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Status](https://sonarcloud.io/api/project_badges/measure?project=itsbigspark_pymetagen\u0026metric=alert_status\u0026token=ca78f8a35c6c2ac9c28a08070e00f0b07d7e2342)](https://sonarcloud.io/summary/new_code?id=itsbigspark_pymetagen)[![Coverage](https://sonarcloud.io/api/project_badges/measure?project=itsbigspark_pymetagen\u0026metric=coverage\u0026token=ca78f8a35c6c2ac9c28a08070e00f0b07d7e2342)](https://sonarcloud.io/summary/new_code?id=itsbigspark_pymetagen)[![PyMetaGen GitHub release](https://github.com/itsbigspark/pymetagen/actions/workflows/release-action.yml/badge.svg)](https://github.com/itsbigspark/pymetagen/actions/workflows/release-action.yml)[![PyMetaGen Publish PyPi](https://github.com/itsbigspark/pymetagen/actions/workflows/pypi-release.yml/badge.svg)](https://github.com/itsbigspark/pymetagen/actions/workflows/pypi-release.yml)[![CircleCI](https://dl.circleci.com/status-badge/img/circleci/E2yL1VmSsVrCRNYYNptvyt/61gfAqXnv7nkeAjCUm2P1s/tree/dev/main.svg?style=svg)](https://dl.circleci.com/status-badge/redirect/circleci/E2yL1VmSsVrCRNYYNptvyt/61gfAqXnv7nkeAjCUm2P1s/tree/dev/main)\n# PyMetaGen\n\n\u003c!-- image of pyMetaGen --\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"./docs/img/pyMetaGenLogo.png\" alt=\"pymetagenlogo\"\n    style=\"width:500px;\"\u003e\n\u003c/p\u003e\n\n**PyMetaGen** is a powerful and fast data quality tool base on [Polars](https://pola.rs/#) designed for generating metadata and extracting useful information from various data file formats. It provides both a Python API and a command-line interface (CLI) to inspect, filter, and extract data from files such as CSV, JSON, Parquet, and Excel.\n\n## Key Features\n\n- **Metadata Generation**: Automatically generates metadata for your datasets, including statistics such as min, max, standard deviation, and more.\n- **Data Extraction**: Easily extract specific rows from your datasets using head, tail, or random sampling.\n- **Command Line Interface**: Perform operations like metadata generation, data inspection, and filtering using an intuitive CLI.\n- **Multiple File Format Support**: Import and export data in various formats, including CSV, Parquet, Excel, and JSON.\n- **SQL Query Support**: Filter data using SQL queries directly on the command line.\n\n## Installation\n\nTo install the package, use the following command:\n\n```bash\npip install pymetagen\n```\n\n## Local Installation\n\nTo install the package locally, use the following command:\n\n```bash\npython -m pip install -U git+ssh://git@github.com/itsbigspark/dotdda.git@dev/main\n```\n\n## Usage\n\n### Python API\n\nYou can use the Python API to load a data file and generate metadata:\n\n```python\nfrom pymetagen import MetaGen\n\n# Create an instance of the MetaGen class reading a data file\n\nmetagen = MetaGen.from_path(\"tests/data/testdata.csv\", loading_mode=\"eager\")\n\n# Display the first few rows of the data\n\nmetagen.data.head()\n```\n\n```python\n# Generate metadata and reset the index\n\nmetadata = metagen.compute_metadata().reset_index()\n\n```\n\n```python\n# Save the metadata to a file\n\nmetagen.write_metadata(\"tests/data/testdata_metadata.csv\")\n```\n\n### Command Line Interface\n\n- **Metadata Generation** Generate metadata for a tabular data file:\n\n```bash\n$ metagen metadata -i tests/data/testdata.csv -o tests/data/testdata_metadata.csv\n\n\u003e\u003e\u003e Generating metadata for tests/data/testdata.csv...\n```\n\n- **Data Inspection** Inspect a data file (e.g., a partitioned Parquet file):\n\n```bash\nmetagen inspect -i tests/data/input_ab_partition.parquet\n```\n\n- **Data Filtering** Filter a data set using an SQL query:\n\n```bash\nmetagen filter -i tests/data/testdata.csv -q \"SELECT * FROM data WHERE imdb_score \u003e 9\"\n```\n\n- **Data Extraction** Extract a specific number of rows from a data set:\n\n```bash\n$ metagen extracts -i tests/data/testdata.csv -o tests.csv -n 3\n\n\u003e\u003e\u003e Writing extract in: tests-head.csv\n\u003e\u003e\u003e Writing extract in: tests-tail.csv\n\u003e\u003e\u003e Writing extract in: tests-sample.csv\n```\n\n### Available Output Formats\n\n- **CSV**\n- **Parquet**\n- **JSON**\n- **Excel**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fitsbigspark%2Fpymetagen","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fitsbigspark%2Fpymetagen","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fitsbigspark%2Fpymetagen/lists"}