القائمة
Compute aggregate statistics (mean, median, sum, count, min, max) grouped by a column.
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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 | stats-aggregator |
| description | Compute aggregate statistics (mean, median, sum, count, min, max) grouped by a column. |
| version | 1.0.0 |
Groups records by a specified column and computes configurable aggregate statistics on a numeric value column. Supports six standard statistical metrics: mean, median, sum, count, min, and max.
--input / -i: Path to JSON records file (required)--group-by / -g: Column name to group records by (required)--value-column / -v: Numeric column to aggregate (required)--metrics: Comma-separated metrics to compute (default: mean,median,sum,count,min,max)--output / -o: Output directory (required)| Metric | Description | Python Function |
|---|---|---|
mean | Arithmetic mean | statistics.mean() |
median | Middle value | statistics.median() |
sum | Sum of all values | sum() |
count | Number of records | len() |
min | Minimum value | min() |
max | Maximum value | max() |
stats_report.jsonstats_report.json with structure:
{
"group_column": "region",
"groups": {
"North": {"mean": 150.5, "median": 140.0, "sum": 3010, "count": 20, "min": 80, "max": 250},
"South": {"mean": 120.3, ...}
}
}
python3 stats-aggregator/aggregator.py -i <file> -g region -v revenue -o <dir>json, statistics, argparse, os)