Iterating Without For Loop In Numpy Array

I need to do logical iteration over numpy array, which's values depend on elements of other array. I've written code below for clarifying my problem. Any suggestions to solve this

Solution 1:

Here's an approach using a combination of np.maximum.accumulate and np.where to create stepped indices that are to be stopped at certain intervals and then simply indexing into b would give us the desired output.

Thus, an implementation would be -

mask = a!="b"idx = np.maximum.accumulate(np.where(mask,np.arange(mask.size),0))
out = b[idx]

Sample step-by-step run -

In [656]: # Inputs 
     ...: a = np.array(['a', 'b', 'a', 'a', 'b', 'a'])
     ...: b = np.array([150, 154, 147, 126, 148, 125])
     ...: 

In [657]: mask = a!="b"

In [658]: mask
Out[658]: array([ True, False,  True,  True, False,  True], dtype=bool)

# Crux of the implmentation happens here :
In [696]: np.where(mask,np.arange(mask.size),0)
Out[696]: array([0, 0, 2, 3, 0, 5])

In [697]: np.maximum.accumulate(np.where(mask,np.arange(mask.size),0))
Out[697]: array([0, 0, 2, 3, 3, 5])# Stepped indices "intervaled" at masked places

In [698]: idx = np.maximum.accumulate(np.where(mask,np.arange(mask.size),0))

In [699]: b[idx]
Out[699]: array([150, 150, 147, 126, 126, 125])

Solution 2:

You could use a more vectorized approach Like so:

np.where(a == "b", np.roll(c, 1), b)

np.where will take the elements from np.roll(c, 1) if the condition is True or it will take from b if the condition is False. np.roll(c, 1) will "roll" forward all the elements of c by 1 so that each element refers to c[i-1].

These type of operations are what make numpy so invaluable. Looping should be avoided if possible.

Solution 3:

If you don't need to wrap around the margin there is a very simply solution:

a = np.array(['a', 'b', 'a', 'a', 'b', 'a'])
b = np.array([150, 154, 147, 126, 148, 125])
c = b.copy()  #removes necessity of elsecase
c[a[:-1]=='b'] = c[a[1:]=='b']

or equally:

a = np.array(['a', 'b', 'a', 'a', 'b', 'a'])
b = np.array([150, 154, 147, 126, 148, 125])
c = b.copy()  #removes necessity of elsecase
mask = a == 'b'
c[mask[:-1]] = c[mask[1:]]

If you want to wrap around the margin (a[0]=='b') then it gets a little more complicated, you either need to use roll or catch this case first with and if.