Flattening
Do you know what it means to flatten an array? Flattening is the process of transforming a multidimensional array into a one-dimensional one.
This transformation can be achieved using two different methods:
- the first one we're already familiar with is the
.reshape(-1)method with an argument of-1; - the other option is to use the
.flatten()method.
Now, let's have a look at both of these methods in practice.
Let's see how to use the .reshape(-1) method:
123456import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.reshape(-1) print(new_array)
Let's see how to use the .flatten() method:
123456import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.flatten() print(new_array)
Let's practice!
Swipe to start coding
Consider the following array:
[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]]
You should transform it into the following array:
[1 2 3 4 5 6 7 8 9 10 11 12].
Solution
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Do you know what it means to flatten an array? Flattening is the process of transforming a multidimensional array into a one-dimensional one.
This transformation can be achieved using two different methods:
- the first one we're already familiar with is the
.reshape(-1)method with an argument of-1; - the other option is to use the
.flatten()method.
Now, let's have a look at both of these methods in practice.
Let's see how to use the .reshape(-1) method:
123456import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.reshape(-1) print(new_array)
Let's see how to use the .flatten() method:
123456import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.flatten() print(new_array)
Let's practice!
Swipe to start coding
Consider the following array:
[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]]
You should transform it into the following array:
[1 2 3 4 5 6 7 8 9 10 11 12].
Solution
Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessousMerci pour vos commentaires !
Awesome!
Completion rate improved to 4.76single
