Data type Object (dtype) in NumPy Python Last Updated : 11 Aug, 2021 Comments Improve Suggest changes 23 Likes Like Report See More 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 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 # 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 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 : docs.scipy.orgStructured Arrays Create Quiz Comment A Ayushi_Asthana 23 Improve A Ayushi_Asthana 23 Improve Article Tags : Python Python-numpy Python numpy-DataType Explore Python FundamentalsPython Introduction 2 min read Input and Output in Python 4 min read Python Variables 4 min read Python Operators 4 min read Python Keywords 2 min read Python Data Types 8 min read Conditional Statements in Python 3 min read Loops in Python - For, While and Nested Loops 5 min read Python Functions 5 min read Recursion in Python 4 min read Python Lambda Functions 5 min read Python Data StructuresPython String 5 min read Python Lists 4 min read Python Tuples 4 min read Python Dictionary 3 min read Python Sets 6 min read Python Arrays 7 min read List Comprehension in Python 4 min read Advanced PythonPython OOP Concepts 11 min read Python Exception Handling 5 min read File Handling in Python 4 min read Python Database Tutorial 4 min read Python MongoDB Tutorial 3 min read Python MySQL 9 min read Python Packages 10 min read Python Modules 3 min read Python DSA Libraries 15 min read List of Python GUI Library and Packages 3 min read Data Science with PythonNumPy Tutorial - Python Library 3 min read Pandas Tutorial 4 min read Matplotlib Tutorial 5 min read Python Seaborn Tutorial 3 min read StatsModel Library - Tutorial 3 min read Learning Model Building in Scikit-learn 6 min read TensorFlow Tutorial 2 min read PyTorch Tutorial 6 min read Web Development with PythonFlask Tutorial 8 min read Django Tutorial | Learn Django Framework 7 min read Django ORM - Inserting, Updating & Deleting Data 4 min read Templating With Jinja2 in Flask 6 min read Django Templates 5 min read Build a REST API using Flask - Python 3 min read Building a Simple API with Django REST Framework 3 min read Python PracticePython Quiz 1 min read Python Coding Practice 1 min read Python Interview Questions and Answers 15+ min read Like