app.py

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


st.title("Hello World!")
#st.markdown
st.markdown("### Header 3")

DATA_URL = ("Motor_Vehicle_Collisions_-_Crashes.csv")

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

@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 injured people 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 ocurred during a given time of day?")
#hour = st.selectbox("Hour to look at", range(0, 24), 1)

#hour = st.sidebar.slider("Hour to look at", 0, 24)

hour = st.slider("Hour to look at", 0, 24)
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=300,
        extruded=True,
        pickable=True,
        elevation_scale=4,
        elevation_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)
st.write(fig)

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

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_motorist >= 1")[["on_street_name", "injured_motorist"]].sort_values(by=['injured_motorist'], ascending=False).dropna(how='any')[:5])








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