Array

In Python, an array is a data structure that stores a collection of elements of the same data type. Unlike lists, which can contain elements of different data types, arrays can only contain elements of a single data type, such as integers, floats, or characters.

Python supports two types of arrays: arrays provided by the built-in array module and arrays provided by the numpy module.

built-in `array` module

The array module provides a simple way to create and manipulate arrays of primitive data types, such as integers, floats, and characters. Here’s an example of using the array module to create an array of integers:

import array

my_array = array.array('i', [1, 2, 3, 4, 5])
print(my_array)

In this example, the array function is used to create a new array of integers with the values [1, 2, 3, 4, 5].

The first argument to the array function is a type code that specifies the data type of the array. In this case, the type code 'i' represents signed integers.

TYPECODE	C TYPE	PYTHON TYPE	SIZE
‘b’	signed char	int	1
‘B’	unsigned char	int	1
‘u’	wchar_t	Unicode character	2
‘h’	signed short	int	2
‘H’	unsigned short	int	2
‘i’	signed int	int	2
‘I’	unsigned int	int	2
’l'	signed long	int	4
‘L’	unsigned long	int	4
‘q’	signed long long	int	8
‘Q’	unsigned long long	int	8
‘f’	float	float	4
’d'	double	float	8

`numpy` module

The numpy module provides a more powerful and flexible way to create and manipulate arrays, including support for multi-dimensional arrays, mathematical operations on arrays, and advanced indexing and slicing. Here’s an example of using the numpy module to create an array of integers:

import numpy as np

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

In this example, the np.array function is used to create a new array of integers with the values [1, 2, 3, 4, 5].

The numpy module also provides many other functions and tools for working with arrays, such as ndarray.shape and ndarray.reshape for manipulating the shape of arrays, ndarray.min, ndarray.max, and ndarray.mean for computing statistics on arrays, and ndarray.dot and ndarray.transpose for performing matrix operations on arrays.

Overall, arrays are a useful data structure in Python for storing and manipulating collections of elements of the same data type, and they can be used in a wide range of applications, such as numerical computing, data analysis, and machine learning.

built-in array module#

numpy module#

built-in `array` module

`numpy` module