New Technical Indicators In Python

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New Technical Indicators in Python

Author : Sofien Kaabar
Publisher :
ISBN 13 :
Total Pages : 282 pages
Book Rating : 4.7/5 (111 download)

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Book Synopsis New Technical Indicators in Python by : Sofien Kaabar

Download or read book New Technical Indicators in Python written by Sofien Kaabar and published by . This book was released on 2021-02-18 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is this book all about?This book is a modest attempt at presenting a more modern version of Technical Analysis based on objective measures rather than subjective ones. A sizeable chunk of this beautiful type of analysis revolves around technical indicators which is exactly the purpose of this book. I believe it is time to be creative and invent our own indicators that fit our profiles. Having had more success with custom indicators than conventional ones, I have decided to share my findings. The following chapters present new indicators that are the fruit of my research as well as indicators created by brilliant people. I also include the functions to create the indicators in Python and provide how to best use them as well as back-testing results. What am I going to gain?You will gain exposure to many new indicators and concepts that will change the way you think about trading and you will find yourself busy experimenting and choosing the strategy that suits you the best. How is it organized?The order of chapters is not important, although reading the introductory technical chapter is helpful. The book is divided into three parts: part 1 deals with trend-following indicators, part 2 deals with contrarian indicators, part 3 deals with market timing indicators, and finally, part 4 deals with risk and performance indicators.What do you mean when you say this book is dynamic and not static?This means that everything inside gets updated regularly with new material on my Medium profile. I always publish new findings and strategies. Make sure to follow me.What level of knowledge do I need to follow this book?Although a basic or a good understanding of trading and coding is considered very helpful, it is not necessary. At the beginning of the book, I have included a chapter that deals with some Python concepts, but this book is not about Python.

Python for Finance Cookbook

Author : Eryk Lewinson
Publisher : Packt Publishing Ltd
ISBN 13 : 1789617324
Total Pages : 426 pages
Book Rating : 4.7/5 (896 download)

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Book Synopsis Python for Finance Cookbook by : Eryk Lewinson

Download or read book Python for Finance Cookbook written by Eryk Lewinson and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.

The Book of Trading Strategies

Author : Sofien Kaabar
Publisher :
ISBN 13 :
Total Pages : 330 pages
Book Rating : 4.5/5 (328 download)

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Book Synopsis The Book of Trading Strategies by : Sofien Kaabar

Download or read book The Book of Trading Strategies written by Sofien Kaabar and published by . This book was released on 2021-07-06 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Trading strategies come in different shapes and colors, and having a detailed view on their structure and functioning is very useful towards the path of creating a robust and profitable trading system. The book presents various technical strategies and the way to back-test them in Python. You can think of the book as a mix between introductory Python and an Encyclopedia of trading strategies with a touch of reality.

Trend Following Strategies in Python

Author : Sofien Kaabar
Publisher : Independently Published
ISBN 13 :
Total Pages : 218 pages
Book Rating : 4.7/5 (569 download)

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Book Synopsis Trend Following Strategies in Python by : Sofien Kaabar

Download or read book Trend Following Strategies in Python written by Sofien Kaabar and published by Independently Published. This book was released on 2021-10-31 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is this book all about? This book is a modest attempt at presenting a more modern version of technical analysis based on objective measures rather than subjective ones. A sizeable chunk of this beautiful type of analysis revolves around trend-following technical indicators which is what this book covers. I believe it is time to be creative with indicators. The following chapters present trend-following indicators and how to code/use them. The code included in the book is available in the GitHub repository. A QR code link will be provided in the book. What am I going to gain? You will gain exposure to many new indicators and strategies that will change the way you think about trading, and you will find yourself busy experimenting and choosing the strategy that suits you the best. How is it organized? The order of the chapter is not very important, although reading the introductory Python chapter is helpful. The book is divided into four parts: Part 1 deals with different types of moving averages, Part 2 deals with trend-following indicators, Part3 deals with market regime detection techniques, and finally, Part 4 will present many different trend-following technical strategies. What level of knowledge do I need to follow this book? Although a basic or a good understanding of trading and coding is considered very helpful, it is not necessary. At the beginning of the book, I have included a chapter that deals with some Python concepts, but this book is not about Python.

Evidence-Based Technical Analysis

Author : David Aronson
Publisher : John Wiley & Sons
ISBN 13 : 1118160584
Total Pages : 572 pages
Book Rating : 4.1/5 (181 download)

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Book Synopsis Evidence-Based Technical Analysis by : David Aronson

Download or read book Evidence-Based Technical Analysis written by David Aronson and published by John Wiley & Sons. This book was released on 2011-07-11 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evidence-Based Technical Analysis examines how you can apply the scientific method, and recently developed statistical tests, to determine the true effectiveness of technical trading signals. Throughout the book, expert David Aronson provides you with comprehensive coverage of this new methodology, which is specifically designed for evaluating the performance of rules/signals that are discovered by data mining.

Machine Learning for Algorithmic Trading

Author : Stefan Jansen
Publisher : Packt Publishing Ltd
ISBN 13 : 1839216786
Total Pages : 822 pages
Book Rating : 4.8/5 (392 download)

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Book Synopsis Machine Learning for Algorithmic Trading by : Stefan Jansen

Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

Dow Theory for the 21st Century

Author : Jack Schannep
Publisher : John Wiley & Sons
ISBN 13 : 0470240598
Total Pages : 246 pages
Book Rating : 4.4/5 (72 download)

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Book Synopsis Dow Theory for the 21st Century by : Jack Schannep

Download or read book Dow Theory for the 21st Century written by Jack Schannep and published by John Wiley & Sons. This book was released on 2008-06-30 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dow Theory for the 21st Century includes everything that the serious investor needs to know about the stock market and how to become financially successful. Expanding upon Charles Dow's 20th century stock market theory, author Jack Schannep provides readers with a better understanding of the ingredients that make up the world of finance, specifically the American stock market, in order to help them achieve investment success.

Python Algorithmic Trading Cookbook

Author : Pushpak Dagade
Publisher : Packt Publishing Ltd
ISBN 13 : 1838982515
Total Pages : 528 pages
Book Rating : 4.8/5 (389 download)

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Book Synopsis Python Algorithmic Trading Cookbook by : Pushpak Dagade

Download or read book Python Algorithmic Trading Cookbook written by Pushpak Dagade and published by Packt Publishing Ltd. This book was released on 2020-08-28 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build a solid foundation in algorithmic trading by developing, testing and executing powerful trading strategies with real market data using Python Key FeaturesBuild a strong foundation in algorithmic trading by becoming well-versed with the basics of financial marketsDemystify jargon related to understanding and placing multiple types of trading ordersDevise trading strategies and increase your odds of making a profit without human interventionBook Description If you want to find out how you can build a solid foundation in algorithmic trading using Python, this cookbook is here to help. Starting by setting up the Python environment for trading and connectivity with brokers, you’ll then learn the important aspects of financial markets. As you progress, you’ll learn to fetch financial instruments, query and calculate various types of candles and historical data, and finally, compute and plot technical indicators. Next, you’ll learn how to place various types of orders, such as regular, bracket, and cover orders, and understand their state transitions. Later chapters will cover backtesting, paper trading, and finally real trading for the algorithmic strategies that you've created. You’ll even understand how to automate trading and find the right strategy for making effective decisions that would otherwise be impossible for human traders. By the end of this book, you’ll be able to use Python libraries to conduct key tasks in the algorithmic trading ecosystem. Note: For demonstration, we're using Zerodha, an Indian Stock Market broker. If you're not an Indian resident, you won't be able to use Zerodha and therefore will not be able to test the examples directly. However, you can take inspiration from the book and apply the concepts across your preferred stock market broker of choice. What you will learnUse Python to set up connectivity with brokersHandle and manipulate time series data using PythonFetch a list of exchanges, segments, financial instruments, and historical data to interact with the real marketUnderstand, fetch, and calculate various types of candles and use them to compute and plot diverse types of technical indicatorsDevelop and improve the performance of algorithmic trading strategiesPerform backtesting and paper trading on algorithmic trading strategiesImplement real trading in the live hours of stock marketsWho this book is for If you are a financial analyst, financial trader, data analyst, algorithmic trader, trading enthusiast or anyone who wants to learn algorithmic trading with Python and important techniques to address challenges faced in the finance domain, this book is for you. Basic working knowledge of the Python programming language is expected. Although fundamental knowledge of trade-related terminologies will be helpful, it is not mandatory.

Beyond Technical Analysis

Author : Tushar S. Chande
Publisher : John Wiley & Sons
ISBN 13 : 9780471415671
Total Pages : 412 pages
Book Rating : 4.4/5 (156 download)

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Book Synopsis Beyond Technical Analysis by : Tushar S. Chande

Download or read book Beyond Technical Analysis written by Tushar S. Chande and published by John Wiley & Sons. This book was released on 2001-05-24 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Der Klassiker zur technischen Analyse erscheint jetzt in der 2. überarbeiteten, aktualisierten und erweiterten Auflage. Diese Neuauflage bietet eine interessante Mischung aus topaktuellen Techniken und Analyseverfahren, Strategien, zeitlos gültigen Grundsätzen und praktischen Tipps. Sie liefert umfassende Information für die Entwicklung und Implementierung eines eigenen Handelssystems und stellt so eine Verbindung her zwischen Analyse und Ausführung. Neu aufgenommen wurde eine Einführung in die technische Analyse sowie Material zu Einstiegs- und Ausstiegsstrategien, zur Aktienanalyse und zu Chandes neuer bahnbrechender Arbeit über die 'Comfort Zone' für richtiges Risiko- und Geldmanagement. "Beyond Technical Analysis" ist ein praktischer Leitfaden für versierte Händler und Neulinge gleichermaßen. Mit umfangreichem Beispielmaterial zu allen neu eingeführten Techniken, einschließlich Aktienfonds und offenen Investmentfonds!

Applied Quantitative Finance

Author : Mauricio Garita
Publisher : Springer Nature
ISBN 13 : 3030291413
Total Pages : 240 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Applied Quantitative Finance by : Mauricio Garita

Download or read book Applied Quantitative Finance written by Mauricio Garita and published by Springer Nature. This book was released on 2021-09-03 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides both conceptual knowledge of quantitative finance and a hands-on approach to using Python. It begins with a description of concepts prior to the application of Python with the purpose of understanding how to compute and interpret results. This book offers practical applications in the field of finance concerning Python, a language that is more and more relevant in the financial arena due to big data. This will lead to a better understanding of finance as it gives a descriptive process for students, academics and practitioners.

Advances in Financial Machine Learning

Author : Marcos Lopez de Prado
Publisher : John Wiley & Sons
ISBN 13 : 1119482119
Total Pages : 400 pages
Book Rating : 4.1/5 (194 download)

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Book Synopsis Advances in Financial Machine Learning by : Marcos Lopez de Prado

Download or read book Advances in Financial Machine Learning written by Marcos Lopez de Prado and published by John Wiley & Sons. This book was released on 2018-01-23 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Algorithmic Trading with Python

Author : Chris Conlan
Publisher : Independently Published
ISBN 13 :
Total Pages : 126 pages
Book Rating : 4.6/5 (327 download)

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Book Synopsis Algorithmic Trading with Python by : Chris Conlan

Download or read book Algorithmic Trading with Python written by Chris Conlan and published by Independently Published. This book was released on 2020-04-09 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and scikit-learn. After establishing an understanding of technical indicators and performance metrics, readers will walk through the process of developing a trading simulator, strategy optimizer, and financial machine learning pipeline. This book maintains a high standard of reprocibility. All code and data is self-contained in a GitHub repo. The data includes hyper-realistic simulated price data and alternative data based on real securities. Algorithmic Trading with Python (2020) is the spiritual successor to Automated Trading with R (2016). This book covers more content in less time than its predecessor due to advances in open-source technologies for quantitative analysis.

Hands-On Financial Trading with Python

Author : Jiri Pik
Publisher : Packt Publishing Ltd
ISBN 13 : 1838988807
Total Pages : 360 pages
Book Rating : 4.8/5 (389 download)

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Book Synopsis Hands-On Financial Trading with Python by : Jiri Pik

Download or read book Hands-On Financial Trading with Python written by Jiri Pik and published by Packt Publishing Ltd. This book was released on 2021-04-29 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build and backtest your algorithmic trading strategies to gain a true advantage in the market Key FeaturesGet quality insights from market data, stock analysis, and create your own data visualisationsLearn how to navigate the different features in Python's data analysis librariesStart systematically approaching quantitative research and strategy generation/backtesting in algorithmic tradingBook Description Creating an effective system to automate your trading can help you achieve two of every trader's key goals; saving time and making money. But to devise a system that will work for you, you need guidance to show you the ropes around building a system and monitoring its performance. This is where Hands-on Financial Trading with Python can give you the advantage. This practical Python book will introduce you to Python and tell you exactly why it's the best platform for developing trading strategies. You'll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. As you progress, you'll pick up lots of skills like time series forecasting, covering pmdarima and Facebook Prophet. By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization to help you get —and stay—ahead of the markets. What you will learnDiscover how quantitative analysis works by covering financial statistics and ARIMAUse core Python libraries to perform quantitative research and strategy development using real datasetsUnderstand how to access financial and economic data in PythonImplement effective data visualization with MatplotlibApply scientific computing and data visualization with popular Python librariesBuild and deploy backtesting algorithmic trading strategiesWho this book is for If you're a financial trader or a data analyst who wants a hands-on introduction to designing algorithmic trading strategies, then this book is for you. You don't have to be a fully-fledged programmer to dive into this book, but knowing how to use Python's core libraries and a solid grasp on statistics will help you get the most out of this book.

The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python

Author : Lucas Amunategui
Publisher :
ISBN 13 : 9781096577867
Total Pages : 96 pages
Book Rating : 4.5/5 (778 download)

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Book Synopsis The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python by : Lucas Amunategui

Download or read book The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python written by Lucas Amunategui and published by . This book was released on 2019-05-02 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this little book is to help you find your way around the chaotic world of the financial markets. Stop trusting other people's opinions and make your own. Here are tools to explore the markets and find answers to your fundamental stock-market questions.We'll start with the S&P 500, my favorite index and the world's economic barometer. This powerful and telling index comprise some 80% of all equity market value in the US and 30% of its revenue comes from outside the United States. It is also the benchmark against which all other financial products are measured. Most chapters in this book will use this index in one form or another. We'll continue by exploring the VIX, the Yield Curve, the Case-Shiller Home Price Index, the Consumer Price Index and much more. This book assumes that you have some Python experience, a working interpreter on your computer and the basics of operating a Jupyter notebook. I will show you in simple terms where to find market data, how to prepare it and visualize it using Python and Jupyter notebooks. You will find a link at the beginning of each chapter to access the source code and a paragraph explaining where and how to download the required market data.You won't find trading setups or financial advice here. This is exactly what this book isn't about. Instead, you will acquire a simple set of scripts and data sources to explore, learn and build anything you want.

Applied Text Analysis with Python

Author : Benjamin Bengfort
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491962992
Total Pages : 332 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Applied Text Analysis with Python by : Benjamin Bengfort

Download or read book Applied Text Analysis with Python written by Benjamin Bengfort and published by "O'Reilly Media, Inc.". This book was released on 2018-06-11 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity

Python for Algorithmic Trading

Author : Yves Hilpisch
Publisher : O'Reilly Media
ISBN 13 : 1492053325
Total Pages : 380 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Python for Algorithmic Trading by : Yves Hilpisch

Download or read book Python for Algorithmic Trading written by Yves Hilpisch and published by O'Reilly Media. This book was released on 2020-11-12 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms

Python Data Science Handbook

Author : Jake VanderPlas
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491912138
Total Pages : 743 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Python Data Science Handbook by : Jake VanderPlas

Download or read book Python Data Science Handbook written by Jake VanderPlas and published by "O'Reilly Media, Inc.". This book was released on 2016-11-21 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms