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 |

see more

`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.