Five small command-line scripts can handle recurring CSV chores: cleaning rows, splitting large files, merging exports, converting CSV to JSON, and organizing files. Wei Li describes these tools as requiring Python 3.8 or later and having no third-party dependencies. The examples below reflect the author’s descriptions; the scripts’ source code is not linked on the article page, so treat their behavior as claims to verify on a copy of your data.
What the five tools do
Each script targets one job. That separation makes the commands easier to understand, but you should still review the output before replacing or deleting source files.
| Tool | Task | Example |
|---|---|---|
csv_cleaner.py |
Remove duplicate rows, trim cell whitespace, normalize headers, and report changes. | python csv_cleaner.py messy.csv --dedupe --trim --headers --summary |
csv_splitter.py |
Divide a CSV into chunks by rows per file or by a requested number of parts. | --rows 100000 or --parts 4 |
csv_merger.py |
Combine files with matching headers, skip repeated header lines within a file, and optionally add a source-file field. | Merge with --add-source |
csv_to_json.py |
Convert CSV data to a JSON array or JSON Lines. | Choose the output format supported by the script. |
file_organizer.py |
Sort files into folders by type, extension, or year-month, with a dry-run preview. | python file_organizer.py ~/Downloads --by type --dry-run |
Clean a CSV and inspect the changes
The cleaner’s example combines four operations in one command: deduplication, whitespace trimming, header normalization, and a summary. For example, it can change a header such as Order Date to order_date. Because each flag represents a transformation, you can choose only the changes you want rather than applying every cleanup rule automatically.
The article shows illustrative output with 4 input rows, 1 duplicate removed, 1 empty row dropped, and 2 output rows. Those counts demonstrate what a report might look like; they are not a benchmark or a promise about what the script will do with another file. Check how the cleaner defines an empty row and duplicate before using it on data where blank records or repeated values matter.
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Split files when a workflow needs smaller chunks
The splitter is described as supporting two ways to divide a file: specify a row count per chunk, such as --rows 100000, or request a number of parts, such as --parts 4. Choose a row limit when a downstream system has a per-file size or record constraint. Choose a part count when you need a fixed number of output files.
Before processing the results, confirm how the tool handles the header row and whether it repeats that header in every chunk. The article does not establish those details, and they affect whether each output file can be opened or imported independently.
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Merge exports while preserving their origin
The merger is described as rejecting inputs with different headers and skipping repeated header lines found inside a file. Its --add-source option can label each output row with the originating file, which is useful when combining annual or monthly exports and tracing an unexpected value back to its input.
Header equality does not guarantee that two exports have identical meaning: a column can keep the same name while its values or interpretation change between reporting periods. Review the headers and representative rows before merging, and check how the source label is named and written in the output.
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Convert CSV values to JSON with schema checks
The author describes the converter as supporting both a JSON array and JSON Lines, and says it may infer types—for example, turning 30 into a number, true into a boolean, and an empty field into null. That can be convenient when those interpretations match the destination schema, but it can also alter values that are meant to remain text. An account number such as 00123, for instance, should not be treated as an ordinary number if its leading zero is significant.
Validate a sample of converted records against the schema expected by the receiving application. In particular, check identifiers, dates, empty fields, and strings that resemble numbers or booleans; the article does not specify controls for overriding inferred types.
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Preview file organization before moving files
The organizer is described as sorting files by type, extension, or year-month. Its dry-run example previews a type-based organization in Downloads:
python file_organizer.py ~/Downloads --by type --dry-run
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Use the preview to check the proposed destinations before allowing a run that moves files. The article does not specify how name collisions, files without extensions, or dates used for year-month grouping are handled, so inspect those cases in the preview if they apply to your folders.
CSV parsing depends on encoding and dialect
CSV is not one universally consistent format. Exporting applications can differ in delimiter and quoting conventions, so a file that looks comma-separated may use a different delimiter. Python’s CSV documentation describes csv.Sniffer for inferring a dialect from a sample, but warns that its header detection is heuristic and can produce false positives and negatives. Treat detection as a starting point and verify the parsed columns against the original file.
Encoding is a separate concern from delimiter choice. Reading as utf-8-sig can remove a UTF-8 byte-order mark; it does not convert a file saved in another encoding. A commenter reports that some Excel installations using Polish or German regional settings may save CSV with a semicolon separator and, in that commenter’s case, CP1250 encoding. That is an example rather than a rule for all European Excel exports. Confirm the actual encoding and separator for the file you received.
When adapting standard-library CSV code, open the file object with newline=''. The Python documentation recommends this so the CSV module can handle embedded newlines correctly and avoid extra carriage returns on some platforms. The author also recommends printing a summary of changes; as Wei Li puts it, “Always print what changed. Silent success is how data bugs survive.”
What is established about availability
The article, dated September 25, 2026, says the scripts require Python 3.8 or later and have no third-party dependencies. It also says the author plans to package them with a README as a downloadable toolkit. The article does not link to the scripts or an available download, so it does not establish that the toolkit can currently be obtained.
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