Welcome to the 8th edition of our Top Python Libraries list! We are excited to present this year's picks for the most innovative developments in the Python ecosystem. From this edition, we are expanding our list to include not only libraries per-se, but also tools that are built to belong in the Python ecosystem -- some of which are not written in Python as you'll see. The rules for selection are the same as in previous years. We are looking for libraries that were launched or gained popularity in the past year, are well-maintained, and are simply cool and worth checking out. Our picks are heavily influenced by AI and data science libraries, but we also include a number of libraries that can be useful for non-data science purposes.

Before forwarding the data to data processing and machine learning training, it is helpful to visualize data using the Matplotlib module in Python. It creates graphs and charts using object-oriented APIs and Python GUI toolkits. Additionally, Matplotlib offers a MATLAB-like user interface so that users may perform operations that MATLAB can perform. This open-source, free package offers multiple extension interfaces that connect the matplotlib API to a variety of other libraries.

Dask is a powerful open-source Python parallel computing framework. Dask scales Python programs from single-core local workstations to huge distributed cloud clusters. Dask provides a familiar user experience by replicating the APIs of other PyData ecosystem programs like Pandas, Scikit-learn, and NumPy. It also offers low-level APIs that allow programmers to execute bespoke algorithms concurrently.

A time series is a sequence of data points, typically consisting of successive measurements made over a time interval.

Welcome to a new edition (7th!) of our yearly Top Python Libraries list! Starting in December 2015 -- and uninterruptedly since then -- we have been compiling the best Python libraries that are launched or popularized every year (or late the previous year). It all started as a "Top 10" series, but although we still have 10 main picks, we are nowadays listing so many more libraries. The work the Python community has been doing is just too good, and we want to give YOU a chance to find these great libraries in case they haven't yet crossed your path. In case you are not a fan of most top-10-style posts, bear with us and give this a chance.

Most Shareable (Viral) Blogs Among the top blogs, here are the blogs with the highest ratio of shares/unique views, which suggests that people who read it really liked it. Top Python Libraries for Data Science, Data Visualization & Machine Learning, by Matthew Mayo (*) The Best Data Science Certification You've Never Heard Of, by Nicole Janeway Bills (*) TabPy: Combining Python and Tableau, by Bima Putra Pratama (*) The Best Data Science Certification You've Never Heard Of, by Nicole Janeway Bills (*) Every Complex DataFrame Manipulation, Explained & Visualized Intuitively, by Andre Ye (*) Is Data Science for Me? 14 Self-Examination Questions to Consider, by Benjamin Obi Tayo (*) Facebook Open Sourced New Frameworks to Advance Deep Learning Research, by Jesus Rodriguez (*) From Y X to Building a Complete Artificial Neural Network, by Ahmed Gad The Rise of the Machine Learning Engineer, by Edward Bullen AI and Automation meets BI, by Assaf Araki and Ben Lorica (*) Dos and Don'ts of Analyzing Time Series Data, by Ahmad Anis (*) 15 Exciting AI Project Ideas for Beginners, by Great Learning (*) Is Data Science for Me? 14 Self-Examination Questions to Consider, by Benjamin Obi Tayo (*)

A dataset of over ten thousand 3D scans of real objects was created. Around 70 operators equipped with consumer-grade mobile 3D scanning setups were asked to scan objects of their choice in their environments without any kind of supervision from the computer vision professionals. As a result, large and diverse objects were collected – toys, grand piano, shoes, mugs, vases, and construction vehicles, etc. This task was carried out with the help of the attorney to ensure the data acquisition do not undergo any type of privacy constraints. This dataset along with the reconstructed models and RGB-D scans are open for public usage with proper attribution.

In a previous post, we had a look at the top python libraries for data science, data visualization, and machine learning. This time, we look at the top libraries for deep learning, natural language processing, and computer vision. These categories really don't need any further clarification. This separation and classification is arbitrary, in some instances more than others, but we have done our best to group tools together by intended use case, hoping this is most useful for readers. Clearly not all NLP and CV work these days is performed using deep learning techniques, but as the trends move toward such techniques for state of the art results, we stand by this otherwise arbitrary categorization logic.

It has been some time since we last performed a Python libraries roundup, and as such we have taken the opportunity to start the month of November with just such a fresh list. Last time we at KDnuggets did this, editor and author Dan Clark split up the vast array of Python data science related libraries up into several smaller collections, including data science libraries, machine learning libraries, and deep learning libraries. While splitting libraries into categories is inherently arbitrary, this made sense at the time of previous publication. This time, however, we have split the collected on open source Python data science libraries in two. This first post (this) covers "data science, data visualization & machine learning," and can be thought of as "traditional" data science tools covering common tasks. The second post, to be published next week, will cover libraries for use in building neural networks, and those for performing natural language processing and computer vision tasks.

Statsmodels is an open-source statistics-driven module that offers various classes and functions to the many statistical models available for statistical analysis and exploration of data. The module covers a vast number of models ranging from Linear Regression, Discrete Models, Time Series Analysis, Survival Analysis, and many other miscellaneous models.