NumPy array()

NumPy's array() function is a powerful method for creating arrays from Python data structures. It allows for efficient storage and manipulation of numerical data, making it essential for scientific and mathematical computing.

Usage

The np.array() function is used to convert Python lists, tuples, other array-like objects such as existing NumPy arrays, or any similar structures into NumPy arrays. This is crucial for performing vectorized operations and leveraging NumPy's optimized routines.

numpy.array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0)

• object: The array-like input data.
• dtype: Desired data type of the array (e.g., int, float).
• copy: If True, the object is copied.
• order: Memory layout order ('C' for row-major, 'F' for column-major, 'A' to preserve input order as much as possible, 'K' for default order).
• subok: If False, the returned array is a base class array; if True, subclasses are passed through.
• ndmin: Minimum number of dimensions required.

Examples

1. Basic Array Creation

import numpy as np

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

This example creates a one-dimensional NumPy array from a Python list containing integers.

2. Multi-dimensional Array

import numpy as np

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

Here, a two-dimensional array is created from a nested list, effectively forming a matrix.

3. Array with Specified Data Type

import numpy as np

arr = np.array([1.5, 2.5, 3.5], dtype=np.int32)

This example creates a one-dimensional array with specified data type int32, converting all floating-point numbers to integers.

4. Using `ndmin` to Add Dimensions

import numpy as np

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

This creates a two-dimensional array from a one-dimensional list, adding an extra dimension automatically.

5. Combining Parameters for Array Creation

import numpy as np

arr = np.array([[1, 2], [3, 4]], dtype=np.float64, order='F')

This creates a two-dimensional array with specified data type float64 and column-major memory layout.

Tips and Best Practices

When using np.array(), consider specifying the dtype to ensure consistency and save memory, especially with large datasets. If a specific number of dimensions is needed, use ndmin to add extra dimensions automatically. By default, np.array() creates a copy of the input data; use copy=False if a duplicate is unnecessary. Leverage broadcasting for efficient operations on arrays of different shapes; for example, adding a scalar to an array. Optimize performance by choosing the appropriate order parameter ('C', 'F', 'A', or 'K') based on your use case.