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In [181]:



    


import pandas as pd
from sklearn.cluster import KMeans

%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (10, 10)



    











In [3]:



    


#Load data
county_facts = pd.read_csv('../kaggle-data/county_facts.csv', index_col=['fips', 'area_name'])
county_facts_columns = pd.read_csv('../kaggle-data/county_facts_dictionary.csv')
primary_results = pd.read_csv('../kaggle-data/primary_results.csv')



    











In [4]:



    


#Take a look at the columns
county_facts_columns



    


















    
Out[4]:










  
    

      
      column_name
      description
    

  
  
    

      0
      PST045214
      Population, 2014 estimate
    

    

      1
      PST040210
      Population, 2010 (April 1) estimates base
    

    

      2
      PST120214
      Population, percent change - April 1, 2010 to ...
    

    

      3
      POP010210
      Population, 2010
    

    

      4
      AGE135214
      Persons under 5 years, percent, 2014
    

    

      5
      AGE295214
      Persons under 18 years, percent, 2014
    

    

      6
      AGE775214
      Persons 65 years and over, percent, 2014
    

    

      7
      SEX255214
      Female persons, percent, 2014
    

    

      8
      RHI125214
      White alone, percent, 2014
    

    

      9
      RHI225214
      Black or African American alone, percent, 2014
    

    

      10
      RHI325214
      American Indian and Alaska Native alone, perce...
    

    

      11
      RHI425214
      Asian alone, percent, 2014
    

    

      12
      RHI525214
      Native Hawaiian and Other Pacific Islander alo...
    

    

      13
      RHI625214
      Two or More Races, percent, 2014
    

    

      14
      RHI725214
      Hispanic or Latino, percent, 2014
    

    

      15
      RHI825214
      White alone, not Hispanic or Latino, percent, ...
    

    

      16
      POP715213
      Living in same house 1 year & over, percent, 2...
    

    

      17
      POP645213
      Foreign born persons, percent, 2009-2013
    

    

      18
      POP815213
      Language other than English spoken at home, pc...
    

    

      19
      EDU635213
      High school graduate or higher, percent of per...
    

    

      20
      EDU685213
      Bachelor's degree or higher, percent of person...
    

    

      21
      VET605213
      Veterans, 2009-2013
    

    

      22
      LFE305213
      Mean travel time to work (minutes), workers ag...
    

    

      23
      HSG010214
      Housing units, 2014
    

    

      24
      HSG445213
      Homeownership rate, 2009-2013
    

    

      25
      HSG096213
      Housing units in multi-unit structures, percen...
    

    

      26
      HSG495213
      Median value of owner-occupied housing units, ...
    

    

      27
      HSD410213
      Households, 2009-2013
    

    

      28
      HSD310213
      Persons per household, 2009-2013
    

    

      29
      INC910213
      Per capita money income in past 12 months (201...
    

    

      30
      INC110213
      Median household income, 2009-2013
    

    

      31
      PVY020213
      Persons below poverty level, percent, 2009-2013
    

    

      32
      BZA010213
      Private nonfarm establishments, 2013
    

    

      33
      BZA110213
      Private nonfarm employment,  2013
    

    

      34
      BZA115213
      Private nonfarm employment, percent change, 20...
    

    

      35
      NES010213
      Nonemployer establishments, 2013
    

    

      36
      SBO001207
      Total number of firms, 2007
    

    

      37
      SBO315207
      Black-owned firms, percent, 2007
    

    

      38
      SBO115207
      American Indian- and Alaska Native-owned firms...
    

    

      39
      SBO215207
      Asian-owned firms, percent, 2007
    

    

      40
      SBO515207
      Native Hawaiian- and Other Pacific Islander-ow...
    

    

      41
      SBO415207
      Hispanic-owned firms, percent, 2007
    

    

      42
      SBO015207
      Women-owned firms, percent, 2007
    

    

      43
      MAN450207
      Manufacturers shipments, 2007 ($1,000)
    

    

      44
      WTN220207
      Merchant wholesaler sales, 2007 ($1,000)
    

    

      45
      RTN130207
      Retail sales, 2007 ($1,000)
    

    

      46
      RTN131207
      Retail sales per capita, 2007
    

    

      47
      AFN120207
      Accommodation and food services sales, 2007 ($...
    

    

      48
      BPS030214
      Building permits, 2014
    

    

      49
      LND110210
      Land area in square miles, 2010
    

    

      50
      POP060210
      Population per square mile, 2010
    

  




















In [199]:



    


#Subselect rows, let's just keep state level data
#df = county_facts[county_facts.state_abbreviation.isnull()][1:]
#Subselect at the county level
df = county_facts[county_facts.state_abbreviation.notnull()]
df.head()



    


















    
Out[199]:










  
    

      
      
      state_abbreviation
      PST045214
      PST040210
      PST120214
      POP010210
      AGE135214
      AGE295214
      AGE775214
      SEX255214
      RHI125214
      ...
      SBO415207
      SBO015207
      MAN450207
      WTN220207
      RTN130207
      RTN131207
      AFN120207
      BPS030214
      LND110210
      POP060210
    

    

      fips
      area_name
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      1001
      Autauga County
      AL
      55395
      54571
      1.5
      54571
      6.0
      25.2
      13.8
      51.4
      77.9
      ...
      0.7
      31.7
      0
      0
      598175
      12003
      88157
      131
      594.44
      91.8
    

    

      1003
      Baldwin County
      AL
      200111
      182265
      9.8
      182265
      5.6
      22.2
      18.7
      51.2
      87.1
      ...
      1.3
      27.3
      1410273
      0
      2966489
      17166
      436955
      1384
      1589.78
      114.6
    

    

      1005
      Barbour County
      AL
      26887
      27457
      -2.1
      27457
      5.7
      21.2
      16.5
      46.6
      50.2
      ...
      0.0
      27.0
      0
      0
      188337
      6334
      0
      8
      884.88
      31.0
    

    

      1007
      Bibb County
      AL
      22506
      22919
      -1.8
      22915
      5.3
      21.0
      14.8
      45.9
      76.3
      ...
      0.0
      0.0
      0
      0
      124707
      5804
      10757
      19
      622.58
      36.8
    

    

      1009
      Blount County
      AL
      57719
      57322
      0.7
      57322
      6.1
      23.6
      17.0
      50.5
      96.0
      ...
      0.0
      23.2
      341544
      0
      319700
      5622
      20941
      3
      644.78
      88.9
    

  



5 rows × 52 columns



















In [200]:



    


#Select 2 columns (this is for visualization purposes)
#df = df.filter(['EDU685213', 'SEX255214'])
df = df.filter(['SBO315207', 'SBO015207'])



    











In [201]:



    


#Rename columns to use their meaning instead their codename
col_names = dict((k, v) for k,v in county_facts_columns.itertuples(index=False, name=None))
df.rename(columns=col_names, inplace=True)
df.head()



    


















    
Out[201]:










  
    

      
      
      Black-owned firms, percent, 2007
      Women-owned firms, percent, 2007
    

    

      fips
      area_name
      
      
    

  
  
    

      1001
      Autauga County
      15.2
      31.7
    

    

      1003
      Baldwin County
      2.7
      27.3
    

    

      1005
      Barbour County
      0.0
      27.0
    

    

      1007
      Bibb County
      14.9
      0.0
    

    

      1009
      Blount County
      0.0
      23.2
    

  




















In [202]:



    


df.plot.scatter(0, 1, s=100)



    


















    
Out[202]:








<matplotlib.axes._subplots.AxesSubplot at 0x1166392d0>










    
























In [204]:



    


#Run a clustering algorithm, group in 4 clusters
model = KMeans(n_clusters=4)
results = model.fit_predict(df.values)



    











In [205]:



    


#Assign cluster number to our dataframe
df['cluster'] = results
#Add a color for each cluster
df['color'] = df.cluster.map({0: 'black', 1:'red', 2:'green', 3:'yellow'})



    











In [206]:



    


df.head()



    


















    
Out[206]:










  
    

      
      
      Black-owned firms, percent, 2007
      Women-owned firms, percent, 2007
      cluster
      color
    

    

      fips
      area_name
      
      
      
      
    

  
  
    

      1001
      Autauga County
      15.2
      31.7
      0
      black
    

    

      1003
      Baldwin County
      2.7
      27.3
      1
      red
    

    

      1005
      Barbour County
      0.0
      27.0
      1
      red
    

    

      1007
      Bibb County
      14.9
      0.0
      2
      green
    

    

      1009
      Blount County
      0.0
      23.2
      3
      yellow
    

  




















In [207]:



    


df.plot.scatter(x=0, y=1, s=100, c=df.color)



    


















    
Out[207]:








<matplotlib.axes._subplots.AxesSubplot at 0x11fe351d0>

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