NumPy - Data Types

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.
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

# 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)

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