Update app.py

app.py

@@ -2,12 +2,13 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 
-st.title('Uber pickups in NYC')
+st.title('I\'m working on this demo')
 
 DATE_COLUMN = 'date/time'
 DATA_URL = ('https://s3-us-west-2.amazonaws.com/'
-         'streamlit-demo-data/uber-raw-data-sep14.csv.gz')
+            'streamlit-demo-data/uber-raw-data-sep14.csv.gz')
 
+@st.cache_data
 def load_data(nrows):
     data = pd.read_csv(DATA_URL, nrows=nrows)
     lowercase = lambda x: str(x).lower()
@@ -15,9 +16,21 @@ def load_data(nrows):
     data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])
     return data
 
-# Create a text element and let the reader know the data is loading.
 data_load_state = st.text('Loading data...')
-# Load 10,000 rows of data into the dataframe.
 data = load_data(10000)
-# Notify the reader that the data was successfully loaded.
-data_load_state.text('Loading data...done!')
+data_load_state.text("Done! (using st.cache_data)")
+
+if st.checkbox('Show raw data'):
+    st.subheader('Raw data')
+    st.write(data)
+
+st.subheader('Number of pickups by hour')
+hist_values = np.histogram(data[DATE_COLUMN].dt.hour, bins=24, range=(0,24))[0]
+st.bar_chart(hist_values)
+
+# Some number in the range 0-23
+hour_to_filter = st.slider('hour', 0, 23, 17)
+filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]
+
+st.subheader('Map of all pickups at %s:00' % hour_to_filter)
+st.map(filtered_data)