<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Healthy Communities Data and Indicators Project (HCI)
Healthy Communities Data and Indicators Project (HCI)
Step2:  First, create a set of views to limit the individual indicators to one record per county. The Ambry SQL parser is 
ver simplistic, and can't handle anything mroe then very simple joins.
Step4:  Now we can run a query to join the indicators.
Step6:  Plot the PM2.5 Concentration, a measure of particulate air polution.
<ASSISTANT_TASK:>
Python Code:
from ambry import get_library
l = get_library()
b = l.bundle('cdph.ca.gov-hci-0.0.2')
Explanation: Healthy Communities Data and Indicators Project (HCI)
Healthy Communities Data and Indicators Project (HCI)
End of explanation
w = b.warehouse('hci_counties')
w.clean()
print w.dsn
w.query(
-- Get only counties in California
CREATE VIEW geo AS SELECT gvid, name AS county_name,  geometry FROM census.gov-tiger-2015-counties
WHERE statefp = 6;
-- Get only records for all race/ethinicities
CREATE VIEW hf_total AS SELECT gvid, mrfei FROM cdph.ca.gov-hci-healthy_food-county
WHERE race_eth_name = 'Total';
-- Get only records for all race/ethinicities
CREATE VIEW aq_total AS SELECT gvid, pm25_concentration FROM cdph.ca.gov-hci-air_quality-county
WHERE race_eth_name = 'Total';
-- THe overty table has a lot of otrher categories, for report year and type of poverty
CREATE VIEW pr_total AS SELECT gvid, percent FROM  cdph.ca.gov-hci-poverty_rate-county
WHERE race_eth_name = 'Total' AND reportyear='2008-2010' AND poverty='Overall';
).close()
Explanation: First, create a set of views to limit the individual indicators to one record per county. The Ambry SQL parser is 
ver simplistic, and can't handle anything mroe then very simple joins.
End of explanation
sql=
SELECT county_name, mrfei, pm25_concentration, percent as percent_poverty FROM geo as counties
JOIN hf_total ON hf_total.gvid = counties.gvid
JOIN aq_total ON aq_total.gvid = counties.gvid
JOIN pr_total ON pr_total.gvid = counties.gvid;
df = w.dataframe(sql)
df.head()
df.corr()
Explanation: Now we can run a query to join the indicators.
End of explanation
%matplotlib inline
sql=
SELECT county_name, mrfei, pm25_concentration, percent as percent_poverty, geometry FROM geo as counties
LEFT JOIN hf_total ON hf_total.gvid = counties.gvid
LEFT JOIN aq_total ON aq_total.gvid = counties.gvid
LEFT JOIN pr_total ON pr_total.gvid = counties.gvid;
w.geoframe(sql).plot(column='pm25_concentration')
Explanation: Plot the PM2.5 Concentration, a measure of particulate air polution.
End of explanation
<END_TASK>