In [1]:

    

from jupyterworkflow.data import get_fremont_data
import pandas as pd

def test_fremont_data():
    data = get_fremont_data()
    assert all(data.columns == ['West', 'East', 'Total'])
    assert isinstance(data.index, pd.DatetimeIndex)


    

In [2]:

    

test_fremont_data()


    

In [4]:

    

data = pd.read_csv('Fremont.csv', index_col='Date')

pd.to_datetime(data.index, format='%m/%d/%Y %H:%M:%S %p')


    

    
Out[4]:





DatetimeIndex(['2012-10-03 12:00:00', '2012-10-03 01:00:00',
               '2012-10-03 02:00:00', '2012-10-03 03:00:00',
               '2012-10-03 04:00:00', '2012-10-03 05:00:00',
               '2012-10-03 06:00:00', '2012-10-03 07:00:00',
               '2012-10-03 08:00:00', '2012-10-03 09:00:00',
               ...
               '2017-08-31 02:00:00', '2017-08-31 03:00:00',
               '2017-08-31 04:00:00', '2017-08-31 05:00:00',
               '2017-08-31 06:00:00', '2017-08-31 07:00:00',
               '2017-08-31 08:00:00', '2017-08-31 09:00:00',
               '2017-08-31 10:00:00', '2017-08-31 11:00:00'],
              dtype='datetime64[ns]', name='Date', length=43056, freq=None)

In [8]:

    

data = pd.read_csv('Fremont.csv', index_col='Date')
pd.to_datetime(data.index, format='%m/%d/%Y %H:%M:%S %p')


    

    
Out[8]:





DatetimeIndex(['2012-10-03 12:00:00', '2012-10-03 01:00:00',
               '2012-10-03 02:00:00', '2012-10-03 03:00:00',
               '2012-10-03 04:00:00', '2012-10-03 05:00:00',
               '2012-10-03 06:00:00', '2012-10-03 07:00:00',
               '2012-10-03 08:00:00', '2012-10-03 09:00:00',
               ...
               '2017-08-31 02:00:00', '2017-08-31 03:00:00',
               '2017-08-31 04:00:00', '2017-08-31 05:00:00',
               '2017-08-31 06:00:00', '2017-08-31 07:00:00',
               '2017-08-31 08:00:00', '2017-08-31 09:00:00',
               '2017-08-31 10:00:00', '2017-08-31 11:00:00'],
              dtype='datetime64[ns]', name='Date', length=43056, freq=None)

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