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Learn web development with Python : Get hands-on with Python Programming and Django web development
If you want to develop complete Python web apps with Django, this Learning Path is for you. It will walk you through Python programming techniques and guide you in implementing them when creating 4 professional Django projects, teaching you how to solve common problems and develop RESTful web services with Django and Python. You will learn how to build a blog application, a social image bookmarking website, an online shop, and an e-learning platform.
Book
La base des données NoSQL pour le réseau électrique: La SNEL SA met en oeuvre un nouveau modèle basé sur le neutre effectivement mis à la terre
Avec une population estimée à 17,07 millions d'habitants en 2021 et un taux d'électrification d'environ 45,5% [1], le déficit en desserte électrique de la Ville-Province de Kinshasa est estimé à 54,5 %. Renverser cette tendance fait partie des préoccupations du Gouvernement de la République. C'est dans ce cadre que plusieurs programmes de réhabilitation et d'extension sont en cours de réalisation. Ces projets sont financés par soit des bailleurs de fonds internationaux tels que le PMURR, le PMEDE, le PEPUR, l'EASE MALT, soit la SNEL SA elle-même sur fonds propres, soit sur intervention directe du gouvernement congolais. La stratégie de mise en oeuvre du projet EASE par la SNEL SA DKO fera l'objet de cette analyse. En considérant le volume des données qu'elle est sensé générer, une gestion informatique à base des données NoSQL est la mieux appropriée au point qu'elle est susceptible de la rendre performante.
Journal Article
Python for teenagers : learn to program like a superhero!
Discover everything you need to know about Python to turn your passion of programming into a job you'll love. Fueled by fun and practical examples, this book gives high schoolers who want to learn an easy programming language ideas for how to leverage them in the workforce. Start with the basics and before you know it, you'll be building your own web sites, doing white-hat hacking, finding code bugs and errors, and creating games, including using Python to roll characters for RPGs. Every chapter is relaxed and informal, like learning with a cool teacher all the time.
BOOK
MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads
Background
PacBio high fidelity (HiFi) sequencing reads are both long (15–20 kb) and highly accurate (> Q20). Because of these properties, they have revolutionised genome assembly leading to more accurate and contiguous genomes. In eukaryotes the mitochondrial genome is sequenced alongside the nuclear genome often at very high coverage. A dedicated tool for mitochondrial genome assembly using HiFi reads is still missing.
Results
MitoHiFi was developed within the Darwin Tree of Life Project to assemble mitochondrial genomes from the HiFi reads generated for target species. The input for MitoHiFi is either the raw reads or the assembled contigs, and the tool outputs a mitochondrial genome sequence fasta file along with annotation of protein and RNA genes. Variants arising from heteroplasmy are assembled independently, and nuclear insertions of mitochondrial sequences are identified and not used in organellar genome assembly. MitoHiFi has been used to assemble 374 mitochondrial genomes (368 Metazoa and 6 Fungi species) for the Darwin Tree of Life Project, the Vertebrate Genomes Project and the Aquatic Symbiosis Genome Project. Inspection of 60 mitochondrial genomes assembled with MitoHiFi for species that already have reference sequences in public databases showed the widespread presence of previously unreported repeats.
Conclusions
MitoHiFi is able to assemble mitochondrial genomes from a wide phylogenetic range of taxa from Pacbio HiFi data. MitoHiFi is written in python and is freely available on GitHub (
https://github.com/marcelauliano/MitoHiFi
). MitoHiFi is available with its dependencies as a Docker container on GitHub (ghcr.io/marcelauliano/mitohifi:master).
Journal Article
Python 3 and Data Visualization Using ChatGPT /GPT-4
This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, leading into data visualization using prominent libraries such as Matplotlib. Chapter 6 includes Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. Further, the book covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. Chapter 7 covers information about the main features of ChatGPT and GPT-4, as well as some of their competitors. Chapter 8 contains examples of using ChatGPT in order to perform data visualization, such as charts and graphs that are based on datasets (e.g., the Titanic dataset). Companion files with code, datasets, and figures are available for downloading. From foundational Python concepts to the intricacies of data visualization, this book is ideal for Python practitioners, data scientists, and anyone in the field of data analytics looking to enhance their storytelling with data through visuals. It's also perfect for educators seeking material for teaching advanced data visualization techniques.
eBook
MacSyFinder: A Program to Mine Genomes for Molecular Systems with an Application to CRISPR-Cas Systems: e110726
Motivation Biologists often wish to use their knowledge on a few experimental models of a given molecular system to identify homologs in genomic data. We developed a generic tool for this purpose. Results Macromolecular System Finder (MacSyFinder) provides a flexible framework to model the properties of molecular systems (cellular machinery or pathway) including their components, evolutionary associations with other systems and genetic architecture. Modelled features also include functional analogs, and the multiple uses of a same component by different systems. Models are used to search for molecular systems in complete genomes or in unstructured data like metagenomes. The components of the systems are searched by sequence similarity using Hidden Markov model (HMM) protein profiles. The assignment of hits to a given system is decided based on compliance with the content and organization of the system model. A graphical interface, MacSyView, facilitates the analysis of the results by showing overviews of component content and genomic context. To exemplify the use of MacSyFinder we built models to detect and class CRISPR-Cas systems following a previously established classification. We show that MacSyFinder allows to easily define an accurate \"Cas-finder\" using publicly available protein profiles. Availability and Implementation MacSyFinder is a standalone application implemented in Python. It requires Python 2.7, Hmmer and makeblastdb (version 2.2.28 or higher). It is freely available with its source code under a GPLv3 license at https://github.com/gem-pasteur/macsyfinder. It is compatible with all platforms supporting Python and Hmmer/makeblastdb. The \"Cas-finder\" (models and HMM profiles) is distributed as a compressed tarball archive as Supporting Information.
Journal Article
Impractical Python projects : playful programming activities to make you smarter
\"A book of fun coding projects for readers who know a little Python already and want to expand their skills. Simulate volcanoes, map Mars, and more, while gaining experience using free modules like Tkinter, matplotlib, Cprofile, Pylint, Pygame, Pillow, and Python-Docx\"-- Provided by publisher.
Book
Causal Inference and Discovery in Python - Machine Learning and Pearlian Perspective - Unlock the Secrets of Modern Causal Machine Learning with DoWhy, EconML, PyTorch and More
The book focuses on using machine learning techniques to uncover cause-and-effect relationships in data, moving beyond mere correlations. From a Pearlian perspective, this involves utilizing causal diagrams (DAGs) and do-calculus to formalize causal reasoning.
eBook