append a NaN (missing value) to ndarray

Development matplotlib-devel
1

Dear all, I have a variable d which has several years plus 11 month data (len(d)%12=11). so I want to append a NaN to the data so that it constitutes complete
several years of data.

but I cannot use “d=np.concatenate(d,np.array(np.nan))” to finish this job.

another question, is there a simple function like is.Nan(ndarray) in numpy to check the missing value? and how can I get the index of the missing value?

Thanks a lot,

Chao

In [246]: d
Out[246]:
array([-24. , -12.9, -14. , 4.2, 7.3, 12.9, 18.5, 16.9, 10.7,
7.6, -1.9, -9.8, -12. , -16.6, -13.3, 5. , 12.2, 14. ,
16.5, 15.6, 11.8, 6.4, -6.6, -14.8, -17.6, -15.4, -3.7,
-2.1, 6.9, 12.8, 17. , 16.7, 9. , 4.5, -11.9, -18.1,
-18.4, -19.5, -3.4, 6.4, 8.5, 13.5, 18.3, 16.5, 10.4,
1.7, -9.6, -19.5, -17.8, -20.9, -10.9, 0.8, 7.7, 14.7,
20.4, 16.2, 9.1, 6.9, -8.6, -17.1, -16.6, -20.8, -14.1,
-4.7, 10.7, 15.8, 18.5, 17.1, 12.9, 5.2, -6. , -18.4,
-20.5, -22.6, -8.3, 7. , 10.9, 14. , 17.1, 18.6, 10.1,
2.1, -1.1, -20.8, -32.9, -17.7, -12.2, -0.4, 9.5, 14.6,
17.3, 15.1, 11.1, 2.6, -9.9, -16.9, -22.9, -18.1, -15.4,
0.5, 11. , 12.4, 16.9, 14.7, 9.7, -0.3, -10.7, -20.7,
-23.1, -13.5, -10.7, 7.6, 11. , 14.4, 16.8, 15.6, 11.2,
4.2, -3.5, -12.2, -19.9, -13.2, -10.1, -0.1, 8.5, 13.6,
17.2, 17.6, 10.1, 6.3, -2.3, -12.4, -27.2, -9.1, -13. ,
-6.2, 7.4, 12.9, 17.3, 14.8, 9.5, 4.3, -1.1, -8.6,
-17.5, -18.5, -15.9, 3.5, 9.6, 15.7, 18.4, 17.1, 9.4,
4.5, -15.1, -21.9, -21.2, -18.5, -12.2, -2.1, 10.4, 16.7,
17. , 16.1, 8.6, 3.9, -4.8, -16. , -20.9, -16.8, -9.3,
0.9, 10.3, 13.8, 19. , 15.3, 11.1, 3.1, -5.5, -14.9,
-12.8, -17.5, -6.9, 3.7, 11.1, 12.5, 16.5, 16.5, 10.2,
4.2, -8.8, -16.3, -24.4, -17.2, -4.7, 3.5, 8. , 14. ,
18.3, 14.6, 8.9, -0.4, -9.1, -8.4, -20.8, -15.5, -12. ,
2.3, 9.8, 13.6, 18.4, 16.8, 11.9, 3.9, -8. , -13.6,
-17.4, -13.1, -6.9, 0.2, 10.6, 17.6, 17.2, 18.8, 6.3,
3.4, -7.6, -22.7, -19.5, -20.9, -11.7, 1.7, 9.8, 16.6,
16.4, 16.2, 10.6, 6.1, -3.6, -14.4, -22.2, -17.8, -7.5,
3.2, 8.9, 14.8, 18.6, 17.7, 12.9, 8.4, -6.9, -16.8,
-16.4, -10.1, -15.6, 3.5, 11.2, 14.7, 19.5, 14.6, 8.3,
4.4, -8.2, -22.3, -23. , -21.8, -14.1, 2.5, 9.1, 15. ,
17.9, 16.4, 5.3, 5.2, -11.6, -14.1, -29. , -20.5, -8.1,
-0.7, 10.1, 13.9, 17.3, 15.5, 12.4, 3.4, -13.3, -15.1,
-21. , -19.5, -12.5, -2.4, 8.9, 13.5, 17.6, 17.3, 14.4,
2.7, -5.1, -15.5, -20.7, -14.5, -3.2, 2.7, 9.6, 14.3,
16. , 13.7, 10.9, 3.2, -4.1, -18.6, -29.2, -16.7, -11. ,
5.1, 9.2, 12.5, 16.2, 17.5, 10.2, -0.8, -5.3, -11.4,
-20.9, -15. , -12.8, 2.1, 8.8, 16.9, 18.2, 16.1, 9.9,
3.4, -9. , -23.5, -25.2, -15.4, -10.3, 2.5, 11.7, 14.5,
15.4, 17.8, 9.9, 3.5, -7.4, -20.7, -25.1, -22.6, -9.7,
1.2, 11.6, 16.3, 14.8, 17.4, 5.9, 0.9, -6.8, -21.4,
-16.6, -15.8, -4.1, 1.7, 10.9, 14.5, 16.8, 16.8, 9.8,
4.2, -12.8, -18.5, -25.2, -16.8, -14.6, 2.4, 6.2, 14.9,
17.9, 13.6, 7.6, 5.3, -4.2, -8.5, -17.5, -17.7, -13.4,
0.1, 8.8, 14.3, 19.3, 14. , 10. , 3.5, -6.1, -16.8,
-16.5, -12.5, -10. , 5.8, 11.6, 14.4, 17.7, 16.7, 11.6,
1.4, -7.3, -16. , -22.2, -7.8, -5.1, 6. , 13.6, 15.5,
16.3, 12.3, 9.8, 5.2, -7.9, -21.1, -24.2, -17.4, -7.6,
2.5, 11.7, 15.4, 16.7, 14.4, 10.6, 5.9, -10.4, -18. ,
-21.5, -25.6, -10.2, -2.9, 7.8, 15. , 19.2, 15.3, 11.3,
4.4, -6. , -14.4, -21. , -15.4, -11.5, 7.4, 13.3, 15.2,
17.6, 15.1, 9.3, 5. , -4.4, -19.4, -12.6, -11.4, -2.6,
3.2, 10.9, 14.4, 17.9, 19.1, 11.8, 1.9, -0.9, -14.2,
-29.7, -17.6, -9.6, 0.5, 9.2, 14.3, 17.7, 14.6, 10.1,
4.2, -10.7, -14.4, -14.5, -13.1, -8.5, 0.5, 8. , 15.1,
18.8, 19.4, 9.3, 4.4, -3.2, -23.6, -14.2, -6.7, -8. ,
6.8, 9. , 15.8, 18.6, 18.8, 7.6, 2.2, -11.3, -21.2,
-17.2, -19.5, -4.9, 4.3, 11.8, 12.3, 17.1, 14.3, 7.7,
3. , -16.7, -14.2, -11.7, -16.4, -3.8, 2.1, 12.1, 15.3,
16.9, 16.2, 8.5, 4.7, -10.7, -10.7, -11.6, -10.2, -6.2,
6.4, 12.7, 17.9, 17.3, 13.7, 12.6, 3.6, -2.7, -10.8,
-20.3, -15.3, -6.8, 4. , 13.9, 19.3, 18.5, 15.8, 10.9,
3. , -5.3, -12.3, -15.7, -16.7, -12.3, 3.5, 10.7, 16. ,
19.8, 17.1, 10.8, 4.1, -9.1, -18.6, -15.9, -17.7, -4.2,
2.6, 10.3, 16.4, 17.3, 16.9, 12.2, 1.7, -9.7, -21.7,
-19.3, -10.7, -9.5, 4.7, 11.3, 16.5, 18.5, 19.8, 10.6,
-0.3, -10.2, -14.2, -13.2, -11.1, -1.8, 3.4, 9.3, 14. ,
16.1, 14.2, 8.4, 3.2, -3.1, -20.8, -18.3, -14.1, -1.5,
3.9, 11. , 12.8, 15.6, 15.8, 8.7, 2.9, -7.4, -11.3,
-23.2, -21.6, -2. , 3.8, 10.3, 15. , 17.1, 15.6, 12.9,
4.6, -8. , -15.6, -16.1, -14.1, -8.4, -1.1, 9.3, 16.9,
16.9, 15.7, 11.4, 4.3, -11.3, -17.1, -25. , -14. , -11.6,
1.4, 7.9, 16.2, 17.6, 17.9, 10. , 2.6, -12.5, -20.1,
-22.3, -11.9, -10.2, 0.4, 9.2, 16.6, 18.4, 17.8, 12.6,
3. , -4.7, -6. , -19.3, -6.1, -7.8, 6.6, 12.3, 14.4,
18.5, 18.9, 12.1, 4.1, -5.6, -14.7, -18.2, -11.7, -4.8,
5.6, 9.9, 13.8, 16.5, 17. , 10.1, 3.2, -3.3, -9. ,
-19.7, -11.9, -3.4, 3.1, 8.9, 13.4, 17.6, 15.9, 10.8,
4.4, -6.9, -21. , -9.8, -18. , -4.1, 3.6, 11.6, 14.8,
18.7, 18.7, 12.8, 1.7, -2.2, -13.9, -16.8, -9.9, -14.6,
-1.7, 7.6, 17.4, 20.3, 16.2, 10.2, -1.9, -6.3, -10.5,
-18.5, -18.6, -10.1, 3.8, 11.7, 15.7, 18.5, 19.6, 10.1,
5.6, -9.5, -11.4, -21.5, -11.2, NaN, 3.5, NaN, 13. ,
16.8, 13.9, 10.1, 2.7, -2.9, -15.8, -18.8, -12.6, -6.3,
5.7, 9.5, 14.2, 17.5, 14.9, 10.8, 4. , -3.4, -9.3,
-8.7, -13. , -8.1, 6.8, 11. , 16.6, 19.5, 17.3, 11.5,
1.5, -10.4, -11.9, -14.2, -18.3, -6.1, 2.9, 10.6, 14.3,
20.6, 14.9, 10. , 4.8, -7.5])

···

–

Chao YUE
Laboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL)
UMR 1572 CEA-CNRS-UVSQ
Batiment 712 - Pe 119
91191 GIF Sur YVETTE Cedex

Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16

2

Here is a quick-and-dirty way to do what you want:

d_full = np.empty(len(d) + 1)
d_full[:len(d)] = d
d_full[-1] = np.nan

As for testing for NaNs, use “np.isnan()”. That returns a boolean array the same shape as the input array (which can be used for boolean indexing). However, if you really need the indices, then you can use “np.nonzero(np.isnan(d_full))[0]”. Note the use of “[0]” because np.nonzero returns a tuple of information, but you usually only need the first one.

I hope that helps!
Ben Root

···

On Thu, Sep 29, 2011 at 9:33 AM, Chao YUE <chaoyuejoy@…714…> wrote:

Dear all, I have a variable d which has several years plus 11 month data (len(d)%12=11). so I want to append a NaN to the data so that it constitutes complete
several years of data.

but I cannot use “d=np.concatenate(d,np.array(np.nan))” to finish this job.

another question, is there a simple function like is.Nan(ndarray) in numpy to check the missing value? and how can I get the index of the missing value?

Thanks a lot,

Chao

In [246]: d
Out[246]:

array([-24. , -12.9, -14. , 4.2, 7.3, 12.9, 18.5, 16.9, 10.7,
7.6, -1.9, -9.8, -12. , -16.6, -13.3, 5. , 12.2, 14. ,
16.5, 15.6, 11.8, 6.4, -6.6, -14.8, -17.6, -15.4, -3.7,
-2.1, 6.9, 12.8, 17. , 16.7, 9. , 4.5, -11.9, -18.1,

   -18.4, -19.5,  -3.4,   6.4,   8.5,  13.5,  18.3,  16.5,  10.4,
     1.7,  -9.6, -19.5, -17.8, -20.9, -10.9,   0.8,   7.7,  14.7,
    20.4,  16.2,   9.1,   6.9,  -8.6, -17.1, -16.6, -20.8, -14.1,
    -4.7,  10.7,  15.8,  18.5,  17.1,  12.9,   5.2,  -6. , -18.4,



   -20.5, -22.6,  -8.3,   7. ,  10.9,  14. ,  17.1,  18.6,  10.1,
     2.1,  -1.1, -20.8, -32.9, -17.7, -12.2,  -0.4,   9.5,  14.6,
    17.3,  15.1,  11.1,   2.6,  -9.9, -16.9, -22.9, -18.1, -15.4,
     0.5,  11. ,  12.4,  16.9,  14.7,   9.7,  -0.3, -10.7, -20.7,



   -23.1, -13.5, -10.7,   7.6,  11. ,  14.4,  16.8,  15.6,  11.2,
     4.2,  -3.5, -12.2, -19.9, -13.2, -10.1,  -0.1,   8.5,  13.6,
    17.2,  17.6,  10.1,   6.3,  -2.3, -12.4, -27.2,  -9.1, -13. ,
    -6.2,   7.4,  12.9,  17.3,  14.8,   9.5,   4.3,  -1.1,  -8.6,



   -17.5, -18.5, -15.9,   3.5,   9.6,  15.7,  18.4,  17.1,   9.4,
     4.5, -15.1, -21.9, -21.2, -18.5, -12.2,  -2.1,  10.4,  16.7,
    17. ,  16.1,   8.6,   3.9,  -4.8, -16. , -20.9, -16.8,  -9.3,
     0.9,  10.3,  13.8,  19. ,  15.3,  11.1,   3.1,  -5.5, -14.9,



   -12.8, -17.5,  -6.9,   3.7,  11.1,  12.5,  16.5,  16.5,  10.2,
     4.2,  -8.8, -16.3, -24.4, -17.2,  -4.7,   3.5,   8. ,  14. ,
    18.3,  14.6,   8.9,  -0.4,  -9.1,  -8.4, -20.8, -15.5, -12. ,
     2.3,   9.8,  13.6,  18.4,  16.8,  11.9,   3.9,  -8. , -13.6,



   -17.4, -13.1,  -6.9,   0.2,  10.6,  17.6,  17.2,  18.8,   6.3,
     3.4,  -7.6, -22.7, -19.5, -20.9, -11.7,   1.7,   9.8,  16.6,
    16.4,  16.2,  10.6,   6.1,  -3.6, -14.4, -22.2, -17.8,  -7.5,
     3.2,   8.9,  14.8,  18.6,  17.7,  12.9,   8.4,  -6.9, -16.8,



   -16.4, -10.1, -15.6,   3.5,  11.2,  14.7,  19.5,  14.6,   8.3,
     4.4,  -8.2, -22.3, -23. , -21.8, -14.1,   2.5,   9.1,  15. ,
    17.9,  16.4,   5.3,   5.2, -11.6, -14.1, -29. , -20.5,  -8.1,
    -0.7,  10.1,  13.9,  17.3,  15.5,  12.4,   3.4, -13.3, -15.1,



   -21. , -19.5, -12.5,  -2.4,   8.9,  13.5,  17.6,  17.3,  14.4,
     2.7,  -5.1, -15.5, -20.7, -14.5,  -3.2,   2.7,   9.6,  14.3,
    16. ,  13.7,  10.9,   3.2,  -4.1, -18.6, -29.2, -16.7, -11. ,
     5.1,   9.2,  12.5,  16.2,  17.5,  10.2,  -0.8,  -5.3, -11.4,



   -20.9, -15. , -12.8,   2.1,   8.8,  16.9,  18.2,  16.1,   9.9,
     3.4,  -9. , -23.5, -25.2, -15.4, -10.3,   2.5,  11.7,  14.5,
    15.4,  17.8,   9.9,   3.5,  -7.4, -20.7, -25.1, -22.6,  -9.7,
     1.2,  11.6,  16.3,  14.8,  17.4,   5.9,   0.9,  -6.8, -21.4,



   -16.6, -15.8,  -4.1,   1.7,  10.9,  14.5,  16.8,  16.8,   9.8,
     4.2, -12.8, -18.5, -25.2, -16.8, -14.6,   2.4,   6.2,  14.9,
    17.9,  13.6,   7.6,   5.3,  -4.2,  -8.5, -17.5, -17.7, -13.4,
     0.1,   8.8,  14.3,  19.3,  14. ,  10. ,   3.5,  -6.1, -16.8,



   -16.5, -12.5, -10. ,   5.8,  11.6,  14.4,  17.7,  16.7,  11.6,
     1.4,  -7.3, -16. , -22.2,  -7.8,  -5.1,   6. ,  13.6,  15.5,
    16.3,  12.3,   9.8,   5.2,  -7.9, -21.1, -24.2, -17.4,  -7.6,
     2.5,  11.7,  15.4,  16.7,  14.4,  10.6,   5.9, -10.4, -18. ,



   -21.5, -25.6, -10.2,  -2.9,   7.8,  15. ,  19.2,  15.3,  11.3,
     4.4,  -6. , -14.4, -21. , -15.4, -11.5,   7.4,  13.3,  15.2,
    17.6,  15.1,   9.3,   5. ,  -4.4, -19.4, -12.6, -11.4,  -2.6,
     3.2,  10.9,  14.4,  17.9,  19.1,  11.8,   1.9,  -0.9, -14.2,



   -29.7, -17.6,  -9.6,   0.5,   9.2,  14.3,  17.7,  14.6,  10.1,
     4.2, -10.7, -14.4, -14.5, -13.1,  -8.5,   0.5,   8. ,  15.1,
    18.8,  19.4,   9.3,   4.4,  -3.2, -23.6, -14.2,  -6.7,  -8. ,
     6.8,   9. ,  15.8,  18.6,  18.8,   7.6,   2.2, -11.3, -21.2,



   -17.2, -19.5,  -4.9,   4.3,  11.8,  12.3,  17.1,  14.3,   7.7,
     3. , -16.7, -14.2, -11.7, -16.4,  -3.8,   2.1,  12.1,  15.3,
    16.9,  16.2,   8.5,   4.7, -10.7, -10.7, -11.6, -10.2,  -6.2,
     6.4,  12.7,  17.9,  17.3,  13.7,  12.6,   3.6,  -2.7, -10.8,



   -20.3, -15.3,  -6.8,   4. ,  13.9,  19.3,  18.5,  15.8,  10.9,
     3. ,  -5.3, -12.3, -15.7, -16.7, -12.3,   3.5,  10.7,  16. ,
    19.8,  17.1,  10.8,   4.1,  -9.1, -18.6, -15.9, -17.7,  -4.2,
     2.6,  10.3,  16.4,  17.3,  16.9,  12.2,   1.7,  -9.7, -21.7,



   -19.3, -10.7,  -9.5,   4.7,  11.3,  16.5,  18.5,  19.8,  10.6,
    -0.3, -10.2, -14.2, -13.2, -11.1,  -1.8,   3.4,   9.3,  14. ,
    16.1,  14.2,   8.4,   3.2,  -3.1, -20.8, -18.3, -14.1,  -1.5,
     3.9,  11. ,  12.8,  15.6,  15.8,   8.7,   2.9,  -7.4, -11.3,



   -23.2, -21.6,  -2. ,   3.8,  10.3,  15. ,  17.1,  15.6,  12.9,
     4.6,  -8. , -15.6, -16.1, -14.1,  -8.4,  -1.1,   9.3,  16.9,
    16.9,  15.7,  11.4,   4.3, -11.3, -17.1, -25. , -14. , -11.6,
     1.4,   7.9,  16.2,  17.6,  17.9,  10. ,   2.6, -12.5, -20.1,



   -22.3, -11.9, -10.2,   0.4,   9.2,  16.6,  18.4,  17.8,  12.6,
     3. ,  -4.7,  -6. , -19.3,  -6.1,  -7.8,   6.6,  12.3,  14.4,
    18.5,  18.9,  12.1,   4.1,  -5.6, -14.7, -18.2, -11.7,  -4.8,
     5.6,   9.9,  13.8,  16.5,  17. ,  10.1,   3.2,  -3.3,  -9. ,



   -19.7, -11.9,  -3.4,   3.1,   8.9,  13.4,  17.6,  15.9,  10.8,
     4.4,  -6.9, -21. ,  -9.8, -18. ,  -4.1,   3.6,  11.6,  14.8,
    18.7,  18.7,  12.8,   1.7,  -2.2, -13.9, -16.8,  -9.9, -14.6,
    -1.7,   7.6,  17.4,  20.3,  16.2,  10.2,  -1.9,  -6.3, -10.5,



   -18.5, -18.6, -10.1,   3.8,  11.7,  15.7,  18.5,  19.6,  10.1,
     5.6,  -9.5, -11.4, -21.5, -11.2,   NaN,   3.5,   NaN,  13. ,
    16.8,  13.9,  10.1,   2.7,  -2.9, -15.8, -18.8, -12.6,  -6.3,
     5.7,   9.5,  14.2,  17.5,  14.9,  10.8,   4. ,  -3.4,  -9.3,



    -8.7, -13. ,  -8.1,   6.8,  11. ,  16.6,  19.5,  17.3,  11.5,
     1.5, -10.4, -11.9, -14.2, -18.3,  -6.1,   2.9,  10.6,  14.3,
    20.6,  14.9,  10. ,   4.8,  -7.5])
3

29.09.2011 17:38, Benjamin Root kirjoitti:
[clip]

but I cannot use "d=np.concatenate(d,np.array(np.nan))" to finish this job.

[clip]

Do

    d = np.concatenate([d, np.array([np.nan])])

or

    d = np.hstack([d, np.nan])

or

    d = np.r_[d, np.nan]

4

Thanks Ben! it works fine! help me a lot.

Chao

2011/9/29 Pauli Virtanen <pav@…278…>

···

29.09.2011 17:38, Benjamin Root kirjoitti:

[clip]

but I cannot use “d=np.concatenate(d,np.array(np.nan))” to finish this job.

[clip]

Do

d = np.concatenate([d, np.array([np.nan])])

or

d = np.hstack([d, np.nan])

or

d = np.r_[d, np.nan]

All the data continuously generated in your IT infrastructure contains a

definitive record of customers, application performance, security

threats, fraudulent activity and more. Splunk takes this data and makes

sense of it. Business sense. IT sense. Common sense.

http://p.sf.net/sfu/splunk-d2dcopy1

Matplotlib-devel mailing list

Matplotlib-devel@lists.sourceforge.net

https://lists.sourceforge.net/lists/listinfo/matplotlib-devel

–

Chao YUE
Laboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL)
UMR 1572 CEA-CNRS-UVSQ
Batiment 712 - Pe 119
91191 GIF Sur YVETTE Cedex

Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16

5

Thanks a lot Pauli!

Best,

Chao

2011/9/29 Pauli Virtanen <pav@…278…>

···

29.09.2011 17:38, Benjamin Root kirjoitti:

[clip]

but I cannot use “d=np.concatenate(d,np.array(np.nan))” to finish this job.

[clip]

Do

d = np.concatenate([d, np.array([np.nan])])

or

d = np.hstack([d, np.nan])

or

d = np.r_[d, np.nan]

All the data continuously generated in your IT infrastructure contains a

definitive record of customers, application performance, security

threats, fraudulent activity and more. Splunk takes this data and makes

sense of it. Business sense. IT sense. Common sense.

http://p.sf.net/sfu/splunk-d2dcopy1

Matplotlib-devel mailing list

Matplotlib-devel@lists.sourceforge.net

https://lists.sourceforge.net/lists/listinfo/matplotlib-devel

–

Chao YUE
Laboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL)
UMR 1572 CEA-CNRS-UVSQ
Batiment 712 - Pe 119
91191 GIF Sur YVETTE Cedex

Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16