Numpy Data Types W3schools

Leo Migdal

-Nov 18, 2025, 2:22 AM

W3Schools offers a wide range of services and products for beginners and professionals, helping millions of people everyday to learn and master new skills. Enjoy our free tutorials like millions of other internet users since 1999 Explore our selection of references covering all popular coding languages Create your own website with W3Schools Spaces - no setup required Test your skills with different exercises NumPy supports a much greater variety of numerical types than Python does.

This section shows which are available, and how to modify an array’s data-type. NumPy numerical types are instances of numpy.dtype (data-type) objects, each having unique characteristics. Once you have imported NumPy using import numpy as np you can create arrays with a specified dtype using the scalar types in the numpy top-level API, e.g. numpy.bool, numpy.float32, etc. These scalar types as arguments to the dtype keyword that many numpy functions or methods accept. For example:

Array types can also be referred to by character codes, for example: See Specifying and constructing data types for more information about specifying and constructing data type objects, including how to specify parameters like the byte order. NumPy is a powerful Python library that can manage different types of data. Here we will explore the Datatypes in NumPy and How we can check and create datatypes of the NumPy array. A data type in NumPy is used to specify the type of data stored in a variable. Here is the list of characters available in NumPy to represent data types.

A fixed chunk of memory for other types (void) The list of various types of data types provided by NumPy are given below: Identical to the integer in C (int32 or int64) By default Python have these data types: NumPy has some extra data types, and refer to data types with one character, like i for integers, u for unsigned integers etc. Below is a list of all data types in NumPy and the characters used to represent them.

The NumPy array object has a property called dtype that returns the data type of the array: Get the data type of an array containing strings: NumPy supports a much greater variety of numerical types than Python does. The following table shows different scalar data types defined in NumPy. Boolean (True or False) stored as a byte Default integer type (same as C long; normally either int64 or int32)

Identical to C int (normally int32 or int64) Integer used for indexing (same as C ssize_t; normally either int32 or int64) Created with over a decade of experience. Created with over a decade of experience and thousands of feedback. A data type is a way to specify the type of data that will be stored in an array. For example,

Here, the array1 array contains three integer elements, so the data type is Integer(int64)), by default. NumPy provides us with several built-in data types to efficiently represent numerical data. W3Schools offers a wide range of services and products for beginners and professionals, helping millions of people everyday to learn and master new skills. Enjoy our free tutorials like millions of other internet users since 1999 Explore our selection of references covering all popular coding languages Create your own website with W3Schools Spaces - no setup required

Test your skills with different exercises A data type object (an instance of numpy.dtype class) describes how the bytes in the fixed-size block of memory corresponding to an array item should be interpreted. It describes the following aspects of the data: Type of the data (integer, float, Python object, etc.) Size of the data (how many bytes is in e.g. the integer)

Byte order of the data (little-endian or big-endian) If the data type is structured data type, an aggregate of other data types, (e.g., describing an array item consisting of an integer and a float), Numpy can be abbreviated as Numeric Python, is a Data analysis library for Python that consists of multi-dimensional array-objects as well as a collection of routines to process these arrays. In this tutorial, you will be learning about the various uses of this library concerning data science. NumPy is a linear algebra library for Python, and it is so famous and commonly used because most of the libraries in PyData's environment rely on Numpy as one of their main building blocks. Moreover, it is fast and reliable.

It is recommended that your system should have Python installed via Anaconda distribution (www.continuum.io or www.anaconda.com ) for making sure all underlying dependencies get sync up with use of conda install. The Python's standard distribution doesn't come with NumPy library module. So for installing Numpy, you have to go to command prompt (Windows users) or terminal (Mac users) and type the following: In this chapter, you will be using numpy arrays. Numpy comes in two different flavors. These are:

Here vectors are 1D (a one-dimensional array of elements), and matrices are 2D (two dimensional) array of elements. It is to be noted that matrices can also hold only either one row or one column as well. NumPy, short for Numerical Python, is a library in Python that is often used for numerical computations. In this tutorial, we will explore a fundamental aspect of NumPy: data types (dtypes). NumPy's array object, ndarray, is a grid that can hold values of the same data type (dtype). The dtype object is an instance of numpy.dtype class and it can be created using numpy.dtype.

The dtype object describes the following aspects of the data: You can define the dtype when you create the array. You can check the dtype of an array using the dtype attribute.

Numpy Data Types W3schools

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