Assigning Values to Indexed Elements

Assigning values to specific elements or subarrays is useful for updating data, correcting errors, or applying conditions in datasets. This is especially helpful in tasks like replacing invalid entries, adjusting values for analysis, or modifying parts of an array for simulations and computations.

First of all, we can assign a value to an indexed element of an array. Here is the general syntax to accomplish this in 1D arrays: array[i] = n, where i is a certain index and n is the value to be assigned.

In 2D arrays, we have the following syntax: array[i, j] = n, where i and j are the row and column indices, respectively. For higher-dimensional arrays, the number of indices corresponds to the number of dimensions.


              123456789
            
import numpy as np
array_1d = np.array([1, 4, 6, 2])
# Assigning 10 to the first element of array_1d
array_1d[0] = 10
print(array_1d)
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
# Assigning 8 to the element in the second row and column of array_2d
array_2d[1, 1] = 8
print(array_2d)

Note

If you assign a value of a higher data type, like a float, to an element with a lower data type, like an integer, the value may be changed or cause an error. For example, assigning 3.5 to an integer element will store it as 3, losing the decimal part.


              12345
            
import numpy as np
array_1d = np.array([1, 4, 6, 2])
# Assigning 10.2 to the first element of array_1d
array_1d[0] = 10.2
print(array_1d)

No exception was thrown, however, the first element was assigned the value of 10 instead of 10.2. The float value was converted to an integer since that's the dtype of the array.

The picture below shows the structure of the employee_data array used in the task:

Task

Swipe to show code editor

You are managing a dataset of employee information, where each row represents an employee, and the columns represent their salary and performance score. The dataset is stored in the employee_data array.

Update the salary (first column) of the fourth employee to 60000.
Use positive indexing to access and modify the value.

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 2. Chapter 9

Assigning Values to Indexed Elements


              123456789
            
import numpy as np
array_1d = np.array([1, 4, 6, 2])
# Assigning 10 to the first element of array_1d
array_1d[0] = 10
print(array_1d)
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
# Assigning 8 to the element in the second row and column of array_2d
array_2d[1, 1] = 8
print(array_2d)

Note

If you assign a value of a higher data type, like a float, to an element with a lower data type, like an integer, the value may be changed or cause an error. For example, assigning 3.5 to an integer element will store it as 3, losing the decimal part.


              12345
            
import numpy as np
array_1d = np.array([1, 4, 6, 2])
# Assigning 10.2 to the first element of array_1d
array_1d[0] = 10.2
print(array_1d)

No exception was thrown, however, the first element was assigned the value of 10 instead of 10.2. The float value was converted to an integer since that's the dtype of the array.

The picture below shows the structure of the employee_data array used in the task:

Task

Swipe to show code editor

Update the salary (first column) of the fourth employee to 60000.
Use positive indexing to access and modify the value.

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 2. Chapter 9

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