Backfill only last N items
2
I have this simple time series
In [1]: df = pd.DataFrame({'fire': [1, 1, 1]},
...: index=pd.to_datetime([
...: '2016-03-16 23:20:10',
...: '2016-03-16 23:28:58',
...: '2016-03-16 23:38:15']))
...:
In [2]: df
Out[2]:
fire
2016-03-16 23:20:10 1
2016-03-16 23:28:58 1
2016-03-16 23:41:15 1
I want to downsample it by 1 minute and add another column named fire_in_the_next_5_minutes. The resampling is done easily but I could not find a way to limit the backfilling to only 5 previous rows. The closest data I get is this:
In [3]: df = df.resample('1min').mean()
...: df['fire_in_the_next_5_minutes'] = df['fire'].fillna(method='backfill')
...:
In [4]: df
Out[4]:
fire fire_in_the_next_5_minutes
2016-03-16 23:20:00 1.0 1.0
2016-03-16 23:21:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:22:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:23:00 NaN 1.0
2016-03-16 23:24:00 NaN 1.0
2016-03-16 23:25:00 NaN 1.0
2016-03-16 23:26:00 NaN 1.0
2016-03-16 23:27:00 NaN 1.0
2016-03-16 23:28:00 1.0 1.0
2016-03-16 23:29:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:30:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:31:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:32:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:33:00 NaN 1.0
2016-03-16 23:34:00 NaN 1.0
2016-03-16 23:35:00 NaN 1.0
2016-03-16 23:36:00 NaN 1.0
2016-03-16 23:37:00 NaN 1.0
2016-03-16 23:38:00 1.0 1.0
Can I backfill another way, not by using fillna method?
pandas
asked Nov 23 '18 at 14:24
tsionyxtsionyx
1,12511326
add a comment |
2
I have this simple time series
In [1]: df = pd.DataFrame({'fire': [1, 1, 1]},
...: index=pd.to_datetime([
...: '2016-03-16 23:20:10',
...: '2016-03-16 23:28:58',
...: '2016-03-16 23:38:15']))
...:
In [2]: df
Out[2]:
fire
2016-03-16 23:20:10 1
2016-03-16 23:28:58 1
2016-03-16 23:41:15 1
I want to downsample it by 1 minute and add another column named fire_in_the_next_5_minutes. The resampling is done easily but I could not find a way to limit the backfilling to only 5 previous rows. The closest data I get is this:
In [3]: df = df.resample('1min').mean()
...: df['fire_in_the_next_5_minutes'] = df['fire'].fillna(method='backfill')
...:
In [4]: df
Out[4]:
fire fire_in_the_next_5_minutes
2016-03-16 23:20:00 1.0 1.0
2016-03-16 23:21:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:22:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:23:00 NaN 1.0
2016-03-16 23:24:00 NaN 1.0
2016-03-16 23:25:00 NaN 1.0
2016-03-16 23:26:00 NaN 1.0
2016-03-16 23:27:00 NaN 1.0
2016-03-16 23:28:00 1.0 1.0
2016-03-16 23:29:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:30:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:31:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:32:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:33:00 NaN 1.0
2016-03-16 23:34:00 NaN 1.0
2016-03-16 23:35:00 NaN 1.0
2016-03-16 23:36:00 NaN 1.0
2016-03-16 23:37:00 NaN 1.0
2016-03-16 23:38:00 1.0 1.0
Can I backfill another way, not by using fillna method?
pandas
asked Nov 23 '18 at 14:24
tsionyxtsionyx
1,12511326
add a comment |
2
2
2
I have this simple time series
In [1]: df = pd.DataFrame({'fire': [1, 1, 1]},
...: index=pd.to_datetime([
...: '2016-03-16 23:20:10',
...: '2016-03-16 23:28:58',
...: '2016-03-16 23:38:15']))
...:
In [2]: df
Out[2]:
fire
2016-03-16 23:20:10 1
2016-03-16 23:28:58 1
2016-03-16 23:41:15 1
I want to downsample it by 1 minute and add another column named fire_in_the_next_5_minutes. The resampling is done easily but I could not find a way to limit the backfilling to only 5 previous rows. The closest data I get is this:
In [3]: df = df.resample('1min').mean()
...: df['fire_in_the_next_5_minutes'] = df['fire'].fillna(method='backfill')
...:
In [4]: df
Out[4]:
fire fire_in_the_next_5_minutes
2016-03-16 23:20:00 1.0 1.0
2016-03-16 23:21:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:22:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:23:00 NaN 1.0
2016-03-16 23:24:00 NaN 1.0
2016-03-16 23:25:00 NaN 1.0
2016-03-16 23:26:00 NaN 1.0
2016-03-16 23:27:00 NaN 1.0
2016-03-16 23:28:00 1.0 1.0
2016-03-16 23:29:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:30:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:31:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:32:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:33:00 NaN 1.0
2016-03-16 23:34:00 NaN 1.0
2016-03-16 23:35:00 NaN 1.0
2016-03-16 23:36:00 NaN 1.0
2016-03-16 23:37:00 NaN 1.0
2016-03-16 23:38:00 1.0 1.0
Can I backfill another way, not by using fillna method?
pandas
asked Nov 23 '18 at 14:24
tsionyxtsionyx
1,12511326
I have this simple time series
In [1]: df = pd.DataFrame({'fire': [1, 1, 1]},
...: index=pd.to_datetime([
...: '2016-03-16 23:20:10',
...: '2016-03-16 23:28:58',
...: '2016-03-16 23:38:15']))
...:
In [2]: df
Out[2]:
fire
2016-03-16 23:20:10 1
2016-03-16 23:28:58 1
2016-03-16 23:41:15 1
I want to downsample it by 1 minute and add another column named fire_in_the_next_5_minutes. The resampling is done easily but I could not find a way to limit the backfilling to only 5 previous rows. The closest data I get is this:
In [3]: df = df.resample('1min').mean()
...: df['fire_in_the_next_5_minutes'] = df['fire'].fillna(method='backfill')
...:
In [4]: df
Out[4]:
fire fire_in_the_next_5_minutes
2016-03-16 23:20:00 1.0 1.0
2016-03-16 23:21:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:22:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:23:00 NaN 1.0
2016-03-16 23:24:00 NaN 1.0
2016-03-16 23:25:00 NaN 1.0
2016-03-16 23:26:00 NaN 1.0
2016-03-16 23:27:00 NaN 1.0
2016-03-16 23:28:00 1.0 1.0
2016-03-16 23:29:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:30:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:31:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:32:00 NaN 1.0 <-- should remain NaN
2016-03-16 23:33:00 NaN 1.0
2016-03-16 23:34:00 NaN 1.0
2016-03-16 23:35:00 NaN 1.0
2016-03-16 23:36:00 NaN 1.0
2016-03-16 23:37:00 NaN 1.0
2016-03-16 23:38:00 1.0 1.0
Can I backfill another way, not by using fillna method?
pandas
pandas
asked Nov 23 '18 at 14:24
tsionyxtsionyx
1,12511326
asked Nov 23 '18 at 14:24
tsionyxtsionyx
1,12511326
asked Nov 23 '18 at 14:24
tsionyxtsionyx
1,12511326
asked Nov 23 '18 at 14:24
tsionyxtsionyx
1,12511326
asked Nov 23 '18 at 14:24
tsionyxtsionyx
1,12511326
1,12511326
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
2
Using bfill with limit
df = df.resample('1min').mean()
df['fire_in_the_next_5_minutes'] = df['fire'].bfill(limit=5)
df
Out[173]:
fire fire_in_the_next_5_minutes
2016-03-16 23:20:00 1.0 1.0
2016-03-16 23:21:00 NaN NaN
2016-03-16 23:22:00 NaN NaN
2016-03-16 23:23:00 NaN 1.0
2016-03-16 23:24:00 NaN 1.0
2016-03-16 23:25:00 NaN 1.0
2016-03-16 23:26:00 NaN 1.0
2016-03-16 23:27:00 NaN 1.0
2016-03-16 23:28:00 1.0 1.0
2016-03-16 23:29:00 NaN NaN
2016-03-16 23:30:00 NaN NaN
2016-03-16 23:31:00 NaN NaN
2016-03-16 23:32:00 NaN NaN
2016-03-16 23:33:00 NaN 1.0
2016-03-16 23:34:00 NaN 1.0
2016-03-16 23:35:00 NaN 1.0
2016-03-16 23:36:00 NaN 1.0
2016-03-16 23:37:00 NaN 1.0
2016-03-16 23:38:00 1.0 1.0
answered Nov 23 '18 at 14:26
Wen-BenWen-Ben
1
add a comment |
0
You need to supply the limit argument to fillna:
df['fire_in_the_next_5_minutes'] = df['fire'].fillna(method='backfill', limit=5)
answered Nov 23 '18 at 14:27
Toby PettyToby Petty
706412
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
Using bfill with limit
df = df.resample('1min').mean()
df['fire_in_the_next_5_minutes'] = df['fire'].bfill(limit=5)
df
Out[173]:
fire fire_in_the_next_5_minutes
2016-03-16 23:20:00 1.0 1.0
2016-03-16 23:21:00 NaN NaN
2016-03-16 23:22:00 NaN NaN
2016-03-16 23:23:00 NaN 1.0
2016-03-16 23:24:00 NaN 1.0
2016-03-16 23:25:00 NaN 1.0
2016-03-16 23:26:00 NaN 1.0
2016-03-16 23:27:00 NaN 1.0
2016-03-16 23:28:00 1.0 1.0
2016-03-16 23:29:00 NaN NaN
2016-03-16 23:30:00 NaN NaN
2016-03-16 23:31:00 NaN NaN
2016-03-16 23:32:00 NaN NaN
2016-03-16 23:33:00 NaN 1.0
2016-03-16 23:34:00 NaN 1.0
2016-03-16 23:35:00 NaN 1.0
2016-03-16 23:36:00 NaN 1.0
2016-03-16 23:37:00 NaN 1.0
2016-03-16 23:38:00 1.0 1.0
answered Nov 23 '18 at 14:26
Wen-BenWen-Ben
1
add a comment |
2
Using bfill with limit
df = df.resample('1min').mean()
df['fire_in_the_next_5_minutes'] = df['fire'].bfill(limit=5)
df
Out[173]:
fire fire_in_the_next_5_minutes
2016-03-16 23:20:00 1.0 1.0
2016-03-16 23:21:00 NaN NaN
2016-03-16 23:22:00 NaN NaN
2016-03-16 23:23:00 NaN 1.0
2016-03-16 23:24:00 NaN 1.0
2016-03-16 23:25:00 NaN 1.0
2016-03-16 23:26:00 NaN 1.0
2016-03-16 23:27:00 NaN 1.0
2016-03-16 23:28:00 1.0 1.0
2016-03-16 23:29:00 NaN NaN
2016-03-16 23:30:00 NaN NaN
2016-03-16 23:31:00 NaN NaN
2016-03-16 23:32:00 NaN NaN
2016-03-16 23:33:00 NaN 1.0
2016-03-16 23:34:00 NaN 1.0
2016-03-16 23:35:00 NaN 1.0
2016-03-16 23:36:00 NaN 1.0
2016-03-16 23:37:00 NaN 1.0
2016-03-16 23:38:00 1.0 1.0
answered Nov 23 '18 at 14:26
Wen-BenWen-Ben
1
add a comment |
2
2
2
Using bfill with limit
df = df.resample('1min').mean()
df['fire_in_the_next_5_minutes'] = df['fire'].bfill(limit=5)
df
Out[173]:
fire fire_in_the_next_5_minutes
2016-03-16 23:20:00 1.0 1.0
2016-03-16 23:21:00 NaN NaN
2016-03-16 23:22:00 NaN NaN
2016-03-16 23:23:00 NaN 1.0
2016-03-16 23:24:00 NaN 1.0
2016-03-16 23:25:00 NaN 1.0
2016-03-16 23:26:00 NaN 1.0
2016-03-16 23:27:00 NaN 1.0
2016-03-16 23:28:00 1.0 1.0
2016-03-16 23:29:00 NaN NaN
2016-03-16 23:30:00 NaN NaN
2016-03-16 23:31:00 NaN NaN
2016-03-16 23:32:00 NaN NaN
2016-03-16 23:33:00 NaN 1.0
2016-03-16 23:34:00 NaN 1.0
2016-03-16 23:35:00 NaN 1.0
2016-03-16 23:36:00 NaN 1.0
2016-03-16 23:37:00 NaN 1.0
2016-03-16 23:38:00 1.0 1.0
answered Nov 23 '18 at 14:26
Wen-BenWen-Ben
1
Using bfill with limit
df = df.resample('1min').mean()
df['fire_in_the_next_5_minutes'] = df['fire'].bfill(limit=5)
df
Out[173]:
fire fire_in_the_next_5_minutes
2016-03-16 23:20:00 1.0 1.0
2016-03-16 23:21:00 NaN NaN
2016-03-16 23:22:00 NaN NaN
2016-03-16 23:23:00 NaN 1.0
2016-03-16 23:24:00 NaN 1.0
2016-03-16 23:25:00 NaN 1.0
2016-03-16 23:26:00 NaN 1.0
2016-03-16 23:27:00 NaN 1.0
2016-03-16 23:28:00 1.0 1.0
2016-03-16 23:29:00 NaN NaN
2016-03-16 23:30:00 NaN NaN
2016-03-16 23:31:00 NaN NaN
2016-03-16 23:32:00 NaN NaN
2016-03-16 23:33:00 NaN 1.0
2016-03-16 23:34:00 NaN 1.0
2016-03-16 23:35:00 NaN 1.0
2016-03-16 23:36:00 NaN 1.0
2016-03-16 23:37:00 NaN 1.0
2016-03-16 23:38:00 1.0 1.0
answered Nov 23 '18 at 14:26
Wen-BenWen-Ben
1
answered Nov 23 '18 at 14:26
Wen-BenWen-Ben
1
answered Nov 23 '18 at 14:26
Wen-BenWen-Ben
1
answered Nov 23 '18 at 14:26
Wen-BenWen-Ben
1
1
add a comment |
add a comment |
0
You need to supply the limit argument to fillna:
df['fire_in_the_next_5_minutes'] = df['fire'].fillna(method='backfill', limit=5)
answered Nov 23 '18 at 14:27
Toby PettyToby Petty
706412
add a comment |
0
You need to supply the limit argument to fillna:
df['fire_in_the_next_5_minutes'] = df['fire'].fillna(method='backfill', limit=5)
answered Nov 23 '18 at 14:27
Toby PettyToby Petty
706412
add a comment |
0
0
0
You need to supply the limit argument to fillna:
df['fire_in_the_next_5_minutes'] = df['fire'].fillna(method='backfill', limit=5)
answered Nov 23 '18 at 14:27
Toby PettyToby Petty
706412
You need to supply the limit argument to fillna:
df['fire_in_the_next_5_minutes'] = df['fire'].fillna(method='backfill', limit=5)
answered Nov 23 '18 at 14:27
Toby PettyToby Petty
706412
answered Nov 23 '18 at 14:27
Toby PettyToby Petty
706412
answered Nov 23 '18 at 14:27
Toby PettyToby Petty
706412
answered Nov 23 '18 at 14:27
Toby PettyToby Petty
706412
706412
add a comment |
add a comment |
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