Data wrangling with Python :: creating actionable data from raw sources /
Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices. Key Features Focus on the basics of data wrangling Study various ways to extract the most out of your data in less time Boost your learning curve with bonus topics like random data generation and data inte...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices. Key Features Focus on the basics of data wrangling Study various ways to extract the most out of your data in less time Boost your learning curve with bonus topics like random data generation and data integrity checks Book Description For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets. By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. What you will learn Use and manipulate complex and simple data structures Harness the full potential of DataFrames and numpy.array at run time Perform web scraping with BeautifulSoup4 and html5lib Execute advanced string search and manipulation with RegEX Handle outliers and perform data imputation with Pandas Use descriptive statistics and plotting techniques Practice data wrangling and modeling using data generation techniques Who this book is for Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have rudimentary knowledge of relational database and SQL. Downloading the example code for this book You can download the ... |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 1789804248 9781789804249 |
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| spelling | Sarkar, Tirthajyoti, author. Data wrangling with Python : creating actionable data from raw sources / Dr. Tirthajyoti Sarkar and Shubhadeep Roychowdhury. Birmingham, UK : Packt Publishing, 2019. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from title page (Safari, viewed April 18, 2019). Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices. Key Features Focus on the basics of data wrangling Study various ways to extract the most out of your data in less time Boost your learning curve with bonus topics like random data generation and data integrity checks Book Description For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets. By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. What you will learn Use and manipulate complex and simple data structures Harness the full potential of DataFrames and numpy.array at run time Perform web scraping with BeautifulSoup4 and html5lib Execute advanced string search and manipulation with RegEX Handle outliers and perform data imputation with Pandas Use descriptive statistics and plotting techniques Practice data wrangling and modeling using data generation techniques Who this book is for Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have rudimentary knowledge of relational database and SQL. Downloading the example code for this book You can download the ... On-line resource; title from PDF title page (EBSCO, viewed June 18, 2019) Introduction to Data Wrangling with Python -- Introduction -- Python for Data Wrangling -- Lists, Sets, Strings, Tuples, and Dictionaries. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Data Mining https://id.nlm.nih.gov/mesh/D057225 Python (Langage de programmation) Exploration de données (Informatique) Visualisation de l'information. COMPUTERS Programming Languages Python. bisacsh Data mining fast Electronic data processing fast Information visualization fast Python (Computer program language) fast Roychowdhury, Shubhadeep, author. FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2037515 Volltext |
| spellingShingle | Sarkar, Tirthajyoti Roychowdhury, Shubhadeep Data wrangling with Python : creating actionable data from raw sources / Introduction to Data Wrangling with Python -- Introduction -- Python for Data Wrangling -- Lists, Sets, Strings, Tuples, and Dictionaries. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Data Mining https://id.nlm.nih.gov/mesh/D057225 Python (Langage de programmation) Exploration de données (Informatique) Visualisation de l'information. COMPUTERS Programming Languages Python. bisacsh Data mining fast Electronic data processing fast Information visualization fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh85042288 http://id.loc.gov/authorities/subjects/sh2002000243 https://id.nlm.nih.gov/mesh/D057225 |
| title | Data wrangling with Python : creating actionable data from raw sources / |
| title_auth | Data wrangling with Python : creating actionable data from raw sources / |
| title_exact_search | Data wrangling with Python : creating actionable data from raw sources / |
| title_full | Data wrangling with Python : creating actionable data from raw sources / Dr. Tirthajyoti Sarkar and Shubhadeep Roychowdhury. |
| title_fullStr | Data wrangling with Python : creating actionable data from raw sources / Dr. Tirthajyoti Sarkar and Shubhadeep Roychowdhury. |
| title_full_unstemmed | Data wrangling with Python : creating actionable data from raw sources / Dr. Tirthajyoti Sarkar and Shubhadeep Roychowdhury. |
| title_short | Data wrangling with Python : |
| title_sort | data wrangling with python creating actionable data from raw sources |
| title_sub | creating actionable data from raw sources / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Data Mining https://id.nlm.nih.gov/mesh/D057225 Python (Langage de programmation) Exploration de données (Informatique) Visualisation de l'information. COMPUTERS Programming Languages Python. bisacsh Data mining fast Electronic data processing fast Information visualization fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Data mining. Electronic data processing. Information visualization. Data Mining Python (Langage de programmation) Exploration de données (Informatique) Visualisation de l'information. COMPUTERS Programming Languages Python. Data mining Electronic data processing Information visualization |
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