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Table of contents
1.
Introduction
2.
NumPy: The Numeric Powerhouse
3.
Numeric Datatypes in NumPy
4.
Examples
4.1.
python
4.2.
python
4.3.
python
4.4.
python
4.5.
python
5.
Frequently Asked Questions
5.1.
Why do Numeric Data Types hold significance in NumPy?
5.2.
Can I Convert Between Different Numeric Data Types?
5.3.
How Do Numeric Data Types Affect Memory Usage?
6.
Conclusion
Last Updated: Mar 27, 2024
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Numeric Datatypes in NumPy

Author Shiva
1 upvote

Introduction

NumPy is a foundational library in the world of numerical computing and data science, offering superb abilities with an array data structure as well as a vast assortment of mathematical functions. A key factor contributing to the effectiveness of NumPy is the support for numerous numeric data types. These types of data are critical in determining storage and manipulation methods, effectively impacting both memory performance and computational accuracy.

Numeric Datatypes in NumPy

In this article, we will explore Numeric Datatypes in NumPy and provide examples to help you get started.

NumPy: The Numeric Powerhouse

NumPy, which stands for Numerical Python is a Python library that offers assistance, with handling arrays and matrices of numeric data. It also includes a range of functions to perform operations on these arrays. Due to its efficiency and versatility NumPy serves as the cornerstone for scientific computing tasks, in Python.

Why Do We Need Numeric Data Types?

When computers store and manipulate data, they need to understand how to interpret that data. For example, consider the numbers 3.14 and 42. Without context, a computer might treat them as mere strings of characters rather than numerical values. Numeric data types provide this crucial context, telling the computer how to interpret and perform operations with the data.

Numeric Datatypes in NumPy

For effective and accurate numerical computations, NumPy, the Numeric Python library, provides a wide range of numeric data types. Within the NumPy framework, these data types act as the fundamental building blocks for representing various forms of numbers and mathematical objects. 

NumPy provides an array of numeric data types:

bool_, byte, ubyte, short, ushort, intc, uintc, int_, uint, longlong, ulonglong: Different integer types with various ranges.

half, single, double, longdouble: Floating-point types with varying precision.

csingle, cdouble, clongdouble: Complex number types with varying precision.

 

These data types influence memory usage, numeric ranges, and precision. For instance, float64 has more precision than float32, but occupies more memory.

The extensive table that follows lists all of the many numeric data types that the NumPy library supports, along with their corresponding C types and descriptions:

Sr. No.  Numpy type C type Description
1. numpy.bool_ bool Represents a boolean value indicating true or false. Stored as a byte.
2.  numpy.byte signed char Stores small integers using one byte. Ranges from -128 to 127 or 0 to 255.
3.  numpy.ubyte unsigned char Similar to numpy.byte but only stores positive values from 0 to 255.
4.  numpy.short short Stores signed short integers in two bytes. Ranges from -32,768 to 32,767.
5.  numpy.ushort unsigned short Similar to numpy.short but only stores positive values from 0 to 65,535.
6.  numpy.intc int A signed integer compatible with C int. Can store 32-bit or 64-bit integers.
7.  numpy.uintc unsigned int An unsigned integer compatible with C unsigned int. Can hold values up to 65,535 or 4,294,967,295.
8.  numpy.int_ long A signed integer compatible with Python int and C long. Stores values up to 2^63-1 or -2^63 to 2^63-1.
9.  numpy.uint unsigned long An unsigned integer equivalent to C unsigned long. Stores values from 0 to 2^64-1.
10.  numpy.longlong long long A signed integer compatible with C long long. Holds values up to 2^63-1 or -2^63 to 2^63-1.
11.  numpy.ulonglong unsigned long long An unsigned integer equivalent to C unsigned long long. Stores values from 0 to 2^64-1.
12.  numpy.half / numpy.float16 half-precision float Represents a floating-point number with limited precision. Contains 5 bits for exponent, 10 bits for mantissa, and 1 bit for sign.
13.  numpy.single float Represents a single-precision floating-point number compatible with C float. Contains 8 bits for exponent, 23 bits for mantissa, and 1 bit for sign.
14. numpy.double double Represents a double-precision floating-point number compatible with Python float and C double. Contains 11 bits for exponent, 52 bits for mantissa, and 1 bit for sign.
15.  numpy.longdouble long double Represents an extended precision floating-point number compatible with C long double. Alias for numpy.float128. Stored in 8 bytes.
16.  numpy.csingle float complex Represents a complex number with single-precision floating-point parts. Real and imaginary parts each have 32 bits.
17.  numpy.cdouble double complex Represents a complex number with double-precision floating-point parts. Real and imaginary parts each have 64 bits.
18.  numpy.clongdouble long double complex Represents a complex number with extended precision floating-point parts. Real and imaginary parts are each 128 bits.

Examples

Here are some examples of how you might use different numeric data types from the NumPy library in Python:

Using numpy.int_ for Whole Numbers:

  • python

python

import numpy as np

# Creating an array of integers
int_array = np.array([10, 20, 30], dtype=np.int_)
print(int_array)
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Output:

output

Working with numpy.float64 for Decimal Numbers:

  • python

python

import numpy as np

# Creating an array of floating-point numbers
float_array = np.array([3.14, 1.618, 2.718], dtype=np.float64)
print(float_array)
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Output:

output

Dealing with numpy.complex128 for Complex Numbers:

  • python

python

import numpy as np

# Creating an array of complex numbers
complex_array = np.array([1 + 2j, 3 - 4j, 5 + 6j], dtype=np.complex128)
print(complex_array)
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Output:

output

Using numpy.bool_ for Boolean Values:

  • python

python

import numpy as np
# Creating an array of boolean values
bool_array = np.array([True, False, True], dtype=np.bool_)
print(bool_array)
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Output:

output

Using numpy.float16 for Precise but Compact Numbers:

  • python

python

import numpy as np
# Creating an array of half-precision floating-point numbers
half_float_array = np.array([3.14159, 2.71828], dtype=np.float16)
print(half_float_array)
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Output:

output

Frequently Asked Questions

Why do Numeric Data Types hold significance in NumPy?

Numeric data types play a role in NumPy as they determine the storage of values in memory and facilitate mathematical operations. Selecting the data type ensures calculations, efficient memory utilization and compatibility, with other libraries and systems.

Can I Convert Between Different Numeric Data Types?

Yes, you can convert between different numeric data types using the astype() method. Keep in mind that converting to a smaller data type might result in loss of precision or data truncation.

How Do Numeric Data Types Affect Memory Usage?

Different data types consume varying amounts of memory. Smaller data types use less memory but might sacrifice precision. Choosing an appropriate data type helps optimize memory usage while maintaining accuracy.

Conclusion

In the world of NumPy having a grasp of data types is crucial, for optimizing memory usage and ensuring accurate calculations. The diverse range of Numeric Datatypes in NumPy empowers data scientists and programmers to tackle intricate mathematical operations and manipulate data. With the knowledge gained from this discussion you will be well prepared to make the most of NumPys capabilities. Confidently navigate the complexities of computations.

Recommended Readings:

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