What are the common data types supported by NumPy?

Common Data Types Supported by NumPy

NumPy, the fundamental library for scientific computing in Python, supports a wide range of data types to accommodate various numerical and non-numerical data. These data types are essential for efficient storage, manipulation, and analysis of data in scientific and numerical computing applications.

The common data types supported by NumPy can be broadly classified into the following categories:

Numeric Data Types

NumPy provides a variety of numeric data types, which are optimized for different use cases and memory requirements. These include:

1. Integer Data Types:

   • int8: 8-bit signed integer (-128 to 127)
   • int16: 16-bit signed integer (-32,768 to 32,767)
   • int32: 32-bit signed integer (-2,147,483,648 to 2,147,483,647)
   • int64: 64-bit signed integer (-9,223,372,036,854,775,808 to 9,223,372,036,854,775,807)

2. Unsigned Integer Data Types:

   • uint8: 8-bit unsigned integer (0 to 255)
   • uint16: 16-bit unsigned integer (0 to 65,535)
   • uint32: 32-bit unsigned integer (0 to 4,294,967,295)
   • uint64: 64-bit unsigned integer (0 to 18,446,744,073,709,551,615)

3. Floating-Point Data Types:

   • float16: 16-bit floating-point number (half precision)
   • float32: 32-bit floating-point number (single precision)
   • float64: 64-bit floating-point number (double precision)

4. Complex Data Types:

   • complex64: 64-bit complex number (two 32-bit floating-point numbers)
   • complex128: 128-bit complex number (two 64-bit floating-point numbers)

Non-Numeric Data Types

NumPy also supports non-numeric data types, which are useful for storing and manipulating textual and categorical data. These include:

1. Boolean Data Type:

   • bool: Stores True or False values

2. String Data Types:

   • str_: Variable-length string
   • bytes_: Variable-length bytes

3. Object Data Type:

   • object: Stores Python objects of any type

To illustrate the usage of these data types, let's consider a simple example:

import numpy as np

# Create a NumPy array with different data types
arr = np.array([1, 2.5, 'hello', True], dtype=object)
print(arr)
print(arr.dtype)

Output:

[1, 2.5, 'hello', True]
object

In this example, we create a NumPy array with a mix of integer, float, string, and boolean values, and assign the object data type to the array. This allows us to store heterogeneous data within a single NumPy array.

Understanding the available data types in NumPy is crucial for efficient data storage, manipulation, and analysis in scientific and numerical computing applications. By choosing the appropriate data type, you can optimize memory usage, improve computational performance, and ensure the integrity of your data.