Data type Object (dtype) in NumPy Python

Difficulty Level : Medium
Last Updated : 29 Nov, 2018

Every ndarray has an associated data type (dtype) object. This data type object (dtype) informs us about the layout of the array. This means it gives us information about :

Type of the data (integer, float, Python object etc.)
Size of the data (number of bytes)
Byte order of the data (little-endian or big-endian)
If the data type is a sub-array, what is its shape and data type.

The values of an ndarray are stored in a buffer which can be thought of as a contiguous block of memory bytes. So how these bytes will be interpreted is given by the dtype object.

Constructing a data type (dtype) object : Data type object is an instance of numpy.dtype class and it can be created using numpy.dtype.

Parameters:

obj: Object to be converted to a data type object.
align : bool, optional
Add padding to the fields to match what a C compiler would output for a similar C-struct.
copy : bool, optional
Make a new copy of the data-type object. If False, the result may just be a reference to a built-in data-type object.

# Python Program to create a data type object 
import numpy as np 
  
# np.int16 is converted into a data type object. 
print(np.dtype(np.int16)) 

Output:

int16

# Python Program to create a data type object  
# containing a 32 bit big-endian integer 
import numpy as np 
  
# i4 represents integer of size 4 byte 
# > represents big-endian byte ordering and < represents little-endian encoding. 
# dt is a dtype object 
dt = np.dtype('>i4') 
  
print("Byte order is:",dt.byteorder) 
  
print("Size is:",dt.itemsize) 
  
print("Data type is:",dt.name) 

Output:

Byte order is: >
Size is: 4
Name of data type is: int32

The type specifier (i4 in above case) can take different forms:

b1, i1, i2, i4, i8, u1, u2, u4, u8, f2, f4, f8, c8, c16, a
(representing bytes, ints, unsigned ints, floats, complex and
fixed length strings of specified byte lengths)
int8,…,uint8,…,float16, float32, float64, complex64, complex128
(this time with bit sizes)

Note :

dtype is different from type.

# Python program to differentiate 
# between type and dtype. 
import numpy as np 
  
a = np.array([1]) 
  
print("type is: ",type(a)) 
print("dtype is: ",a.dtype) 

Output:

type is:    
dtype is:  int32

Data type Objects with Structured Arrays : Data type objects are useful for creating structured arrays. A structured array is the one which contains different types of data. Structured arrays can be accessed with the help of fields.
A field is like specifying a name to the object. In case of structured arrays the dtype object will also be structured.

# Python program for demonstrating  
# the use of fields 
import numpy as np 
  
# A structured data type containing a 16-character string (in field ‘name’)  
# and a sub-array of two 64-bit floating-point number (in field ‘grades’): 
  
dt = np.dtype([('name', np.unicode_, 16), ('grades', np.float64, (2,))]) 
  
# Data type of object with field grades 
print(dt['grades']) 
  
# Data type of object with field name  
print(dt['name']) 

Output:

('<f8', (2,))

# Python program to demonstrate  
# the use of data type object with structured array. 
import numpy as np 
  
dt = np.dtype([('name', np.unicode_, 16), ('grades', np.float64, (2,))]) 
  
# x is a structured array with names and marks of students. 
# Data type of name of the student is np.unicode_ and  
# data type of marks is np.float(64) 
x = np.array([('Sarah', (8.0, 7.0)), ('John', (6.0, 7.0))], dtype=dt) 
  
print(x[1]) 
print("Grades of John are: ",x[1]['grades']) 
print("Names are: ",x['name']) 

Output:

('John', [ 6.,  7.])
Grades of John are:  [ 6.  7.]
Names are:  ['Sarah' 'John']

References :

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