Implement Data Retrieval and Transformation Logic (Steps 1-9)#1
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wekesawgodwin wants to merge 2 commits intolearn-co-curriculum:mainfrom
Open
Implement Data Retrieval and Transformation Logic (Steps 1-9)#1wekesawgodwin wants to merge 2 commits intolearn-co-curriculum:mainfrom
wekesawgodwin wants to merge 2 commits intolearn-co-curriculum:mainfrom
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Implemented SQL queries using sqlite3 and pandas to retrieve and transform employee and order data. Completed steps 5-9, which include: - Categorizing employee roles using CASE statements. - Applying string functions (LENGTH, SUBSTR) for data extraction. - Calculating and rounding total order amounts. - Formatting date strings using STRFTIME.
Implemented SQL queries using sqlite3 and pandas to retrieve and transform employee and order data. Completed steps 5-9, which include: - Categorizing employee roles using CASE statements. - Applying string functions (LENGTH, SUBSTR) for data extraction. - Calculating and rounding total order amounts. - Formatting date strings using STRFTIME.
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Description
This PR completes the remaining data analysis tasks in the SQLSelectLab.ipynb notebook. The focus of this work was on leveraging advanced SQL syntax within the sqlite3 and pandas environment to transform raw employee and order data into actionable reports.
Key Changes
Conditional Logic: Implemented a CASE statement to categorize employee seniority into Executive and Not Executive roles.
String Manipulation: Applied LENGTH() and SUBSTR() functions to process last names and generate abbreviated job titles.
Mathematical Operations: Created a calculated column for total order prices using arithmetic operators and the ROUND() function.
Date Formatting: Utilized STRFTIME() to parse and extract specific date components (Day, Month, Year) for international reporting standards.