In [1]:

    

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt


    

In [2]:

    

traveltime = pd.read_csv(r'datasets/ACS_15_5YR_S0801_bymetro/ACS_15_5YR_S0801.csv',
                    header=0,sep=",")
population = pd.read_csv(r'datasets/DEC_10_SF1_GCTPH1.US24PR/census2010populationdensity.csv',\
                        header=0, encoding='latin-1')
modeofcommute = pd.read_csv(r'datasets/ACS_15_5YR_S0801_bymetro/ACS_15_5YR_S0801_with_ann.csv',\
                           header=0)


    

In [3]:

    

merged = traveltime.merge(population, left_on="GEO.display-label", right_on="GCT_STUB.display-label.1")
merged["Mean Travel Time"] = (merged["HC01_EST_VC46"]*7 + merged["HC01_EST_VC47"]*12 + merged["HC01_EST_VC48"]*17\
+ merged["HC01_EST_VC49"]*22 + merged["HC01_EST_VC50"]*27 + merged["HC01_EST_VC51"]*32 + merged["HC01_EST_VC52"]*40 +\
merged["HC01_EST_VC53"]*52 + merged["HC01_EST_VC54"]*60)/100
merged = merged.merge(modeofcommute, left_on="GEO.display-label_x", right_on="GEO.display-label")
merged.head()


    

    
Out[3]:






  

    

      

      
GEO.id_x
      
GEO.id2_x
      
GEO.display-label_x
      
HC01_EST_VC01_x
      
HC01_MOE_VC01_x
      
HC02_EST_VC01_x
      
HC02_MOE_VC01_x
      
HC03_EST_VC01_x
      
HC03_MOE_VC01_x
      
HC01_EST_VC03_x
      
...
      
HC02_EST_VC69_y
      
HC02_MOE_VC69_y
      
HC03_EST_VC69_y
      
HC03_MOE_VC69_y
      
HC01_EST_VC70_y
      
HC01_MOE_VC70_y
      
HC02_EST_VC70_y
      
HC02_MOE_VC70_y
      
HC03_EST_VC70_y
      
HC03_MOE_VC70_y
    
  
  

    

      
0
      
310M200US10180
      
10180
      
Abilene, TX Metro Area
      
72827
      
2659
      
38393
      
2538
      
34434
      
792
      
93.1
      
...
      
(X)
      
(X)
      
(X)
      
(X)
      
0.7
      
(X)
      
(X)
      
(X)
      
(X)
      
(X)
    
    

      
1
      
310M200US10420
      
10420
      
Akron, OH Metro Area
      
332619
      
2222
      
171612
      
1502
      
161007
      
1590
      
92.8
      
...
      
(X)
      
(X)
      
(X)
      
(X)
      
0.7
      
(X)
      
(X)
      
(X)
      
(X)
      
(X)
    
    

      
2
      
310M200US10500
      
10500
      
Albany, GA Metro Area
      
59147
      
1214
      
29018
      
866
      
30129
      
846
      
92.7
      
...
      
(X)
      
(X)
      
(X)
      
(X)
      
1.6
      
(X)
      
(X)
      
(X)
      
(X)
      
(X)
    
    

      
3
      
310M200US10580
      
10580
      
Albany-Schenectady-Troy, NY Metro Area
      
430433
      
2584
      
222574
      
1899
      
207859
      
1778
      
88.0
      
...
      
(X)
      
(X)
      
(X)
      
(X)
      
1.2
      
(X)
      
(X)
      
(X)
      
(X)
      
(X)
    
    

      
4
      
310M200US10740
      
10740
      
Albuquerque, NM Metro Area
      
395799
      
3225
      
207589
      
1894
      
188210
      
2360
      
89.5
      
...
      
(X)
      
(X)
      
(X)
      
(X)
      
1.0
      
(X)
      
(X)
      
(X)
      
(X)
      
(X)
    
  

5 rows × 705 columns

In [4]:

    

g = sns.regplot(x="SUBHD0401", y="Mean Travel Time", data=merged, lowess=True) #, scatter_kws={"s":merged["Car%"]*25000}
g.figure.set_size_inches(8,4)
g.set(xlim=(0, 810)) 
g.set(ylim=(15,31))
g.set(xlabel=("Population per Square Mile"),ylabel=("Average Commute in Minutes"))
g.set(title="Average Commute Time vs Population Density")
plt.savefig("Population vs Commute.pdf")
plt.show()


    

In [ ]: