In [1]:
import numpy as np, pandas as pd, os

In [2]:
hh = pd.read_csv('c://users//janowicz//desktop//drcog_new_households.csv')
persons = pd.read_csv('c://users//janowicz//desktop//drcog_new_persons.csv')

In [4]:
hh = hh.set_index('household_id')
persons = persons.set_index('person_id')

In [9]:
hh.describe()


Out[9]:
age_of_head cars children income persons tenure workers serialno taz building_id
count 1160869.000000 1160869.000000 1160869.000000 1160869.000000 1160869.000000 1160869.000000 1160869.000000 1.160869e+06 1160869.00000 1160869.000000
mean 46.663054 1.809641 0.758610 82869.044468 2.450459 1.334716 1.329384 2.009001e+12 377636.07443 473059.299294
std 15.610260 1.071603 0.837915 81489.992173 1.460441 0.471891 1.007229 1.426441e+09 170954.09554 294924.760137
min 15.000000 0.000000 0.000000 -17254.156200 1.000000 1.000000 0.000000 2.007000e+12 101020.00000 1.000000
25% 35.000000 1.000000 0.000000 31720.496880 1.000000 1.000000 1.000000 2.008000e+12 211180.00000 191967.000000
50% 46.000000 2.000000 1.000000 61603.338500 2.000000 1.000000 1.000000 2.009001e+12 405010.00000 466724.000000
75% 56.000000 2.000000 1.000000 107322.179800 3.000000 2.000000 2.000000 2.010001e+12 506240.00000 711175.000000
max 93.000000 6.000000 11.000000 1124531.898000 15.000000 2.000000 8.000000 2.011002e+12 803120.00000 1015771.000000

In [8]:
store = pd.HDFStore('c:\\urbansim\\data\\drcog.h5')

In [10]:
store['households_previous'] = store['households']

In [11]:
store['households'] = hh
store['persons'] = persons

In [12]:
store.close()