# Installation Pointblank can be installed using various package managers. The base installation gives you the core validation functionality, with optional dependencies for working with different data sources. # Basic Installation You can install Pointblank using your preferred package manager: - pip - uv - conda - pixi ``` bash pip install pointblank ``` ``` bash uv pip install pointblank ``` ``` bash conda install -c conda-forge pointblank ``` ``` bash # add pointblank to project pixi init name-of-project cd name-of-project pixi add pointblank ``` # DataFrame Libraries Pointblank requires a DataFrame library but doesn't include one by default, giving you the flexibility to choose either [Pandas](https://pandas.pydata.org) or [Polars](https://pola.rs): - Polars - Pandas ``` bash # Using pip pip install pointblank[pl] # Or manually pip install polars>=1.24.0 ``` ``` bash # Using pip pip install pointblank[pd] # Or manually pip install pandas>=2.2.3 ``` Pointblank works seamlessly with both libraries, and you can choose the one that best fits your workflow and performance requirements. # Optional Dependencies ## Ibis Backends To work with various database systems through [Ibis](https://ibis-project.org), you can install additional backends: - pip - uv - conda - pixi ``` bash pip install pointblank[sqlite] # SQLite pip install pointblank[duckdb] # DuckDB pip install pointblank[postgres] # PostgreSQL pip install pointblank[mysql] # MySQL pip install pointblank[mssql] # Microsoft SQL Server pip install pointblank[bigquery] # BigQuery pip install pointblank[pyspark] # Apache Spark pip install pointblank[databricks] # Databricks pip install pointblank[snowflake] # Snowflake # Example of installing multiple backends pip install pointblank[duckdb,postgres,sqlite] ``` ``` bash uv pip install pointblank[sqlite] # SQLite uv pip install pointblank[duckdb] # DuckDB uv pip install pointblank[postgres] # PostgreSQL uv pip install pointblank[mysql] # MySQL uv pip install pointblank[mssql] # Microsoft SQL Server uv pip install pointblank[bigquery] # BigQuery uv pip install pointblank[pyspark] # Apache Spark uv pip install pointblank[databricks] # Databricks uv pip install pointblank[snowflake] # Snowflake # Example of installing multiple backends uv pip install pointblank[duckdb,postgres,sqlite] ``` ``` bash conda install -c conda-forge pointblank-sqlite # SQLite conda install -c conda-forge pointblank-duckdb # DuckDB conda install -c conda-forge pointblank-postgres # PostgreSQL conda install -c conda-forge pointblank-mysql # MySQL conda install -c conda-forge pointblank-mssql # Microsoft SQL Server conda install -c conda-forge pointblank-bigquery # BigQuery conda install -c conda-forge pointblank-pyspark # Apache Spark conda install -c conda-forge pointblank-databricks # Databricks conda install -c conda-forge pointblank-snowflake # Snowflake # Example of installing multiple backends conda install -c conda-forge pointblank-duckdb pointblank-postgres pointblank-sqlite ``` ``` bash pixi add pointblank-sqlite # SQLite pixi add pointblank-duckdb # DuckDB pixi add pointblank-postgres # PostgreSQL pixi add pointblank-mysql # MySQL pixi add pointblank-mssql # Microsoft SQL Server pixi add pointblank-bigquery # BigQuery pixi add pointblank-pyspark # Apache Spark pixi add pointblank-databricks # Databricks pixi add pointblank-snowflake # Snowflake # Example of installing multiple backends pixi add pointblank-duckdb pointblank-postgres pointblank-sqlite ``` > **Note: Note** > > Even when using exclusively Ibis backends, you still need either Pandas or Polars installed since Pointblank's reporting functionality (powered by [Great Tables](https://posit-dev.github.io/great-tables)) requires a DataFrame library for rendering tabular reporting results. ## AI-Assisted Validation (Experimental) Pointblank includes experimental support for AI-assisted validation plan generation: ``` bash pip install pointblank[generate] ``` This installs the necessary dependencies for working with LLM providers to help generate validation plans. See the [Draft Validation](../../user-guide/advanced-validation/draft-validation.md) article for how to create validation plans from existing data. ## Development Version If you want the latest development version with the newest features, you can install directly from GitHub: ``` bash pip install git+https://github.com/posit-dev/pointblank.git ``` # Verifying Your Installation You can verify your installation by importing Pointblank and checking the version: ``` python import pointblank as pb print(pb.__version__) ``` # System Requirements - Python 3.10 or higher - a supported DataFrame library (Pandas or Polars) - optional: Ibis (for database connectivity) # Next Steps Now that you've installed Pointblank, you're ready to start validating your data. If you haven't read the [Introduction](../../user-guide/getting-started/index.md) yet, consider starting there to learn the basic concepts. If you encounter any installation issues, please [open an issue on GitHub](https://github.com/posit-dev/pointblank/issues/new) with details about your system and the specific error messages you're seeing. The maintainers actively monitor these issues and can help troubleshoot problems. For a quick test of your installation, try running a simple validation: ``` python import pointblank as pb # Load a small dataset data = pb.load_dataset("small_table") # Create a simple validation validation = ( pb.Validate(data=data) .col_exists(columns=["a", "b", "c"]) .interrogate() ) # Display the validation results validation ``` # Command Line Interface Once installed, Pointblank also provides a powerful command-line interface for quick data validation tasks: ``` bash # Test the CLI with a built-in dataset pb validate small_table --check rows-distinct # Check if a column exists pb validate small_table --check col-exists --column a # Validate data ranges pb validate small_table --check col-vals-lt --column a --value 10 ``` The CLI is useful for: - quick data quality checks in CI/CD pipelines - exploratory data analysis from the terminal - integration with shell scripts and automation workflows > **Tip: See the CLI in Action** > > Watch our [interactive CLI demonstrations](../../examples/advanced-topics/cli-interactive.md) to see these commands executing in real-time with actual output formatting. Learn more about the CLI capabilities in the [Command Line Interface](../../user-guide/the-pointblank-cli/index.md) guide.