Update app.py

app.py

@@ -1,13 +1,36 @@
-"""
-# My first app
-Here's our first attempt at using data to create a table:
-"""
-
 import streamlit as st
 import pandas as pd
-df = pd.DataFrame({
-  'first column': [1, 2, 3, 4],
-  'second column': [10, 20, 30, 40]
-})
+import numpy as np
+
+st.title('Uber pickups in NYC')
+
+DATE_COLUMN = 'date/time'
+DATA_URL = ('https://s3-us-west-2.amazonaws.com/'
+            '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()
+    data.rename(lowercase, axis='columns', inplace=True)
+    data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])
+    return data
+
+data_load_state = st.text('Loading data...')
+data = load_data(10000)
+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]
 
-df
+st.subheader('Map of all pickups at %s:00' % hour_to_filter)
+st.map(filtered_data)