def get_state_scores(jobs, prices):
"""
Scale jobs/state by average house price/state
"""
# 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'])
jobs_per_state = jobs.groupby(['state'])['id'].count().to_dict()
prices_per_state = prices[['state', 'average_price'
]].set_index('state').to_dict()['average_price']
scores_jobs = []
scores_prices = []
for state in jobs_per_state:
scores_jobs.append(float(jobs_per_state[state]))
scores_prices.append(float(prices_per_state[state]))
max_job_score = max(scores_jobs)
max_price_score = max(scores_prices)
scores = [(x / max_job_score) / (y / max_price_score)
for x, y in zip(scores_jobs, scores_prices)]
max_score = max(scores)
scores = [x / max_score for x in scores]
return dict(zip(jobs_per_state.keys(), scores))
def get_state_scores(jobs, prices):
"""
Scale jobs/state by average house price/state
"""
# 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'])
jobs_per_state = jobs.groupby(['state'])['id'].count().to_dict()
prices_per_state = prices[['state','average_price']].set_index('state').to_dict()['average_price']
scores_jobs = []
scores_prices = []
for state in jobs_per_state:
scores_jobs.append(float(jobs_per_state[state]))
scores_prices.append(float(prices_per_state[state]))
max_job_score = max(scores_jobs)
max_price_score = max(scores_prices)
scores = [(x/max_job_score)/(y/max_price_score) for x,y in zip(scores_jobs,scores_prices)]
max_score = max(scores)
scores = [x/max_score for x in scores]
return dict(zip(jobs_per_state.keys(), scores))
def get_populations_scores(populations):
fips = get_fips_data()
popfips = pd.merge(fips[['state', 'county', 'fips_state', 'fips_county']],
populations,
on=['county', 'state'])
growth_per_county = dict(
zip(zip(popfips['fips_state'], popfips['fips_county']),
(popfips['population2010'] / popfips['population2000']) - 1.))
return calculate_scores(growth_per_county)
def get_houseprices_scores(houseprices):
fips = get_fips_data()
pricefips = pd.merge(fips[['state', 'county', 'fips_state',
'fips_county']],
houseprices,
on=['county', 'state'])
price_per_county = dict(
zip(zip(pricefips['fips_state'], pricefips['fips_county']),
-pricefips['average_price'].values))
return calculate_scores(price_per_county)
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:
continue
max_jobs = max(counts)
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 = []
def get_houseprices_scores(houseprices):
fips = get_fips_data()
pricefips = pd.merge(fips[['state','county','fips_state','fips_county']], houseprices, on=['county', 'state'])
price_per_county = dict(zip(zip(pricefips['fips_state'], pricefips['fips_county']), -pricefips['average_price'].values))
return calculate_scores(price_per_county)
def get_populations_scores(populations):
fips = get_fips_data()
popfips = pd.merge(fips[['state','county','fips_state','fips_county']], populations, on=['county', 'state'])
growth_per_county = dict(zip(zip(popfips['fips_state'], popfips['fips_county']), (popfips['population2010']/popfips['population2000']) - 1.))
return calculate_scores(growth_per_county)
