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| 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') | |
| 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] | |
| st.subheader('Map of all pickups at %s:00' % hour_to_filter) | |
| st.map(filtered_data) |