NumPy - Data Types


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NumPy supports a much greater variety of numerical types than Python does. The following table shows different scalar data types defined in NumPy.

Sr.No. Data Types & Description
1

bool_

Boolean (True or False) stored as a byte

2

int_

Default integer type (same as C long; normally either int64 or int32)

3

intc

Identical to C int (normally int32 or int64)

4

intp

Integer used for indexing (same as C ssize_t; normally either int32 or int64)

5

int8

Byte (-128 to 127)

6

int16

Integer (-32768 to 32767)

7

int32

Integer (-2147483648 to 2147483647)

8

int64

Integer (-9223372036854775808 to 9223372036854775807)

9

uint8

Unsigned integer (0 to 255)

10

uint16

Unsigned integer (0 to 65535)

11

uint32

Unsigned integer (0 to 4294967295)

12

uint64

Unsigned integer (0 to 18446744073709551615)

13

float_

Shorthand for float64

14

float16

Half precision float: sign bit, 5 bits exponent, 10 bits mantissa

15

float32

Single precision float: sign bit, 8 bits exponent, 23 bits mantissa

16

float64

Double precision float: sign bit, 11 bits exponent, 52 bits mantissa

17

complex_

Shorthand for complex128

18

complex64

Complex number, represented by two 32-bit floats (real and imaginary components)

19

complex128

Complex number, represented by two 64-bit floats (real and imaginary components)

NumPy numerical types are instances of dtype (data-type) objects, each having unique characteristics. The dtypes are available as np.bool_, np.float32, etc.

Data Type Objects (dtype)

A data type object describes interpretation of fixed block of memory corresponding to an array, depending on the following aspects −

  • Type of data (integer, float or Python object)

  • Size of data

  • Byte order (little-endian or big-endian)

  • In case of structured type, the names of fields, data type of each field and part of the memory block taken by each field.

  • If data type is a subarray, its shape and data type

The byte order is decided by prefixing '<' or '>' to data type. '<' means that encoding is little-endian (least significant is stored in smallest address). '>' means that encoding is big-endian (most significant byte is stored in smallest address).

A dtype object is constructed using the following syntax −

numpy.dtype(object, align, copy)

The parameters are −

  • Object − To be converted to data type object

  • Align − If true, adds padding to the field to make it similar to C-struct

  • Copy − Makes a new copy of dtype object. If false, the result is reference to builtin data type object

Example 1

Live Demo
# using array-scalar type 
import numpy as np 
dt = np.dtype(np.int32) 
print dt

The output is as follows −

int32

Example 2

Live Demo
#int8, int16, int32, int64 can be replaced by equivalent string 'i1', 'i2','i4', etc. 
import numpy as np 

dt = np.dtype('i4')
print dt 

The output is as follows −

int32

Example 3

Live Demo
# using endian notation 
import numpy as np 
dt = np.dtype('>i4') 
print dt

The output is as follows −

>i4

The following examples show the use of structured data type. Here, the field name and the corresponding scalar data type is to be declared.

Example 4

Live Demo
# first create structured data type 
import numpy as np 
dt = np.dtype([('age',np.int8)]) 
print dt 

The output is as follows −

[('age', 'i1')] 

Example 5

Live Demo
# now apply it to ndarray object 
import numpy as np 

dt = np.dtype([('age',np.int8)]) 
a = np.array([(10,),(20,),(30,)], dtype = dt) 
print a

The output is as follows −

[(10,) (20,) (30,)]

Example 6

Live Demo
# file name can be used to access content of age column 
import numpy as np 

dt = np.dtype([('age',np.int8)]) 
a = np.array([(10,),(20,),(30,)], dtype = dt) 
print a['age']

The output is as follows −

[10 20 30]

Example 7

The following examples define a structured data type called student with a string field 'name', an integer field 'age' and a float field 'marks'. This dtype is applied to ndarray object.

Live Demo
import numpy as np 
student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')]) 
print student

The output is as follows −

[('name', 'S20'), ('age', 'i1'), ('marks', '<f4')])

Example 8

Live Demo
import numpy as np 

student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')]) 
a = np.array([('abc', 21, 50),('xyz', 18, 75)], dtype = student) 
print a

The output is as follows −

[('abc', 21, 50.0), ('xyz', 18, 75.0)]

Each built-in data type has a character code that uniquely identifies it.

  • 'b' − boolean

  • 'i' − (signed) integer

  • 'u' − unsigned integer

  • 'f' − floating-point

  • 'c' − complex-floating point

  • 'm' − timedelta

  • 'M' − datetime

  • 'O' − (Python) objects

  • 'S', 'a' − (byte-)string

  • 'U' − Unicode

  • 'V' − raw data (void)



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