Python. Automatyzacja zadań. Jak efektywnie pracować z danymi, arkuszami Excela, raportami i e-mailami. Wydanie II

Jaime Buelta

Ile czasu marnujesz na wykonywanie monotonnych, długotrwałych zadań? Mowa o przeglądaniu setek stron czy plików, ręcznym przekształcaniu danych, rozsyłaniu e-maili, tworzeniu wykresów i wielu innych nudnych czynnościach. A gdyby tak zlecić tę pracę komputerowi, a samemu zająć się bardziej odpowiedzialnymi i kreatywnymi zadaniami? To jest do zrobienia — trzeba tylko poświęcić nieco czasu i odpowiednio wykorzystać dostępne rozwiązania, takie jak Python i imponująca kolekcja opracowanych dla tego języka narzędzi, bibliotek i rozszerzeń. Ta książka jest praktycznym zbiorem gotowych receptur, przeznaczonym dla początkujących użytkowników Pythona. Wydanie zostało dostosowane do wersji 3.8 języka, dodano też nowy materiał dotyczący automatyzowania testów, uczenia maszynowego i pracy z nieuporządkowanymi danymi. Dzięki lekturze zaczniesz automatyzować procesy biznesowe — napiszesz aplikację do pobierania informacji ze stron internetowych, tworzenia raportów z wykresami i diagramami na podstawie arkuszów kalkulacyjnych, a także automatycznego generowania e-maili. Będziesz również tworzyć zaawansowane grafiki z potrzebnymi informacjami, automatyzować kampanie marketingowe oraz stosować techniki testowania i debugowania. W książce znajdziesz receptury, dzięki którym: przekształcisz dane na potrzeby data science za pomocą biblioteki pandas zautomatyzujesz klasyfikowanie tekstu, filtrowanie e-maili i pobieranie informacji ze stron WWW użyjesz biblioteki Matplotlib do generowania wykresów, diagramów i map zautomatyzujesz różne zadania związane z generowaniem raportów nauczysz się pracy z Beautiful Soup, programem cron, a także z dziennikami i wyrażeniami regularnymi napiszesz bot dla komunikatora Telegram, czytnik kanałów RSS i model uczenia maszynowego Automatyzacja: monotonne zadania zostaw Pythonowi!

Python Business Intelligence Cookbook. Leverage the computational power of Python with more than 60 recipes that arm you with the required skills to make informed business decisions

Robert Dempsey, Stefan Urbanek, Saurabh Chhajed

The amount of data produced by businesses and devices is going nowhere but up. In this scenario, the major advantage of Python is that it's a general-purpose language and gives you a lot of flexibility in data structures. Python is an excellent tool for more specialized analysis tasks, and is powered with related libraries to process data streams, to visualize datasets, and to carry out scientific calculations. Using Python for business intelligence (BI) can help you solve tricky problems in one go.Rather than spending day after day scouring Internet forums for “how-to” information, here you’ll find more than 60 recipes that take you through the entire process of creating actionable intelligence from your raw data, no matter what shape or form it’s in. Within the first 30 minutes of opening this book, you’ll learn how to use the latest in Python and NoSQL databases to glean insights from data just waiting to be exploited.We’ll begin with a quick-fire introduction to Python for BI and show you what problems Python solves. From there, we move on to working with a predefined data set to extract data as per business requirements, using the Pandas library and MongoDB as our storage engine.Next, we will analyze data and perform transformations for BI with Python. Through this, you will gather insightful data that will help you make informed decisions for your business. The final part of the book will show you the most important task of BI—visualizing data by building stunning dashboards using Matplotlib, PyTables, and iPython Notebook.

Python, C++, JavaScript. Zadania z programowania

Marek Luliński, Gniewomir Sarbicki

Zadania z programowania? Zbiór programów do napisania! Pascal, C++, JavaScript i Python — oto zbiór języków, które przećwiczysz dzięki zadaniom w tej książce. Każdy z nich ma nieco inne zastosowania, wszystkie zaś są lub będą w najbliższym czasie wykorzystywane na lekcjach informatyki i na egzaminach, a ich zastosowanie w obliczu realnych problemów może wydatnie uprościć Ci życie. Jak najłatwiej i najszybciej nauczyć się programować w tych językach? Oczywiście dzięki ćwiczeniom! Z tą książką napiszesz konkretne programy rozwiązujące zadania numeryczne, logiczne i matematyczne — w każdym z powyższych języków. Z praktycznym zestawem zawartych tu zadań spróbujesz znaleźć permutacje zbioru, zaszyfrować tekst szyfrem Cezara, zbudować trójkąt Pascala i wskazać punkty kratowe wewnątrz koła o promieniu r. Napiszesz według własnego pomysłu programy odpowiadające na postawione w zadaniach pytania. Jeśli jesteś uczniem, będziesz mógł w ten sposób ćwiczyć nie tylko umiejętności programistyczne, lecz także matematyczne. Jeśli jesteś nauczycielem, znajdziesz tu wiele inspiracji dla swoich uczniów. Gotowy pogram będziesz mógł później porównać z rozwiązaniami proponowanymi przez autorów. Dogadaj się z komputerem — w każdym z czterech języków! --- UWAGA: Skala trudności zadań odpowiada poziomowi szkoły średniej.

Python. Ćwiczenia praktyczne

Andrzej Kierzkowski, Marek Gawryszewski

Poznaj programowanie z bliska! Naucz się Pythona! Dowiedz się, jak czytać i implementować algorytmy Naucz się analizować i rozwiązywać problemy Poznaj podstawy Pythona na praktycznych przykładach Python to niezwykle wydajny i wszechstronny język programowania, który znajduje zastosowanie w różnych dziedzinach informatyki. Stanowi fundament rozwiązań wykorzystywanych przez YouTube’a czy aplikacje Google'a. Czytelność i zwięzłość kodu, szeroki wybór bibliotek standardowych oraz wsparcie ze strony producentów różnych systemów operacyjnych sprawiają, że język ten z roku na rok zyskuje coraz większą popularność wśród profesjonalnych programistów i wśród osób amatorsko tworzących wtyczki czy skrypty uruchamiane w aplikacjach komercyjnych. Czas do nich dołączyć! Sięgnij po książkę Python. Ćwiczenia praktyczne, która w prosty i przystępny sposób wprowadzi Cię w świat programowania komputerów przy użyciu jednego z najpopularniejszych języków! Naucz się czytać algorytmy i implementować je na praktycznych przykładach, poznaj podstawy Pythona, odkryj składnię oraz konstrukcje stosowane w tym języku i zacznij myśleć jak zawodowy programista! Tylko krok dzieli Cię od rozpoczęcia przygody z programowaniem, więc nie trać czasu i już dziś weź się do ćwiczeń! Wprowadzenie do algorytmiki Analiza i rozwiązywanie problemów Instalacja i korzystanie z IDE Struktura programu i instrukcje Pythona Zmienne, proste typy danych i operatory Korzystanie z bibliotek standardowych Złożone typy danych i programowanie obiektowe Praktyczne zadania do samodzielnego wykonania Dowiedz się, jak oswoić Pythona!

Python Data Analysis Cookbook. Clean, scrape, analyze, and visualize data with the power of Python!

Ivan Idris

Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning.Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining.In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code.By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.

Python Data Analysis. Data manipulation and complex data analysis with Python - Second Edition

Armando Fandango, Ivan Idris

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.

Python Data Analysis. Learn how to apply powerful data analysis techniques with popular open source Python modules

Ivan Idris

Python Data Analysis. Perform data collection, data processing, wrangling, visualization, and model building using Python - Third Edition

Avinash Navlani, Armando Fandango, Ivan Idris

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines.Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask.By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.

Python: Data Analytics and Visualization. Perform data processing and analysis with the help of python libraries, gain practical insights into predictive modeling and generate effective results in a variety of visually appealing charts using the plotting packages in Python

Martin Czygan, Phuong Vo.T.H, Ashish Kumar, Kirthi Raman

You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn. After this, you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You’ll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling.After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examplesThis Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan•Learning Predictive Analytics with Python, Ashish Kumar•Mastering Python Data Visualization, Kirthi Raman

Python Data Cleaning and Preparation Best Practices. A practical guide to organizing and handling data from various sources and formats using Python

Maria Zervou

Professionals face several challenges in effectively leveraging data in today's data-driven world. One of the main challenges is the low quality of data products, often caused by inaccurate, incomplete, or inconsistent data. Another significant challenge is the lack of skills among data professionals to analyze unstructured data, leading to valuable insights being missed that are difficult or impossible to obtain from structured data alone.To help you tackle these challenges, this book will take you on a journey through the upstream data pipeline, which includes the ingestion of data from various sources, the validation and profiling of data for high-quality end tables, and writing data to different sinks. You’ll focus on structured data by performing essential tasks, such as cleaning and encoding datasets and handling missing values and outliers, before learning how to manipulate unstructured data with simple techniques. You’ll also be introduced to a variety of natural language processing techniques, from tokenization to vector models, as well as techniques to structure images, videos, and audio.By the end of this book, you’ll be proficient in data cleaning and preparation techniques for both structured and unstructured data.

Python Data Cleaning Cookbook. Modern techniques and Python tools to detect and remove dirty data and extract key insights

Michael Walker

Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data. By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it.

Python Data Cleaning Cookbook. Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI - Second Edition

Michael Walker

Jumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook - Second Edition will show you tools and techniques for cleaning and handling data with Python for better outcomes.Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate and clean data to get it into a useful form. he current edition focuses on advanced techniques like machine learning and AI-specific approaches and tools for data cleaning along with the conventional ones. The book also delves into tips and techniques to process and clean data for ML, AI, and NLP models. You will learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Next, you’ll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify unexpected values. Finally, you’ll build functions and classes that you can reuse without modification when you have new data.By the end of this Data Cleaning book, you'll know how to clean data and diagnose problems within it.

Python Data Mining Quick Start Guide. A beginner's guide to extracting valuable insights from your data

Nathan Greeneltch

Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining.This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques.By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle.

Python Data Science Essentials. A practitioner’s guide covering essential data science principles, tools, and techniques - Third Edition

Alberto Boschetti, Luca Massaron

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users

Python Data Science Essentials. Become an efficient data science practitioner by thoroughly understanding the key concepts of Python

Alberto Boschetti, Luca Massaron

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results.In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

Python Data Science Essentials. Learn the fundamentals of Data Science with Python - Second Edition

Alberto Boschetti, Luca Massaron

Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.