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Author: AtharvaDev

app.py

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+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/Atharva/Desktop/Project/project 2/streamlit-nyc/Motor_Vehicle_Collisions_-_Crashes.csv"
+)
+
+st.title("Motor Vehicle Collisions in New York City")
+st.markdown("This application s 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 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 occur 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 collision 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, #radius of hexagon
+ extruded=True, #3-d visulations
+ pickable=True,
+ elevation_range=[0,1000],
+ ),
+ ]
+))
+
+st.subheader("Breakdown by minute between %i:00 and %i:00" % (hour,(hour+1)))
+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', 'Motorists'])
+
+if select == 'Pedestrains':
+ st.write(original_data.query("injured_pedstrains >=1")[["on_street_name", "injured_pedstrains"]].sort_values(by=['injured_pedstrains'], 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_cyclistss'], 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)

requirements.txt

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+numpy==1.16.4
+pandas==0.24.2
+pydeck==0.3.0
+streamlit==0.57.3
+plotly==4.0.0