streamlit_app.py

# -*- coding: utf-8 -*-
# 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.

"""An example of showing geographic data."""

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

# SETTING PAGE CONFIG TO WIDE MODE
st.set_page_config(layout="wide")

# 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.experimental_memo
def load_data(nrows):
    data = pd.read_csv("uber-raw-data-sep14.csv.gz", 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,
            ),
        ]
    ))

# 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=0.2,
        color='red'
    ), use_container_width=True)