How to delete columns in numpy array

Question

I would like to delete selected columns in a numpy array   This is what I do   n  397   a   array    NaN    2     3   NaN                       1     2     3   9     In  398   print a    NaN   2    3   NaN      1    2    3    9     In  399   z   any isnan a   axis 0   In  400   print z   True False False  True   In  401   delete a  z  axis   1  Out 401    array     3    NaN             3     9       In this example my goal is to delete all the columns that contain NaN s  I expect the last command  to result in   array   2   3            2   3       How can I do that

User · Answer

In your situation  you can extract the desired data with   a    -z     -z  is the logical negation of the boolean array  z   This is the same as   a    logical not z

User · Answer

gt  gt  gt  A   array    1   2   3   4                    5   6   7   8                    9  10  11  12      gt  gt  gt  A   A transpose     gt  gt  gt  A   A 1   transpose

User · Answer

Removing Matrix columns that contain NaN  This is a lengthy answer  but hopefully easy to follow   def column to vector matrix  i       return  row i  for row in matrix  import numpy def remove NaN columns matrix       import scipy     import math     from numpy import column stack  vstack      columns   A shape 1       print  columns   columns      result          skip column   True     for column in range 0  columns           vector   column to vector A  column          skip column   False         for value in vector                print column  vector  value  math isnan value                if math isnan value                   skip column   True         if skip column    False              result append vector      return column stack result       test it A   vstack    float  NaN    2   3   float  NaN       1   2   3   9    print  A shape   A shape    n   A  B   remove NaN columns A  print  B shape   B shape    n   B   A shape  2  4       nan   2    3   nan      1    2    3    9    B shape  2  2       2   3      2   3

User · Answer

This creates another array without those columns     b   a compress logical not z   axis 1

User · Answer

Example from the numpy documentation    gt  gt  gt  a   numpy array    0   1   2   3                    4   5   6   7                    8   9  10  11                   12  13  14  15      gt  gt  gt  numpy delete a  numpy s  1 3   axis 0                          remove rows 1 and 2  array    0   1   2   3           12  13  14  15      gt  gt  gt  numpy delete a  numpy s  1 3   axis 1                          remove columns 1 and 2  array    0   3            4   7            8  11           12  15

User · Answer

Given its name  I think the standard way should be delete   import numpy as np  A   np delete A  1  0     delete second row of A B   np delete B  2  0     delete third row of B C   np delete C  1  1     delete second column of C   According to numpy s documentation page  the parameters for numpy delete are as follow    numpy delete arr  obj  axis None    arr refers to the input array   obj refers to which sub-arrays  e g  column row no  or slice of the array  and axis refers to either column wise  axis   1  or row-wise  axis   0  delete operation

User · Answer

From Numpy Documentation  np delete arr  obj  axis None      Return a new array with sub-arrays along an axis deleted    gt  gt  gt  arr array    1   2   3   4            5   6   7   8            9  10  11  12     gt  gt  gt  np delete arr  1  0  array    1   2   3   4            9  10  11  12      gt  gt  gt  np delete arr  np s    2   1  array    2   4            6   8           10  12     gt  gt  gt  np delete arr   1 3 5   None  array   1   3   5   7   8   9  10  11  12

User · Answer

Another way is to use masked arrays   import numpy as np a   np array    np nan    2     3   np nan      1     2     3   9    print a       NaN   2    3   NaN        1    2    3    9      The np ma masked invalid method returns a masked array with nans and infs masked out   print np ma masked invalid a     -- 2 0 3 0 --    1 0 2 0 3 0 9 0     The np ma compress cols method returns a 2-D array with any column containing a  masked value suppressed   a np ma compress cols np ma masked invalid a   print a       2   3        2   3      See  manipulating-a-maskedarray

[python] How to delete columns in numpy.array

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