Python Basics

What are Numpy data types

There are three broad categories under which data types of Numpy are divided-

Numpy Numeric Types
Numpy String Types
Numpy Boolean Types

Numpy Numeric Types

Numeric types include signed and unsigned integer, floating-point numbers, and complex numbers.

Numpy Numeric Data Types	Character Codes	Description
Signed Integer
int8	b, i1 or \|i1	It is an 8-bit(1 byte) signed integer and its range is -128 to 127.
int16	h, i2 or <i2	It is a 16-bit(2 bytes) signed integer and its range is -32768 to 32767.
int32	i, i4 or <i4	It is a 32-bit(4 bytes) signed integer and its range is -2³¹ to 2³¹ - 1.
int64	l, i8 or <i8	It is a 64-bit(8 bytes) signed integer and its range is -2⁶³ to 2⁶³ - 1.
Unsigned Integer
uint8	B, u1 or \|u1	It is an 8-bit(1 byte) unsigned integer and its range is 0 to 255.
uint16	H, u2 or <u2	It is a 16-bit(2 bytes) unsigned integer and its range is 0 to 65535.
uint32	I, u4 or <u4	It is a 32-bit(4 bytes) unsigned integer and its range is 0 to 2³² - 1.
uint64	L, u8 or <u8	It is a 64-bit(8 bytes) unsigned integer and its range is 0 to 2⁶⁴ - 1.
Single and Half Precision Floating-Point Number
float16	e, f2 or <f2	It is a half precision float with signed bit- 5 bits exponent and 10 bits mantissa.
float32	f, f4 or <f4	It is a single precision float with signed bit- 8 bits exponent and 23 bits mantissa.
Double Precision Floating-Point Number
float64	d, f8 or <f8	It is a double precision float with signed bit- 11 bits exponent and 52 bits mantissa.
Quadruple Precision Floating-Point Number
float128	g, f16 or <f16	It is a quadruple precision float with signed bit- 15 bits exponent and 112 bits mantissa.
Complex Number
complex64	F, c8 or <c8	It is a two 32-bit floating complex number
complex128	D, c16 or <c16	It is a two 64-bit floating complex number

Numpy String Types

String types include String and Unicode.

Numpy String Data Types	Character Codes	Description
S or string_	Sx or \|Sx x is the number of characters.	It is a string data type whose size is equal to x bytes.
U, unicode_ or str_	Ux or <Ux x is the number of characters.	It is a unicode data type whose size is equal to 4*x bytes.

Numpy Boolean Types

It represents True or False.

Numpy Boolean Data Type	Character Codes	Description
b	b	It is a boolean data type that takes either True or False.

Numpy Data Types Character Codes

Character codes are mainly used in two places-

You specify the data type of fields using character codes passed to dtype() constructor.
```
t = np.dtype([('name', 'S30'), ('age', 'i2')])
```
Character codes are passed to Numpy functions where you specify the data type using dtype parameter.
```
arr = np.array([12, 34.65, 98.333, 67], dtype='f4')
print(arr)
```
Output of the above program
```
[12.    34.65  98.333 67.   ]
```

dtype constructor

dtype() constructor is used to create homogeneous and heterogeneous data types. To the constructor, you can pass numpy data types, their character codes, and a list of tuples having fields and their data types.

homogeneous = dtype('float32')

The below code will create record data type that contains product name represented by a 50-character string, its quantity represented by 16-bit integer and its price represented by 16-bit floating point number.

hetero = dtype([('productname', S50), ('quantity', int16), ('price', float16)])

Attributes of dtype

There are three attributes of dtype class-

char- It provides the character code of a data type.
```
import numpy as np
d = np.dtype('float16')
print(d.char)
```
It will give e in the output.
str- It provides the string representation of a data type. The representation starts with '<' then character code, and finally the number of bytes that data type requires.
```
import numpy as np
d = np.dtype('int32')
print(d.str)
```
It will give <i4 in the output.
itemsize- It returns the size of a data type in bytes.
```
import numpy as np
d = np.dtype('int64')
print(d.itemsize)
```
It will give 8 in the output.