import streamlit as st import pandas as pd 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 # 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) hour_to_filter = st.slider('hour', 0, 23, 17) # min: 0h, max: 23h, default: 17h filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter] st.subheader(f'Map of all pickups at {hour_to_filter}:00') st.map(filtered_data)