NumPy Integrations

Train in Data is your go-to online school for mastering machine learning. We offer intermediate and advanced courses in Python programming, data science and machine learning, taught by industry experts with extensive experience in developing, optimizing, and deploying machine learning models in enterprise production environments. We focus on building a solid, intuitive grasp of machine learning concepts, backed by hands-on Python coding to make sure you can actually apply what you learn. Our approach? Simple: learn the theory, understand the why behind it, then get coding. We give you the complete package—theory, coding, and troubleshooting skills—so you can confidently handle real-world projects from start to finish.

Visual Studio Code (VS Code) is Microsoft’s open-source AI code editor designed to make coding faster, smarter, and more collaborative. It supports thousands of extensions and nearly every programming language, offering developers a lightweight yet powerful environment for writing, testing, and debugging code. With AI-powered features like GitHub Copilot, Next Edit Suggestions, and Agent Mode, VS Code helps you code with precision, automate complex tasks, and streamline development workflows. It integrates seamlessly with cloud services, remote repositories, and tools like Git, Docker, and Azure. The editor is fully customizable, allowing you to personalize your layout, color themes, and keyboard shortcuts. Whether coding locally or in the browser, VS Code delivers a complete development experience for individuals and teams alike.

All the Python tools in one place. Save time while PyCharm takes care of the routine. Focus on the bigger things and embrace the keyboard-centric approach to get the most of PyCharm's many productivity features. PyCharm knows everything about your code. Rely on it for intelligent code completion, on-the-fly error checking and quick-fixes, easy project navigation, and much more. Write neat and maintainable code while the IDE helps you keep control of the quality with PEP8 checks, testing assistance, smart refactorings, and a host of inspections. PyCharm is designed by programmers, for programmers, to provide all the tools you need for productive Python development. PyCharm provides smart code completion, code inspections, on-the-fly error highlighting and quick-fixes, along with automated code refactorings and rich navigation capabilities.

The h5py package is a Pythonic interface to the HDF5 binary data format. It lets you store huge amounts of numerical data, and easily manipulate that data from NumPy. For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. Thousands of datasets can be stored in a single file, categorized and tagged however you want. H5py uses straightforward NumPy and Python metaphors, like dictionary and NumPy array syntax. For example, you can iterate over datasets in a file, or check out the .shape or .dtype attributes of datasets. You don't need to know anything special about HDF5 to get started. In addition to the easy-to-use high level interface, h5py rests on a object-oriented Cython wrapping of the HDF5 C API. Almost anything you can do from C in HDF5, you can do from h5py.

Over the last years, high performance computing has become an affordable resource to many more researchers in the scientific community than ever before. The conjunction of quality open source software and commodity hardware strongly influenced the now widespread popularity of Beowulf class clusters and cluster of workstations. Among many parallel computational models, message-passing has proven to be an effective one. This paradigm is specially suited for (but not limited to) distributed memory architectures and is used in today’s most demanding scientific and engineering application related to modeling, simulation, design, and signal processing. However, portable message-passing parallel programming used to be a nightmare in the past because of the many incompatible options developers were faced to. Fortunately, this situation definitely changed after the MPI Forum released its standard specification.

You select the size of the cluster, node capacity, and a set of services, and Yandex Data Proc automatically creates and configures Spark and Hadoop clusters and other components. Collaborate by using Zeppelin notebooks and other web apps via a UI proxy. You get full control of your cluster with root permissions for each VM. Install your own applications and libraries on running clusters without having to restart them. Yandex Data Proc uses instance groups to automatically increase or decrease computing resources of compute subclusters based on CPU usage indicators. Data Proc allows you to create managed Hive clusters, which can reduce the probability of failures and losses caused by metadata unavailability. Save time on building ETL pipelines and pipelines for training and developing models, as well as describing other iterative tasks. The Data Proc operator is already built into Apache Airflow.

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Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

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Gensim is a free, open source Python library designed for unsupervised topic modeling and natural language processing, focusing on large-scale semantic modeling. It enables the training of models like Word2Vec, FastText, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), facilitating the representation of documents as semantic vectors and the discovery of semantically related documents. Gensim is optimized for performance with highly efficient implementations in Python and Cython, allowing it to process arbitrarily large corpora using data streaming and incremental algorithms without loading the entire dataset into RAM. It is platform-independent, running on Linux, Windows, and macOS, and is licensed under the GNU LGPL, promoting both personal and commercial use. The library is widely adopted, with thousands of companies utilizing it daily, over 2,600 academic citations, and more than 1 million downloads per week.

Flower is an open source federated learning framework designed to simplify the development and deployment of machine learning models across decentralized data sources. It enables training on data located on devices or servers without transferring the data itself, thereby enhancing privacy and reducing bandwidth usage. Flower supports a wide range of machine learning frameworks, including PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and XGBoost, and is compatible with various platforms and cloud services like AWS, GCP, and Azure. It offers flexibility through customizable strategies and supports both horizontal and vertical federated learning scenarios. Flower's architecture allows for scalable experiments, with the capability to handle workloads involving tens of millions of clients. It also provides built-in support for privacy-preserving techniques like differential privacy and secure aggregation.

NVIDIA FLARE (Federated Learning Application Runtime Environment) is an open source, extensible SDK designed to facilitate federated learning across diverse industries, including healthcare, finance, and automotive. It enables secure, privacy-preserving AI model training by allowing multiple parties to collaboratively train models without sharing raw data. FLARE supports various machine learning frameworks such as PyTorch, TensorFlow, RAPIDS, and XGBoost, making it adaptable to existing workflows. FLARE's componentized architecture allows for customization and scalability, supporting both horizontal and vertical federated learning. It is suitable for applications requiring data privacy and regulatory compliance, such as medical imaging and financial analytics. It is available for download via the NVIDIA NVFlare GitHub repository and PyPi.

Codédex is an online, interactive coding-learning platform that uses a gamified, adventure-style format to teach real programming languages and skills. Learners travel through “fantasy lands” corresponding to languages such as Python, HTML/CSS, JavaScript, React, and command-line tools (Git, GitHub), proceeding at their own pace while earning experience points, badges, and unlocking new regions as they progress. It combines bite-sized interactive lessons, an in-browser code editor for instant practice, and project-based tutorials to give users hands-on experience rather than just theory. With more than 200 hours of content, Codédex supports beginners with no prior coding experience and gradually builds up to more advanced topics, reinforcing learning through code challenges, exercises, and real-world projects. It fosters a supportive community through forums and events like monthly challenges and hackathons, helping motivate learners and provide peer support.

Spyder’s multi-language editor integrates a number of powerful tools right out of the box for an easy to use, efficient editing experience. The editor’s key features include syntax highlighting (pygments); real-time code and style analysis (pyflakes and pycodestyle); on-demand completion, calltips and go-to-definition features (rope and jedi); a function/class browser, horizontal and vertical splitting, and much more. The IPython console allows you to execute commands and interact with data inside IPython interpreters. The variable explorer allows you to interactively browse and manage the objects generated running your code. It shows the namespace contents (including all global objects, variables, class instances and more) of the currently selected IPython console session, and allows you to add, remove, and edit their values through a variety of GUI-based editors.

Imageio is a Python library that provides an easy interface to read and write a wide range of image data, including animated images, volumetric data, and scientific formats. It is cross-platform, runs on Python 3.5+, and is easy to install. Imageio is written in pure Python, so installation is easy. Imageio works on Python 3.5+. It also works on Pypy. Imageio depends on Numpy and Pillow. For some formats, imageio needs additional libraries/executables (e.g. ffmpeg), which imageio helps you to download/install. If something doesn’t work as it should, you need to know where to search for causes. The overview on this page aims to help you in this regard by giving you an idea of how things work, and - hence - where things may go sideways.

Coiled is enterprise-grade Dask made easy. Coiled manages Dask clusters in your AWS or GCP account, making it the easiest and most secure way to run Dask in production. Coiled manages cloud infrastructure for you, deploying on your AWS or Google Cloud account in minutes. Giving you a rock-solid deployment solution with zero effort. Customize cluster node types to fit your analysis needs. Run Dask in Jupyter Notebooks with real-time dashboards and cluster insights. Create software environments easily with customized dependencies for your Dask analysis. Enjoy enterprise-grade security. Reduce costs with SLAs, user-level management, and auto-termination of clusters. Coiled makes it easy to deploy your cluster on AWS or GCP. You can do it in minutes, without a credit card. Launch code from anywhere, including cloud services like AWS SageMaker, open source solutions, like JupyterHub, or even from the comfort of your very own laptop.

Cython is an optimizing static compiler for both the Python programming language and the extended Cython programming language (based on Pyrex). It makes writing C extensions for Python as easy as Python itself. Cython gives you the combined power of Python and C to let you write Python code that calls back and forth from and to C or C++ code natively at any point. Easily tune readable Python code into plain C performance by adding static type declarations, also in Python syntax. Use combined source code level debugging to find bugs in your Python, Cython, and C code. Interact efficiently with large data sets, e.g. using multi-dimensional NumPy arrays. Quickly build your applications within the large, mature, and widely used CPython ecosystem. The Cython language is a superset of the Python language that additionally supports calling C functions and declaring C types on variables and class attributes.

Dash gives your Mac instant offline access to 200+ API documentation sets. Dash is an API documentation browser and code snippet manager. Dash instantly searches offline documentation sets for 200+ APIs, 100+ cheat sheets, and more. You can even generate your own docsets or request docsets to be included. Dash comes with 200+ offline documentation sets. You can choose which documentation sets to download and Dash will take care of the rest, making sure they are kept up to date. You can also generate your own docsets, request docsets or download docsets from third-party sources. All documentation sets have been generated and are maintained with the utmost care. Dash integrates with package managers to generate documentation sets for anything you might need, as well as provide custom documentation sources of their own. Store snippets of code. Easily reuse snippets. Expand snippets in any app. Organize snippets with tags, syntax highlighting, and variable placeholders.

Avanzai helps accelerate your financial data analysis by letting you use natural language to output production-ready Python code. Avanzai speeds up financial data analysis for both beginners and experts using plain English. Plot times series data, equity index members, and even stock performance data using natural prompts. Skip the boring parts of financial analysis by leveraging AI to generate code with relevant Python packages already installed. Further edit the code if you wish, once you're ready copy and paste the code into your local environment and get straight to business. Leverage commonly used Python packages for quant analysis such as Pandas, Numpy, etc using plain English. Take financial analysis to the next level, quickly pull fundamental data and calculate the performance of nearly all US stocks. Enhance your investment decisions with accurate and up-to-date information. Avanzai empowers you to write the same Python code that quants use to analyze complex financial data.

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Light up the black box and pip install 3LC to gain the clarity you need to make meaningful changes to your models in moments. Remove the guesswork from your model training and iterate fast. Collect per-sample metrics and visualize them in your browser. Analyze your training and eliminate issues in your dataset. Model-guided, interactive data debugging and enhancements. Find important or inefficient samples. Understand what samples work and where your model struggles. Improve your model in different ways by weighting your data. Make sparse, non-destructive edits to individual samples or in a batch. Maintain a lineage of all changes and restore any previous revisions. Dive deeper than standard experiment trackers with per-sample per epoch metrics and data tracking. Aggregate metrics by sample features, rather than just epoch, to spot hidden trends. Tie each training run to a specific dataset revision for full reproducibility.

​JAX is a Python library designed for high-performance numerical computing and machine learning research. It offers a NumPy-like API, facilitating seamless adoption for those familiar with NumPy. Key features of JAX include automatic differentiation, just-in-time compilation, vectorization, and parallelization, all optimized for execution on CPUs, GPUs, and TPUs. These capabilities enable efficient computation for complex mathematical functions and large-scale machine-learning models. JAX also integrates with various libraries within its ecosystem, such as Flax for neural networks and Optax for optimization tasks. Comprehensive documentation, including tutorials and user guides, is available to assist users in leveraging JAX's full potential. ​