# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))

# Any results you write to the current directory are saved as output.

train = pd.read_csv('../input/train.csv',
                    dtype={'is_booking':bool,'srch_destination_id':np.int32, 'hotel_cluster':np.int32},
                    usecols=['srch_destination_id','is_booking','hotel_cluster'],
                    chunksize=1000000)
aggs = []
print('-'*38)
for chunk in train:
    agg = chunk.groupby(['srch_destination_id',
                         'hotel_cluster'])['is_booking'].agg(['sum','count'])
    agg.reset_index(inplace=True)
    aggs.append(agg)
    print('.',end='')
print('')
aggs = pd.concat(aggs, axis=0)
aggs.head()