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)
def counties_map():
criteria_scores = []
if 'jobs' in app.vars['criteria']:
scores = get_jobs_scores(load_jobs(app.vars['profession_key']))
criteria_scores.append(scores)
if 'houseprices' in app.vars['criteria']:
scores = get_houseprices_scores(load_houseprices())
criteria_scores.append(scores)
if 'population' in app.vars['criteria']:
scores = get_populations_scores(load_populations())
criteria_scores.append(scores)
final_scores = get_scores(criteria_scores)
svg = choropleth_svg(final_scores)
response = make_response(svg)
response.headers['Content-Type'] = 'image/svg+xml'
return response
import pandas as pd
from getdata import load_jobs, get_average_prices_data, get_fips_data
from plotchoropleth import choropleth_usa_states
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 = []
from bs4 import BeautifulSoup
from getdata import load_houseprices, load_populations, load_jobs
from getscores import get_houseprices_scores, get_populations_scores, get_jobs_scores, get_scores
#scores = get_houseprices_scores(load_houseprices())
#scores = get_populations_scores(load_populations())
#scores = get_jobs_scores(load_jobs('information-technology'))
scores = get_scores(load_jobs('financial-services'), load_populations(),
load_houseprices())
# Load the SVG map
svg = open('data/us_counties.svg', 'r').read()
soup = BeautifulSoup(svg, 'xml')
# Find counties
paths = soup.findAll('path')
# Map colors
colors = ["#F1EEF6", "#D4B9DA", "#C994C7", "#DF65B0", "#DD1C77", "#980043"]
path_style = 'font-size:12px;fill-rule:nonzero;stroke:#FFFFFF;stroke-opacity:1;'
path_style += 'stroke-width:0.1;stroke-miterlimit:4;stroke-dasharray:none;'
path_style += 'stroke-linecap:butt;marker-start:none;stroke-linejoin:bevel;fill:'
# Color the counties based on unemployment rate
for p in paths:
if p['id'] not in ['State_Lines', 'separator']:
# pass
try:
fips = (int(p['id'][:2]), int(p['id'][2:]))
from bs4 import BeautifulSoup
from getdata import load_houseprices, load_populations, load_jobs
from getscores import get_houseprices_scores, get_populations_scores, get_jobs_scores, get_scores
#scores = get_houseprices_scores(load_houseprices())
#scores = get_populations_scores(load_populations())
#scores = get_jobs_scores(load_jobs('information-technology'))
scores = get_scores(load_jobs('financial-services'), load_populations(), load_houseprices())
# Load the SVG map
svg = open('data/us_counties.svg', 'r').read()
soup = BeautifulSoup(svg, 'xml')
# Find counties
paths = soup.findAll('path')
# Map colors
colors = ["#F1EEF6", "#D4B9DA", "#C994C7", "#DF65B0", "#DD1C77", "#980043"]
path_style = 'font-size:12px;fill-rule:nonzero;stroke:#FFFFFF;stroke-opacity:1;'
path_style += 'stroke-width:0.1;stroke-miterlimit:4;stroke-dasharray:none;'
path_style += 'stroke-linecap:butt;marker-start:none;stroke-linejoin:bevel;fill:'
# Color the counties based on unemployment rate
for p in paths:
if p['id'] not in ['State_Lines', 'separator']:
# pass
try:
fips = (int(p['id'][:2]), int(p['id'][2:]))
from getdata import load_jobs, get_average_prices_data
from getscores import get_state_scores
from plotchoropleth import choropleth_usa_states
"""
Generate (and save) choropleths of available jobs (scaled by house prices)
"""
prices = get_average_prices_data()
profession_keys = ["data-science", "financial-services", "information-technology", "mobile-app"]
for pk in profession_keys:
jobs = load_jobs(pk)
scores = get_state_scores(jobs, prices)
choropleth_usa_states(scores, "templates/jobsdata_%s.html" % pk, title="")
import pandas as pd
from getdata import load_jobs, get_average_prices_data, get_fips_data
from plotchoropleth import choropleth_usa_states
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'])