Cleaning a spreadsheet does not mean changing everything that looks unusual. The goal is to remove inconsistencies that make later work unreliable while preserving the meaning of the original data.
A good cleaning pass is especially useful before merging exports, comparing versions, importing records into another system or converting a file to another format.
Keep the source file
Before changing important data, keep an untouched copy. If a rule turns out to be wrong, you can return to the source rather than trying to undo several transformations.
This simple habit also makes a workflow easier to reproduce later.
Start with empty rows and obvious noise
Blank rows can create misleading counts and sometimes interfere with imports. Leading or trailing spaces are equally subtle: Paris and Paris may look identical to a person but can be treated as different values by software.
The Excel and CSV Cleaner is designed for these broad preparation steps.
Standardize headers before merging files
Headers define the schema of a table. If three monthly exports use customer_id, Customer ID and id_client, merging them may create separate columns even though the business meaning is the same.
Choose clear, stable names before combining sources. When only header names need to change, Rename CSV columns lets you correct them without altering the rest of the table.
Be careful with identifiers
A value such as 00125 may be an identifier, not the number 125. Converting it to a numeric type would remove the leading zeros and change the business value.
The same caution applies to phone numbers, postal codes, product references and account IDs. Clean based on the semantics of the column, not only on how the values look.
Missing values need context
An empty value can mean “unknown”, “not applicable”, “not collected” or simply “error”. Replacing every blank cell with zero or an empty string may introduce incorrect information.
Decide which columns are required and which may legitimately be empty.