Numeric datatypes in NumPy

Overview

A data type is a variable property that indicates to the compiler or interpreter how the developer wants to employ the variable. It specifies the actions that may be performed on the data as well as the types of values that can be stored. NumPy is an abbreviation for numeric Python. So, while dealing with NumPy, we need numeric data types, which are commonly used to store and do computations on NumPy objects. Numpy numeric data types are important when working with NumPy objects.

Introduction

Before we begin, we will assume that you have successfully installed Python on your system and are familiar with the foundations of the NumPy library.

Understanding how data is stored and changed is required for robust data-driven research and computing. Python users are frequently pulled in by its simplicity of use, one of which is dynamic typing. Whereas a statically typed language such as C or Java needs every variable to be manually defined, a dynamically typed language such as Python does not. In C, for example, you may define a specific operation as follows:

Code :

In Python, a similar operation might be expressed as follows:

Code :

Did you observe anything interesting? The primary distinction is that each variable's data type is explicitly stated in C, but in Python, the types are assumed dynamically.

What exactly do we mean by data type? In programming, a data type is a categorization that determines which kind of information a variable has, as well as which mathematical, relational, or logical operations may be performed on it without creating an error. A string, for illustration, is a data type used to describe text, whereas an integer is a data type used to describe entire integers.

Now that we've covered the basics of data types let's look at the significance of numeric data types in Numpy.

Importance of Numeric Data Types in NumPy

You may have guessed by now that a data type object indicates how the bytes in the fixed-size memory block associated with an array item must be processed. It denotes the following data characteristics:

1. It Informs us Regarding the Type of Data.

To represent the kind of data, NumPy has multiple built-in data types such as int, float, bool, and so on (to know more about scalar type in NumPy please refer to this link for various precisions of integers, floating-point numbers, and so on. An item retrieved from an array, for example, via indexing, is a Python object whose type seems to be the type corresponding with the array's data type.

2. It Informs us Regarding the Size of the Data.

Data Types provides a summary of data sizes. Consider the following example to better understand it. Assume we have an int data type that represents an integer. A decimal point will never appear in an integer. As a result, if we were to save 2.13 as an integer, we would store it as 2. Likewise, 1907.345 would be saved as 1907. In Python, the data type Int64, which stands for a 64-bit integer, is often used. The 64 signifies the amount of memory allocated to hold data in each cell, which essentially relates to the number of digits that may be stored in each "cell." Computers can enhance storage and processing efficiency by allocating space ahead of time.

3. It Informs us Regarding the Byte Order.

The data type tells us a lot about the byte order or how data is physically stored in our system, which might be in little-endian or big-endian format. Have you never heard of these phrases before? Let's review the little-endian and big-endian formats quickly.

If you think of memory as a one-dimensional tape made up of bytes, you'll need to divide the data into individual bytes and arrange them in a contiguous block. Some people like to begin from the left end since that is how they read, but others prefer to begin at the right end. When bytes are arranged in a row from left to right, the most significant byte is allocated to the lowest memory address. This is referred to as big-endian order. When bytes are placed from right to left, the least-significant byte is saved first. This is known as the little-endian order.

4. In the Case of Structured Type, it Informs us About the Names of the Fields, the Data Type of Every Field, and the Portion of the Memory Block Occupied by Each Field.

Structured data types are created by constructing a data type that contains additional data types in its fields. Every field has a title that may be used to access it. The parent data type should be large enough to hold all of its fields; the parent is almost commonly based on the void type, which allows for arbitrary item sizes. A structured data type is used to build Numpy Structured Array. Numpy Structured Array can store and access the same data effectively. It does this by storing the full array as a contiguous array in the same memory space. If you are unfamiliar with the phrase "NumPy structured array," don't worry, we will discuss it in future posts.

5. If the Data Type is a Subarray, we can Learn About its Structure and Data Type.

Lastly, a data type can express objects that are arrays of other data types' items. These subarrays, however, must have a defined size. When an array is constructed with a data type that describes a subarray, the dimensions of the subarray are appended to the array's shape. As a result, it is probable to state We can learn about the structure and data type of a data type if it is a subarray.

Now that you understand what a data type is and why it is important in Numpy let's look at some numeric data types provided by the NumPy library.

Numeric Data Types in NumPy

The NumPy library primarily provides a wider choice of numeric data types in Python. The following table contains a list of numeric data types supported by NumPy library:

Sr No.	Numpy type	C type	Description
1	numpy.bool_	bool	This is employed to express a boolean value that indicates whether something is either true or false. It is employed to denote the expression's truth value. As a byte, it is stored. For instance, the statement 3 >= 2 is True, but -1 == 1 is False.
2	numpy.byte	signed char	This data type only requires one byte of memory. Any of the following can be stored in a variable of this data type: either an eight-bit data element or values ranging from 0 to 255
3	numpy.ubyte	unsigned char	This data type is nearly identical to numpy.byte. It also needs only one byte of Memory space to hold the data. The main difference is that it accepts values ranging from -128 to 127.
4	numpy.short	short	A signed integer is stored in the short data type. It simply requires two bytes of memory space to store the info. It can store values ranging from -32,768 to 32,767.
5	numpy.ushort	unsigned short	This data type is similar to numpy.short. It also requires two bytes of memory to store the data. The key distinction is that it takes values from 0 to 65,535.
6	numpy.intc	int	numpy.intc is a type of signed integer that is additionally compatible with C int. It can store up to two bytes or four bytes of data. It denotes a 32-bit or 64-bit int. It can store values ranging from -32,768 to 32,767, or -2,147,483,648 to 2,147,483,647.
7	numpy.uintc	unsigned int	numpy.uintc is an unsigned integer type that is likewise compatible with C unsigned int. It has a storage capacity of two or four bytes. It represents a 32-bit or 64-bit integer. It can hold values between 0 and 65,535 or 0 and 4,294,967,295.
8	numpy.int_	long	The numpy.int_ type is a signed integer data type that is compatible with Python int and C long. It has a capacity of eight bytes. It is capable of storing values ranging from $-2^{63}$ to $2^{63}-1$ .
9	numpy.uint	unsigned long	The unsigned variant of the numpy.int_ data type is numpy.uint. The numpy.uint data type is an unsigned integer that is equivalent to the C unsigned long data type. It has an eight-byte capacity. It can store values ranging from $0 to 2^{64}-1$ .
10	numpy.longlong	long long	It's quite similar to the numpy.int_ data type. It is a signed integer type that is equivalent to C long long. It has an eight byte capacity, equivalent to numpy.int_. It has the ability to store values ranging from $-2^{63} to 2^{63}-1$ .
11	numpy.ulonglong	unsigned long long	It is the unsigned counterpart of the numpy.longlong data type. It is a signed integer that is equivalent to unsigned long long in C. It, like numpy.longlong, takes 8 bytes of Memory space to hold the data. It is capable of storing data ranging from 0 to $2^{64}-1$ .
12	numpy.half / numpy.float16		The half-precision float is represented by this. The exponent is assigned 5 bits. The mantissa is allocated 10 bits, and the sign is allocated 1 bit.
13	numpy.single	float	This is a single precision floating point integer type that is compatible with C float. It requires 4 bytes to store the data, among which the exponent is allocated 8 bits, the mantissa is allocated 23 bits, and the sign is allocated 1 bit.
14	numpy.double	double	This type represents a double-precision floating-point number that is equivalent to Python float and C double. The data is stored in 8 bytes, with 11 bits allocated for the exponent, 52 bits reserved for the mantissa, and 1 bit reserved for the sign.
15	numpy.longdouble	long double	This represents the Extended precision floating-point number type, which is compatible with C long double. It is an alias for the numpy.float128 data type. It takes 8 bytes to store data. This 128-bit Extended precision is intended not only for applications that require high-accuracy results but also, as a main role, to enable the arithmetic of double precision results more precisely and reliably by reducing overflow and round-off mistakes in intermediate calculations as well as scratch variables.
16	numpy.csingle	float complex	It represents the complex number type, which is made up of two single precision floating point values. It is an abbreviation for numpy.complex64. It takes 8 bytes to store data. This is employed to denote a complex number in which the real and imaginary parts each have 32 bits.
17	numpy.cdouble	double complex	It represents the Complex number type, which is made up of two double precision floating point numbers and is compatible with Python complex. It is an abbreviation for numpy.complex128. It takes 16 bytes to store data. This is employed to denote a complex number in which the real and imaginary parts each have 64 bits.
18	numpy.clongdouble	long double complex	It represents the Complex number type, which is made up of two extended precision floating point numbers. It is an abbreviation for numpy.complex256. This is the complex number type, which is made up of two 128-bit extended precision floating point values.

We've reached the conclusion of our essay now that you've grasped the notion of Numeric Data Types in Numpy. Kudos :tada: ! You now have a firm grasp on what data type is in NumPy.

Conclusion

This article taught us :

The data type specifies what kind of values the data item may hold.
Data type tells us the type of data, the amount of the data, the byte order, and whether the data is stored in little-endian or big-endian format. In the case of a structured type, it tells us the names of the fields, the data type of each field, and the fraction of the memory block occupied by each field; if the data type is a subarray, we may learn about its structure and data type.
Numpy offers a variety of numeric data types, such as numpy.bool_, numpy.byte, numpy.short, numpy.intc, numpy.int_, numpy.longlong,numpy.half, numpy.single,numpy.double, numpy.csingle, numpy.cdouble, and so on.