For this kind of regression, I usually convert the dates or timestamps to an integer number of days since the start of the data.

This does the trick nicely:

df = pd.read_csv('test.csv')
df['date'] = pd.to_datetime(df['date'])    
df['date_delta'] = (df['date'] - df['date'].min())  / np.timedelta64(1,'D')
city_data = df[df['city'] == 'London']
result = sm.ols(formula = 'sales ~ date_delta', data = city_data).fit()

The advantage of this method is that you're sure of the units involved in the regression (days), whereas an automatic conversion may implicitly use other units, creating confusing coefficients in your linear model. It also allows you to combine data from multiple sales campaigns that started at different times into your regression (say you're interested in effectiveness of a campaign as a function of days into the campaign). You could also pick Jan 1st as your 0 if you're interested in measuring the day of year trend. Picking your own 0 date puts you in control of all that.

There's also evidence that statsmodels supports timeseries from pandas. You may be able to apply this to linear models as well: http://statsmodels.sourceforge.net/stable/examples/generated/ex_dates.html

Also, a quick note: You should be able to read column names directly out of the csv automatically as in the sample code I posted. In your example I see there are spaces between the commas in the first line of the csv file, resulting in column names like ' date'. Remove the spaces and automatic csv header reading should just work.

You can use time deltas to do this more directly:

In [11]: s = pd.Series(["00:10:30"])

In [12]: s = pd.to_timedelta(s)

In [13]: s
Out[13]:
0   00:10:30
dtype: timedelta64[ns]

In [14]: s / pd.offsets.Minute(1)
Out[14]:
0    10.5
dtype: float64

One way to do this would be to use Series.dt.seconds and ´Series.dt.days´ and multiply with a factor for the desired unit:

(Series.dt.seconds/3600) + (Series.dt.days*24)  # for values with [hours]

Convert to Timedelta and extract the total seconds from dt.total_seconds:

df

        date
0 2013-01-01
1 2013-01-02
2 2013-01-03
3 2013-01-04
4 2013-01-05
5 2013-01-06
6 2013-01-07
7 2013-01-08
8 2013-01-09
9 2013-01-10

pd.to_timedelta(df.date).dt.total_seconds()

0    1.356998e+09
1    1.357085e+09
2    1.357171e+09
3    1.357258e+09
4    1.357344e+09
5    1.357430e+09
6    1.357517e+09
7    1.357603e+09
8    1.357690e+09
9    1.357776e+09
Name: date, dtype: float64

Or, maybe, the data would be more useful presented as an int type:

pd.to_timedelta(df.date).dt.total_seconds().astype(int)

0    1356998400
1    1357084800
2    1357171200
3    1357257600
4    1357344000
5    1357430400
6    1357516800
7    1357603200
8    1357689600
9    1357776000
Name: date, dtype: int64

You can use:

df['TradeDate'] = pd.to_datetime(df['TradeDate'], format='%Y%m%d.0')
print (df)
   TradeDate
0 2010-03-29
1 2010-03-28
2 2010-03-29

But if some bad values, add errors='coerce' for replace them to NaT

print (df)
    TradeDate
0  20100329.0
1  20100328.0
2  20100329.0
3  20153030.0
4         yyy

df['TradeDate'] = pd.to_datetime(df['TradeDate'], format='%Y%m%d.0', errors='coerce')
print (df)
   TradeDate
0 2010-03-29
1 2010-03-28
2 2010-03-29
3        NaT
4        NaT

If your time column is already datetime then you can access the time components using .dt accessor:

In [18]:
df['Time'].dt.hour + df['Time'].dt.minute/60

Out[18]:
0    16.533333
1    17.216667
2    18.166667
Name: Time, dtype: float64

if required then you can convert doing df['Time'] = pd.to_datetime(df['Time'])

When you read the excel file specify the dtype of col itime as a str:

df = pd.read_excel("test.xlsx", dtype={'itime':str})

then you will have a time column of strings looking like:

df = pd.DataFrame({'itime':['2300', '0100', '0500', '1000']})

Then specify the format and convert to time:

df['Time'] = pd.to_datetime(df['itime'], format='%H%M').dt.time

    itime   Time
0   2300    23:00:00
1   0100    01:00:00
2   0500    05:00:00
3   1000    10:00:00