Python

Robotic Process Automation with Automation Anywhere. Techniques to fuel business productivity and intelligent automation using RPA

Husan Mahey

With an increase in the number of organizations deploying RPA solutions, Robotic Process Automation (RPA) is quickly becoming the most desired skill set for both developers starting their career and seasoned professionals. This book will show you how to use Automation Anywhere A2019, one of the leading platforms used widely for RPA.Starting with an introduction to RPA and Automation Anywhere, the book will guide you through the registration, installation, and configuration of the Bot agent and Control Room. With the help of easy-to-follow instructions, you’ll build your first bot and discover how you can automate tasks with Excel, Word, emails, XML, and PDF files. You’ll learn from practical examples based on real-world business scenarios, and gain insights into building more robust and resilient bots, executing external scripts such as VBScripts and Python, and adding error handling routines.By the end of this RPA book, you’ll have developed the skills required to install and configure an RPA platform confidently and have a solid understanding of how to build complex and robust, yet performant, bots.

ROS Robotics By Example. Bring life to your robot using ROS robotic applications

Carol Fairchild, Dr. Thomas L. Harman

The visionaries who created ROS developed a framework for robotics centered on the commonality of robotic systems and exploited this commonality in ROS to expedite the development of future robotic systems. From the fundamental concepts to advanced practical experience, this book will provide you with an incremental knowledge of the ROS framework, the backbone of the robotics evolution. ROS standardizes many layers of robotics functionality from low-level device drivers to process control to message passing to software package management. This book provides step-by-step examples of mobile, armed, and flying robots, describing the ROS implementation as the basic model for other robots of these types. By controlling these robots, whether in simulation or in reality, you will use ROS to drive, move, and fly robots using ROS control.

Rozwijanie mikrousług w Pythonie. Budowa, testowanie, instalacja i skalowanie

Tarek Ziade

Rozwijanie mikrousług w Pythonie. Budowa, testowanie, instalacja i skalowanie Mikrousługi są bardzo ciekawym trendem tworzenia kodu. Pojawił się on kilka lat temu z uwagi na potrzebę przyspieszenia cyklu udostępniania oprogramowania. Nowe produkty i funkcje musiały być oferowane użytkownikom możliwie najszybciej. Wkrótce okazało się, że tworzenie architektury aplikacji składającej się z małych, funkcjonalnych jednostek - właśnie mikrousług - jest bardzo obiecującym sposobem pracy. Pozwala na zwiększenie się elastyczności oraz szybkości wprowadzania innowacji, gdyż programista może zająć się jednym elementem bez zastanawiania się nad całością aplikacji. W świecie, w którym rządzą wydajność i krótki czas dostarczania kodu, jest to duża wartość! Dzięki tej książce dowiesz się, w jaki sposób niewielkie, standardowe elementy kodu mogą złożyć się na kompletną, działającą aplikację. Nauczysz się tworzyć takie mikrousługi, rozwiązywać pojawiające się problemy i nabierzesz nawyku stosowania dobrych praktyk. Szybko zaczniesz pisać aplikacje w Pythonie za pomocą szerokiego wachlarza dostępnych narzędzi, włączając w to Flask czy Tox. Przy okazji nauczysz się zasad programowania zorientowanego na testy. Dowiesz się, jak zabezpieczać komunikację pomiędzy usługami i kodować funkcjonalności zapory aplikacyjnej w języku Lua dla serwera Nginx. Poznasz też możliwości instalowania mikrousług w chmurze AWS z wykorzystaniem kontenerów Docker. W tej książce między innymi: mikrousługi i ich rola w tworzeniu nowoczesnych aplikacji WWW cykl tworzenia kodu pod kątem testów i ciągłej integracji monitorowanie i zabezpieczanie mikrousług tworzenie mikrousług w JavaScript budowa mikrousług niezależnie od operatorów chmurowych i technologii wirtualizacyjnych unikanie problemów wynikających z centralizacji aplikacji Mikrousługi w języku Python: integracja doskonała!

Sage Beginner's Guide. Unlock the full potential of Sage for simplifying and automating mathematical computing with this book and

Craig Finch

This is a beginner's guide with clear step-by-step instructions, explanations, and advice. Each concept is illustrated with a complete example that you can use as a starting point for your own work. If you are an engineer, scientist, mathematician, or student, this book is for you. To get the most from Sage by using the Python programming language, we'll give you the basics of the language to get you started. For this, it will be helpful if you have some experience with basic programming concepts.

Scalable Data Analytics with Azure Data Explorer. Modern ways to query, analyze, and perform real-time data analysis on large volumes of data

Jason Myerscough

Azure Data Explorer (ADX) enables developers and data scientists to make data-driven business decisions. This book will help you rapidly explore and query your data at scale and secure your ADX clusters.The book begins by introducing you to ADX, its architecture, core features, and benefits. You'll learn how to securely deploy ADX instances and navigate through the ADX Web UI, cover data ingestion, and discover how to query and visualize your data using the powerful Kusto Query Language (KQL). Next, you'll get to grips with KQL operators and functions to efficiently query and explore your data, as well as perform time series analysis and search for anomalies and trends in your data. As you progress through the chapters, you'll explore advanced ADX topics, including deploying your ADX instances using Infrastructure as Code (IaC). The book also shows you how to manage your cluster performance and monthly ADX costs by handling cluster scaling and data retention periods. Finally, you'll understand how to secure your ADX environment by restricting access with best practices for improving your KQL query performance.By the end of this Azure book, you'll be able to securely deploy your own ADX instance, ingest data from multiple sources, rapidly query your data, and produce reports with KQL and Power BI.

Scalable Data Streaming with Amazon Kinesis. Design and secure highly available, cost-effective data streaming applications with Amazon Kinesis

Tarik Makota, Brian Maguire, Danny Gagne, Rajeev...

Amazon Kinesis is a collection of secure, serverless, durable, and highly available purpose-built data streaming services. This data streaming service provides APIs and client SDKs that enable you to produce and consume data at scale.Scalable Data Streaming with Amazon Kinesis begins with a quick overview of the core concepts of data streams, along with the essentials of the AWS Kinesis landscape. You'll then explore the requirements of the use case shown through the book to help you get started and cover the key pain points encountered in the data stream life cycle. As you advance, you'll get to grips with the architectural components of Kinesis, understand how they are configured to build data pipelines, and delve into the applications that connect to them for consumption and processing. You'll also build a Kinesis data pipeline from scratch and learn how to implement and apply practical solutions. Moving on, you'll learn how to configure Kinesis on a cloud platform. Finally, you’ll learn how other AWS services can be integrated into Kinesis. These services include Redshift, Dynamo Database, AWS S3, Elastic Search, and third-party applications such as Splunk.By the end of this AWS book, you’ll be able to build and deploy your own Kinesis data pipelines with Kinesis Data Streams (KDS), Kinesis Data Firehose (KFH), Kinesis Video Streams (KVS), and Kinesis Data Analytics (KDA).

Scientific Computing with Python. High-performance scientific computing with NumPy, SciPy, and pandas - Second Edition

Claus Führer, Claus Fuhrer, Jan Erik Solem,...

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python.This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations.By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.

scikit-learn Cookbook , Second Edition. Over 80 recipes for machine learning in Python with scikit-learn - Second Edition

Julian Avila, Trent Hauck

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively.The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model. By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.