Count number of registers in interval & location Recently I asked how one could count the number of registers by the interval as answered in https://stackoverflow.com/questions/49240140/count-number-of-registers-in-interval.

The solution works great, but I had to adapt it to also take into account some localization key.

I did it through the following code:

def time_features(df, time_key, T, location_key, output_key):
    """
    Create features based on time such as: how many BDs are open in the same GRA at this moment (hour)?
    """ 
    from datetime import date
    assert np.issubdtype(df[time_key], np.datetime64)
    output = pd.DataFrame()

    grouped = df.groupby(location_key)
    for name, group in grouped:
        # initialize times registers open as 1, close as -1
        start_times = group.copy()
        start_times[time_key] = group[time_key]-pd.Timedelta(hours=T)
        start_times[output_key] =  1

        aux = group.copy()
        all_times = start_times.copy()
        aux[output_key] = -1  
        all_times = all_times.append(aux, ignore_index=True)

        # sort by time and perform a cumulative sum to get opened registers
        # (subtract 1 since you don't want to include the current time as opened)
        all_times = all_times.sort_values(by=time_key)
        all_times[output_key] = all_times[output_key].cumsum() - 1

        # revert the index back to original order, and truncate closed times
        all_times = all_times.sort_index().iloc[:len(all_times)//2]
        output = output.append(all_times, ignore_index=True)
    return output


Output: 

time    loc1    loc2
0   2013-01-01 12:56:00 1   "a"
1   2013-01-01 12:00:12 1   "b"
2   2013-01-01 10:34:28 2   "c"
3   2013-01-01 09:34:54 2   "c"
4   2013-01-01 08:34:55 3   "d"
5   2013-01-01 08:34:55 5   "d"
6   2013-01-01 16:35:19 4   "e"
7   2013-01-01 16:35:30 4   "e"

time_features(df, time_key='time', T=2, location_key='loc1', output_key='count')


This works great for small data, but for longer data (I using it with a file with 1 million rows) it takes "forever" to run. I wonder if I could optimize this computation somehow.
Consider not expanding a dataframe inside the for loop but build a list or dictionary and then concatenate all dataframe elements outside loop. 

Expanding objects within a loop causes substantial memory resources to allocate before and after blocks with lots of copying of objects to do so. Running one call on outside should substantially run faster as virtually no copying is done.

Specifically change:

output = pd.DataFrame()


To a list:

output = []


And then append to list inside loop and then pd.concat(list) outside loop.

def time_features(df, time_key, T, location_key, output_key):
    """
    Create features based on time such as: how many BDs are open in the same GRA at this moment (hour)?
    """ 
    from datetime import date
    assert np.issubdtype(df[time_key], np.datetime64)
    output = []

    grouped = df.groupby(location_key)
    for name, group in grouped:
        # initialize times registers open as 1, close as -1
        start_times = group.copy()
        start_times[time_key] = group[time_key]-pd.Timedelta(hours=T)
        start_times[output_key] =  1

        aux = group.copy()
        all_times = start_times.copy()
        aux[output_key] = -1  
        all_times = all_times.append(aux, ignore_index=True)

        # sort by time and perform a cumulative sum to get opened registers
        # (subtract 1 since you don't want to include the current time as opened)
        all_times = all_times.sort_values(by=time_key)
        all_times[output_key] = all_times[output_key].cumsum() - 1

        # revert the index back to original order, and truncate closed times
        all_times = all_times.sort_index().iloc[:len(all_times)//2]
        # APPEND TO LIST
        output.append(all_times)

    # CONCATENATE ALL DF ELEMENTS
    final_df = pd.concat(output, ignore_index=True)

    return final_df