extract windowed data from pandas dataframe efficiently
let say i have a DataFrame with two columns as follow
1. 'a' 0.1
2. 'b' 0.2
3. 'c' 0.3
4. 'd' 0.4
and i want to extract 'windowed data' from it as follows :
(window size : 2)
[['a' 0.1], ['b' 0.2]], [['b' 0.2], ['c' 0.3]], [['c' 0.3], ['d' 0.4]]
currently, im using the simplest way with loop like this :
[df.loc[i - window_size : i, features].values for i in target_data_idx]
since im handling almost 1000k data, this procedure requires huge runtime
is there any better solution for this using parallel ways(like Dask framework)?
python pandas parallel-processing
edited Nov 21 '18 at 14:34
Naga Kiran
2,3971617
asked Nov 21 '18 at 14:01
김동규김동규
313
add a comment |
let say i have a DataFrame with two columns as follow
1. 'a' 0.1
2. 'b' 0.2
3. 'c' 0.3
4. 'd' 0.4
and i want to extract 'windowed data' from it as follows :
(window size : 2)
[['a' 0.1], ['b' 0.2]], [['b' 0.2], ['c' 0.3]], [['c' 0.3], ['d' 0.4]]
currently, im using the simplest way with loop like this :
[df.loc[i - window_size : i, features].values for i in target_data_idx]
since im handling almost 1000k data, this procedure requires huge runtime
is there any better solution for this using parallel ways(like Dask framework)?
python pandas parallel-processing
edited Nov 21 '18 at 14:34
Naga Kiran
2,3971617
asked Nov 21 '18 at 14:01
김동규김동규
313
add a comment |
let say i have a DataFrame with two columns as follow
1. 'a' 0.1
2. 'b' 0.2
3. 'c' 0.3
4. 'd' 0.4
and i want to extract 'windowed data' from it as follows :
(window size : 2)
[['a' 0.1], ['b' 0.2]], [['b' 0.2], ['c' 0.3]], [['c' 0.3], ['d' 0.4]]
currently, im using the simplest way with loop like this :
[df.loc[i - window_size : i, features].values for i in target_data_idx]
since im handling almost 1000k data, this procedure requires huge runtime
is there any better solution for this using parallel ways(like Dask framework)?
python pandas parallel-processing
edited Nov 21 '18 at 14:34
Naga Kiran
2,3971617
asked Nov 21 '18 at 14:01
김동규김동규
313
let say i have a DataFrame with two columns as follow
1. 'a' 0.1
2. 'b' 0.2
3. 'c' 0.3
4. 'd' 0.4
and i want to extract 'windowed data' from it as follows :
(window size : 2)
[['a' 0.1], ['b' 0.2]], [['b' 0.2], ['c' 0.3]], [['c' 0.3], ['d' 0.4]]
currently, im using the simplest way with loop like this :
[df.loc[i - window_size : i, features].values for i in target_data_idx]
since im handling almost 1000k data, this procedure requires huge runtime
is there any better solution for this using parallel ways(like Dask framework)?
python pandas parallel-processing
python pandas parallel-processing
edited Nov 21 '18 at 14:34
Naga Kiran
2,3971617
asked Nov 21 '18 at 14:01
김동규김동규
313
edited Nov 21 '18 at 14:34
Naga Kiran
2,3971617
asked Nov 21 '18 at 14:01
김동규김동규
313
edited Nov 21 '18 at 14:34
Naga Kiran
2,3971617
edited Nov 21 '18 at 14:34
Naga Kiran
2,3971617
edited Nov 21 '18 at 14:34
Naga Kiran
2,3971617
2,3971617
asked Nov 21 '18 at 14:01
김동규김동규
313
asked Nov 21 '18 at 14:01
김동규김동규
313
asked Nov 21 '18 at 14:01
김동규김동규
313
313
add a comment |
add a comment |
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