computer science

History of Python

History of Python is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand History of Python rather than just read about it. In short: The programming language Python was conceived in the late 1980s, and its implementation was started in December 1989 by Guido van Rossum at CWI in the Netherlands as a successor to ABC capable of exception handling and interfacing with the Amoeba operating system. Van Rossum was Python's principal author and had a central role in deciding the direction of Python (as reflected in the title given to him by the Python…

Key takeaways

History of Python belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect History of Python to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of History of Python from memory before moving on to harder problems.

Reference excerpt

The programming language Python was conceived in the late 1980s, and its implementation was started in December 1989 by Guido van Rossum at CWI in the Netherlands as a successor to ABC capable of exception handling and interfacing with the Amoeba operating system. Van Rossum was Python's principal author and had a central role in deciding the direction of Python (as reflected in the title given to him by the Python community, Benevolent Dictator for Life (BDFL)) until stepping down as leader on July 12, 2018. Python was named after the BBC TV show Monty Python's Flying Circus. Python 2.0 was released on October 16, 2000, with many major new features, such as list comprehensions, cycle-detecting garbage collector, reference counting, memory management and support for Unicode, along with a change to the development process itself, with a shift to a more transparent and community-backed process. Python 3.0, a major, backwards-incompatible release, was released on December 3, 2008 after a long period of testing. Many of its major features were also backported to the backwards-compatible Python versions 2.6 and 2.7 until support for Python 2 finally ceased at the beginning of 2020. Releases of Python 3 up through 3.12 include the 2to3 utility, which automates the translation of Python 2 code to Python 3. As of June 2026, Python 3.14.6 is the latest stable release. This version currently receives full bug-fix and security updates, while Python 3.13—released in October 2024—will continue to receive bug-fixes until October 2026, and after that will only receive security fixes until its end-of-life in 2029. Python 3.10 is the oldest supported branch of Python (albeit in the 'security support' phase).

Early history Van Rossum first published the code (for Python version 0.9.1) to alt.sources in February 1991. Several features of the language were already present at this stage, among them classes with inheritance, exception handling, functions, and various core datatypes such as list, dict, and str. The initial release also contained a module system borrowed from Modula-3; Van Rossum describes the module as "one of Python's major programming units". Python's exception model also resembled Modula-3's, with the addition of an else clause. In 1994 comp.lang.python, the primary discussion forum for Python, was formed.

Version 1 Python reached version 1.0 in January 1994. The major new features included in this release were the functional programming tools lambda, map, filter and reduce. Van Rossum stated that "Python acquired lambda, reduce(), filter() and map(), courtesy of a Lisp hacker who missed them and submitted working patches". The last version released while Van Rossum was at CWI was Python 1.2. In 1995, Van Rossum continued his work on Python at the Corporation for National Research Initiatives (CNRI) in Reston, Virginia from where he released several versions. By version 1.4, Python had acquired several new features. Notable among these are the Modula-3 inspired keyword arguments (which are also similar to Common Lisp's keyword arguments) and built-in support for complex numbers. Also included is a basic form of data hiding by name mangling, though this is easily bypassed. During Van Rossum's stay at CNRI, he launched the Computer Programming for Everybody (CP4E) initiative, intending to make programming more accessible to more people, with a basic "literacy" in programming languages, similar to the basic English literacy and mathematics skills required by most employers. Python served a central role in this: because of its focus on clean syntax, it was already suitable, and CP4E's goals bore similarities to its predecessor, ABC. The project was funded by DARPA. As of 2007, the CP4E project is inactive, and while Python attempts to be easily learnable and not too arcane in its syntax and semantics, outreach to non-programmers is not an active concern.

BeOpen In 2000, the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team. CNRI requested that a version 1.6 be released, summarizing Python's development up to the point at which the development team left CNRI. Consequently, the release schedules for 1.6 and 2.0 had a significant amount of overlap. Python 2.0 was the only release from BeOpen.com. After Python 2.0 was released by BeOpen.com, Guido van Rossum and the other PythonLabs developers joined Digital Creations. The Python 1.6 release included a new CNRI license that was substantially longer than the CWI license that had been used for earlier releases. The new license included a clause stating that the license was governed by the laws of the State of Virginia. The Free Software Foundation argued that the choice-of-law clause was incompatible with the GNU General Public License. BeOpen, CNRI and the FSF negotiated a change to Python's free-software license that would make it GPL-compatible. Python 1.6.1 is essentially the same as Python 1.6, with a few minor bug fixes, and with the new GPL-compatible license.

Version 2 Python 2.0, released October 2000, introduced list comprehensions, a feature borrowed from the functional programming languages SETL and Haskell. Python's syntax for this construct is very similar to Haskell's, apart from Haskell's preference for punctuation characters and Python's preference for alphabetic keywords. Python 2.0 also introduced a garbage collector able to collect reference cycles. Python 2.1 was close to Python 1.6.1, as well as Python 2.0. Its license was renamed Python Software Foundation License. All code, documentation and specifications added, from the time of Python 2.1's alpha release on, is owned by the Python Software Foundation (PSF), a nonprofit organization formed in 2001, modeled after the Apache Software Foundation. The release included a change to the language specification to support nested scopes, like other statically scoped languages. (The feature was turned off by default, and not required, until Python 2.2.) Python 2.2 was released in December 2001; a major innovation was the unification of Python's types (types written in C) and classes (types written in Python) into one hierarchy. This single unification made Python's object model purely and consistently object oriented. Also added were generators which were inspired by Icon.

… excerpt ends here. Continue reading the full article.

Illustrations

Old Python logo, 1990s–2006

New Python logo, 2006–present

Worked examples

Example 1 — a first encounter with History of Python

Start with the simplest possible case. Write down what History of Python claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to History of Python before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about History of Python ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of History of Python

In research: History of Python appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses History of Python in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: History of Python is common in secondary-school and first-year university syllabi. It links to neighbouring topics History of software, Python (programming language), Software version histories, so understanding it makes those chapters shorter.
In everyday life: Look for History of Python outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study History of Python in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what History of Python means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain History of Python out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is History of Python in simple terms?

Why does History of Python matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study History of Python?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on History of Python.