Geopandas - geocoding, interactive plotting¶

More IO, interactive visualization using folium and geocoding

In [1]:

import matplotlib.pyplot as plt
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point, Polygon, LineString
import seaborn as sns
import pyepsg
%matplotlib inline
plt.style.use('bmh')

import matplotlib.pyplot as plt import pandas as pd import geopandas as gpd from shapely.geometry import Point, Polygon, LineString import seaborn as sns import pyepsg %matplotlib inline plt.style.use('bmh')

Read CSV data¶

In [2]:

df = pd.read_csv('data/2015-06-21.csv')
df.head()

df = pd.read_csv('data/2015-06-21.csv') df.head()

Out[2]:

	Start time	End time	Calories (kcal)	Distance (m)	Average heart rate (bpm)	Max heart rate (bpm)	Min heart rate (bpm)	Height (m)	Low latitude (deg)	Low longitude (deg)	...	High longitude (deg)	Average speed (m/s)	Max speed (m/s)	Min speed (m/s)	Step count	Average weight (kg)	Max weight (kg)	Min weight (kg)	Inactive duration (ms)	Walking duration (ms)
0	00:00:00.000-07:00	00:15:00.000-07:00	16.215914	NaN	NaN	NaN	NaN	NaN	34.054939	-117.242691	...	-117.242432	NaN	NaN	NaN	NaN	NaN	NaN	NaN	900000	NaN
1	00:15:00.000-07:00	00:30:00.000-07:00	16.215914	NaN	NaN	NaN	NaN	NaN	34.054955	-117.242424	...	-117.242401	NaN	NaN	NaN	NaN	NaN	NaN	NaN	900000	NaN
2	00:30:00.000-07:00	00:45:00.000-07:00	16.215914	NaN	NaN	NaN	NaN	NaN	34.054939	-117.242706	...	-117.242416	NaN	NaN	NaN	NaN	NaN	NaN	NaN	900000	NaN
3	00:45:00.000-07:00	01:00:00.000-07:00	16.215914	NaN	NaN	NaN	NaN	NaN	34.054970	-117.242661	...	-117.242447	NaN	NaN	NaN	NaN	NaN	NaN	NaN	900000	NaN
4	01:00:00.000-07:00	01:15:00.000-07:00	16.215914	NaN	NaN	NaN	NaN	NaN	34.054977	-117.242668	...	-117.242409	NaN	NaN	NaN	NaN	NaN	NaN	NaN	900000	NaN

5 rows × 21 columns

In [3]:

df.shape

df.shape

Out[3]:

(96, 21)

In [4]:

df.columns

df.columns

Out[4]:

Index(['Start time', 'End time', 'Calories (kcal)', 'Distance (m)',
       'Average heart rate (bpm)', 'Max heart rate (bpm)',
       'Min heart rate (bpm)', 'Height (m)', 'Low latitude (deg)',
       'Low longitude (deg)', 'High latitude (deg)', 'High longitude (deg)',
       'Average speed (m/s)', 'Max speed (m/s)', 'Min speed (m/s)',
       'Step count', 'Average weight (kg)', 'Max weight (kg)',
       'Min weight (kg)', 'Inactive duration (ms)', 'Walking duration (ms)'],
      dtype='object')

filter out columns without data

In [5]:

df.describe()

df.describe()

Out[5]:

	Calories (kcal)	Distance (m)	Average heart rate (bpm)	Max heart rate (bpm)	Min heart rate (bpm)	Height (m)	Low latitude (deg)	Low longitude (deg)	High latitude (deg)	High longitude (deg)	Average speed (m/s)	Max speed (m/s)	Min speed (m/s)	Step count	Average weight (kg)	Max weight (kg)	Min weight (kg)	Inactive duration (ms)	Walking duration (ms)
count	96.000000	2.000000	0.0	0.0	0.0	0.0	91.000000	91.000000	91.000000	91.000000	2.000000	2.000000	2.000000	15.000000	0.0	0.0	0.0	96.000000	5.000000
mean	16.411472	31.000000	NaN	NaN	NaN	NaN	34.055033	-117.242620	34.055120	-117.242453	0.799705	0.799705	0.799705	116.600000	NaN	NaN	NaN	880833.302083	150074.200000
std	2.665210	2.828427	NaN	NaN	NaN	NaN	0.000113	0.000092	0.000075	0.000097	0.333833	0.333833	0.333833	157.019016	NaN	NaN	NaN	87443.309515	142937.658018
min	3.434296	29.000000	NaN	NaN	NaN	NaN	34.054150	-117.242844	34.054996	-117.242722	0.563649	0.563649	0.563649	1.000000	NaN	NaN	NaN	190607.000000	40929.000000
25%	16.215914	30.000000	NaN	NaN	NaN	NaN	34.054996	-117.242691	34.055063	-117.242500	0.681677	0.681677	0.681677	16.000000	NaN	NaN	NaN	900000.000000	50464.000000
50%	16.215914	31.000000	NaN	NaN	NaN	NaN	34.055023	-117.242653	34.055122	-117.242447	0.799705	0.799705	0.799705	64.000000	NaN	NaN	NaN	900000.000000	62575.000000
75%	16.215914	32.000000	NaN	NaN	NaN	NaN	34.055098	-117.242538	34.055174	-117.242405	0.917733	0.917733	0.917733	121.000000	NaN	NaN	NaN	900000.000000	233586.000000
max	34.519853	33.000000	NaN	NaN	NaN	NaN	34.055172	-117.242409	34.055359	-117.242027	1.035761	1.035761	1.035761	581.000000	NaN	NaN	NaN	900000.000000	362817.000000

In [7]:

df2 = df[['Start time', 'End time','Calories (kcal)','High latitude (deg)',
          'High longitude (deg)','Average speed (m/s)','Step count']]
df2.head(5)

df2 = df[['Start time', 'End time','Calories (kcal)','High latitude (deg)', 'High longitude (deg)','Average speed (m/s)','Step count']] df2.head(5)

Out[7]:

	Start time	End time	Calories (kcal)	High latitude (deg)	High longitude (deg)	Average speed (m/s)	Step count
0	00:00:00.000-07:00	00:15:00.000-07:00	16.215914	34.054996	-117.242432	NaN	NaN
1	00:15:00.000-07:00	00:30:00.000-07:00	16.215914	34.055000	-117.242401	NaN	NaN
2	00:30:00.000-07:00	00:45:00.000-07:00	16.215914	34.055065	-117.242416	NaN	NaN
3	00:45:00.000-07:00	01:00:00.000-07:00	16.215914	34.055058	-117.242447	NaN	NaN
4	01:00:00.000-07:00	01:15:00.000-07:00	16.215914	34.055061	-117.242409	NaN	NaN

Create a geopandas data frame from pandas dataframe¶

In [8]:

point_list = [Point(xy) for xy in zip(df2['High longitude (deg)'],df['High latitude (deg)'])]
point_list[:4]

point_list = [Point(xy) for xy in zip(df2['High longitude (deg)'],df['High latitude (deg)'])] point_list[:4]

Out[8]:

[<shapely.geometry.point.Point at 0x10f8b5630>,
 <shapely.geometry.point.Point at 0x10f8b5668>,
 <shapely.geometry.point.Point at 0x10f8b5710>,
 <shapely.geometry.point.Point at 0x10f8b56a0>]

It is not necessary to construct a geoseries, but Folium needs a geoseries and that to have a coordinate system set. So I am building it here

In [9]:

gseries = gpd.GeoSeries(point_list)

gseries = gpd.GeoSeries(point_list)

In [10]:

wgs84=pyepsg.get(4326)

wgs84=pyepsg.get(4326)

In [11]:

# gseries.crs = pyepsg.get(4326)
gseries.crs ={'init':'epsg:4326'}

# gseries.crs = pyepsg.get(4326) gseries.crs ={'init':'epsg:4326'}

In [12]:

gdf = gpd.GeoDataFrame(data=df2, geometry=gseries)
gdf.head(4)

gdf = gpd.GeoDataFrame(data=df2, geometry=gseries) gdf.head(4)

Out[12]:

	Start time	End time	Calories (kcal)	High latitude (deg)	High longitude (deg)	Average speed (m/s)	Step count	geometry
0	00:00:00.000-07:00	00:15:00.000-07:00	16.215914	34.054996	-117.242432	NaN	NaN	POINT (-117.242431640625 34.05499649047852)
1	00:15:00.000-07:00	00:30:00.000-07:00	16.215914	34.055000	-117.242401	NaN	NaN	POINT (-117.2424011230469 34.05500030517578)
2	00:30:00.000-07:00	00:45:00.000-07:00	16.215914	34.055065	-117.242416	NaN	NaN	POINT (-117.2424163818359 34.0550651550293)
3	00:45:00.000-07:00	01:00:00.000-07:00	16.215914	34.055058	-117.242447	NaN	NaN	POINT (-117.2424468994141 34.05505752563477)

In [13]:

gdf[['High latitude (deg)','High longitude (deg)']].describe()

gdf[['High latitude (deg)','High longitude (deg)']].describe()

Out[13]:

	High latitude (deg)	High longitude (deg)
count	91.000000	91.000000
mean	34.055120	-117.242453
std	0.000075	0.000097
min	34.054996	-117.242722
25%	34.055063	-117.242500
50%	34.055122	-117.242447
75%	34.055174	-117.242405
max	34.055359	-117.242027

Drop the rows that don't have coordinates

In [14]:

gdf.dropna(subset=['High latitude (deg)'], inplace=True)
gdf.shape

gdf.dropna(subset=['High latitude (deg)'], inplace=True) gdf.shape

Out[14]:

(91, 8)

In [15]:

fig,ax= plt.subplots(1, figsize=(5,5))
gdf.plot(ax=ax)
# ax.set_xlim([-117.2,-117.4])
# ax.set_ylim([34.054, 34.055])

fig,ax= plt.subplots(1, figsize=(5,5)) gdf.plot(ax=ax) # ax.set_xlim([-117.2,-117.4]) # ax.set_ylim([34.054, 34.055])

Out[15]:

<matplotlib.axes._subplots.AxesSubplot at 0x10f99f0f0>

Viz on Folium map¶

In [16]:

import folium
folium.__version__

import folium folium.__version__

Out[16]:

'0.5.0'

In [17]:

# from folium.plugins import HeatMap
# hm = HeatMap(data=[xy for xy in zip(gdf['geometry'].x, gdf['geometry'].y)])

# from folium.plugins import HeatMap # hm = HeatMap(data=[xy for xy in zip(gdf['geometry'].x, gdf['geometry'].y)])

In [135]:

m = folium.Map(location=[34.054,-117.2], zoom_start=15)
folium.GeoJson(data=gdf).add_to(m)
m

m = folium.Map(location=[34.054,-117.2], zoom_start=15) folium.GeoJson(data=gdf).add_to(m) m

Out[135]:

Geocoding¶

Geocoding is the process of turning addresses into coordinates by plotting them on a map. It is a combination of search and computation (when you interpolate street addresses for unknown extents) that is typically performed on server side due to the amount of data required on a global scale.

Geopandas uses geopy to perform geocoding and returns the result of hits as a geodataframe.

In [18]:

from geopandas.tools import geocode, geocoding, reverse_geocode

from geopandas.tools import geocode, geocoding, reverse_geocode

In [19]:

type(geocode), type(reverse_geocode), type(geocoding)

type(geocode), type(reverse_geocode), type(geocoding)

Out[19]:

(function, function, module)

Get list of providers from geopy

In [15]:

import geopy, inspect
print(geopy.__version__)

# use inspection, but limit to just classes
inspect.getmembers(geopy, predicate=inspect.isclass)

import geopy, inspect print(geopy.__version__) # use inspection, but limit to just classes inspect.getmembers(geopy, predicate=inspect.isclass)

1.14.0

Out[15]:

[('ArcGIS', geopy.geocoders.arcgis.ArcGIS),
 ('Baidu', geopy.geocoders.baidu.Baidu),
 ('Bing', geopy.geocoders.bing.Bing),
 ('DataBC', geopy.geocoders.databc.DataBC),
 ('GeoNames', geopy.geocoders.geonames.GeoNames),
 ('GeocodeFarm', geopy.geocoders.geocodefarm.GeocodeFarm),
 ('GoogleV3', geopy.geocoders.googlev3.GoogleV3),
 ('IGNFrance', geopy.geocoders.ignfrance.IGNFrance),
 ('LiveAddress', geopy.geocoders.smartystreets.LiveAddress),
 ('Location', geopy.location.Location),
 ('Mapzen', geopy.geocoders.mapzen.Mapzen),
 ('Nominatim', geopy.geocoders.osm.Nominatim),
 ('OpenCage', geopy.geocoders.opencage.OpenCage),
 ('OpenMapQuest', geopy.geocoders.openmapquest.OpenMapQuest),
 ('Photon', geopy.geocoders.photon.Photon),
 ('PickPoint', geopy.geocoders.pickpoint.PickPoint),
 ('Point', geopy.point.Point),
 ('What3Words', geopy.geocoders.what3words.What3Words),
 ('Yandex', geopy.geocoders.yandex.Yandex)]

In [20]:

geocode(strings='Hollywood sign') # default provider is googlev3

geocode(strings='Hollywood sign') # default provider is googlev3

Out[20]:

	address	geometry
0	Los Angeles, CA 90068, USA	POINT (-118.3215482 34.1341151)

In [21]:

geocode(strings='Hollywood sign', provider='arcgis')

geocode(strings='Hollywood sign', provider='arcgis')

Out[21]:

	address	geometry
0	Hollywood Sign	POINT (-118.3219799999999 34.13438000000008)

In [26]:

address_list = ['hospitals near Redlands, CA', 
                'groceries near Redlands, CA', 
                'schools near Redlands, CA']

geocoded_gdf = geocode(strings=address_list, provider='arcgis')
geocoded_gdf

address_list = ['hospitals near Redlands, CA', 'groceries near Redlands, CA', 'schools near Redlands, CA'] geocoded_gdf = geocode(strings=address_list, provider='arcgis') geocoded_gdf

Out[26]:

	address	geometry
0	Redlands Community Hospital-ER	POINT (-117.2044100362101 34.03633005943772)
1	Target	POINT (-117.2072099744647 34.07031003505633)
2	Redlands East Valley High School	POINT (-117.12776 34.06201000000004)

In [30]:

m2 = folium.Map(location=[34.054,-117.2], zoom_start=12)
folium.GeoJson(data=geocoded_gdf).add_to(m2)
m2

m2 = folium.Map(location=[34.054,-117.2], zoom_start=12) folium.GeoJson(data=geocoded_gdf).add_to(m2) m2

Out[30]:

In [31]:

m2.crs

m2.crs

Out[31]:

'EPSG3857'

In [32]:

m2.to_json()

m2.to_json()

Out[32]:

'{"name": "Map", "id": "94aa73e804f245ec8f4e34436a4a2412", "children": {"openstreetmap": {"name": "TileLayer", "id": "6e327110ed23459f86840082b427835d", "children": {}}, "geo_json_bd54f09d34e648ffbd9a583dc626582f": {"name": "GeoJson", "id": "bd54f09d34e648ffbd9a583dc626582f", "children": {}}}}'