Pandas cookbook :: recipes for scientific computing, time series analysis and data visualization using Python /
Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2017.
|
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Zusammenfassung: | Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas Who This Book Is For This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory. What You Will Learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn In Detail This book will provide 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. 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 like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas library to generate results. |
| Beschreibung: | 1 online resource (xv, 510 pages) : illustrations |
| ISBN: | 9781784393342 1784393347 |
Internformat
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | ZDB-4-EBA-on1011595407 | ||
| 003 | OCoLC | ||
| 005 | 20250103110447.0 | ||
| 006 | m o d | ||
| 007 | cr mnu|||||||| | ||
| 008 | 171115s2017 enka o 000 0 eng d | ||
| 040 | |a UMI |b eng |e rda |e pn |c UMI |d STF |d OCLCF |d IDEBK |d NLE |d TOH |d BCD |d COO |d EBLCP |d MERUC |d IDB |d YDX |d UIU |d VT2 |d SCB |d N$T |d UOK |d CEF |d KSU |d ORU |d OCLCQ |d DEBBG |d TEFOD |d OCLCQ |d UKMGB |d WYU |d C6I |d UKAHL |d OCLCQ |d OCLCO |d NZAUC |d OCLCQ |d INARC |d OCLCQ |d OCLCO |d OCLCL | ||
| 015 | |a GBB7O3358 |2 bnb | ||
| 016 | 7 | |a 018610857 |2 Uk | |
| 019 | |a 1007930453 |a 1009299781 |a 1019733956 |a 1027100721 | ||
| 020 | |a 9781784393342 |q (electronic bk.) | ||
| 020 | |a 1784393347 |q (electronic bk.) | ||
| 020 | |z 9781784393878 | ||
| 020 | |z 1784393878 | ||
| 035 | |a (OCoLC)1011595407 |z (OCoLC)1007930453 |z (OCoLC)1009299781 |z (OCoLC)1019733956 |z (OCoLC)1027100721 | ||
| 037 | |a CL0500000912 |b Safari Books Online | ||
| 037 | |a 39DED06D-8ED2-42A0-8FDE-87510D9CCBE7 |b OverDrive, Inc. |n http://www.overdrive.com | ||
| 050 | 4 | |a HD30.25 | |
| 050 | 4 | |a QA76.73.P98 |b .P487 2017eb | |
| 072 | 7 | |a COM |x 018000 |2 bisacsh | |
| 072 | 7 | |a COM |x 051360 |2 bisacsh | |
| 072 | 7 | |a COM |x 077000 |2 bisacsh | |
| 082 | 7 | |a 005.133 |2 23 | |
| 049 | |a MAIN | ||
| 100 | 1 | |a Petrou, Theodore, |e author. | |
| 245 | 1 | 0 | |a Pandas cookbook : |b recipes for scientific computing, time series analysis and data visualization using Python / |c Theodore Petrou. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2017. | |
| 300 | |a 1 online resource (xv, 510 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 505 | 0 | |a Pandas foundations -- Dissecting the anatomy of a DataFrame -- Accessing the main DataFrame components -- Understanding data types -- Selecting a single column of data as a series -- Calling series methods -- Working with operators on a series -- Chaining series methods together -- Making the index meaningful -- Renaming row and column names -- Creating and deleting columns -- Essential Chapter 2: DataFrame operations -- Selecting multiple DataFrame columns -- Selecting columns with methods -- Ordering column names sensibly -- Operating on the entire DataFrame -- Chaining DataFrame methds together -- Working with operators on a DataFrame -- Comparing missing values -- Transposing the direction of a DataFrame operation -- Determining college campus diversity -- Chapter 3: Beginning data analysis -- Developing a data analysis routine. | |
| 588 | 0 | |a Online resource; title from title page (Safari, viewed November 14, 2017). | |
| 520 | |a Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas Who This Book Is For This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory. What You Will Learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn In Detail This book will provide 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. 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 like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas library to generate results. | ||
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
| 650 | 0 | |a Management |x Data processing. |0 http://id.loc.gov/authorities/subjects/sh85080339 | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Information visualization. |0 http://id.loc.gov/authorities/subjects/sh2002000243 | |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 0 | |a Electronic data processing. |0 http://id.loc.gov/authorities/subjects/sh85042288 | |
| 650 | 2 | |a Data Mining |0 https://id.nlm.nih.gov/mesh/D057225 | |
| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Gestion |x Informatique. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Visualisation de l'information. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 7 | |a COMPUTERS |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Programming Languages |x Python. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Mathematical & Statistical Software. |2 bisacsh | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Electronic data processing |2 fast | |
| 650 | 7 | |a Information visualization |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Management |x Data processing |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 758 | |i has work: |a Pandas Cookbook (Text) |1 https://id.oclc.org/worldcat/entity/E39PCXQM8PxGpyJPVBF4PbqV98 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 966 | 4 | 0 | |l DE-862 |p ZDB-4-EBA |q FWS_PDA_EBA |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1630654 |3 Volltext |
| 966 | 4 | 0 | |l DE-863 |p ZDB-4-EBA |q FWS_PDA_EBA |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1630654 |3 Volltext |
| 938 | |a Internet Archive |b INAR |n pandascookbookre0000petr | ||
| 938 | |a Askews and Holts Library Services |b ASKH |n BDZ0035302679 | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL5112952 | ||
| 938 | |a EBSCOhost |b EBSC |n 1630654 | ||
| 938 | |a ProQuest MyiLibrary Digital eBook Collection |b IDEB |n cis39169728 | ||
| 938 | |a YBP Library Services |b YANK |n 14934124 | ||
| 994 | |a 92 |b GEBAY | ||
| 912 | |a ZDB-4-EBA | ||
| 049 | |a DE-862 | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-on1011595407 |
|---|---|
| _version_ | 1829095123581927424 |
| adam_text | |
| any_adam_object | |
| author | Petrou, Theodore |
| author_facet | Petrou, Theodore |
| author_role | aut |
| author_sort | Petrou, Theodore |
| author_variant | t p tp |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | H - Social Science |
| callnumber-label | HD30 |
| callnumber-raw | HD30.25 QA76.73.P98 .P487 2017eb |
| callnumber-search | HD30.25 QA76.73.P98 .P487 2017eb |
| callnumber-sort | HD 230.25 |
| callnumber-subject | HD - Industries, Land Use, Labor |
| collection | ZDB-4-EBA |
| contents | Pandas foundations -- Dissecting the anatomy of a DataFrame -- Accessing the main DataFrame components -- Understanding data types -- Selecting a single column of data as a series -- Calling series methods -- Working with operators on a series -- Chaining series methods together -- Making the index meaningful -- Renaming row and column names -- Creating and deleting columns -- Essential Chapter 2: DataFrame operations -- Selecting multiple DataFrame columns -- Selecting columns with methods -- Ordering column names sensibly -- Operating on the entire DataFrame -- Chaining DataFrame methds together -- Working with operators on a DataFrame -- Comparing missing values -- Transposing the direction of a DataFrame operation -- Determining college campus diversity -- Chapter 3: Beginning data analysis -- Developing a data analysis routine. |
| ctrlnum | (OCoLC)1011595407 |
| dewey-full | 005.133 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.133 |
| dewey-search | 005.133 |
| dewey-sort | 15.133 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>07027cam a2200805 i 4500</leader><controlfield tag="001">ZDB-4-EBA-on1011595407</controlfield><controlfield tag="003">OCoLC</controlfield><controlfield tag="005">20250103110447.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr mnu||||||||</controlfield><controlfield tag="008">171115s2017 enka o 000 0 eng d</controlfield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">UMI</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="e">pn</subfield><subfield code="c">UMI</subfield><subfield code="d">STF</subfield><subfield code="d">OCLCF</subfield><subfield code="d">IDEBK</subfield><subfield code="d">NLE</subfield><subfield code="d">TOH</subfield><subfield code="d">BCD</subfield><subfield code="d">COO</subfield><subfield code="d">EBLCP</subfield><subfield code="d">MERUC</subfield><subfield code="d">IDB</subfield><subfield code="d">YDX</subfield><subfield code="d">UIU</subfield><subfield code="d">VT2</subfield><subfield code="d">SCB</subfield><subfield code="d">N$T</subfield><subfield code="d">UOK</subfield><subfield code="d">CEF</subfield><subfield code="d">KSU</subfield><subfield code="d">ORU</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">DEBBG</subfield><subfield code="d">TEFOD</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">UKMGB</subfield><subfield code="d">WYU</subfield><subfield code="d">C6I</subfield><subfield code="d">UKAHL</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">OCLCO</subfield><subfield code="d">NZAUC</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">INARC</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCL</subfield></datafield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">GBB7O3358</subfield><subfield code="2">bnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">018610857</subfield><subfield code="2">Uk</subfield></datafield><datafield tag="019" ind1=" " ind2=" "><subfield code="a">1007930453</subfield><subfield code="a">1009299781</subfield><subfield code="a">1019733956</subfield><subfield code="a">1027100721</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781784393342</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1784393347</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9781784393878</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">1784393878</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1011595407</subfield><subfield code="z">(OCoLC)1007930453</subfield><subfield code="z">(OCoLC)1009299781</subfield><subfield code="z">(OCoLC)1019733956</subfield><subfield code="z">(OCoLC)1027100721</subfield></datafield><datafield tag="037" ind1=" " ind2=" "><subfield code="a">CL0500000912</subfield><subfield code="b">Safari Books Online</subfield></datafield><datafield tag="037" ind1=" " ind2=" "><subfield code="a">39DED06D-8ED2-42A0-8FDE-87510D9CCBE7</subfield><subfield code="b">OverDrive, Inc.</subfield><subfield code="n">http://www.overdrive.com</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">HD30.25</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">QA76.73.P98</subfield><subfield code="b">.P487 2017eb</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="x">018000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="x">051360</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="x">077000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="7" ind2=" "><subfield code="a">005.133</subfield><subfield code="2">23</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">MAIN</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Petrou, Theodore,</subfield><subfield code="e">author.</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Pandas cookbook :</subfield><subfield code="b">recipes for scientific computing, time series analysis and data visualization using Python /</subfield><subfield code="c">Theodore Petrou.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham, UK :</subfield><subfield code="b">Packt Publishing,</subfield><subfield code="c">2017.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xv, 510 pages) :</subfield><subfield code="b">illustrations</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Pandas foundations -- Dissecting the anatomy of a DataFrame -- Accessing the main DataFrame components -- Understanding data types -- Selecting a single column of data as a series -- Calling series methods -- Working with operators on a series -- Chaining series methods together -- Making the index meaningful -- Renaming row and column names -- Creating and deleting columns -- Essential Chapter 2: DataFrame operations -- Selecting multiple DataFrame columns -- Selecting columns with methods -- Ordering column names sensibly -- Operating on the entire DataFrame -- Chaining DataFrame methds together -- Working with operators on a DataFrame -- Comparing missing values -- Transposing the direction of a DataFrame operation -- Determining college campus diversity -- Chapter 3: Beginning data analysis -- Developing a data analysis routine.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Online resource; title from title page (Safari, viewed November 14, 2017).</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas Who This Book Is For This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory. What You Will Learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn In Detail This book will provide 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. 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 like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas library to generate results.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Python (Computer program language)</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh96008834</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Management</subfield><subfield code="x">Data processing.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85080339</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85079324</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Information visualization.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh2002000243</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Data mining.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh97002073</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Electronic data processing.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85042288</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Data Mining</subfield><subfield code="0">https://id.nlm.nih.gov/mesh/D057225</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Machine Learning</subfield><subfield code="0">https://id.nlm.nih.gov/mesh/D000069550</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Python (Langage de programmation)</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Gestion</subfield><subfield code="x">Informatique.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Apprentissage automatique.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Visualisation de l'information.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Exploration de données (Informatique)</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS</subfield><subfield code="x">Data Processing.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS</subfield><subfield code="x">Programming Languages</subfield><subfield code="x">Python.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS</subfield><subfield code="x">Mathematical & Statistical Software.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Data mining</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Electronic data processing</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Information visualization</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Management</subfield><subfield code="x">Data processing</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Python (Computer program language)</subfield><subfield code="2">fast</subfield></datafield><datafield tag="758" ind1=" " ind2=" "><subfield code="i">has work:</subfield><subfield code="a">Pandas Cookbook (Text)</subfield><subfield code="1">https://id.oclc.org/worldcat/entity/E39PCXQM8PxGpyJPVBF4PbqV98</subfield><subfield code="4">https://id.oclc.org/worldcat/ontology/hasWork</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-862</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1630654</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-863</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1630654</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">Internet Archive</subfield><subfield code="b">INAR</subfield><subfield code="n">pandascookbookre0000petr</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">Askews and Holts Library Services</subfield><subfield code="b">ASKH</subfield><subfield code="n">BDZ0035302679</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBL - Ebook Library</subfield><subfield code="b">EBLB</subfield><subfield code="n">EBL5112952</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBSCOhost</subfield><subfield code="b">EBSC</subfield><subfield code="n">1630654</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">ProQuest MyiLibrary Digital eBook Collection</subfield><subfield code="b">IDEB</subfield><subfield code="n">cis39169728</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">14934124</subfield></datafield><datafield tag="994" ind1=" " ind2=" "><subfield code="a">92</subfield><subfield code="b">GEBAY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-EBA</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-862</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| id | ZDB-4-EBA-on1011595407 |
| illustrated | Illustrated |
| indexdate | 2025-04-11T08:44:01Z |
| institution | BVB |
| isbn | 9781784393342 1784393347 |
| language | English |
| oclc_num | 1011595407 |
| open_access_boolean | |
| owner | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 1 online resource (xv, 510 pages) : illustrations |
| psigel | ZDB-4-EBA FWS_PDA_EBA ZDB-4-EBA |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Petrou, Theodore, author. Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python / Theodore Petrou. Birmingham, UK : Packt Publishing, 2017. 1 online resource (xv, 510 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Pandas foundations -- Dissecting the anatomy of a DataFrame -- Accessing the main DataFrame components -- Understanding data types -- Selecting a single column of data as a series -- Calling series methods -- Working with operators on a series -- Chaining series methods together -- Making the index meaningful -- Renaming row and column names -- Creating and deleting columns -- Essential Chapter 2: DataFrame operations -- Selecting multiple DataFrame columns -- Selecting columns with methods -- Ordering column names sensibly -- Operating on the entire DataFrame -- Chaining DataFrame methds together -- Working with operators on a DataFrame -- Comparing missing values -- Transposing the direction of a DataFrame operation -- Determining college campus diversity -- Chapter 3: Beginning data analysis -- Developing a data analysis routine. Online resource; title from title page (Safari, viewed November 14, 2017). Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas Who This Book Is For This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory. What You Will Learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn In Detail This book will provide 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. 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 like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas library to generate results. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Management Data processing. http://id.loc.gov/authorities/subjects/sh85080339 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Data Mining https://id.nlm.nih.gov/mesh/D057225 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Gestion Informatique. Apprentissage automatique. Visualisation de l'information. Exploration de données (Informatique) COMPUTERS Data Processing. bisacsh COMPUTERS Programming Languages Python. bisacsh COMPUTERS Mathematical & Statistical Software. bisacsh Data mining fast Electronic data processing fast Information visualization fast Machine learning fast Management Data processing fast Python (Computer program language) fast has work: Pandas Cookbook (Text) https://id.oclc.org/worldcat/entity/E39PCXQM8PxGpyJPVBF4PbqV98 https://id.oclc.org/worldcat/ontology/hasWork |
| spellingShingle | Petrou, Theodore Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python / Pandas foundations -- Dissecting the anatomy of a DataFrame -- Accessing the main DataFrame components -- Understanding data types -- Selecting a single column of data as a series -- Calling series methods -- Working with operators on a series -- Chaining series methods together -- Making the index meaningful -- Renaming row and column names -- Creating and deleting columns -- Essential Chapter 2: DataFrame operations -- Selecting multiple DataFrame columns -- Selecting columns with methods -- Ordering column names sensibly -- Operating on the entire DataFrame -- Chaining DataFrame methds together -- Working with operators on a DataFrame -- Comparing missing values -- Transposing the direction of a DataFrame operation -- Determining college campus diversity -- Chapter 3: Beginning data analysis -- Developing a data analysis routine. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Management Data processing. http://id.loc.gov/authorities/subjects/sh85080339 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Data Mining https://id.nlm.nih.gov/mesh/D057225 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Gestion Informatique. Apprentissage automatique. Visualisation de l'information. Exploration de données (Informatique) COMPUTERS Data Processing. bisacsh COMPUTERS Programming Languages Python. bisacsh COMPUTERS Mathematical & Statistical Software. bisacsh Data mining fast Electronic data processing fast Information visualization fast Machine learning fast Management Data processing fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh85080339 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2002000243 http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh85042288 https://id.nlm.nih.gov/mesh/D057225 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python / |
| title_auth | Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python / |
| title_exact_search | Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python / |
| title_full | Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python / Theodore Petrou. |
| title_fullStr | Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python / Theodore Petrou. |
| title_full_unstemmed | Pandas cookbook : recipes for scientific computing, time series analysis and data visualization using Python / Theodore Petrou. |
| title_short | Pandas cookbook : |
| title_sort | pandas cookbook recipes for scientific computing time series analysis and data visualization using python |
| title_sub | recipes for scientific computing, time series analysis and data visualization using Python / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Management Data processing. http://id.loc.gov/authorities/subjects/sh85080339 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Data Mining https://id.nlm.nih.gov/mesh/D057225 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Python (Langage de programmation) Gestion Informatique. Apprentissage automatique. Visualisation de l'information. Exploration de données (Informatique) COMPUTERS Data Processing. bisacsh COMPUTERS Programming Languages Python. bisacsh COMPUTERS Mathematical & Statistical Software. bisacsh Data mining fast Electronic data processing fast Information visualization fast Machine learning fast Management Data processing fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Management Data processing. Machine learning. Information visualization. Data mining. Electronic data processing. Data Mining Machine Learning Python (Langage de programmation) Gestion Informatique. Apprentissage automatique. Visualisation de l'information. Exploration de données (Informatique) COMPUTERS Data Processing. COMPUTERS Programming Languages Python. COMPUTERS Mathematical & Statistical Software. Data mining Electronic data processing Information visualization Machine learning Management Data processing |
| work_keys_str_mv | AT petroutheodore pandascookbookrecipesforscientificcomputingtimeseriesanalysisanddatavisualizationusingpython |