I have a python array that i got using

array = np.arange(2,201,2).reshape(25,4)

which gave me this:

[[  2   4   6   8]
 [ 18  20  22  24]
 [ 34  36  38  40]
 [ 50  52  54  56]
 [ 66  68  70  72]
 [ 82  84  86  88]
 [ 98 100 102 104]
 [114 116 118 120]
 [130 132 134 136]
 [146 148 150 152]
 [162 164 166 168]
 [178 180 182 184]
 [194 196 198 200]]

but now i'm instructed to select only the values below 50 from "array", add 5 to these values, and then multiply by 2. The other values should remain unchanged and everything should be saved as "array". This is a school assignment so I don't have the output but basically the output should be the array in the same 25x4 shape and the first ~3 rows will be changed (since those are the ones under 50) and the other rows/values will be the same (since they're over 50). I've tried the following code:

for i in array:
    if array < 50:
        print((i 5)*2)
    else:
        print(i)

and I'm getting an error that says -
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

any help would be greatly appreciated since I can't find any other articles with similar questions

CodePudding user response：

There are 2 ways to address this question. A Python one and a numpy one (numpy is not Python...).

1. Python way:

   You have a sequence of sequence containers. You can use a double iteration to test the values one at a time and replace the ones that have to be:

    for row in array:                   # iterate over the rows
        for i, val in enumerate(row):   # then the values in the row
            if val <=50:                # test them
                row[i] = (val   5) * 2  # and replace
   

   This works as soon as the outer iteration gives you a direct access to the row container. This is true for both Python containers (lists) and numpy arrays but may not be guaranteed for any type of containers. The super safe way would be to keep the indexes and directly modify array:

    for i in range(len(array)):
        for j in range(len(array[i])):
            if array[i][j]< 50:
                array[i][j] = (array[i][j]   2) * 5
   

2. Numpy way:

   The power of numpy is to provide high speed iterations on its arrays. In numpy wordings it is called vectorization. You should first extract the relevant indexes and then change the values in one single vectorized operation:

    ix = np.where(array < 50)
    array[ix] = (array[ix]   5) * 2
   

   For large arrays, this second way should be at least one magnitude order faster than the first one.

For your question, the correct way is the one that matches your current lesson, either Python or numpy...

CodePudding user response：

import numpy as np

array = np.arange(2,201,2).reshape(25,4)

values = [ (element 5)*2 if element < 50 else element for innerList in array for element in innerList ]

print(values)