Contenido del Curso

NumPy in a Nutshell

1. Getting Started with NumPy

What is NumPy?Function array()Basic Array Creation Quiz

2. Dimensions in Arrays

1-D Arrays Mathematical Operations with 1-D Arrays 2-D Arrays 3-D Arrays Multidimensional Arrays Quiz

3. Indexing and Slicing

Access Array Elements Match Math Operations Access 2-D and 3-D Arrays Match the Slicing Negative Indexing Match the Indices Get One Dimensional Array Using Slice and Only Positive Indexes Get One Dimensional Array Using Only Negative Indexes

4. Important Functions

Reshaping Flattening Joining Sorting Arrays Copying Arrays

Joining

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)

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)

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)

Tarea

Swipe to show code editor

You have two arrays:

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

You have to create the following combined array:

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 4. Capítulo 3

Joining

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)

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)

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)

Tarea

Swipe to show code editor

You have two arrays:

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

You have to create the following combined array:

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 4. Capítulo 3

Joining

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)

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)

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)

Tarea

Swipe to show code editor

You have two arrays:

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

You have to create the following combined array:

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

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)

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)

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)

Tarea

Swipe to show code editor

You have two arrays:

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

You have to create the following combined array:

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 4. Capítulo 3

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones