Adip Rai - Fab Futures - Data Science

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DATA VISUALIZATION

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Task

• Import required libraries
• Load dataset
• Plot the data

1. Imported the required libraries

# ---------------------------------------------------------
# 1. Import the required libraries
# ---------------------------------------------------------
import pandas as pd
import matplotlib.pyplot as plt
!pip install folium
import folium
from folium.plugins import HeatMap

Requirement already satisfied: folium in /opt/conda/lib/python3.13/site-packages (0.20.0)
Requirement already satisfied: branca>=0.6.0 in /opt/conda/lib/python3.13/site-packages (from folium) (0.8.2)
Requirement already satisfied: jinja2>=2.9 in /opt/conda/lib/python3.13/site-packages (from folium) (3.1.6)
Requirement already satisfied: numpy in /opt/conda/lib/python3.13/site-packages (from folium) (2.3.3)
Requirement already satisfied: requests in /opt/conda/lib/python3.13/site-packages (from folium) (2.32.5)
Requirement already satisfied: xyzservices in /opt/conda/lib/python3.13/site-packages (from folium) (2025.4.0)
Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.13/site-packages (from jinja2>=2.9->folium) (3.0.3)
Requirement already satisfied: charset_normalizer<4,>=2 in /opt/conda/lib/python3.13/site-packages (from requests->folium) (3.4.4)
Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.13/site-packages (from requests->folium) (3.11)
Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.13/site-packages (from requests->folium) (2.5.0)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.13/site-packages (from requests->folium) (2025.10.5)

2. Loaded dataset

# ---------------------------------------------------------
# 2. Load your dataset
# ---------------------------------------------------------
df2 = pd.read_csv("datasets/Motor_Vehicle_Collisions_-_Crashes.csv", low_memory=False)

# Show first 50 rows
df2.head(50)

	CRASH DATE	CRASH TIME	BOROUGH	ZIP CODE	LATITUDE	LONGITUDE	LOCATION	ON STREET NAME	CROSS STREET NAME	OFF STREET NAME	...	CONTRIBUTING FACTOR VEHICLE 2	CONTRIBUTING FACTOR VEHICLE 3	CONTRIBUTING FACTOR VEHICLE 4	CONTRIBUTING FACTOR VEHICLE 5	COLLISION_ID	VEHICLE TYPE CODE 1	VEHICLE TYPE CODE 2	VEHICLE TYPE CODE 3	VEHICLE TYPE CODE 4	VEHICLE TYPE CODE 5
0	09/11/2021	2:39	NaN	NaN	NaN	NaN	NaN	WHITESTONE EXPRESSWAY	20 AVENUE	NaN	...	Unspecified	NaN	NaN	NaN	4455765	Sedan	Sedan	NaN	NaN	NaN
1	03/26/2022	11:45	NaN	NaN	NaN	NaN	NaN	QUEENSBORO BRIDGE UPPER	NaN	NaN	...	NaN	NaN	NaN	NaN	4513547	Sedan	NaN	NaN	NaN	NaN
2	11/01/2023	1:29	BROOKLYN	11230	40.621790	-73.970024	(40.62179, -73.970024)	OCEAN PARKWAY	AVENUE K	NaN	...	Unspecified	Unspecified	NaN	NaN	4675373	Moped	Sedan	Sedan	NaN	NaN
3	06/29/2022	6:55	NaN	NaN	NaN	NaN	NaN	THROGS NECK BRIDGE	NaN	NaN	...	Unspecified	NaN	NaN	NaN	4541903	Sedan	Pick-up Truck	NaN	NaN	NaN
4	09/21/2022	13:21	NaN	NaN	NaN	NaN	NaN	BROOKLYN BRIDGE	NaN	NaN	...	Unspecified	NaN	NaN	NaN	4566131	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	NaN
5	04/26/2023	13:30	NaN	NaN	NaN	NaN	NaN	WEST 54 STREET	NaN	NaN	...	Unspecified	NaN	NaN	NaN	4623759	Sedan	Box Truck	NaN	NaN	NaN
6	11/01/2023	7:12	NaN	NaN	NaN	NaN	NaN	HUTCHINSON RIVER PARKWAY	NaN	NaN	...	Driver Inattention/Distraction	NaN	NaN	NaN	4675709	Sedan	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN
7	11/01/2023	8:01	NaN	NaN	NaN	NaN	NaN	WEST 35 STREET	HENRY HUDSON RIVER	NaN	...	NaN	NaN	NaN	NaN	4675769	Sedan	NaN	NaN	NaN	NaN
8	04/26/2023	22:20	NaN	NaN	NaN	NaN	NaN	NaN	NaN	61 Ed Koch queensborough bridge	...	NaN	NaN	NaN	NaN	4623865	Sedan	Pick-up Truck	NaN	NaN	NaN
9	09/11/2021	9:35	BROOKLYN	11208	40.667202	-73.866500	(40.667202, -73.8665)	NaN	NaN	1211 LORING AVENUE	...	NaN	NaN	NaN	NaN	4456314	Sedan	NaN	NaN	NaN	NaN
10	12/14/2021	8:13	BROOKLYN	11233	40.683304	-73.917274	(40.683304, -73.917274)	SARATOGA AVENUE	DECATUR STREET	NaN	...	NaN	NaN	NaN	NaN	4486609	NaN	NaN	NaN	NaN	NaN
11	04/14/2021	12:47	NaN	NaN	NaN	NaN	NaN	MAJOR DEEGAN EXPRESSWAY RAMP	NaN	NaN	...	Unspecified	NaN	NaN	NaN	4407458	Dump	Sedan	NaN	NaN	NaN
12	12/14/2021	17:05	NaN	NaN	40.709183	-73.956825	(40.709183, -73.956825)	BROOKLYN QUEENS EXPRESSWAY	NaN	NaN	...	Unspecified	NaN	NaN	NaN	4486555	Sedan	Tractor Truck Diesel	NaN	NaN	NaN
13	12/14/2021	8:17	BRONX	10475	40.868160	-73.831480	(40.86816, -73.83148)	NaN	NaN	344 BAYCHESTER AVENUE	...	Unspecified	NaN	NaN	NaN	4486660	Sedan	Sedan	NaN	NaN	NaN
14	12/14/2021	21:10	BROOKLYN	11207	40.671720	-73.897100	(40.67172, -73.8971)	NaN	NaN	2047 PITKIN AVENUE	...	Unspecified	NaN	NaN	NaN	4487074	Sedan	NaN	NaN	NaN	NaN
15	12/14/2021	14:58	MANHATTAN	10017	40.751440	-73.973970	(40.75144, -73.97397)	3 AVENUE	EAST 43 STREET	NaN	...	Unspecified	NaN	NaN	NaN	4486519	Sedan	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN
16	12/13/2021	0:34	NaN	NaN	40.701275	-73.888870	(40.701275, -73.88887)	MYRTLE AVENUE	NaN	NaN	...	Unspecified	NaN	NaN	NaN	4486934	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	NaN
17	12/14/2021	16:50	QUEENS	11413	40.675884	-73.755770	(40.675884, -73.75577)	SPRINGFIELD BOULEVARD	EAST GATE PLAZA	NaN	...	Unspecified	NaN	NaN	NaN	4487127	Sedan	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN
18	12/14/2021	8:30	NaN	NaN	NaN	NaN	NaN	broadway	west 80 street -west 81 street	NaN	...	Unspecified	NaN	NaN	NaN	4486634	Station Wagon/Sport Utility Vehicle	Sedan	NaN	NaN	NaN
19	12/14/2021	0:59	NaN	NaN	40.596620	-74.002310	(40.59662, -74.00231)	BELT PARKWAY	NaN	NaN	...	NaN	NaN	NaN	NaN	4486564	Sedan	NaN	NaN	NaN	NaN
20	12/14/2021	23:10	QUEENS	11434	40.666840	-73.789410	(40.66684, -73.78941)	NORTH CONDUIT AVENUE	150 STREET	NaN	...	Unspecified	NaN	NaN	NaN	4486635	Sedan	Sedan	NaN	NaN	NaN
21	12/14/2021	17:58	BROOKLYN	11217	40.681580	-73.974630	(40.68158, -73.97463)	NaN	NaN	480 DEAN STREET	...	Unspecified	NaN	NaN	NaN	4486604	Tanker	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN
22	12/14/2021	20:03	BROOKLYN	11226	40.650680	-73.958810	(40.65068, -73.95881)	NaN	NaN	878 FLATBUSH AVENUE	...	NaN	NaN	NaN	NaN	4486991	Sedan	NaN	NaN	NaN	NaN
23	12/14/2021	1:28	NaN	NaN	NaN	NaN	NaN	MEEKER AVENUE	LORIMER STREET	NaN	...	Unspecified	NaN	NaN	NaN	4486284	Station Wagon/Sport Utility Vehicle	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN
24	12/11/2021	19:43	BRONX	10463	40.872620	-73.904686	(40.87262, -73.904686)	WEST KINGSBRIDGE ROAD	HEATH AVENUE	NaN	...	Unspecified	NaN	NaN	NaN	4487040	Station Wagon/Sport Utility Vehicle	Sedan	NaN	NaN	NaN
25	12/14/2021	14:30	NaN	NaN	40.783268	-73.824850	(40.783268, -73.82485)	WHITESTONE EXPRESSWAY	NaN	NaN	...	Unspecified	Unspecified	NaN	NaN	4486537	Station Wagon/Sport Utility Vehicle	Sedan	Sedan	NaN	NaN
26	12/11/2021	4:45	MANHATTAN	10001	40.748917	-73.993546	(40.748917, -73.993546)	NaN	NaN	232 WEST 30 STREET	...	Unspecified	NaN	NaN	NaN	4486905	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	NaN
27	12/14/2021	5:46	NaN	NaN	40.744644	-73.770410	(40.744644, -73.77041)	LONG ISLAND EXPRESSWAY	NaN	NaN	...	Other Vehicular	NaN	NaN	NaN	4487122	Station Wagon/Sport Utility Vehicle	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN
28	12/13/2021	6:30	QUEENS	11372	40.753730	-73.885050	(40.75373, -73.88505)	82 STREET	34 AVENUE	NaN	...	NaN	NaN	NaN	NaN	4486967	Sedan	NaN	NaN	NaN	NaN
29	12/14/2021	3:43	NaN	NaN	40.804375	-73.937420	(40.804375, -73.93742)	LEXINGTON AVENUE	NaN	NaN	...	NaN	NaN	NaN	NaN	4486304	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	NaN
30	12/13/2021	17:40	STATEN ISLAND	10301	40.631650	-74.087620	(40.63165, -74.08762)	VICTORY BOULEVARD	WOODSTOCK AVENUE	NaN	...	Unspecified	NaN	NaN	NaN	4487001	Sedan	Sedan	NaN	NaN	NaN
31	12/14/2021	17:31	BROOKLYN	11230	40.623104	-73.958090	(40.623104, -73.95809)	EAST 18 STREET	AVENUE K	NaN	...	NaN	NaN	NaN	NaN	4486516	Sedan	NaN	NaN	NaN	NaN
32	12/14/2021	20:13	BROOKLYN	11215	40.665760	-73.984500	(40.66576, -73.9845)	NaN	NaN	366 12 STREET	...	Unspecified	NaN	NaN	NaN	4486605	Sedan	NaN	NaN	NaN	NaN
33	12/14/2021	12:54	BROOKLYN	11217	40.687534	-73.977500	(40.687534, -73.9775)	FULTON STREET	SAINT FELIX STREET	NaN	...	Unspecified	NaN	NaN	NaN	4487052	Sedan	Bike	NaN	NaN	NaN
34	12/14/2021	17:15	BROOKLYN	11211	40.710957	-73.951126	(40.710957, -73.951126)	GRAND STREET	UNION AVENUE	NaN	...	Unspecified	NaN	NaN	NaN	4486556	Bus	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN
35	12/14/2021	22:49	BRONX	10455	40.818130	-73.910126	(40.81813, -73.910126)	NaN	NaN	713 EAGLE AVENUE	...	Unspecified	NaN	NaN	NaN	4486875	Taxi	NaN	NaN	NaN	NaN
36	12/12/2021	9:00	QUEENS	11385	40.704470	-73.901480	(40.70447, -73.90148)	NaN	NaN	59-14 67 AVENUE	...	Unspecified	NaN	NaN	NaN	4486933	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	NaN
37	12/14/2021	16:25	NaN	NaN	40.784615	-73.953964	(40.784615, -73.953964)	EAST 93 STREET	NaN	NaN	...	Driver Inattention/Distraction	NaN	NaN	NaN	4486581	Van	Bike	NaN	NaN	NaN
38	11/02/2023	9:20	NaN	NaN	NaN	NaN	NaN	35 AVENUE	NaN	NaN	...	NaN	NaN	NaN	NaN	4675877	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	NaN
39	04/14/2021	14:30	NaN	NaN	NaN	NaN	NaN	EASTCHESTER ROAD	PELHAM PARKWAY NORTH	NaN	...	Unspecified	NaN	NaN	NaN	4407520	Bus	NaN	NaN	NaN	NaN
40	12/16/2021	6:59	NaN	NaN	NaN	NaN	NaN	KINGSLAND AVENUE	MEEKER AVENUE	NaN	...	NaN	NaN	NaN	NaN	4486960	NaN	NaN	NaN	NaN	NaN
41	04/27/2023	15:40	NaN	NaN	NaN	NaN	NaN	WILLIAMSBURG BRIDGE OUTER ROADWA	NaN	NaN	...	Unspecified	NaN	NaN	NaN	4624078	Sedan	Sedan	NaN	NaN	NaN
42	09/22/2022	16:16	QUEENS	11418	40.698257	-73.826320	(40.698257, -73.82632)	123 STREET	89 AVENUE	NaN	...	Unspecified	NaN	NaN	NaN	4566408	Sedan	E-Scooter	NaN	NaN	NaN
43	01/12/2023	21:00	BROOKLYN	11208	NaN	NaN	NaN	NaN	NaN	97-16 DREW STREET	...	Unspecified	NaN	NaN	NaN	4598234	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	NaN
44	06/29/2022	16:00	NaN	NaN	NaN	NaN	NaN	WILLIAMSBURG BRIDGE OUTER ROADWA	NaN	NaN	...	Unspecified	NaN	NaN	NaN	4542336	Motorscooter	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN
45	04/15/2021	16:15	NaN	NaN	NaN	NaN	NaN	HUTCHINSON RIVER PARKWAY	NaN	NaN	...	NaN	NaN	NaN	NaN	4407665	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	NaN
46	07/07/2021	11:42	NaN	NaN	NaN	NaN	NaN	THROGS NECK BRIDGE	NaN	NaN	...	Unspecified	NaN	NaN	NaN	4456591	Sedan	Sedan	NaN	NaN	NaN
47	04/27/2023	22:13	NaN	NaN	NaN	NaN	NaN	PELHAM PARKWAY SOUTH	WILLIAMSBRIDGE ROAD	NaN	...	Unspecified	NaN	NaN	NaN	4624101	Sedan	NaN	NaN	NaN	NaN
48	07/12/2022	17:50	BROOKLYN	11225	40.663303	-73.960490	(40.663303, -73.96049)	NaN	NaN	44 EMPIRE BOULEVARD	...	Unspecified	NaN	NaN	NaN	4545699	Sedan	NaN	NaN	NaN	NaN
49	03/23/2022	10:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	71 EAST DRIVE	...	NaN	NaN	NaN	NaN	4512922	Bike	NaN	NaN	NaN	NaN

50 rows × 29 columns

3. Cleaned the data and Plotted

df2['CRASH DATE'] = pd.to_datetime(df['CRASH DATE'], errors='coerce')

df2_clean = df2.dropna(subset=['LATITUDE', 'LONGITUDE'])

plt.figure(figsize=(12,5))
df['VEHICLE TYPE CODE 1'].value_counts().head(15).plot(kind='bar')
plt.title("Top 15 Vehicle Types Involved in Collisions")
plt.xlabel("Vehicle Type")
plt.ylabel("Number of Collisions")
plt.show()

plt.figure(figsize=(12,5))
df.groupby(df['CRASH DATE'].dt.date).size().plot()
plt.title("Accidents Over Time")
plt.xlabel("Date")
plt.ylabel("Number of Accidents")
plt.show()

I tried my favourite data visualization - showing the crash/collision location on map. However I could not succeed.

# Convert LAT & LON to numeric values
df2['LATITUDE'] = pd.to_numeric(df['LATITUDE'], errors='coerce')
df2['LONGITUDE'] = pd.to_numeric(df['LONGITUDE'], errors='coerce')

# Remove missing or invalid values
df2_map = df2.dropna(subset=['LATITUDE', 'LONGITUDE'])

# Remove impossible coordinates (0,0)
df2_map = df2_map[(df2_map['LATITUDE'] != 0) & (df2_map['LONGITUDE'] != 0)]

m = folium.Map(
    location=[df2_map['LATITUDE'].mean(), df2_map['LONGITUDE'].mean()],
    zoom_start=11,
    tiles='OpenStreetMap'
)

for _, row in df2_map.iterrows():
    folium.CircleMarker(
        location=[row['LATITUDE'], row['LONGITUDE']],
        radius=2,
        color='red',
        fill=True,
        fill_opacity=0.5
    ).add_to(m)

m

heat_data = df2_clean[['LATITUDE', 'LONGITUDE']].values.tolist()

m2 = folium.Map(
    location=[df2_clean['LATITUDE'].mean(), df2_clean['LONGITUDE'].mean()],
    zoom_start=11
)

HeatMap(heat_data, radius=6).add_to(m2)

m2

import folium

# Set starting point (center of NYC)
m = folium.Map(location=[40.7128, -74.0060], zoom_start=11)

# Add points
for _, row in df2.iterrows():
    folium.CircleMarker(
        location=[row["LATITUDE"], row["LONGITUDE"]],
        radius=2,
        color="red",
        fill=True,
        fill_opacity=0.6
    ).add_to(m)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[33], line 8
      6 # Add points
      7 for _, row in df2.iterrows():
----> 8     folium.CircleMarker(
      9         location=[row["LATITUDE"], row["LONGITUDE"]],
     10         radius=2,
     11         color="red",
     12         fill=True,
     13         fill_opacity=0.6
     14     ).add_to(m)

File /opt/conda/lib/python3.13/site-packages/folium/vector_layers.py:387, in CircleMarker.__init__(self, location, radius, popup, tooltip, **kwargs)
    379 def __init__(
    380     self,
    381     location: Optional[Sequence[float]] = None,
   (...)    385     **kwargs: TypePathOptions,
    386 ):
--> 387     super().__init__(location, popup=popup, tooltip=tooltip)
    388     self._name = "CircleMarker"
    389     self.options = path_options(line=False, radius=radius, **kwargs)

File /opt/conda/lib/python3.13/site-packages/folium/map.py:451, in Marker.__init__(self, location, popup, tooltip, icon, draggable, **kwargs)
    449 super().__init__()
    450 self._name = "Marker"
--> 451 self.location = validate_location(location) if location is not None else None
    452 self.options = remove_empty(
    453     draggable=draggable or None, autoPan=draggable or None, **kwargs
    454 )
    455 # this attribute is not used by Marker, but by GeoJson

File /opt/conda/lib/python3.13/site-packages/folium/utilities.py:110, in validate_location(location)
    105         raise ValueError(
    106             "Location should consist of two numerical values, "
    107             f"but {coord!r} of type {type(coord)} is not convertible to float."
    108         )
    109     if math.isnan(float(coord)):
--> 110         raise ValueError("Location values cannot contain NaNs.")
    111 return [float(x) for x in coords]

ValueError: Location values cannot contain NaNs.

m

Make this Notebook Trusted to load map: File -> Trust Notebook

Challenges I faced related to foliam

I wanted to mark the crash site on the map but I could not write the right code.

Mattermost Help

ChatGPT Help

Prompted the AI for assistance.

.

Explored quite a lot!

.

Explored quite a lot!

.

import pandas as pd
import matplotlib.pyplot as plt

# Load your dataset
df = pd.read_csv("datasets/Motor_Vehicle_Collisions_-_Crashes.csv",low_memory=False)

# Drop rows with missing coordinates
df_geo = df.dropna(subset=["LATITUDE", "LONGITUDE"])

# Plot
plt.figure(figsize=(8, 10))
plt.scatter(df_geo["LONGITUDE"], df_geo["LATITUDE"], s=1)
plt.xlabel("Longitude")
plt.ylabel("Latitude")
plt.title("Crash Locations Scatter Plot")
plt.show(200)

import pandas as pd
import folium.map
from folium.plugins import HeatMap

df = pd.read_csv("datasets/Motor_Vehicle_Collisions_-_Crashes.csv")
df_geo = df.dropna(subset=["LATITUDE", "LONGITUDE"])

# Create base map centered on median coordinates
center_lat = df_geo["LATITUDE"].median()
center_lon = df_geo["LONGITUDE"].median()

m = folium.Map(location=[center_lat, center_lon], zoom_start=11)

# Prepare data for heatmap
heat_data = df_geo[["LATITUDE", "LONGITUDE"]].values.tolist()

HeatMap(heat_data, radius=8).add_to(m)

m

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[9], line 2
      1 import pandas as pd
----> 2 import folium.map
      3 from folium.plugins import HeatMap
      5 df = pd.read_csv("datasets/Motor_Vehicle_Collisions_-_Crashes.csv")

ModuleNotFoundError: No module named 'folium'

Never Give UP!!

Tried again on 19th December, 2025. It was rewarding!

Sought the HELP from ChatGPT

%pip install folium
import pandas as pd
import folium

Requirement already satisfied: folium in /opt/conda/lib/python3.13/site-packages (0.20.0)
Requirement already satisfied: branca>=0.6.0 in /opt/conda/lib/python3.13/site-packages (from folium) (0.8.2)
Requirement already satisfied: jinja2>=2.9 in /opt/conda/lib/python3.13/site-packages (from folium) (3.1.6)
Requirement already satisfied: numpy in /opt/conda/lib/python3.13/site-packages (from folium) (2.3.3)
Requirement already satisfied: requests in /opt/conda/lib/python3.13/site-packages (from folium) (2.32.5)
Requirement already satisfied: xyzservices in /opt/conda/lib/python3.13/site-packages (from folium) (2025.4.0)
Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.13/site-packages (from jinja2>=2.9->folium) (3.0.3)
Requirement already satisfied: charset_normalizer<4,>=2 in /opt/conda/lib/python3.13/site-packages (from requests->folium) (3.4.4)
Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.13/site-packages (from requests->folium) (3.11)
Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.13/site-packages (from requests->folium) (2.5.0)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.13/site-packages (from requests->folium) (2025.10.5)
Note: you may need to restart the kernel to use updated packages.

df = pd.read_csv("datasets/Motor_Vehicle_Collisions_-_Crashes.csv", low_memory=False)

df.head(200)

	CRASH DATE	CRASH TIME	BOROUGH	ZIP CODE	LATITUDE	LONGITUDE	LOCATION	ON STREET NAME	CROSS STREET NAME	OFF STREET NAME	...	CONTRIBUTING FACTOR VEHICLE 2	CONTRIBUTING FACTOR VEHICLE 3	CONTRIBUTING FACTOR VEHICLE 4	CONTRIBUTING FACTOR VEHICLE 5	COLLISION_ID	VEHICLE TYPE CODE 1	VEHICLE TYPE CODE 2	VEHICLE TYPE CODE 3	VEHICLE TYPE CODE 4	VEHICLE TYPE CODE 5
0	09/11/2021	2:39	NaN	NaN	NaN	NaN	NaN	WHITESTONE EXPRESSWAY	20 AVENUE	NaN	...	Unspecified	NaN	NaN	NaN	4455765	Sedan	Sedan	NaN	NaN	NaN
1	03/26/2022	11:45	NaN	NaN	NaN	NaN	NaN	QUEENSBORO BRIDGE UPPER	NaN	NaN	...	NaN	NaN	NaN	NaN	4513547	Sedan	NaN	NaN	NaN	NaN
2	11/01/2023	1:29	BROOKLYN	11230	40.621790	-73.970024	(40.62179, -73.970024)	OCEAN PARKWAY	AVENUE K	NaN	...	Unspecified	Unspecified	NaN	NaN	4675373	Moped	Sedan	Sedan	NaN	NaN
3	06/29/2022	6:55	NaN	NaN	NaN	NaN	NaN	THROGS NECK BRIDGE	NaN	NaN	...	Unspecified	NaN	NaN	NaN	4541903	Sedan	Pick-up Truck	NaN	NaN	NaN
4	09/21/2022	13:21	NaN	NaN	NaN	NaN	NaN	BROOKLYN BRIDGE	NaN	NaN	...	Unspecified	NaN	NaN	NaN	4566131	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
195	03/23/2022	21:00	QUEENS	11354	40.776764	-73.848015	(40.776764, -73.848015)	NaN	NaN	25-03 120 STREET	...	NaN	NaN	NaN	NaN	4514334	Sedan	NaN	NaN	NaN	NaN
196	03/26/2022	12:00	QUEENS	11377	40.747498	-73.913090	(40.747498, -73.91309)	NaN	NaN	39-34 51 STREET	...	NaN	NaN	NaN	NaN	4513548	Sedan	NaN	NaN	NaN	NaN
197	03/26/2022	13:28	BROOKLYN	11206	40.701637	-73.942276	(40.701637, -73.942276)	GRAHAM AVENUE	DEBEVOISE STREET	NaN	...	Unspecified	NaN	NaN	NaN	4514046	Station Wagon/Sport Utility Vehicle	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN
198	03/26/2022	19:12	QUEENS	11419	40.693490	-73.826546	(40.69349, -73.826546)	NaN	NaN	94-39 120 STREET	...	Unspecified	NaN	NaN	NaN	4513581	Station Wagon/Sport Utility Vehicle	Sedan	NaN	NaN	NaN
199	03/26/2022	21:29	QUEENS	11374	40.726463	-73.859430	(40.726463, -73.85943)	WETHEROLE STREET	65 ROAD	NaN	...	NaN	NaN	NaN	NaN	4513772	Bike	NaN	NaN	NaN	NaN

200 rows × 29 columns

df = df[['LATITUDE', 'LONGITUDE', 'CRASH DATE', 'VEHICLE TYPE CODE 1']]
df = df.dropna(subset=['LATITUDE', 'LONGITUDE'])

df.head(200)

	LATITUDE	LONGITUDE	CRASH DATE	VEHICLE TYPE CODE 1
2	40.621790	-73.970024	11/01/2023	Moped
9	40.667202	-73.866500	09/11/2021	Sedan
10	40.683304	-73.917274	12/14/2021	NaN
12	40.709183	-73.956825	12/14/2021	Sedan
13	40.868160	-73.831480	12/14/2021	Sedan
...	...	...	...	...
226	40.761040	-73.881620	12/10/2021	Sedan
227	40.669777	-73.958300	12/10/2021	Station Wagon/Sport Utility Vehicle
228	40.839443	-73.904360	12/10/2021	Ambulance
229	40.752450	-73.945070	12/10/2021	Station Wagon/Sport Utility Vehicle
230	40.687748	-73.980125	12/08/2021	Station Wagon/Sport Utility Vehicle

200 rows × 4 columns

center_lat = df['LATITUDE'].mean()
center_lon = df['LONGITUDE'].mean()

m = folium.Map(
    location=[center_lat, center_lon],
    zoom_start=11,
    tiles='OpenStreetMap'
)

for _, row in df.sample(1000).iterrows():  # limit to 1000 for performance
    folium.CircleMarker(
        location=[row['LATITUDE'], row['LONGITUDE']],
        radius=0.5,
        color='red',
        fill=True,
        fill_opacity=0.6,
        popup=f"Date: {row['CRASH DATE']}<br>Vehicle: {row['VEHICLE TYPE CODE 1']}"
    ).add_to(m)

m

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