--- name: pandera-b9699ef77402 description: >- Pandera is a Python library for validating DataFrames (pandas, Polars, PySpark, Dask and others) against declarative schemas with column types, constraints and custom checks. Use this skill when asked to validate a DataFrame, define a data contract, add data quality checks to a pipeline or pytest suite, or report which rows fail validation. license: Apache-2.0 compatibility: "Python 3.9+; pandas or polars installed through pandera extras" metadata: author: terminal-skills version: "1.1.0" category: data-ai repository: https://github.com/unionai-oss/pandera tags: "data-validation, pandas, polars, python, schema" --- # Pandera — Data Validation for DataFrames ## Overview Pandera validates DataFrames against schemas you declare as classes (`DataFrameModel`) or objects (`DataFrameSchema`). Each column gets a type, constraints (`ge`, `isin`, `str_matches`, `unique`, `nullable`) and optional custom checks; whole-frame rules go in `@pa.dataframe_check`. It works with pandas (`pandera.pandas`), Polars (`pandera.polars`), PySpark, Dask, Modin and Ibis. The pandas API lives in `pandera.pandas`, and the base `pandera` package no longer installs pandas for you. ## Instructions ### Schema Definition Define column types, constraints, and checks: ```python # schemas/orders.py — Order data validation schema import pandas as pd import pandera.pandas as pa from pandera.typing import Series, DataFrame class OrderSchema(pa.DataFrameModel): """Schema for validated order records (one row per order).""" order_id: Series[str] = pa.Field( unique=True, str_matches=r"^ORD-\d{8}$", # Format: ORD-00000001 description="Unique order identifier", ) customer_id: Series[str] = pa.Field( nullable=False, str_length={"min_value": 1, "max_value": 50}, ) amount: Series[float] = pa.Field( ge=0.01, # Minimum $0.01 le=100_000, # Maximum $100,000 (sanity check) description="Order total in USD", ) status: Series[str] = pa.Field( isin=["pending", "processing", "completed", "cancelled", "refunded"], ) currency: Series[str] = pa.Field( isin=["USD", "EUR", "GBP"], default="USD", ) created_at: Series[pd.Timestamp] = pa.Field( nullable=False, description="Order creation timestamp (UTC)", ) shipped_at: Series[pd.Timestamp] = pa.Field( nullable=True, # Not all orders are shipped yet ) items_count: Series[int] = pa.Field( ge=1, # At least one item per order le=100, # Max 100 items ) # DataFrame-level validation (checks across columns) @pa.dataframe_check def shipped_after_created(cls, df: pd.DataFrame) -> Series[bool]: """Shipped date must be after creation date (when present).""" mask = df["shipped_at"].notna() result = pd.Series(True, index=df.index) result[mask] = df.loc[mask, "shipped_at"] > df.loc[mask, "created_at"] return result @pa.dataframe_check def completed_must_be_shipped(cls, df: pd.DataFrame) -> Series[bool]: """Completed orders must have a shipped date.""" completed = df["status"] == "completed" return ~completed | df["shipped_at"].notna() class Config: strict = True # Reject extra columns not in schema coerce = True # Auto-coerce types (str → int, etc.) name = "OrderSchema" description = "Validated order records for the analytics pipeline" ``` ### Using Schemas in Pipelines ```python # pipelines/orders.py — Data pipeline with validation import pandas as pd import pandera.pandas as pa from pandera.typing import DataFrame from schemas.orders import OrderSchema @pa.check_types # Validates return type at runtime def load_orders(filepath: str) -> DataFrame[OrderSchema]: """Load order data from CSV; raises pa.errors.SchemaError if invalid.""" df = pd.read_csv(filepath, parse_dates=["created_at", "shipped_at"]) return df # Auto-validated by @check_types def process_orders(orders: DataFrame[OrderSchema]) -> pd.DataFrame: """Process validated orders into daily revenue summary.""" return ( orders .query("status == 'completed'") .groupby(orders["created_at"].dt.date) .agg( revenue=("amount", "sum"), order_count=("order_id", "count"), avg_items=("items_count", "mean"), ) .reset_index() ) # Usage try: orders = load_orders("data/orders_2026_03.csv") summary = process_orders(orders) print(f"Processed {len(orders)} orders → {len(summary)} daily summaries") except pa.errors.SchemaError as err: print(f"Validation failed:\n{err.failure_cases}") # failure_cases is a DataFrame showing exactly which rows/columns failed ``` ### Custom Checks ```python # schemas/custom_checks.py — Reusable validation checks import pandas as pd import pandera.pandas as pa import pandera.extensions as extensions from pandera.typing import Series @extensions.register_check_method(statistics=["threshold"]) def no_outliers_iqr(series: pd.Series, *, threshold: float = 1.5) -> pd.Series: """Flag values outside the IQR fence (1.5 = standard, 3.0 = extreme only).""" q1 = series.quantile(0.25) q3 = series.quantile(0.75) iqr = q3 - q1 lower = q1 - threshold * iqr upper = q3 + threshold * iqr return (series >= lower) & (series <= upper) # Usage in schema class MetricsSchema(pa.DataFrameModel): revenue: Series[float] = pa.Field(no_outliers_iqr={"threshold": 3.0}) latency_ms: Series[float] = pa.Field(no_outliers_iqr={"threshold": 1.5}) ``` ### Polars Support ```python # schemas/polars_schema.py — Validate Polars DataFrames import pandera.polars as pa import polars as pl class UserSchema(pa.DataFrameModel): user_id: int = pa.Field(unique=True, gt=0) email: str = pa.Field(str_matches=r"^[\w.-]+@[\w.-]+\.\w+$") plan: str = pa.Field(isin=["free", "pro", "enterprise"]) mrr: float = pa.Field(ge=0) # Validate a Polars DataFrame df = pl.read_parquet("users.parquet") validated = UserSchema.validate(df) # Returns validated Polars DataFrame ``` ### Integration with Pytest ```python # tests/test_data_quality.py — Data quality tests import pandas as pd import pytest import pandera.pandas as pa from schemas.orders import OrderSchema def test_orders_schema_on_sample_data(): """Verify the schema accepts known-good data.""" good_data = pd.DataFrame({ "order_id": ["ORD-00000001", "ORD-00000002"], "customer_id": ["cust-1", "cust-2"], "amount": [29.99, 149.00], "status": ["completed", "pending"], "currency": ["USD", "EUR"], "created_at": pd.to_datetime(["2026-01-01", "2026-01-02"]), "shipped_at": pd.to_datetime(["2026-01-03", pd.NaT]), "items_count": [2, 5], }) validated = OrderSchema.validate(good_data) assert len(validated) == 2 def test_orders_schema_rejects_negative_amount(): """Schema must reject orders with negative amounts.""" bad_data = pd.DataFrame({ "order_id": ["ORD-00000001"], "customer_id": ["cust-1"], "amount": [-10.00], "status": ["completed"], "currency": ["USD"], "created_at": pd.to_datetime(["2026-01-01"]), "shipped_at": pd.to_datetime(["2026-01-02"]), "items_count": [1], }) with pytest.raises(pa.errors.SchemaError): OrderSchema.validate(bad_data) ``` ## Installation ```bash pip install "pandera[pandas]" # pandas DataFrames (import pandera.pandas) pip install "pandera[polars]" # Polars DataFrames (import pandera.polars) pip install "pandera[io]" # to_yaml / from_yaml schema serialization pip install "pandera[hypotheses]" # hypothesis checks (needs scipy) pip install "pandera[strategies]" # data synthesis with hypothesis ``` The plain `pip install pandera` no longer pulls in pandas, so install the extra for the backend you use. ## Examples ### Example 1: Report every bad row in a CSV before loading it **User request:** "Our nightly orders.csv sometimes has malformed IDs and negative amounts. Validate it and tell me every failing row, not just the first." By default Pandera stops at the first failure. Pass `lazy=True` to collect all of them; they raise `pa.errors.SchemaErrors` (plural) with a `failure_cases` DataFrame. ```python import pandas as pd import pandera.pandas as pa from schemas.orders import OrderSchema df = pd.read_csv("data/orders_2026_03.csv", parse_dates=["created_at", "shipped_at"]) try: OrderSchema.validate(df, lazy=True) except pa.errors.SchemaErrors as err: print(err.failure_cases[["index", "column", "check", "failure_case"]]) ``` Result: one row per violation, for example `order_id | str_matches('^ORD-\\d{8}$') | ORD-0002` and `amount | greater_than_or_equal_to(0.01) | -1.0`. Without `lazy=True` only the first violation raises `pa.errors.SchemaError`. ### Example 2: Data contract for a Polars users table in CI **User request:** "Add a check to our pipeline that fails if users.parquet has duplicate ids or an unknown plan." ```python import pandera.polars as pa import polars as pl class UserSchema(pa.DataFrameModel): user_id: int = pa.Field(unique=True, gt=0) plan: str = pa.Field(isin=["free", "pro", "enterprise"]) UserSchema.validate(pl.read_parquet("users.parquet"), lazy=True) ``` A valid frame is returned unchanged. A frame with `user_id` 1 twice and `plan` "x" raises `SchemaErrors`; `failure_cases` is a Polars DataFrame listing `field_uniqueness` failures for `user_id` and the `isin` failure for `plan`. Without `lazy=True` Polars raises `SchemaError` at the first failure. ## Guidelines 1. **Schema at the boundary** — Validate data at ingestion points (file loads, API responses, database queries); don't trust upstream 2. **Use DataFrameModel over raw SchemaModel** — Class-based schemas give you type hints, IDE autocomplete, and cleaner code 3. **Strict mode** — Enable `strict = True` to reject unexpected columns; prevents schema drift 4. **Coercion for robustness** — Enable `coerce = True` to auto-convert types (string "123" → int 123) before validation 5. **Cross-column checks** — Use `@dataframe_check` for rules that span multiple columns (shipped_at > created_at) 6. **Test your schemas** — Write pytest tests with known-good and known-bad data to verify schema behavior 7. **Descriptive error messages** — Pandera's `failure_cases` DataFrame shows exactly which rows and columns failed and why 8. **Schema evolution** — When requirements change, update the schema first; let validation catch all affected data 9. **Pick the right import** — `import pandera.pandas as pa` for pandas, `import pandera.polars as pa` for Polars; the old top-level `import pandera as pa` still works for pandas, but the submodule is what the documentation uses 10. **Lazy validation for reports** — use `lazy=True` when you want all failures; leave it off for fail-fast pipelines 11. **Cost** — validation runs on every call; for large frames validate at boundaries, or use `head`, `tail` or `sample` arguments on `validate` for a cheaper spot check --- Source attribution: TerminalSkills Original project: https://github.com/TerminalSkills/skills/blob/3bfbf7f76459d89422eddc0e5f572ad0e6c393c9/skills/pandera/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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