In [12]:

    

import pandas as pd

from jupyterworkflow.data import get_fremont_data

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


    

In [13]:

    

test_fremont_data()


    

In [15]:

    

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


    

In [22]:

    

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


    

In [23]:

    

data.index


    

    
Out[23]:





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',
               ...
               '2017-02-28 02:00:00', '2017-02-28 03:00:00',
               '2017-02-28 04:00:00', '2017-02-28 05:00:00',
               '2017-02-28 06:00:00', '2017-02-28 07:00:00',
               '2017-02-28 08:00:00', '2017-02-28 09:00:00',
               '2017-02-28 10:00:00', '2017-02-28 11:00:00'],
              dtype='datetime64[ns]', name='Date', length=38640, freq=None)

In [ ]:

    

data.columns = ['West', 'East']
data['Total'] = data.sum(axis = 1)