Sorting is a fundamental operation in data processing, and NumPy makes it efficient and intuitive with its powerful array sorting capabilities. Whether you're sorting numeric data, strings, or complex multi-dimensional arrays, NumPy provides optimized tools to get the job done quickly.

In this guide, you'll learn:

• ✅ What array sorting is in NumPy

• How to use numpy.sort(), ndarray.sort(), and argsort()

• Sorting multi-dimensional arrays

• Full code examples

• Tips and Common pitfalls

Why Sort with NumPy?

Sorting helps in:

• Preparing data for binary search or analysis

• Organizing results for visualization

• Cleaning or restructuring large datasets

NumPy’s built-in methods are much faster than Python’s list sorting, especially for large numerical datasets.

Basic Sorting with numpy.sort()

The most common function to sort arrays.

✅ Syntax:

numpy.sort(a, axis=-1, kind='quicksort', order=None)

• a: Array to sort

• axis: Axis along which to sort

• kind: Sorting algorithm — 'quicksort', 'mergesort', 'heapsort', 'stable'

• order: For structured arrays

✅ Example:

import numpy as np

arr = np.array([3, 1, 5, 2])
sorted_arr = np.sort(arr)
print(sorted_arr)

Output:

[1 2 3 5]

numpy.sort() returns a sorted copy and does not modify the original array.

In-Place Sorting with ndarray.sort()

Sorts the array in place, modifying the original data.

arr = np.array([9, 7, 3, 1])
arr.sort()
print(arr)

Output:

[1 3 7 9]

Use this for memory efficiency when you don’t need the original array.

Getting Sorted Indices with numpy.argsort()

Returns the indices that would sort the array.

arr = np.array([40, 10, 20])
indices = np.argsort(arr)
print(indices)

Output:

[1 2 0]

You can use these indices to reorder another related array:

names = np.array(['apple', 'banana', 'cherry'])
sorted_names = names[indices]
print(sorted_names)

Sorting Multi-dimensional Arrays

✅ Sorting Row-wise (default):

arr = np.array([[3, 2, 1], [6, 5, 4]])
sorted_rows = np.sort(arr, axis=1)
print(sorted_rows)

Output:

[[1 2 3]
 [4 5 6]]

✅ Sorting Column-wise:

sorted_cols = np.sort(arr, axis=0)
print(sorted_cols)

Output:

[[3 2 1]
 [6 5 4]]

Sorting Structured Arrays by Field

If you're working with structured arrays (like CSVs or databases), you can sort by field.

data = np.array([(3, 'apple'), (1, 'banana'), (2, 'cherry')],
                dtype=[('id', int), ('name', 'U10')])
sorted_data = np.sort(data, order='id')
print(sorted_data)

Output:

[(1, 'banana') (2, 'cherry') (3, 'apple')]

Full Working Code Example

import numpy as np

# 1D Sorting
arr1d = np.array([12, 4, 7, 9])
print("Sorted:", np.sort(arr1d))

# In-place sorting
arr1d.sort()
print("In-place Sorted:", arr1d)

# argsort usage
scores = np.array([88, 70, 96])
names = np.array(['Alice', 'Bob', 'Charlie'])
order = np.argsort(scores)
print("Sorted Names by Scores:", names[order])

# 2D Array sorting
arr2d = np.array([[9, 2, 4], [7, 1, 5]])
print("Row-wise Sort:\n", np.sort(arr2d, axis=1))
print("Column-wise Sort:\n", np.sort(arr2d, axis=0))

# Structured array sorting
data = np.array([(25, 'David'), (30, 'Anna')],
                dtype=[('age', int), ('name', 'U10')])
print("Sorted by age:\n", np.sort(data, order='age'))

Tips and Best Practices

Common Pitfalls

Conclusion

Sorting is one of the most common and powerful operations in data processing. NumPy provides multiple ways to:

• Sort arrays (1D, 2D, structured)

• Sort in place or return sorted copies

• Retrieve sorted indices for indirect sorting

With the tools like sort(), argsort(), and sort(order=...), you're well-equipped to handle everything from simple sorting to advanced use cases in data science and machine learning.

Next Steps

• NumPy Array Join

• ✂️ NumPy Array Split

• NumPy Data Aggregation & Statistics