import numpy as np
A and B arrays are in good order.
A = np.array(['a','b','c','d','e'])
B = np.array([5,7,3,9,11])
C = np.array(['a','b','k','j','p','x'])
For each element of array C, if that element is in A, get value from B of the same position as B. If not in A, write np.nan.
The expected result would be:
result = np.array([5,7,na,na,na,na])
How is the easeist way of doing it in numPy?
You could use np.in1d(C, A) to determine if C is in A.
In [110]: np.in1d(C, A)
Out[115]: array([ True, True, False, False, False], dtype=bool)
Then use np.where to select the desired values from B or np.nan:
In [116]: np.where(np.in1d(C, A), B, np.nan)
Out[116]: array([ 5., 7., nan, nan, nan])
np.where(cond, B, np.nan) returns an array of the same shape as the boolean array cond.
The returned array takes a value from B if the corresponding value in cond is True, and is np.nan otherwise.
If len(C) > len(B), and if you would like the final array to contain NaNs for the last len(C)-len(B) values, then you could use:
N = len(B)
result = np.full(len(C), np.nan)
result[:N] = np.where(np.in1d(C[:N], A), B, np.nan)
B is shorter than C. What do you want to happen if an element in C is in A but there is no corresponding value in B?C = np.array(['a','b','k','j','p','a']) then what should the result be?