GeoPandas

Data analysis with GeoPandas

Python
GeoPandas
Data Analysis
Author

Kunal Khurana

Published

January 3, 2024

import geopandas as gpd
import pandas as pd
from shapely.geometry import LineString

# for interactive maps
import folium
from folium import Choropleth, Circle, Marker
from folium.plugins import HeatMap, MarkerCluster
import math

GeoDataFrame which has all capabilities of Pandas DataFrame

gdf.info()

gdf.columns

gdf.shape

gdf.dtypes

gdf.geometry

gdf.crs

gdf.geom_type - provides geometry for each row (ponit, LineString, Polygon, etc.)

gdf.head()

gdf.describe()

gdf.area

gdf.distance

gdf.buffer

gdf.plot()

data = gpd.read_file("C:\\Users\\Khurana_Kunal\\Downloads\\DEC_lands 2\\DEC_lands")
data.head()
OBJECTID CATEGORY UNIT FACILITY CLASS UMP DESCRIPTIO REGION COUNTY URL SOURCE UPDATE_ OFFICE ACRES LANDS_UID GREENCERT SHAPE_AREA SHAPE_LEN geometry
0 1 FOR PRES DET PAR CFP HANCOCK FP DETACHED PARCEL WILD FOREST None DELAWARE COUNTY DETACHED PARCEL 4 DELAWARE http://www.dec.ny.gov/ DELAWARE RPP 5/12 STAMFORD 738.620192 103 N 2.990365e+06 7927.662385 POLYGON ((486093.245 4635308.586, 486787.235 4...
1 2 FOR PRES DET PAR CFP HANCOCK FP DETACHED PARCEL WILD FOREST None DELAWARE COUNTY DETACHED PARCEL 4 DELAWARE http://www.dec.ny.gov/ DELAWARE RPP 5/12 STAMFORD 282.553140 1218 N 1.143940e+06 4776.375600 POLYGON ((491931.514 4637416.256, 491305.424 4...
2 3 FOR PRES DET PAR CFP HANCOCK FP DETACHED PARCEL WILD FOREST None DELAWARE COUNTY DETACHED PARCEL 4 DELAWARE http://www.dec.ny.gov/ DELAWARE RPP 5/12 STAMFORD 234.291262 1780 N 9.485476e+05 5783.070364 POLYGON ((486000.287 4635834.453, 485007.550 4...
3 4 FOR PRES DET PAR CFP GREENE COUNTY FP DETACHED PARCEL WILD FOREST None None 4 GREENE http://www.dec.ny.gov/ GREENE RPP 5/12 STAMFORD 450.106464 2060 N 1.822293e+06 7021.644833 POLYGON ((541716.775 4675243.268, 541217.579 4...
4 6 FOREST PRESERVE AFP SARANAC LAKES WILD FOREST WILD FOREST SARANAC LAKES None 5 ESSEX http://www.dec.ny.gov/lands/22593.html DECRP, ESSEX RPP 12/96 RAY BROOK 69.702387 1517 N 2.821959e+05 2663.909932 POLYGON ((583896.043 4909643.187, 583891.200 4... 
data_selected = data.loc[:, ['CLASS', 'COUNTY', 'geometry']].copy()
data_selected.CLASS.value_counts()
CLASS
WILD FOREST                   965
INTENSIVE USE                 108
PRIMITIVE                      60
WILDERNESS                     52
ADMINISTRATIVE                 17
UNCLASSIFIED                    7
HISTORIC                        5
PRIMITIVE BICYCLE CORRIDOR      4
CANOE AREA                      1
Name: count, dtype: int64
# select lands under 'wild forest' or 'wilderness' category
wild_lands = data_selected.loc[data_selected.CLASS.isin(['WILD FOREST', 'WILDERNESS'])].copy()
wild_lands.head()
CLASS COUNTY geometry
0 WILD FOREST DELAWARE POLYGON ((486093.245 4635308.586, 486787.235 4...
1 WILD FOREST DELAWARE POLYGON ((491931.514 4637416.256, 491305.424 4...
2 WILD FOREST DELAWARE POLYGON ((486000.287 4635834.453, 485007.550 4...
3 WILD FOREST GREENE POLYGON ((541716.775 4675243.268, 541217.579 4...
4 WILD FOREST ESSEX POLYGON ((583896.043 4909643.187, 583891.200 4... 
wild_lands.plot()
<Axes: >

wild_lands.geometry.head()
0    POLYGON ((486093.245 4635308.586, 486787.235 4...
1    POLYGON ((491931.514 4637416.256, 491305.424 4...
2    POLYGON ((486000.287 4635834.453, 485007.550 4...
3    POLYGON ((541716.775 4675243.268, 541217.579 4...
4    POLYGON ((583896.043 4909643.187, 583891.200 4...
Name: geometry, dtype: geometry
world_filepath ='geopandas\\ne_110m_admin_0_countries\\ne_110m_admin_0_countries.shx'
world = gpd.read_file(world_filepath)
world.head()
featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 ADM0_DIF LEVEL TYPE TLC ADMIN ... FCLASS_TR FCLASS_ID FCLASS_PL FCLASS_GR FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD FCLASS_UA geometry
0 Admin-0 country 1 6 Fiji FJI 0 2 Sovereign country 1 Fiji ... None None None None None None None None None MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1 Admin-0 country 1 3 United Republic of Tanzania TZA 0 2 Sovereign country 1 United Republic of Tanzania ... None None None None None None None None None POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2 Admin-0 country 1 7 Western Sahara SAH 0 2 Indeterminate 1 Western Sahara ... Unrecognized Unrecognized Unrecognized None None Unrecognized None None None POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3 Admin-0 country 1 2 Canada CAN 0 2 Sovereign country 1 Canada ... None None None None None None None None None MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4 Admin-0 country 1 2 United States of America US1 1 2 Country 1 United States of America ... None None None None None None None None None MULTIPOLYGON (((-122.84000 49.00000, -120.0000...

5 rows × 169 columns

ax = world.plot()

#plotting a map with coordinates
ax = world.plot(figsize=(20,20), color='whitesmoke', linestyle=':', edgecolor='black')

PHL_loans = data.loc[data.COUNTY=="Philippines"].copy()
print(world.columns)
Index(['featurecla', 'scalerank', 'LABELRANK', 'SOVEREIGNT', 'SOV_A3',
       'ADM0_DIF', 'LEVEL', 'TYPE', 'TLC', 'ADMIN',
       ...
       'FCLASS_TR', 'FCLASS_ID', 'FCLASS_PL', 'FCLASS_GR', 'FCLASS_IT',
       'FCLASS_NL', 'FCLASS_SE', 'FCLASS_BD', 'FCLASS_UA', 'geometry'],
      dtype='object', length=169)
world.info()
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 177 entries, 0 to 176
Columns: 169 entries, featurecla to geometry
dtypes: float64(6), geometry(1), int64(25), object(137)
memory usage: 233.8+ KB
world.head()
featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 ADM0_DIF LEVEL TYPE TLC ADMIN ... FCLASS_TR FCLASS_ID FCLASS_PL FCLASS_GR FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD FCLASS_UA geometry
0 Admin-0 country 1 6 Fiji FJI 0 2 Sovereign country 1 Fiji ... None None None None None None None None None MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1 Admin-0 country 1 3 United Republic of Tanzania TZA 0 2 Sovereign country 1 United Republic of Tanzania ... None None None None None None None None None POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2 Admin-0 country 1 7 Western Sahara SAH 0 2 Indeterminate 1 Western Sahara ... Unrecognized Unrecognized Unrecognized None None Unrecognized None None None POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3 Admin-0 country 1 2 Canada CAN 0 2 Sovereign country 1 Canada ... None None None None None None None None None MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4 Admin-0 country 1 2 United States of America US1 1 2 Country 1 United States of America ... None None None None None None None None None MULTIPOLYGON (((-122.84000 49.00000, -120.0000...

5 rows × 169 columns

Coordinate Reference Systems

  • shape file imports CRS automatically
  • settings (DataFrame uses EPSG 32630; csv file uses EPSG 4326)
facilities_df = pd.read_csv('geopandas\health_facilities.csv')

# convert to GeoDataFrame
facilities = gpd.GeoDataFrame(facilities_df, geometry = gpd.points_from_xy
                              (facilities_df.Longitude, facilities_df.Latitude))

# set CRS
facilities.crs = ('epsg:4326')

#view first 5 rows
facilities.head()
Region District FacilityName Type Town Ownership Latitude Longitude geometry
0 Ashanti Offinso North A.M.E Zion Clinic Clinic Afrancho CHAG 7.40801 -1.96317 POINT (-1.96317 7.40801)
1 Ashanti Bekwai Municipal Abenkyiman Clinic Clinic Anwiankwanta Private 6.46312 -1.58592 POINT (-1.58592 6.46312)
2 Ashanti Adansi North Aboabo Health Centre Health Centre Aboabo No 2 Government 6.22393 -1.34982 POINT (-1.34982 6.22393)
3 Ashanti Afigya-Kwabre Aboabogya Health Centre Health Centre Aboabogya Government 6.84177 -1.61098 POINT (-1.61098 6.84177)
4 Ashanti Kwabre Aboaso Health Centre Health Centre Aboaso Government 6.84177 -1.61098 POINT (-1.61098 6.84177) 
# plotting facilities of Ghana on world map
ax = world.plot(figsize=(20,20), color='whitesmoke', linestyle=':', edgecolor='black')
facilities.to_crs(epsg=4326).plot(markersize=.25, ax=ax)
C:\Users\Khurana_Kunal\anaconda3\Lib\site-packages\shapely\measurement.py:103: RuntimeWarning: invalid value encountered in bounds
  return lib.bounds(geometry_arr, out=out, **kwargs)
<Axes: >

#get the x coordinates of each point
facilities.geometry.head().x
0   -1.96317
1   -1.58592
2   -1.34982
3   -1.61098
4   -1.61098
dtype: float64
birds_df = pd.read_csv("data_for_all_courses\purple_martin.csv")
birds_df.head()
timestamp location-long location-lat tag-local-identifier
0 2014-08-15 05:56:00 -88.146014 17.513049 30448
1 2014-09-01 05:59:00 -85.243501 13.095782 30448
2 2014-10-30 23:58:00 -62.906089 -7.852436 30448
3 2014-11-15 04:59:00 -61.776826 -11.723898 30448
4 2014-11-30 09:59:00 -61.241538 -11.612237 30448 
print(f"There are {birds_df['tag-local-identifier'].nunique()} different birds in the dataset.")
There are 11 different birds in the dataset.
birds = gpd.GeoDataFrame(birds_df,
                         geometry = gpd.points_from_xy(birds_df["location-long"],
                                                      birds_df['location-lat']))
birds.head()
timestamp location-long location-lat tag-local-identifier geometry
0 2014-08-15 05:56:00 -88.146014 17.513049 30448 POINT (-88.14601 17.51305)
1 2014-09-01 05:59:00 -85.243501 13.095782 30448 POINT (-85.24350 13.09578)
2 2014-10-30 23:58:00 -62.906089 -7.852436 30448 POINT (-62.90609 -7.85244)
3 2014-11-15 04:59:00 -61.776826 -11.723898 30448 POINT (-61.77683 -11.72390)
4 2014-11-30 09:59:00 -61.241538 -11.612237 30448 POINT (-61.24154 -11.61224) 
# set the CRS

birds.crs = ('epsg:4326')
# plot the data
americas = world.loc[world['CONTINENT'].isin(['North America', 'South America',])]
americas.head()
featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 ADM0_DIF LEVEL TYPE TLC ADMIN ... FCLASS_TR FCLASS_ID FCLASS_PL FCLASS_GR FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD FCLASS_UA geometry
3 Admin-0 country 1 2 Canada CAN 0 2 Sovereign country 1 Canada ... None None None None None None None None None MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4 Admin-0 country 1 2 United States of America US1 1 2 Country 1 United States of America ... None None None None None None None None None MULTIPOLYGON (((-122.84000 49.00000, -120.0000...
9 Admin-0 country 1 2 Argentina ARG 0 2 Sovereign country 1 Argentina ... None None None None None None None None None MULTIPOLYGON (((-68.63401 -52.63637, -68.25000...
10 Admin-0 country 1 2 Chile CHL 0 2 Sovereign country 1 Chile ... None None None None None None None None None MULTIPOLYGON (((-68.63401 -52.63637, -68.63335...
16 Admin-0 country 1 5 Haiti HTI 0 2 Sovereign country 1 Haiti ... None None None None None None None None None POLYGON ((-71.71236 19.71446, -71.62487 19.169...

5 rows × 169 columns

world.head()
featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 ADM0_DIF LEVEL TYPE TLC ADMIN ... FCLASS_TR FCLASS_ID FCLASS_PL FCLASS_GR FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD FCLASS_UA geometry
0 Admin-0 country 1 6 Fiji FJI 0 2 Sovereign country 1 Fiji ... None None None None None None None None None MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1 Admin-0 country 1 3 United Republic of Tanzania TZA 0 2 Sovereign country 1 United Republic of Tanzania ... None None None None None None None None None POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2 Admin-0 country 1 7 Western Sahara SAH 0 2 Indeterminate 1 Western Sahara ... Unrecognized Unrecognized Unrecognized None None Unrecognized None None None POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3 Admin-0 country 1 2 Canada CAN 0 2 Sovereign country 1 Canada ... None None None None None None None None None MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4 Admin-0 country 1 2 United States of America US1 1 2 Country 1 United States of America ... None None None None None None None None None MULTIPOLYGON (((-122.84000 49.00000, -120.0000...

5 rows × 169 columns

# checking for all the columns in a data frame with for loop
for column in world.columns:
    print(column)
featurecla
scalerank
LABELRANK
SOVEREIGNT
SOV_A3
ADM0_DIF
LEVEL
TYPE
TLC
ADMIN
ADM0_A3
GEOU_DIF
GEOUNIT
GU_A3
SU_DIF
SUBUNIT
SU_A3
BRK_DIFF
NAME
NAME_LONG
BRK_A3
BRK_NAME
BRK_GROUP
ABBREV
POSTAL
FORMAL_EN
FORMAL_FR
NAME_CIAWF
NOTE_ADM0
NOTE_BRK
NAME_SORT
NAME_ALT
MAPCOLOR7
MAPCOLOR8
MAPCOLOR9
MAPCOLOR13
POP_EST
POP_RANK
POP_YEAR
GDP_MD
GDP_YEAR
ECONOMY
INCOME_GRP
FIPS_10
ISO_A2
ISO_A2_EH
ISO_A3
ISO_A3_EH
ISO_N3
ISO_N3_EH
UN_A3
WB_A2
WB_A3
WOE_ID
WOE_ID_EH
WOE_NOTE
ADM0_ISO
ADM0_DIFF
ADM0_TLC
ADM0_A3_US
ADM0_A3_FR
ADM0_A3_RU
ADM0_A3_ES
ADM0_A3_CN
ADM0_A3_TW
ADM0_A3_IN
ADM0_A3_NP
ADM0_A3_PK
ADM0_A3_DE
ADM0_A3_GB
ADM0_A3_BR
ADM0_A3_IL
ADM0_A3_PS
ADM0_A3_SA
ADM0_A3_EG
ADM0_A3_MA
ADM0_A3_PT
ADM0_A3_AR
ADM0_A3_JP
ADM0_A3_KO
ADM0_A3_VN
ADM0_A3_TR
ADM0_A3_ID
ADM0_A3_PL
ADM0_A3_GR
ADM0_A3_IT
ADM0_A3_NL
ADM0_A3_SE
ADM0_A3_BD
ADM0_A3_UA
ADM0_A3_UN
ADM0_A3_WB
CONTINENT
REGION_UN
SUBREGION
REGION_WB
NAME_LEN
LONG_LEN
ABBREV_LEN
TINY
HOMEPART
MIN_ZOOM
MIN_LABEL
MAX_LABEL
LABEL_X
LABEL_Y
NE_ID
WIKIDATAID
NAME_AR
NAME_BN
NAME_DE
NAME_EN
NAME_ES
NAME_FA
NAME_FR
NAME_EL
NAME_HE
NAME_HI
NAME_HU
NAME_ID
NAME_IT
NAME_JA
NAME_KO
NAME_NL
NAME_PL
NAME_PT
NAME_RU
NAME_SV
NAME_TR
NAME_UK
NAME_UR
NAME_VI
NAME_ZH
NAME_ZHT
FCLASS_ISO
TLC_DIFF
FCLASS_TLC
FCLASS_US
FCLASS_FR
FCLASS_RU
FCLASS_ES
FCLASS_CN
FCLASS_TW
FCLASS_IN
FCLASS_NP
FCLASS_PK
FCLASS_DE
FCLASS_GB
FCLASS_BR
FCLASS_IL
FCLASS_PS
FCLASS_SA
FCLASS_EG
FCLASS_MA
FCLASS_PT
FCLASS_AR
FCLASS_JP
FCLASS_KO
FCLASS_VN
FCLASS_TR
FCLASS_ID
FCLASS_PL
FCLASS_GR
FCLASS_IT
FCLASS_NL
FCLASS_SE
FCLASS_BD
FCLASS_UA
geometry
# plot americas
ax_americas = americas.plot(figsize=(10,10), color='whitesmoke', linestyle=':', edgecolor='black')

Starting and end journey of birds

# GeoDataFrame showing path for each bird
path_df = birds.groupby("tag-local-identifier")['geometry'].apply(list).apply(lambda x: LineString(x)).reset_index()
path_gdf = gpd.GeoDataFrame(path_df, geometry = path_df.geometry)
path_gdf.crs = ('epsg:4326')

# GeoDataFrame showing starting point for each bird
start_df = birds.groupby("tag-local-identifier")['geometry'].apply(list).apply(lambda x: x[0]).reset_index()
start_gdf = gpd.GeoDataFrame(start_df, geometry = start_df.geometry)
start_gdf.crs = ('epsg:4326')

# Show first five rows of GeoDataFrame
start_gdf.head()
tag-local-identifier geometry
0 30048 POINT (-90.12992 20.73242)
1 30054 POINT (-93.60861 46.50563)
2 30198 POINT (-80.31036 25.92545)
3 30263 POINT (-76.78146 42.99209)
4 30275 POINT (-76.78213 42.99207) 
# end point of each bird
end_df = birds.groupby("tag-local-identifier")['geometry'].apply(list).apply(lambda x: x[-1]).reset_index()
end_gdf = gpd.GeoDataFrame(end_df, geometry = end_df.geometry)
end_gdf.crs = ('epsg:4326')
# plot americas
ax_americas = americas.plot(figsize=(10,10), color='whitesmoke', linestyle=':', edgecolor='black')

start_gdf.plot(ax = ax_americas, color = 'red', markersize = 10)
path_gdf.plot(ax = ax_americas, cmap = 'tab20b', linestyle= '-', linewidth = 1, zorder = 1)
end_gdf.plot(ax = ax_americas, color = 'blue', markersize = 10)
<Axes: >

# no file found; gives 'Driver Error' - à voir plustard
protected_filepath = 'data_for_all_courses/add_0.shp'
protected_area = gpd.read_file(protected_filepath)

Interactive maps

# Create a map
montréal_1 = folium.Map(location=[45.50, -73.56], tiles='openstreetmap', zoom_start=10)


# Display the map
montréal_1
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# crimes

crimes = pd.read_csv("data_for_all_courses\crime.csv", encoding = 'latin-1')
crimes.describe()
OFFENSE_CODE YEAR MONTH HOUR Lat Long
count 319073.000000 319073.000000 319073.000000 319073.000000 299074.000000 299074.000000
mean 2317.546956 2016.560586 6.609719 13.118205 42.214381 -70.908272
std 1185.285543 0.996344 3.273691 6.294205 2.159766 3.493618
min 111.000000 2015.000000 1.000000 0.000000 -1.000000 -71.178674
25% 1001.000000 2016.000000 4.000000 9.000000 42.297442 -71.097135
50% 2907.000000 2017.000000 7.000000 14.000000 42.325538 -71.077524
75% 3201.000000 2017.000000 9.000000 18.000000 42.348624 -71.062467
max 3831.000000 2018.000000 12.000000 23.000000 42.395042 -1.000000 
crimes.head()
INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART STREET Lat Long Location
0 I182070945 619 Larceny LARCENY ALL OTHERS D14 808 NaN 2018-09-02 13:00:00 2018 9 Sunday 13 Part One LINCOLN ST 42.357791 -71.139371 (42.35779134, -71.13937053)
1 I182070943 1402 Vandalism VANDALISM C11 347 NaN 2018-08-21 00:00:00 2018 8 Tuesday 0 Part Two HECLA ST 42.306821 -71.060300 (42.30682138, -71.06030035)
2 I182070941 3410 Towed TOWED MOTOR VEHICLE D4 151 NaN 2018-09-03 19:27:00 2018 9 Monday 19 Part Three CAZENOVE ST 42.346589 -71.072429 (42.34658879, -71.07242943)
3 I182070940 3114 Investigate Property INVESTIGATE PROPERTY D4 272 NaN 2018-09-03 21:16:00 2018 9 Monday 21 Part Three NEWCOMB ST 42.334182 -71.078664 (42.33418175, -71.07866441)
4 I182070938 3114 Investigate Property INVESTIGATE PROPERTY B3 421 NaN 2018-09-03 21:05:00 2018 9 Monday 21 Part Three DELHI ST 42.275365 -71.090361 (42.27536542, -71.09036101) 
# drop missing locations
crimes.dropna(subset= ['Lat', 'Long', 'DISTRICT'], inplace = True)
# focus on major crimes
crimes = crimes[crimes.OFFENSE_CODE_GROUP.isin([
    'Larceny', 'Auto Theft', 'Robbery', 'Larceny From Motor Vehicle', 'Residential Burglary',
    'Simple Assault', 'Harassment', 'Ballistics', 'Aggravated Assault', 'Other Burglary', 
    'Arson', 'Commercial Burglary'    
])]

crimes = crimes[crimes.YEAR>=2018]

crimes.head()
INCIDENT_NUMBER OFFENSE_CODE OFFENSE_CODE_GROUP OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING OCCURRED_ON_DATE YEAR MONTH DAY_OF_WEEK HOUR UCR_PART STREET Lat Long Location
0 I182070945 619 Larceny LARCENY ALL OTHERS D14 808 NaN 2018-09-02 13:00:00 2018 9 Sunday 13 Part One LINCOLN ST 42.357791 -71.139371 (42.35779134, -71.13937053)
6 I182070933 724 Auto Theft AUTO THEFT B2 330 NaN 2018-09-03 21:25:00 2018 9 Monday 21 Part One NORMANDY ST 42.306072 -71.082733 (42.30607218, -71.08273260)
8 I182070931 301 Robbery ROBBERY - STREET C6 177 NaN 2018-09-03 20:48:00 2018 9 Monday 20 Part One MASSACHUSETTS AVE 42.331521 -71.070853 (42.33152148, -71.07085307)
19 I182070915 614 Larceny From Motor Vehicle LARCENY THEFT FROM MV - NON-ACCESSORY B2 181 NaN 2018-09-02 18:00:00 2018 9 Sunday 18 Part One SHIRLEY ST 42.325695 -71.068168 (42.32569490, -71.06816778)
24 I182070908 522 Residential Burglary BURGLARY - RESIDENTIAL - NO FORCE B2 911 NaN 2018-09-03 18:38:00 2018 9 Monday 18 Part One ANNUNCIATION RD 42.335062 -71.093168 (42.33506218, -71.09316781) 
# crimes between 9 to 18 

daytime_robberies = crimes[((crimes.OFFENSE_CODE_GROUP == 'Robbery') & 
                            crimes.HOUR.isin(range(9,18)))]
# create a map

map2 = folium.Map(location=[42.32,-71.0589], tiles='openstreetmap', zoom_start=13)

# add points
for idx, row in daytime_robberies.iterrows():
    Marker([row['Lat'], row['Long']]).add_to(map2)
    
    
# display
map2
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folium.plugin.MarkerCluster

# plotting points
m_3 = folium.Map(location= [42.32,-71.0589], tiles='cartodbpositron', zoom_start=13)

# add points
mc = MarkerCluster()
for idx, row in daytime_robberies.iterrows():
    if not math.isnan(row['Long']) and not math.isnan(row['Lat']):
        mc.add_child(Marker([row['Lat'], row['Long']]))
m_3.add_child(mc)
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Bubble maps

# create a base map

m_4 = folium.Map(location=[42.32,-71.0589], tiles='cartodbpositron', zoom_start=11)

def color_producer(val):
    if val >=12:
        return 'forestgreen'
    else:
        return 'darkred'
    
# add bubble map to the base map
for i in range(0, len(daytime_robberies)):
    Circle(
    location = [daytime_robberies.iloc[i]['Lat'], 
               daytime_robberies.iloc[i]['Long']],
    radius = 20,
    color = color_producer(daytime_robberies.iloc[i]['HOUR'])).add_to(m_4)
    
# display
m_4
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Heatmaps

# basemaps
m_5 = folium.Map(location=[42.32,-71.0589], tiles='cartodbpositron', zoom_start=11)

# add heatmaps to the base map
HeatMap(data= crimes[['Lat', 'Long']], radius = 10).add_to(m_5)

# display
m_5
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Choropleth maps

help(folium.Choropleth)
Help on class Choropleth in module folium.features:

class Choropleth(folium.map.FeatureGroup)
 |  Choropleth(geo_data: Any, data: Optional[Any] = None, columns: Optional[Sequence[Any]] = None, key_on: Optional[str] = None, bins: Union[int, Sequence[float]] = 6, fill_color: Optional[str] = None, nan_fill_color: str = 'black', fill_opacity: float = 0.6, nan_fill_opacity: Optional[float] = None, line_color: str = 'black', line_weight: float = 1, line_opacity: float = 1, name: Optional[str] = None, legend_name: str = '', overlay: bool = True, control: bool = True, show: bool = True, topojson: Optional[str] = None, smooth_factor: Optional[float] = None, highlight: bool = False, use_jenks: bool = False, **kwargs)
 |  
 |  Apply a GeoJSON overlay to the map.
 |  
 |  Plot a GeoJSON overlay on the base map. There is no requirement
 |  to bind data (passing just a GeoJSON plots a single-color overlay),
 |  but there is a data binding option to map your columnar data to
 |  different feature objects with a color scale.
 |  
 |  If data is passed as a Pandas DataFrame, the "columns" and "key-on"
 |  keywords must be included, the first to indicate which DataFrame
 |  columns to use, the second to indicate the layer in the GeoJSON
 |  on which to key the data. The 'columns' keyword does not need to be
 |  passed for a Pandas series.
 |  
 |  Colors are generated from color brewer (https://colorbrewer2.org/)
 |  sequential palettes. By default, linear binning is used between
 |  the min and the max of the values. Custom binning can be achieved
 |  with the `bins` parameter.
 |  
 |  TopoJSONs can be passed as "geo_data", but the "topojson" keyword must
 |  also be passed with the reference to the topojson objects to convert.
 |  See the topojson.feature method in the TopoJSON API reference:
 |  https://github.com/topojson/topojson/wiki/API-Reference
 |  
 |  
 |  Parameters
 |  ----------
 |  geo_data: string/object
 |      URL, file path, or data (json, dict, geopandas, etc) to your GeoJSON
 |      geometries
 |  data: Pandas DataFrame or Series, default None
 |      Data to bind to the GeoJSON.
 |  columns: tuple with two values, default None
 |      If the data is a Pandas DataFrame, the columns of data to be bound.
 |      Must pass column 1 as the key, and column 2 the values.
 |  key_on: string, default None
 |      Variable in the `geo_data` GeoJSON file to bind the data to. Must
 |      start with 'feature' and be in JavaScript objection notation.
 |      Ex: 'feature.id' or 'feature.properties.statename'.
 |  bins: int or sequence of scalars or str, default 6
 |      If `bins` is an int, it defines the number of equal-width
 |      bins between the min and the max of the values.
 |      If `bins` is a sequence, it directly defines the bin edges.
 |      For more information on this parameter, have a look at
 |      numpy.histogram function.
 |  fill_color: string, optional
 |      Area fill color, defaults to blue. Can pass a hex code, color name,
 |      or if you are binding data, one of the following color brewer palettes:
 |      'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',
 |      'YlGn', 'YlGnBu', 'YlOrBr', and 'YlOrRd'.
 |  nan_fill_color: string, default 'black'
 |      Area fill color for nan or missing values.
 |      Can pass a hex code, color name.
 |  fill_opacity: float, default 0.6
 |      Area fill opacity, range 0-1.
 |  nan_fill_opacity: float, default fill_opacity
 |      Area fill opacity for nan or missing values, range 0-1.
 |  line_color: string, default 'black'
 |      GeoJSON geopath line color.
 |  line_weight: int, default 1
 |      GeoJSON geopath line weight.
 |  line_opacity: float, default 1
 |      GeoJSON geopath line opacity, range 0-1.
 |  legend_name: string, default empty string
 |      Title for data legend.
 |  topojson: string, default None
 |      If using a TopoJSON, passing "objects.yourfeature" to the topojson
 |      keyword argument will enable conversion to GeoJSON.
 |  smooth_factor: float, default None
 |      How much to simplify the polyline on each zoom level. More means
 |      better performance and smoother look, and less means more accurate
 |      representation. Leaflet defaults to 1.0.
 |  highlight: boolean, default False
 |      Enable highlight functionality when hovering over a GeoJSON area.
 |  use_jenks: bool, default False
 |      Use jenkspy to calculate bins using "natural breaks"
 |      (Fisher-Jenks algorithm). This is useful when your data is unevenly
 |      distributed.
 |  name : string, optional
 |      The name of the layer, as it will appear in LayerControls
 |  overlay : bool, default True
 |      Adds the layer as an optional overlay (True) or the base layer (False).
 |  control : bool, default True
 |      Whether the Layer will be included in LayerControls.
 |  show: bool, default True
 |      Whether the layer will be shown on opening.
 |  
 |  Returns
 |  -------
 |  GeoJSON data layer in obj.template_vars
 |  
 |  Examples
 |  --------
 |  >>> Choropleth(geo_data="us-states.json", line_color="blue", line_weight=3)
 |  >>> Choropleth(
 |  ...     geo_data="geo.json",
 |  ...     data=df,
 |  ...     columns=["Data 1", "Data 2"],
 |  ...     key_on="feature.properties.myvalue",
 |  ...     fill_color="PuBu",
 |  ...     bins=[0, 20, 30, 40, 50, 60],
 |  ... )
 |  >>> Choropleth(geo_data="countries.json", topojson="objects.countries")
 |  >>> Choropleth(
 |  ...     geo_data="geo.json",
 |  ...     data=df,
 |  ...     columns=["Data 1", "Data 2"],
 |  ...     key_on="feature.properties.myvalue",
 |  ...     fill_color="PuBu",
 |  ...     bins=[0, 20, 30, 40, 50, 60],
 |  ...     highlight=True,
 |  ... )
 |  
 |  Method resolution order:
 |      Choropleth
 |      folium.map.FeatureGroup
 |      folium.map.Layer
 |      branca.element.MacroElement
 |      branca.element.Element
 |      builtins.object
 |  
 |  Methods defined here:
 |  
 |  __init__(self, geo_data: Any, data: Optional[Any] = None, columns: Optional[Sequence[Any]] = None, key_on: Optional[str] = None, bins: Union[int, Sequence[float]] = 6, fill_color: Optional[str] = None, nan_fill_color: str = 'black', fill_opacity: float = 0.6, nan_fill_opacity: Optional[float] = None, line_color: str = 'black', line_weight: float = 1, line_opacity: float = 1, name: Optional[str] = None, legend_name: str = '', overlay: bool = True, control: bool = True, show: bool = True, topojson: Optional[str] = None, smooth_factor: Optional[float] = None, highlight: bool = False, use_jenks: bool = False, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  render(self, **kwargs) -> None
 |      Render the GeoJson/TopoJson and color scale objects.
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from branca.element.Element:
 |  
 |  __getstate__(self)
 |      Modify object state when pickling the object.
 |      jinja2 Environment cannot be pickled, so set
 |      the ._env attribute to None. This will be added back
 |      when unpickling (see __setstate__)
 |  
 |  __setstate__(self, state: dict)
 |      Re-add ._env attribute when unpickling
 |  
 |  add_child(self, child, name=None, index=None)
 |      Add a child.
 |  
 |  add_children(self, child, name=None, index=None)
 |      Add a child.
 |  
 |  add_to(self, parent, name=None, index=None)
 |      Add element to a parent.
 |  
 |  get_bounds(self)
 |      Computes the bounds of the object and all it's children
 |      in the form [[lat_min, lon_min], [lat_max, lon_max]].
 |  
 |  get_name(self)
 |      Returns a string representation of the object.
 |      This string has to be unique and to be a python and
 |      javascript-compatible
 |      variable name.
 |  
 |  get_root(self)
 |      Returns the root of the elements tree.
 |  
 |  save(self, outfile, close_file=True, **kwargs)
 |      Saves an Element into a file.
 |      
 |      Parameters
 |      ----------
 |      outfile : str or file object
 |          The file (or filename) where you want to output the html.
 |      close_file : bool, default True
 |          Whether the file has to be closed after write.
 |  
 |  to_dict(self, depth=-1, ordered=True, **kwargs)
 |      Returns a dict representation of the object.
 |  
 |  to_json(self, depth=-1, **kwargs)
 |      Returns a JSON representation of the object.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from branca.element.Element:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)