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Exploratory Data Analysis with Python Cookbook
Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesGain practical experience in conducting EDA on a single variable of interest in PythonLearn the different techniques for analyzing and exploring tabular, time series, and textual data in PythonGet well versed in data visualization using leading Python libraries like Matplotlib and seabornBook DescriptionIn today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data.This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights.Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries.By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.What you will learnPerform EDA with leading python data visualization librariesExecute univariate, bivariate and multivariate analysis on tabular dataUncover patterns and relationships within time series dataIdentify hidden patterns within textual dataLearn different techniques to prepare data for analysisOvercome challenge of outliers and missing values during data analysisLeverage automated EDA for fast and efficient analysisWho this book is forWhether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights.It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic stati
R 1,693.00
Python Data Analysis Cookbook
Over 140 practical recipes to help you make sense of your data with ease and build production-ready data appsAbout This Book• Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types• Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning• Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed booksWho This Book Is ForThis book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed.What You Will Learn• Set up reproducible data analysis• Clean and transform data• Apply advanced statistical analysis• Create attractive data visualizations• Web scrape and work with databases, Hadoop, and Spark• Analyze images and time series data• Mine text and analyze social networks• Use machine learning and evaluate the results• Take advantage of parallelism and concurrencyIn DetailData 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.Style and ApproachThe book is written in “cookbook” style striving for high realism in data analysis. Through the recipe-based format, you can read each recipe separately as required and immediately apply the knowledge gained.
R 1,889.00
Hands-On Exploratory Data Analysis with Python
Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandasKey FeaturesUnderstand the fundamental concepts of exploratory data analysis using PythonFind missing values in your data and identify the correlation between different variablesPractice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python packageBook DescriptionExploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization.You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence.By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes.What you will learnImport, clean, and explore data to perform preliminary analysis using powerful Python packagesIdentify and transform erroneous data using different data wrangling techniquesExplore the use of multiple regression to describe non-linear relationshipsDiscover hypothesis testing and explore techniques of time-series analysisUnderstand and interpret results obtained from graphical analysisBuild, train, and optimize predictive models to estimate resultsPerform complex EDA techniques on open source datasetsWho this book is forThis EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.
R 2,735.00
Python Data Analysis
Understand data analysis pipelines using Python Data Analysis, machine learning, pandas, scikit-learn, and data visualization techniques. Build scalable workflows for time series, NLP, image analytics, and big data processing. Key FeaturesPrepare, clean, and transform data with Python, pandas, and exploratory data analysis techniquesApply machine learning with Python using regression, classification, clustering, PCA, and Bayesian methodsScale analytics workflows using Dask, Ray, Modin, and PySparkBook DescriptionModern data analysis goes beyond cleaning and visualizing data. Today's practitioners need to build scalable data pipelines, apply machine learning, work with text and image data, and understand emerging AI techniques such as Generative AI and Large Language Models (LLMs). This guide shows you how to tackle these challenges using Python's modern data ecosystem.Unlike books focused on a single library or technique, this book provides an end-to-end approach to Python data analysis. You'll learn how to move from data preparation and exploratory analysis to machine learning, NLP, image analytics, scalable processing, and AI-powered workflows.Starting with statistical foundations, you'll learn how to clean, transform, wrangle, and visualize data. You'll then explore time series analysis, signal processing, forecasting, and predictive analytics before applying machine learning techniques such as regression, classification, clustering, PCA, probabilistic methods, and Bayesian approaches.The book also covers graph analytics, sentiment analysis, NLP, image analytics, Generative AI, and LLMs. Finally, you'll learn to scale analytics workflows using Dask, Modin, Ray, and PySpark.By the end of the book, you'll be able to build end-to-end data analysis pipelines and apply modern data science and AI techniques to solve real-world challenges.What you will learnPrepare, clean, and transform data for exploratory data analysis and data wranglingAnalyze and visualize data using Python and pandasPerform time series analysis, forecasting, and signal processingApply machine learning with Python using scikit-learn techniquesUse regression, classification, clustering, PCA, and Bayesian methodsPerform sentiment analysis, NLP, graph analytics, and image analyticsAccelerate workflows using Dask, Modin, and RayBuild scalable big data analytics pipelines with PySparkWho this book is forThis book is for data analysts, data scientists, business analysts, statisticians, students, and academic professionals who want to strengthen their Python Data Analysis skills. It is ideal for readers looking to apply data science with Python to real-world problems involving data preparation, visualization, machine learning, NLP, image analytics, and big data processing. A basic understanding of mathematics and working knowledge of Python will help you get the most from this book.
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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 decisionsAbout This Book• Want to minimize risk and optimize profits of your business? Learn to create efficient analytical reports with ease using this highly practical, easy-to-follow guide• Learn to apply Python for business intelligence tasks—preparing, exploring, analyzing, visualizing and reporting—in order to make more informed business decisions using data at hand• Learn to explore and analyze business data, and build business intelligence dashboards with the help of various insightful recipesWho This Book Is ForThis book is intended for data analysts, managers, and executives with a basic knowledge of Python, who now want to use Python for their BI tasks. If you have a good knowledge and understanding of BI applications and have a “working” system in place, this book will enhance your toolbox.What You Will Learn• Install Anaconda, MongoDB, and everything you need to get started with your data analysis• Prepare data for analysis by querying cleaning and standardizing data• Explore your data by creating a Pandas data frame from MongoDB• Gain powerful insights, both statistical and predictive, to make informed business decisions• Visualize your data by building dashboards and generating reports• Create a complete data processing and business intelligence systemIn DetailThe 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.Style and approa
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Exploratory Data Analysis
This book is a comprehensive guide to exploratory data analysis (EDA), providing readers with the tools, techniques, and knowledge needed to conduct effective and thorough data exploration. Throughout the seven main chapters, this book details various aspects of EDA, from data description and preprocessing to visualization, storytelling, and dashboard design. We will explore real‑world datasets, uncovering hidden patterns and gaining insights along the way. This book is filled with illustrations using practical examples, Python codes, and different types of exercises designed to reinforce the concepts and processes discussed. Whether you are a student just starting out in the field of data science, a senior professional looking to improve your skills, or a curious individual interested in the power of data, this book is for you.
R 6,960.00
Time Series Analysis with Python Cookbook
Perform time series analysis and forecasting confidently with this Python code bank and reference manualKey FeaturesExplore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithmsLearn different techniques for evaluating, diagnosing, and optimizing your modelsWork with a variety of complex data with trends, multiple seasonal patterns, and irregularitiesBook DescriptionTime series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you'll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you'll work with ML and DL models using TensorFlow and PyTorch. Finally, you'll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.What you will learnUnderstand what makes time series data different from other dataApply various imputation and interpolation strategies for missing dataImplement different models for univariate and multivariate time seriesUse different deep learning libraries such as TensorFlow, Keras, and PyTorchPlot interactive time series visualizations using hvPlotExplore state-space models and the unobserved components model (UCM)Detect anomalies using statistical and machine learning methodsForecast complex time series with multiple seasonal patternsWho this book is forThis book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.
R 2,279.00
Python Data Analysis
Understand data analysis pipelines using machine learning algorithms and techniques with this practical guideKey FeaturesPrepare and clean your data to use it for exploratory analysis, data manipulation, and data wranglingDiscover supervised, unsupervised, probabilistic, and Bayesian machine learning methodsGet to grips with graph processing and sentiment analysisBook DescriptionData 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.What you will learnExplore data science and its various process modelsPerform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing valuesCreate interactive visualizations using Matplotlib, Seaborn, and BokehRetrieve, process, and store data in a wide range of formatsUnderstand data preprocessing and feature engineering using pandas and scikit-learnPerform time series analysis and signal processing using sunspot cycle dataAnalyze textual data and image data to perform advanced analysisGet up to speed with parallel computing using DaskWho this book is forThis book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.
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Time Series Analysis with Python Cookbook
Perform time series analysis and forecasting confidently with this Python code bank and reference manual. Access exclusive GitHub bonus chapters and hands-on recipes covering Python setup, probabilistic deep learning forecasts, frequency-domain analysis, large-scale data handling, databases, InfluxDB, and advanced visualizations.Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithmsLearn different techniques for evaluating, diagnosing, and optimizing your modelsWork with a variety of complex data with trends, multiple seasonal patterns, and irregularitiesBook DescriptionTo use time series data to your advantage, you need to master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You’ll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples.You'll hit the ground running by ingesting time series data from various sources and formats and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods.Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you’ll also be able to evaluate, compare, and optimize models, finishing with a strong command of wrangling data with Python.What you will learnUnderstand what makes time series data different from other dataApply imputation and interpolation strategies to handle missing dataImplement an array of models for univariate and multivariate time seriesPlot interactive time series visualizations using hvPlotExplore state-space models and the unobserved components model (UCM)Detect anomalies using statistical and machine learning methodsForecast complex time series with multiple seasonal patternsUse conformal prediction for constructing prediction intervals for time seriesWho this book is forThis book is for data analysts, business analysts, data scientists, data engineers, and Python developers who want to learn time series analysis and forecasting techniques step by step through practical Python recipes. To get the most out of this book, you’ll need fundamental Python programming knowledge. Prior experience working with time series data to solve business problems will help
R 1,954.00
Exploratory Data Analysis
This book is a comprehensive guide to exploratory data analysis (EDA), providing readers with the tools, techniques, and knowledge needed to conduct effective and thorough data exploration. Throughout the seven main chapters, this book details various aspects of EDA, from data description and preprocessing to visualization, storytelling, and dashboard design. We will explore real‑world datasets, uncovering hidden patterns and gaining insights along the way. This book is filled with illustrations using practical examples, Python codes, and different types of exercises designed to reinforce the concepts and processes discussed. Whether you are a student just starting out in the field of data science, a senior professional looking to improve your skills, or a curious individual interested in the power of data, this book is for you.
R 2,454.00
Python for Data Analysis 3e
Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the Jupyter notebook and IPython shell for exploratory computingLearn basic and advanced features in NumPyGet started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples
R 1,978.00
Python for Finance Cookbook
Use modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problemsPurchase of the print or Kindle book includes a free eBook in the PDF formatKey FeaturesExplore unique recipes for financial data processing and analysis with PythonApply classical and machine learning approaches to financial time series analysisCalculate various technical analysis indicators and backtest trading strategiesBook DescriptionPython is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as modern machine learning and deep learning solutions.You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses.Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them.What you will learnPreprocess, analyze, and visualize financial dataExplore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning modelsUncover advanced time series forecasting algorithms such as Meta's ProphetUse Monte Carlo simulations for derivatives valuation and risk assessmentExplore volatility modeling using univariate and multivariate GARCH modelsInvestigate various approaches to asset allocationLearn how to approach ML-projects using an example of default predictionExplore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphetWho this book is forThis book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems.Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.
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Python Data Cleaning Cookbook
Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricksKey FeaturesGet well-versed with various data cleaning techniques to reveal key insightsManipulate data of different complexities to shape them into the right form as per your business needsClean, monitor, and validate large data volumes to diagnose problems before moving on to data analysisBook DescriptionGetting 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.What you will learnFind out how to read and analyze data from a variety of sourcesProduce summaries of the attributes of data frames, columns, and rowsFilter data and select columns of interest that satisfy given criteriaAddress messy data issues, including working with dates and missing valuesImprove your productivity in Python pandas by using method chainingUse visualizations to gain additional insights and identify potential data issuesEnhance your ability to learn what is going on in your dataBuild user-defined functions and classes to automate data cleaningWho this book is forThis book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book.
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Python for Finance Cookbook
Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandasKey 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 DescriptionPython 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 forThis 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.
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Python Cookbook
If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms. Inside, you'll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works. Topics include: Data Structures and Algorithms Strings and Text Numbers, Dates, and Times Iterators and Generators Files and I/O Data Encoding and Processing Functions Classes and Objects Metaprogramming Modules and Packages Network and Web Programming Concurrency Utility Scripting and System Administration Testing, Debugging, and Exceptions C Extensions
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Python Data Visualization Cookbook
This book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python. Python Data Visualization Cookbook is for developers that already know about Python programming in general. If you have heard about data visualization but you don't know where to start, then this book will guide you from the start and help you understand data, data formats, data visualization, and how to use Python to visualize data.You will need to know some general programming concepts, and any kind of programming experience will be helpful, but the code in this book is explained almost line by line. You don't need maths for this book, every concept that is introduced is thoroughly explained in plain English, and references are available for further interest in the topic.
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Exploratory Data Analysis
An Introduction to the underlying principles, central concepts, and basic techniques for conducting and understanding exploratory data analysis - with numerous social science examples.
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Data Analysis and Visualization Using Python
Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you’ll get a chance to revisit the concepts you’ve covered so far.What You Will LearnUse Python programming techniques for data scienceMaster data collections in Python Create engaging visualizations for BI systemsDeploy effective strategies for gathering and cleaning dataIntegrate the Seaborn and Matplotlib plotting systemsWho This Book Is ForDevelopers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.
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Getting Started with Python Data Analysis
Learn to use powerful Python libraries for effective data processing and analysisAbout This Book• Learn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and Matplotlib• Create, manipulate, and analyze your data to extract useful information to optimize your system• A hands-on guide to help you learn data analysis using PythonWho This Book Is ForIf you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.What You Will Learn• Understand the importance of data analysis and get familiar with its processing steps• Get acquainted with Numpy to use with arrays and array-oriented computing in data analysis• Create effective visualizations to present your data using Matplotlib• Process and analyze data using the time series capabilities of Pandas• Interact with different kind of database systems, such as file, disk format, Mongo, and Redis• Apply the supported Python package to data analysis applications through examples• Explore predictive analytics and machine learning algorithms using Scikit-learn, a Python libraryIn DetailData analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.With this book, we will get you started with Python data analysis and show you what its advantages are.The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn. Style and approachThis is an easy-to-follow, step-by-step guide to get you familiar with data analysis and the libraries supported by Python. Topics are explained with real-world examples wherever required.
R 1,335.00
Modern Python Cookbook
Complete recipes spread across 15 chapters to help you overcome commonly faced issues by Python for everybody across the globe. Each recipe takes a problem-solution approach to resolve for effective Python.Key FeaturesDevelop expressive and effective Python programsBest practices and common idioms through carefully explained recipesDiscover new ways to apply Python for data-focused developmentMake use of Python’s optional type annotationsBook DescriptionPython is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand.This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications.The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it.By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.What you will learnSee the intricate details of the Python syntax and how to use it to your advantageImprove your coding with Python readability through functionsManipulate data effectively using built-in data structuresGet acquainted with advanced programming techniques in PythonEquip yourself with functional and statistical programming featuresWrite proper tests to be sure a program works as advertisedIntegrate application software using PythonWho this book is forThe Python book is for web developers, programmers, enterprise programmers, engineers, and big data scientists. If you are a beginner, this book will get you started. If you are experienced, it will expand your knowledge base. A basic knowledge of programming would help.
R 3,256.00
R 891.43
Learn Data Analysis with Python
Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it. Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects.If you aren’t using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished.What You Will LearnGet data into and out of Python codePrepare the data and its formatFind the meaning of the dataVisualize the data using iPythonWho This Book Is For Those who want to learn data analysis using Python. Some experience with Python is recommended but not required, as is some prior experience with data analysis or data science.
R 1,067.67
Hands-On Data Analysis with Pandas
Get to grips with pandas by working with real datasets and master data discovery, data manipulation, data preparation, and handling data for analytical tasksKey FeaturesPerform efficient data analysis and manipulation tasks using pandas 1.xApply pandas to different real-world domains with the help of step-by-step examplesMake the most of pandas as an effective data exploration toolBook DescriptionExtracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time.This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn.Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data.This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling using PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsSolve common data representation and analysis problems using pandasBuild Python scripts, modules, and packages for reusable analysis codeWho this book is forThis book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress.You’ll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.
R 2,214.00
Hands-On Data Analysis with Pandas
Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discoveryKey FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook DescriptionData analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value.Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data.By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is forThis book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.
R 1,661.00
Machine Learning with Python Cookbook
This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems all the way from loading data to training models and leveraging neural networks.Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.You'll find recipes for:Vectors, matrices, and arraysWorking with data from CSV, JSON, SQL, databases, cloud storage, and other sourcesHandling numerical and categorical data, text, images, and dates and timesDimensionality reduction using feature extraction or feature selectionModel evaluation and selectionLinear and logical regression, trees and forests, and k-nearest neighborsSupport vector machines (SVM), naive Bayes, clustering, and tree-based modelsSaving and loading trained models from multiple frameworks
R 1,824.00
Mastering Python Data Analysis
Become an expert at using Python for advanced statistical analysis of data using real-world examplesAbout This Book• Clean, format, and explore data using graphical and numerical summaries• Leverage the IPython environment to efficiently analyze data with Python• Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex dataWho This Book Is ForIf you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you. Familiarity with the basics of applying Python libraries to data sets is assumed.What You Will Learn• Read, sort, and map various data into Python and Pandas• Recognise patterns so you can understand and explore data• Use statistical models to discover patterns in data• Review classical statistical inference using Python, Pandas, and SciPy• Detect similarities and differences in data with clustering• Clean your data to make it useful• Work in Jupyter Notebook to produce publication ready figures to be included in reportsIn DetailPython, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want!Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You'll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making.You'll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you'll move on to review statistical inference using Python, Pandas, and SciPy. After that, we'll focus on performing regression using computational tools and you'll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you'll discover how to use Python's tools for supervised machine learning.Style and approachThis book takes a step-by-step approach to reading, processing, and analyzing data in Python using various methods and tools. Rich in examples, each topic connects to real-world examples and retrieves data directly online where possible. With this book, you are given the knowledge and tools to explore any data on your own, encouraging a curiosity befitting all data scientists.
R 1,889.00
Big Data Analysis with Python
Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.Key FeaturesGet a hands-on, fast-paced introduction to the Python data science stackExplore ways to create useful metrics and statistics from large datasetsCreate detailed analysis reports with real-world dataBook DescriptionProcessing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools.By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.What you will learnUse Python to read and transform data into different formatsGenerate basic statistics and metrics using data on diskWork with computing tasks distributed over a clusterConvert data from various sources into storage or querying formatsPrepare data for statistical analysis, visualization, and machine learningPresent data in the form of effective visualsWho this book is forBig Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.
R 1,140.00
Data Analysis Workshop
Learn how to analyze data using Python models with the help of real-world use cases and guidance from industry expertsKey FeaturesGet to grips with data analysis by studying use cases from different fieldsDevelop your critical thinking skills by following tried-and-true data analysisLearn how to use conclusions from data analyses to make better business decisionsBook DescriptionBusinesses today operate online and generate data almost continuously. While not all data in its raw form may seem useful, if processed and analyzed correctly, it can provide you with valuable hidden insights. The Data Analysis Workshop will help you learn how to discover these hidden patterns in your data, to analyze them, and leverage the results to help transform your business. The book begins by taking you through the use case of a bike rental shop. You'll be shown how to correlate data, plot histograms, and analyze temporal features. As you progress, you'll learn how to plot data for a hydraulic system using the Seaborn and Matplotlib libraries, and explore a variety of use cases that show you how to join and merge databases, prepare data for analysis, and handle imbalanced data. By the end of the book, you'll have learned different data analysis techniques, including hypothesis testing, correlation, and null-value imputation, and will have become a confident data analyst.What you will learnGet to grips with the fundamental concepts and conventions of data analysisUnderstand how different algorithms help you to analyze the data effectivelyDetermine the variation between groups of data using hypothesis testingVisualize your data correctly using appropriate plotting pointsUse correlation techniques to uncover the relationship between variablesFind hidden patterns in data using advanced techniques and strategiesWho this book is forThe Data Analysis Workshop is for programmers who already know how to code in Python and want to use it to perform data analysis. If you are looking to gain practical experience in data science with Python, this book is for you.
R 1,335.00
Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
R 2,585.49
