Join Tutorial#
Join points to closest BBL shapes tutorial.
Importing Libraries#
import os
import glob
import pandas as pd
import numpy as np
import seaborn as sns
import geopandas as gpd
import fiona
from fiona.crs import from_epsg
import geoplot
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.dates as mdates
from matplotlib.ticker import FuncFormatter
from mpl_toolkits.axes_grid1 import make_axes_locatable
import datetime
import statsmodels.api as sm
plt.rcParams['savefig.facecolor'] = 'white'
%matplotlib inline
Printing versions of Python modules and packages with watermark - the IPython magic extension.
%load_ext watermark
%watermark -v -p numpy,pandas,geopandas,geoplot,fiona,matplotlib.pyplot,seaborn
Python implementation: CPython
Python version : 3.8.13
IPython version : 8.4.0
numpy : 1.23.1
pandas : 1.4.3
geopandas : 0.11.1
geoplot : 0.5.1
fiona : 1.8.21
matplotlib.pyplot: unknown
seaborn : 0.11.2
Documention for installing watermark: rasbt/watermark
Retrieve Data#
path = 'https://raw.githubusercontent.com/mebauer/nyc-311-street-flooding/main/data/\
street-flooding-complaints.csv'
df = pd.read_csv(path, low_memory=False)
gdf = gpd.GeoDataFrame(
df, geometry=gpd.points_from_xy(df.longitude, df.latitude, crs=4263))
gdf = gdf.dropna(subset=['longitude']).reset_index(drop=True)
gdf = gdf.to_crs(2263)
print(gdf.shape)
gdf.head()
(26959, 35)
| unique_key | created_date | agency | agency_name | complaint_type | descriptor | incident_zip | incident_address | street_name | cross_street_1 | ... | intersection_street_1 | intersection_street_2 | closed_date | resolution_description | resolution_action_updated_date | location_type | landmark | facility_type | due_date | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 48542220 | 2020-12-31T15:41:00.000 | DEP | Department of Environmental Protection | Sewer | Street Flooding (SJ) | 11420.0 | 117-17 135 STREET | 135 STREET | FOCH BLVD | ... | NaN | NaN | 2021-01-01T00:20:00.000 | Please call 311 for further information. If yo... | 2021-01-01T00:20:00.000 | NaN | NaN | NaN | NaN | POINT (1038500.000 186000.014) |
| 1 | 48536430 | 2020-12-31T14:49:00.000 | DEP | Department of Environmental Protection | Sewer | Street Flooding (SJ) | 11357.0 | 20-24 150 STREET | 150 STREET | 20 AVE | ... | NaN | NaN | 2021-01-04T10:15:00.000 | The Department of Environment Protection inspe... | 2021-01-04T10:15:00.000 | NaN | NaN | NaN | NaN | POINT (1035682.000 223771.014) |
| 2 | 48539361 | 2020-12-31T14:03:00.000 | DEP | Department of Environmental Protection | Sewer | Street Flooding (SJ) | 11228.0 | 7223 8 AVENUE | 8 AVENUE | 72 ST | ... | NaN | NaN | 2021-01-02T11:25:00.000 | The Department of Environmental Protection has... | 2021-01-02T11:25:00.000 | NaN | NaN | NaN | NaN | POINT (979584.000 168255.014) |
| 3 | 48543132 | 2020-12-31T13:48:00.000 | DEP | Department of Environmental Protection | Sewer | Street Flooding (SJ) | 10032.0 | NaN | NaN | NaN | ... | RIVERSIDE DRIVE | WEST 165 STREET | 2020-12-31T14:50:00.000 | Please call 311 for further information. If yo... | 2020-12-31T14:50:00.000 | NaN | NaN | NaN | NaN | POINT (999557.000 245700.014) |
| 4 | 48536441 | 2020-12-31T13:10:00.000 | DEP | Department of Environmental Protection | Sewer | Street Flooding (SJ) | 11234.0 | 3123 FILLMORE AVENUE | FILLMORE AVENUE | E 31 ST | ... | NaN | NaN | 2021-01-03T10:45:00.000 | The Department of Environmental Protection ins... | 2021-01-03T10:45:00.000 | NaN | NaN | NaN | NaN | POINT (1001868.000 161232.014) |
5 rows × 35 columns
gdf.plot()
<AxesSubplot:>
path = '~/Downloads/nyc_mappluto_22v3_1_fgdb/MapPLUTO_22v3_1.gdb'
pluto_gdf = gpd.read_file(path, rows=500)
pluto_gdf = pluto_gdf.to_crs(2263)
print(pluto_gdf.shape)
pluto_gdf.head()
(500, 95)
| Borough | Block | Lot | CD | BCT2020 | BCTCB2020 | CT2010 | CB2010 | SchoolDist | Council | ... | FIRM07_FLAG | PFIRM15_FLAG | Version | DCPEdited | Latitude | Longitude | Notes | Shape_Leng | Shape_Area | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | MN | 1 | 10 | 101 | 1000500 | 10005000003 | 5 | 1000 | 02 | 1 | ... | 1 | 1 | 22v3.1 | None | 40.688766 | -74.018682 | None | 0.0 | 7.478663e+06 | MULTIPOLYGON (((980783.787 191526.763, 980898.... |
| 1 | MN | 1 | 101 | 101 | 1000100 | None | 1 | 1001 | 02 | 1 | ... | None | 1 | 22v3.1 | None | 40.689920 | -74.045337 | None | 0.0 | 5.018973e+05 | MULTIPOLYGON (((972409.690 190685.560, 972428.... |
| 2 | MN | 1 | 201 | 101 | 1000100 | 10001001000 | 1 | 1000 | 02 | 1 | ... | None | 1 | 22v3.1 | None | 40.698188 | -74.041329 | None | 0.0 | 1.148539e+06 | MULTIPOLYGON (((973648.661 193712.461, 973648.... |
| 3 | MN | 2 | 1 | 101 | 1000900 | 10009001022 | 9 | 1025 | 02 | 1 | ... | 1 | 1 | 22v3.1 | t | 40.700369 | -74.012911 | None | 0.0 | 1.008250e+05 | MULTIPOLYGON (((980639.785 194217.691, 980609.... |
| 4 | MN | 2 | 2 | 101 | 1000900 | 10009001022 | 9 | 1025 | 02 | 1 | ... | 1 | 1 | 22v3.1 | None | 40.700550 | -74.011588 | None | 0.0 | 8.724425e+04 | MULTIPOLYGON (((980915.002 194319.141, 980795.... |
5 rows × 95 columns
pluto_gdf.plot()
<AxesSubplot:>
pluto_gdf.sindex
rtree.index.Index(bounds=[971045.2731347084, 188447.4288892746, 985249.228328228, 201211.1846499443], size=70)
offset = 200
bbox = gdf.bounds + [-offset, -offset, offset, offset]
bbox.head()
| minx | miny | maxx | maxy | |
|---|---|---|---|---|
| 0 | 1.038300e+06 | 185800.014393 | 1.038700e+06 | 186200.014393 |
| 1 | 1.035482e+06 | 223571.014417 | 1.035882e+06 | 223971.014417 |
| 2 | 9.793840e+05 | 168055.014382 | 9.797840e+05 | 168455.014382 |
| 3 | 9.993570e+05 | 245500.014431 | 9.997570e+05 | 245900.014431 |
| 4 | 1.001668e+06 | 161032.014377 | 1.002068e+06 | 161432.014377 |
hits = bbox.apply(lambda row: list(pluto_gdf.sindex.intersection(row)), axis=1)
print(hits.shape)
hits.head()
(26959,)
0 []
1 []
2 []
3 []
4 []
dtype: object
tmp = pd.DataFrame(
{
# index of points table
"pt_idx": np.repeat(hits.index, hits.apply(len)),
# ordinal position of line - access via iloc later
"line_i": np.concatenate(hits.values)
}
)
print(tmp.shape)
tmp.head()
(721, 2)
| pt_idx | line_i | |
|---|---|---|
| 0 | 459 | 128.0 |
| 1 | 459 | 145.0 |
| 2 | 459 | 137.0 |
| 3 | 459 | 139.0 |
| 4 | 459 | 140.0 |
# Join back to the lines on line_i; we use reset_index() to
# give us the ordinal position of each line
tmp = tmp.join(pluto_gdf.reset_index(drop=True), on="line_i")
tmp.head()
| pt_idx | line_i | Borough | Block | Lot | CD | BCT2020 | BCTCB2020 | CT2010 | CB2010 | ... | FIRM07_FLAG | PFIRM15_FLAG | Version | DCPEdited | Latitude | Longitude | Notes | Shape_Leng | Shape_Area | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 459 | 128.0 | MN | 79 | 27 | 101 | 1001502 | 10015021000 | 15.02 | 1000 | ... | None | None | 22v3.1 | None | 40.710322 | -74.008466 | None | 0.0 | 5670.917438 | MULTIPOLYGON (((981955.698 198099.048, 981894.... |
| 1 | 459 | 145.0 | MN | 92 | 23 | 101 | 1001501 | 10015011011 | 15.01 | 1007 | ... | None | None | 22v3.1 | None | 40.710594 | -74.007185 | None | 0.0 | 1702.936740 | MULTIPOLYGON (((982289.308 198169.543, 982267.... |
| 2 | 459 | 137.0 | MN | 89 | 3 | 101 | 1001501 | 10015011017 | 15.01 | 1005 | ... | None | None | 22v3.1 | None | 40.710635 | -74.007964 | None | 0.0 | 5632.008015 | MULTIPOLYGON (((982020.597 198116.096, 981985.... |
| 3 | 459 | 139.0 | MN | 90 | 23 | 101 | 1001501 | 10015011009 | 15.01 | 1004 | ... | None | None | 22v3.1 | None | 40.710956 | -74.007773 | None | 0.0 | 1021.570578 | MULTIPOLYGON (((982110.692 198297.452, 982090.... |
| 4 | 459 | 140.0 | MN | 90 | 24 | 101 | 1001501 | 10015011009 | 15.01 | 1004 | ... | None | None | 22v3.1 | None | 40.711005 | -74.007817 | None | 0.0 | 1242.680263 | MULTIPOLYGON (((982106.447 198327.138, 982072.... |
5 rows × 97 columns
# Join back to the original points to get their geometry
# rename the point geometry as "point"
tmp = tmp.join(gdf.geometry.rename("point"), on="pt_idx")
# Convert back to a GeoDataFrame, so we can do spatial ops
tmp = gpd.GeoDataFrame(tmp, geometry="geometry", crs=gdf.crs)
tmp.head()
| pt_idx | line_i | Borough | Block | Lot | CD | BCT2020 | BCTCB2020 | CT2010 | CB2010 | ... | PFIRM15_FLAG | Version | DCPEdited | Latitude | Longitude | Notes | Shape_Leng | Shape_Area | geometry | point | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 459 | 128.0 | MN | 79 | 27 | 101 | 1001502 | 10015021000 | 15.02 | 1000 | ... | None | 22v3.1 | None | 40.710322 | -74.008466 | None | 0.0 | 5670.917438 | MULTIPOLYGON (((981955.698 198099.048, 981894.... | POINT (982027.000 198304.014) |
| 1 | 459 | 145.0 | MN | 92 | 23 | 101 | 1001501 | 10015011011 | 15.01 | 1007 | ... | None | 22v3.1 | None | 40.710594 | -74.007185 | None | 0.0 | 1702.936740 | MULTIPOLYGON (((982289.308 198169.543, 982267.... | POINT (982027.000 198304.014) |
| 2 | 459 | 137.0 | MN | 89 | 3 | 101 | 1001501 | 10015011017 | 15.01 | 1005 | ... | None | 22v3.1 | None | 40.710635 | -74.007964 | None | 0.0 | 5632.008015 | MULTIPOLYGON (((982020.597 198116.096, 981985.... | POINT (982027.000 198304.014) |
| 3 | 459 | 139.0 | MN | 90 | 23 | 101 | 1001501 | 10015011009 | 15.01 | 1004 | ... | None | 22v3.1 | None | 40.710956 | -74.007773 | None | 0.0 | 1021.570578 | MULTIPOLYGON (((982110.692 198297.452, 982090.... | POINT (982027.000 198304.014) |
| 4 | 459 | 140.0 | MN | 90 | 24 | 101 | 1001501 | 10015011009 | 15.01 | 1004 | ... | None | 22v3.1 | None | 40.711005 | -74.007817 | None | 0.0 | 1242.680263 | MULTIPOLYGON (((982106.447 198327.138, 982072.... | POINT (982027.000 198304.014) |
5 rows × 98 columns
tmp["snap_dist"] = tmp.geometry.distance(gpd.GeoSeries(tmp.point))
tmp.head()
| pt_idx | line_i | Borough | Block | Lot | CD | BCT2020 | BCTCB2020 | CT2010 | CB2010 | ... | Version | DCPEdited | Latitude | Longitude | Notes | Shape_Leng | Shape_Area | geometry | point | snap_dist | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 459 | 128.0 | MN | 79 | 27 | 101 | 1001502 | 10015021000 | 15.02 | 1000 | ... | 22v3.1 | None | 40.710322 | -74.008466 | None | 0.0 | 5670.917438 | MULTIPOLYGON (((981955.698 198099.048, 981894.... | POINT (982027.000 198304.014) | 210.186017 |
| 1 | 459 | 145.0 | MN | 92 | 23 | 101 | 1001501 | 10015011011 | 15.01 | 1007 | ... | 22v3.1 | None | 40.710594 | -74.007185 | None | 0.0 | 1702.936740 | MULTIPOLYGON (((982289.308 198169.543, 982267.... | POINT (982027.000 198304.014) | 244.527951 |
| 2 | 459 | 137.0 | MN | 89 | 3 | 101 | 1001501 | 10015011017 | 15.01 | 1005 | ... | 22v3.1 | None | 40.710635 | -74.007964 | None | 0.0 | 5632.008015 | MULTIPOLYGON (((982020.597 198116.096, 981985.... | POINT (982027.000 198304.014) | 69.911787 |
| 3 | 459 | 139.0 | MN | 90 | 23 | 101 | 1001501 | 10015011009 | 15.01 | 1004 | ... | 22v3.1 | None | 40.710956 | -74.007773 | None | 0.0 | 1021.570578 | MULTIPOLYGON (((982110.692 198297.452, 982090.... | POINT (982027.000 198304.014) | 49.550503 |
| 4 | 459 | 140.0 | MN | 90 | 24 | 101 | 1001501 | 10015011009 | 15.01 | 1004 | ... | 22v3.1 | None | 40.711005 | -74.007817 | None | 0.0 | 1242.680263 | MULTIPOLYGON (((982106.447 198327.138, 982072.... | POINT (982027.000 198304.014) | 30.673000 |
5 rows × 99 columns
# Discard any lines that are greater than tolerance from points
tmp = tmp.loc[tmp.snap_dist <= offset]
# Sort on ascending snap distance, so that closest goes to top
tmp = tmp.sort_values(by=["snap_dist"])
tmp.head()
| pt_idx | line_i | Borough | Block | Lot | CD | BCT2020 | BCTCB2020 | CT2010 | CB2010 | ... | Version | DCPEdited | Latitude | Longitude | Notes | Shape_Leng | Shape_Area | geometry | point | snap_dist | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 448 | 19254 | 340.0 | MN | 16 | 3 | 101 | 1031704 | 10317044000 | 317.04 | 4001 | ... | 22v3.1 | t | 40.712618 | -74.016586 | None | 0.0 | 2.052775e+06 | MULTIPOLYGON (((980584.841 200985.007, 980594.... | POINT (979355.000 197234.014) | 0.000000 |
| 483 | 19475 | 340.0 | MN | 16 | 3 | 101 | 1031704 | 10317044000 | 317.04 | 4001 | ... | 22v3.1 | t | 40.712618 | -74.016586 | None | 0.0 | 2.052775e+06 | MULTIPOLYGON (((980584.841 200985.007, 980594.... | POINT (979355.000 197234.014) | 0.000000 |
| 324 | 15235 | 340.0 | MN | 16 | 3 | 101 | 1031704 | 10317044000 | 317.04 | 4001 | ... | 22v3.1 | t | 40.712618 | -74.016586 | None | 0.0 | 2.052775e+06 | MULTIPOLYGON (((980584.841 200985.007, 980594.... | POINT (979759.000 199686.014) | 0.000000 |
| 518 | 21495 | 455.0 | MN | 25 | 7502 | 101 | 1000900 | 10009001001 | 9 | 1000 | ... | 22v3.1 | None | 40.705919 | -74.010766 | None | 0.0 | 2.585314e+04 | MULTIPOLYGON (((981284.442 196374.824, 981236.... | POINT (981399.000 196469.014) | 11.267715 |
| 516 | 21495 | 47.0 | MN | 25 | 27 | 101 | 1000900 | 10009001000 | 9 | 1000 | ... | 22v3.1 | None | 40.705686 | -74.010287 | None | 0.0 | 1.625568e+04 | MULTIPOLYGON (((981492.934 196409.111, 981478.... | POINT (981399.000 196469.014) | 11.310903 |
5 rows × 99 columns
# group by the index of the points and take the first, which is the
# closest line
closest = tmp.groupby("pt_idx").first()
# construct a GeoDataFrame of the closest lines
closest = gpd.GeoDataFrame(closest, geometry="geometry")
closest.head()
| line_i | Borough | Block | Lot | CD | BCT2020 | BCTCB2020 | CT2010 | CB2010 | SchoolDist | ... | Version | DCPEdited | Latitude | Longitude | Notes | Shape_Leng | Shape_Area | geometry | point | snap_dist | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pt_idx | |||||||||||||||||||||
| 459 | 138.0 | MN | 90 | 1 | 101 | 1001501 | 10015011009 | 15.01 | 1004 | 02 | ... | 22v3.1 | None | 40.711176 | -74.008123 | None | 0.0 | 6523.195602 | MULTIPOLYGON (((982041.663 198346.135, 982046.... | POINT (982027.000 198304.014) | 13.819315 |
| 1096 | 353.0 | MN | 143 | 7503 | 101 | 1003900 | 10039003007 | 39 | 4007 | 02 | ... | 22v3.1 | t | 40.717834 | -74.009967 | None | 0.0 | 5299.637906 | MULTIPOLYGON (((981511.400 200756.768, 981461.... | POINT (981500.000 200877.014) | 26.688052 |
| 1859 | 356.0 | MN | 144 | 31 | 101 | 1003300 | 10033002018 | 33 | 3022 | 02 | ... | 22v3.1 | t | 40.716852 | -74.008459 | None | 0.0 | 1353.898306 | MULTIPOLYGON (((981934.351 200443.725, 981920.... | POINT (981942.000 200380.014) | 46.337050 |
| 2563 | 386.0 | MN | 151 | 1 | 101 | 1003300 | 10033003004 | 33 | 1010 | 02 | ... | 22v3.1 | t | 40.715820 | -74.005977 | None | 0.0 | 26217.794000 | MULTIPOLYGON (((982672.512 200047.743, 982690.... | POINT (982569.000 199967.014) | 26.450837 |
| 4135 | 212.0 | MN | 38 | 17 | 101 | 1000700 | 10007007002 | 7 | 1012 | 02 | ... | 22v3.1 | None | 40.705661 | -74.006168 | None | 0.0 | 28946.530441 | MULTIPOLYGON (((982494.655 196247.048, 982403.... | POINT (982649.000 196479.014) | 31.676819 |
5 rows × 98 columns
# Join back to the original points:
updated_points = gdf.join(closest.drop(columns=['geometry']))
updated_points.head()
| unique_key | created_date | agency | agency_name | complaint_type | descriptor | incident_zip | incident_address | street_name | cross_street_1 | ... | PFIRM15_FLAG | Version | DCPEdited | Latitude | Longitude | Notes | Shape_Leng | Shape_Area | point | snap_dist | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 48542220 | 2020-12-31T15:41:00.000 | DEP | Department of Environmental Protection | Sewer | Street Flooding (SJ) | 11420.0 | 117-17 135 STREET | 135 STREET | FOCH BLVD | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | None | NaN |
| 1 | 48536430 | 2020-12-31T14:49:00.000 | DEP | Department of Environmental Protection | Sewer | Street Flooding (SJ) | 11357.0 | 20-24 150 STREET | 150 STREET | 20 AVE | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | None | NaN |
| 2 | 48539361 | 2020-12-31T14:03:00.000 | DEP | Department of Environmental Protection | Sewer | Street Flooding (SJ) | 11228.0 | 7223 8 AVENUE | 8 AVENUE | 72 ST | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | None | NaN |
| 3 | 48543132 | 2020-12-31T13:48:00.000 | DEP | Department of Environmental Protection | Sewer | Street Flooding (SJ) | 10032.0 | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | None | NaN |
| 4 | 48536441 | 2020-12-31T13:10:00.000 | DEP | Department of Environmental Protection | Sewer | Street Flooding (SJ) | 11234.0 | 3123 FILLMORE AVENUE | FILLMORE AVENUE | E 31 ST | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | None | NaN |
5 rows × 132 columns
updated_points.describe()
| unique_key | incident_zip | bbl | x_coordinate_state_plane | y_coordinate_state_plane | latitude | longitude | location_type | landmark | facility_type | ... | BBL | CondoNo | XCoord | YCoord | APPBBL | Latitude | Longitude | Shape_Leng | Shape_Area | snap_dist | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2.695900e+04 | 26959.000000 | 1.615900e+04 | 2.695900e+04 | 26959.000000 | 26959.000000 | 26959.000000 | 0.0 | 0.0 | 0.0 | ... | 8.600000e+01 | 45.000000 | 86.000000 | 86.000000 | 6.100000e+01 | 86.000000 | 86.000000 | 86.0 | 8.600000e+01 | 86.000000 |
| mean | 3.254573e+07 | 10969.384436 | 3.699429e+09 | 1.005498e+06 | 186184.607441 | 40.677615 | -73.923323 | NaN | NaN | NaN | ... | 1.000691e+09 | 1692.355556 | 981860.232558 | 197816.534884 | 1.000704e+09 | 40.709637 | -74.008620 | 0.0 | 1.464337e+05 | 55.102735 |
| std | 9.167418e+06 | 536.377470 | 1.075864e+09 | 3.505257e+04 | 30576.150750 | 0.083924 | 0.126329 | NaN | NaN | NaN | ... | 4.924506e+05 | 891.678528 | 1349.550990 | 1455.157179 | 4.997937e+05 | 0.003994 | 0.004868 | 0.0 | 4.086507e+05 | 41.886663 |
| min | 1.563993e+07 | 10001.000000 | 0.000000e+00 | 9.133570e+05 | 121032.000000 | 40.498628 | -74.254937 | NaN | NaN | NaN | ... | 1.000078e+09 | 65.000000 | 979652.000000 | 195554.000000 | 1.000070e+09 | 40.703427 | -74.016586 | 0.0 | 6.592921e+02 | 0.000000 |
| 25% | 2.455603e+07 | 10312.000000 | 3.060255e+09 | 9.868870e+05 | 161248.000000 | 40.609123 | -73.990504 | NaN | NaN | NaN | ... | 1.000210e+09 | 960.000000 | 980937.250000 | 196635.250000 | 1.000250e+09 | 40.706394 | -74.011948 | 0.0 | 6.331987e+03 | 28.184895 |
| 50% | 3.291181e+07 | 11221.000000 | 4.067910e+09 | 1.007480e+06 | 183618.000000 | 40.670610 | -73.916267 | NaN | NaN | NaN | ... | 1.000610e+09 | 1704.000000 | 981720.000000 | 197685.000000 | 1.000710e+09 | 40.709276 | -74.009125 | 0.0 | 1.725629e+04 | 38.098773 |
| 75% | 4.043790e+07 | 11385.000000 | 4.156250e+09 | 1.033980e+06 | 206488.500000 | 40.733373 | -73.820554 | NaN | NaN | NaN | ... | 1.001055e+09 | 2442.000000 | 982535.000000 | 198845.750000 | 1.000970e+09 | 40.712461 | -74.006186 | 0.0 | 4.467984e+04 | 75.728271 |
| max | 4.854313e+07 | 11697.000000 | 5.080500e+09 | 1.067279e+06 | 271876.000000 | 40.912869 | -73.700384 | NaN | NaN | NaN | ... | 1.001620e+09 | 3035.000000 | 984839.000000 | 200803.000000 | 1.001621e+09 | 40.717834 | -73.997875 | 0.0 | 2.052775e+06 | 165.787876 |
8 rows × 59 columns
updated_points['unique_key'].is_unique
True