import pandas as pd
pj = '/Users/danielmsheehan/Desktop/data/'
pj = '/Users/danielmsheehan/Dropbox/data/'
df = pd.read_csv(pj+'output/all/taxi_2013.csv', dtype={'geoid':object})
df['count'] = 1
dfg = df.groupby('geoid').sum()
dfg.to_csv(pj+'output/all/taxi_2013_groupby_geoid_temp.csv')
import pandas as pd
pj = '/Users/danielmsheehan/Desktop/data/'
pj = '/Users/danielmsheehan/Dropbox/data/'
pj = '/Volumes/Hotel/Dropbox/data/'
df = pd.read_csv(pj+'output/all/taxi_2013_groupby_geoid_temp.csv', dtype={'geoid':object})
df['avg_dist_roadbed'] = df['dist_roadbed'] / df['count']
df = df[['geoid','avg_dist_roadbed','count']]
df.to_csv(pj+'output/all/taxi_2013_geoid_avg_dist.csv', index=False)
df
geoid | avg_dist_roadbed | count | |
---|---|---|---|
0 | 360050001001000 | 25.326407 | 24 |
1 | 360050001001001 | 10.642221 | 3 |
2 | 360050001001002 | 38.103541 | 93 |
3 | 360050001001003 | 25.087721 | 80 |
4 | 360050001001004 | 26.286546 | 1 |
5 | 360050001001006 | 32.498545 | 111 |
6 | 360050001001007 | 28.543215 | 15 |
7 | 360050001001008 | 32.507626 | 5 |
8 | 360050001001009 | 21.961709 | 9 |
9 | 360050001001010 | 59.102415 | 5 |
10 | 360050001001011 | 52.472313 | 12 |
11 | 360050001001012 | 22.333181 | 46 |
12 | 360050001001013 | 20.817396 | 11 |
13 | 360050001001014 | 24.936051 | 10 |
14 | 360050001001015 | 23.299182 | 67 |
15 | 360050001001016 | 52.595001 | 4 |
16 | 360050001001018 | 24.304408 | 20 |
17 | 360050001001019 | 36.761402 | 11 |
18 | 360050001001020 | 0.000000 | 1 |
19 | 360050001001022 | 0.000000 | 2 |
20 | 360050001001023 | 17.231906 | 3 |
21 | 360050001001024 | 8.517224 | 2 |
22 | 360050002001000 | 8.337034 | 38 |
23 | 360050002001001 | 7.982848 | 57 |
24 | 360050002001002 | 6.525385 | 47 |
25 | 360050002001003 | 8.180328 | 40 |
26 | 360050002001004 | 7.141355 | 36 |
27 | 360050002001005 | 2.991294 | 9 |
28 | 360050002002000 | 10.914435 | 42 |
29 | 360050002002001 | 9.535214 | 96 |
... | ... | ... | ... |
37324 | 360850319023000 | 23.332381 | 22 |
37325 | 360850319023001 | 34.424242 | 1 |
37326 | 360850319023002 | 0.410695 | 7 |
37327 | 360850319023003 | 10.263497 | 5 |
37328 | 360850319023004 | 6.255302 | 2 |
37329 | 360850319023005 | 0.000000 | 2 |
37330 | 360850319023006 | 3.730935 | 3 |
37331 | 360850319023007 | 0.000000 | 2 |
37332 | 360850319023008 | 23.744708 | 1 |
37333 | 360850319023009 | 6.222475 | 4 |
37334 | 360850319023010 | 0.000000 | 1 |
37335 | 360850319023012 | 2.181627 | 13 |
37336 | 360850323001000 | 201.471068 | 2 |
37337 | 360850323001001 | 128.727339 | 14 |
37338 | 360850323001002 | 194.489426 | 8 |
37339 | 360850323001003 | 284.506587 | 1 |
37340 | 360850323001005 | 6.254239 | 2 |
37341 | 360850323001006 | 7.261832 | 2 |
37342 | 360850323001007 | 0.000000 | 3 |
37343 | 360850323001008 | 31.932629 | 4 |
37344 | 360850323001009 | 2.677777 | 19 |
37345 | 360850323001010 | 5.298159 | 78 |
37346 | 360850323001011 | 0.486334 | 8 |
37347 | 360850323001012 | 57.396573 | 16 |
37348 | 360850323001014 | 0.000000 | 2 |
37349 | 360850323001016 | 0.000000 | 3 |
37350 | 360850323001019 | 51.921060 | 15 |
37351 | 360850323001023 | 0.000000 | 1 |
37352 | 360850323001024 | 27.442990 | 4 |
37353 | 360850323001025 | 0.000000 | 1 |
37354 rows × 3 columns