Replace columns in a 2D numpy array by columns from another2D array

Question

I have two 2D arrays, I want to create arrays that are copy of the first one and then replace some columns by others from the second one.

M1 = np.array([[1.0, 2.0, 3.0, 1.0, 2.0, 3.0], 
               [4.0, 5.0, 6.0, 4.0, 5.0, 6.0]])

M2 = np.array([[1.1, 2.1, 3.1, 1.2, 2.2, 3.2],
               [4.1, 5.1, 6.1., 4.2, 5.2, 6.2]])

I want to do a loop that can give the following arrays:

M3 = np.array([[1.1, 2.0, 3.0, 1.2, 2.0, 3.0], 
               [4.1, 5.0, 6.0, 4.2, 5.0, 6.0]])

M4 = np.array([[1.0, 2.1, 3.0, 1.0, 2.2, 3.0], 
               [4.0, 5.1, 6.0, 4.0, 5.2, 6.0]])

M5 = np.array([[1.0, 2.0, 3.1, 1.0, 2.0, 3.2], 
               [4.0, 5.0, 6.1, 4.0, 5.0, 6.2]])

Paul Panzer · Accepted Answer · 2019-06-05 14:24:40Z

1

You can use np.where:

selector = [1,0,0,1,0,0]
np.where(selector,M2,M1)
# array([[1.1, 2. , 3. , 1.2, 2. , 3. ],
#        [4.1, 5. , 6. , 4.2, 5. , 6. ]])
selector = [0,1,0,0,1,0]
np.where(selector,M2,M1)
# array([[1. , 2.1, 3. , 1. , 2.2, 3. ],
#        [4. , 5.1, 6. , 4. , 5.2, 6. ]])

etc.

Or in a loop:

M3,M4,M5 = (np.where(s,M2,M1) for s in np.tile(np.identity(3,bool), (1,2)))
M3
# array([[1.1, 2. , 3. , 1.2, 2. , 3. ],
#        [4.1, 5. , 6. , 4.2, 5. , 6. ]])
M4
# array([[1. , 2.1, 3. , 1. , 2.2, 3. ],
#        [4. , 5.1, 6. , 4. , 5.2, 6. ]])
M5
# array([[1. , 2. , 3.1, 1. , 2. , 3.2],
#        [4. , 5. , 6.1, 4. , 5. , 6.2]])

Alternatively, you can copy M1 and then slice in M2. This is more verbose but should be faster:

n = 3
Mj = []
for j in range(n):
    Mp = M1.copy()
    Mp[:,j::n] = M2[:,j::n]
    Mj.append(Mp)

M3,M4,M5 = Mj

edited Jun 5, 2019 at 14:24

answered Jun 5, 2019 at 13:33

Paul Panzer

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3 Comments

Elena Popa Over a year ago

what if i have a very large number of columns and I want to specify that every 'n' columns pick one.

Paul Panzer Over a year ago

@ElenaPopa is the number of columns a mulitple of n?

Paul Panzer Over a year ago

@ElenaPopa never mind. I've added a method that works regardless. I'm pretty sure it will also be faster, so if your arrays are big I recommend it over the other.

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