_educational purposes only. A 5yr rolling mean transformation has been applied to the original data series values_
_and so is still representative of actual level values. Please do not redistribute this data without the express_
_permission of the owner, Oxford Economics._
'''
st.sidebar.header("Settings")
region_type = st.sidebar.selectbox('Select region type',
['Cities', 'Countries', 'Both'])
top_n = st.sidebar.selectbox('Select group',
['All', 'Top 100', 'Top 50', 'Top 20'])
# GET DATA
gc_data = load_data(region_type)
geojson = load_geojson()
gc_data_unpivoted, merged = clean_and_reshape_data(gc_data, geojson)
# >> DISPLAY WIDGETS <<
# FILTER TO SELECTED INDICATOR & YEAR
year = st.sidebar.slider(
'Select year', min_value=2015, max_value=2022, value=2020
) # data starts at 2000, but selection is from 2010 because of 10 yr GAGR
indicators_list = list(
merged.loc[:, 'Indicator'].sort_values(ascending=True).unique())
indicator = st.sidebar.selectbox('Select indicator', indicators_list)
merged_filtered_view = merged.loc[(merged.loc[:, 'Indicator'] == indicator)
& (gc_data_unpivoted.loc[:, 'Year'] == year)]
# FILTER TO SELECTED MEASUREMENT
measurements_list = list(merged_filtered_view.loc[:,
'Measurement'].sort_values(
def main():
st.sidebar.header("Settings")
# GET DATA
data = load_data()
data_unpivoted = clean_and_reshape_data(data)
# >> DISPLAY WIDGETS <<
# FILTER TO SELECTED LOCATION & YEAR
year = st.sidebar.slider('Select year',
min_value=2015,
max_value=2022,
value=2020)
locations_list = list(
data_unpivoted.loc[:, 'Location'].sort_values(ascending=True).unique())
location = st.sidebar.selectbox(
'Select location',
locations_list,
index=locations_list.index('United Kingdom'))
data_view = data_unpivoted.loc[(data_unpivoted.loc[:,
'Location'] == location)
& (data_unpivoted.loc[:, 'Year'] == year)]
if st.checkbox('Show data', False):
'''
### Data
_The sample data used in this application is property of Oxford Economics and provided for personal use and_
_educational purposes only. A 5yr rolling mean transformation has been applied to the original data series values_
_and so is still representative of actual level values. Please do not redistribute this data without the express_
_permission of the owner, Oxford Economics._
'''
# TABLE
if st.checkbox('Show DataFrame', True):
data_view
if st.checkbox('Show Table'):
st.table(data_view)
'''
### Chart
'''
# chart data is calculated in two steps
# step 1 (using data_unpivoted, filtered by location)
chart_data = data_unpivoted[(data_unpivoted['Location'] == location)]
indicators_list = list(
chart_data.loc[:, 'Indicator'].sort_values(ascending=True).unique())
# this selection box is put into the reserved widget slot created above
indicator = st.sidebar.selectbox('Select indicator', indicators_list)
# step 2 (using chart_data, filtered by location's indicators)
chart_data = chart_data[(chart_data['Indicator'] == indicator)]
fig = alt.Chart(chart_data, title=f'{location} | {indicator}').mark_bar().encode(
alt.X('Year:O', axis=alt.Axis(domain=False, tickSize=0)),
alt.Y('Value', axis=alt.Axis(domain=False, tickSize=0, title='Value')),
color='Value', tooltip=['Id','Year','Value']) \
.properties(width=600).interactive()
st.altair_chart(fig)
# ABOUT
st.sidebar.header('About')
st.sidebar.info('Using Streamlit to build a Web App.\n\n' + \
'(c) 2020. Oxford Economics Ltd. All rights reserved.')
st.sidebar.markdown('---')
# Display Readme.md
if st.sidebar.checkbox('Readme', False):
st.markdown('---')
'''
### Readme
'''
with open('./README.md', 'r', encoding='utf-8') as f:
readme = f.read()
st.markdown(readme)
# TESTS
if st.sidebar.checkbox('Run Tests', False):
st.markdown('---')
st.title('Test Suite')
'''
### Data Load Test
'''
suite = unittest.TestLoader().loadTestsFromModule(TestFixtures)
result = unittest.TextTestRunner(verbosity=2).run(suite)
if result.wasSuccessful():
st.info(f'Test PASSED :-)')
st.balloons()
else:
st.error(f'Test FAILED :-(')
# Style
st.sidebar.markdown('---')
if st.sidebar.checkbox('Configure Style'):
BlockContainerStyler().block_container_styler()