Five small command-line scripts can take recurring CSV chores off your hands: clean rows and headers, split large files, merge exports, convert to JSON, and sort files into folders. Wei Li describes these tools as requiring Python 3.8 or later and having no dependencies. The examples below explain the intended workflows; the scripts were not independently run or inspected, and the article does not link to a downloadable package.
What the five tools are for
Each script targets one repeatable task rather than trying to be a general-purpose spreadsheet application. The commands and capabilities here are those Wei Li describes in the article; check the resulting files before relying on them for important data.
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1. Clean rows and normalize headers
csv_cleaner.py is described as trimming whitespace in cells, removing duplicate rows, normalizing headers—for example, changing Order Date to order_date—and reporting what changed.
The Tool Desk
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2. Split a CSV into manageable chunks
csv_splitter.py is intended to divide a file by a chosen number of rows per chunk or by a chosen number of parts. The examples use these alternatives:
python csv_splitter.py large.csv --rows 100000python csv_splitter.py large.csv --parts 4Choose the option that matches the job: a row limit sets the target chunk size, while a part count sets how many pieces to make. Confirm how the script treats headers and whether output files meet your downstream system’s limits.
3. Merge exports and identify their source
csv_merger.py is described as rejecting files whose headers differ, skipping repeated header lines found inside a file, and optionally adding a source-file tag to each row. The example uses --add-source when combining annual and monthly files:
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4. Convert CSV data to JSON
csv_to_json.py is described as producing either a JSON array or JSON Lines. The article says it can infer types so that values such as 30 become a number, true becomes a boolean, and an empty field becomes null. Treat that as a behavior to verify, not an assumption: automatic conversion can change the meaning of identifiers, codes, or values that look numeric or boolean but should remain text. Compare the output with the schema your receiving application expects.
5. Organize files into folders
file_organizer.py is intended to sort files by type, extension, or year-month. Its dry-run option is meant to preview proposed moves before changing the folder:
python file_organizer.py ~/Downloads --by type --dry-runReview the preview, especially if files with the same name could land in one destination folder, before running a mode that moves files.
What you need to run them
Li says the scripts need Python 3.8 or later and have zero dependencies. The article does not provide a repository, installation package, or download link, so these requirements describe the author’s scripts rather than a verified downloadable toolkit. Li says they plan to package the scripts with a README; no availability or price is established in the article.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.CSV cleanup still depends on the export’s format
CSV is not one universally consistent format: applications can differ in delimiters and quoting conventions. Python’s CSV documentation describes csv.Sniffer for inferring a dialect from a sample, but warns that its header detection is a rough heuristic that can return false positives and false negatives. Detection is a useful clue, not proof that a file has been interpreted correctly.
A commenter on Li’s article reports an edge case from some Polish or German regional Excel settings: a semicolon separator and a non-UTF-8 encoding such as cp1250. That is an individual report, not a rule for every European Excel installation. It highlights two separate checks: whether the text decodes correctly and whether the delimiter and quoting match the file.
Handle encoding and dialect as separate decisions
Reading with utf-8-sig can remove a UTF-8 byte-order mark (BOM), as Li recommends. It does not detect or convert other encodings such as cp1250, and it does not determine whether the delimiter is a comma or semicolon. If a file looks garbled or lands in the wrong columns, identify its actual encoding and dialect rather than treating the BOM option as a complete fix.
Keep Python’s CSV file-opening convention
When adapting code that uses Python’s standard csv module, open CSV files with newline="". The Python 3.14.8 CSV documentation says this supports correct handling of embedded newlines and avoids extra carriage returns on some platforms. Li also recommends this when writing CSV files.
Make each cleanup run auditable
Li’s practical design advice is to give each command-line flag one clear transformation and print a summary of the changes. A report lets you catch unexpected row counts or transformations before using the output. As Li puts it: “Always print what changed. Silent success is how data bugs survive.”
Quick Recap
- Keep an untouched copy of the original export until the output has been checked.
- Inspect column names, row counts, and representative values after cleaning or conversion.
- Test format detection against the actual file, particularly when exports come from different locales or applications.
- Check inferred JSON types against the receiving system’s intended schema.
- Use a dry run for file moves where available, and review the proposed destinations first.
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