# Path: twip/constant.py
# DATA_PATH = os.path.abspath(os.path.join(BASE_PATH, '..', 'data'))


from past.builtins import basestring
from twip.constant import DATA_PATH
from IPython.display import display, HTML 
from gensim.models import TfidfModel
from gensim.corpora import Dictionary
import os
import pandas as pd
import string
import matplotlib
import gzip

# coding: utf-8

# In[3]:

from __future__ import division, print_function, absolute_import





# In[4]:

get_ipython().magic(u'matplotlib inline')
np = pd.np
display(HTML("<style>.container { width:100% !important; }</style>"))
pd.set_option('display.max_rows', 6)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 500)


# In[5]:



# Load previously cleaned data

# In[6]:

dates = pd.read_csv(os.path.join(DATA_PATH, 'datetimes.csv.gz'), engine='python')

# Path: twip/constant.py
# DATA_PATH = os.path.abspath(os.path.join(BASE_PATH, '..', 'data'))


# Path: docs/notebooks/08 Features -- TFIDF with Gensim.py
from past.builtins import basestring
from twip.constant import DATA_PATH
from IPython.display import display, HTML 
from gensim.models import TfidfModel
from gensim.corpora import Dictionary
import os
import pandas as pd
import string
import matplotlib
import gzip


# coding: utf-8

# In[3]:

from __future__ import division, print_function, absolute_import





# In[4]:

get_ipython().magic(u'matplotlib inline')
np = pd.np
display(HTML("<style>.container { width:100% !important; }</style>"))
pd.set_option('display.max_rows', 6)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 500)


# In[5]:



# Load previously cleaned data

# In[6]:

dates = pd.read_csv(os.path.join(DATA_PATH, 'datetimes.csv.gz'), engine='python')

nums = pd.read_csv(os.path.join(DATA_PATH, 'numbers.csv.gz'), engine='python')