Sorting and Searching

Sorting Arrays

Sorting is essential for data analysis, ranking, and organizing datasets.

Sorting a 1D Array

Copy Code
a = np.array([3, 1, 4, 1, 5])
print("Original Array:", a)
print("Sorted Array:", np.sort(a))
# Sorts in ascending orderTry it Yourself

Sorting a 2D Array

Copy Code
matrix = np.array([[3, 2, 1], [6, 5, 4]])
print("Original Matrix:\n", matrix)
print("Sorted along each row:\n", np.sort(matrix, axis=1)) # Row-wise sorting
print("Sorted along each column:\n", np.sort(matrix, axis=0)) # Column-wise sortingTry it Yourself

Searching for Elements in an Array

NumPy provides fast searching operations to find values in an array.

Finding the Index of the Maximum and Minimum Values

Copy Code
a = np.array([3, 1, 4, 1, 5, 9, 2])
print("Index of Maximum Value:", np.argmax(a))
print("Index of Minimum Value:", np.argmin(a))
# Returns index of max value
# Returns index of min valueTry it Yourself

Finding Elements that Satisfy a Condition

Copy Code
a = np.array([10, 20, 30, 40, 50])
# Find elements greater than 25
filtered_elements = a[a > 25]
print("Elements greater than 25:", filtered_elements)Try it Yourself

Why is Searching Important?

• Used in data preprocessing to filter values in large datasets.
• Helps in feature selection and anomaly detection in machine learning.