# 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.

clients = pd.read_csv('../input/cliente_tabla.csv',nrows=5000)
products = pd.read_csv('../input/producto_tabla.csv',nrows=5000)
sample = pd.read_csv('../input/sample_submission.csv',nrows=5000)
test = pd.read_csv('../input/test.csv',nrows=5000)
location = pd.read_csv('../input/town_state.csv',nrows=5000)
train  = pd.read_csv('../input/train.csv',nrows=5000)

np_products = np.array(products)

name = np_products[:,1]
name.tolist()

print(name)

name[0].split(" ")