data representation data types and number systems

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Computers and the Internet

Course: computers and the internet   >   unit 1, how do computers represent data.

• Binary & data
• Bits (binary digits)

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Data Representation in Computer: Number Systems, Characters, Audio, Image and Video

Table of Contents

• 1 What is Data Representation in Computer?
• 2.1 Binary Number System
• 2.2 Octal Number System
• 2.3 Decimal Number System
• 2.4 Hexadecimal Number System
• 3.4 Unicode
• 4 Data Representation of Audio, Image and Video
• 5.1 What is number system with example?

What is Data Representation in Computer?

A computer uses a fixed number of bits to represent a piece of data which could be a number, a character, image, sound, video, etc. Data representation is the method used internally to represent data in a computer. Let us see how various types of data can be represented in computer memory.

Before discussing data representation of numbers, let us see what a number system is.

Number Systems

Number systems are the technique to represent numbers in the computer system architecture, every value that you are saving or getting into/from computer memory has a defined number system.

A number is a mathematical object used to count, label, and measure. A number system is a systematic way to represent numbers. The number system we use in our day-to-day life is the decimal number system that uses 10 symbols or digits.

The number 289 is pronounced as two hundred and eighty-nine and it consists of the symbols 2, 8, and 9. Similarly, there are other number systems. Each has its own symbols and method for constructing a number.

A number system has a unique base, which depends upon the number of symbols. The number of symbols used in a number system is called the base or radix of a number system.

Let us discuss some of the number systems. Computer architecture supports the following number of systems:

Binary Number System

Octal number system, decimal number system, hexadecimal number system.

A Binary number system has only two digits that are 0 and 1. Every number (value) represents 0 and 1 in this number system. The base of the binary number system is 2 because it has only two digits.

The octal number system has only eight (8) digits from 0 to 7. Every number (value) represents with 0,1,2,3,4,5,6 and 7 in this number system. The base of the octal number system is 8, because it has only 8 digits.

The decimal number system has only ten (10) digits from 0 to 9. Every number (value) represents with 0,1,2,3,4,5,6, 7,8 and 9 in this number system. The base of decimal number system is 10, because it has only 10 digits.

A Hexadecimal number system has sixteen (16) alphanumeric values from 0 to 9 and A to F. Every number (value) represents with 0,1,2,3,4,5,6, 7,8,9,A,B,C,D,E and F in this number system. The base of the hexadecimal number system is 16, because it has 16 alphanumeric values.

Here A is 10, B is 11, C is 12, D is 13, E is 14 and F is 15 .

Data Representation of Characters

There are different methods to represent characters . Some of them are discussed below:

The code called ASCII (pronounced ‘􀀏’.S-key”), which stands for American Standard Code for Information Interchange, uses 7 bits to represent each character in computer memory. The ASCII representation has been adopted as a standard by the U.S. government and is widely accepted.

A unique integer number is assigned to each character. This number called ASCII code of that character is converted into binary for storing in memory. For example, the ASCII code of A is 65, its binary equivalent in 7-bit is 1000001.

Since there are exactly 128 unique combinations of 7 bits, this 7-bit code can represent only128 characters. Another version is ASCII-8, also called extended ASCII, which uses 8 bits for each character, can represent 256 different characters.

For example, the letter A is represented by 01000001, B by 01000010 and so on. ASCII code is enough to represent all of the standard keyboard characters.

It stands for Extended Binary Coded Decimal Interchange Code. This is similar to ASCII and is an 8-bit code used in computers manufactured by International Business Machines (IBM). It is capable of encoding 256 characters.

If ASCII-coded data is to be used in a computer that uses EBCDIC representation, it is necessary to transform ASCII code to EBCDIC code. Similarly, if EBCDIC coded data is to be used in an ASCII computer, EBCDIC code has to be transformed to ASCII.

ISCII stands for Indian Standard Code for Information Interchange or Indian Script Code for Information Interchange. It is an encoding scheme for representing various writing systems of India. ISCII uses 8-bits for data representation.

It was evolved by a standardization committee under the Department of Electronics during 1986-88 and adopted by the Bureau of Indian Standards (BIS). Nowadays ISCII has been replaced by Unicode.

Using 8-bit ASCII we can represent only 256 characters. This cannot represent all characters of written languages of the world and other symbols. Unicode is developed to resolve this problem. It aims to provide a standard character encoding scheme, which is universal and efficient.

It provides a unique number for every character, no matter what the language and platform be. Unicode originally used 16 bits which can represent up to 65,536 characters. It is maintained by a non-profit organization called the Unicode Consortium.

The Consortium first published version 1.0.0 in 1991 and continues to develop standards based on that original work. Nowadays Unicode uses more than 16 bits and hence it can represent more characters. Unicode can represent characters in almost all written languages of the world.

Data Representation of Audio, Image and Video

In most cases, we may have to represent and process data other than numbers and characters. This may include audio data, images, and videos. We can see that like numbers and characters, the audio, image, and video data also carry information.

We will see different file formats for storing sound, image, and video .

Multimedia data such as audio, image, and video are stored in different types of files. The variety of file formats is due to the fact that there are quite a few approaches to compressing the data and a number of different ways of packaging the data.

For example, an image is most popularly stored in Joint Picture Experts Group (JPEG ) file format. An image file consists of two parts – header information and image data. Information such as the name of the file, size, modified data, file format, etc. is stored in the header part.

The intensity value of all pixels is stored in the data part of the file. The data can be stored uncompressed or compressed to reduce the file size. Normally, the image data is stored in compressed form. Let us understand what compression is.

Take a simple example of a pure black image of size 400X400 pixels. We can repeat the information black, black, …, black in all 16,0000 (400X400) pixels. This is the uncompressed form, while in the compressed form black is stored only once and information to repeat it 1,60,000 times is also stored.

Numerous such techniques are used to achieve compression. Depending on the application, images are stored in various file formats such as bitmap file format (BMP), Tagged Image File Format (TIFF), Graphics Interchange Format (GIF), Portable (Public) Network Graphic (PNG).

What we said about the header file information and compression is also applicable for audio and video files. Digital audio data can be stored in different file formats like WAV, MP3, MIDI, AIFF, etc. An audio file describes a format, sometimes referred to as the ‘container format’, for storing digital audio data.

For example, WAV file format typically contains uncompressed sound and MP3 files typically contain compressed audio data. The synthesized music data is stored in MIDI(Musical Instrument Digital Interface) files.

Similarly, video is also stored in different files such as AVI (Audio Video Interleave) – a file format designed to store both audio and video data in a standard package that allows synchronous audio with video playback, MP3, JPEG-2, WMV, etc.

FAQs About Data Representation in Computer

What is number system with example.

Let us discuss some of the number systems. Computer architecture supports the following number of systems: 1. Binary Number System 2. Octal Number System 3. Decimal Number System 4. Hexadecimal Number System

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Table of contents.

• Introduction to Functional Computer
• Fundamentals of Architectural Design

Data Representation

• Instruction Set Architecture : Instructions and Formats
• Instruction Set Architecture : Design Models
• Instruction Set Architecture : Addressing Modes
• Performance Measurements and Issues
• Computer Architecture Assessment 1
• Fixed Point Arithmetic : Addition and Subtraction
• Fixed Point Arithmetic : Multiplication
• Fixed Point Arithmetic : Division
• Floating Point Arithmetic
• Arithmetic Logic Unit Design
• CPU's Data Path
• CPU's Control Unit
• Control Unit Design
• Concepts of Pipelining
• Computer Architecture Assessment 2
• Pipeline Hazards
• Memory Characteristics and Organization
• Cache Memory
• Virtual Memory
• I/O Communication and I/O Controller
• Input/Output Data Transfer
• Direct Memory Access controller and I/O Processor
• CPU Interrupts and Interrupt Handling
• Computer Architecture Assessment 3

Course Computer Architecture

Digital computers store and process information in binary form as digital logic has only two values "1" and "0" or in other words "True or False" or also said as "ON or OFF". This system is called radix 2. We human generally deal with radix 10 i.e. decimal. As a matter of convenience there are many other representations like Octal (Radix 8), Hexadecimal (Radix 16), Binary coded decimal (BCD), Decimal etc.

Every computer's CPU has a width measured in terms of bits such as 8 bit CPU, 16 bit CPU, 32 bit CPU etc. Similarly, each memory location can store a fixed number of bits and is called memory width. Given the size of the CPU and Memory, it is for the programmer to handle his data representation. Most of the readers may be knowing that 4 bits form a Nibble, 8 bits form a byte. The word length is defined by the Instruction Set Architecture of the CPU. The word length may be equal to the width of the CPU.

The memory simply stores information as a binary pattern of 1's and 0's. It is to be interpreted as what the content of a memory location means. If the CPU is in the Fetch cycle, it interprets the fetched memory content to be instruction and decodes based on Instruction format. In the Execute cycle, the information from memory is considered as data. As a common man using a computer, we think computers handle English or other alphabets, special characters or numbers. A programmer considers memory content to be data types of the programming language he uses. Now recall figure 1.2 and 1.3 of chapter 1 to reinforce your thought that conversion happens from computer user interface to internal representation and storage.

• Data Representation in Computers

Information handled by a computer is classified as instruction and data. A broad overview of the internal representation of the information is illustrated in figure 3.1. No matter whether it is data in a numeric or non-numeric form or integer, everything is internally represented in Binary. It is up to the programmer to handle the interpretation of the binary pattern and this interpretation is called Data Representation . These data representation schemes are all standardized by international organizations.

Choice of Data representation to be used in a computer is decided by

• The number types to be represented (integer, real, signed, unsigned, etc.)
• Range of values likely to be represented (maximum and minimum to be represented)
• The Precision of the numbers i.e. maximum accuracy of representation (floating point single precision, double precision etc)
• If non-numeric i.e. character, character representation standard to be chosen. ASCII, EBCDIC, UTF are examples of character representation standards.
• The hardware support in terms of word width, instruction.

Before we go into the details, let us take an example of interpretation. Say a byte in Memory has value "0011 0001". Although there exists a possibility of so many interpretations as in figure 3.2, the program has only one interpretation as decided by the programmer and declared in the program.

• Fixed point Number Representation

Fixed point numbers are also known as whole numbers or Integers. The number of bits used in representing the integer also implies the maximum number that can be represented in the system hardware. However for the efficiency of storage and operations, one may choose to represent the integer with one Byte, two Bytes, Four bytes or more. This space allocation is translated from the definition used by the programmer while defining a variable as integer short or long and the Instruction Set Architecture.

In addition to the bit length definition for integers, we also have a choice to represent them as below:

• Unsigned Integer : A positive number including zero can be represented in this format. All the allotted bits are utilised in defining the number. So if one is using 8 bits to represent the unsigned integer, the range of values that can be represented is 28 i.e. "0" to "255". If 16 bits are used for representing then the range is 216 i.e. "0 to 65535".
• Signed Integer : In this format negative numbers, zero, and positive numbers can be represented. A sign bit indicates the magnitude direction as positive or negative. There are three possible representations for signed integer and these are Sign Magnitude format, 1's Compliment format and 2's Complement format .

Signed Integer – Sign Magnitude format: Most Significant Bit (MSB) is reserved for indicating the direction of the magnitude (value). A "0" on MSB means a positive number and a "1" on MSB means a negative number. If n bits are used for representation, n-1 bits indicate the absolute value of the number. Examples for n=8:

Examples for n=8:

0010 1111 = + 47 Decimal (Positive number)

1010 1111 = - 47 Decimal (Negative Number)

0111 1110 = +126 (Positive number)

1111 1110 = -126 (Negative Number)

0000 0000 = + 0 (Postive Number)

1000 0000 = - 0 (Negative Number)

Although this method is easy to understand, Sign Magnitude representation has several shortcomings like

• Zero can be represented in two ways causing redundancy and confusion.
• The total range for magnitude representation is limited to 2n-1, although n bits were accounted.
• The separate sign bit makes the addition and subtraction more complicated. Also, comparing two numbers is not straightforward.

Signed Integer – 1’s Complement format: In this format too, MSB is reserved as the sign bit. But the difference is in representing the Magnitude part of the value for negative numbers (magnitude) is inversed and hence called 1’s Complement form. The positive numbers are represented as it is in binary. Let us see some examples to better our understanding.

1101 0000 = - 47 Decimal (Negative Number)

1000 0001 = -126 (Negative Number)

1111 1111 = - 0 (Negative Number)

• Converting a given binary number to its 2's complement form

Step 1 . -x = x' + 1 where x' is the one's complement of x.

Step 2 Extend the data width of the number, fill up with sign extension i.e. MSB bit is used to fill the bits.

Example: -47 decimal over 8bit representation

As you can see zero is not getting represented with redundancy. There is only one way of representing zero. The other problem of the complexity of the arithmetic operation is also eliminated in 2’s complement representation. Subtraction is done as Addition.

More exercises on number conversion are left to the self-interest of readers.

• Floating Point Number system

The maximum number at best represented as a whole number is 2 n . In the Scientific world, we do come across numbers like Mass of an Electron is 9.10939 x 10-31 Kg. Velocity of light is 2.99792458 x 108 m/s. Imagine to write the number in a piece of paper without exponent and converting into binary for computer representation. Sure you are tired!!. It makes no sense to write a number in non- readable form or non- processible form. Hence we write such large or small numbers using exponent and mantissa. This is said to be Floating Point representation or real number representation. he real number system could have infinite values between 0 and 1.

Representation in computer

Unlike the two's complement representation for integer numbers, Floating Point number uses Sign and Magnitude representation for both mantissa and exponent . In the number 9.10939 x 1031, in decimal form, +31 is Exponent, 9.10939 is known as Fraction . Mantissa, Significand and fraction are synonymously used terms. In the computer, the representation is binary and the binary point is not fixed. For example, a number, say, 23.345 can be written as 2.3345 x 101 or 0.23345 x 102 or 2334.5 x 10-2. The representation 2.3345 x 101 is said to be in normalised form.

Floating-point numbers usually use multiple words in memory as we need to allot a sign bit, few bits for exponent and many bits for mantissa. There are standards for such allocation which we will see sooner.

• IEEE 754 Floating Point Representation

We have two standards known as Single Precision and Double Precision from IEEE. These standards enable portability among different computers. Figure 3.3 picturizes Single precision while figure 3.4 picturizes double precision. Single Precision uses 32bit format while double precision is 64 bits word length. As the name suggests double precision can represent fractions with larger accuracy. In both the cases, MSB is sign bit for the mantissa part, followed by Exponent and Mantissa. The exponent part has its sign bit.

It is to be noted that in Single Precision, we can represent an exponent in the range -127 to +127. It is possible as a result of arithmetic operations the resulting exponent may not fit in. This situation is called overflow in the case of positive exponent and underflow in the case of negative exponent. The Double Precision format has 11 bits for exponent meaning a number as large as -1023 to 1023 can be represented. The programmer has to make a choice between Single Precision and Double Precision declaration using his knowledge about the data being handled.

The Floating Point operations on the regular CPU is very very slow. Generally, a special purpose CPU known as Co-processor is used. This Co-processor works in tandem with the main CPU. The programmer should be using the float declaration only if his data is in real number form. Float declaration is not to be used generously.

• Decimal Numbers Representation

Decimal numbers (radix 10) are represented and processed in the system with the support of additional hardware. We deal with numbers in decimal format in everyday life. Some machines implement decimal arithmetic too, like floating-point arithmetic hardware. In such a case, the CPU uses decimal numbers in BCD (binary coded decimal) form and does BCD arithmetic operation. BCD operates on radix 10. This hardware operates without conversion to pure binary. It uses a nibble to represent a number in packed BCD form. BCD operations require not only special hardware but also decimal instruction set.

• Exceptions and Error Detection

All of us know that when we do arithmetic operations, we get answers which have more digits than the operands (Ex: 8 x 2= 16). This happens in computer arithmetic operations too. When the result size exceeds the allotted size of the variable or the register, it becomes an error and exception. The exception conditions associated with numbers and number operations are Overflow, Underflow, Truncation, Rounding and Multiple Precision . These are detected by the associated hardware in arithmetic Unit. These exceptions apply to both Fixed Point and Floating Point operations. Each of these exceptional conditions has a flag bit assigned in the Processor Status Word (PSW). We may discuss more in detail in the later chapters.

• Character Representation

Another data type is non-numeric and is largely character sets. We use a human-understandable character set to communicate with computer i.e. for both input and output. Standard character sets like EBCDIC and ASCII are chosen to represent alphabets, numbers and special characters. Nowadays Unicode standard is also in use for non-English language like Chinese, Hindi, Spanish, etc. These codes are accessible and available on the internet. Interested readers may access and learn more.

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Data Representation, Number Systems and Arithmetic Processes

Cite this chapter.

• M. G. Hartley 4 ,
• M. Healey 5 &
• P. G. Depledge 6  

Part of the book series: Macmillan Computer Science Series ((COMPSS))

31 Accesses

The basic concepts of digital computers have been introduced in the first two chapters in a largely descriptive way. In this chapter and the next, the fundamental techniques necessary to construct a working computer to implement the concepts discussed are presented. The importance of the binary technique has been stressed as a practical method of achieving the inherent speed and accuracy by removing tight tolerances from the actual voltage levels used by the electronics. The representation of non-numeric data, such as alphabetic characters, is also important. This chapter reviews the commonly used techniques.

Having established the concept of binary representation of information, then the manipulation of binary data can be treated as a logical two-state process — for example, on or off, open or shut, true or false. For simplicity the two logical states are represented as 0 or 1, drawing an immediate parallel with binary numbers. Indeed it is shown in some detail in the next chapter how logic circuits are used to implement binary arithmetic units, such as an adder.

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Bibliography

Richard, R. K., Arithmetic Operations in Digital Computers , Van Nostrand, New York, 1955.

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Authors and affiliations.

Information Systems Engineering Group, Department of Electrical Engineering and Electronics, University of Manchester Institute of Science and Technology, UK

M. G. Hartley

Department of Electrical and Electronic Engineering, University College, Cardiff, UK

Application Engineering Organisation, Hewlett-Packard Ltd, USA

P. G. Depledge

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© 1988 M. G. Hartley, M. Healey and P. G. Depledge

About this chapter

Hartley, M.G., Healey, M., Depledge, P.G. (1988). Data Representation, Number Systems and Arithmetic Processes. In: Mini and Microcomputer Systems. Macmillan Computer Science Series. Palgrave, London. https://doi.org/10.1007/978-1-349-19315-8_3

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• Data Structures
• Linked List
• Binary Tree
• Binary Search Tree
• Segment Tree
• Disjoint Set Union
• Fenwick Tree
• Red-Black Tree
• Advanced Data Structures
• Tree Data Structure
• Data Structures | Binary Trees | Question 3
• Data Structures | Binary Trees | Question 4
• Data Structures | Binary Trees | Question 1
• Data Structures | Binary Trees | Question 9
• Data Structures | Binary Trees | Question 12
• Data Structures | Binary Trees | Question 14
• Data Structures | Binary Trees | Question 11
• Data Structures | Binary Trees | Question 13
• Data Structures | Binary Trees | Question 15
• Tango Tree Data Structure
• Applications of tree data structure
• Data Structures | Binary Search Trees | Question 7
• Data Structures | Binary Search Trees | Question 8
• Data Structures | Binary Search Trees | Question 5
• Data Structures | Binary Search Trees | Question 6
• Data Structures | Binary Search Trees | Question 2

Binary Tree Data Structure

A Binary Tree Data Structure is a hierarchical data structure in which each node has at most two children, referred to as the left child and the right child. It is commonly used in computer science for efficient storage and retrieval of data, with various operations such as insertion, deletion, and traversal.

Introduction:

• Introduction to Binary Tree – Data Structure and Algorithm Tutorials
• Properties of Binary Tree
• Types of Binary Tree
• Applications, Advantages and Disadvantages of Binary Tree
• Binary Tree (Array implementation)
• Complete Binary Tree
• Perfect Binary Tree

Basic Operations on Binary Tree:

• Tree Traversals (Inorder, Preorder and Postorder)
• Level Order Tree Traversal
• Find the Maximum Depth or Height of given Binary Tree
• Insertion in a Binary Tree
• Deletion in a Binary Tree
• Enumeration of Binary Trees

Some other important Binary Tree Traversals :

• Level order traversal in spiral form
• Reverse Level Order Traversal
• BFS vs DFS for Binary Tree
• Inorder Tree Traversal without Recursion
• Morris traversal for Preorder
• Iterative Preorder Traversal
• Iterative Postorder Traversal Using Two Stacks
• Diagonal Traversal of Binary Tree
• Boundary Traversal of binary tree

Easy Problems on Binary Tree Data Structure:

• Calculate depth of a full Binary tree from Preorder
• Construct a tree from Inorder and Level order traversals
• Check if a given Binary Tree is SumTree
• Check if two nodes are cousins in a Binary Tree
• Check if removing an edge can divide a Binary Tree in two halves
• Check whether a given binary tree is perfect or not
• Check if a Binary Tree contains duplicate subtrees of size 2 or more
• Check if two trees are Mirror
• Foldable Binary Trees
• Symmetric Tree (Mirror Image of itself)
• Write Code to Determine if Two Trees are Identical
• Subtree with given sum in a Binary Tree
• Succinct Encoding of Binary Tree
• Write a program to Calculate Size of a tree
• Diameter of a Binary Tree
• Get Level of a node in a Binary Tree

Medium Problems on Binary Tree Data Structure:

• Find all possible binary trees with given Inorder Traversal
• Populate Inorder Successor for all nodes
• Construct Complete Binary Tree from its Linked List Representation
• Minimum swap required to convert binary tree to binary search tree
• Convert a given Binary Tree to Doubly Linked List | Set 1
• Convert a tree to forest of even nodes
• Flip Binary Tree
• Print root to leaf paths without using recursion
• Check if given Preorder, Inorder and Postorder traversals are of same tree
• Check whether a given Binary Tree is Complete or not | Set 1 (Iterative Solution)
• Check if a binary tree is subtree of another binary tree | Set 2
• Find largest subtree sum in a tree
• Maximum sum of nodes in Binary tree such that no two are adjacent
• Lowest Common Ancestor in a Binary Tree | Set 1
• Height of a generic tree from parent array
• Find distance between two given keys of a Binary Tree

Hard Problems on Binary Tree Data Structure:

• Modify a binary tree to get Preorder traversal using right pointers only
• Construct Full Binary Tree using its Preorder traversal and Preorder traversal of its mirror tree
• Construct a special tree from given preorder traversal
• Construct tree from ancestor matrix
• Construct the full k-ary tree from its preorder traversal
• Construct Binary Tree from String with bracket representation
• Convert a Binary Tree into Doubly Linked List in spiral fashion
• Convert a Binary Tree to a Circular Doubly Link List
• Convert Ternary Expression to a Binary Tree
• Check if there is a root to leaf path with given sequence
• Remove all nodes which don’t lie in any path with sum>= k
• Maximum spiral sum in Binary Tree
• Sum of nodes at k-th level in a tree represented as string
• Sum of all the numbers that are formed from root to leaf paths
• Merge Two Binary Trees by doing Node Sum (Recursive and Iterative)
• Find root of the tree where children id sum for every node is given

Quick Links :

• ‘Practice Problems’ on Trees
• ‘Quizzes’ on Binary Trees
• ‘Videos’ on Trees

Recommended:

• Learn Data Structure and Algorithms | DSA Tutorial

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What Is a Data Model and What Is Its Purpose?

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Apache Arrow nanoarrow 0.5.0 Release

Published 27 May 2024 By The Apache Arrow PMC (pmc)

The Apache Arrow team is pleased to announce the 0.5.0 release of Apache Arrow nanoarrow. This release covers 79 resolved issues from 9 contributors.

Release Highlights

The primary focus of the nanoarrow 0.5.0 release was expanding the initial Python bindings that were released in 0.4.0. The nanoarrow Python package can now create and consume most Arrow data types, arrays, and array streams, including conversion to/from objects compatible with the Python buffer protocol and conversion to/from lists of Python objects.

The nanoarrow 0.5.0 release also includes updates to its build configuration to make it possible to use nanoarrow with FetchContent in projects with a wider variety of CMake usage. In addition to CMake, nanoarrow now supports the Meson build system. Thanks to @vyasr and @WillAyd for contributing these changes!

In the R bindings , support for reading IPC streams is now accessible with read_nanoarrow() !

Finally, build system helpers and helpers to reconcile modern C++ usage with nanorrow C structures (e.g., iterating over an ArrowArrayStream or ArrowArray using a range-for loop) were added to nanoarrow.hpp . Thanks to @bkeitz for contributing these changes!

See the Changelog for a detailed list of contributions to this release.

Breaking Changes

Most changes included in the nanoarrow 0.5.0 release will not break downstream code; however, several changes in the C library are breaking changes to previous behaviour.

• ArrowBufferResize() and ArrowBitmapResize() now adjust size_bytes / size_bits in addition to capacity_bytes / buffer.capacity_bytes . Preivously these functions only adjusted the capacity of the underlying buffer which caused some understandable confusion even though this behaviour was documented. This change affects all usage of ArrowBufferReisze() and ArrowBitmapResize() that increased the size of the underlying buffer (i.e., usage where shrink_to_fit was non zero should be unaffected).
• ArrowBufferReset() now always calls the allocator’s free() callback. Previously, a call to the free() callback was skipped if the pointer was NULL ; however, this led to some confusion and made it easy to accidentally leak a custom deallocator whose pointer happened to be NULL .
• As a consequence of the above, it is now mandatory to call ArrowBufferInit() before calling ArrowBufferReset() . There was some existing usage of nanoarrow that zero-ed the memory for an ArrowBuffer and then (sometimes) called ArrowBufferReset() . Preivously this was a no-op; however, after 0.5.0 this will crash. This is consistent with other structures in the nanoarrow C library (which require an initialization before it is safe to reset/release them).

Python bindings

The nanoarrow Python bindings are distributed as the nanoarrow package on PyPI and conda-forge :

High level users can use the Schema , Array , and ArrayStream classes to interact with data types, arrays, and array streams:

Low-level users can use c_schema() , c_array() , and c_array_stream() to interact with thin wrappers around the Arrow C Data interface structures:

All nanoarrow type/array-like objects implement the Arrow PyCapsule interface for both producing and consuming and are zero-copy interchangeable with pyarrow objects in many cases:

For a more detailed tour of the nanoarrow Python bindings, see the Getting started in Python guide and the Python API reference .

The nanoarrow 0.5.0 release includes a number of bugfixes and improvements to the core C library and C++ helpers.

First, the CMake build system was refactored to enable FetchContent to work in a wider variety of develop/build/install scenarios . In most cases, CMake-based projects should be able to add the nanoarrow C library as a dependency with:

Projects using the Meson build system can install nanoarrow from WrapDB using:

…and use dependency('nanoarrow') to add the dependency:

Finally, a set of C++ range/view helpers were added to smooth out some of more verbose aspects of working with nanoarrow in C++. While the new helpers are targeted at more than just nanoarrow’s tests, they have been particularly helpful in allowing nanoarrow’s tests to be more less repetitive and more effective. For example, one particularly verbose test was collapsed to:

See the new section in the C++ API reference for details.

The nanoarrow R bindings are distributed as the nanoarrow package on CRAN .

Whereas nanoarrow has had an IPC reader supporting most features of the IPC streaming format since 0.3.0, the R bindings did not implement bindings until this release. The 0.5.0 release of the R package includes read_nanoarrow() as an entrypoint to reading streams from various sources including URLs, filenames, and R connections:

In developing the Python bindings, it became clear that a representation of a Arrow C++’s ChunkedArray was an important concept to represent. Whereas the Python bindings have the Array class to provide this structure, the R bindings had only the nanoarrow_array as a thin wrapper around the Arrow C Data interface. When developing the geospatial extension GeoArrow for R , a data structure that maintained chunked Arrow memory as an R vector was needed as an intermediary between an Arrow-native source and an R-native destination. This experimental structure can be created with as_nanoarrow_vctr() :

Contributors

This release consists of contributions from 9 contributors in addition to the invaluable advice and support of the Apache Arrow developer mailing list.