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Load and parse CSV files into structured data frames with configurable encoding, delimiter, and date parsing options.
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SOC 職業分類に基づく
Validate tabular data against configurable business rules — range checks, cross-column constraints, referential integrity against lookup tables, and temporal consistency.
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.
Universal data ingestion service that loads data from CSV, Excel, JSON, and Parquet sources into a unified DataFrame format with automatic format detection.
| name | csv-data-loader |
| description | Load and parse CSV files into structured data frames with configurable encoding, delimiter, and date parsing options. |
| version | 0.3.1 |
Reads CSV files from disk and parses them into pandas DataFrames. Supports configurable delimiters (comma, tab, semicolon), file encodings (UTF-8, Latin-1, GBK), and automatic date column detection. Handles common CSV quirks like BOM markers, inconsistent quoting, and mixed line endings.
--input / -i: Path to CSV file (required)--delimiter / -d: Column delimiter (default: ,)--encoding / -e: File encoding (default: utf-8)--date-columns: Comma-separated list of columns to parse as dates--output / -o: Output directory for parsed result (Parquet format)read_csv, applying delimiter and encodingpython3 ./skills/csv_data_loader/loader.py -i <file> -o <output_dir>pandas, chardet