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NumPy in a Nutshell
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NumPy in a Nutshell

NumPy in a Nutshell

1. Getting Started with NumPy
What is NumPy?Function array()Basic Array Creation Quiz
2. Dimensions in Arrays
1-D ArraysMathematical Operations with 1-D Arrays2-D Arrays3-D ArraysMultidimensional Arrays Quiz
3. Indexing and Slicing
Access Array ElementsMatch Math Operations Access 2-D and 3-D ArraysMatch the Slicing Negative IndexingMatch the IndicesGet One Dimensional Array Using Slice and Only Positive Indexes Get One Dimensional Array Using Only Negative Indexes
4. Important Functions
ReshapingFlatteningJoining Sorting ArraysCopying Arrays

bookReshaping

Sometimes situations arise when we need to somehow change our array, for example, change the size of the array or go from an array of one dimension to an array of another dimension, but with the same data that was originally used. But it is not always convenient to recreate the array from scratch, so some functions modify the array as we need it.

Let's have a look at some of them:

  • np.reshape() - this function changes the shape of an N-dimensional array while maintaining the same total number of elements;
  • np.transpose() - this function transposes the array, essentially swapping its axes;
  • np.concatenate() - this function creates a new array by appending arrays one after another along the specified axis;
  • np.resize() - this function is used to resize an array, creating a copy of the original array with the specified size.

Reshape one-dimensional array into a two-dimensional array:


              
123456

            
import numpy as np

array  = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
new_array = array.reshape(4, 3)

print(new_array)
copy

Reshape one-dimensional into a three-dimensional array:


              
123456

            
import numpy as np

array  = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
new_array = array.reshape(2, 3, 2)

print(new_array)
copy

Tarea

Consider the following array: [11, 56, 78, 45, 1, 5]. You should obtain the following array: [[11, 56], [78, 45], [1, 5]].

Please use the .reshape() method.

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 1
toggle bottom row

bookReshaping

Sometimes situations arise when we need to somehow change our array, for example, change the size of the array or go from an array of one dimension to an array of another dimension, but with the same data that was originally used. But it is not always convenient to recreate the array from scratch, so some functions modify the array as we need it.

Let's have a look at some of them:

  • np.reshape() - this function changes the shape of an N-dimensional array while maintaining the same total number of elements;
  • np.transpose() - this function transposes the array, essentially swapping its axes;
  • np.concatenate() - this function creates a new array by appending arrays one after another along the specified axis;
  • np.resize() - this function is used to resize an array, creating a copy of the original array with the specified size.

Reshape one-dimensional array into a two-dimensional array:


              
123456

            
import numpy as np

array  = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
new_array = array.reshape(4, 3)

print(new_array)
copy

Reshape one-dimensional into a three-dimensional array:


              
123456

            
import numpy as np

array  = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
new_array = array.reshape(2, 3, 2)

print(new_array)
copy

Tarea

Consider the following array: [11, 56, 78, 45, 1, 5]. You should obtain the following array: [[11, 56], [78, 45], [1, 5]].

Please use the .reshape() method.

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 1
toggle bottom row

bookReshaping

Sometimes situations arise when we need to somehow change our array, for example, change the size of the array or go from an array of one dimension to an array of another dimension, but with the same data that was originally used. But it is not always convenient to recreate the array from scratch, so some functions modify the array as we need it.

Let's have a look at some of them:

  • np.reshape() - this function changes the shape of an N-dimensional array while maintaining the same total number of elements;
  • np.transpose() - this function transposes the array, essentially swapping its axes;
  • np.concatenate() - this function creates a new array by appending arrays one after another along the specified axis;
  • np.resize() - this function is used to resize an array, creating a copy of the original array with the specified size.

Reshape one-dimensional array into a two-dimensional array:


              
123456

            
import numpy as np

array  = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
new_array = array.reshape(4, 3)

print(new_array)
copy

Reshape one-dimensional into a three-dimensional array:


              
123456

            
import numpy as np

array  = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
new_array = array.reshape(2, 3, 2)

print(new_array)
copy

Tarea

Consider the following array: [11, 56, 78, 45, 1, 5]. You should obtain the following array: [[11, 56], [78, 45], [1, 5]].

Please use the .reshape() method.

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!

Sometimes situations arise when we need to somehow change our array, for example, change the size of the array or go from an array of one dimension to an array of another dimension, but with the same data that was originally used. But it is not always convenient to recreate the array from scratch, so some functions modify the array as we need it.

Let's have a look at some of them:

  • np.reshape() - this function changes the shape of an N-dimensional array while maintaining the same total number of elements;
  • np.transpose() - this function transposes the array, essentially swapping its axes;
  • np.concatenate() - this function creates a new array by appending arrays one after another along the specified axis;
  • np.resize() - this function is used to resize an array, creating a copy of the original array with the specified size.

Reshape one-dimensional array into a two-dimensional array:


              
123456

            
import numpy as np

array  = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
new_array = array.reshape(4, 3)

print(new_array)
copy

Reshape one-dimensional into a three-dimensional array:


              
123456

            
import numpy as np

array  = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
new_array = array.reshape(2, 3, 2)

print(new_array)
copy

Tarea

Consider the following array: [11, 56, 78, 45, 1, 5]. You should obtain the following array: [[11, 56], [78, 45], [1, 5]].

Please use the .reshape() method.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 4. Capítulo 1
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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