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


from past.builtins import basestring
from collections import Counter
from matplotlib import pyplot as plt 
from twip.constant import DATA_PATH
from IPython.display import display, HTML 
import os
import pandas as pd
import matplotlib
import gzip
import gzip
df.drop_duplicates('id_str', keep='last', inplace=True)
rawlen - len(df)


# In[ ]:




# In[ ]:

mask = np.array([bool('date' in c) for c in df.columns])
df.columns[mask]


# In[ ]:

df.columns[np.array([bool('time' in c) for c in df.columns])]


# Remember any date or time columns from Dan's tutorial?

# In[8]:

mask = np.array([c.endswith('_at') for c in df.columns])
df.columns[mask]


# In[9]:


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


# Path: docs/notebooks/05 Features -- Time.py
from past.builtins import basestring
from collections import Counter
from matplotlib import pyplot as plt 
from twip.constant import DATA_PATH
from IPython.display import display, HTML 
import os
import pandas as pd
import matplotlib
import gzip
import gzip

df.drop_duplicates('id_str', keep='last', inplace=True)
rawlen - len(df)


# In[ ]:




# In[ ]:

mask = np.array([bool('date' in c) for c in df.columns])
df.columns[mask]


# In[ ]:

df.columns[np.array([bool('time' in c) for c in df.columns])]


# Remember any date or time columns from Dan's tutorial?

# In[8]:

mask = np.array([c.endswith('_at') for c in df.columns])
df.columns[mask]


# In[9]:


dates = df[df.columns[mask]]