Vfrdtyky

I have a dataframe with columns: Date of a transaction , Time of the transaction and Price. I want to drop the first and last 5 minutes in each day.

Here is an example:

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Date       | Time         | Price

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03/03/2014 | 09:36:36.814 |  43.90

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03/03/2014 | 09:37:02.381  | 43.40

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03/03/2014 | 09:41:02.381  | 43.40

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03/03/2014 | 09:50:02.381  | 43.40

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I want to get this output:

----------------------------------------

Date       | Time         | Price

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03/03/2014 | 09:50:02.381  | 43.40

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I need to do this for each day of time serie.
I tried this code:

  trades14081.insert(2,'DateTime',pd.to_datetime(trades14081['Date']+trades14081['Time'], format = "%d/%m/%Y%H:%M:%S.%f" ))

delta=datetime.timedelta(minutes=5)

i=0

j=0

start=

end=

while trades14081['Date'][i]==trades14081['Date'][j] and j<len(trades14081):

    if trades14081['DateTime'][j]-trades14081['DateTime'][i]<delta:

        j=j+1

    else:

        start.append(i)

        end.append(j)

        j=j+1

        while trades14081['Date'][i]==trades14081['Date'][j] and j<len(trades14081):

            j=j+1

        i=j

for i in range(len(start)):

    trades14081=trades14081.drop(trades14081.index[start[i]:end[i]])

But I keep on getting this error:

KeyError: 19996

>      12             l.append(j)

>      13             j=j+1

> ---> 14             while trades14081['Date'][i]==trades14081['Date'][j]:

>      15                 j=j+1

>      16             i=j

19996 is the length of my dataframe trades14081.

Any ideas?

groupby + Boolean indexing

You can and should avoid Python-level loops. Here you can use groupby:

# convert strings to timedelta

df['Time'] = pd.to_timedelta(df['Time'])



# define offset from start to omit

offset = pd.Timedelta(minutes=5)



# apply Boolean filter to dataframe

res = df.loc[df['Time'] > df.groupby('Date')['Time'].transform('min') + offset]



print(res)



          Date     Time  Price

4  03/03/2014  09:40:00     41

5  03/03/2014  09:46:00     42