Alternatives to pandas

OpenRefine, which was formerly known as Google Refine, serves as an exceptional resource for managing chaotic data by enabling users to clean it, convert it between different formats, and enhance it with external data and web services. This tool prioritizes your privacy, as it operates exclusively on your local machine until you decide to share or collaborate with others; your data remains securely on your computer unless you choose to upload it. It functions by setting up a lightweight server on your device, allowing you to engage with it through your web browser, making data exploration of extensive datasets both straightforward and efficient. Additionally, users can discover more about OpenRefine's capabilities through instructional videos available online. Beyond cleaning your data, OpenRefine offers the ability to connect and enrich your dataset with various web services, and certain platforms even permit the uploading of your refined data to central repositories like Wikidata. Furthermore, a continually expanding selection of extensions and plugins is accessible on the OpenRefine wiki, enhancing its versatility and functionality for users. These features make OpenRefine an invaluable asset for anyone looking to manage and utilize complex datasets effectively.

ML.NET is a versatile, open-source machine learning framework that is free to use and compatible across platforms, enabling .NET developers to create tailored machine learning models using C# or F# while remaining within the .NET environment. This framework encompasses a wide range of machine learning tasks such as classification, regression, clustering, anomaly detection, and recommendation systems. Additionally, ML.NET seamlessly integrates with other renowned machine learning frameworks like TensorFlow and ONNX, which broadens the possibilities for tasks like image classification and object detection. It comes equipped with user-friendly tools such as Model Builder and the ML.NET CLI, leveraging Automated Machine Learning (AutoML) to streamline the process of developing, training, and deploying effective models. These innovative tools automatically analyze various algorithms and parameters to identify the most efficient model for specific use cases. Moreover, ML.NET empowers developers to harness the power of machine learning without requiring extensive expertise in the field.

Polars offers a comprehensive Python API that reflects common data wrangling practices, providing a wide array of functionalities for manipulating DataFrames through an expression language that enables the creation of both efficient and clear code. Developed in Rust, Polars makes deliberate choices to ensure a robust DataFrame API that caters to the Rust ecosystem's needs. It serves not only as a library for DataFrames but also as a powerful backend query engine for your data models, allowing for versatility in data handling and analysis. This flexibility makes it a valuable tool for data scientists and engineers alike.

Developed and continuously improved by a dedicated team of professionals specializing in differential privacy, this system is actively utilized by organizations such as the U.S. Census Bureau. It operates on the Spark framework, seamlessly handling input tables with billions of entries. The platform offers an extensive and expanding array of aggregation functions, data transformation operations, and privacy frameworks. Users can execute public and private joins, apply filters, or utilize custom functions on their datasets. It enables the computation of counts, sums, quantiles, and more under various privacy models, ensuring that differential privacy is accessible through straightforward tutorials and comprehensive documentation. Tumult Analytics is constructed on our advanced privacy architecture, Tumult Core, which regulates access to confidential data, ensuring that every program and application inherently includes a proof of privacy. The system is designed by integrating small, easily scrutinized components, ensuring a high level of safety through proven stability tracking and floating-point operations. Furthermore, it employs a flexible framework grounded in peer-reviewed academic research, guaranteeing that users can trust the integrity and security of their data handling processes. This commitment to transparency and security sets a new standard in the field of data privacy.

List & Label is a reporting tool made for software developers which adds powerful report functions to your application. Itis the preferred reporting component used by thousands worldwide software development teams. List & Label supports a huge variety of data sources, is seamless to integrate and extends applications with convenient print, export and preview functions. The reporting tool is made for development environments such as .NET, C#, Delphi, C++, ASP.NET, ASP.NET MVC, .NET Core etc. The WYSIWYG Report Designer in Office Look & Feel is included in all editions. It helps developers and end users to create flexible reports and dashboards the way they like it. The additionally included and entirely browser-based Web Report Designer for ASP.NET MVC offers more flexibility in development and is independent from printer drivers. Reports for web applications can be designed anywhere at any time in the browser of your choice. List & Label is "Made in Germany" by combit.

At Vaex.io, our mission is to make big data accessible to everyone, regardless of the machine or scale they are using. By reducing development time by 80%, we transform prototypes directly into solutions. Our platform allows for the creation of automated pipelines for any model, significantly empowering data scientists in their work. With our technology, any standard laptop can function as a powerful big data tool, eliminating the need for clusters or specialized engineers. We deliver dependable and swift data-driven solutions that stand out in the market. Our cutting-edge technology enables the rapid building and deployment of machine learning models, outpacing competitors. We also facilitate the transformation of your data scientists into proficient big data engineers through extensive employee training, ensuring that you maximize the benefits of our solutions. Our system utilizes memory mapping, an advanced expression framework, and efficient out-of-core algorithms, enabling users to visualize and analyze extensive datasets while constructing machine learning models on a single machine. This holistic approach not only enhances productivity but also fosters innovation within your organization.

Statsmodels is a Python library designed for the estimation of various statistical models, enabling users to perform statistical tests and explore data effectively. Each estimator comes with a comprehensive array of result statistics, which are validated against established statistical software to ensure accuracy. This package is distributed under the open-source Modified BSD (3-clause) license, promoting free use and modification. Users can specify models using R-style formulas or utilize pandas DataFrames for convenience. To discover available results, you can check dir(results), and you will find that attributes are detailed in results.__doc__, while methods include their own docstrings for further guidance. Additionally, numpy arrays can be employed as an alternative to formulas. For most users, the simplest way to install statsmodels is through the Anaconda distribution, which caters to data analysis and scientific computing across various platforms. Overall, statsmodels serves as a powerful tool for statisticians and data analysts alike.

Avanzai accelerates your financial data analysis by allowing you to generate production-ready Python code through natural language commands. This innovative tool streamlines the financial analysis process for novices and seasoned professionals alike, utilizing simple English for interaction. You can effortlessly plot time series data, equity index components, and stock performance metrics with straightforward prompts. Eliminate tedious aspects of financial analysis by using AI to produce code with the necessary Python libraries pre-installed. Once the code is generated, you can modify it as needed, then easily transfer it into your local setup to dive right into your projects. Benefit from popular Python libraries tailored for quantitative analysis, including Pandas and Numpy, all while communicating in plain English. Elevate your financial analysis capabilities by swiftly accessing fundamental data and assessing the performance of nearly every US stock. With Avanzai, you can enhance your investment strategies using precise and timely information, empowering you to write the same Python scripts that quantitative analysts rely on for dissecting intricate financial datasets. This revolutionary approach not only simplifies the coding process but also enriches your understanding of data-driven investment decisions.

The h5py library serves as a user-friendly interface for the HDF5 binary data format in Python. It allows users to handle vast quantities of numerical data and efficiently work with it alongside NumPy. For instance, you can access and manipulate multi-terabyte datasets stored on your disk as if they were standard NumPy arrays. You can organize thousands of datasets within a single file, applying your own categorization and tagging methods. H5py embraces familiar NumPy and Python concepts, such as dictionary and array syntax. For example, it enables you to loop through datasets in a file or examine the .shape and .dtype properties of those datasets. Getting started with h5py requires no prior knowledge of HDF5, making it accessible for newcomers. Besides its intuitive high-level interface, h5py is built on an object-oriented Cython wrapper for the HDF5 C API, ensuring that nearly any operation possible in C with HDF5 can also be performed using h5py. This combination of simplicity and power makes it a popular choice for data handling in the scientific community.

Warcat is a tool and library specifically designed for managing Web ARChive (WARC) files, enabling users to naively combine archives into a single file, extract contents, and perform a variety of commands such as listing available operations and the contents of the archive itself. Users can load an archive, write it back out, split it into individual records, and ensure data integrity by verifying digests and validating conformance to standards. Although the library may not yet be fully thread-safe, its primary aim is to provide a user-friendly and rapid experience akin to manipulating traditional archives like tar and zip. Warcat efficiently handles large, gzip-compressed files by allowing partial extraction as necessary, thus optimizing resource use. It is important to note that Warcat is distributed without any warranty, meaning users should exercise caution by backing up their data and thoroughly testing it prior to use. Each WARC file consists of multiple records joined together, with each record comprising named fields, a content block, and appropriate newline separators, while the content block itself can either be binary data or a structured combination of named fields followed by binary data. By understanding the structure and functionality of WARC files, users can effectively utilize Warcat to streamline their archival processes.

Boost your productivity and reduce development time with the IMSL numerical libraries. Leverage IMSL's build tools to attain your strategic goals effectively. With the IMSL library, you can perform tasks such as modeling regression, constructing decision trees, developing neural networks, and predicting time series. The IMSL C Numerical Library has been rigorously tested and trusted for decades across various sectors, providing businesses with a reliable, high-return solution for creating advanced analytics tools. It aids teams in rapidly incorporating complex features into their analytic applications, ranging from data mining and forecasting to sophisticated statistical analysis. Furthermore, the IMSL C library simplifies integration and deployment processes, ensuring smooth migrations and support for various popular platforms and combinations without requiring additional infrastructure for embedding in databases or applications. By utilizing IMSL libraries, organizations can enhance their analytical capabilities and remain competitive in an ever-evolving market.

The Python Imaging Library enhances your Python interpreter with advanced image processing features. This library offers a wide range of file format compatibility, an efficient internal structure, and robust image processing functionalities. Its core design focuses on enabling quick access to data in several fundamental pixel formats, serving as a reliable base for general image processing applications. For enterprises, Pillow is accessible through a Tidelift subscription, catering to professional needs. The Python Imaging Library is particularly well-suited for tasks related to image archiving and batch processing workflows. Users can leverage the library to generate thumbnails, switch between file formats, print images, and more. The latest version supports a diverse array of formats, while write capabilities are carefully limited to the most prevalent interchange and display formats. Additionally, the library includes essential image processing features such as point operations, filtering through built-in convolution kernels, and converting color spaces, making it a comprehensive tool for both casual and advanced users alike. Its versatility ensures that developers can efficiently handle various image-related tasks with ease.

nuvo delivers AI-powered, secure, and scalable data onboarding solutions that allow both you and your customers to effortlessly map, validate, and clean data, no matter the input format. With its advanced capabilities, it simplifies the process of handling complex data from various sources. Say goodbye to the burdens of intricate data mappings and transformations that can slow down your development team. Cut down on internal time spent managing customer data imports by 90%, or switch entirely to a customer self-service model, empowering your users to import their data through a simple, intuitive interface. nuvo's Data Importer SDK helps software companies such as Sastrify, Prewave, and Insurwave by automating data imports, reducing errors, and allowing development teams to concentrate on improving core features. Our solution ensures seamless integration with your existing systems, minimizing disruptions. Discover the power of nuvo firsthand by starting a free trial or booking a quick demo.

PyQtGraph is a graphics and GUI library developed in pure Python, utilizing PyQt/PySide alongside NumPy, designed primarily for applications in mathematics, science, and engineering. Despite its complete implementation in Python, the library achieves impressive speed by effectively utilizing NumPy for numerical computations and the Qt GraphicsView framework for efficient rendering. Released under the MIT open-source license, PyQtGraph supports fundamental 2D plotting through interactive view boxes, enabling line and scatter plots with user-friendly mouse control for panning and scaling. Its ability to handle various data types, including integers, floats, and different bit depths, is complemented by functionalities for slicing multidimensional images at various angles, making it particularly useful for MRI data analysis. Furthermore, it facilitates rapid updates suitable for video display or real-time interactions, along with image display features that include interactive lookup tables and level adjustments. The library also provides mesh rendering capabilities with isosurface generation, while interactive viewports allow users to rotate and zoom with ease using the mouse. Additionally, it incorporates a basic 3D scenegraph, simplifying the programming process for three-dimensional data visualization. With its robust set of features, PyQtGraph caters to a wide range of visualization needs and enhances user experience through interactivity.

NetworkX is a Python library designed for constructing, altering, and analyzing the intricacies, behaviors, and functionalities of complex networks. It offers generators for various types of graphs, including traditional, random, and synthetic networks. The advantages of using Python further enhance the experience, providing quick prototyping capabilities, ease of learning, and compatibility across multiple platforms. Additionally, it facilitates a comprehensive examination of network structures and the application of various analytical measures. This makes NetworkX an invaluable tool for researchers and practitioners in the field of network science.

An intuitive, code-free platform designed for sophisticated data analysis, enabling quick and precise insights. Why choose Datavore? Accelerate your workflow by integrating various signals seamlessly. Identify and monitor indicators across multiple datasets to effectively validate and test these signals. Organize your data comprehensively, ensuring everything is cataloged in a single location. Utilize dynamic filters for swift access to both internal and external data. Dive into exploration by creating dashboards that allow for comparison, assessment, and ongoing monitoring of key metrics. Efficiently evaluate and track numerous indicators across different datasets. Conduct thorough proprietary research by building advanced forecasting models and performing regression analyses. This platform offers the flexibility of Excel combined with the scalability of the cloud, allowing for streamlined quantitative research and the automation of repetitive tasks. With Excel-like syntax, you can create custom functions and leverage existing time series formulas. The patented ingestion engine facilitates the discovery of concepts and relationships within expansive datasets. Align your data with the company’s fiscal calendar or other defined timeframes to ensure accuracy in reporting. Aggregate data effectively to enhance your analysis.

gTTS, which stands for Google Text-to-Speech, is a Python library and command-line interface tool that allows users to interact with the text-to-speech API provided by Google Translate. This tool enables users to write spoken audio data in mp3 format to various outputs, such as a file, a bytestring for additional audio processing, or even directly to stdout. Additionally, it offers the option to pre-generate URLs for Google Translate TTS requests, which can be utilized by other external applications. The library features a customizable tokenizer specifically designed for speech, allowing for arbitrary lengths of text to be processed while maintaining correct intonation, handling of abbreviations, decimal numbers, and more. Furthermore, it includes customizable text preprocessing capabilities that can address pronunciation issues, enhancing the overall quality of the speech output. With these diverse functionalities, gTTS serves as a versatile tool for generating high-quality spoken audio from text.

Bokeh simplifies the creation of standard visualizations while also accommodating unique or specialized scenarios. It allows users to publish plots, dashboards, and applications seamlessly on web pages or within Jupyter notebooks. The Python ecosystem boasts a remarkable collection of robust analytical libraries such as NumPy, Scipy, Pandas, Dask, Scikit-Learn, and OpenCV. With its extensive selection of widgets, plotting tools, and user interface events that can initiate genuine Python callbacks, the Bokeh server serves as a vital link, enabling the integration of these libraries into dynamic, interactive visualizations accessible via the browser. Additionally, Microscopium, a project supported by researchers at Monash University, empowers scientists to uncover new functions of genes or drugs through the exploration of extensive image datasets facilitated by Bokeh’s interactive capabilities. Another useful tool, Panel, which is developed by Anaconda, enhances data presentation by leveraging the Bokeh server. It streamlines the creation of custom interactive web applications and dashboards by linking user-defined widgets to a variety of elements, including plots, images, tables, and textual information, thus broadening the scope of data interaction possibilities. This combination of tools fosters a rich environment for data analysis and visualization, making it easier for researchers and developers to share their insights.

The ROOT data analysis framework is widely utilized in High Energy Physics (HEP) and features its own file output format (.root). It seamlessly integrates with software developed in C++, while for Python users, there is an interface called pyROOT. However, pyROOT has compatibility issues with python3.4. To address this, broot is a compact library designed to transform data stored in Python's numpy ndarrays into ROOT files, structuring them with a branch for each array. This library aims to offer a standardized approach for exporting Python numpy data structures into ROOT files. Furthermore, it is designed to be portable and compatible with both Python2 and Python3, as well as ROOT versions 5 and 6, without necessitating changes to the ROOT components themselves—only a standard installation is needed. Users should find that installing the library requires minimal effort, as they only need to compile the library once or choose to install it as a Python package, making it a convenient tool for data analysis. Additionally, this ease of use encourages more researchers to adopt ROOT in their workflows.

JDeli can read and write images in HEIC format. It also provides a complete HEIC conversion between HEIC file formats and many other image file formats. JDeli can convert the image in one step. You can also read and write the HEIC file separately if you need to process the image using Java. Threading can improve the performance of your code reading and writing. JDeli can be used with threads, unlike ImageIO. JDeli has a similar API to ImageIO. It is also easy to switch between the two in your code. JDeli is actively being developed by IDRsolutions' development team, who use it in their other products. Unable to add a platform dependence? JDeli is 100% Java, and does not require plugins or native dependencies.

The Pylons web framework is tailored for the straightforward and efficient creation of web applications and websites. These applications can vary significantly in size, ranging from a simple Python module to an extensive directory structure suitable for more intricate web solutions. With the availability of project templates, Pylons allows developers to quickly initiate a new web application, or they can opt to create a custom setup from square one to meet their specific requirements. This framework simplifies the process of developing web applications in Python, embracing a minimalist and component-based approach that enhances expandability. It also leverages developers' existing knowledge of Python, promoting an extensible application design that is both fast and efficient. The framework boasts an impressively small per-request call stack, which ensures exceptional performance levels and relies on established, reliable Python packages. While the Pylons 1.0 series is recognized as stable and ready for production, it is currently only in maintenance mode. Moving forward, the Pylons Project has shifted its focus to the Pyramid web framework for ongoing development, and users of Pylons 1.0 are highly encouraged to consider transitioning to Pyramid for their upcoming projects, which offers even more advanced functionalities and support. This transition can significantly enhance the development experience and provide access to new features that are continuously being improved.

The websockets library offers a comprehensive implementation of the WebSocket Protocol (RFC 6455 & 7692) for creating both WebSocket servers and clients in Python, emphasizing accuracy, simplicity, durability, and high performance. Utilizing asyncio, which is Python’s built-in asynchronous I/O framework, it presents a sophisticated coroutine-based API that streamlines development. The library has undergone extensive testing to ensure it meets the requirements outlined in RFC 6455, and its continuous integration process mandates that every branch achieves 100% coverage. Designed specifically for production environments, websockets was notably the first library to effectively address backpressure issues before they gained widespread attention in the Python ecosystem. Furthermore, it offers optimized and adjustable memory usage, and utilizes a C extension to enhance performance for demanding operations. The library is conveniently pre-compiled for Linux, macOS, and Windows, and is distributed in wheel format tailored for each system and Python version. With websockets managing the intricate details, developers can dedicate their efforts to building robust applications without concern for the underlying complexities. This makes it an essential tool for developers looking to harness the full potential of WebSocket technology.

DHTMLX is a powerful and easy-to-use JavaScript UI library that provides a wide range of customizable and flexible components for building modern and responsive web applications. It offers 30+ full-featured UI widgets, including grids, charts, diagrams, schedulers, gantt charts, calendars, trees, forms, and more. These components are optimized for fast rendering, ensuring that your application runs smoothly in all browsers and devices. DHTMLX is compatible with popular web frameworks such as React, Angular, and Vue.js. This makes it an excellent choice for developers who are already working with these frameworks and want to add a powerful UI library to their projects. Moreover, DHTMLX supports different data sources and formats, making it easy to integrate with various back-end technologies. DHTMLX provides extensive configuration and customization abilities for its UI components, allowing developers to tailor their appearance and behavior to meet specific application requirements and extend its functionality with custom features if needed. DHTMLX also has comprehensive documentation that covers every aspect of the library, including detailed API references, tutorials, and code examples, as well as an active community.

Imageio is a versatile Python library that simplifies the process of reading and writing various types of image data, such as animated images, volumetric data, and scientific formats. It is designed to be cross-platform, compatible with Python versions 3.5 and later, and installation is straightforward. Since Imageio is developed entirely in Python, users can expect a seamless setup. It supports Python 3.5+ and is also functional on Pypy. The library relies on Numpy and Pillow for its operations, and for certain image formats, additional libraries or executables like ffmpeg may be required, which Imageio assists users in acquiring. In case of issues, understanding where to look for potential problems is crucial. This overview aims to provide insights into the workings of Imageio, enabling users to identify possible points of failure. By familiarizing yourself with these functionalities, you can enhance your troubleshooting skills when using the library.

Seaborn is a versatile data visualization library for Python that builds upon matplotlib. It offers a user-friendly interface for creating visually appealing and insightful statistical graphics. To gain a foundational understanding of the library's concepts, you can explore the introductory notes or relevant academic papers. For installation instructions, check out the dedicated page that guides you on how to download and set up the package. You can also explore the example gallery to discover various visualizations you can create with Seaborn, and further your knowledge by diving into the tutorials or API reference for detailed guidance. If you wish to examine the source code or report any issues, the GitHub repository is the place to go. Additionally, for general inquiries and community support, StackOverflow features a specific section for Seaborn discussions. Engaging with these resources will enhance your ability to effectively use the library.

DataMelt, or "DMelt", is an environment for numeric computations, data analysis, data mining and computational statistics. DataMelt allows you to plot functions and data in 2D or 3D, perform statistical testing, data mining, data analysis, numeric computations and function minimization. It also solves systems of linear and differential equations. There are also options for symbolic, non-linear, and linear regression. Java API integrates neural networks and data-manipulation techniques using various data-manipulation algorithms. Support is provided for elements of symbolic computations using Octave/Matlab programming. DataMelt provides a Java platform-based computational environment. It can be used on different operating systems and programming languages. It is not limited to one programming language, unlike other statistical programs. This software combines Java, the most widely used enterprise language in the world, with the most popular data science scripting languages, Jython (Python), Groovy and JRuby.

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).

Waiting is a compact library designed to facilitate the process of waiting for specific conditions to be met. It fundamentally pauses execution until a designated function returns True, offering various operational modes. Additionally, Waiting is designed to work seamlessly with flux for simulating timelines. The simplest way to utilize it is by providing a function to monitor. It’s straightforward to wait indefinitely; if your predicate yields a value, that value will be returned as the output of wait(). You can also set a timeout, and if this period lapses without the predicate being satisfied, an exception will occur. The library polls the predicate at a default interval of one second, which can be adjusted using the sleep_seconds parameter. When dealing with multiple predicates, Waiting offers two efficient methods for aggregation: any and all. These methods are similar to Python's built-in any() and all(), but they ensure that a predicate is not invoked more than necessary, which is particularly beneficial when working with predicates that are resource-intensive and time-consuming. By streamlining these functions, Waiting enhances both the efficiency and user experience of handling asynchronous operations.

React Spectrum serves as a React-based version of Adobe's Spectrum design system, offering a variety of components that adapt seamlessly to different interactions and screen sizes on various devices. Notably, it ensures full accessibility with support for screen readers and keyboard navigation, making it user-friendly for all. The components are crafted to accommodate mouse, touch, and keyboard inputs, adhering to responsive design principles to provide an optimal user experience across all devices. Furthermore, React Spectrum automatically adjusts to dark and light modes in alignment with the user's operating system settings. In addition to this, the framework supports more than 30 languages, which includes features for right-to-left languages, as well as date and number formatting. The extensive library of components encompasses buttons, pickers, collections, date and time inputs, color selection tools, forms, navigation elements, overlays, status indicators, and various content components. This wide-ranging set of tools ensures that developers can create accessible and versatile applications tailored to diverse user needs.

Enterprise apps require high-performance JavaScript UI controls. Fast, lightweight HTML5/JavaScript apps that are fast and have zero dependencies. New in Wijmo 2022 v1: - Inventory management reference app built in Angular - Website traffic monitoring reference app built in Angular - FlexMap added to GeoDashboard reference app - Rotated Spline Chart - Re-order FlexGrid rows with drag and drop Wijmo's General Features: - CellTemplates for Vue FlexGrids, Angular, and React - Wijmo's single set true JavaScript controls can be used in any framework including Angular and React. Wijmo Web Components allows you to use controls as native HTML elements FlexGrid, JavaScript’s fastest data grid, allows you to filter, group, sort and much more - Visualize data using 80+ charts and controls OLAP Pivot Grid - Instantly analyze, aggregate and visualize data

Stata delivers everything you need for reproducible data analysis—powerful statistics, visualization, data manipulation, and automated reporting—all in one intuitive platform. Stata is quick and accurate. The extensive graphical interface makes it easy to use, but is also fully programable. Stata's menus, dialogs and buttons give you the best of both worlds. All Stata's data management, statistical, and graphical features are easy to access by dragging and dropping or point-and-click. To quickly execute commands, you can use Stata's intuitive command syntax. You can log all actions and results, regardless of whether you use the menus or dialogs. This will ensure reproducibility and integrity in your analysis. Stata also offers complete command-line programming and programming capabilities, including a full matrix language. All the commands that Stata ships with are available to you, whether you want to create new Stata commands or script your analysis.

Parsel is a Python library licensed under BSD that facilitates the extraction and removal of data from HTML and XML documents using XPath and CSS selectors, with the option to integrate regular expressions. To begin, you create a selector object for the HTML or XML content you wish to analyze. After that, you can utilize either CSS or XPath expressions to target specific elements. CSS serves as a styling language for HTML, defining selectors that link styles to designated HTML elements, while XPath is utilized for selecting nodes within XML documents and can also be applied to HTML. Although both CSS and XPath can be used, CSS tends to offer greater readability, whereas XPath provides capabilities that may not be achievable through CSS alone. Built on top of lxml, parsel selectors incorporate some EXSLT extensions and come with pre-registered namespaces available for use in XPath queries. Furthermore, parsel selectors allow for the chaining of selectors, enabling users to primarily select by class using CSS and seamlessly transition to XPath when the situation demands it, enhancing flexibility in data extraction tasks. This dual capability makes parsel a powerful tool for developers working with web data.

kdb Insights is an advanced analytics platform built for the cloud, enabling high-speed real-time analysis of both live and past data streams. It empowers users to make informed decisions efficiently, regardless of the scale or speed of the data, and boasts exceptional price-performance ratios, achieving analytics performance that is up to 100 times quicker while costing only 10% compared to alternative solutions. The platform provides interactive data visualization through dynamic dashboards, allowing for immediate insights that drive timely decision-making. Additionally, it incorporates machine learning models to enhance predictive capabilities, identify clusters, detect patterns, and evaluate structured data, thereby improving AI functionalities on time-series datasets. With remarkable scalability, kdb Insights can manage vast amounts of real-time and historical data, demonstrating effectiveness with loads of up to 110 terabytes daily. Its rapid deployment and straightforward data ingestion process significantly reduce the time needed to realize value, while it natively supports q, SQL, and Python, along with compatibility for other programming languages through RESTful APIs. This versatility ensures that users can seamlessly integrate kdb Insights into their existing workflows and leverage its full potential for a wide range of analytical tasks.

Flutter is a user interface toolkit developed by Google that enables the creation of stunning, natively compiled applications for mobile, web, and desktop platforms from a single codebase. With its Stateful Hot Reload feature, you can bring your app to life in mere milliseconds. The toolkit offers a vast array of fully-customizable widgets that allow developers to construct native interfaces in just minutes. This swift development process emphasizes delivering features that enhance the native user experience. Its layered architecture provides extensive customization options, leading to rapid rendering and designs that are both expressive and adaptable. Flutter’s widgets are designed to accommodate essential platform-specific variations, including scrolling, navigation, icons, and fonts, while the Flutter code is compiled into native ARM machine code using Dart's native compilers. The hot reload capability significantly streamlines workflows, enabling developers to experiment, create user interfaces, introduce new features, and resolve bugs more efficiently. With Flutter, you can enjoy sub-second reload times without losing the state of your application, whether you're working on emulators, simulators, or physical devices. This seamless experience fosters a more agile development process, making it easier to innovate and iterate on app designs.

SparkBeyond Discovery independently examines intricate data sets, uncovering solutions to business challenges in unexpected areas. It allows for the effortless incorporation of external data into your investigations, enhancing your understanding of the key factors influencing outcomes and providing a comprehensive view of your business landscape. By enabling users to engage with data and insights in natural language, it fosters a stronger collaboration between analytics and business leaders, pushing analytics initiatives beyond mere experimentation. To ensure that the advantages gained from analytics remain relevant, it promotes a continuous cycle of inputs and outputs that adapt to changing circumstances. As the world evolves, so too must your insights. With the ability to automatically connect various data types, from time-series to geo-spatial, in their original detailed form without any coding required, you can gain valuable perspectives effortlessly. Moreover, by integrating a well-curated repository of global knowledge, including maps, demographic data, and Wikipedia, or by tapping into a network of external data partners, you can significantly enrich your analytical capabilities. This holistic approach ensures that organizations are well-equipped to navigate the complexities of modern business environments.

It is more than a new generation "Decision Cube"; it is also more than Pivot Grid control. It is a standalone, high-speed OLAP-cube engine. Integrate FastCube OLAP library to your application, prepare data (or multiple data sets) for your end-users and bring Business Intelligence directly to your application! Your end-users have full control over the creation of reports. This will help them make reasonable decisions. FastCube VCL is a set that can be used to create pivot table reports. It supports Delphi 7 - XE8, C++Builder 2005 - XE8, Embarcadero RAD Studio 10 – 11 and Lazarus. Customers will no longer require Excel-tables to perform cross-tab and multi-dimensional data analysis. FastCube components are easily integrated into host applications. FastCube users don't need to be proficient in programming to create reports. FastCube is a set OLAP Desktop components that Delphi can use.

Molecula serves as an enterprise feature store that streamlines, enhances, and manages big data access to facilitate large-scale analytics and artificial intelligence. By consistently extracting features, minimizing data dimensionality at the source, and channeling real-time feature updates into a centralized repository, it allows for millisecond-level queries, computations, and feature re-utilization across various formats and locations without the need to duplicate or transfer raw data. This feature store grants data engineers, scientists, and application developers a unified access point, enabling them to transition from merely reporting and interpreting human-scale data to actively forecasting and recommending immediate business outcomes using comprehensive data sets. Organizations often incur substantial costs when preparing, consolidating, and creating multiple copies of their data for different projects, which delays their decision-making processes. Molecula introduces a groundbreaking approach for continuous, real-time data analysis that can be leveraged for all mission-critical applications, dramatically improving efficiency and effectiveness in data utilization. This transformation empowers businesses to make informed decisions swiftly and accurately, ensuring they remain competitive in an ever-evolving landscape.

Coheris Spad, developed by ChapsVision, serves as a self-service data analysis platform tailored for Data Scientists across diverse sectors and industries. This tool is widely recognized and incorporated into numerous prestigious French and international educational institutions, solidifying its esteemed status among Data Scientists. Coheris Spad offers an extensive methodological framework that encompasses a wide array of data analysis techniques. Users benefit from a friendly and intuitive interface that equips them with the necessary capabilities to explore, prepare, and analyze their data effectively. The platform supports connections to multiple data sources for efficient data preparation. Additionally, it boasts a comprehensive library of data processing functions, including filtering, stacking, aggregation, transposition, joining, handling of missing values, identification of unusual distributions, statistical or supervised recoding, and formatting options, empowering users to perform thorough and insightful analyses. Furthermore, the flexibility and versatility of Coheris Spad make it an invaluable asset for both novice and experienced data practitioners.

TADA by MyDataModels offers a top-tier predictive analytics solution that enables professionals to leverage their Small Data for business improvement through a user-friendly and easily deployable tool. With TADA, users can quickly develop predictive models that deliver actionable insights in a fraction of the time, transforming what once took days into mere hours thanks to an automated data preparation process that reduces time by 40%. This platform empowers individuals to extract valuable outcomes from their data without the need for programming expertise or advanced machine learning knowledge. By utilizing intuitive and transparent models composed of straightforward formulas, users can efficiently optimize their time and turn raw data into meaningful insights effortlessly across various platforms. The complexity of predictive model construction is significantly diminished as TADA automates the generative machine learning process, making it as simple as inputting data to receive a model output. Moreover, TADA allows for the creation and execution of machine learning models on a wide range of devices and platforms, ensuring accessibility through its robust web-based pre-processing capabilities, thereby enhancing operational efficiency and decision-making.

The world's only free modern embeddable data and analytics platform that provides a self-service, powerful, secure & seamless end-to-end experience. DataClarity Unlimited Analytics Benefits: SIMPLIFIED DATA INTEGRATION – Easily connect, join, curate, cache & catalog diverse data through drag and drop, custom SQL builder and AI-powered data profiling. | INTERACTIVE REPORTS & DASHBOARDS – craft compelling storyboards using 80 stunning visualizations, geospatial maps & flexibility to bring your own charts. | REAL-TIME ANALYSIS – Perform advanced analysis & data exploration using drill-down, drill-through, filters, built-in statistical & predictive models, or your own Python and R code. | SMOOTH APPLICATION INTEGRATION – Achieve seamless integration with robust APIs, tailor-made configurations and flexible embedding features. | SECURITY and GOVERNANCE – Comply with your security guidelines, governance standards, multitenancy, row-level data protection, and SSO (Single Sign-On). DataClarity Unlimited Analytics is tailored for ISVs , SaaS providers, consultancies, and IT teams and includes: free forever software license, free version updates & access to support resources, and optional paid 24/7 production support with SLA.

Dataproc enhances the speed, simplicity, and security of open source data and analytics processing in the cloud. You can swiftly create tailored OSS clusters on custom machines to meet specific needs. Whether your project requires additional memory for Presto or GPUs for machine learning in Apache Spark, Dataproc facilitates the rapid deployment of specialized clusters in just 90 seconds. The platform offers straightforward and cost-effective cluster management options. Features such as autoscaling, automatic deletion of idle clusters, and per-second billing contribute to minimizing the overall ownership costs of OSS, allowing you to allocate your time and resources more effectively. Built-in security measures, including default encryption, guarantee that all data remains protected. With the JobsAPI and Component Gateway, you can easily manage permissions for Cloud IAM clusters without the need to configure networking or gateway nodes, ensuring a streamlined experience. Moreover, the platform's user-friendly interface simplifies the management process, making it accessible for users at all experience levels.

Secure is a cutting-edge platform tailored to address the evolving needs of facility management, equipping landlords and property managers with real-time insights that facilitate informed decision-making. Centered around Data Analytics, Digital Twin technology, and ESG principles, Secure effectively gathers, organizes, and oversees a vast array of information, presenting it in a user-friendly manner. By compiling data from various building systems, Secure offers comprehensive reports that shed light on critical factors such as energy usage, occupancy rates, and sanitation practices. The platform's digital twin feature creates a 3D representation of your building, granting facility managers unparalleled visibility into operations and maintenance in real time, ultimately enhancing efficiencies and boosting overall performance. Additionally, Secure simplifies the path towards achieving net-zero carbon emissions by employing intelligent technology to monitor energy consumption, allowing for a complete overview of all properties managed through the platform. With Secure, users can not only streamline their operations but also contribute to sustainable practices in facility management.

Over the last two decades, numerous organizations have created SAS language programs that are essential for their functioning. Altair SLC efficiently executes programs that are written in SAS language syntax directly, eliminating the need for translation or the licensing of external products. This results in significant reductions in both capital costs and operating expenses for users, owing to its exceptional capacity to manage extensive data processing demands. Furthermore, Altair SLC comes equipped with a native SAS language compiler that not only processes SAS language and SQL code but also incorporates Python and R compilers, enabling seamless execution of Python and R code while facilitating the exchange of SAS language datasets, Pandas, and R data frames. The platform is versatile, operating on IBM mainframes, cloud environments, and a variety of servers and workstations across different operating systems. Additionally, it offers features for remote job submission and robust data exchange capabilities among mainframe, cloud, and on-premises systems, ensuring seamless integration across diverse computing environments.

Engage with robust and transparent audiences or similar profiles for your advertising needs. Enhance your sales by utilizing the highly valuable Enhencer audiences across Facebook, Instagram, and Google advertising campaigns. Quickly integrate your e-commerce platform with Google Tag Manager or Shopify in just a few minutes. Enhencer will gather essential data on your website visitors automatically, across all preferred e-commerce platforms. The AI-Engine from Enhencer learns independently, allowing it to predict and identify the most valuable visitors based on their online behavior. Acquire the most pertinent real-time retargeting and lookalike audiences tailored to your brand and products, thus elevating your advertising efforts on Facebook and Google Ads. Enhencer provides a clear and detailed view of valuable audience segments, allowing you to discern which website behaviors ultimately lead to purchases. Real-time audiences generated by Enhencer ensure you stay informed about the rapidly evolving trends within the e-commerce landscape, ultimately improving your marketing strategies and customer engagement. With this insightful approach, you can refine your targeting and maximize your advertising investment effectively.

All components of a URL, including scheme, user, password, host, port, path, query, and fragment, can be accessed through their respective properties. Every manipulation of a URL results in a newly generated URL object, and the strings provided to the constructor or modification functions are automatically encoded to yield a canonical format. While standard properties return percent-decoded values, the raw_ variants should be used to obtain encoded strings. A human-readable version of the URL can be accessed using the .human_repr() method. Binary wheels for yarl are available on PyPI for operating systems such as Linux, Windows, and MacOS. In cases where you wish to install yarl on different systems like Alpine Linux—which does not comply with manylinux standards due to the absence of glibc—you will need to compile the library from the source using the provided tarball. This process necessitates having a C compiler and the necessary Python headers installed on your machine. It is important to remember that the uncompiled, pure-Python version is significantly slower. Nevertheless, PyPy consistently employs a pure-Python implementation, thus remaining unaffected by performance variations. Additionally, this means that regardless of the environment, PyPy users can expect consistent behavior from the library.