#!/usr/bin/env python
# coding: utf-8

# # BLS MSA stats
# 
# By Andy Wheeler
# 
# See blog post, [Use circles instead of choropleth for MSAs](https://andrewpwheeler.com/2022/03/13/use-circles-instead-of-choropleth-for-msas/) for overview.
# 
# This scrapes the data from BLS website for particular employment codes.

# In[1]:


# Creating example bls maps
from bls_geo import *

# can check out https://www.bls.gov/oes/current/oes_stru.htm
# or use this function
os_cats = ocodes()
os_cats


# In[2]:


# 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[3]:


# 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


# In[4]:


# Data science jobs
ds = '152051'
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)


# In[ ]: