Python data cleaning cookbook: prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham
Packt Publishing Ltd.
May 2024
|
| Ausgabe: | Second edition |
| Schriftenreihe: | Expert Insight
|
| Links: | https://ebookcentral.proquest.com/lib/hsansbach/detail.action?docID=31355096 https://portal.igpublish.com/iglibrary/search/PACKT0007203.html https://portal.igpublish.com/iglibrary/search/PACKT0007203.html https://portal.igpublish.com/iglibrary/search/PACKT0007203.html https://portal.igpublish.com/iglibrary/search/PACKT0007203.html |
| Abstract: | Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: Anticipating Data Cleaning Issues When Importing Tabular Data with pandas -- Technical requirements -- Importing CSV files -- Importing Excel files -- Importing data from SQL databases -- Importing SPSS, Stata, and SAS data -- Importing R data -- Persisting tabular data -- Summary -- Chapter 2: Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data -- Technical requirements -- Importing simple JSON data -- Importing more complicated JSON data from an API -- Importing data from web pages -- Working with Spark data -- Persisting JSON data -- Versioning data -- Summary -- Chapter 3: Taking the Measure of Your Data -- Technical requirements -- Getting a first look at your data -- Selecting and organizing columns -- Selecting rows -- Generating frequencies for categorical variables -- Generating summary statistics for continuous variables -- Using generative AI to display descriptive statistics -- Summary -- Chapter 4: Identifying Outliers in Subsets of Data -- Technical requirements -- Identifying outliers with one variable -- Identifying outliers and unexpected values in bivariate relationships -- Using subsetting to examine logical inconsistencies in variable relationships -- Using linear regression to identify data points with significant influence -- Using k-nearest neighbors to find outliers -- Using Isolation Forest to find anomalies -- Using PandasAI to identify outliers -- Summary -- Chapter 5: Using Visualizations for the Identification of Unexpected Values -- Technical requirements -- Using histograms to examine the distribution of continuous variables -- Using boxplots to identify outliers for continuous variables -- Using grouped boxplots to uncover unexpected values in a particular group. |
| Umfang: | 1 Online-Ressource (xvii, 453 Seiten) Illustrationen |
| ISBN: | 9781803246291 |
Internformat
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| 520 | 3 | |a Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: Anticipating Data Cleaning Issues When Importing Tabular Data with pandas -- Technical requirements -- Importing CSV files -- Importing Excel files -- Importing data from SQL databases -- Importing SPSS, Stata, and SAS data -- Importing R data -- Persisting tabular data -- Summary -- Chapter 2: Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data -- Technical requirements -- Importing simple JSON data -- Importing more complicated JSON data from an API -- Importing data from web pages -- Working with Spark data -- Persisting JSON data -- Versioning data -- Summary -- Chapter 3: Taking the Measure of Your Data -- Technical requirements -- Getting a first look at your data -- Selecting and organizing columns -- Selecting rows -- Generating frequencies for categorical variables -- Generating summary statistics for continuous variables -- Using generative AI to display descriptive statistics -- Summary -- Chapter 4: Identifying Outliers in Subsets of Data -- Technical requirements -- Identifying outliers with one variable -- Identifying outliers and unexpected values in bivariate relationships -- Using subsetting to examine logical inconsistencies in variable relationships -- Using linear regression to identify data points with significant influence -- Using k-nearest neighbors to find outliers -- Using Isolation Forest to find anomalies -- Using PandasAI to identify outliers -- Summary -- Chapter 5: Using Visualizations for the Identification of Unexpected Values -- Technical requirements -- Using histograms to examine the distribution of continuous variables -- Using boxplots to identify outliers for continuous variables -- Using grouped boxplots to uncover unexpected values in a particular group. | |
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Datensatz im Suchindex
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| id | DE-604.BV049746289 |
| illustrated | Illustrated |
| indexdate | 2025-02-11T15:01:18Z |
| institution | BVB |
| isbn | 9781803246291 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035088081 |
| oclc_num | 1443577560 |
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| physical | 1 Online-Ressource (xvii, 453 Seiten) Illustrationen |
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| publishDate | 2024 |
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| publisher | Packt Publishing Ltd. |
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| series2 | Expert Insight |
| spellingShingle | Walker, Michael Python data cleaning cookbook prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI |
| 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 |
| url | https://portal.igpublish.com/iglibrary/search/PACKT0007203.html |
| work_keys_str_mv | AT walkermichael pythondatacleaningcookbookprepareyourdataforanalysiswithpandasnumpymatplotlibscikitlearnandopenai |