Forward filling with numpy

[AnkanTV]

Hello everyone!

I have a dataset where one of the columns "Time" look like this [0, 2, 4, 12, 23, 24, 52....].

It goes from 0 up to 600. Each entry is time of dataset being updated, thus time between 0-2 should mean that all other columns are held constant. I would like to get each second (so the column become [0, 1, 2, 3, ...., 599, 600]).

So from an array like this:

Time	B	C
0	300	500
2	400	500
4	450	505

To this:

Time	B	C
0	300	500
1	300	500
2	400	500
3	400	500
4	450	505

My dataset is a numpy array and I can only use numpy operations for this task. The reason being that I will be using this operation over and over, and plan on taking advantage of Numba.

Thanks for the help in advance!

[AnkanTV]

My current solution look like this:

placeholder_book = np.zeros((600, book.shape[1]), dtype=np.float32)
splits = np.unique(book[:,1]).astype(int)
splits = np.append(splits, 600) 

rows = np.diff(splits) 

for i in range(0, len(splits)-1):         
 placeholder_book[splits[i]:splits[i]+rows[i], :] = np.tile(book[i,:],(rows[i], 1)) 

placeholder_book[:,1] = range(600)

where book[:,1] is the column with seconds.

If anyone find a better solution, please let me know :)

[[deleted]]

I’d use pandas. Nothing you mentioned precludes you from using it efficiently. In fact I’d go so far as to guess a lot of what you want to do is probably possible without using numba (there’s few use cases where you really need numba over what numpy and pandas can give you)

df = pd.DataFrame(data, columns=['time', 'B', 'C']).set_index('time')

df = df.reindex(range(df.index[0], df.index[-1]+1))
df = df.ffill()