#Add demographic Data
SPDem = pd.read_csv('MSP Neighborhoods_2013-2017.csv', skiprows=[1])
SPDem.columns = SPDem.columns.str.replace(' ', '')
SPDem['City'].value_counts().to_frame()
SPDem= SPDem.query('geography in ["Summit Hill","Hamline-Midway","Frogtown/Thomas-Dale","Union Park"]')
for i,square in enumerate(SPDem.columns.values):
print(str(i) + ' '+ square)
SPDem = SPDem.iloc[:,[1,2,27,39,43,47,63,71,107,123,127,131,135,159,215,243,267,271,275,279,363,371,375,379,383,387,391,395,399,403,471,475,479,495,511,515,519,523]]
col_names= ['TotHH','Neigh','Unemploy','<HighSDip','HighSDip','AssDeg','BacDeg+','Foreign%','Child<18','Inc<35k','Inc_35to50','Inc_50to75','Inc75to100','Age65+','Renter','LangNotEng','Age18_24','Age25_34','Age35_44','Age45_54','Male','Race_Black','Race_Native','Race_Asian','Race_Other','Race_Two+','Race_His','Race_White','Race_POC','Poverty','Poverty_At','Poverty_100to150','Poverty_150to200','CarOwn','Commute<10','Commute_10to19','Commute_20-29','Commute_30+']
SPDem.columns= col_names