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Fast Actual/Expected Data Processing.

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Τίτλος: Fast Actual/Expected Data Processing.
Συγγραφείς: Wesley, David (AUTHOR)
Πηγή: Journal of Insurance Medicine. 2026, Vol. 53 Issue 1, p105-114. 10p.
Θεματικοί όροι: *Electronic data processing, *Real-time computing, *Big data, Python programming language, Death rate
Περίληψη: A common problem with mortality analyses on company or registry data is that the processing time on large datasets can be an impediment to the interactive process of the analysis. The following paper delineates an approach using the Polars dataframe library and the programming language Python to speed up the processing time considerably. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Insurance Medicine is the property of American Academy of Insurance Medicine and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Business Source Index
FullText Text:
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DbLabel: Business Source Index
An: 191841995
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PubType: Academic Journal
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  Data: Fast Actual/Expected Data Processing.
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  Data: A common problem with mortality analyses on company or registry data is that the processing time on large datasets can be an impediment to the interactive process of the analysis. The following paper delineates an approach using the Polars dataframe library and the programming language Python to speed up the processing time considerably. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Insurance Medicine is the property of American Academy of Insurance Medicine and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.17849/insm-53-1-1-10.1
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 10
        StartPage: 105
    Subjects:
      – SubjectFull: Electronic data processing
        Type: general
      – SubjectFull: Real-time computing
        Type: general
      – SubjectFull: Big data
        Type: general
      – SubjectFull: Python programming language
        Type: general
      – SubjectFull: Death rate
        Type: general
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      – TitleFull: Fast Actual/Expected Data Processing.
        Type: main
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            NameFull: Wesley, David
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            – D: 01
              M: 01
              Text: 2026
              Type: published
              Y: 2026
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              Value: 07436661
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              Value: 53
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            – TitleFull: Journal of Insurance Medicine
              Type: main
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