Pandas 1.x cookbook :: practical recipes for scientific computing, time series analysis and exploratory data analysis using Python /
Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x. Key Features This is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quick...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2020.
|
| Ausgabe: | Second edition. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x. Key Features This is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book Description The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results. What you will learn Master data exploration in pandas through dozens of practice problems Group, aggregate, transform, reshape, and filter data Merge data from different sources through pandas SQL-like operations Create visualizations via pandas hooks to matplotlib and seaborn Use pandas, time series functionality to perform powerful analyses Import, clean, and prepare real-world datasets for machine learning Create workflows for processing big data that doesn't fit in memory Who this book is for This book is for Python developers, data scientists, engineers, and analysts. Pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas. |
| Beschreibung: | 1 online resource |
| ISBN: | 1839218916 9781839218910 |
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| 520 | |a Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x. Key Features This is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book Description The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results. What you will learn Master data exploration in pandas through dozens of practice problems Group, aggregate, transform, reshape, and filter data Merge data from different sources through pandas SQL-like operations Create visualizations via pandas hooks to matplotlib and seaborn Use pandas, time series functionality to perform powerful analyses Import, clean, and prepare real-world datasets for machine learning Create workflows for processing big data that doesn't fit in memory Who this book is for This book is for Python developers, data scientists, engineers, and analysts. Pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas. | ||
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| contents | Pandas Foundations -- Essential DataFrame Operations -- Creating and Persisting DataFrames -- Beginning Data Analysis -- Exploratory Data Analysis -- Selecting Subsets of Data -- Filtering Rows -- Index Alignment -- Grouping for Aggregation, Filtration and Transformation -- Restructuring Data into a Tidy Form -- Combining Pandas Objects -- Time Series Analysis -- Visualization with Matplotlib, Pandas, and Seaborn -- Debugging and Testing Pandas. |
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| spelling | Harrison, Matt, 1975- author. https://id.oclc.org/worldcat/entity/E39PCjyRWYXKfpPHQKX7dkD7Dy http://id.loc.gov/authorities/names/no2016124289 Pandas 1.x cookbook : practical recipes for scientific computing, time series analysis and exploratory data analysis using Python / Matt Harrison, Theodore Petrou. Second edition. Birmingham : Packt Publishing, 2020. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x. Key Features This is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book Description The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results. What you will learn Master data exploration in pandas through dozens of practice problems Group, aggregate, transform, reshape, and filter data Merge data from different sources through pandas SQL-like operations Create visualizations via pandas hooks to matplotlib and seaborn Use pandas, time series functionality to perform powerful analyses Import, clean, and prepare real-world datasets for machine learning Create workflows for processing big data that doesn't fit in memory Who this book is for This book is for Python developers, data scientists, engineers, and analysts. Pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas. Pandas Foundations -- Essential DataFrame Operations -- Creating and Persisting DataFrames -- Beginning Data Analysis -- Exploratory Data Analysis -- Selecting Subsets of Data -- Filtering Rows -- Index Alignment -- Grouping for Aggregation, Filtration and Transformation -- Restructuring Data into a Tidy Form -- Combining Pandas Objects -- Time Series Analysis -- Visualization with Matplotlib, Pandas, and Seaborn -- Debugging and Testing Pandas. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Programming languages (Electronic computers) http://id.loc.gov/authorities/subjects/sh85107313 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Science Mathematics Computer programs. Data Mining https://id.nlm.nih.gov/mesh/D057225 Python (Langage de programmation) Exploration de données (Informatique) Sciences Mathématiques Logiciels. Data mining fast Programming languages (Electronic computers) fast Python (Computer program language) fast Science Mathematics Computer programs fast Petrou, Theodore, author. has work: Pandas 1.x cookbook (Text) https://id.oclc.org/worldcat/entity/E39PCG4wTXCWPJ3M6DtmyqcbMP https://id.oclc.org/worldcat/ontology/hasWork Print version: 9781839213106 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2382014 Volltext |
| spellingShingle | Harrison, Matt, 1975- Petrou, Theodore Pandas 1.x cookbook : practical recipes for scientific computing, time series analysis and exploratory data analysis using Python / Pandas Foundations -- Essential DataFrame Operations -- Creating and Persisting DataFrames -- Beginning Data Analysis -- Exploratory Data Analysis -- Selecting Subsets of Data -- Filtering Rows -- Index Alignment -- Grouping for Aggregation, Filtration and Transformation -- Restructuring Data into a Tidy Form -- Combining Pandas Objects -- Time Series Analysis -- Visualization with Matplotlib, Pandas, and Seaborn -- Debugging and Testing Pandas. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Programming languages (Electronic computers) http://id.loc.gov/authorities/subjects/sh85107313 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Science Mathematics Computer programs. Data Mining https://id.nlm.nih.gov/mesh/D057225 Python (Langage de programmation) Exploration de données (Informatique) Sciences Mathématiques Logiciels. Data mining fast Programming languages (Electronic computers) fast Python (Computer program language) fast Science Mathematics Computer programs fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh85107313 http://id.loc.gov/authorities/subjects/sh97002073 https://id.nlm.nih.gov/mesh/D057225 |
| title | Pandas 1.x cookbook : practical recipes for scientific computing, time series analysis and exploratory data analysis using Python / |
| title_auth | Pandas 1.x cookbook : practical recipes for scientific computing, time series analysis and exploratory data analysis using Python / |
| title_exact_search | Pandas 1.x cookbook : practical recipes for scientific computing, time series analysis and exploratory data analysis using Python / |
| title_full | Pandas 1.x cookbook : practical recipes for scientific computing, time series analysis and exploratory data analysis using Python / Matt Harrison, Theodore Petrou. |
| title_fullStr | Pandas 1.x cookbook : practical recipes for scientific computing, time series analysis and exploratory data analysis using Python / Matt Harrison, Theodore Petrou. |
| title_full_unstemmed | Pandas 1.x cookbook : practical recipes for scientific computing, time series analysis and exploratory data analysis using Python / Matt Harrison, Theodore Petrou. |
| title_short | Pandas 1.x cookbook : |
| title_sort | pandas 1 x cookbook practical recipes for scientific computing time series analysis and exploratory data analysis using python |
| title_sub | practical recipes for scientific computing, time series analysis and exploratory data analysis using Python / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Programming languages (Electronic computers) http://id.loc.gov/authorities/subjects/sh85107313 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Science Mathematics Computer programs. Data Mining https://id.nlm.nih.gov/mesh/D057225 Python (Langage de programmation) Exploration de données (Informatique) Sciences Mathématiques Logiciels. Data mining fast Programming languages (Electronic computers) fast Python (Computer program language) fast Science Mathematics Computer programs fast |
| topic_facet | Python (Computer program language) Programming languages (Electronic computers) Data mining. Science Mathematics Computer programs. Data Mining Python (Langage de programmation) Exploration de données (Informatique) Sciences Mathématiques Logiciels. Data mining Science Mathematics Computer programs |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2382014 |
| work_keys_str_mv | AT harrisonmatt pandas1xcookbookpracticalrecipesforscientificcomputingtimeseriesanalysisandexploratorydataanalysisusingpython AT petroutheodore pandas1xcookbookpracticalrecipesforscientificcomputingtimeseriesanalysisandexploratorydataanalysisusingpython |