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Joining | Important Functions
NumPy in a Nutshell
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NumPy in a Nutshell

NumPy in a Nutshell

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

bookJoining

Another equally important operation in array manipulation is joining arrays. Joining arrays involves combining multiple arrays into a single array that includes all the elements from each of the original arrays.

We perform this concatenation along the specified axes:

  • if axis = 0 (which is the default value), this implies concatenating the arrays by rows;
  • if axis = 1, this means concatenating the arrays by columns.

Join two arrays:

12345678
import numpy as np array_1 = np.array([54, 6, 23, 1, 6]) array_2 = np.array([12, 67, 94, 2, 8 ]) array = np.concatenate((array_1, array_2)) print(array)
copy

Concatenate two 2-D arrays along columns (axis=1):

12345678
import numpy as np array_1 = np.array([[0, 1, 2], [3, 4, 5]]) array_2 = np.array([[6, 7, 8], [9, 10, 11]]) array = np.concatenate((array_1, array_2), axis=1) print(array)
copy

Concatenate two 2-D arrays along rows (default axis=0):

12345678
import numpy as np array_1 = np.array([[0, 1, 2], [3, 4, 5]]) array_2 = np.array([[6, 7, 8], [9, 10, 11]]) array = np.concatenate((array_1, array_2)) print(array)
copy

Tarea

You have two arrays:

  1. [[12, 56, 78], [35, 1, 5]];
  2. [[ 8, 65, 3], [ 1, 2, 3]].

You have to create the following combined array:

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 3
toggle bottom row

bookJoining

Another equally important operation in array manipulation is joining arrays. Joining arrays involves combining multiple arrays into a single array that includes all the elements from each of the original arrays.

We perform this concatenation along the specified axes:

  • if axis = 0 (which is the default value), this implies concatenating the arrays by rows;
  • if axis = 1, this means concatenating the arrays by columns.

Join two arrays:

12345678
import numpy as np array_1 = np.array([54, 6, 23, 1, 6]) array_2 = np.array([12, 67, 94, 2, 8 ]) array = np.concatenate((array_1, array_2)) print(array)
copy

Concatenate two 2-D arrays along columns (axis=1):

12345678
import numpy as np array_1 = np.array([[0, 1, 2], [3, 4, 5]]) array_2 = np.array([[6, 7, 8], [9, 10, 11]]) array = np.concatenate((array_1, array_2), axis=1) print(array)
copy

Concatenate two 2-D arrays along rows (default axis=0):

12345678
import numpy as np array_1 = np.array([[0, 1, 2], [3, 4, 5]]) array_2 = np.array([[6, 7, 8], [9, 10, 11]]) array = np.concatenate((array_1, array_2)) print(array)
copy

Tarea

You have two arrays:

  1. [[12, 56, 78], [35, 1, 5]];
  2. [[ 8, 65, 3], [ 1, 2, 3]].

You have to create the following combined array:

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 3
toggle bottom row

bookJoining

Another equally important operation in array manipulation is joining arrays. Joining arrays involves combining multiple arrays into a single array that includes all the elements from each of the original arrays.

We perform this concatenation along the specified axes:

  • if axis = 0 (which is the default value), this implies concatenating the arrays by rows;
  • if axis = 1, this means concatenating the arrays by columns.

Join two arrays:

12345678
import numpy as np array_1 = np.array([54, 6, 23, 1, 6]) array_2 = np.array([12, 67, 94, 2, 8 ]) array = np.concatenate((array_1, array_2)) print(array)
copy

Concatenate two 2-D arrays along columns (axis=1):

12345678
import numpy as np array_1 = np.array([[0, 1, 2], [3, 4, 5]]) array_2 = np.array([[6, 7, 8], [9, 10, 11]]) array = np.concatenate((array_1, array_2), axis=1) print(array)
copy

Concatenate two 2-D arrays along rows (default axis=0):

12345678
import numpy as np array_1 = np.array([[0, 1, 2], [3, 4, 5]]) array_2 = np.array([[6, 7, 8], [9, 10, 11]]) array = np.concatenate((array_1, array_2)) print(array)
copy

Tarea

You have two arrays:

  1. [[12, 56, 78], [35, 1, 5]];
  2. [[ 8, 65, 3], [ 1, 2, 3]].

You have to create the following combined array:

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!

Another equally important operation in array manipulation is joining arrays. Joining arrays involves combining multiple arrays into a single array that includes all the elements from each of the original arrays.

We perform this concatenation along the specified axes:

  • if axis = 0 (which is the default value), this implies concatenating the arrays by rows;
  • if axis = 1, this means concatenating the arrays by columns.

Join two arrays:

12345678
import numpy as np array_1 = np.array([54, 6, 23, 1, 6]) array_2 = np.array([12, 67, 94, 2, 8 ]) array = np.concatenate((array_1, array_2)) print(array)
copy

Concatenate two 2-D arrays along columns (axis=1):

12345678
import numpy as np array_1 = np.array([[0, 1, 2], [3, 4, 5]]) array_2 = np.array([[6, 7, 8], [9, 10, 11]]) array = np.concatenate((array_1, array_2), axis=1) print(array)
copy

Concatenate two 2-D arrays along rows (default axis=0):

12345678
import numpy as np array_1 = np.array([[0, 1, 2], [3, 4, 5]]) array_2 = np.array([[6, 7, 8], [9, 10, 11]]) array = np.concatenate((array_1, array_2)) print(array)
copy

Tarea

You have two arrays:

  1. [[12, 56, 78], [35, 1, 5]];
  2. [[ 8, 65, 3], [ 1, 2, 3]].

You have to create the following combined array:

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 4. Capítulo 3
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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