Course Content

Ultimate NumPy

Array 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)

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}')

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.

Task

Swipe to show code editor

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

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 6

Array Concatenation

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)

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}')

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.

Task

Swipe to show code editor

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

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 6

Array Concatenation

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)

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}')

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.

Task

Swipe to show code editor

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

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

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)

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}')

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.

Task

Swipe to show code editor

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

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 3. Chapter 6

Switch to desktop for real-world practiceContinue from where you are using one of the options below