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Learning Numpy - Simple Tutorial For Beginners - NumPy - Data Types Part 3

Learning Numpy - Simple Tutorial For Beginners - NumPy - Data Types Part 3

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

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

What Character Code Does The Data Type Have?

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)

Data Types Description
bool_ Boolean (True or False) stored as a byte
int_ Default integer type (same as C long; normally either int64 or int32)
intc Identical to C int (normally int32 or int64)
intp Integer used for indexing (same as C ssize_t; normally either int32 or int64)
int8 Byte (-128 to 127)
int16 Integer (-32768 to 32767)
int32 Integer (-2147483648 to 2147483647)
int64 Integer (-9223372036854775808 to 9223372036854775807)
uint8 Unsigned integer (0 to 255)
uint16 Unsigned integer (0 to 65535)
uint32 Unsigned integer (0 to 4294967295)
uint64 Unsigned integer (0 to 18446744073709551615)
float_ Shorthand for float64
float16 Half precision float: sign bit, 5 bits exponent, 10 bits mantissa
float32 Single precision float: sign bit, 8 bits exponent, 23 bits mantissa
float64 Double precision float: sign bit, 11 bits exponent, 52 bits mantissa
complex_ Shorthand for complex128
complex64 Complex number, represented by two 32-bit floats (real and imaginary components)
complex128 Complex number, represented by two 64-bit floats (real and imaginary components)

Example 1

In [1]:
# using array-scalar type 
import numpy as np
dt = np.dtype(np.int32)
print (dt)
int32

Example 2

In [3]:
#int8, int16, int32, int64 can be replaced by equivalent string 'i1', 'i2','i4', etc. 
import numpy as np

dt = np.dtype('i4')
print (dt)
int32

Example 3

In [4]:
# using endian notation 
import numpy as np
dt = np.dtype('>i4')
print (dt)
>i4

Example 4

In [5]:
# first create structured data type 
import numpy as np
dt = np.dtype([('age',np.int8)])
print (dt)
[('age', 'i1')]

Example 5

In [6]:
# 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)
[(10,) (20,) (30,)]


Dolly Solanki  Dolly Solanki