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hakuba_resorts_app.py
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import streamlit as st
import plotly.express as px
import requests
import parsel
import pandas as pd
import io
SOURCE_URL = 'https://www.hakubavalley.com/en/ski_resort_info_en/'
def _parse_ski_resort_info(html, debug_log=False):
"""Parses the HTML on SOURCE_URL"""
resort_dicts = []
document = parsel.Selector(html)
resorts = document.css('.spec-item')
if debug_log:
print(f'Found {len(resorts)}')
for resort in resorts:
name = resort.css('.gelande_name::text').get()
specs = resort.css('.spec-info dl dd::text').getall()[0:6]
elevation = resort.css('.altitude p::text').getall()
levels = resort.css('.course-level p::text').getall()
website = resort.css('.site_url a::attr(href)').get()
trail_map = resort.css('.btn-wht-blk a::attr(href)').get()
resort_dicts.append(dict(
name=name,
length=int(specs[0].replace(',', '')),
total_trails_length=int(specs[1].replace(',', '')),
area=int(specs[2].replace(',', '')),
gondolas=int(specs[3].replace(',', '')),
chairs=int(specs[4].replace(',', '')),
trails=int(specs[5].replace(',', '')),
max_elevation=int(elevation[0].replace(',', '')),
base_elevation=int(elevation[2].replace(',', '')),
vertical=int(elevation[1].replace(',', '')),
beginner_pct=int(levels[0]) / 100.0,
intermediate_pct=int(levels[1]) / 100.0,
advanced_pct=int(levels[2]) / 100.0,
website=website,
trail_map=trail_map,
))
return pd.DataFrame(resort_dicts)
@st.experimental_memo
def get_resort_info(debug_log=True):
"""Gets info from SOURCE_URL"""
# Pull down page
response = requests.get(SOURCE_URL)
df = _parse_ski_resort_info(response.text, debug_log)
# Cleanup resort names
df = df.assign(name=lambda df: (
df['name']
.str.replace(' Snow Resort', '')
.str.replace(' Snow Field', '')
.str.replace(" Park", '')
.str.replace(" Resort", '')
.str.replace(" Mountain", '')
.str.replace(' Winter Sports', '')
.str.replace('ABLE ', '')
.str.replace('Hakuba ', ''))
.str.replace('47', 'Hakuba 47'))
# Get trails by type
df = (df
.assign(beginner_trails=lambda df: df['beginner_pct'] * df['trails'])
.assign(intermediate_trails=lambda df: df['intermediate_pct'] * df['trails'])
.assign(advanced_trails=lambda df: df['advanced_pct'] * df['trails'])
.set_index('name')
)
return df.sort_values('area', ascending=False)
def _combine_resorts(df, remove_parts_of_group=True):
"""Combine Goryu and Hakuba 47"""
combined = (df
.loc[['Goryu', 'Hakuba 47']]
.agg({'length': 'max',
'total_trails_length': 'sum',
'area': 'sum',
'gondolas': 'sum',
'chairs': 'sum',
'trails': 'sum',
'max_elevation': 'max',
'base_elevation': 'min',
'beginner_trails': 'sum',
'intermediate_trails': 'sum',
'advanced_trails': 'sum'})
)
combined['vertical'] = combined['max_elevation'] - combined['base_elevation']
combined['beginner_pct'] = combined['beginner_trails'] / combined['trails']
combined['intermediate_pct'] = combined['intermediate_trails'] / combined['trails']
combined['advanced_pct'] = combined['advanced_trails'] / combined['trails']
df.loc['Hakuba 47 + Goryu'] = combined
df = df.astype({'gondolas': 'int', 'chairs': 'int'})
if remove_parts_of_group:
df = df.drop(index=['Hakuba 47', 'Goryu'])
return df.sort_values('area', ascending=False)
@st.experimental_memo
def convert_to_excel(df: pd.DataFrame):
file = io.BytesIO()
df.to_excel(file)
file.seek(0)
return file
@st.experimental_memo
def convert_to_csv(df: pd.DataFrame):
file = io.BytesIO()
df.to_csv(file, encoding='utf-8')
file.seek(0)
return file
def run():
st.set_page_config(layout='wide', page_title='Hakuba Valley Resorts')
st.title('Hakuba Valley Ski Resort Comparison!!!')
st.markdown(f'''
Taking the data from {SOURCE_URL} and presenting them into generic charts to make it easier to compare.
Hakuba 47 and Goryu Ski Resorts are connected so by default they're shown together.
Hello World
''')
tab_chart, tab_maps = st.tabs(['Charts', 'Maps'])
with tab_chart:
resorts_df = get_resort_info(True)
with st.expander("Source Data", expanded=False):
st.write(resorts_df)
st.download_button(
'Download as Excel', convert_to_excel(resorts_df), 'hakuba_data.xlsx', mime='application/vnd.ms-excel')
st.download_button(
'Download as CSV', convert_to_csv(resorts_df), 'hakuba_data.csv', mime='text/csv')
combine_resorts = st.checkbox('Combine Hakuba 47 and Goryu?', value=True)
if combine_resorts:
resorts_df = _combine_resorts(resorts_df)
st.plotly_chart(px.bar(resorts_df.assign(
label=lambda df: df['gondolas'].apply(lambda f: f'{f} gondolas, ') + df['chairs'].apply(
lambda f: f'{f} chairs')),
y='area',
title='Skiable Area by Resort',
text='label',
).update_layout(xaxis_title='', yaxis_title='ha'), use_container_width=True)
st.plotly_chart(
px.bar(resorts_df,
y='total_trails_length',
title='Total Trail Length by Resort'
).update_layout(xaxis_title='', yaxis_title='m')
, use_container_width=True)
st.plotly_chart(
px.bar(resorts_df,
y=['beginner_trails', 'intermediate_trails', 'advanced_trails'],
title='Trail Type by Resort',
color_discrete_map={'beginner_trails': '#86c96b', 'intermediate_trails': '#db3a2e',
'advanced_trails': '#555'},
).update_layout(showlegend=False, yaxis_title='trails', xaxis_title='')
, use_container_width=True)
st.plotly_chart(
px.bar(resorts_df,
y=['beginner_pct', 'intermediate_pct', 'advanced_pct'],
title='Trail Type by Resort',
color_discrete_map={'beginner_pct': '#86c96b', 'intermediate_pct': '#db3a2e', 'advanced_pct': '#555'},
).update_layout(showlegend=False, yaxis_title='%', yaxis_tickformat='0.0%', xaxis_title='')
, use_container_width=True)
st.plotly_chart(
px.bar(resorts_df.assign(label=lambda df: df['max_elevation'].apply(lambda x: f'max elev={x:.0f}m')),
y='vertical',
text='label',
title='Vertical and Max Elevation by Resort',
barmode='group'
).update_layout(showlegend=False, yaxis_title='vertical (m)', xaxis_title='')
, use_container_width=True)
with tab_maps:
st.markdown('''
Old, from Google Image Search
From a handy site with village maps, trail maps, etc: https://www.samuraisnow.com/hakuba-maps
''')
if __name__ == '__main__':
run()