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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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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-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