NumPy - Data Types

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

S.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

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

The output is as follows −

int32

Example 2

#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

# 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

# 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

# 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

# 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.

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

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)