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Parse CSV files into structured records with configurable delimiter and encoding.
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基于 SOC 职业分类
Validate tabular data against configurable business rules — range checks, cross-column constraints, referential integrity against lookup tables, and temporal consistency.
Load and parse CSV files into structured data frames with configurable encoding, delimiter, and date parsing options.
Export pandas DataFrames or processed datasets to well-formatted CSV files with configurable encoding, delimiter, quoting, and column ordering.
Aggregate tabular data by one or more dimensions with configurable metrics — sum, mean, count, min, max, median. Supports multi-level grouping and pivot table generation.
Comprehensive data cleaning toolkit — handles missing values, duplicate removal, type coercion, whitespace trimming, and outlier detection in a single pass over the dataset.
Identify and remove duplicate records from tabular datasets using exact-match, fuzzy-match, or composite-key-based deduplication strategies.
| name | csv-parser |
| description | Parse CSV files into structured records with configurable delimiter and encoding. |
| version | 1.0.0 |
Reads CSV files and converts them into a list of dictionaries (records). Supports comma and tab delimiters, configurable file encoding (defaults to UTF-8), and automatic header detection from the first row.
--input / -i: Path to CSV file (required)--delimiter / -d: Column delimiter: , or \t (default: ,)--encoding / -e: File encoding (default: utf-8)--output / -o: Output directory (required)parsed_data.jsonparsed_data.json: JSON array of record objectspython3 csv-parser/parser.py -i <file> -d "," -o <output_dir>csv, json, argparse, os)