In [1]:
import pandas as pd
import numpy as np
In [4]:
print "hi" #comment me
hi
In [9]:
In [15]:
print ("These are the data types in Building Permits\n" + str(bp_file.dtypes))
These are the data types in Building Permits
ID int64
PERMIT# object
PERMIT_TYPE object
ISSUE_DATE object
ESTIMATED_COST object
AMOUNT_WAIVED object
AMOUNT_PAID object
TOTAL_FEE object
STREET_NUMBER int64
STREET DIRECTION object
STREET_NAME object
SUFFIX object
WORK_DESCRIPTION object
PIN1 object
PIN2 object
...
CONTRACTOR_14_ADDRESS object
CONTRACTOR_14_CITY object
CONTRACTOR_14_STATE object
CONTRACTOR_14_ZIPCODE object
CONTRACTOR_14_PHONE object
CONTRACTOR_15_TYPE object
CONTRACTOR_15_NAME object
CONTRACTOR_15_ADDRESS object
CONTRACTOR_15_CITY object
CONTRACTOR_15_STATE object
CONTRACTOR_15_ZIPCODE float64
CONTRACTOR_15_PHONE float64
LATITUDE float64
LONGITUDE float64
LOCATION object
Length: 131, dtype: object
In [17]:
bp_file.head()
Out[17]:
ID
PERMIT#
PERMIT_TYPE
ISSUE_DATE
ESTIMATED_COST
AMOUNT_WAIVED
AMOUNT_PAID
TOTAL_FEE
STREET_NUMBER
STREET DIRECTION
...
CONTRACTOR_15_TYPE
CONTRACTOR_15_NAME
CONTRACTOR_15_ADDRESS
CONTRACTOR_15_CITY
CONTRACTOR_15_STATE
CONTRACTOR_15_ZIPCODE
CONTRACTOR_15_PHONE
LATITUDE
LONGITUDE
LOCATION
0
911610
EV000008
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
406
N
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.889204
-87.654722
(41.88920399822612, -87.65472214051763)
1
911617
EV000015
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
1664
N
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.912568
-87.662167
(41.91256808104945, -87.66216724918101)
2
911654
EV000074
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$105.00
$105.00
536
W
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.948277
-87.644878
(41.948276832456244, -87.64487812656894)
3
911698
EV000113
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
3255
W
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.926405
-87.709663
(41.92640488498051, -87.70966289254984)
4
911713
EV000128
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
4177
S
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.817495
-87.698459
(41.817494775576236, -87.69845934497967)
5 rows × 131 columns
In [18]:
bp_file.columns
Out[18]:
Index([u'ID', u'PERMIT#', u' PERMIT_TYPE', u' ISSUE_DATE', u' ESTIMATED_COST', u' AMOUNT_WAIVED', u' AMOUNT_PAID', u' TOTAL_FEE', u'STREET_NUMBER', u'STREET DIRECTION', u'STREET_NAME', u' SUFFIX', u'WORK_DESCRIPTION', u' PIN1', u' PIN2', u' PIN3', u' PIN4', u' PIN5', u' PIN6', u' PIN7', u' PIN8', u' PIN9', u' PIN10', u'CONTRACTOR_1_TYPE', u'CONTRACTOR_1_NAME', u'CONTRACTOR_1_ADDRESS', u'CONTRACTOR_1_CITY', u'CONTRACTOR_1_STATE', u'CONTRACTOR_1_ZIPCODE', u'CONTRACTOR_1_PHONE', u'CONTRACTOR_2_TYPE', u'CONTRACTOR_2_NAME', u'CONTRACTOR_2_ADDRESS', u'CONTRACTOR_2_CITY', u'CONTRACTOR_2_STATE', u'CONTRACTOR_2_ZIPCODE', u'CONTRACTOR_2_PHONE', u'CONTRACTOR_3_TYPE', u'CONTRACTOR_3_NAME', u'CONTRACTOR_3_ADDRESS', u'CONTRACTOR_3_CITY', u'CONTRACTOR_3_STATE', u'CONTRACTOR_3_ZIPCODE', u'CONTRACTOR_3_PHONE', u'CONTRACTOR_4_TYPE', u'CONTRACTOR_4_NAME', u'CONTRACTOR_4_ADDRESS', u'CONTRACTOR_4_CITY', u'CONTRACTOR_4_STATE', u'CONTRACTOR_4_ZIPCODE', u'CONTRACTOR_4_PHONE', u'CONTRACTOR_5_TYPE', u'CONTRACTOR_5_NAME', u'CONTRACTOR_5_ADDRESS', u'CONTRACTOR_5_CITY', u'CONTRACTOR_5_STATE', u'CONTRACTOR_5_ZIPCODE', u'CONTRACTOR_5_PHONE', u'CONTRACTOR_6_TYPE', u'CONTRACTOR_6_NAME', u'CONTRACTOR_6_ADDRESS', u'CONTRACTOR_6_CITY', u'CONTRACTOR_6_STATE', u'CONTRACTOR_6_ZIPCODE', u'CONTRACTOR_6_PHONE', u'CONTRACTOR_7_TYPE', u'CONTRACTOR_7_NAME', u'CONTRACTOR_7_ADDRESS', u'CONTRACTOR_7_CITY', u'CONTRACTOR_7_STATE', u'CONTRACTOR_7_ZIPCODE', u'CONTRACTOR_7_PHONE', u'CONTRACTOR_8_TYPE', u'CONTRACTOR_8_NAME', u'CONTRACTOR_8_ADDRESS', u'CONTRACTOR_8_CITY', u'CONTRACTOR_8_STATE', u'CONTRACTOR_8_ZIPCODE', u'CONTRACTOR_8_PHONE', u'CONTRACTOR_9_TYPE', u'CONTRACTOR_9_NAME', u'CONTRACTOR_9_ADDRESS', u'CONTRACTOR_9_CITY', u'CONTRACTOR_9_STATE', u'CONTRACTOR_9_ZIPCODE', u'CONTRACTOR_9_PHONE', u'CONTRACTOR_10_TYPE', u'CONTRACTOR_10_NAME', u'CONTRACTOR_10_ADDRESS', u'CONTRACTOR_10_CITY', u'CONTRACTOR_10_STATE', u'CONTRACTOR_10_ZIPCODE', u'CONTRACTOR_10_PHONE', u'CONTRACTOR_11_TYPE', u'CONTRACTOR_11_NAME', u'CONTRACTOR_11_ADDRESS', u'CONTRACTOR_11_CITY', u'CONTRACTOR_11_STATE', u'CONTRACTOR_11_ZIPCODE', u'CONTRACTOR_11_PHONE', ...], dtype='object')
In [39]:
def removeSpace(file):
new_names=[]
for col_name in file.columns:
col_name=str.lower(col_name)
col_name = str.strip(col_name)
str.replace(col_name,'\s','_')
new_names[len(new_names):]=[col_name]
file.columns = new_names
In [40]:
removeSpace(bp_file)
print bp_file.columns
Index([u'id', u'permit#', u'permit_type', u'issue_date', u'estimated_cost', u'amount_waived', u'amount_paid', u'total_fee', u'street_number', u'street direction', u'street_name', u'suffix', u'work_description', u'pin1', u'pin2', u'pin3', u'pin4', u'pin5', u'pin6', u'pin7', u'pin8', u'pin9', u'pin10', u'contractor_1_type', u'contractor_1_name', u'contractor_1_address', u'contractor_1_city', u'contractor_1_state', u'contractor_1_zipcode', u'contractor_1_phone', u'contractor_2_type', u'contractor_2_name', u'contractor_2_address', u'contractor_2_city', u'contractor_2_state', u'contractor_2_zipcode', u'contractor_2_phone', u'contractor_3_type', u'contractor_3_name', u'contractor_3_address', u'contractor_3_city', u'contractor_3_state', u'contractor_3_zipcode', u'contractor_3_phone', u'contractor_4_type', u'contractor_4_name', u'contractor_4_address', u'contractor_4_city', u'contractor_4_state', u'contractor_4_zipcode', u'contractor_4_phone', u'contractor_5_type', u'contractor_5_name', u'contractor_5_address', u'contractor_5_city', u'contractor_5_state', u'contractor_5_zipcode', u'contractor_5_phone', u'contractor_6_type', u'contractor_6_name', u'contractor_6_address', u'contractor_6_city', u'contractor_6_state', u'contractor_6_zipcode', u'contractor_6_phone', u'contractor_7_type', u'contractor_7_name', u'contractor_7_address', u'contractor_7_city', u'contractor_7_state', u'contractor_7_zipcode', u'contractor_7_phone', u'contractor_8_type', u'contractor_8_name', u'contractor_8_address', u'contractor_8_city', u'contractor_8_state', u'contractor_8_zipcode', u'contractor_8_phone', u'contractor_9_type', u'contractor_9_name', u'contractor_9_address', u'contractor_9_city', u'contractor_9_state', u'contractor_9_zipcode', u'contractor_9_phone', u'contractor_10_type', u'contractor_10_name', u'contractor_10_address', u'contractor_10_city', u'contractor_10_state', u'contractor_10_zipcode', u'contractor_10_phone', u'contractor_11_type', u'contractor_11_name', u'contractor_11_address', u'contractor_11_city', u'contractor_11_state', u'contractor_11_zipcode', u'contractor_11_phone', ...], dtype='object')
In [43]:
bp_file.issue_date = pd.to_datetime(bp_file.issue_date)
bp_file.issue_date.head()
Out[43]:
0 NaT
1 NaT
2 NaT
3 NaT
4 NaT
Name: issue_date, dtype: datetime64[ns]
In [44]:
pd.value_counts(bp_file.id)
Out[44]:
2355917 1
2634579 1
2134855 1
2157384 1
1637194 1
1635147 1
2149196 1
1622861 1
2108240 1
2630481 1
2112338 1
2626391 1
2431251 1
2653018 1
2518408 1
...
2636973 1
2245665 1
2108591 1
2147506 1
2336984 1
2381100 1
2157751 1
2476687 1
2133179 1
2143420 1
2301850 1
2491474 1
1791168 1
2311362 1
2378075 1
Length: 390864, dtype: int64
In [46]:
bp_file.describe()
Out[46]:
id
street_number
contractor_15_zipcode
contractor_15_phone
latitude
longitude
count
390864.000000
390864.000000
1
0
389725.000000
389725.000000
mean
2160625.416012
3494.807035
60641
NaN
41.869630
-87.673014
std
309811.615913
2982.947473
NaN
NaN
0.084681
0.059308
min
911610.000000
1.000000
60641
NaN
41.644702
-87.914534
25%
1881983.250000
1006.000000
60641
NaN
41.801236
-87.707729
50%
2181551.000000
2744.000000
60641
NaN
41.885822
-87.663094
75%
2437348.750000
5411.000000
60641
NaN
41.931697
-87.631885
max
2660888.000000
111601.000000
60641
NaN
42.022645
-87.524677
In [48]:
bp_file.groupby("latitude").mean()
Out[48]:
id
street_number
contractor_15_zipcode
contractor_15_phone
longitude
latitude
41.644702
1659976.000000
244
NaN
NaN
-87.615151
41.644712
2055233.908046
440
NaN
NaN
-87.610545
41.644713
2032439.000000
304
NaN
NaN
-87.614148
41.644713
2298827.000000
430
NaN
NaN
-87.610893
41.644715
2117366.000000
306
NaN
NaN
-87.614075
41.644717
2153995.134454
612
NaN
NaN
-87.606793
41.644722
2013874.000000
826
NaN
NaN
-87.601401
41.644746
1974911.666667
1220
NaN
NaN
-87.592011
41.644798
2428705.395349
13707
NaN
NaN
-87.574634
41.645098
2203029.000000
13779
NaN
NaN
-87.614856
41.645528
2338712.666667
13733
NaN
NaN
-87.539888
41.645547
2187627.500000
13750
NaN
NaN
-87.615869
41.645671
1806449.000000
13744
NaN
NaN
-87.616035
41.645743
1973962.000000
13749
NaN
NaN
-87.615717
41.646033
2323624.000000
13735
NaN
NaN
-87.616104
41.646075
2381503.000000
13733
NaN
NaN
-87.616160
41.646272
2089645.000000
13700
NaN
NaN
-87.540225
41.646489
1875196.000000
13713
NaN
NaN
-87.616713
41.646662
2078135.000000
13685
NaN
NaN
-87.543379
41.646722
2486828.000000
13651
NaN
NaN
-87.542684
41.646885
1866683.500000
13673
NaN
NaN
-87.543661
41.646928
2622665.000000
337
NaN
NaN
-87.613624
41.646929
2522471.000000
311
NaN
NaN
-87.614480
41.647148
1862080.000000
330
NaN
NaN
-87.613821
41.647148
919076.000000
344
NaN
NaN
-87.613361
41.647149
1848028.000000
246
NaN
NaN
-87.615443
41.647365
1827735.000000
13647
NaN
NaN
-87.544277
41.647439
2494150.500000
13643
NaN
NaN
-87.544372
41.647440
1671191.000000
13625
NaN
NaN
-87.560384
41.647796
2045840.000000
13610
NaN
NaN
-87.540181
...
...
...
...
...
...
42.022312
2120971.500000
7736
NaN
NaN
-87.666980
42.022342
2440772.000000
1404
NaN
NaN
-87.667087
42.022344
2624524.000000
1408
NaN
NaN
-87.667249
42.022352
2460691.666667
1428
NaN
NaN
-87.668059
42.022361
2460217.500000
1518
NaN
NaN
-87.669066
42.022363
2536321.500000
1522
NaN
NaN
-87.669211
42.022369
2639443.000000
1536
NaN
NaN
-87.669718
42.022371
1949901.750000
1542
NaN
NaN
-87.669936
42.022372
1814571.000000
1546
NaN
NaN
-87.670081
42.022372
2184769.750000
1548
NaN
NaN
-87.670154
42.022409
2020055.000000
7742
NaN
NaN
-87.673211
42.022411
1778062.000000
1609
NaN
NaN
-87.671052
42.022412
2338119.500000
1625
NaN
NaN
-87.671568
42.022415
1898185.000000
7759
NaN
NaN
-87.666318
42.022417
2338087.000000
1647
NaN
NaN
-87.672322
42.022544
2254839.000000
7746
NaN
NaN
-87.666677
42.022580
2456731.000000
7750
NaN
NaN
-87.666555
42.022630
2214631.000000
1608
NaN
NaN
-87.671051
42.022631
2258303.000000
1616
NaN
NaN
-87.671309
42.022631
1724361.000000
1622
NaN
NaN
-87.671503
42.022632
2509988.000000
1624
NaN
NaN
-87.671567
42.022632
2220273.000000
1626
NaN
NaN
-87.671632
42.022634
2432020.500000
1634
NaN
NaN
-87.671940
42.022636
1697900.000000
1646
NaN
NaN
-87.672316
42.022638
2422669.000000
1658
NaN
NaN
-87.672691
42.022642
2083770.272727
1700
NaN
NaN
-87.673119
42.022643
1945289.000000
1702
NaN
NaN
-87.673196
42.022644
2422552.333333
1710
NaN
NaN
-87.673498
42.022645
2419406.000000
1716
NaN
NaN
-87.673726
42.022645
1881222.000000
1726
NaN
NaN
-87.674104
165089 rows × 5 columns
In [53]:
def remove_na(file):
file= file.dropna(axis={0,1}, how='all')
file.head()
In [62]:
remove_na(bp_file)
#bp_file.head()
In [60]:
bp_file.interpolate().plot()
Out[60]:
<matplotlib.axes._subplots.AxesSubplot at 0x107af4350>
In [71]:
bp_file= bp_file.dropna(axis=[0,1], how='all')
bp_file.head()
Out[71]:
In [72]:
bp_file= bp_file.dropna(axis=[0,1], how='all')
bp_file.head()
Out[72]:
In [73]:
bp_file = pd.read_csv("/Users/Elissa/dssg/project-inspector-gadget/Building_Permits.csv")
In [78]:
#bp_file= bp_file.dropna(axis=[0,1], thresh=100)
bp_file.head()
Out[78]:
In [79]:
bp_file = pd.read_csv("/Users/Elissa/dssg/project-inspector-gadget/Building_Permits.csv")
In [80]:
bp_file.head()
Out[80]:
ID
PERMIT#
PERMIT_TYPE
ISSUE_DATE
ESTIMATED_COST
AMOUNT_WAIVED
AMOUNT_PAID
TOTAL_FEE
STREET_NUMBER
STREET DIRECTION
...
CONTRACTOR_15_TYPE
CONTRACTOR_15_NAME
CONTRACTOR_15_ADDRESS
CONTRACTOR_15_CITY
CONTRACTOR_15_STATE
CONTRACTOR_15_ZIPCODE
CONTRACTOR_15_PHONE
LATITUDE
LONGITUDE
LOCATION
0
911610
EV000008
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
406
N
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.889204
-87.654722
(41.88920399822612, -87.65472214051763)
1
911617
EV000015
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
1664
N
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.912568
-87.662167
(41.91256808104945, -87.66216724918101)
2
911654
EV000074
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$105.00
$105.00
536
W
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.948277
-87.644878
(41.948276832456244, -87.64487812656894)
3
911698
EV000113
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
3255
W
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.926405
-87.709663
(41.92640488498051, -87.70966289254984)
4
911713
EV000128
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
4177
S
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.817495
-87.698459
(41.817494775576236, -87.69845934497967)
5 rows × 131 columns
In [81]:
bp_file= bp_file.dropna(axis=[0,1], how="all")
In [82]:
bp_file.head()
Out[82]:
ID
PERMIT#
PERMIT_TYPE
ISSUE_DATE
ESTIMATED_COST
AMOUNT_WAIVED
AMOUNT_PAID
TOTAL_FEE
STREET_NUMBER
STREET DIRECTION
...
CONTRACTOR_14_PHONE
CONTRACTOR_15_TYPE
CONTRACTOR_15_NAME
CONTRACTOR_15_ADDRESS
CONTRACTOR_15_CITY
CONTRACTOR_15_STATE
CONTRACTOR_15_ZIPCODE
LATITUDE
LONGITUDE
LOCATION
0
911610
EV000008
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
406
N
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.889204
-87.654722
(41.88920399822612, -87.65472214051763)
1
911617
EV000015
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
1664
N
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.912568
-87.662167
(41.91256808104945, -87.66216724918101)
2
911654
EV000074
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$105.00
$105.00
536
W
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.948277
-87.644878
(41.948276832456244, -87.64487812656894)
3
911698
EV000113
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
3255
W
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.926405
-87.709663
(41.92640488498051, -87.70966289254984)
4
911713
EV000128
PERMIT - ELEVATOR EQUIPMENT
NaN
$0.00
$0.00
$52.50
$52.50
4177
S
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.817495
-87.698459
(41.817494775576236, -87.69845934497967)
5 rows × 130 columns
In [87]:
bp_file= bp_file.dropna(axis=[0,1], thresh=40)
removeSpace(bp_file)
bp_file.head()
Out[87]:
id
permit#
permit_type
issue_date
estimated_cost
amount_waived
amount_paid
total_fee
street_number
street direction
...
contractor_11_phone
contractor_12_type
contractor_12_name
contractor_12_address
contractor_12_city
contractor_12_state
contractor_12_zipcode
latitude
longitude
location
467
1358358
B20414313
PERMIT - NEW CONSTRUCTION
05/18/2006
$2000000.00
$0.00
$18726.95
$18726.95
321
S
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.876481
-87.715602
(41.8764808798513, -87.71560203300689)
476
1378485
100002267
PERMIT - RENOVATION/ALTERATION
03/10/2006
$45000.00
$0.00
$1945.00
$1945.00
2421
N
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.925857
-87.664452
(41.92585684158289, -87.66445154091303)
480
1381541
100004342
PERMIT - RENOVATION/ALTERATION
09/29/2005
$38000.00
$0.00
$1560.00
$1560.00
2525
W
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.910173
-87.690678
(41.91017264385976, -87.69067799640872)
495
1392510
100010675
PERMIT - NEW CONSTRUCTION
09/30/2005
$800000.00
$0.00
$13475.14
$13475.14
693
N
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.894941
-87.649976
(41.89494059376751, -87.64997609685338)
500
1393747
100011654
PERMIT - NEW CONSTRUCTION
06/09/2006
$450000.00
$0.00
$8003.98
$8003.98
3637
N
...
NaN
NaN
NaN
NaN
NaN
NaN
NaN
41.948342
-87.654216
(41.94834184745331, -87.65421574924672)
5 rows × 109 columns
In [92]:
bp_file.sort_index(axis=1, ascending=False)
bp_file.groupby("permit_type").describe()
Out[92]:
id
latitude
longitude
street_number
permit_type
PERMIT - EASY PERMIT PROCESS
count
12398.000000
12362.000000
12362.000000
12398.000000
mean
2239580.565898
41.872826
-87.670125
3435.804243
std
277411.088461
0.081466
0.054998
2869.067300
min
1631654.000000
41.648034
-87.914428
1.000000
25%
2030754.000000
41.805878
-87.702304
1040.000000
50%
2252920.000000
41.891114
-87.661723
2650.000000
75%
2488714.000000
41.931647
-87.631010
5345.000000
max
2660859.000000
42.022645
-87.524793
13601.000000
PERMIT - ELECTRIC WIRING
count
171.000000
171.000000
171.000000
171.000000
mean
2344623.017544
41.902194
-87.639942
1234.356725
std
244473.956561
0.038029
0.035431
1572.506387
min
1648495.000000
41.774379
-87.836841
1.000000
25%
2181232.500000
41.884606
-87.640514
221.500000
50%
2388227.000000
41.895585
-87.627700
600.000000
75%
2524884.000000
41.907800
-87.624126
1408.500000
max
2659993.000000
42.019287
-87.586674
6625.000000
PERMIT - ELEVATOR EQUIPMENT
count
13.000000
13.000000
13.000000
13.000000
mean
1784027.000000
41.913713
-87.642516
1393.307692
std
153059.804522
0.051593
0.024074
1649.742878
min
1653052.000000
41.838141
-87.701599
30.000000
25%
1694335.000000
41.887469
-87.648656
400.000000
50%
1769358.000000
41.898465
-87.636605
828.000000
75%
1783210.000000
41.941080
-87.626333
1791.000000
max
2248622.000000
42.019287
-87.617560
6030.000000
PERMIT - FOR EXTENSION OF PMT
count
20.000000
20.000000
20.000000
20.000000
mean
1840212.850000
41.883573
-87.687679
4378.800000
std
49174.752160
0.088078
0.059219
2550.941531
min
1784330.000000
41.693591
-87.789381
500.000000
25%
1810337.250000
41.816878
-87.755429
2401.250000
50%
1828003.500000
41.888869
-87.684555
4926.500000
...
...
...
...
...
...
PERMIT - RENOVATION/ALTERATION
std
301040.574380
0.073391
0.054047
2723.095179
min
1378485.000000
41.644717
-87.914436
1.000000
25%
1893060.500000
41.856858
-87.697580
550.000000
50%
2191642.500000
41.889038
-87.655734
2056.000000
75%
2434379.500000
41.931165
-87.630801
4610.000000
max
2660428.000000
42.022644
-87.525094
13749.000000
PERMIT - SCAFFOLDING
count
148.000000
148.000000
148.000000
148.000000
mean
2266617.283784
41.906027
-87.636442
1683.195946
std
170289.800028
0.042928
0.023654
2017.432023
min
1788346.000000
41.763235
-87.790577
1.000000
25%
2180531.750000
41.884642
-87.644034
332.250000
50%
2297329.500000
41.899627
-87.632183
909.500000
75%
2366474.250000
41.921132
-87.626984
1624.000000
max
2640511.000000
42.016879
-87.559617
7423.000000
PERMIT - SIGNS
count
417.000000
417.000000
417.000000
417.000000
mean
2149261.302158
41.890323
-87.643081
1276.712230
std
274846.546650
0.039096
0.031956
1795.182396
min
1645683.000000
41.714554
-87.820686
1.000000
25%
1884105.000000
41.881589
-87.650259
135.000000
50%
2134402.000000
41.886373
-87.632167
600.000000
75%
2422446.000000
41.900097
-87.626054
1550.000000
max
2642009.000000
42.019287
-87.575487
9831.000000
PERMIT - WRECKING/DEMOLITION
count
7.000000
7.000000
7.000000
7.000000
mean
1822022.000000
41.881582
-87.680151
4230.142857
std
254799.225204
0.083528
0.062387
2644.988811
min
1563936.000000
41.767494
-87.820686
1130.000000
25%
1637477.500000
41.814693
-87.664391
2420.500000
50%
1666787.000000
41.925965
-87.657137
2745.000000
75%
2041025.000000
41.931981
-87.653905
6576.500000
max
2166426.000000
41.984271
-87.646643
7742.000000
88 rows × 4 columns
In [ ]:
Content source: eredmiles/project-inspector-gadget
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