Best Component Libraries for Python

python_docx is a Python library that allows you to create and update Microsoft Word (.docx), files. Word's fundamental structure is the paragraph. They are used for body text as well as headings and list items such bullets. Although you can specify width and height, it is not recommended. If you only specify one, python docx uses it for the correct scaled value of both. This ensures that your image doesn't look stretched and preserves the aspect ratio. You should learn what a Word paragraph format is. It allows you to apply multiple formatting options to a paragraph at a time. Python-docx can be used to create new documents and make changes to existing documents. It only allows you to make changes to existing documents. However, if you start with a blank document, it may feel like you are creating one from scratch.

Requests is an elegant and simple HTTP library. Requests makes it easy to send HTTP/1.1 queries. You don't need to manually add query strings or form-encode your PUT and POST data to your URLs. Instead, you can use the JSON method. Requests is the most downloaded Python package today, pulling in approximately 30M downloads per week, according to GitHub. Requests is currently dependent upon 1,000,000+ repositories. This code is certainly worth your trust. PyPI has Requests. Requests is available to meet the demands of building reliable and robust HTTP-speaking applications for today's needs. Automated content decompression and decoding. International URLs and domains. Sessions with cookie persistence. Browser-style TLS/SSL verification. Basic and digest authentication, as well as familiar dict-like cookies. Multi-part file uploads. SOCKS proxy support. Connection timeouts and streaming downloading.

urllib3 is a powerful and user-friendly HTTP client that Python uses. You should use urllib3 as a client for your Python environment. Many critical features are missing from the Python standard library, and urllib3 provides them. Thread safety, connection pooling and client-side SSL/TLS verification. Multipart encryption is used for file uploads. Helpers for dealing with HTTP redirects and retrying requests. Support for deflate, gzip, and brotli encryption. Proxy support for HTTPS and SOCKS. 100% coverage of the test. urllib3 is one the most downloaded Python packages. It is a dependency on many popular Python packages such as Requests, Pip and more! urllib3 is available under the MIT License. API Reference documentation provides API-level documentation. The User Guide explains how to use the library to accomplish common tasks. For lower-level tweaking, the Advanced Usage Guide is a more detailed guide.

High performance computing has become a more affordable resource for researchers in the scientific community over the past years. The popularity of clusters of workstations and clusters of Beowulf classes has been strongly influenced by the combination of high quality open-source software and cheap hardware. Message-passing is one of the most effective parallel computational models. This paradigm is especially suited for distributed memory architectures. It is used in today’s most complex engineering and scientific applications related to modeling, simulation and design. Because of the incompatible options developers had to choose from, portable message-passing parallel programming used to be a nightmare. This situation has changed significantly since the MPI Forum published its standard specification.

Dash & Dash Enterprise allow you to build and deploy analytic web applications using Python, R, or Julia. No JavaScript or DevOps are required. The world's most successful companies offer AI, ML and Python analytics at a fraction of the cost of full-stack development. Dash is the way they do it. Apps and dashboards that run advanced analytics such as NLP, forecasting and computer vision can be delivered. You can work in Python, R, or Julia. Reduce costs by migrating legacy per-seat license software to Dash Enterprise's unlimited end-user pricing model. You can deploy and update Dash apps faster without an IT or DevOps staff. You can create pixel-perfect web apps and dashboards without having to write any CSS. Kubernetes makes it easy to scale. High availability support for mission-critical Python apps

The Pylons web framework was designed to make it easy and quick to build web applications and websites. They can be as simple as one Python module or as complex as a directory layout for larger, more complex web applications. Pylons includes project templates to help you set up a new web app project. Or, you can create your own and customize it as you wish. This framework makes it easy to write Python web applications. It is simple to expand on the minimalist, component-based philosophy. Use existing Python knowledge. Extensible application design. High performance, fast and efficient with a very small per-request stack. Uses well-tested Python packages. The Pylons 1.0 series is stable, production-ready, but in maintenance-only mode. The Pylons Project maintains the Pyramid web framework to support future development. Pylons 1.0 users are strongly encouraged to use Pyramid for their next project.

With BuildVu, you will unlock precise PDF-to-HTML/SVG conversion, giving you greater control and added functionality over PDF in your web application. -Optimized Content: BuildVu intelligently converts PDFs, optimizing for smaller file sizes and fast rendering in browsers. -File Metadata: Access PDF data in JSON format, including metadata, word lists, outlines (bookmarks), and annotations. -Thumbnails: Generate high-quality page thumbnails with customizable dimensions. -Annotations: Enjoy support for various annotation types (Links, Popups, Sound/Video, Text, Highlight, Underline) in easy-to-use JSON format. -search.json: Extract all text from the document alongside the HTML content. -Font Conversion: Restructure embedded fonts for compatibility across web browsers. -Office Conversion: Combine BuildVu with LibreOffice for seamless conversion from Office formats (Word, PowerPoint, Excel).

Parsel is a BSD licensed Python library that extracts and removes data from HTML and XML. It uses XPath, CSS selectors and regular expressions. Make a selector object to parse the HTML or XML text. Next, use CSS or XPath expressions for selecting elements. CSS is a language that allows you to apply styles to HTML documents. It defines selectors that allow you to associate styles with specific HTML elements. XPath, which can be used with HTML, is a language that allows you to select nodes from XML documents. You can choose to use CSS or XPath. While CSS is more readable than XPath, some things cannot be done with XPath. Parsel selectors are built on top of lxml and support certain EXSLT extensions. They also come with pre-registered namesspaces for use in XPath statements. Parsel selectors let you chain selectors. This means that you can select by class using CSS, and then switch to XPath if necessary.

Pexpect makes Python an easier tool for controlling other programs. Pexpect can be used to create child applications, control them and respond to their output. Pexpect works like Don Libes' Expect. Your script can use Pexpect to create a child application. You can also control it like a human would. Pexpect is a tool that automates interactive applications like ssh and FTP, passwd and telnet. It can also be used to automate setup scripts that duplicate software package installations on different servers. It can also be used to automate software testing. Pexpect has a similar spirit to Don Libes' Expect but is pure Python. Pexpect is not like other Expect-like Python modules. It doesn't require Expect or TCL to be compiled. It should work on any platform that can support the standard Python pty modules. The Pexpect interface is easy to use.

Pygame is a collection of Python modules that can be used to create video games. Pygame is an extension of the SDL library. This allows you create full-featured games and multimedia programs using the python programming language. Pygame is portable and can be used on almost every platform and operating system. Pygame is completely free. Pygame is free and open-source. You can use it to create commercial, freeware, shareware, or open-source games. Dual-core CPUs are common and 8-core CPUs easily available on desktop systems, multi-core CPUs allow you to do more with your game. Select pygame functions will release the dreaded Python GIL. This is something you can do with C code. Optimized C and assembly code are used for core functions. C code is often 10-20x faster than Python code, while assembly code can easily be 100x faster than Python code. It is compatible with many operating systems. You just need to apt-get or emerge.

The Python multi-platform windowing and multimedia library. pyglet, a powerful and easy-to-use Python library, allows you to create games and other visually rich applications on Windows, Mac OS X and Linux. It supports windowing, user event handling, Joysticks and OpenGL graphics. It also supports loading images and videos. This is all done with a friendly Pythonic API that's easy to use and doesn't get in the way. pyglet is available under the BSD open source license. This allows you to use it for commercial and open-source projects without restriction. No external dependencies or installation requirements. pyglet doesn't require any additional Python to meet most application or game requirements. This simplifies distribution and installation. This makes it easy for you to package your project using freezers like PyInstaller. pyglet offers real platform native windows that allow you to use multiple windows and multi-monitor desktops.

Bokeh allows you to create simple plots but can also handle custom or specialized use cases. Apps, dashboards and plots can be published on web pages or in Jupyter notebooks. Python offers a wide range of powerful analytics tools, including NumPy and Scipy, Pandas. Scikit-Learn, OpenCV and Scikit-Learn. Bokeh server allows you to connect these tools to rich interactive visualizations in your browser. It has a wide range of widgets, plot tools and UI events that trigger Python callbacks. Researchers at Monash University maintain Microscopium. Researchers can use it to explore large image datasets using Bokeh's interactive tools. Panel is a tool that allows for polished data presentation and makes use of the Bokeh server. Anaconda created it and supports it. Panel allows you to easily create interactive web apps and dashboards using user-definable widgets. These widgets can be connected to plots, images or tables, text, or any other data.

PyQtGraph is a pure Python graphics and GUI library based on PyQt/PySide or NumPy. It is intended for use in mathematics/scientific/engineering applications. Although written entirely in Python, the library is extremely fast thanks to its heavy use of NumPy to perform number crunching and Qt GraphicsView framework for fast display. PyQtGraph is available under the MIT open source license. Interactive view boxes for basic 2D plotting. Line and scatter plots. Data can be scaled/panned with the mouse. Rapid drawing allows for real-time data display. Displays all data types (int, float, any bit depth, RGB, RGBA or luminance). Multidimensional images can be sliced at arbitrary angles using these functions (great for MRI data). Rapid update for video display and real-time interaction Image display with interactive level control and lookup tables. Mesh rendering with isosurface generator. Interactive viewports can be rotated/zoomed with the mouse. For easier programming, use the basic 3D scenegraph.

The Python Imaging Library provides image processing capabilities for your Python interpreter. This library supports many file formats, an efficient internal representation, as well as powerful image processing capabilities. The core image library allows for quick access to data stored only in basic pixel formats. It should be a solid foundation for an image processing tool. Tidelift subscribers can get Pillow for Enterprise. The Python Imaging Library is perfect for batch processing and image archival applications. The library can be used to create thumbnails, convert file formats, and print images. The current version can identify and read a wide range of formats. The most common interchange and presentation formats are the only ones that support writing. The library includes basic image processing functionality such as point operations, filtering using a set of convolution kernels and color space conversions.

Python-sql allows you to create SQL queries in a pythonic manner. Select with where condition, simple selects Select with multiple joins or with join. Select with group_by or select with the output name Select with order_by or sub-select. Select another schema and insert query using default values. Insert query with query and values. Update query with values. Update query with where condition. Update query with from list. Delete query with where condition and delete query using sub-query. Provides limit style and qmark style as well as numeric style.

Waiting is a small library that waits for things to happen. It simply waits for a function in various modes to return True. Flux can be used to simulate timelines. Waiting is compatible. When you have a function that you need to wait for, waiting is the most basic use. It is easy to wait forever. If your predicate returns value, it will be returned by wait(). You can also specify a timeout parameter. An exception is made if the timeout expires and the predicate has not been fulfilled. The predicate is polled at a specified interval (default 1 second). You can change the interval with the sleep_seconds argument. Waiting provides two simple options to aggregate multiple predicates. They are similar to Python's built in any() and all() but don't call predicates once they have been satisfied. This is useful for predicates that are slow or inefficient.

Dedupe.io, a powerful tool, can help you find similar rows in your data. Machine learning and cutting-edge research allow us to quickly and accurately identify matches within your Excel spreadsheet or database. This will save you time and money. There is more data than ever in today's big data world. All this data can be difficult to use, especially if it was entered manually or comes from different systems. It can be difficult and time-consuming to figure out who is who in a spreadsheet. Dedupe.io is here to help. We created the most dynamic and scalable solution to de-duplicating or linking datasets and created a step-by-step wizard that anyone can use.

Implementation of the WebSocket Protocol. RFC 6455 & RFC 7692. websockets is an open-source library that allows you to build WebSocket clients and servers in Python. It focuses on correctness and simplicity, robustness and performance. It is built on top of Python's standard asynchronous I/O framework asyncio and provides an elegant coroutine-based API. websockets has been rigorously tested to ensure compliance with RFC 6455. Continuous integration fails below 100% branch coverage. websockets was designed for production. It was the first library to correctly handle backpressure before the issue became well-known in the Python community. Memory usage is optimized and configurable. C extensions accelerate expensive operations. It is pre-compiled for Linux and macOS and packaged in the wheel format according to each Python version. websockets handles everything so you can concentrate on your application.

Xlrd is a library that allows developers to extract data from Microsoft Excel™,.xls spreadsheet files. Xlrd is a library that reads and formats data from Excel files in the historical format.xls. This library cannot read files other than.xls. Charts, macros and pictures are ignored. However, formula calculations are extracted and comments, hyperlinks, advanced filters, pivot table, conditional formatting and data validation are all possible. This library cannot read password-protected files. This command will display the first, second, or last rows of each sheet within each file. xlrd is licensed by the BSD license.

Ruffus is a Python computation pipeline library. It is open-sourced and powerful, user-friendly, and widely used for science and bioinformatics. Ruffus was designed to automate scientific and other analyses with minimal fuss and effort. It is suitable for even the most basic tasks. Even complex pipelines can be handled. This will prevent make or scons from becoming cross-eyed and recursive. No "clever magic", no pre-processing. The lightweight syntax, which does one small thing well, is unambitious. Ruffus is licensed under the permissive MIT-free software license. This license allows for free use and inclusion in proprietary software. It is a good idea to run your pipeline in a temporary directory that is not connected to your original data. Ruffus is a lightweight Python module that can be used to build computational pipelines. Ruffus requires Python 2.6 and higher, or Python 3.0 and higher.

Scapy is an interactive packet manipulation program. It can forge and decode packets from a variety of protocols, send them over the wire, capture them and match requests and responses. It can handle most of the classic tasks such as scanning, tracerouting and probing, attacks, or network detection (it can replace Hping, 85% nmap, arpspoofsk, arping), tcpdumpshark, p0f and tshark). It can also handle a lot more specific tasks than most other tools, such as sending invalid frames, injecting 802.11 frames, combining technologynics (VLAN hopping+ARP caching poisoning, VOIP descoding on WEP encrypted channel), and so forth. Scapy works natively on Linux and Windows as well as OSX and most Unixes that have libpcap. The same code base runs natively on Python 2 and Python 3 Scapy development uses Git version control system. The GitHub repository hosts the Scapy reference repository.

gTTS (Google Text-to-Speech) is a Python library and CLI to interface with Google Translate’s text-to-speech API. For further audio manipulation or to write spoken mp3 data to files, you can use bytestring (file-like object) or stdout. You can also pre-generate Google Translate request URLs to feed an external program. A speech-specific sentence tokenizer that can be customized to allow for unlimited text lengths, while maintaining proper intonation, abbreviations and decimals. You can also make pronunciation corrections with custom-designed text pre-processors.

statsmodels is an Python module that provides classes, functions, and methods for the estimation of various statistical models. It also allows for statistical data exploration and statistical tests. Each estimator has a comprehensive list of available result statistics. To ensure accuracy, the results are compared to existing statistical packages. The package is available under the Modified BSD (3 clause) license. statsmodels allows you to specify models using R-style formulas or pandas DataFrames. To see the available results, take a look at dir (results). Results describe attributes. __doc__, results methods have their own documentation strings. Numpy arrays can be used in place of formulas. Installing statsmodels as part of the Anaconda distribution is the easiest way to do so. This cross-platform distribution allows for data analysis and scientific computing. This is the preferred method of installation for most users.

Tox aims automate and standardize Python testing. It is part of a larger vision to simplify the packaging, testing, and release of Python software. Tox is a generic virtualenv management tool and test command-line tool that you can use to verify that your package works with different Python versions. You can also run your tests in each environment. It also acts as a frontend for continuous integration servers, reducing boilerplate and merging CI. First, install tox using pip install tox. Next, add basic information about your project as well as the test environments that you would like your project to run into a tox.ini.py file. To generate a tox.ini automatically, run tox-quickstart. Answer a few questions. Test your project against Python2.7 or Python3.6.

The h5py package provides a Pythonic interface for the HDF5 binary data format. It allows you to store large amounts of numerical data and allow you to easily manipulate that data using NumPy. You can cut into multi-terabyte datasets on disk as if they were NumPy arrays. You can store thousands of datasets in one file. You can categorize and tag them however you like. H5py makes use of simple NumPy and Python metaphors like dictionary syntax and NumPy array syntax. You can, for example, iterate over files or examine the.shape and.dtype attributes of data. To get started, you don't need any knowledge about HDF5. In addition to an easy-to-use interface, h5py relies on an object-oriented Cython wrap of the HDF5 CAP API. You can do almost anything from C in HDF5 with h5py.