Explain the NumPy Data Types.

In NumPy, data types (called dtypes) define the kind of elements stored in an array — such as integers, floats, booleans, or custom data. NumPy provides finer control over data types than standard Python because it’s designed for performance and memory efficiency in numerical computing.

Let’s go over it clearly:

1. What Are NumPy Data Types?

A NumPy data type (dtype) describes the type of value and memory size each element in an array uses.

Example:

import numpy as np

arr = np.array([1, 2, 3], dtype=np.int32)
print(arr.dtype)

Output:

int32

This means each integer takes 4 bytes (32 bits).

2. Categories of NumPy Data Types

Category	Description	Examples of dtype
Integer	Whole numbers (positive/negative)	`int8`, `int16`, `int32`, `int64`
Unsigned Integer	Whole numbers (only positive)	`uint8`, `uint16`, `uint32`, `uint64`
Float	Decimal numbers	`float16`, `float32`, `float64`
Complex	Complex numbers (a + bj)	`complex64`, `complex128`
Boolean	True/False values	`bool_`
String	Fixed-size byte or Unicode strings	`string_`, `unicode_`
Object	Python objects (mixed types)	`object_`
Datetime	Date and time values	`datetime64`
Timedelta	Difference between two datetime values	`timedelta64`

3. Examples

Integers

np.array([10, 20, 30], dtype=np.int8)   # 1 byte per element
np.array([10, 20, 30], dtype=np.int64)  # 8 bytes per element

Unsigned Integers

np.array([0, 255], dtype=np.uint8)  # 0–255 range

Floats

np.array([3.14, 2.71], dtype=np.float32)

Complex Numbers

np.array([1+2j, 3+4j], dtype=np.complex128)

Boolean

np.array([True, False, True], dtype=np.bool_)

String

np.array(['Hello', 'World'], dtype=np.string_)   # ASCII
np.array(['Hello', 'World'], dtype=np.unicode_)  # Unicode

Datetime

np.array(['2024-01-01', '2024-02-01'], dtype='datetime64')

Object

np.array([1, 'two', 3.0], dtype=object)

4. Checking and Converting Data Types

Check the dtype

arr = np.array([1.2, 3.4, 5.6])
print(arr.dtype)

Convert dtype

arr_int = arr.astype(np.int32)
print(arr_int, arr_int.dtype)

Output:

[1 3 5] int32

5. Why NumPy Has Its Own Data Types

Python’s native types (int, float, etc.) are object-based, meaning they take more memory and are slower for numerical operations.
NumPy’s types are fixed-size, C-like types designed for fast computation and minimal memory usage.

For example:

Type	Bytes	Range
`int8`	1	-128 to 127
`int16`	2	-32,768 to 32,767
`int32`	4	-2,147,483,648 to 2,147,483,647
`float64`	8	~15–16 decimal digits precision

6. Custom Structured Data Types

You can define your own compound data type (like a record or struct):

person_dtype = np.dtype([
    ('name', 'U10'),
    ('age', 'i4'),
    ('height', 'f4')
])

people = np.array([
    ('Alice', 25, 5.5),
    ('Bob', 30, 6.0)
], dtype=person_dtype)

print(people['name'])
print(people['age'])

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