In [9]:
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 [10]:
#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 [11]:
#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[11]:
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 [12]:
#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 [13]:
#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[13]:
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 [14]:
data_2013.columns
Out[14]:
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 [15]:
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 [16]:
data_2013.head()
Out[16]:
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 [17]:
#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 [18]:
# 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[18]:
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 [19]:
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 [20]:
nyc_shape.head()
Out[20]:
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 [21]:
bky_shape = nyc_shape[[u'GEO.id', u'GEO.id2', u'geometry']]
bky_shape.head()
Out[21]:
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 [22]:
#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[22]:
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 [23]:
#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[23]:
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 [24]:
#Saving the dataset as csv
rent_2000.to_csv('rent_2000.csv')
rent_2013.to_csv('rent_2013.csv')
In [27]:
#Checking the CSV
bky2000 = pd.read_csv('rent_2000.csv')
bky2000.head()
Out[27]:
Unnamed: 0
GEO.id
GEO.id2
GEO.display-label
VD01
VD02
VD03
VD04
VD05
VD06
VD07
VD08
VD09
VD10
VD11
geometry
0
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
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
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
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
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 [28]:
bky2000.columns
Out[28]:
Index([u'Unnamed: 0', u'GEO.id', u'GEO.id2', u'GEO.display-label', u'VD01',
u'VD02', u'VD03', u'VD04', u'VD05', u'VD06', u'VD07', u'VD08', u'VD09',
u'VD10', u'VD11', u'geometry'],
dtype='object')
In [29]:
bky00 = bky2000[[u'GEO.id2', u'VD01', u'VD02', u'VD03',
u'VD04', u'VD05', u'VD06', u'VD07', u'VD08', u'VD09', u'VD10', u'VD11']]
In [30]:
bky00.loc['Total']= bky00.astype(float).sum()
bky00.tail()
ValueErrorTraceback (most recent call last)
<ipython-input-30-0e22f42ceea0> in <module>()
----> 1 bky00.loc['Total']= bky00.astype(float).sum()
2 bky00.tail()
/opt/rh/anaconda/root/envs/PUI2016_Python2/lib/python2.7/site-packages/pandas/core/generic.pyc in astype(self, dtype, copy, raise_on_error, **kwargs)
2948
2949 mgr = self._data.astype(dtype=dtype, copy=copy,
-> 2950 raise_on_error=raise_on_error, **kwargs)
2951 return self._constructor(mgr).__finalize__(self)
2952
/opt/rh/anaconda/root/envs/PUI2016_Python2/lib/python2.7/site-packages/pandas/core/internals.pyc in astype(self, dtype, **kwargs)
2936
2937 def astype(self, dtype, **kwargs):
-> 2938 return self.apply('astype', dtype=dtype, **kwargs)
2939
2940 def convert(self, **kwargs):
/opt/rh/anaconda/root/envs/PUI2016_Python2/lib/python2.7/site-packages/pandas/core/internals.pyc in apply(self, f, axes, filter, do_integrity_check, consolidate, raw, **kwargs)
2888
2889 kwargs['mgr'] = self
-> 2890 applied = getattr(b, f)(**kwargs)
2891 result_blocks = _extend_blocks(applied, result_blocks)
2892
/opt/rh/anaconda/root/envs/PUI2016_Python2/lib/python2.7/site-packages/pandas/core/internals.pyc in astype(self, dtype, copy, raise_on_error, values, **kwargs)
432 **kwargs):
433 return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,
--> 434 values=values, **kwargs)
435
436 def _astype(self, dtype, copy=False, raise_on_error=True, values=None,
/opt/rh/anaconda/root/envs/PUI2016_Python2/lib/python2.7/site-packages/pandas/core/internals.pyc in _astype(self, dtype, copy, raise_on_error, values, klass, mgr, **kwargs)
475
476 # _astype_nansafe works fine with 1-d only
--> 477 values = com._astype_nansafe(values.ravel(), dtype, copy=True)
478 values = values.reshape(self.shape)
479
/opt/rh/anaconda/root/envs/PUI2016_Python2/lib/python2.7/site-packages/pandas/core/common.pyc in _astype_nansafe(arr, dtype, copy)
1918
1919 if copy:
-> 1920 return arr.astype(dtype)
1921 return arr.view(dtype)
1922
ValueError: could not convert string to float: Not computed
In [ ]:
In [71]:
#Unzipping downloaded nyc shapefile in a dataframe
zipfile.ZipFile(os.path.join("data/metr200.zip")).extractall(r"data/metr200")
In [72]:
# loading shape file for NYC
nyc_shape = gpd.read_file("data/metr200/metr200.shp")
nyc_shape
Out[72]:
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
44
1500000US360470031001
(POLYGON ((-73.98244284225586 40.6928752668677...
15
Block Group 2, Census Tract 31, Kings County, ...
Block Group 2, Census Tract 31, Kings County, ...
1500000US360470031002
360470031002
0.000000e+00
1.782760e+06
83
108
62
14
...
0
0
0
0
0
0
47
45
1500000US360470031002
(POLYGON ((-73.98158772483383 40.6909854662658...
16
Block Group 1, Census Tract 37, Kings County, ...
Block Group 1, Census Tract 39, Kings County, ...
1500000US360470039001
360470039001
2.760843e+06
3.556554e+06
158
154
13
17
...
8
8
19
15
0
8
63
58
1500000US360470037001
POLYGON ((-73.97983016638672 40.68680535933928...
17
Block Group 1, Census Tract 39, Kings County, ...
Block Group 1, Census Tract 39, Kings County, ...
1500000US360470039001
360470039001
5.301568e+06
1.133788e+05
158
154
13
17
...
8
8
19
15
0
8
63
61
1500000US360470039001
(POLYGON ((-73.9786719698802 40.68522967010515...
18
Block Group 1, Census Tract 35, Kings County, ...
Block Group 1, Census Tract 35, Kings County, ...
1500000US360470035001
360470035001
2.122820e+06
2.187168e+06
527
489
64
49
...
69
49
10
18
0
41
55
53
1500000US360470035001
(POLYGON ((-73.97604749487012 40.6851941333957...
19
Block Group 3, Census Tract 41, Kings County, ...
Block Group 3, Census Tract 41, Kings County, ...
1500000US360470041003
360470041003
4.171970e+06
1.114244e+06
365
269
13
14
...
38
53
28
44
8
23
70
66
1500000US360470041003
POLYGON ((-73.98322167230421 40.68615376466381...
20
Block Group 2, Census Tract 39, Kings County, ...
Block Group 2, Census Tract 39, Kings County, ...
1500000US360470039002
360470039002
1.537886e+06
4.512899e+06
148
128
8
0
...
22
0
14
15
0
0
64
62
1500000US360470039002
POLYGON ((-73.98103731274125 40.68505412831944...
21
Block Group 3, Census Tract 39, Kings County, ...
Block Group 3, Census Tract 39, Kings County, ...
1500000US360470039003
360470039003
1.840572e+06
7.748789e+05
353
305
149
22
...
5
24
17
10
9
6
65
63
1500000US360470039003
POLYGON ((-73.97871150496528 40.6844054180393,...
22
Block Group 1, Census Tract 129.01, Kings Coun...
Block Group 1, Census Tract 129.01, Kings Coun...
1500000US360470129011
360470129011
1.513746e+06
2.047302e+06
353
229
52
23
...
11
26
27
0
12
7
255
259
1500000US360470129011
POLYGON ((-73.97617836053597 40.68321422955896...
23
Block Group 3, Census Tract 7, Kings County, N...
Block Group 3, Census Tract 7, Kings County, N...
1500000US360470007003
360470007003
8.541489e+06
2.914260e+06
516
276
10
46
...
52
75
47
16
40
120
19
18
1500000US360470007003
POLYGON ((-73.99347679990953 40.69230129739747...
24 rows × 38 columns
In [78]:
metro = pd.DataFrame(nyc_shape['GEO.id'])
metro.to_csv('metrotech.csv')
data = pd.read_csv('metrotech.csv')
data
Out[78]:
Unnamed: 0
GEO.id
0
0
1500000US360470033002
1
1
1500000US360470037001
2
2
1500000US360470009002
3
3
1500000US360470043002
4
4
1500000US360470043001
5
5
1500000US360470043004
6
6
1500000US360470041001
7
7
1500000US360470043003
8
8
1500000US360470041002
9
9
1500000US360470013001
10
10
1500000US360470011001
11
11
1500000US360470009001
12
12
1500000US360470009001
13
13
1500000US360470031001
14
14
1500000US360470031001
15
15
1500000US360470031002
16
16
1500000US360470039001
17
17
1500000US360470039001
18
18
1500000US360470035001
19
19
1500000US360470041003
20
20
1500000US360470039002
21
21
1500000US360470039003
22
22
1500000US360470129011
23
23
1500000US360470007003
In [79]:
np.sum(nyc_shape.TotalTax20)
Out[79]:
102314497.07960001
In [80]:
np.sum(nyc_shape.TotalTax_1)
Out[80]:
55302022.024999999
In [81]:
np.sum(nyc_shape.VD01_x.astype(float))
Out[81]:
8273.0
In [82]:
np.sum(nyc_shape.VD01_y.astype(float))
Out[82]:
5488.0
In [83]:
#Unzipping downloaded nyc shapefile in a dataframe
zipfile.ZipFile(os.path.join("data/will200.zip")).extractall(r"data/will200")
In [84]:
# loading shape file for NYC
nyc_shape = gpd.read_file("data/will200/will200.shp")
nyc_shape
Out[84]:
CensusBloo
CensusTrac
GEO.id
GEO.id2
MediamGros
Median hou
TotalTax20
VD01
VD02
VD03
VD04
VD05
VD06
VD07
VD08
VD09
VD10
VD11
geometry
0
3001
055100
1500000US360470551003
360470551003
925
54313
3.008752e+06
104
10
10
27
20
0
0
0
0
28
9
POLYGON ((994763.4347482463 199218.6635385387,...
1
3002
055100
1500000US360470551003
360470551003
925
54313
3.008752e+06
104
10
10
27
20
0
0
0
0
28
9
POLYGON ((994645.8196441368 198974.3437943111,...
2
3000
055100
1500000US360470551003
360470551003
925
54313
3.008752e+06
104
10
10
27
20
0
0
0
0
28
9
POLYGON ((994863.0397403392 199425.4936187313,...
3
2001
055100
1500000US360470551002
360470551002
563
30000
2.023738e+06
454
40
46
52
93
49
41
6
9
88
30
POLYGON ((995207.7795806602 199072.4836984025,...
4
2000
055100
1500000US360470551002
360470551002
563
30000
2.023738e+06
454
40
46
52
93
49
41
6
9
88
30
POLYGON ((995073.0296285347 199305.2936826193,...
5
2002
055100
1500000US360470551002
360470551002
563
30000
2.023738e+06
454
40
46
52
93
49
41
6
9
88
30
POLYGON ((995079.1495645404 198794.853746144, ...
6
3006
051900
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
POLYGON ((996239.1595496207 199071.6936824018,...
7
3000
051900
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
POLYGON ((996702.1496300519 199079.2436344088,...
8
3008
051900
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
POLYGON ((996618.9795819744 198908.8236662501,...
9
3007
051900
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
POLYGON ((995805.6030692169 199254.0337785716,...
10
3001
051900
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
POLYGON ((996342.7397097172 199281.7037945974,...
11
3002
051900
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
POLYGON ((995907.7995493121 199461.3937787648,...
12
3005
051900
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
POLYGON ((996208.1195495918 199561.3136508578,...
13
3003
051900
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
POLYGON ((996022.6895334191 199706.6837629932,...
14
1008
055100
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
POLYGON ((993811.3996753597 198999.993714335, ...
15
1009
055100
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
POLYGON ((993739.5796432928 199102.8038264308,...
16
1007
055100
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
POLYGON ((994228.6796757483 199179.3936505021,...
17
1006
055100
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
POLYGON ((994348.1198038596 199431.1038587365,...
18
1005
055100
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
POLYGON ((993908.9098194505 199602.9136508965,...
19
1000
055100
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
POLYGON ((994513.8196440139 199779.483763061, ...
20
1001
055100
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
POLYGON ((993928.2897874685 199850.3437951269,...
21
1004
055100
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
POLYGON ((993678.4396752359 199306.4436346204,...
22
4000
052300
1500000US360470523004
360470523004
533
35125
9.540152e+05
170
16
47
0
31
7
8
6
19
28
8
POLYGON ((995652.6297570744 199018.3637623521,...
23
4001
052300
1500000US360470523004
360470523004
533
35125
9.540152e+05
170
16
47
0
31
7
8
6
19
28
8
POLYGON ((995619.1495650433 198872.7138422165,...
24
5000
052300
1500000US360470523005
360470523005
461
20357
1.433303e+06
284
23
19
56
29
56
8
0
10
50
33
POLYGON ((996151.8295015394 198894.753650237, ...
25
5001
052300
1500000US360470523005
360470523005
461
20357
1.433303e+06
284
23
19
56
29
56
8
0
10
50
33
POLYGON ((996030.9695974268 198640.9437300006,...
26
2002
054900
1500000US360470549002
360470549002
633
36875
2.712518e+06
199
59
23
17
5
18
0
18
21
34
4
POLYGON ((994007.9598035428 198721.9436660761,...
27
2003
054900
1500000US360470549002
360470549002
633
36875
2.712518e+06
199
59
23
17
5
18
0
18
21
34
4
POLYGON ((993502.7297230721 198796.2238581452,...
28
2004
054900
1500000US360470549002
360470549002
633
36875
2.712518e+06
199
59
23
17
5
18
0
18
21
34
4
POLYGON ((993585.5797711494 198652.3137140112,...
29
2001
054900
1500000US360470549002
360470549002
633
36875
2.712518e+06
199
59
23
17
5
18
0
18
21
34
4
POLYGON ((994405.6895959132 198474.5936498457,...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
93
1009
055500
1500000US360470555001
360470555001
1224
56016
1.514461e+07
222
0
15
38
30
29
6
9
19
62
14
POLYGON ((995016.7697884822 200406.7336836451,...
94
1010
055500
1500000US360470555001
360470555001
1224
56016
1.514461e+07
222
0
15
38
30
29
6
9
19
62
14
POLYGON ((994601.3995480954 200709.4137479271,...
95
1005
055500
1500000US360470555001
360470555001
1224
56016
1.514461e+07
222
0
15
38
30
29
6
9
19
62
14
POLYGON ((994624.8197081173 201171.8237323577,...
96
1003
055300
1500000US360470553001
360470553001
720
37823
2.652623e+06
307
35
33
35
37
31
52
8
26
50
0
POLYGON ((995931.0696933337 200113.2836993719,...
97
1002
055300
1500000US360470553001
360470553001
720
37823
2.652623e+06
307
35
33
35
37
31
52
8
26
50
0
POLYGON ((996012.2895334094 200174.4136834288,...
98
1000
055300
1500000US360470553001
360470553001
720
37823
2.652623e+06
307
35
33
35
37
31
52
8
26
50
0
POLYGON ((996335.1096297101 200575.2436358021,...
99
1001
055300
1500000US360470553001
360470553001
720
37823
2.652623e+06
307
35
33
35
37
31
52
8
26
50
0
POLYGON ((996206.5095655903 200418.1336196557,...
100
3003
055300
1500000US360470553003
360470553003
669
31688
1.475774e+06
289
28
47
0
41
39
29
13
0
72
20
POLYGON ((995536.1697249659 200322.0437635663,...
101
3004
055300
1500000US360470553003
360470553003
669
31688
1.475774e+06
289
28
47
0
41
39
29
13
0
72
20
POLYGON ((995347.5796447904 200096.6536833564,...
102
3000
055300
1500000US360470553003
360470553003
669
31688
1.475774e+06
289
28
47
0
41
39
29
13
0
72
20
POLYGON ((996006.1795814037 200892.7937160978,...
103
3002
055300
1500000US360470553003
360470553003
669
31688
1.475774e+06
289
28
47
0
41
39
29
13
0
72
20
POLYGON ((995648.7695330709 200461.1935876959,...
104
3001
055300
1500000US360470553003
360470553003
669
31688
1.475774e+06
289
28
47
0
41
39
29
13
0
72
20
POLYGON ((995853.1996132613 200707.7937799256,...
105
1002
055700
1500000US360470557001
360470557001
669
37841
2.597763e+06
390
47
50
49
34
41
29
20
27
66
27
POLYGON ((995338.2696927816 201363.2436365359,...
106
1001
055700
1500000US360470557001
360470557001
669
37841
2.597763e+06
390
47
50
49
34
41
29
20
27
66
27
POLYGON ((995282.1096927293 201535.9036046968,...
107
1003
051700
1500000US360470517001
360470517001
864
61023
1.531597e+06
161
7
29
22
44
18
18
9
9
0
5
POLYGON ((996654.3495660074 201202.7736203865,...
108
1004
051700
1500000US360470517001
360470517001
864
61023
1.531597e+06
161
7
29
22
44
18
18
9
9
0
5
POLYGON ((996590.6055659479 200892.5835400976,...
109
1002
051700
1500000US360470517001
360470517001
864
61023
1.531597e+06
161
7
29
22
44
18
18
9
9
0
5
POLYGON ((996882.59967822 201239.3736844206, 9...
110
1001
051700
1500000US360470517001
360470517001
864
61023
1.531597e+06
161
7
29
22
44
18
18
9
9
0
5
POLYGON ((997030.6096623577 201580.5837967384,...
111
2004
051700
1500000US360470517002
360470517002
781
45469
4.429708e+06
432
41
38
76
106
57
32
16
14
44
8
POLYGON ((996146.8695015347 201070.6236042635,...
112
2002
051700
1500000US360470517002
360470517002
781
45469
4.429708e+06
432
41
38
76
106
57
32
16
14
44
8
POLYGON ((996478.0495338432 201468.513652634, ...
113
2003
051700
1500000US360470517002
360470517002
781
45469
4.429708e+06
432
41
38
76
106
57
32
16
14
44
8
POLYGON ((996314.4596456909 201268.9936524482,...
114
2001
051700
1500000US360470517002
360470517002
781
45469
4.429708e+06
432
41
38
76
106
57
32
16
14
44
8
POLYGON ((996644.179709998 201670.2137488219, ...
115
2014
055700
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
POLYGON ((995791.6495332039 201363.1135885359,...
116
2012
055700
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
POLYGON ((995959.6195813604 201564.9437327238,...
117
2010
055700
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
POLYGON ((995400.0296928391 201679.1837328302,...
118
2013
055700
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
POLYGON ((996055.3597414496 202041.1336851673,...
119
2011
055700
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
POLYGON ((995594.7297250205 201916.6437010514,...
120
2006
055700
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
POLYGON ((996316.2595816925 202005.2637331339,...
121
2008
055700
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
POLYGON ((995727.369725144 202083.6437652069, ...
122
2007
055700
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
POLYGON ((995963.7096933641 202374.2636694775,...
123 rows × 19 columns
In [85]:
nyc_shape.drop(['CensusBloo', 'CensusTrac', 'geometry'], axis =1, inplace=True)
nyc_shape
Out[85]:
GEO.id
GEO.id2
MediamGros
Median hou
TotalTax20
VD01
VD02
VD03
VD04
VD05
VD06
VD07
VD08
VD09
VD10
VD11
0
1500000US360470551003
360470551003
925
54313
3.008752e+06
104
10
10
27
20
0
0
0
0
28
9
1
1500000US360470551003
360470551003
925
54313
3.008752e+06
104
10
10
27
20
0
0
0
0
28
9
2
1500000US360470551003
360470551003
925
54313
3.008752e+06
104
10
10
27
20
0
0
0
0
28
9
3
1500000US360470551002
360470551002
563
30000
2.023738e+06
454
40
46
52
93
49
41
6
9
88
30
4
1500000US360470551002
360470551002
563
30000
2.023738e+06
454
40
46
52
93
49
41
6
9
88
30
5
1500000US360470551002
360470551002
563
30000
2.023738e+06
454
40
46
52
93
49
41
6
9
88
30
6
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
7
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
8
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
9
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
10
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
11
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
12
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
13
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
14
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
15
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
16
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
17
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
18
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
19
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
20
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
21
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
22
1500000US360470523004
360470523004
533
35125
9.540152e+05
170
16
47
0
31
7
8
6
19
28
8
23
1500000US360470523004
360470523004
533
35125
9.540152e+05
170
16
47
0
31
7
8
6
19
28
8
24
1500000US360470523005
360470523005
461
20357
1.433303e+06
284
23
19
56
29
56
8
0
10
50
33
25
1500000US360470523005
360470523005
461
20357
1.433303e+06
284
23
19
56
29
56
8
0
10
50
33
26
1500000US360470549002
360470549002
633
36875
2.712518e+06
199
59
23
17
5
18
0
18
21
34
4
27
1500000US360470549002
360470549002
633
36875
2.712518e+06
199
59
23
17
5
18
0
18
21
34
4
28
1500000US360470549002
360470549002
633
36875
2.712518e+06
199
59
23
17
5
18
0
18
21
34
4
29
1500000US360470549002
360470549002
633
36875
2.712518e+06
199
59
23
17
5
18
0
18
21
34
4
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
93
1500000US360470555001
360470555001
1224
56016
1.514461e+07
222
0
15
38
30
29
6
9
19
62
14
94
1500000US360470555001
360470555001
1224
56016
1.514461e+07
222
0
15
38
30
29
6
9
19
62
14
95
1500000US360470555001
360470555001
1224
56016
1.514461e+07
222
0
15
38
30
29
6
9
19
62
14
96
1500000US360470553001
360470553001
720
37823
2.652623e+06
307
35
33
35
37
31
52
8
26
50
0
97
1500000US360470553001
360470553001
720
37823
2.652623e+06
307
35
33
35
37
31
52
8
26
50
0
98
1500000US360470553001
360470553001
720
37823
2.652623e+06
307
35
33
35
37
31
52
8
26
50
0
99
1500000US360470553001
360470553001
720
37823
2.652623e+06
307
35
33
35
37
31
52
8
26
50
0
100
1500000US360470553003
360470553003
669
31688
1.475774e+06
289
28
47
0
41
39
29
13
0
72
20
101
1500000US360470553003
360470553003
669
31688
1.475774e+06
289
28
47
0
41
39
29
13
0
72
20
102
1500000US360470553003
360470553003
669
31688
1.475774e+06
289
28
47
0
41
39
29
13
0
72
20
103
1500000US360470553003
360470553003
669
31688
1.475774e+06
289
28
47
0
41
39
29
13
0
72
20
104
1500000US360470553003
360470553003
669
31688
1.475774e+06
289
28
47
0
41
39
29
13
0
72
20
105
1500000US360470557001
360470557001
669
37841
2.597763e+06
390
47
50
49
34
41
29
20
27
66
27
106
1500000US360470557001
360470557001
669
37841
2.597763e+06
390
47
50
49
34
41
29
20
27
66
27
107
1500000US360470517001
360470517001
864
61023
1.531597e+06
161
7
29
22
44
18
18
9
9
0
5
108
1500000US360470517001
360470517001
864
61023
1.531597e+06
161
7
29
22
44
18
18
9
9
0
5
109
1500000US360470517001
360470517001
864
61023
1.531597e+06
161
7
29
22
44
18
18
9
9
0
5
110
1500000US360470517001
360470517001
864
61023
1.531597e+06
161
7
29
22
44
18
18
9
9
0
5
111
1500000US360470517002
360470517002
781
45469
4.429708e+06
432
41
38
76
106
57
32
16
14
44
8
112
1500000US360470517002
360470517002
781
45469
4.429708e+06
432
41
38
76
106
57
32
16
14
44
8
113
1500000US360470517002
360470517002
781
45469
4.429708e+06
432
41
38
76
106
57
32
16
14
44
8
114
1500000US360470517002
360470517002
781
45469
4.429708e+06
432
41
38
76
106
57
32
16
14
44
8
115
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
116
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
117
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
118
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
119
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
120
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
121
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
122
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
123 rows × 16 columns
In [86]:
nyc_shape.drop_duplicates(inplace=True)
nyc_shape
Out[86]:
GEO.id
GEO.id2
MediamGros
Median hou
TotalTax20
VD01
VD02
VD03
VD04
VD05
VD06
VD07
VD08
VD09
VD10
VD11
0
1500000US360470551003
360470551003
925
54313
3.008752e+06
104
10
10
27
20
0
0
0
0
28
9
3
1500000US360470551002
360470551002
563
30000
2.023738e+06
454
40
46
52
93
49
41
6
9
88
30
6
1500000US360470519003
360470519003
755
35278
2.765746e+06
575
17
105
68
60
32
31
27
60
143
32
14
1500000US360470551001
360470551001
666
26212
2.816056e+06
424
12
67
57
40
50
40
0
43
102
13
22
1500000US360470523004
360470523004
533
35125
9.540152e+05
170
16
47
0
31
7
8
6
19
28
8
24
1500000US360470523005
360470523005
461
20357
1.433303e+06
284
23
19
56
29
56
8
0
10
50
33
26
1500000US360470549002
360470549002
633
36875
2.712518e+06
199
59
23
17
5
18
0
18
21
34
4
38
1500000US360470551004
360470551004
431
26429
3.405331e+06
119
6
10
29
14
0
0
27
7
19
7
40
1500000US360470523003
360470523003
567
36429
9.807110e+05
344
64
50
8
67
37
23
7
26
52
10
45
1500000US360470523001
360470523001
354
20129
2.034168e+06
535
89
73
15
80
54
46
42
40
57
39
49
1500000US360470523002
360470523002
679
16979
3.269767e+06
456
32
45
45
53
7
37
26
25
171
15
52
1500000US360470525001
360470525001
315
12750
1.613152e+06
733
53
84
73
92
97
86
59
52
107
30
56
1500000US360470519002
360470519002
775
34531
4.265974e+06
224
7
23
15
47
7
32
8
14
64
7
64
1500000US360470553002
360470553002
557
35463
3.284624e+06
376
73
48
46
37
65
14
30
13
43
7
71
1500000US360470519001
360470519001
695
32008
5.146695e+06
347
23
27
34
55
33
26
22
8
101
18
82
1500000US360470555001
360470555001
1224
56016
1.514461e+07
222
0
15
38
30
29
6
9
19
62
14
96
1500000US360470553001
360470553001
720
37823
2.652623e+06
307
35
33
35
37
31
52
8
26
50
0
100
1500000US360470553003
360470553003
669
31688
1.475774e+06
289
28
47
0
41
39
29
13
0
72
20
105
1500000US360470557001
360470557001
669
37841
2.597763e+06
390
47
50
49
34
41
29
20
27
66
27
107
1500000US360470517001
360470517001
864
61023
1.531597e+06
161
7
29
22
44
18
18
9
9
0
5
111
1500000US360470517002
360470517002
781
45469
4.429708e+06
432
41
38
76
106
57
32
16
14
44
8
115
1500000US360470557002
360470557002
638
45875
4.059491e+06
127
17
15
0
36
13
0
0
14
32
0
In [87]:
willy = pd.DataFrame(nyc_shape['GEO.id'])
willy.to_csv('willy.csv')
data = pd.read_csv('willy.csv')
data
Out[87]:
Unnamed: 0
GEO.id
0
0
1500000US360470551003
1
3
1500000US360470551002
2
6
1500000US360470519003
3
14
1500000US360470551001
4
22
1500000US360470523004
5
24
1500000US360470523005
6
26
1500000US360470549002
7
38
1500000US360470551004
8
40
1500000US360470523003
9
45
1500000US360470523001
10
49
1500000US360470523002
11
52
1500000US360470525001
12
56
1500000US360470519002
13
64
1500000US360470553002
14
71
1500000US360470519001
15
82
1500000US360470555001
16
96
1500000US360470553001
17
100
1500000US360470553003
18
105
1500000US360470557001
19
107
1500000US360470517001
20
111
1500000US360470517002
21
115
1500000US360470557002
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]:
CensusBloc
CensusTrac
GEO.id
GEO.id2
Margin o_1
Margin of
MediamGros
MedianHous
TotalTax20
VD01
...
VD03
VD04
VD05
VD06
VD07
VD08
VD09
VD10
VD11
geometry
0
3003
055300
1500000US360470553003
360470553003
30680
843
1164
61875
1.451061e+06
289
...
47
0
41
39
29
13
0
72
20
POLYGON ((995533.5128540844 200319.9265096784,...
1
3001
055300
1500000US360470553003
360470553003
30680
843
1164
61875
1.451061e+06
289
...
47
0
41
39
29
13
0
72
20
POLYGON ((995853.0955482572 200707.734461084, ...
2
3000
055300
1500000US360470553003
360470553003
30680
843
1164
61875
1.451061e+06
289
...
47
0
41
39
29
13
0
72
20
POLYGON ((996005.1483937502 200891.6530565023,...
3
3002
055300
1500000US360470553003
360470553003
30680
843
1164
61875
1.451061e+06
289
...
47
0
41
39
29
13
0
72
20
POLYGON ((995647.4706314206 200459.5823983401,...
4
3004
055300
1500000US360470553003
360470553003
30680
843
1164
61875
1.451061e+06
289
...
47
0
41
39
29
13
0
72
20
POLYGON ((995344.1913823336 200092.6454681754,...
5
3007
051900
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
...
105
68
60
32
31
27
60
143
32
POLYGON ((995799.4480177611 199247.6071320027,...
6
3006
051900
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
...
105
68
60
32
31
27
60
143
32
POLYGON ((996242.3155703396 199078.3377855122,...
7
3002
051900
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
...
105
68
60
32
31
27
60
143
32
POLYGON ((995901.2942707539 199457.4886020124,...
8
3008
051900
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
...
105
68
60
32
31
27
60
143
32
POLYGON ((996620.7295116782 198912.101569429, ...
9
3000
051900
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
...
105
68
60
32
31
27
60
143
32
POLYGON ((996699.6548787504 199074.0789357573,...
10
3001
051900
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
...
105
68
60
32
31
27
60
143
32
POLYGON ((996340.8009376675 199277.4732140005,...
11
3003
051900
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
...
105
68
60
32
31
27
60
143
32
POLYGON ((996026.0443493426 199704.0540537536,...
12
3005
051900
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
...
105
68
60
32
31
27
60
143
32
POLYGON ((996208.1197725832 199561.3135255873,...
13
2005
055300
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
...
48
46
37
65
14
30
13
43
7
POLYGON ((995356.172985673 199527.3431210816, ...
14
2004
055300
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
...
48
46
37
65
14
30
13
43
7
POLYGON ((995009.4830622524 199681.0140735954,...
15
2006
055300
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
...
48
46
37
65
14
30
13
43
7
POLYGON ((995482.9922858477 199533.9904175103,...
16
2002
055300
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
...
48
46
37
65
14
30
13
43
7
POLYGON ((995169.795405671 199950.6674056649, ...
17
2003
055300
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
...
48
46
37
65
14
30
13
43
7
POLYGON ((995076.6492665112 199904.7813425958,...
18
2000
055300
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
...
48
46
37
65
14
30
13
43
7
POLYGON ((995713.8261417598 199807.4751067609,...
19
2001
055300
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
...
48
46
37
65
14
30
13
43
7
POLYGON ((995376.6020786762 199886.8092656732,...
20
1002
055700
1500000US360470557001
360470557001
18396
388
1712
53438
1.129687e+06
390
...
50
49
34
41
29
20
27
66
27
POLYGON ((995633.658979252 201170.5120788366, ...
21
1001
055700
1500000US360470557001
360470557001
18396
388
1712
53438
1.129687e+06
390
...
50
49
34
41
29
20
27
66
27
POLYGON ((995278.7830485851 201538.1438895017,...
22
1007
055100
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
...
67
57
40
50
40
0
43
102
13
POLYGON ((994227.2192068398 199176.260161832, ...
23
1006
055100
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
...
67
57
40
50
40
0
43
102
13
POLYGON ((994347.9138473421 199430.6566346735,...
24
1001
055100
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
...
67
57
40
50
40
0
43
102
13
POLYGON ((993932.8346250057 199848.5130824149,...
25
1008
055100
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
...
67
57
40
50
40
0
43
102
13
POLYGON ((993814.7603860945 198998.8589418381,...
26
1004
055100
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
...
67
57
40
50
40
0
43
102
13
POLYGON ((993678.6031780839 199306.3875262588,...
27
1005
055100
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
...
67
57
40
50
40
0
43
102
13
POLYGON ((993908.37010701 199601.3478943408, 9...
28
1009
055100
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
...
67
57
40
50
40
0
43
102
13
POLYGON ((993739.699512586 199103.1251375079, ...
29
1000
055100
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
...
67
57
40
50
40
0
43
102
13
POLYGON ((994513.8196835071 199779.4836929291,...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
93
1020
055500
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
...
15
38
30
29
6
9
19
62
14
POLYGON ((994840.820998013 200195.1485439986, ...
94
1001
055500
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
...
15
38
30
29
6
9
19
62
14
POLYGON ((995113.575045675 201337.3762464225, ...
95
1006
055500
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
...
15
38
30
29
6
9
19
62
14
POLYGON ((995327.1261277497 200791.7759596705,...
96
1008
055500
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
...
15
38
30
29
6
9
19
62
14
POLYGON ((995178.7031962574 200612.5788115114,...
97
1004
055500
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
...
15
38
30
29
6
9
19
62
14
POLYGON ((994953.4670981765 201261.4754795879,...
98
1009
055500
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
...
15
38
30
29
6
9
19
62
14
POLYGON ((995040.0079355836 200440.6378824264,...
99
1016
055500
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
...
15
38
30
29
6
9
19
62
14
POLYGON ((994079.541352585 200591.4922395051, ...
100
1021
055500
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
...
15
38
30
29
6
9
19
62
14
POLYGON ((994134.9447850883 200138.5695889294,...
101
3002
055100
1500000US360470551003
360470551003
24594
155
1104
57599
3.673678e+05
104
...
10
27
20
0
0
0
0
28
9
POLYGON ((994636.245619759 198955.3688713461, ...
102
3001
055100
1500000US360470551003
360470551003
24594
155
1104
57599
3.673678e+05
104
...
10
27
20
0
0
0
0
28
9
POLYGON ((994763.4336855859 199218.6606675833,...
103
3000
055100
1500000US360470551003
360470551003
24594
155
1104
57599
3.673678e+05
104
...
10
27
20
0
0
0
0
28
9
POLYGON ((994863.0555335879 199425.526723668, ...
104
1006
052500
1500000US360470525001
360470525001
1366
51
325
15867
1.812191e+06
733
...
84
73
92
97
86
59
52
107
30
POLYGON ((994781.3772192597 198040.8149261773,...
105
1005
052500
1500000US360470525001
360470525001
1366
51
325
15867
1.812191e+06
733
...
84
73
92
97
86
59
52
107
30
POLYGON ((995121.7342827102 197775.7393795627,...
106
1003
052500
1500000US360470525001
360470525001
1366
51
325
15867
1.812191e+06
733
...
84
73
92
97
86
59
52
107
30
POLYGON ((995623.9704738221 197801.7597871438,...
107
1002
052500
1500000US360470525001
360470525001
1366
51
325
15867
1.812191e+06
733
...
84
73
92
97
86
59
52
107
30
POLYGON ((995729.7253417312 198018.6963564213,...
108
2002
055100
1500000US360470551002
360470551002
28122
272
1104
52656
5.806674e+05
454
...
46
52
93
49
41
6
9
88
30
POLYGON ((995079.35070467 198795.2615800053, 9...
109
2001
055100
1500000US360470551002
360470551002
28122
272
1104
52656
5.806674e+05
454
...
46
52
93
49
41
6
9
88
30
POLYGON ((995207.5853563398 199072.0713938326,...
110
2000
055100
1500000US360470551002
360470551002
28122
272
1104
52656
5.806674e+05
454
...
46
52
93
49
41
6
9
88
30
POLYGON ((995283.7121725976 199224.5792909265,...
111
2010
055700
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
...
15
0
36
13
0
0
14
32
0
POLYGON ((995397.984581843 201679.8765456676, ...
112
2013
055700
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
...
15
0
36
13
0
0
14
32
0
POLYGON ((996055.7057073414 202040.8541701734,...
113
2014
055700
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
...
15
0
36
13
0
0
14
32
0
POLYGON ((995789.0546658337 201360.0556630045,...
114
2011
055700
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
...
15
0
36
13
0
0
14
32
0
POLYGON ((995589.0714700073 201919.8980472535,...
115
2012
055700
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
...
15
0
36
13
0
0
14
32
0
POLYGON ((995955.0812366754 201560.1704202592,...
116
2008
055700
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
...
15
0
36
13
0
0
14
32
0
POLYGON ((995726.6689638346 202082.8219339997,...
117
2006
055700
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
...
15
0
36
13
0
0
14
32
0
POLYGON ((996072.4481279254 202103.6076535881,...
118
2007
055700
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
...
15
0
36
13
0
0
14
32
0
POLYGON ((995963.3240023404 202373.7704990953,...
119
4001
052300
1500000US360470523004
360470523004
30817
620
641
44786
8.704884e+05
170
...
47
0
31
7
8
6
19
28
8
POLYGON ((995619.7731802613 198874.0028923452,...
120
4000
052300
1500000US360470523004
360470523004
30817
620
641
44786
8.704884e+05
170
...
47
0
31
7
8
6
19
28
8
POLYGON ((995652.9286257625 199019.8844903409,...
121
4001
055100
1500000US360470551004
360470551004
34606
394
1012
67697
2.040639e+05
119
...
10
29
14
0
0
27
7
19
7
POLYGON ((994517.110031262 198702.0937873423, ...
122
4000
055100
1500000US360470551004
360470551004
34606
394
1012
67697
2.040639e+05
119
...
10
29
14
0
0
27
7
19
7
POLYGON ((994940.2046575099 198501.0502261668,...
123 rows × 21 columns
In [67]:
nyc_shape.drop(['CensusBloc', 'CensusTrac', 'geometry'], axis =1, inplace=True)
nyc_shape
Out[67]:
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
1
1500000US360470553003
360470553003
30680
843
1164
61875
1.451061e+06
289
28
47
0
41
39
29
13
0
72
20
2
1500000US360470553003
360470553003
30680
843
1164
61875
1.451061e+06
289
28
47
0
41
39
29
13
0
72
20
3
1500000US360470553003
360470553003
30680
843
1164
61875
1.451061e+06
289
28
47
0
41
39
29
13
0
72
20
4
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
6
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
17
105
68
60
32
31
27
60
143
32
7
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
17
105
68
60
32
31
27
60
143
32
8
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
17
105
68
60
32
31
27
60
143
32
9
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
17
105
68
60
32
31
27
60
143
32
10
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
17
105
68
60
32
31
27
60
143
32
11
1500000US360470519003
360470519003
22896
222
1748
60134
1.214220e+06
575
17
105
68
60
32
31
27
60
143
32
12
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
14
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
73
48
46
37
65
14
30
13
43
7
15
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
73
48
46
37
65
14
30
13
43
7
16
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
73
48
46
37
65
14
30
13
43
7
17
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
73
48
46
37
65
14
30
13
43
7
18
1500000US360470553002
360470553002
39190
423
1701
73750
1.342436e+06
376
73
48
46
37
65
14
30
13
43
7
19
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
21
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
23
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
12
67
57
40
50
40
0
43
102
13
24
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
12
67
57
40
50
40
0
43
102
13
25
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
12
67
57
40
50
40
0
43
102
13
26
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
12
67
57
40
50
40
0
43
102
13
27
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
12
67
57
40
50
40
0
43
102
13
28
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
12
67
57
40
50
40
0
43
102
13
29
1500000US360470551001
360470551001
50458
419
1640
65200
5.302822e+05
424
12
67
57
40
50
40
0
43
102
13
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
93
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
0
15
38
30
29
6
9
19
62
14
94
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
0
15
38
30
29
6
9
19
62
14
95
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
0
15
38
30
29
6
9
19
62
14
96
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
0
15
38
30
29
6
9
19
62
14
97
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
0
15
38
30
29
6
9
19
62
14
98
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
0
15
38
30
29
6
9
19
62
14
99
1500000US360470555001
360470555001
18454
206
1997
96350
6.779472e+05
222
0
15
38
30
29
6
9
19
62
14
100
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
102
1500000US360470551003
360470551003
24594
155
1104
57599
3.673678e+05
104
10
10
27
20
0
0
0
0
28
9
103
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
105
1500000US360470525001
360470525001
1366
51
325
15867
1.812191e+06
733
53
84
73
92
97
86
59
52
107
30
106
1500000US360470525001
360470525001
1366
51
325
15867
1.812191e+06
733
53
84
73
92
97
86
59
52
107
30
107
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
109
1500000US360470551002
360470551002
28122
272
1104
52656
5.806674e+05
454
40
46
52
93
49
41
6
9
88
30
110
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
112
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
17
15
0
36
13
0
0
14
32
0
113
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
17
15
0
36
13
0
0
14
32
0
114
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
17
15
0
36
13
0
0
14
32
0
115
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
17
15
0
36
13
0
0
14
32
0
116
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
17
15
0
36
13
0
0
14
32
0
117
1500000US360470557002
360470557002
34954
572
1888
85962
7.972190e+05
127
17
15
0
36
13
0
0
14
32
0
118
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
120
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
122
1500000US360470551004
360470551004
34606
394
1012
67697
2.040639e+05
119
6
10
29
14
0
0
27
7
19
7
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 [ ]:
df.loc['Total']= df.sum()
Content source: pichot/was-wburg-worth-it
Similar notebooks: