3. Data model
3. Data model¶ 3.1. Objects, values and types¶ Objects are Python’s abstraction for data. All data in a Python program is represented by objects or by relations between objects. (In a sense, and in conformance to Von Neumann’s model of a “stored program computer”, code is also represented by objects.) Every object has an identity, a type and a value. An object’s identity never changes once it has
setuptools 74.1.2.post20240906 documentation
Documentation¶ Setuptools is a fully-featured, actively-maintained, and stable library designed to facilitate packaging Python projects. It helps developers to easily share reusable code (in the form of a library) and programs (e.g., CLI/GUI tools implemented in Python), that can be installed with pip and uploaded to PyPI.
Dask — Dask documentation
Dask is a Python library for parallel and distributed computing. Dask is: Easy to use and set up (it’s just a Python library) Powerful at providing scale, and unlocking complex algorithms and Fun 🎉 How to Use Dask¶ Dask provides several APIs. Choose one that works best for you: Dask Futures parallelize arbitrary for-loop style Python code, providing: Flexible tooling allowing you to construct cus
Home - papermill 2.4.0 documentation
Welcome to papermill¶ Papermill is a tool for parameterizing and executing Jupyter Notebooks. Papermill lets you: parameterize notebooks execute notebooks This opens up new opportunities for how notebooks can be used. For example: Perhaps you have a financial report that you wish to run with different values on the first or last day of a month or at the beginning or end of the year, using paramete
pytest documentation
pytest: helps you write better programs¶ The pytest framework makes it easy to write small, readable tests, and can scale to support complex functional testing for applications and libraries. pytest requires: Python 3.8+ or PyPy3. PyPI package name: pytest A quick example¶ $ pytest =========================== test session starts ============================ platform linux -- Python 3.x.y, pytest-8
Keras Documentation
KERAS 3.0 RELEASED A superpower for ML developers Keras is a deep learning API designed for human beings, not machines. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier to iterate on. inputs = keras.Input(shape=(32, 32, 3)) x = layers.Conv2D(32, 3, activation="relu")(inputs) x =
Imageio’s user API — imageio 2.4.1 documentation
imwrite() - write an image to the specified uri mimwrite() - write a series of images to the specified uri volwrite() - write a volume to the specified uri mvolwrite() - write a series of volumes to the specified uri More control: For a larger degree of control, imageio provides functions get_reader() and get_writer(). They respectively return an Reader and an Writer object, which can be used to r
Scrapy at a glance — Scrapy 2.12.0 documentation
First steps Scrapy at a glance Walk-through of an example spider What just happened? What else? What’s next? Installation guide Scrapy Tutorial Examples Basic concepts Command line tool Spiders Selectors Items Item Loaders Scrapy shell Item Pipeline Feed exports Requests and Responses Link Extractors Settings Exceptions Built-in services Logging Stats Collection Sending e-mail Telnet Console Solvi
boltons — boltons 25.0.0 documentation
boltons boltons should be builtins. Boltons is a set of pure-Python utilities in the same spirit as — and yet conspicuously missing from — the standard library, including: Atomic file saving, bolted on with fileutils A highly-optimized OrderedMultiDict, in dictutils Two types of PriorityQueue, in queueutils Chunked and windowed iteration, in iterutils A full-featured TracebackInfo type, for repre
Tweepy Documentation — tweepy 4.14.0 documentation
» Tweepy Documentation Edit on GitHub Tweepy Documentation Contents: Installation Getting Started Models Example Streaming Authentication Introduction Twitter API v1.1 Twitter API v2 3-legged OAuth Reference Logging Twitter API v1.1 Reference API Tweets Get Tweet timelines Post, retrieve, and engage with Tweets Search Tweets Accounts and users Create and manage lists Follow, search, and get users
Overview — Sphinx 2.0.0+/ed411fd documentation
📝 Rich Text Formatting Author in reStructuredText or MyST Markdown to create highly structured technical documents, including tables, highlighted code blocks, mathematical notations, and more. 🔗 Powerful Cross-Referencing Create cross-references within your project, and even across different projects. Include references to sections, figures, tables, citations, glossaries, code objects, and more.
Πούλησε το smartphone σου σε 3 λεπτά | pandas.io
Κάνε κάτι καλό με το smartphone που έχειςΟκ, αλλά πώς ή ποιούς βοηθάω; Θα σου εξηγήσουμε αμέσως γιατί έχει τόση αξία το trade-in 😊
Usage — Matplotlib 2.0.2 documentation
General Concepts¶ matplotlib has an extensive codebase that can be daunting to many new users. However, most of matplotlib can be understood with a fairly simple conceptual framework and knowledge of a few important points. Plotting requires action on a range of levels, from the most general (e.g., ‘contour this 2-D array’) to the most specific (e.g., ‘color this screen pixel red’). The purpose of
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Pythonプログラミング入門 — Pythonプログラミング入門 documentation
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Pythonのデコレータにはwrapsをつけるべきという覚え書き - Qiita
Cookiecutter: Better Project Templates — cookiecutter 2.6.0 documentation
Optimization with PuLP — PuLP 1.6.0 documentation
TextAdapter — Anaconda documentation
PySpark Overview — PySpark 3.5.5 documentation