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Flattening | Important Functions
NumPy in a Nutshell
course content

Contenido del Curso

NumPy in a Nutshell

NumPy in a Nutshell

1. Getting Started with NumPy
2. Dimensions in Arrays
3. Indexing and Slicing
4. Important Functions

bookFlattening

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:

123456
import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.reshape(-1) print(new_array)
copy

Let's see how to use the .flatten() method:

123456
import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.flatten() print(new_array)
copy

Let's practice!

Tarea

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].

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 4. Capítulo 2
toggle bottom row

bookFlattening

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:

123456
import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.reshape(-1) print(new_array)
copy

Let's see how to use the .flatten() method:

123456
import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.flatten() print(new_array)
copy

Let's practice!

Tarea

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].

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 4. Capítulo 2
toggle bottom row

bookFlattening

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:

123456
import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.reshape(-1) print(new_array)
copy

Let's see how to use the .flatten() method:

123456
import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.flatten() print(new_array)
copy

Let's practice!

Tarea

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].

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

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:

123456
import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.reshape(-1) print(new_array)
copy

Let's see how to use the .flatten() method:

123456
import numpy as np array = np.array([[12, 45, 78, 34, 0], [13, 5, 78, 3, 1]]) new_array = array.flatten() print(new_array)
copy

Let's practice!

Tarea

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].

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 4. Capítulo 2
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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