Exemplo n.º 1
0
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)
Exemplo n.º 2
0
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
Exemplo n.º 3
0
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 = []
Exemplo n.º 4
0
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:]))
Exemplo n.º 5
0
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:]))
Exemplo n.º 6
0
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="")
Exemplo n.º 7
0
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'])