app.py

import streamlit as st
import pandas as pd
import numpy as np
import pydeck as pdk
import plotly.express as px


DATA_URL=("C:/Users/MEGHAJ SINGH/Desktop/Data_Science_Web_App/Motor_Vehicle_Collisions_-_Crashes.csv")

st.title("Motor vehile Collisions in New York City")
st.markdown("This application is a Streamlit dashboard that can be used to analyze motor vehicle collisions in NYC")

@st.cache(persist=True)
def load_data(nrows):
    data=pd.read_csv(DATA_URL,nrows=nrows,parse_dates=[['CRASH_DATE','CRASH_TIME']])
    data.dropna(subset=['LATITUDE','LONGITUDE'],inplace=True)
    lowercase = lambda x: str(x).lower()
    data.rename(lowercase,axis='columns',inplace=True)
    data.rename(columns={'crash_date_crash_time':'date/time'},inplace=True)
    return data


data = load_data(100000)
original_data = data

st.header("Where are the most people injured in NYC?")
injured_people = st.slider("Number of persons injured in vehicle collisions",0, 19)
st.map(data.query("injured_persons >= @injured_people")[['latitude','longitude']].dropna(how="any"))



st.header("How many collisions have occured during a given time of day?")
hour = st.slider("Hour to look at",0,23)
data = data[data['date/time'].dt.hour == hour]

st.markdown("Vehicle Collisions between %i:00 and %i:00" % (hour,(hour+1) % 24))
midpoint = (np.average(data['latitude']),np.average(data['longitude']))

st.write(pdk.Deck(
    map_style = "mapbox://styles/mapbox/light-v9",
    initial_view_state = {
        "latitude":midpoint[0],
        "longitude":midpoint[1],
        "zoom":11,
        "pitch":50,
    },

    layers = [
        pdk.Layer(
            "HexagonLayer",
            data = data[['date/time','latitude','longitude']],
            get_position = ['longitude','latitude'],
            radius=100, 
            extruded=True,
            pickable=True,
            elevation_scale=4,
            elevation_range=[0,1000],
        ),
    ],

))


st.subheader("Breakdown by minute %i:00 and %i:00" % (hour,(hour+1) % 24))
filtered = data[
    (data['date/time'].dt.hour>=hour & (data['date/time'].dt.hour<(hour+1)))
]

hist = np.histogram(filtered['date/time'].dt.minute,bins=60,range=(0,60))[0]
chart_data = pd.DataFrame({'minute':range(60),'crashes':hist})
fig = px.bar(chart_data, x='minute',y='crashes',hover_data=['minute','crashes'],height=400)
st.write(fig)

st.header("Top 5 dangerous streets by affected class")
select = st.selectbox('Affected class', ['Pedestrians', 'Cyclists', 'Motorists'])

if select == 'Pedestrians':
    st.write(original_data.query("injured_pedestrians >= 1")[["on_street_name", "injured_pedestrians"]].sort_values(by=['injured_pedestrians'], ascending=False).dropna(how="any")[:5])

elif select == 'Cyclists':
    st.write(original_data.query("injured_cyclists >= 1")[["on_street_name", "injured_cyclists"]].sort_values(by=['injured_cyclists'], ascending=False).dropna(how="any")[:5])

else:
    st.write(original_data.query("injured_motorists >= 1")[["on_street_name", "injured_motorists"]].sort_values(by=['injured_motorists'], ascending=False).dropna(how="any")[:5])


if st.checkbox("Show Raw Data",False):
    st.subheader('Raw Data')
    st.write(data)