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Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data and extract key insights
Provides a comprehensive guide to data cleaning techniques using Python, specifically focusing on the pandas library. It covers essential steps from importing various data formats like CSV, Excel, SQL, SPSS, Stata, SAS, and R files, to addressing common data quality issues. The text details methods for identifying missing values and outliers through statistical analysis and visualizations, cleaning and transforming data series, and combining datasets through vertical concatenation and different types of merges. Ultimately, the book emphasizes automating data cleaning processes by developing reusable functions and classes to efficiently manage and prepare data for analysis.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Python-Data-Cleaning-Cookbook-techniques/dp/1800565666?&linkCode=ll1&tag=cvthunderx-20&linkId=49cb1a93b896e2f724376b0710211ef7&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Python-Data-Cleaning-Cookbook-techniques/dp/1800565666?&linkCode=ll1&tag=cvthunderx-20&linkId=49cb1a93b896e2f724376b0710211ef7&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
View all episodes
By CyberSecurity Summary
2.2
55 ratings
Provides a comprehensive guide to data cleaning techniques using Python, specifically focusing on the pandas library. It covers essential steps from importing various data formats like CSV, Excel, SQL, SPSS, Stata, SAS, and R files, to addressing common data quality issues. The text details methods for identifying missing values and outliers through statistical analysis and visualizations, cleaning and transforming data series, and combining datasets through vertical concatenation and different types of merges. Ultimately, the book emphasizes automating data cleaning processes by developing reusable functions and classes to efficiently manage and prepare data for analysis.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Python-Data-Cleaning-Cookbook-techniques/dp/1800565666?&linkCode=ll1&tag=cvthunderx-20&linkId=49cb1a93b896e2f724376b0710211ef7&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Python-Data-Cleaning-Cookbook-techniques/dp/1800565666?&linkCode=ll1&tag=cvthunderx-20&linkId=49cb1a93b896e2f724376b0710211ef7&language=en_US&ref_=as_li_ss_tl
Discover our free courses in tech and cybersecurity, Start learning today:
https://linktr.ee/cybercode_academy
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