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.
