apps/uber_app.py

# Copyright 2018-2019 Streamlit Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import streamlit as st
import pandas as pd
import numpy as np
import altair as alt
import pydeck as pdk
from hydralit import HydraHeadApp


class UberNYC(HydraHeadApp):

    def __init__(self, title = '', **kwargs):
        self.__dict__.update(kwargs)
        self.title = title

    def run(self):

        #st.experimental_set_query_params(selected=self.title)
        print(self.title)

        # LOADING DATA
        DATE_TIME = "date/time"
        DATA_URL = (
            "http://s3-us-west-2.amazonaws.com/streamlit-demo-data/uber-raw-data-sep14.csv.gz"
        )

        @st.cache(persist=True)
        def load_data(nrows):
            data = pd.read_csv(DATA_URL, nrows=nrows)
            lowercase = lambda x: str(x).lower()
            data.rename(lowercase, axis="columns", inplace=True)
            data[DATE_TIME] = pd.to_datetime(data[DATE_TIME])
            return data

        data = load_data(100000)

        # CREATING FUNCTION FOR MAPS

        def map(data, lat, lon, zoom):
            st.write(pdk.Deck(
                map_style="mapbox://styles/mapbox/light-v9",
                initial_view_state={
                    "latitude": lat,
                    "longitude": lon,
                    "zoom": zoom,
                    "pitch": 50,
                },
                layers=[
                    pdk.Layer(
                        "HexagonLayer",
                        data=data,
                        get_position=["lon", "lat"],
                        radius=100,
                        elevation_scale=4,
                        elevation_range=[0, 1000],
                        pickable=True,
                        extruded=True,
                    ),
                ]
            ))

        st.subheader('Source for this great app is from the Streamlit gallery [NYC Uber Ridesharing Data](https://github.com/streamlit/demo-uber-nyc-pickups). An example of how easy it is to convert an existing application and use within a Hydralit multi-page application, see the secret saurce [here] (https://github.com/TangleSpace/hydralit).')
        st.markdown('<br><br>',unsafe_allow_html=True)
        
        # LAYING OUT THE TOP SECTION OF THE APP
        row1_1, row1_2 = st.columns((2,3))

        with row1_1:
            st.title("NYC Uber Ridesharing Data")

            hour_selected = st.slider("Select hour of pickup", 0, 23)

        with row1_2:
            st.write(
            """
            ##
            Examining how Uber pickups vary over time in New York City's and at its major regional airports.
            By sliding the slider on the left you can view different slices of time and explore different transportation trends.
            """)

        # FILTERING DATA BY HOUR SELECTED
        data = data[data[DATE_TIME].dt.hour == hour_selected]

        # LAYING OUT THE MIDDLE SECTION OF THE APP WITH THE MAPS
        row2_1, row2_2, row2_3, row2_4 = st.columns((2,1,1,1))

        # SETTING THE ZOOM LOCATIONS FOR THE AIRPORTS
        la_guardia= [40.7900, -73.8700]
        jfk = [40.6650, -73.7821]
        newark = [40.7090, -74.1805]
        zoom_level = 12
        midpoint = (np.average(data["lat"]), np.average(data["lon"]))

        with row2_1:
            st.write("**All New York City from %i:00 and %i:00**" % (hour_selected, (hour_selected + 1) % 24))
            map(data, midpoint[0], midpoint[1], 11)

        with row2_2:
            st.write("**La Guardia Airport**")
            map(data, la_guardia[0],la_guardia[1], zoom_level)

        with row2_3:
            st.write("**JFK Airport**")
            map(data, jfk[0],jfk[1], zoom_level)

        with row2_4:
            st.write("**Newark Airport**")
            map(data, newark[0],newark[1], zoom_level)

        # FILTERING DATA FOR THE HISTOGRAM
        filtered = data[
            (data[DATE_TIME].dt.hour >= hour_selected) & (data[DATE_TIME].dt.hour < (hour_selected + 1))
            ]

        hist = np.histogram(filtered[DATE_TIME].dt.minute, bins=60, range=(0, 60))[0]

        chart_data = pd.DataFrame({"minute": range(60), "pickups": hist})

        # LAYING OUT THE HISTOGRAM SECTION

        st.write("")

        st.write("**Breakdown of rides per minute between %i:00 and %i:00**" % (hour_selected, (hour_selected + 1) % 24))

        st.altair_chart(alt.Chart(chart_data)
            .mark_area(
                interpolate='step-after',
            ).encode(
                x=alt.X("minute:Q", scale=alt.Scale(nice=False)),
                y=alt.Y("pickups:Q"),
                tooltip=['minute', 'pickups']
            ).configure_mark(
                opacity=0.5,
                color='red'
            ), use_container_width=True)