Create app.py

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
+
+DATE_TIME = "date/time"
+DATA_URL = (
+    "Motor_Vehicle_Collisions_-_Crashes.csv"
+)
+
+st.title("Motor Vehicle 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)
+    #data = data[['date/time', 'latitude', 'longitude']]
+    return data
+
+data = load_data(100000)
+
+
+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 occur during a given time of day?")
+hour = st.slider("Hour to look at", 0, 23)
+original_data = data
+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"],
+        auto_highlight=True,
+        radius=100,
+        extruded=True,
+        pickable=True,
+        elevation_scale=4,
+        elevation_range=[0, 1000],
+        ),
+    ],
+))
+if st.checkbox("Show raw data", False):
+    st.subheader("Raw data by minute between %i:00 and %i:00" % (hour, (hour + 1) % 24))
+    st.write(data)
+
+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 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])