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Data type Object (dtype) in NumPy Python

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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)
  • The 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 a 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.  

1. Constructing a data type (dtype) object: A data type object is an instance of the 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

# 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:

int16

Python


                    

Output:

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

The type specifier (i4 in the 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

# 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

2. Data type Objects with Structured Arrays: Data type objects are useful for creating structured arrays.  A structured array is one that 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 the case of structured arrays, the dtype object will also be structured.  

Python

# 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

# 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 :  



 



Last Updated : 11 Aug, 2021
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