Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham, UK
Packt Publishing Ltd.
2024
|
| Edition: | Second edition. |
| Series: | Expert insight
|
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781803239873/?ar |
| Summary: | Jumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook - Second Edition will show you tools and techniques for cleaning and handling data with Python for better outcomes. Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate and clean data to get it into a useful form. he current edition focuses on advanced techniques like machine learning and AI-specific approaches and tools for data cleaning along with the conventional ones. The book also delves into tips and techniques to process and clean data for ML, AI, and NLP models. You will 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. Next, you'll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify 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 Data Cleaning book, you'll know how to clean data and diagnose problems within it. |
| Item Description: | Includes index |
| Physical Description: | 1 Online-Ressource (486 pages) illustrations |
| ISBN: | 9781803239873 |
Staff View
MARC
| LEADER | 00000nam a22000002c 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-104372583 | ||
| 003 | DE-627-1 | ||
| 005 | 20240701091204.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240701s2024 xx |||||o 00| ||eng c | ||
| 020 | |a 9781803239873 |9 978-1-80323-987-3 | ||
| 035 | |a (DE-627-1)104372583 | ||
| 035 | |a (DE-599)KEP104372583 | ||
| 035 | |a (ORHE)9781803239873 | ||
| 035 | |a (DE-627-1)104372583 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | |a 005.75/65 |2 23/eng/20240612 | |
| 100 | 1 | |a Walker, Michael |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Python data cleaning cookbook |b prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI |c Michael Walker |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Birmingham, UK |b Packt Publishing Ltd. |c 2024 | |
| 300 | |a 1 Online-Ressource (486 pages) |b illustrations | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Expert insight | |
| 500 | |a Includes index | ||
| 520 | |a Jumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook - Second Edition will show you tools and techniques for cleaning and handling data with Python for better outcomes. Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate and clean data to get it into a useful form. he current edition focuses on advanced techniques like machine learning and AI-specific approaches and tools for data cleaning along with the conventional ones. The book also delves into tips and techniques to process and clean data for ML, AI, and NLP models. You will 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. Next, you'll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify 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 Data Cleaning book, you'll know how to clean data and diagnose problems within it. | ||
| 650 | 0 | |a Analysis of variance | |
| 650 | 0 | |a Database management | |
| 650 | 0 | |a Data mining | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 4 | |a Analyse de variance | |
| 650 | 4 | |a Bases de données ; Gestion | |
| 650 | 4 | |a Exploration de données (Informatique) | |
| 650 | 4 | |a Python (Langage de programmation) | |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781803239873/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Record in the Search Index
| DE-BY-TUM_katkey | ZDB-30-ORH-104372583 |
|---|---|
| _version_ | 1835903247005515776 |
| adam_text | |
| any_adam_object | |
| author | Walker, Michael |
| author_facet | Walker, Michael |
| author_role | aut |
| author_sort | Walker, Michael |
| author_variant | m w mw |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)104372583 (DE-599)KEP104372583 (ORHE)9781803239873 |
| dewey-full | 005.75/65 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.75/65 |
| dewey-search | 005.75/65 |
| dewey-sort | 15.75 265 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | Second edition. |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02859nam a22004452c 4500</leader><controlfield tag="001">ZDB-30-ORH-104372583</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240701091204.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240701s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781803239873</subfield><subfield code="9">978-1-80323-987-3</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)104372583</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP104372583</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781803239873</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)104372583</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">005.75/65</subfield><subfield code="2">23/eng/20240612</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Walker, Michael</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Python data cleaning cookbook</subfield><subfield code="b">prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI</subfield><subfield code="c">Michael Walker</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Second edition.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham, UK</subfield><subfield code="b">Packt Publishing Ltd.</subfield><subfield code="c">2024</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (486 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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Expert insight</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes index</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Jumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook - Second Edition will show you tools and techniques for cleaning and handling data with Python for better outcomes. Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate and clean data to get it into a useful form. he current edition focuses on advanced techniques like machine learning and AI-specific approaches and tools for data cleaning along with the conventional ones. The book also delves into tips and techniques to process and clean data for ML, AI, and NLP models. You will 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. Next, you'll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify 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 Data Cleaning book, you'll know how to clean data and diagnose problems within it.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Analysis of variance</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Database management</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Data mining</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Analyse de variance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bases de données ; Gestion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Exploration de données (Informatique)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Langage de programmation)</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781803239873/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-104372583 |
| illustrated | Illustrated |
| indexdate | 2025-06-25T12:16:14Z |
| institution | BVB |
| isbn | 9781803239873 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (486 pages) illustrations |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Packt Publishing Ltd. |
| record_format | marc |
| series2 | Expert insight |
| spelling | Walker, Michael VerfasserIn aut Python data cleaning cookbook prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI Michael Walker Second edition. Birmingham, UK Packt Publishing Ltd. 2024 1 Online-Ressource (486 pages) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Expert insight Includes index Jumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook - Second Edition will show you tools and techniques for cleaning and handling data with Python for better outcomes. Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate and clean data to get it into a useful form. he current edition focuses on advanced techniques like machine learning and AI-specific approaches and tools for data cleaning along with the conventional ones. The book also delves into tips and techniques to process and clean data for ML, AI, and NLP models. You will 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. Next, you'll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify 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 Data Cleaning book, you'll know how to clean data and diagnose problems within it. Analysis of variance Database management Data mining Python (Computer program language) Analyse de variance Bases de données ; Gestion Exploration de données (Informatique) Python (Langage de programmation) |
| spellingShingle | Walker, Michael Python data cleaning cookbook prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI Analysis of variance Database management Data mining Python (Computer program language) Analyse de variance Bases de données ; Gestion Exploration de données (Informatique) Python (Langage de programmation) |
| title | Python data cleaning cookbook prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI |
| title_auth | Python data cleaning cookbook prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI |
| title_exact_search | Python data cleaning cookbook prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI |
| title_full | Python data cleaning cookbook prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI Michael Walker |
| title_fullStr | Python data cleaning cookbook prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI Michael Walker |
| title_full_unstemmed | Python data cleaning cookbook prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI Michael Walker |
| title_short | Python data cleaning cookbook |
| title_sort | python data cleaning cookbook prepare your data for analysis with pandas numpy matplotlib scikit learn and openai |
| title_sub | prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn and OpenAI |
| topic | Analysis of variance Database management Data mining Python (Computer program language) Analyse de variance Bases de données ; Gestion Exploration de données (Informatique) Python (Langage de programmation) |
| topic_facet | Analysis of variance Database management Data mining Python (Computer program language) Analyse de variance Bases de données ; Gestion Exploration de données (Informatique) Python (Langage de programmation) |
| work_keys_str_mv | AT walkermichael pythondatacleaningcookbookprepareyourdataforanalysiswithpandasnumpymatplotlibscikitlearnandopenai |