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Ultimate NumPy
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Contenido del Curso

Ultimate NumPy

Ultimate NumPy

1. NumPy Basics
Introduction to NumPyBasic Array CreationCreating Higher Dimensional ArraysCreation Functions for 1D ArraysCreation Functions for 2D ArraysGeneral Array Creation FunctionsRandom Arrays
2. Indexing and Slicing
Basic IndexingMultidimensional IndexingSlicingSlicing in 2D ArraysInteger Array IndexingInteger Array Indexing in 2D ArraysBoolean IndexingMore about Comparisons Boolean Indexing in 2D ArraysAssigning Values to Indexed ElementsAssigning Values to Indexed Subarrays
3. Commonly used NumPy Functions
Sorting ArraysSorting 2D ArraysCopying ArraysReshaping ArraysFlattening ArraysArray Concatenation
4. Math with NumPy
BroadcastingBasic Mathematical OperationsStatistical OperationsBasic Linear Algebra with NumPy

bookArray Concatenation

Array concatenation is a fundamental operation in NumPy that allows combining arrays along a specified axis to create larger, more comprehensive datasets. Essentially, concatenation involves joining arrays together to form a new array.

NumPy has a concatenate() function that enables you to concatenate arrays along a specified axis:

  • axis=0 (the default value) concatenates the arrays by rows;
  • axis=1 concatenates the arrays by columns.

The first parameter of this function is the sequence of arrays (a tuple or list of arrays) to concatenate, while axis is the second parameter.

Here is an example with 1D arrays:


              
123456

            
import numpy as np
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
# Concatenating 1D arrays along their only axis 0
concatenated_array = np.concatenate((array1, array2))
print(concatenated_array)
copy

As you can see, with 1D arrays, everything is quite simple. Concatenation creates a 1D array with the elements of the first array followed by the elements of the second array.

Now let’s concatenate 2D arrays:


              
123456789

            
import numpy as np
array1 = np.array([[1, 2], [3, 4]])
array2 = np.array([[5, 6], [7, 8]])
# Concatenating along the axis 0 (rows)
concatenated_array_rows = np.concatenate((array1, array2))
print(f'Axis = 0:\n{concatenated_array_rows}')
# Concatenating along the axis 1 (columns)
concatenated_array_columns = np.concatenate((array1, array2), axis=1)
print(f'Axis = 1:\n{concatenated_array_columns}')
copy

Here is the visualization:

The purple elements correspond to array1, and the green ones to array2.

In fact, we can concatenate any number of arrays, and it will work the same way.

Tarea
test

Swipe to show code editor

Your task is to concatenate the sales data for both products by columns:

  1. Use the correct function for concatenation.
  2. Use sales_data_2021 and sales_data_2022 in this order for concatenation.
  3. Specify the second keyword argument correctly to concatenate by columns.

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 3. Capítulo 6
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bookArray Concatenation

Array concatenation is a fundamental operation in NumPy that allows combining arrays along a specified axis to create larger, more comprehensive datasets. Essentially, concatenation involves joining arrays together to form a new array.

NumPy has a concatenate() function that enables you to concatenate arrays along a specified axis:

  • axis=0 (the default value) concatenates the arrays by rows;
  • axis=1 concatenates the arrays by columns.

The first parameter of this function is the sequence of arrays (a tuple or list of arrays) to concatenate, while axis is the second parameter.

Here is an example with 1D arrays:


              
123456

            
import numpy as np
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
# Concatenating 1D arrays along their only axis 0
concatenated_array = np.concatenate((array1, array2))
print(concatenated_array)
copy

As you can see, with 1D arrays, everything is quite simple. Concatenation creates a 1D array with the elements of the first array followed by the elements of the second array.

Now let’s concatenate 2D arrays:


              
123456789

            
import numpy as np
array1 = np.array([[1, 2], [3, 4]])
array2 = np.array([[5, 6], [7, 8]])
# Concatenating along the axis 0 (rows)
concatenated_array_rows = np.concatenate((array1, array2))
print(f'Axis = 0:\n{concatenated_array_rows}')
# Concatenating along the axis 1 (columns)
concatenated_array_columns = np.concatenate((array1, array2), axis=1)
print(f'Axis = 1:\n{concatenated_array_columns}')
copy

Here is the visualization:

The purple elements correspond to array1, and the green ones to array2.

In fact, we can concatenate any number of arrays, and it will work the same way.

Tarea
test

Swipe to show code editor

Your task is to concatenate the sales data for both products by columns:

  1. Use the correct function for concatenation.
  2. Use sales_data_2021 and sales_data_2022 in this order for concatenation.
  3. Specify the second keyword argument correctly to concatenate by columns.

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 3. Capítulo 6
toggle bottom row

bookArray Concatenation

Array concatenation is a fundamental operation in NumPy that allows combining arrays along a specified axis to create larger, more comprehensive datasets. Essentially, concatenation involves joining arrays together to form a new array.

NumPy has a concatenate() function that enables you to concatenate arrays along a specified axis:

  • axis=0 (the default value) concatenates the arrays by rows;
  • axis=1 concatenates the arrays by columns.

The first parameter of this function is the sequence of arrays (a tuple or list of arrays) to concatenate, while axis is the second parameter.

Here is an example with 1D arrays:


              
123456

            
import numpy as np
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
# Concatenating 1D arrays along their only axis 0
concatenated_array = np.concatenate((array1, array2))
print(concatenated_array)
copy

As you can see, with 1D arrays, everything is quite simple. Concatenation creates a 1D array with the elements of the first array followed by the elements of the second array.

Now let’s concatenate 2D arrays:


              
123456789

            
import numpy as np
array1 = np.array([[1, 2], [3, 4]])
array2 = np.array([[5, 6], [7, 8]])
# Concatenating along the axis 0 (rows)
concatenated_array_rows = np.concatenate((array1, array2))
print(f'Axis = 0:\n{concatenated_array_rows}')
# Concatenating along the axis 1 (columns)
concatenated_array_columns = np.concatenate((array1, array2), axis=1)
print(f'Axis = 1:\n{concatenated_array_columns}')
copy

Here is the visualization:

The purple elements correspond to array1, and the green ones to array2.

In fact, we can concatenate any number of arrays, and it will work the same way.

Tarea
test

Swipe to show code editor

Your task is to concatenate the sales data for both products by columns:

  1. Use the correct function for concatenation.
  2. Use sales_data_2021 and sales_data_2022 in this order for concatenation.
  3. Specify the second keyword argument correctly to concatenate by columns.

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!

Array concatenation is a fundamental operation in NumPy that allows combining arrays along a specified axis to create larger, more comprehensive datasets. Essentially, concatenation involves joining arrays together to form a new array.

NumPy has a concatenate() function that enables you to concatenate arrays along a specified axis:

  • axis=0 (the default value) concatenates the arrays by rows;
  • axis=1 concatenates the arrays by columns.

The first parameter of this function is the sequence of arrays (a tuple or list of arrays) to concatenate, while axis is the second parameter.

Here is an example with 1D arrays:


              
123456

            
import numpy as np
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
# Concatenating 1D arrays along their only axis 0
concatenated_array = np.concatenate((array1, array2))
print(concatenated_array)
copy

As you can see, with 1D arrays, everything is quite simple. Concatenation creates a 1D array with the elements of the first array followed by the elements of the second array.

Now let’s concatenate 2D arrays:


              
123456789

            
import numpy as np
array1 = np.array([[1, 2], [3, 4]])
array2 = np.array([[5, 6], [7, 8]])
# Concatenating along the axis 0 (rows)
concatenated_array_rows = np.concatenate((array1, array2))
print(f'Axis = 0:\n{concatenated_array_rows}')
# Concatenating along the axis 1 (columns)
concatenated_array_columns = np.concatenate((array1, array2), axis=1)
print(f'Axis = 1:\n{concatenated_array_columns}')
copy

Here is the visualization:

The purple elements correspond to array1, and the green ones to array2.

In fact, we can concatenate any number of arrays, and it will work the same way.

Tarea
test

Swipe to show code editor

Your task is to concatenate the sales data for both products by columns:

  1. Use the correct function for concatenation.
  2. Use sales_data_2021 and sales_data_2022 in this order for concatenation.
  3. Specify the second keyword argument correctly to concatenate by columns.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 3. Capítulo 6
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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