clean-data-xls

Cleaning spreadsheet data involves removing leading and trailing whitespace, standardizing text casing, and converting numbers stored as text into proper numeric formats. This Skill also profiles columns to detect mixed data types, blanks, and encoding errors.

Deduplicating rows and standardizing dates in an Excel file is handled by identifying and flagging duplicate or near-duplicate rows while correcting inconsistent date formats. The Skill operates directly on specified ranges or the active sheet within .xlsx files.

Yes, you can clean standalone .xlsx files without Excel installed because the Skill processes spreadsheets using Python and the openpyxl library. It operates directly on the file data rather than relying on the Excel desktop application.

Spreadsheet data normalization handles currency symbols by detecting mixed types and ensuring monetary values are recognized as actual numbers. This prepares financial data for accurate analysis by stripping text formatting from numeric values.

Data quality problems detected in an xlsx spreadsheet include duplicates, blanks, mixed data types, encoding errors, and formula errors. The Skill profiles columns to identify these issues and proposes targeted fixes to standardize the dataset.