD11
HD02_VD11
0
Id
Id2
Geography
Estimate; Total:
Margin of Error; Total:
Estimate; Total: - Less than 10.0 percent
Margin of Error; Total: - Less than 10.0 percent
Estimate; Total: - 10.0 to 14.9 percent
Margin of Error; Total: - 10.0 to 14.9 percent
Estimate; Total: - 15.0 to 19.9 percent
...
Estimate; Total: - 30.0 to 34.9 percent
Margin of Error; Total: - 30.0 to 34.9 percent
Estimate; Total: - 35.0 to 39.9 percent
Margin of Error; Total: - 35.0 to 39.9 percent
Estimate; Total: - 40.0 to 49.9 percent
Margin of Error; Total: - 40.0 to 49.9 percent
Estimate; Total: - 50.0 percent or more
Margin of Error; Total: - 50.0 percent or more
Estimate; Total: - Not computed
Margin of Error; Total: - Not computed
1
1500000US360470001001
360470001001
Block Group 1, Census Tract 1, Kings County, N...
182
74
0
12
41
66
31
...
0
12
0
12
14
22
67
51
0
12
2
1500000US360470001002
360470001002
Block Group 2, Census Tract 1, Kings County, N...
188
80
0
12
43
42
0
...
0
12
40
64
18
29
61
46
0
12
3
1500000US360470001003
360470001003
Block Group 3, Census Tract 1, Kings County, N...
502
122
71
63
33
31
31
...
15
24
0
12
32
35
119
83
0
12
4
1500000US360470001004
360470001004
Block Group 4, Census Tract 1, Kings County, N...
195
96
28
34
14
23
14
...
0
12
0
12
0
12
31
35
30
36
5 rows × 25 columns
In [6]:
data_2013.columns
Out[6]:
Index([u'GEO.id', u'GEO.id2', u'GEO.display-label', u'HD01_VD01', u'HD02_VD01',
u'HD01_VD02', u'HD02_VD02', u'HD01_VD03', u'HD02_VD03', u'HD01_VD04',
u'HD02_VD04', u'HD01_VD05', u'HD02_VD05', u'HD01_VD06', u'HD02_VD06',
u'HD01_VD07', u'HD02_VD07', u'HD01_VD08', u'HD02_VD08', u'HD01_VD09',
u'HD02_VD09', u'HD01_VD10', u'HD02_VD10', u'HD01_VD11', u'HD02_VD11'],
dtype='object')
In [7]:
data_2013 = data_2013[[u'GEO.id', u'GEO.id2', u'GEO.display-label', u'HD01_VD01', u'HD01_VD02', u'HD01_VD03', u'HD01_VD04',
u'HD01_VD05', u'HD01_VD06', u'HD01_VD07', u'HD01_VD08', u'HD01_VD09', u'HD01_VD10', u'HD01_VD11']]
In [8]:
data_2013.head()
Out[8]:
GEO.id
GEO.id2
GEO.display-label
HD01_VD01
HD01_VD02
HD01_VD03
HD01_VD04
HD01_VD05
HD01_VD06
HD01_VD07
HD01_VD08
HD01_VD09
HD01_VD10
HD01_VD11
0
Id
Id2
Geography
Estimate; Total:
Estimate; Total: - Less than 10.0 percent
Estimate; Total: - 10.0 to 14.9 percent
Estimate; Total: - 15.0 to 19.9 percent
Estimate; Total: - 20.0 to 24.9 percent
Estimate; Total: - 25.0 to 29.9 percent
Estimate; Total: - 30.0 to 34.9 percent
Estimate; Total: - 35.0 to 39.9 percent
Estimate; Total: - 40.0 to 49.9 percent
Estimate; Total: - 50.0 percent or more
Estimate; Total: - Not computed
1
1500000US360470001001
360470001001
Block Group 1, Census Tract 1, Kings County, N...
182
0
41
31
18
11
0
0
14
67
0
2
1500000US360470001002
360470001002
Block Group 2, Census Tract 1, Kings County, N...
188
0
43
0
13
13
0
40
18
61
0
3
1500000US360470001003
360470001003
Block Group 3, Census Tract 1, Kings County, N...
502
71
33
31
99
102
15
0
32
119
0
4
1500000US360470001004
360470001004
Block Group 4, Census Tract 1, Kings County, N...
195
28
14
14
17
61
0
0
0
31
30
In [9]:
#Unzipping downloaded nyc shapefile in a dataframe
zipfile.ZipFile(os.path.join("data/cb_2015_36_bg_500k.zip")).extractall(r"data/cb_2015_36_bg_500k")
In [10]:
# loading shape file for NYC
nyc_shape = gpd.read_file("data/cb_2015_36_bg_500k/cb_2015_36_bg_500k.shp")
nyc_shape.head()
Out[10]:
AFFGEOID
ALAND
AWATER
BLKGRPCE
COUNTYFP
GEOID
LSAD
NAME
STATEFP
TRACTCE
geometry
0
1500000US360610211000
0
307945
0
061
360610211000
BG
0
36
021100
POLYGON ((-73.968082 40.8207, -73.967982575439...
1
1500000US360290131014
557271
351509
4
029
360290131014
BG
4
36
013101
POLYGON ((-78.89711856256349 42.75125713600959...
2
1500000US360050516002
256313
0
2
005
360050516002
BG
2
36
051600
POLYGON ((-73.791504 40.855456, -73.7874279999...
3
1500000US360810183004
33973
0
4
081
360810183004
BG
4
36
018300
POLYGON ((-73.92145099999999 40.743634, -73.92...
4
1500000US360470276003
70988
0
3
047
360470276003
BG
3
36
027600
POLYGON ((-74.001302 40.611068, -74.0010729999...
In [11]:
nyc_shape.columns = [[u'GEO.id', u'ALAND', u'AWATER', u'BLKGRPCE', u'COUNTYFP',
u'GEO.id2', u'LSAD', u'NAME', u'STATEFP', u'TRACTCE',
u'geometry']]
In [12]:
nyc_shape.head()
Out[12]:
GEO.id
ALAND
AWATER
BLKGRPCE
COUNTYFP
GEO.id2
LSAD
NAME
STATEFP
TRACTCE
geometry
0
1500000US360610211000
0
307945
0
061
360610211000
BG
0
36
021100
POLYGON ((-73.968082 40.8207, -73.967982575439...
1
1500000US360290131014
557271
351509
4
029
360290131014
BG
4
36
013101
POLYGON ((-78.89711856256349 42.75125713600959...
2
1500000US360050516002
256313
0
2
005
360050516002
BG
2
36
051600
POLYGON ((-73.791504 40.855456, -73.7874279999...
3
1500000US360810183004
33973
0
4
081
360810183004
BG
4
36
018300
POLYGON ((-73.92145099999999 40.743634, -73.92...
4
1500000US360470276003
70988
0
3
047
360470276003
BG
3
36
027600
POLYGON ((-74.001302 40.611068, -74.0010729999...
In [13]:
bky_shape = nyc_shape[[u'GEO.id', u'GEO.id2', u'geometry']]
bky_shape.head()
Out[13]:
GEO.id
GEO.id2
geometry
0
1500000US360610211000
360610211000
POLYGON ((-73.968082 40.8207, -73.967982575439...
1
1500000US360290131014
360290131014
POLYGON ((-78.89711856256349 42.75125713600959...
2
1500000US360050516002
360050516002
POLYGON ((-73.791504 40.855456, -73.7874279999...
3
1500000US360810183004
360810183004
POLYGON ((-73.92145099999999 40.743634, -73.92...
4
1500000US360470276003
360470276003
POLYGON ((-74.001302 40.611068, -74.0010729999...
In [ ]:
In [14]:
#Merging 2000 the dataframes to a mother dataframe
rent_2000 = pd.merge(data_2000, bky_shape, how='left', on=['GEO.id', 'GEO.id2'])
rent_2000.head()
Out[14]:
GEO.id
GEO.id2
GEO.display-label
VD01
VD02
VD03
VD04
VD05
VD06
VD07
VD08
VD09
VD10
VD11
geometry
0
Id
Id2
Geography
Total:
Less than 10 percent
10 to 14 percent
15 to 19 percent
20 to 24 percent
25 to 29 percent
30 to 34 percent
35 to 39 percent
40 to 49 percent
50 percent or more
Not computed
NaN
1
1500000US360470001001
360470001001
Block Group 1, Census Tract 1, Kings County, N...
497
53
106
60
49
27
28
22
67
64
21
POLYGON ((-73.99669799999999 40.700877, -73.99...
2
1500000US360470001002
360470001002
Block Group 2, Census Tract 1, Kings County, N...
550
85
108
78
68
67
62
12
0
59
11
POLYGON ((-73.995379 40.700309, -73.993672 40....
3
1500000US360470001003
360470001003
Block Group 3, Census Tract 1, Kings County, N...
118
14
19
15
9
11
2
4
5
30
9
POLYGON ((-73.993672 40.699836, -73.9926119999...
4
1500000US360470002001
360470002001
Block Group 1, Census Tract 2, Kings County, N...
129
6
11
15
29
6
5
12
16
19
10
POLYGON ((-74.012844 40.653016, -74.0150479999...
In [15]:
#Merging 2013 the dataframes to a mother dataframe
rent_2013 = pd.merge(data_2013, bky_shape, how='left', on=['GEO.id', 'GEO.id2'])
rent_2013.head()
Out[15]:
GEO.id
GEO.id2
GEO.display-label
HD01_VD01
HD01_VD02
HD01_VD03
HD01_VD04
HD01_VD05
HD01_VD06
HD01_VD07
HD01_VD08
HD01_VD09
HD01_VD10
HD01_VD11
geometry
0
Id
Id2
Geography
Estimate; Total:
Estimate; Total: - Less than 10.0 percent
Estimate; Total: - 10.0 to 14.9 percent
Estimate; Total: - 15.0 to 19.9 percent
Estimate; Total: - 20.0 to 24.9 percent
Estimate; Total: - 25.0 to 29.9 percent
Estimate; Total: - 30.0 to 34.9 percent
Estimate; Total: - 35.0 to 39.9 percent
Estimate; Total: - 40.0 to 49.9 percent
Estimate; Total: - 50.0 percent or more
Estimate; Total: - Not computed
NaN
1
1500000US360470001001
360470001001
Block Group 1, Census Tract 1, Kings County, N...
182
0
41
31
18
11
0
0
14
67
0
POLYGON ((-73.99669799999999 40.700877, -73.99...
2
1500000US360470001002
360470001002
Block Group 2, Census Tract 1, Kings County, N...
188
0
43
0
13
13
0
40
18
61
0
POLYGON ((-73.995379 40.700309, -73.993672 40....
3
1500000US360470001003
360470001003
Block Group 3, Census Tract 1, Kings County, N...
502
71
33
31
99
102
15
0
32
119
0
POLYGON ((-73.993672 40.699836, -73.9926119999...
4
1500000US360470001004
360470001004
Block Group 4, Census Tract 1, Kings County, N...
195
28
14
14
17
61
0
0
0
31
30
POLYGON ((-73.99271 40.698097, -73.991759 40.7...
In [ ]:
In [16]:
#Saving the dataset as csv
rent_2000.to_csv('rent_2000.csv')
rent_2013.to_csv('rent_2013.csv')
In [17]:
#Checking the CSV
data = pd.read_csv('rent_2013.csv')
data.head()
Out[17]:
Unnamed: 0
GEO.id
GEO.id2
GEO.display-label
HD01_VD01
HD01_VD02
HD01_VD03
HD01_VD04
HD01_VD05
HD01_VD06
HD01_VD07
HD01_VD08
HD01_VD09
HD01_VD10
HD01_VD11
geometry
0
0
Id
Id2
Geography
Estimate; Total:
Estimate; Total: - Less than 10.0 percent
Estimate; Total: - 10.0 to 14.9 percent
Estimate; Total: - 15.0 to 19.9 percent
Estimate; Total: - 20.0 to 24.9 percent
Estimate; Total: - 25.0 to 29.9 percent
Estimate; Total: - 30.0 to 34.9 percent
Estimate; Total: - 35.0 to 39.9 percent
Estimate; Total: - 40.0 to 49.9 percent
Estimate; Total: - 50.0 percent or more
Estimate; Total: - Not computed
NaN
1
1
1500000US360470001001
360470001001
Block Group 1, Census Tract 1, Kings County, N...
182
0
41
31
18
11
0
0
14
67
0
POLYGON ((-73.99669799999999 40.700877, -73.99...
2
2
1500000US360470001002
360470001002
Block Group 2, Census Tract 1, Kings County, N...
188
0
43
0
13
13
0
40
18
61
0
POLYGON ((-73.995379 40.700309, -73.993672 40....
3
3
1500000US360470001003
360470001003
Block Group 3, Census Tract 1, Kings County, N...
502
71
33
31
99
102
15
0
32
119
0
POLYGON ((-73.993672 40.699836, -73.9926119999...
4
4
1500000US360470001004
360470001004
Block Group 4, Census Tract 1, Kings County, N...
195
28
14
14
17
61
0
0
0
31
30
POLYGON ((-73.99271 40.698097, -73.991759 40.7...
In [ ]:
In [53]:
#Unzipping downloaded nyc shapefile in a dataframe
zipfile.ZipFile(os.path.join("data/metr200.zip")).extractall(r"data/metr200")
In [54]:
# loading shape file for NYC
nyc_shape = gpd.read_file("data/metr200/metr200.shp")
nyc_shape
Out[54]:
GEO.disp_1
GEO.displa
GEO.id
GEO.id2
TotalTax20
TotalTax_1
VD01_x
VD01_y
VD02_x
VD02_y
...
VD12
VD13
VD14
VD15
VD16
VD17
field_1
field_1_2
geo.id_2
geometry
0
Block Group 2, Census Tract 33, Kings County, ...
Block Group 2, Census Tract 33, Kings County, ...
1500000US360470033002
360470033002
6.794355e+06
2.751153e+06
676
486
144
38
...
81
18
48
0
44
17
51
48
1500000US360470033002
(POLYGON ((-73.97870734680457 40.6868858825688...
1
Block Group 1, Census Tract 37, Kings County, ...
Block Group 1, Census Tract 37, Kings County, ...
1500000US360470037001
360470037001
2.760843e+06
3.556554e+06
129
109
0
10
...
8
0
0
7
0
0
60
58
1500000US360470037001
(POLYGON ((-73.97987536126175 40.6871320892793...
2
Block Group 2, Census Tract 9, Kings County, N...
Block Group 2, Census Tract 9, Kings County, N...
1500000US360470009002
360470009002
9.948306e+06
4.825448e+06
366
289
40
59
...
25
35
29
59
19
39
21
20
1500000US360470009002
(POLYGON ((-73.99247153124475 40.6905331370807...
3
Block Group 2, Census Tract 43, Kings County, ...
Block Group 2, Census Tract 43, Kings County, ...
1500000US360470043002
360470043002
3.455649e+06
2.152118e+06
330
253
28
0
...
53
0
34
0
16
56
74
68
1500000US360470043002
(POLYGON ((-73.98901204293372 40.6900246370137...
4
Block Group 1, Census Tract 43, Kings County, ...
Block Group 1, Census Tract 43, Kings County, ...
1500000US360470043001
360470043001
0.000000e+00
4.121107e+06
0
0
0
0
...
0
0
0
0
0
0
73
67
1500000US360470043001
POLYGON ((-73.98933584733204 40.68881232913714...
5
Block Group 4, Census Tract 43, Kings County, ...
Block Group 4, Census Tract 43, Kings County, ...
1500000US360470043004
360470043004
5.156167e+06
2.133868e+06
310
288
50
25
...
51
23
16
13
10
13
76
70
1500000US360470043004
(POLYGON ((-73.99111171049765 40.6888930639022...
6
Block Group 1, Census Tract 41, Kings County, ...
Block Group 1, Census Tract 41, Kings County, ...
1500000US360470041001
360470041001
5.289765e+06
5.839860e+05
462
275
42
0
...
49
40
51
8
13
23
68
64
1500000US360470041001
(POLYGON ((-73.98240685744892 40.6868788202442...
7
Block Group 3, Census Tract 43, Kings County, ...
Block Group 3, Census Tract 43, Kings County, ...
1500000US360470043003
360470043003
2.804964e+06
3.004778e+06
430
282
25
9
...
51
39
44
7
64
19
75
69
1500000US360470043003
POLYGON ((-73.98723838793184 40.68771396645536...
8
Block Group 2, Census Tract 41, Kings County, ...
Block Group 2, Census Tract 41, Kings County, ...
1500000US360470041002
360470041002
3.246929e+06
8.994885e+05
323
157
8
36
...
32
57
25
7
85
46
69
65
1500000US360470041002
POLYGON ((-73.98521659577254 40.68668959319085...
9
Block Group 1, Census Tract 13, Kings County, ...
Block Group 1, Census Tract 13, Kings County, ...
1500000US360470013001
360470013001
7.166677e+06
1.468350e+05
1147
254
34
10
...
159
145
127
60
34
34
23
22
1500000US360470013001
(POLYGON ((-73.9881549547864 40.69508114344289...
10
Block Group 1, Census Tract 11, Kings County, ...
Block Group 1, Census Tract 11, Kings County, ...
1500000US360470011001
360470011001
2.523074e+06
2.588893e+06
75
83
0
41
...
0
8
0
0
0
0
22
21
1500000US360470011001
(POLYGON ((-73.98682423503936 40.6938937250703...
11
Block Group 1, Census Tract 9, Kings County, N...
Block Group 1, Census Tract 9, Kings County, N...
1500000US360470009001
360470009001
1.044992e+07
4.826333e+06
682
450
76
56
...
62
125
44
50
52
83
20
19
1500000US360470009001
(POLYGON ((-73.99073068241701 40.6934277380062...
12
Block Group 1, Census Tract 11, Kings County, ...
Block Group 1, Census Tract 9, Kings County, N...
1500000US360470009001
360470009001
2.523074e+06
2.588893e+06
682
450
76
56
...
62
125
44
50
52
83
20
21
1500000US360470011001
POLYGON ((-73.98968108308657 40.69225601248485...
13
Block Group 3, Census Tract 15, Kings County, ...
Block Group 1, Census Tract 31, Kings County, ...
1500000US360470031001
360470031001
8.143788e+06
2.965691e+05
0
0
0
0
...
0
0
0
0
0
0
46
26
1500000US360470015003
POLYGON ((-73.98244284225586 40.69287526686779...
14
Block Group 1, Census Tract 31, Kings County, ...
Block Group 1, Census Tract 31, Kings County, ...
1500000US360470031001
360470031001
4.260095e+06
1.822554e+06
0
0
0
0
...
0
0
0
0
0
0
46
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1500000US360470031001
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In [55]:
np.sum(nyc_shape.TotalTax20)
Out[55]:
102314497.07960001
In [56]:
np.sum(nyc_shape.TotalTax_1)
Out[56]:
55302022.024999999
In [57]:
np.sum(nyc_shape.VD01_x.astype(float))
Out[57]:
8273.0
In [58]:
np.sum(nyc_shape.VD01_y.astype(float))
Out[58]:
5488.0
In [59]:
#Unzipping downloaded nyc shapefile in a dataframe
zipfile.ZipFile(os.path.join("data/will200.zip")).extractall(r"data/will200")
In [60]:
# loading shape file for NYC
nyc_shape = gpd.read_file("data/will200/will200.shp")
nyc_shape
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In [61]:
nyc_shape.drop(['CensusBloo', 'CensusTrac', 'geometry'], axis =1, inplace=True)
nyc_shape
Out[61]:
GEO.id
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123 rows × 16 columns
In [62]:
nyc_shape.drop_duplicates(inplace=True)
nyc_shape
Out[62]:
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GEO.id2
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In [63]:
np.sum(nyc_shape.TotalTax20)
Out[63]:
71606118.739999995
In [64]:
np.sum(nyc_shape.VD01.astype(float))
Out[64]:
7272.0
In [65]:
#Unzipping downloaded nyc shapefile in a dataframe
zipfile.ZipFile(os.path.join("data/will2010.zip")).extractall(r"data/will2010")
In [66]:
# loading shape file for NYC
nyc_shape = gpd.read_file("data/will2010/will210.shp")
nyc_shape
Out[66]:
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...
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VD04
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VD06
VD07
VD08
VD09
VD10
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1500000US360470557002
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1500000US360470557002
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1500000US360470557002
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1500000US360470557002
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1500000US360470557002
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1500000US360470557002
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1500000US360470523004
360470523004
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1500000US360470523004
360470523004
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1500000US360470551004
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1500000US360470551004
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123 rows × 18 columns
In [68]:
nyc_shape.drop_duplicates(inplace=True)
nyc_shape
Out[68]:
GEO.id
GEO.id2
Margin o_1
Margin of
MediamGros
MedianHous
TotalTax20
VD01
VD02
VD03
VD04
VD05
VD06
VD07
VD08
VD09
VD10
VD11
0
1500000US360470553003
360470553003
30680
843
1164
61875
1.451061e+06
289
28
47
0
41
39
29
13
0
72
20
5
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
17
105
68
60
32
31
27
60
143
32
13
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
73
48
46
37
65
14
30
13
43
7
20
1500000US360470557001
360470557001
18396
388
1712
53438
1.129687e+06
390
47
50
49
34
41
29
20
27
66
27
22
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
12
67
57
40
50
40
0
43
102
13
30
1500000US360470523003
360470523003
29650
480
1004
47775
1.444762e+06
344
64
50
8
67
37
23
7
26
52
10
35
1500000US360470523002
360470523002
52860
358
1138
39231
2.220642e+06
456
32
45
45
53
7
37
26
25
171
15
38
1500000US360470517002
360470517002
24219
439
1542
81827
1.703405e+06
432
41
38
76
106
57
32
16
14
44
8
42
1500000US360470553001
360470553001
34648
***
2,000+
83828
9.771189e+05
307
35
33
35
37
31
52
8
26
50
0
46
1500000US360470519001
360470519001
21572
***
2,000+
80733
1.516244e+06
347
23
27
34
55
33
26
22
8
101
18
57
1500000US360470523001
360470523001
13653
258
839
32981
1.048107e+06
535
89
73
15
80
54
46
42
40
57
39
61
1500000US360470519002
360470519002
13226
263
1750
90542
9.252660e+05
224
7
23
15
47
7
32
8
14
64
7
69
1500000US360470549002
360470549002
52323
***
2,000+
72019
1.294321e+06
199
59
23
17
5
18
0
18
21
34
4
81
1500000US360470517001
360470517001
26052
422
1433
60625
4.270027e+06
161
7
29
22
44
18
18
9
9
0
5
85
1500000US360470523005
360470523005
14807
120
426
29236
5.039769e+05
284
23
19
56
29
56
8
0
10
50
33
87
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
0
15
38
30
29
6
9
19
62
14
101
1500000US360470551003
360470551003
24594
155
1104
57599
3.673678e+05
104
10
10
27
20
0
0
0
0
28
9
104
1500000US360470525001
360470525001
1366
51
325
15867
1.812191e+06
733
53
84
73
92
97
86
59
52
107
30
108
1500000US360470551002
360470551002
28122
272
1104
52656
5.806674e+05
454
40
46
52
93
49
41
6
9
88
30
111
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
17
15
0
36
13
0
0
14
32
0
119
1500000US360470523004
360470523004
30817
620
641
44786
8.704884e+05
170
16
47
0
31
7
8
6
19
28
8
121
1500000US360470551004
360470551004
34606
394
1012
67697
2.040639e+05
119
6
10
29
14
0
0
27
7
19
7
In [69]:
np.sum(nyc_shape.TotalTax20)
Out[69]:
26881499.657499995
In [70]:
np.sum(nyc_shape.VD01.astype(float))
Out[70]:
7272.0
In [ ]:
Content source: pichot/was-wburg-worth-it
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