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/patrick.roxas/Documents/portfolio/Streamlit Project/Motor_Vehicle_Collisions_-_Crashes.csv")

st.title("🚕 Motor Vehicle Collision in New York City")
st.markdown("This application is Streamlit dashboard that can be used to analyze motor-vehicle collisions in NYC")

@st.cache_data
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 injured people", 0, 19)
st.map(data.query("injured_persons >= @injured_people")[["latitude", "longitude"]].dropna(how="any"), )

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

midpoint = (np.average(data["latitude"]), np.average(data["longitude"]))
st.markdown("Vehicle Collision between %i:00 and %i:00" % (hour, (hour + 1) % 24))
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,
        elavation_range=[0, 1000],
        ),
    ],
))

st.subheader("Breakdown by minute between %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,
)

fig.update_layout(template='plotly_white')

st.write(fig)

st.header("Top 5 dangerous streets by affected type")
select = st.selectbox("Affected type of people", ['Pedestrian', 'Cyclist', 'Motorist'])

if select == 'Pedestrian':
    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 == 'Cyclist':
    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)