NumPy Array Sort

NumPy provides several functions to sort arrays. This chapter covers how to sort elements within arrays, sort along different axes, and handle structured arrays.

Basic Sorting

To sort an array, you can use the numpy.sort() function, which returns a sorted copy of the array.

import numpy as np

arr = np.array([3, 1, 2, 5, 4])

# Sort the array
sorted_arr = np.sort(arr)

print(sorted_arr)

Sorting Along an Axis

When dealing with multi-dimensional arrays, you can sort along a specific axis using the axis parameter.

import numpy as np

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

# Sort along the first axis (rows)
sorted_arr = np.sort(arr, axis=0)

print(sorted_arr)

# Sort along the second axis (columns)
sorted_arr = np.sort(arr, axis=1)

print(sorted_arr)

In-Place Sorting

To sort an array in-place, you can use the sort() method of the array object.

import numpy as np

arr = np.array([3, 1, 2, 5, 4])

# Sort the array in-place
arr.sort()

print(arr)

Structured Arrays

NumPy allows you to create structured arrays with named fields and sort them based on specific fields.

import numpy as np

# Create a structured array
arr = np.array([(1, 'B'), (3, 'A'), (2, 'C')],
               dtype=[('number', 'i4'), ('letter', 'U1')])

# Sort the array by the 'number' field
sorted_arr = np.sort(arr, order='number')

print(sorted_arr)

Conclusion

NumPy provides flexible and efficient sorting functions that allow you to sort arrays in various ways. Understanding these functions helps in managing and organizing data effectively.