Practical Python Data Wrangling And Data Quality

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Practical Python Data Wrangling And Data Quality

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Author : Susan E. McGregor
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-12-03

Practical Python Data Wrangling And Data Quality written by Susan E. McGregor and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-03 with Computers categories.

There are awesome discoveries to be made and valuable stories to be told in datasets--and this book will help you uncover them. Whether you already work with data or just want to understand its possibilities, the techniques and advice in this practical book will help you learn how to better clean, evaluate, and analyze data to generate meaningful insights and compelling visualizations. Through foundational concepts and worked examples, author Susan McGregor provides the concepts and tools you need to evaluate and analyze all kinds of data and communicate your findings effectively. This book provides a methodical, jargon-free way for practitioners of all levels to harness the power of data. Use Python 3.8+ to read, write, and transform data from a variety of sources Understand and use programming basics in Python to wrangle data at scale Organize, document, and structure your code using best practices Complete exercises either on your own machine or on the web Collect data from structured data files, web pages, and APIs Perform basic statistical analysis to make meaning from data sets Visualize and present data in clear and compelling ways.

Python Data Wrangling For Business Analytics

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Author : George Snypes
language : en
Publisher:
Release Date : 2024-11-18

Python Data Wrangling For Business Analytics written by George Snypes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-18 with Business & Economics categories.

Master the essential skills of modern data analysis with this comprehensive guide to Python data wrangling, data cleaning, and business analytics. Whether you're a business analyst moving from Excel to Python, a data scientist optimizing workflows, or an analytics professional handling large datasets, this practical guide bridges the gap between basic Python programming and real-world data challenges. What Makes This Book Different: Unlike theoretical guides, this hands-on manual tackles actual business scenarios you'll encounter daily. Learn through practical exercises using real-world datasets from various industries. Master professional-grade data cleaning techniques used by leading companies for customer analysis, sales reporting, financial data processing, and marketing analytics. Essential Skills You'll Master: Data cleaning and preprocessing with pandas and numpy form the foundation of your learning journey. You'll advance to automated data validation and quality checks, ensuring your analyses are built on reliable data. Through hands-on practice, you'll develop expertise in advanced data transformation techniques and complex dataset merging. Time series data handling becomes second nature as you work through real examples. The book covers text data processing, standardization techniques, ETL pipeline development, and crucial performance optimization methods for large datasets. Real-World Applications: Your journey through data wrangling will focus on practical business scenarios. You'll learn to handle data challenges in customer analytics, transforming raw customer data into actionable segments. Sales performance tracking becomes straightforward as you master data integration techniques. Financial reporting transforms from a manual process into an automated workflow. Marketing campaign analysis, supply chain analytics, and operations management datasets become opportunities rather than obstacles. You'll work with multiple data sources, from Excel files and databases to APIs and cloud services. Technical Coverage: The comprehensive guide to pandas for data manipulation starts with fundamentals and progresses to advanced techniques. You'll master step-by-step data cleaning workflows that can be applied immediately in your daily work. Missing data handling strategies ensure no valuable information is lost. Data validation frameworks protect the integrity of your analysis. Automated reporting techniques save hours of manual work. Best practices for reproducible analysis ensure your work meets professional standards. Code optimization methods keep your solutions scalable and efficient.

Practical Data Wrangling

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Author : Allan Visochek
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-11-15

Practical Data Wrangling written by Allan Visochek and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-15 with Computers categories.

Turn your noisy data into relevant, insight-ready information by leveraging the data wrangling techniques in Python and R About This Book This easy-to-follow guide takes you through every step of the data wrangling process in the best possible way Work with different types of datasets, and reshape the layout of your data to make it easier for analysis Get simple examples and real-life data wrangling solutions for data pre-processing Who This Book Is For If you are a data scientist, data analyst, or a statistician who wants to learn how to wrangle your data for analysis in the best possible manner, this book is for you. As this book covers both R and Python, some understanding of them will be beneficial. What You Will Learn Read a csv file into python and R, and print out some statistics on the data Gain knowledge of the data formats and programming structures involved in retrieving API data Make effective use of regular expressions in the data wrangling process Explore the tools and packages available to prepare numerical data for analysis Find out how to have better control over manipulating the structure of the data Create a dexterity to programmatically read, audit, correct, and shape data Write and complete programs to take in, format, and output data sets In Detail Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them. You'll start by understanding the data wrangling process and get a solid foundation to work with different types of data. You'll work with different data structures and acquire and parse data from various locations. You'll also see how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, we conclude with a quick primer on accessing and processing data from databases, conducting data exploration, and storing and retrieving data quickly using databases. The book includes practical examples on each of these points using simple and real-world data sets to give you an easier understanding. By the end of the book, you'll have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way. Style and approach This is a practical book on data wrangling designed to give you an insight into the practical application of data wrangling. It takes you through complex concepts and tasks in an accessible way, featuring information on a wide range of data wrangling techniques with Python and R

Python Data Cleaning And Preparation Best Practices

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Author : Maria Zervou
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-09-27

Python Data Cleaning And Preparation Best Practices written by Maria Zervou and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-27 with Computers categories.

Take your data preparation skills to the next level by converting any type of data asset into a structured, formatted, and readily usable dataset Key Features Maximize the value of your data through effective data cleaning methods Enhance your data skills using strategies for handling structured and unstructured data Elevate the quality of your data products by testing and validating your data pipelines Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionProfessionals face several challenges in effectively leveraging data in today's data-driven world. One of the main challenges is the low quality of data products, often caused by inaccurate, incomplete, or inconsistent data. Another significant challenge is the lack of skills among data professionals to analyze unstructured data, leading to valuable insights being missed that are difficult or impossible to obtain from structured data alone. To help you tackle these challenges, this book will take you on a journey through the upstream data pipeline, which includes the ingestion of data from various sources, the validation and profiling of data for high-quality end tables, and writing data to different sinks. You’ll focus on structured data by performing essential tasks, such as cleaning and encoding datasets and handling missing values and outliers, before learning how to manipulate unstructured data with simple techniques. You’ll also be introduced to a variety of natural language processing techniques, from tokenization to vector models, as well as techniques to structure images, videos, and audio. By the end of this book, you’ll be proficient in data cleaning and preparation techniques for both structured and unstructured data.What you will learn Ingest data from different sources and write it to the required sinks Profile and validate data pipelines for better quality control Get up to speed with grouping, merging, and joining structured data Handle missing values and outliers in structured datasets Implement techniques to manipulate and transform time series data Apply structure to text, image, voice, and other unstructured data Who this book is for Whether you're a data analyst, data engineer, data scientist, or a data professional responsible for data preparation and cleaning, this book is for you. Working knowledge of Python programming is needed to get the most out of this book.

Python For Data Analysis

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Author : Wes Mckinney
language : en
Publisher:
Release Date : 2017

Python For Data Analysis written by Wes Mckinney and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Data mining categories.

Data Wrangling With Python

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Author : Jacqueline Kazil
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-02-04

Data Wrangling With Python written by Jacqueline Kazil and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-04 with Computers categories.

How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started. Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain. Quickly learn basic Python syntax, data types, and language concepts Work with both machine-readable and human-consumable data Scrape websites and APIs to find a bounty of useful information Clean and format data to eliminate duplicates and errors in your datasets Learn when to standardize data and when to test and script data cleanup Explore and analyze your datasets with new Python libraries and techniques Use Python solutions to automate your entire data-wrangling process

Practical Python Data Science Techniques

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Author : Marco Bonzanini
language : en
Publisher:
Release Date : 2017

Practical Python Data Science Techniques written by Marco Bonzanini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.

"Data Science is an interdisciplinary field that employs techniques to extract knowledge from data. As one of the fast growing fields in technology, the interest for Data Science is booming, and the demand for specialized talent is on the rise. This course takes a practical approach to Data Science, presenting solutions for common and not-so-common problems in the form of recipes. This video will begin from exploring your data using the different methods like data acquisition, data cleaning, data mining, machine learning, and data visualization, applied to a variety of different data types like structured data or free-form text. It will show how to deal with text using different methods like text normalization and calculating word frequencies. The audience will learn how to deal with data with a time dimension and how to build a recommendation system as well as about supervised learning problems (regression and classification) and unsupervised learning problems (clustering). They will learn how to perform text preprocessing steps that are necessary for every text analysis applications. Specifically, the course will cover tokenization, stop-word removal, stemming and other preprocessing techniques. The video takes you through with machine learning problems that you may encounter in your everyday use. In the end, the video will cover the time series and recommender system. By the end of the video course, you will become an expert in Data Science Techniques using Python."--Resource description page.

Python Data Analysis

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Author : Avinash Navlani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-05

Python Data Analysis written by Avinash Navlani and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-05 with Computers categories.

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

Time Series Analysis With Python Cookbook

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Author : Tarek A. Atwan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-06-30

Time Series Analysis With Python Cookbook written by Tarek A. Atwan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-30 with Computers categories.

Perform time series analysis and forecasting confidently with this Python code bank and reference manual Key Features Explore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms Learn different techniques for evaluating, diagnosing, and optimizing your models Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities Book 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 learn Understand what makes time series data different from other data Apply various imputation and interpolation strategies for missing data Implement different models for univariate and multivariate time series Use different deep learning libraries such as TensorFlow, Keras, and PyTorch Plot interactive time series visualizations using hvPlot Explore state-space models and the unobserved components model (UCM) Detect anomalies using statistical and machine learning methods Forecast complex time series with multiple seasonal patterns Who this book is for This 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.

Python Crash Course

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Author : Richard Johnson
language : en
Publisher: Richard Johnson
Release Date : 2021-04-14

Python Crash Course written by Richard Johnson and has been published by Richard Johnson this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-14 with categories.

★55% OFF for Bookstores! NOW at $24.95 instead of $34.95!★ IF YOU REALLY WANT TO DISCOVER THE SECRETS BEHIND PYTHON CRASH COURSE, THEN KEEP READING... Data analysis plays an important role in many aspects of life today. From the moment you wake up, you interact with data at different levels. A lot of important decisions are made based on data analytics. Companies need data to help them meet many of their goals. As the population of the world keeps growing, its customer base keeps expanding. In light of this, they must find ways of keeping their customers happy while at the same time meeting their business goals. Given the nature of competition in the business world, it is not easy to keep customers happy. Competitors keep preying on each other's customers, and those who win have another challenge ahead - how to maintain the customers lest they slide back to their former business partners. This is one area where Data Analysis comes in handy. This book covers: What is Data Analysis Python Crash Course Data Munging Why Data Preprocessing Is Important What is Data Wrangling? Reading and writing data The Different Types of Data We Can Work With The Importance of Data Visualization Indexing and selecting arrays Common Debugging Tools Neural Network and What to Use for? And much more. To understand their customers better, companies rely on data. They collect all manner of data at each point of interaction with their customers. Data are useful in several ways. The companies learn more about their customers, thereafter clustering them according to their specific needs. Through such segmentation, the company can attend to the customers' needs better and hope to keep them satisfied for longer. But data analytics is not just about customers and the profit motive. It is also about governance. Governments are the biggest data consumers all over the world. They collect data about citizens, businesses, and every other entity that they interact with at any given point. This is important information because it helps in a lot of instances. For planning purposes, governments need accurate data on their population so that funds can be allocated accordingly. Equitable distribution of resources is something that cannot be achieved without proper Data Analysis. Other than planning, there is also the security angle. To protect the country, the government must maintain different databases for different reasons. There are high profile individuals who must be accorded special security detail, top threats who must be monitored at all times, and so forth. To meet the security objective, the government has to obtain and maintain updated data on persons of interest at all times. There is so much more to Data Analysis than the corporate and government decisions. As a programmer, you are venturing into an industry that is challenging and exciting at the same time. Data doesn't lie unless it is manipulated, in which case you need insane Data Analysis and handling skills. As a data analyst, you will come across many challenges and problems that need solutions that can only be handled through Data Analysis. The way you interact with data can make a big difference, bigger than you can imagine. Buy It Now and Let Your Customers Get Addicted To This Amazing Book