def index():
if request.method == 'GET':
return render_template('jobs_vs_prices.html')
else:
#request was a POST
jobs_app.vars['profession_key'] = request.form['profession_key']
jobs = load_jobs(jobs_app.vars['profession_key'])
prices = get_average_prices_data()
scores = get_state_scores(jobs, prices)
title = jobs_app.vars['profession_key'].replace('-',' ')
title = 'Available ' + title.title() + ' jobs (scaled by average house price)'
script, div = choropleth_usa(scores, title)
print script
return render_template('jobs_vs_prices_graph.html', script=script, div=div)
profession_keys = ['data-science', 'financial-services', 'information-technology', 'mobile-app']
jobs = None
for pk in profession_keys:
if jobs is None:
jobs = load_jobs(pk)
else:
new_jobs = load_jobs(pk)
jobs = pd.concat((jobs, new_jobs), ignore_index=True)
jobs_per_state = jobs.groupby(['state'])['id'].count().to_dict()
prices = get_average_prices_data()
# Add state abbreviations to prices dataframe
fips = get_fips_data()
prices = pd.merge(prices, fips.groupby(['state_long','state']).count().reset_index()[['state_long','state']], how='left', on=['state_long'])
for p in range(12):
max_price = 150000. + float(p)*50000
prices_per_state = prices.loc[prices['average_price'] < max_price, ['state','average_price']].set_index('state').to_dict()['average_price']
if len(prices_per_state) > 0:
counts = []
for state in prices_per_state:
try:
counts.append(float(jobs_per_state[state]))
except KeyError:
'data-science', 'financial-services', 'information-technology',
'mobile-app'
]
jobs = None
for pk in profession_keys:
if jobs is None:
jobs = load_jobs(pk)
else:
new_jobs = load_jobs(pk)
jobs = pd.concat((jobs, new_jobs), ignore_index=True)
jobs_per_state = jobs.groupby(['state'])['id'].count().to_dict()
prices = get_average_prices_data()
# Add state abbreviations to prices dataframe
fips = get_fips_data()
prices = pd.merge(
prices,
fips.groupby(['state_long',
'state']).count().reset_index()[['state_long', 'state']],
how='left',
on=['state_long'])
for p in range(12):
max_price = 150000. + float(p) * 50000
prices_per_state = prices.loc[prices['average_price'] < max_price,
['state', 'average_price']].set_index(
'state').to_dict()['average_price']
