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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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st.title('Uber pickups in NYC') |
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DATE_COLUMN = 'date/time' |
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DATA_URL = ('https://s3-us-west-2.amazonaws.com/' |
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'streamlit-demo-data/uber-raw-data-sep14.csv.gz') |
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@st.cache_data |
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def load_data(nrows): |
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data = pd.read_csv(DATA_URL, nrows=nrows) |
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lowercase = lambda x: str(x).lower() |
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data.rename(lowercase, axis='columns', inplace=True) |
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data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN]) |
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return data |
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data_load_state = st.text('Loading data...') |
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data = load_data(10000) |
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data_load_state.text("Done! (using st.cache_data)") |
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if st.checkbox('Show raw data'): |
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st.subheader('Raw data') |
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st.write(data) |
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st.subheader('Number of pickups by hour') |
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hist_values = np.histogram(data[DATE_COLUMN].dt.hour, bins=24, range=(0,24))[0] |
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st.bar_chart(hist_values) |
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hour_to_filter = st.slider('hour', 0, 23, 17) |
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filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter] |
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st.subheader('Map of all pickups at %s:00' % hour_to_filter) |
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st.map(filtered_data) |
