In [1]:
# Creating example bls maps
from bls_geo import *

# can check out https://www.bls.gov/oes/current/oes_stru.htm
bio = '172031'
bio_stats = oes_geo(bio)
areas = get_areas() # this takes a few minutes
state = state_albers()
geo_bio = merge_occgeo(bio_stats,areas)

ax = geo_bio.plot(column='Employment',cmap='inferno',legend=True,zorder=2)
state.boundary.plot(ax=ax,color='grey',linewidth=0.5,zorder=1)
ax.set_ylim(0.1*1e6,3.3*1e6)
ax.set_xlim(-0.3*1e7,0.3*1e7)   # lower 48 focus (for Albers proj)
ax.set_axis_off()
plt.show()
In [2]:
# Can export to excel (csv parsing is tough, maybe use tsv instead)
bio_stats.to_excel('biojobs.xlsx')

att = ['areaName','Employment','Location Quotient','Employment per 1,000 jobs','Annual mean wage']
form = ['',',.0f','.2f','.2f',',.0f']

map_bio = fol_map(geo_bio,'Employment',['lat', 'lon'],att,form)
#map_bio.save('biomap.html')
map_bio
Out[2]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [3]:
# Data science jobs
ds = '152098'
ds_stats = oes_geo(ds)
geo_ds = merge_occgeo(ds_stats,areas)
geo_ds.sort_values(by='Employment',inplace=True,ascending=False)
lim_fields = ['areaName','Employment','Annual mean wage','Annual 75th percentile wage']
geo_ds[lim_fields].head(10)
Out[3]:
areaName Employment Annual mean wage Annual 75th percentile wage
69 New York-Newark-Jersey City, NY-NJ-PA 4730 129250.0 158950.0
92 San Francisco-Oakland-Hayward, CA 3170 148680.0 175410.0
105 Washington-Arlington-Alexandria, DC-VA-MD-WV 2410 108060.0 132720.0
24 Chicago-Naperville-Elgin, IL-IN-WI 2410 101180.0 126170.0
95 Seattle-Tacoma-Bellevue, WA 2330 121090.0 149700.0
93 San Jose-Sunnyvale-Santa Clara, CA 2190 146400.0 178300.0
30 Dallas-Fort Worth-Arlington, TX 2160 103860.0 124100.0
8 Atlanta-Sandy Springs-Roswell, GA 1810 92240.0 118250.0
60 Los Angeles-Long Beach-Anaheim, CA 1640 117550.0 145070.0
78 Phoenix-Mesa-Scottsdale, AZ 1350 109000.0 130500.0
In [4]:
map_ds = fol_map(geo_ds,'Annual 75th percentile wage',['lat', 'lon'],att,form)
map_ds
Out[4]:
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