Mixing
Pandas: How to apply function row by row in descening order to multiple columns
0
I have a dataframe df1 with 1000 columns. In each column there is a random value. It looks like:
0 1 2 3 4 5 6 7 8 9 ... 990 991 992 993 994 995 996 997 998 999
0 23 15 4 4 23 0 38 14 11 14 ... 22 3 25 3 24 8 1 14 18 27
I have a second dataframe df2 with second-by-second values f that Looks like:
dtm f
0 2018-03-01 00:00:00 +0000 50.135
1 2018-03-01 00:00:01 +0000 50.130
2 2018-03-01 00:00:02 +0000 50.120
3 2018-03-01 00:00:03 +0000 50.112
4 2018-03-01 00:00:04 +0000 50.102
5 2018-03-01 00:00:05 +0000 50.097
6 2018-03-01 00:00:06 +0000 50.095
7 2018-03-01 00:00:07 +0000 50.095
8 2018-03-01 00:00:08 +0000 50.092
9 2018-03-01 00:00:09 +0000 50.095
10 2018-03-01 00:00:10 +0000 50.097
11 2018-03-01 00:00:11 +0000 50.097
12 2018-03-01 00:00:12 +0000 50.097
13 2018-03-01 00:00:13 +0000 50.100
14 2018-03-01 00:00:14 +0000 50.102
15 2018-03-01 00:00:15 +0000 50.105
16 2018-03-01 00:00:16 +0000 50.102
17 2018-03-01 00:00:17 +0000 50.102
18 2018-03-01 00:00:18 +0000 50.100
19 2018-03-01 00:00:19 +0000 50.100
20 2018-03-01 00:00:20 +0000 50.100
21 2018-03-01 00:00:21 +0000 50.097
22 2018-03-01 00:00:22 +0000 50.097
23 2018-03-01 00:00:23 +0000 50.095
24 2018-03-01 00:00:24 +0000 50.092
25 2018-03-01 00:00:25 +0000 50.090
26 2018-03-01 00:00:26 +0000 50.090
27 2018-03-01 00:00:27 +0000 50.087
28 2018-03-01 00:00:28 +0000 50.085
29 2018-03-01 00:00:29 +0000 50.082
... ... ...
86371 2018-03-01 23:59:31 +0000 49.925
86372 2018-03-01 23:59:32 +0000 49.925
86373 2018-03-01 23:59:33 +0000 49.925
86374 2018-03-01 23:59:34 +0000 49.927
86375 2018-03-01 23:59:35 +0000 49.927
86376 2018-03-01 23:59:36 +0000 49.930
86377 2018-03-01 23:59:37 +0000 49.930
86378 2018-03-01 23:59:38 +0000 49.930
86379 2018-03-01 23:59:39 +0000 49.930
86380 2018-03-01 23:59:40 +0000 49.930
86381 2018-03-01 23:59:41 +0000 49.930
86382 2018-03-01 23:59:42 +0000 49.930
86383 2018-03-01 23:59:43 +0000 49.927
86384 2018-03-01 23:59:44 +0000 49.925
86385 2018-03-01 23:59:45 +0000 49.925
86386 2018-03-01 23:59:46 +0000 49.920
86387 2018-03-01 23:59:47 +0000 49.920
86388 2018-03-01 23:59:48 +0000 49.920
86389 2018-03-01 23:59:49 +0000 49.920
86390 2018-03-01 23:59:50 +0000 49.920
86391 2018-03-01 23:59:51 +0000 49.917
86392 2018-03-01 23:59:52 +0000 49.917
86393 2018-03-01 23:59:53 +0000 49.915
86394 2018-03-01 23:59:54 +0000 49.915
86395 2018-03-01 23:59:55 +0000 49.915
86396 2018-03-01 23:59:56 +0000 49.912
86397 2018-03-01 23:59:57 +0000 49.915
86398 2018-03-01 23:59:58 +0000 49.917
86399 2018-03-01 23:59:59 +0000 49.917
86400 2018-03-02 00:00:00 +0000 49.915
Starting from the Initial values of df1, I Need to increase them by 1 each time that f>50 and decrease by 1 whenever f<50. The result should be another dataframe, with 1 row for each second, the relative value, and 1000 columns.
I have tried:
if (f.f>50).any():
df1=df1.apply(lambda x: ((f.f/f.f)*x+1).cumsum())
But it results just in a table with the first line correct and then NaN everywhere else in 86400 rows.
Any help? Thank you in advance
python pandas
asked Nov 18 '18 at 19:06
Luca91
1728
add a comment |
0
I have a dataframe df1 with 1000 columns. In each column there is a random value. It looks like:
0 1 2 3 4 5 6 7 8 9 ... 990 991 992 993 994 995 996 997 998 999
0 23 15 4 4 23 0 38 14 11 14 ... 22 3 25 3 24 8 1 14 18 27
I have a second dataframe df2 with second-by-second values f that Looks like:
dtm f
0 2018-03-01 00:00:00 +0000 50.135
1 2018-03-01 00:00:01 +0000 50.130
2 2018-03-01 00:00:02 +0000 50.120
3 2018-03-01 00:00:03 +0000 50.112
4 2018-03-01 00:00:04 +0000 50.102
5 2018-03-01 00:00:05 +0000 50.097
6 2018-03-01 00:00:06 +0000 50.095
7 2018-03-01 00:00:07 +0000 50.095
8 2018-03-01 00:00:08 +0000 50.092
9 2018-03-01 00:00:09 +0000 50.095
10 2018-03-01 00:00:10 +0000 50.097
11 2018-03-01 00:00:11 +0000 50.097
12 2018-03-01 00:00:12 +0000 50.097
13 2018-03-01 00:00:13 +0000 50.100
14 2018-03-01 00:00:14 +0000 50.102
15 2018-03-01 00:00:15 +0000 50.105
16 2018-03-01 00:00:16 +0000 50.102
17 2018-03-01 00:00:17 +0000 50.102
18 2018-03-01 00:00:18 +0000 50.100
19 2018-03-01 00:00:19 +0000 50.100
20 2018-03-01 00:00:20 +0000 50.100
21 2018-03-01 00:00:21 +0000 50.097
22 2018-03-01 00:00:22 +0000 50.097
23 2018-03-01 00:00:23 +0000 50.095
24 2018-03-01 00:00:24 +0000 50.092
25 2018-03-01 00:00:25 +0000 50.090
26 2018-03-01 00:00:26 +0000 50.090
27 2018-03-01 00:00:27 +0000 50.087
28 2018-03-01 00:00:28 +0000 50.085
29 2018-03-01 00:00:29 +0000 50.082
... ... ...
86371 2018-03-01 23:59:31 +0000 49.925
86372 2018-03-01 23:59:32 +0000 49.925
86373 2018-03-01 23:59:33 +0000 49.925
86374 2018-03-01 23:59:34 +0000 49.927
86375 2018-03-01 23:59:35 +0000 49.927
86376 2018-03-01 23:59:36 +0000 49.930
86377 2018-03-01 23:59:37 +0000 49.930
86378 2018-03-01 23:59:38 +0000 49.930
86379 2018-03-01 23:59:39 +0000 49.930
86380 2018-03-01 23:59:40 +0000 49.930
86381 2018-03-01 23:59:41 +0000 49.930
86382 2018-03-01 23:59:42 +0000 49.930
86383 2018-03-01 23:59:43 +0000 49.927
86384 2018-03-01 23:59:44 +0000 49.925
86385 2018-03-01 23:59:45 +0000 49.925
86386 2018-03-01 23:59:46 +0000 49.920
86387 2018-03-01 23:59:47 +0000 49.920
86388 2018-03-01 23:59:48 +0000 49.920
86389 2018-03-01 23:59:49 +0000 49.920
86390 2018-03-01 23:59:50 +0000 49.920
86391 2018-03-01 23:59:51 +0000 49.917
86392 2018-03-01 23:59:52 +0000 49.917
86393 2018-03-01 23:59:53 +0000 49.915
86394 2018-03-01 23:59:54 +0000 49.915
86395 2018-03-01 23:59:55 +0000 49.915
86396 2018-03-01 23:59:56 +0000 49.912
86397 2018-03-01 23:59:57 +0000 49.915
86398 2018-03-01 23:59:58 +0000 49.917
86399 2018-03-01 23:59:59 +0000 49.917
86400 2018-03-02 00:00:00 +0000 49.915
Starting from the Initial values of df1, I Need to increase them by 1 each time that f>50 and decrease by 1 whenever f<50. The result should be another dataframe, with 1 row for each second, the relative value, and 1000 columns.
I have tried:
if (f.f>50).any():
df1=df1.apply(lambda x: ((f.f/f.f)*x+1).cumsum())
But it results just in a table with the first line correct and then NaN everywhere else in 86400 rows.
Any help? Thank you in advance
python pandas
asked Nov 18 '18 at 19:06
Luca91
1728
add a comment |
0
0
0
I have a dataframe df1 with 1000 columns. In each column there is a random value. It looks like:
0 1 2 3 4 5 6 7 8 9 ... 990 991 992 993 994 995 996 997 998 999
0 23 15 4 4 23 0 38 14 11 14 ... 22 3 25 3 24 8 1 14 18 27
I have a second dataframe df2 with second-by-second values f that Looks like:
dtm f
0 2018-03-01 00:00:00 +0000 50.135
1 2018-03-01 00:00:01 +0000 50.130
2 2018-03-01 00:00:02 +0000 50.120
3 2018-03-01 00:00:03 +0000 50.112
4 2018-03-01 00:00:04 +0000 50.102
5 2018-03-01 00:00:05 +0000 50.097
6 2018-03-01 00:00:06 +0000 50.095
7 2018-03-01 00:00:07 +0000 50.095
8 2018-03-01 00:00:08 +0000 50.092
9 2018-03-01 00:00:09 +0000 50.095
10 2018-03-01 00:00:10 +0000 50.097
11 2018-03-01 00:00:11 +0000 50.097
12 2018-03-01 00:00:12 +0000 50.097
13 2018-03-01 00:00:13 +0000 50.100
14 2018-03-01 00:00:14 +0000 50.102
15 2018-03-01 00:00:15 +0000 50.105
16 2018-03-01 00:00:16 +0000 50.102
17 2018-03-01 00:00:17 +0000 50.102
18 2018-03-01 00:00:18 +0000 50.100
19 2018-03-01 00:00:19 +0000 50.100
20 2018-03-01 00:00:20 +0000 50.100
21 2018-03-01 00:00:21 +0000 50.097
22 2018-03-01 00:00:22 +0000 50.097
23 2018-03-01 00:00:23 +0000 50.095
24 2018-03-01 00:00:24 +0000 50.092
25 2018-03-01 00:00:25 +0000 50.090
26 2018-03-01 00:00:26 +0000 50.090
27 2018-03-01 00:00:27 +0000 50.087
28 2018-03-01 00:00:28 +0000 50.085
29 2018-03-01 00:00:29 +0000 50.082
... ... ...
86371 2018-03-01 23:59:31 +0000 49.925
86372 2018-03-01 23:59:32 +0000 49.925
86373 2018-03-01 23:59:33 +0000 49.925
86374 2018-03-01 23:59:34 +0000 49.927
86375 2018-03-01 23:59:35 +0000 49.927
86376 2018-03-01 23:59:36 +0000 49.930
86377 2018-03-01 23:59:37 +0000 49.930
86378 2018-03-01 23:59:38 +0000 49.930
86379 2018-03-01 23:59:39 +0000 49.930
86380 2018-03-01 23:59:40 +0000 49.930
86381 2018-03-01 23:59:41 +0000 49.930
86382 2018-03-01 23:59:42 +0000 49.930
86383 2018-03-01 23:59:43 +0000 49.927
86384 2018-03-01 23:59:44 +0000 49.925
86385 2018-03-01 23:59:45 +0000 49.925
86386 2018-03-01 23:59:46 +0000 49.920
86387 2018-03-01 23:59:47 +0000 49.920
86388 2018-03-01 23:59:48 +0000 49.920
86389 2018-03-01 23:59:49 +0000 49.920
86390 2018-03-01 23:59:50 +0000 49.920
86391 2018-03-01 23:59:51 +0000 49.917
86392 2018-03-01 23:59:52 +0000 49.917
86393 2018-03-01 23:59:53 +0000 49.915
86394 2018-03-01 23:59:54 +0000 49.915
86395 2018-03-01 23:59:55 +0000 49.915
86396 2018-03-01 23:59:56 +0000 49.912
86397 2018-03-01 23:59:57 +0000 49.915
86398 2018-03-01 23:59:58 +0000 49.917
86399 2018-03-01 23:59:59 +0000 49.917
86400 2018-03-02 00:00:00 +0000 49.915
Starting from the Initial values of df1, I Need to increase them by 1 each time that f>50 and decrease by 1 whenever f<50. The result should be another dataframe, with 1 row for each second, the relative value, and 1000 columns.
I have tried:
if (f.f>50).any():
df1=df1.apply(lambda x: ((f.f/f.f)*x+1).cumsum())
But it results just in a table with the first line correct and then NaN everywhere else in 86400 rows.
Any help? Thank you in advance
python pandas
asked Nov 18 '18 at 19:06
Luca91
1728
I have a dataframe df1 with 1000 columns. In each column there is a random value. It looks like:
0 1 2 3 4 5 6 7 8 9 ... 990 991 992 993 994 995 996 997 998 999
0 23 15 4 4 23 0 38 14 11 14 ... 22 3 25 3 24 8 1 14 18 27
I have a second dataframe df2 with second-by-second values f that Looks like:
dtm f
0 2018-03-01 00:00:00 +0000 50.135
1 2018-03-01 00:00:01 +0000 50.130
2 2018-03-01 00:00:02 +0000 50.120
3 2018-03-01 00:00:03 +0000 50.112
4 2018-03-01 00:00:04 +0000 50.102
5 2018-03-01 00:00:05 +0000 50.097
6 2018-03-01 00:00:06 +0000 50.095
7 2018-03-01 00:00:07 +0000 50.095
8 2018-03-01 00:00:08 +0000 50.092
9 2018-03-01 00:00:09 +0000 50.095
10 2018-03-01 00:00:10 +0000 50.097
11 2018-03-01 00:00:11 +0000 50.097
12 2018-03-01 00:00:12 +0000 50.097
13 2018-03-01 00:00:13 +0000 50.100
14 2018-03-01 00:00:14 +0000 50.102
15 2018-03-01 00:00:15 +0000 50.105
16 2018-03-01 00:00:16 +0000 50.102
17 2018-03-01 00:00:17 +0000 50.102
18 2018-03-01 00:00:18 +0000 50.100
19 2018-03-01 00:00:19 +0000 50.100
20 2018-03-01 00:00:20 +0000 50.100
21 2018-03-01 00:00:21 +0000 50.097
22 2018-03-01 00:00:22 +0000 50.097
23 2018-03-01 00:00:23 +0000 50.095
24 2018-03-01 00:00:24 +0000 50.092
25 2018-03-01 00:00:25 +0000 50.090
26 2018-03-01 00:00:26 +0000 50.090
27 2018-03-01 00:00:27 +0000 50.087
28 2018-03-01 00:00:28 +0000 50.085
29 2018-03-01 00:00:29 +0000 50.082
... ... ...
86371 2018-03-01 23:59:31 +0000 49.925
86372 2018-03-01 23:59:32 +0000 49.925
86373 2018-03-01 23:59:33 +0000 49.925
86374 2018-03-01 23:59:34 +0000 49.927
86375 2018-03-01 23:59:35 +0000 49.927
86376 2018-03-01 23:59:36 +0000 49.930
86377 2018-03-01 23:59:37 +0000 49.930
86378 2018-03-01 23:59:38 +0000 49.930
86379 2018-03-01 23:59:39 +0000 49.930
86380 2018-03-01 23:59:40 +0000 49.930
86381 2018-03-01 23:59:41 +0000 49.930
86382 2018-03-01 23:59:42 +0000 49.930
86383 2018-03-01 23:59:43 +0000 49.927
86384 2018-03-01 23:59:44 +0000 49.925
86385 2018-03-01 23:59:45 +0000 49.925
86386 2018-03-01 23:59:46 +0000 49.920
86387 2018-03-01 23:59:47 +0000 49.920
86388 2018-03-01 23:59:48 +0000 49.920
86389 2018-03-01 23:59:49 +0000 49.920
86390 2018-03-01 23:59:50 +0000 49.920
86391 2018-03-01 23:59:51 +0000 49.917
86392 2018-03-01 23:59:52 +0000 49.917
86393 2018-03-01 23:59:53 +0000 49.915
86394 2018-03-01 23:59:54 +0000 49.915
86395 2018-03-01 23:59:55 +0000 49.915
86396 2018-03-01 23:59:56 +0000 49.912
86397 2018-03-01 23:59:57 +0000 49.915
86398 2018-03-01 23:59:58 +0000 49.917
86399 2018-03-01 23:59:59 +0000 49.917
86400 2018-03-02 00:00:00 +0000 49.915
Starting from the Initial values of df1, I Need to increase them by 1 each time that f>50 and decrease by 1 whenever f<50. The result should be another dataframe, with 1 row for each second, the relative value, and 1000 columns.
I have tried:
if (f.f>50).any():
df1=df1.apply(lambda x: ((f.f/f.f)*x+1).cumsum())
But it results just in a table with the first line correct and then NaN everywhere else in 86400 rows.
Any help? Thank you in advance
python pandas
python pandas
asked Nov 18 '18 at 19:06
Luca91
1728
asked Nov 18 '18 at 19:06
Luca91
1728
asked Nov 18 '18 at 19:06
Luca91
1728
asked Nov 18 '18 at 19:06
Luca91
1728
asked Nov 18 '18 at 19:06
Luca91
1728
1728
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
0
Probably not the most memory-efficient solution...
# Preallocate the result DataFrame
res = pd.DataFrame(np.tile(df1, (len(df2), 1)))
# Compute a numpy array of corrections to add to each cell in `res`
mask = np.where(df2.f > 50, 1, -1)
adjust = np.tile(mask, (len(res), 1)).T.cumsum(axis=0)
# Add the adjustment array to the result DataFrame
res += adjust
answered Nov 19 '18 at 0:12
Peter Leimbigler
3,7391415
+= adjust? it is not defined
– Luca91
Nov 19 '18 at 7:18
Argh, sorry, I missed a line - too late in the day. Edited my answer!
– Peter Leimbigler
Nov 19 '18 at 12:57
add a comment |
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1 Answer
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active
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
Probably not the most memory-efficient solution...
# Preallocate the result DataFrame
res = pd.DataFrame(np.tile(df1, (len(df2), 1)))
# Compute a numpy array of corrections to add to each cell in `res`
mask = np.where(df2.f > 50, 1, -1)
adjust = np.tile(mask, (len(res), 1)).T.cumsum(axis=0)
# Add the adjustment array to the result DataFrame
res += adjust
answered Nov 19 '18 at 0:12
Peter Leimbigler
3,7391415
+= adjust? it is not defined
– Luca91
Nov 19 '18 at 7:18
Argh, sorry, I missed a line - too late in the day. Edited my answer!
– Peter Leimbigler
Nov 19 '18 at 12:57
add a comment |
0
Probably not the most memory-efficient solution...
# Preallocate the result DataFrame
res = pd.DataFrame(np.tile(df1, (len(df2), 1)))
# Compute a numpy array of corrections to add to each cell in `res`
mask = np.where(df2.f > 50, 1, -1)
adjust = np.tile(mask, (len(res), 1)).T.cumsum(axis=0)
# Add the adjustment array to the result DataFrame
res += adjust
answered Nov 19 '18 at 0:12
Peter Leimbigler
3,7391415
+= adjust? it is not defined
– Luca91
Nov 19 '18 at 7:18
Argh, sorry, I missed a line - too late in the day. Edited my answer!
– Peter Leimbigler
Nov 19 '18 at 12:57
add a comment |
0
0
0
Probably not the most memory-efficient solution...
# Preallocate the result DataFrame
res = pd.DataFrame(np.tile(df1, (len(df2), 1)))
# Compute a numpy array of corrections to add to each cell in `res`
mask = np.where(df2.f > 50, 1, -1)
adjust = np.tile(mask, (len(res), 1)).T.cumsum(axis=0)
# Add the adjustment array to the result DataFrame
res += adjust
answered Nov 19 '18 at 0:12
Peter Leimbigler
3,7391415
Probably not the most memory-efficient solution...
# Preallocate the result DataFrame
res = pd.DataFrame(np.tile(df1, (len(df2), 1)))
# Compute a numpy array of corrections to add to each cell in `res`
mask = np.where(df2.f > 50, 1, -1)
adjust = np.tile(mask, (len(res), 1)).T.cumsum(axis=0)
# Add the adjustment array to the result DataFrame
res += adjust
answered Nov 19 '18 at 0:12
Peter Leimbigler
3,7391415
edited Nov 19 '18 at 12:57
answered Nov 19 '18 at 0:12
Peter Leimbigler
3,7391415
answered Nov 19 '18 at 0:12
Peter Leimbigler
3,7391415
answered Nov 19 '18 at 0:12
Peter Leimbigler
3,7391415
3,7391415
+= adjust? it is not defined
– Luca91
Nov 19 '18 at 7:18
Argh, sorry, I missed a line - too late in the day. Edited my answer!
– Peter Leimbigler
Nov 19 '18 at 12:57
add a comment |
+= adjust? it is not defined
– Luca91
Nov 19 '18 at 7:18
Argh, sorry, I missed a line - too late in the day. Edited my answer!
– Peter Leimbigler
Nov 19 '18 at 12:57
+= adjust? it is not defined
– Luca91
Nov 19 '18 at 7:18
+= adjust? it is not defined
– Luca91
Nov 19 '18 at 7:18
Argh, sorry, I missed a line - too late in the day. Edited my answer!
– Peter Leimbigler
Nov 19 '18 at 12:57
Argh, sorry, I missed a line - too late in the day. Edited my answer!
– Peter Leimbigler
Nov 19 '18 at 12:57
add a comment |
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