### DataVar Initialization Example

Source: https://github.com/unionai-oss/pandera/blob/main/specs/xarray.md

Example of initializing a DataVar with various parameters.

```APIDOC
```python
from pandera.api.pandera.schema_components import DataVar

# Example DataVar definition
temperature_var = DataVar(
    dtype="float64",
    dims=("time", "lat", "lon"),
    required=True,
    alias="temp",
    checks=[
        # Add checks here, e.g., pandera.Check.greater_than(0)
    ],
    description="Temperature data variable"
)

# Example of an optional variable with a default
pressure_var = DataVar(
    dtype="float32",
    dims=("time", "lat", "lon"),
    required=False,
    default=0.0,
    description="Pressure data variable (optional)"
)

# Example using regex for matching variable names
wind_vars = DataVar(
    regex=True,
    dtype="float32",
    dims=("time", "lat", "lon"),
    description="Wind components (e.g., u_wind, v_wind)"
)
```
```

--------------------------------

### Install Pandera strategies

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/xarray_guide/hypothesis_strategies.md

Install the necessary dependencies to use Hypothesis strategies with Pandera.

```bash
pip install 'pandera[strategies]'
```

--------------------------------

### Install Pandera

Source: https://github.com/unionai-oss/pandera/blob/main/README.md

Installation commands for Pandera using various package managers.

```bash
pip install 'pandera[pandas]'
```

```bash
uv pip install 'pandera[pandas]'
```

```bash
conda install -c conda-forge pandera-pandas
```

--------------------------------

### Install Pandera with Narwhals support

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/narwhals_backend.md

Install the required extras for Narwhals and the specific backend being used.

```bash
pip install 'pandera[narwhals,polars]'   # Polars
pip install 'pandera[narwhals,ibis]'     # Ibis
pip install 'pandera[narwhals,pyspark]'  # PySpark SQL
```

--------------------------------

### Install Pandera extras

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/index.md

Install additional functionality and library support using pip or conda.

```bash
pip install 'pandera[hypotheses]'   # hypothesis checks
pip install 'pandera[io]'           # yaml/script schema io utilities
pip install 'pandera[frictionless]' # frictionless schema serialization/deserialization
pip install 'pandera[strategies]'   # data synthesis strategies
pip install 'pandera[mypy]'         # enable static type-linting of pandas
pip install 'pandera[fastapi]'      # fastapi integration
pip install 'pandera[dask]'         # validate dask dataframes
pip install 'pandera[pyspark]'      # validate pyspark dataframes
pip install 'pandera[modin]'        # validate modin dataframes
pip install 'pandera[modin-ray]'    # validate modin dataframes with ray
pip install 'pandera[modin-dask]'   # validate modin dataframes with dask
pip install 'pandera[geopandas]'    # validate geopandas geodataframes
pip install 'pandera[polars]'       # validate polars dataframes
pip install 'pandera[ibis]'         # validate ibis tables
pip install 'pandera[xarray]'       # validate xarray data structures
pip install 'pandera[narwhals]'     # use the Narwhals-powered backend
```

```bash
conda install -c conda-forge pandera-hypotheses   # hypothesis checks
conda install -c conda-forge pandera-io           # yaml/script schema io utilities
conda install -c conda-forge pandera-frictionless # frictionless schema serialization/deserialization
conda install -c conda-forge pandera-strategies   # data synthesis strategies
conda install -c conda-forge pandera-mypy         # enable static type-linting of pandas
conda install -c conda-forge pandera-fastapi      # fastapi integration
conda install -c conda-forge pandera-dask         # validate dask dataframes
conda install -c conda-forge pandera-pyspark      # validate pyspark dataframes
conda install -c conda-forge pandera-modin        # validate modin dataframes
conda install -c conda-forge pandera-modin-ray    # validate modin dataframes with ray
conda install -c conda-forge pandera-modin-dask   # validate modin dataframes with dask
conda install -c conda-forge pandera-geopandas    # validate geopandas geodataframes
conda install -c conda-forge pandera-polars       # validate polars dataframes
conda install -c conda-forge pandera-ibis         # validate ibis tables
conda install -c conda-forge pandera-narwhals     # use the Narwhals-powered backend
```

--------------------------------

### Install Pandera Mypy support

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/mypy_integration.md

Install the necessary extra dependencies to enable Mypy integration.

```bash
pip install pandera[mypy]
```

--------------------------------

### Install Fugue with Spark and Dask

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/fugue.md

Installs the Fugue library with support for Spark and Dask. This command ensures that PySpark is also installed when the 'spark' extra is selected.

```bash
pip install 'fugue[spark]'
```

--------------------------------

### Install Pandera with Polars support

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/polars.md

Install the necessary dependencies to use Pandera with Polars.

```bash
pip install 'pandera[polars]'
```

--------------------------------

### Install and enable Narwhals-powered backend

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/ibis.md

Install the Narwhals extra and enable the backend via environment variable or configuration function.

```bash
pip install 'pandera[ibis,narwhals]'
export PANDERA_USE_NARWHALS_BACKEND=True
```

```python
import pandera.ibis as pa

pa.set_config(use_narwhals_backend=True)
```

--------------------------------

### Install Pandera with Ibis support

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/ibis.md

Install the necessary packages to use Pandera with Ibis and a specific backend.

```bash
pip install 'pandera[ibis]' 'ibis-framework[duckdb]'
```

--------------------------------

### Install Pandera with Modin Support

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/modin.md

Installs pandera with the necessary extras for Modin integration. This includes options for installing with both Dask and Ray backends, or specific backends.

```bash
pip install 'pandera[modin]'       # installs both ray and dask backends
pip install 'pandera[modin-ray]'   # only ray backend
pip install 'pandera[modin-dask]'  # only dask backend
```

--------------------------------

### Install Pandera with Dask support

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/dask.md

Command to install the necessary dependencies for using Pandera with Dask.

```bash
pip install 'pandera[dask]'
```

--------------------------------

### Install Narwhals backend for PySpark

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/pyspark_sql.md

Install the required extras and set the environment variable to enable the lazy Narwhals backend.

```bash
pip install 'pandera[pyspark,narwhals]'
export PANDERA_USE_NARWHALS_BACKEND=True
```

--------------------------------

### Install Pandera

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/notebooks/try_pandera.ipynb

Installs the Pandera library using pip. This is a prerequisite for using Pandera in your Python environment.

```python
!pip install pandera
```

--------------------------------

### Install Pandera with Xarray Support

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/xarray_guide/index.md

Install pandera with the xarray extra to enable xarray data validation.

```bash
pip install 'pandera[xarray]'
```

--------------------------------

### Install Pandera with pandas support

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/index.md

Install the core Pandera package with pandas integration using common package managers.

```bash
pip install 'pandera[pandas]'
```

```bash
uv pip install 'pandera[pandas]'
```

```bash
conda install -c conda-forge pandera-pandas
```

--------------------------------

### Install Pandera with GeoPandas support

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/geopandas.md

Install the necessary dependencies to enable GeoPandas validation features.

```bash
pip install 'pandera[geopandas]'
```

--------------------------------

### Install pandera with PySpark support

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/pyspark.md

Install the pandera library with the necessary extras for PySpark integration. This command ensures all dependencies for using pandera with PySpark DataFrames are met.

```bash
pip install 'pandera[pyspark]'
```

--------------------------------

### Create a DataFrame for Validation

Source: https://github.com/unionai-oss/pandera/blob/main/README.md

Initial setup of a pandas DataFrame to be validated by Pandera.

```python
import pandas as pd
import pandera.pandas as pa

# data to validate
df = pd.DataFrame({
    "column1": [1, 2, 3],
    "column2": [1.1, 1.2, 1.3],
    "column3": ["a", "b", "c"],
})
```

--------------------------------

### Strategies and Examples from DataFrame Models

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/data_synthesis_strategies.md

Generate examples and strategies from DataFrame models.

```python
from pandera.typing import Series, DataFrame

class InSchema(pa.DataFrameModel):
    column1: Series[int] = pa.Field(eq=10)
    column2: Series[float] = pa.Field(eq=0.25)
    column3: Series[str] = pa.Field(eq="foo")

class OutSchema(InSchema):
    column4: Series[float]

@pa.check_types
def processing_fn(df: DataFrame[InSchema]) -> DataFrame[OutSchema]:
    return df.assign(column4=df.column1 * df.column2)

@hypothesis.given(InSchema.strategy(size=5))
def test_processing_fn(dataframe):
    processing_fn(dataframe)
```

--------------------------------

### Importing Frictionless Data Schema

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/frictionless.md

Converts a Frictionless Data schema into a Pandera schema. Requires the 'io' extension to be installed.

```python
.. autofunction:: pandera.io.pandas_io.from_frictionless_schema
```

--------------------------------

### Enable Narwhals backend via environment variable

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/narwhals_backend.md

Set the environment variable before starting the Python process to enable the backend.

```bash
export PANDERA_USE_NARWHALS_BACKEND=True
python your_script.py
```

--------------------------------

### Basic Usage

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/data_synthesis_strategies.md

Generate example data from a pandera schema.

```python
import pandera.pandas as pa

schema = pa.DataFrameSchema(
    {
        "column1": pa.Column(int, pa.Check.eq(10)),
        "column2": pa.Column(float, pa.Check.eq(0.25)),
        "column3": pa.Column(str, pa.Check.eq("foo")),
    }
)
schema.example(size=3)
```

--------------------------------

### Implement Path-Based DataTreeSchema

Source: https://github.com/unionai-oss/pandera/blob/main/specs/xarray.md

Example of defining a schema for a hierarchical DataTree using path-based child definitions.

```python
schema = pa.DataTreeSchema(
    dataset=pa.DatasetSchema(attrs={"conventions": "CF-1.8"}),
    children={
        "surface": pa.DatasetSchema(
            data_vars={
                "temperature": pa.DataVar(
                    dtype=np.float64,
                    dims=("time", "lat", "lon"),
                ),
            },
        ),
        "surface/diagnostics": pa.DatasetSchema(
            data_vars={"rmse": pa.DataVar(dtype=np.float64)},
        ),
    },
    strict=True,
)
```

--------------------------------

### Stacked constraints example

Source: https://github.com/unionai-oss/pandera/blob/main/specs/optimized-strategies.md

Demonstrates how multiple user-defined constraints can be stacked on the same column, showing the intersection of bounds and the effect of `post_merge_hook`.

```python
pa.Column(int, checks=[
    Check.gt(0),                               # min_value=0,    excl
    Check.lt(100_000),                         # max_value=100k, excl
    Check.divisible_by(divisor=5),             # residual + (v2: isin)
    Check.percentile_clipped(ref_low=50, ref_high=90_000),  # min=50, max=90k
])
```

--------------------------------

### Registering and Using Statistic DataFrame Checks

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/extensions.md

This example shows how to register custom check methods for DataFrame-level validation and then use them within a DataFrameModel's Config.

```python
import pandera.pandas as pa
import pandera.extensions as extensions
import numpy as np
import pandas as pd


@extensions.register_check_method()
def is_small(df):
    return sum(df.shape) < 1000


@extensions.register_check_method(statistics=["fraction"])
def total_missing_fraction_less_than(df, *, fraction: float):
    return (1 - df.count().sum().item() / df.apply(len).sum().item()) < fraction


@extensions.register_check_method(statistics=["col_a", "col_b"])
def col_mean_a_greater_than_b(df, *, col_a: str, col_b: str):
    return df[col_a].mean() > df[col_b].mean()


from pandera.typing import Series


class Schema(pa.DataFrameModel):
    col1: Series[float] = pa.Field(nullable=True, ignore_na=False)
    col2: Series[float] = pa.Field(nullable=True, ignore_na=False)

    class Config:
        is_small = ()
        total_missing_fraction_less_than = 0.6
        col_mean_a_greater_than_b = {"col_a": "col2", "col_b": "col1"}


data = pd.DataFrame({
    "col1": [float('nan')] * 3 + [0.5, 0.3, 0.1],
    "col2": np.arange(6.),
})

Schema.validate(data)
```

--------------------------------

### Initialize Development Environment

Source: https://github.com/unionai-oss/pandera/blob/main/AGENTS.md

Commands to set up the local development environment using uv.

```bash
# Install uv and sync all extras
make setup

# macOS (uses polars-lts-cpu)
make setup-macos
```

--------------------------------

### Build Documentation

Source: https://github.com/unionai-oss/pandera/blob/main/AGENTS.md

Commands to generate project documentation using Sphinx.

```bash
# Full build with doctests (cleans first)
make docs

# Quick build (no clean, no -W flag)
make quick-docs

# Via nox
nox -db uv -s docs
```

--------------------------------

### Publish and Preview ASV Benchmark Results

Source: https://github.com/unionai-oss/pandera/blob/main/asv_bench/README.md

Builds the HTML documentation for the benchmark results and provides a local preview. This is a crucial step before publishing the results to a website or repository. It uses the specified ASV configuration file.

```bash
asv publish --config asv_bench/asv.conf.json
asv preview --config asv_bench/asv.conf.json
```

--------------------------------

### Enable Narwhals backend via configuration

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/index.md

Use the pandera configuration API to enable the Narwhals backend at runtime.

```python
import pandera

pandera.set_config(use_narwhals_backend=True)
```

--------------------------------

### Numeric Strategy Example

Source: https://github.com/unionai-oss/pandera/blob/main/specs/optimized-strategies.md

Microbenchmark example for generating rows with a combination of numeric checks.

```python
Check.gt(0) & Check.lt(1e10) & Check.notin([-100,-10,0])
```

--------------------------------

### Enable Narwhals backend via configuration

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/pyspark_sql.md

Programmatically enable the Narwhals backend using the Pandera configuration API.

```python
import pandera.pyspark as pa

pa.set_config(use_narwhals_backend=True)
```

--------------------------------

### ConstraintConflictError Message Example

Source: https://github.com/unionai-oss/pandera/blob/main/specs/optimized-strategies.md

Example of the error message format when a ConstraintConflictError is caught and re-raised as SchemaDefinitionError.

```text
Cannot construct a data-generation strategy for column 'x' with checks
[gt(10), lt(5)]: constraints are jointly unsatisfiable
(min_value=10 > max_value=5).
```

--------------------------------

### SchemaDefinitionError example

Source: https://github.com/unionai-oss/pandera/blob/main/specs/optimized-strategies.md

An example of a SchemaDefinitionError raised when checks are jointly unsatisfiable, indicating a conflict between constraints.

```python
SchemaDefinitionError: Cannot construct a data-generation strategy
for column 'amount_cents' with checks
[gt(0), lt(100000), divisible_by(divisor=5),
 percentile_clipped(ref_low=200000, ref_high=300000)]:
constraints are jointly unsatisfiable
(min_value=200000 > max_value=100000;
 contributed by 'percentile_clipped' and 'lt').
```

--------------------------------

### ASV Reporting Example

Source: https://github.com/unionai-oss/pandera/blob/main/specs/optimized-strategies.md

Example markdown table generated by the ASV reporting CLI for comparing benchmarks.

```markdown
| Scenario | Metric | Baseline | New | Δ | Pass? |
|---|---|---|---|---|---|
| AggregatedNumericChecks(n=8) | time_example_size_100 | 4.20 s | 0.62 s | 6.8× | ✓ |
| AggregatedNumericChecks(n=8) | filter_node_count | 10 | 1 | -9 | ✓ |
| StringRegexMerging | time_example_size_100 | 1.10 s | 0.18 s | 6.1× | ✓ |
| StringRegexMerging | regex_filter_node_count | 3 | 0 | -3 | ✓ |
| ... | | | | | |
```

--------------------------------

### Run Linting and Formatting Hooks

Source: https://github.com/unionai-oss/pandera/blob/main/AGENTS.md

Manually trigger pre-commit hooks to enforce code quality standards.

```bash
prek run --all-files
```

--------------------------------

### Example schema with custom divisible_by check

Source: https://github.com/unionai-oss/pandera/blob/main/specs/optimized-strategies.md

An example of how to define a pandera schema with a custom 'divisible_by_5' check, demonstrating the use of Check.gt, Check.lt, and a lambda function for the divisibility constraint.

```python
import pandera.pandas as pa
from pandera.api.checks import Check

schema = pa.DataFrameSchema({
    "amount_cents": pa.Column(int, checks=[
        Check.gt(0),
        Check.lt(100_000),
        Check(
            lambda s: (s % 5) == 0,
            name="divisible_by_5",
            error="must be a multiple of 5 cents",
        ),
    ]),
})

schema.example(size=100)   # slow; can hit Unsatisfiable

```

--------------------------------

### Strategy for in_between check

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/extensions.md

The strategy for the is_between function example.

```python
def in_between_strategy(
    pandera_dtype: pa.DataType,
    strategy: Optional[st.SearchStrategy] = None,
    *,
    min_value,
    max_value
):
    if strategy is None:
        return st.pandas_dtype_strategy(
            pandera_dtype,
            min_value=min_value,
            max_value=max_value,
            exclude_min=False,
            exclude_max=False,
        )
    return strategy.filter(lambda x: min_value <= x <= max_value)
```

--------------------------------

### Enable Narwhals backend programmatically

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/narwhals_backend.md

Use set_config to toggle the backend within a running Python process.

```python
import pandera

pandera.set_config(use_narwhals_backend=True)

import pandera.polars as pa
```

--------------------------------

### Inline Custom Checks

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/extensions.md

Examples of inline custom checks for pandera.

```python
import pandera.pandas as pa

# checks elements in a column/dataframe
element_wise_check = pa.Check(lambda x: x < 0, element_wise=True)

# applies the check function to a dataframe/series
vectorized_check = pa.Check(lambda series_or_df: series_or_df < 0)
```

--------------------------------

### Convert model to schema and validate

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/xarray_guide/data_models.md

Demonstrates converting a model to a schema object and performing validation.

```python
schema = Temperature.to_schema()
print(type(schema))

Temperature.validate(da)
```

--------------------------------

### Using type-checking decorators

Source: https://github.com/unionai-oss/pandera/blob/main/specs/xarray.md

Example of using the @check_types decorator with xarray type annotations.

```python
from pandera.typing.xarray import DataArray, Dataset, Coordinate

@pa.check_types
def process(
    ds: Dataset[ClimateData],
) -> Dataset[ClimateData]:
    ...
```

--------------------------------

### Update Pandera Imports

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/index.md

Demonstrates the transition from the deprecated top-level module to the recommended pandera.pandas module.

```python
import pandera as pa

schema = pa.DataFrameSchema({"col": pa.Column(str)})
```

```python
import pandera.pandas as pa
```

--------------------------------

### Execute Test Suites

Source: https://github.com/unionai-oss/pandera/blob/main/AGENTS.md

Commands for running backend-specific tests, coverage reports, and parameterized tests via nox.

```bash
# Run core + pandas tests
pytest tests/core tests/pandas

# Run a specific backend's tests
pytest tests/polars/
pytest tests/pyspark/
pytest tests/ibis/

# Run all tests with coverage
pytest --cov=pandera --cov-report=term-missing tests/

# Run via nox (parameterized across Python/pandas/pydantic/polars versions)
nox -db uv -s tests

# Run a specific nox session
nox -db uv -s "tests(extra='polars', pandas=None, pydantic=None, polars='1.33.1')"
```

--------------------------------

### Registering Custom Check Methods

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/extensions.md

Example of registering a custom check method 'is_between' to pandera.

```python
import pandera.pandas as pa
import pandera.extensions as extensions
import pandas as pd

@extensions.register_check_method(statistics=["min_value", "max_value"])
def is_between(pandas_obj, *, min_value, max_value):
    return (min_value <= pandas_obj) & (pandas_obj <= max_value)

schema = pa.DataFrameSchema({
    "col": pa.Column(int, pa.Check.is_between(min_value=1, max_value=10))
})

data = pd.DataFrame({"col": [1, 5, 10]})
schema.validate(data)
```

--------------------------------

### Implement Custom Converters with Callables

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/data_format_conversion.md

Illustrates how to use custom functions for serialization by passing callables to the 'to_format' and 'to_format_buffer' configuration options.

```python
import tempfile

def custom_to_pickle(data, *args, **kwargs):
    return data.to_pickle(*args, **kwargs)

def custom_to_pickle_buffer():
    return tempfile.NamedTemporaryFile()

class OutSchemaPickleCallable(OutSchema):
    class Config:
        to_format = custom_to_pickle
        to_format_buffer = custom_to_pickle_buffer
```

--------------------------------

### Validate DataArray attributes via regex

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/xarray_guide/data_array_schema.md

Use regex patterns starting with ^ to validate attribute values.

```python
schema = pa.DataArraySchema(
    attrs={"units": "^(K|degC|degF)$"},
)

da_units = xr.DataArray(
    np.ones(3), dims="x",
    attrs={"units": "K"},
)
schema.validate(da_units)
```

```python
da_bad_units = xr.DataArray(
    np.ones(3), dims="x",
    attrs={"units": "meters"},
)

try:
    schema.validate(da_bad_units)
except pa.errors.SchemaError as exc:
    print(exc)
```

--------------------------------

### Configuring Data Format Conversion

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/data_format_conversion.md

How to use the Config class to define input and output formats for Pandera schemas.

```APIDOC
## Data Format Configuration

### Description
Configure a schema to automatically handle data serialization (to_format) and deserialization (from_format) when used with the @check_types decorator.

### Parameters
#### Config Options
- **from_format** (str or callable) - Optional - Specifies the format or function to read input data (e.g., 'parquet', 'csv', or custom callable).
- **to_format** (str or callable) - Optional - Specifies the format or function to write output data (e.g., 'dict', 'json', or custom callable).
- **to_format_kwargs** (dict) - Optional - Keyword arguments passed to the pandas serialization method.
- **to_format_buffer** (callable) - Optional - A function that returns a buffer or file handle for writing data.

### Request Example
class InSchemaParquet(pa.DataFrameModel):
    class Config:
        from_format = "parquet"

class OutSchemaDict(pa.DataFrameModel):
    class Config:
        to_format = "dict"
        to_format_kwargs = {"orient": "records"}

### Supported Formats
- dict
- csv
- json
- feather
- parquet
- pickle
- Custom Callable
```

--------------------------------

### Enable Narwhals backend

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/configuration.md

Enable the Narwhals-powered backend using either environment variables or the Python configuration API.

```bash
export PANDERA_USE_NARWHALS_BACKEND=True
```

```python
import pandera

pandera.set_config(use_narwhals_backend=True)
```

--------------------------------

### Using DataTree with @check_types

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/xarray_guide/data_tree.md

Use DataTree[Model] from pandera.typing.xarray with the @check_types decorator. Ensure pandera and xarray are installed.

```python
from pandera.typing.xarray import DataTree

@pa.check_types
def process_tree(tree: DataTree[ClimateTree]) -> DataTree[ClimateTree]:
    return tree

process_tree(dt)
```

--------------------------------

### GET /pandera/strategies/pandas_strategies

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/reference/strategies.rst

Access the pandas-specific data synthesis strategies for generating synthetic dataframes based on schema definitions.

```APIDOC
## GET /pandera/strategies/pandas_strategies

### Description
Retrieves the collection of strategies used for generating synthetic pandas objects. These strategies are typically used in conjunction with schema definitions to create mock data for testing pipelines.

### Method
GET

### Endpoint
/pandera/strategies/pandas_strategies

### Parameters
None

### Request Example
GET /pandera/strategies/pandas_strategies

### Response
#### Success Response (200)
- **strategies** (object) - A dictionary mapping data types or schema components to their respective synthesis strategies.

#### Response Example
{
  "strategies": {
    "int64": "<strategy_function>",
    "float64": "<strategy_function>",
    "string": "<strategy_function>"
  }
}
```

--------------------------------

### Lazy Backend Registration

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/narwhals_backend.md

Demonstrates setting the Narwhals backend configuration before schema construction to trigger lazy registration.

```python
import pandera.polars as pa

# CONFIG.use_narwhals_backend is read here — not at import time above
pa.config.set_config(use_narwhals_backend=True)

schema = pa.DataFrameSchema({"name": pa.Column(str)})  # narwhals backends registered
schema.validate(df)
```

--------------------------------

### Report DataTree Validation Errors

Source: https://github.com/unionai-oss/pandera/blob/main/specs/xarray.md

Example of a SchemaError indicating the specific path to a failing node within a DataTree structure.

```python
SchemaError(
    "DataTreeSchema failed at path '/surface/diagnostics': "
    "data variable 'rmse' not found in Dataset"
)
```

--------------------------------

### Run All Benchmarks with ASV

Source: https://github.com/unionai-oss/pandera/blob/main/asv_bench/README.md

Executes all defined benchmarks using Airspeed Velocity. This command requires both the pandera and pandera-asv-logs repositories to be checked out in the same parent directory. It utilizes a specific configuration file for the benchmark run.

```bash
asv run ALL --config asv_bench/asv.conf.json
```

--------------------------------

### DatasetSchema Initialization

Source: https://github.com/unionai-oss/pandera/blob/main/specs/xarray.md

Demonstrates the initialization of a DatasetSchema with various parameters to define validation rules for an xarray.Dataset.

```APIDOC
## DatasetSchema Initialization

### Description
Initializes a `DatasetSchema` object to define validation rules for an `xr.Dataset`.

### Method
`__init__`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **data_vars** (`dict[str, DataVar | None] | None`) - Required/Optional - A dictionary mapping data variable names to their `DataVar` specifications. Keys can also be regex patterns. `None` indicates required presence without value-level validation.
- **coords** (`dict[str, Coordinate] | list[str] | None`) - Required/Optional - Dataset-level coordinate schemas or a list of coordinate names to check for existence.
- **dims** (`tuple[str, ...] | None`) - Required/Optional - Expected dataset-level dimension names. If `ordered_dims` is `False`, only the set of names is checked.
- **ordered_dims** (`bool`) - Required/Optional - If `True` (default), checks dimension names in order. If `False`, only checks for the presence of the set of names.
- **sizes** (`dict[str, int | None] | None`) - Required/Optional - Expected dataset-level dimension sizes as a name-to-length mapping.
- **attrs** (`dict[str, Any] | type[BaseModel] | None`) - Required/Optional - Expected dataset-level attributes. Can be a dictionary with literal values, regex patterns (strings starting with `^`), callables, or a pydantic `BaseModel` class.
- **checks** (`Check | list[Check] | None`) - Required/Optional - Dataset-wide checks that receive the full `xr.Dataset`.
- **parsers** (`Parser | list[Parser] | None`) - Required/Optional - Parsers to apply to the dataset.
- **strict** (`bool | "filter"`) - Required/Optional - If `True`, fail on unexpected data variables. If `"filter"`, drop them.
- **strict_coords** (`bool`) - Required/Optional - If `True`, fail on unexpected dataset-level coordinate keys.
- **strict_attrs** (`bool`) - Required/Optional - If `True`, fail on unexpected attributes.
- **name** (`str | None`) - Required/Optional - The name of the schema.
- **title** (`str | None`) - Required/Optional - The title of the schema.
- **description** (`str | None`) - Required/Optional - The description of the schema.
- **metadata** (`dict | None`) - Required/Optional - Additional metadata for the schema.

### Request Example
```python
import pandera as pa
import xarray as xr
import numpy as np

schema = pa.DatasetSchema(
    data_vars={
        "temperature": pa.DataVar(
            dtype=np.float64,
            dims=("time", "lat", "lon"),
            checks=pa.Check.in_range(150, 350),
        ),
        "precipitation": pa.DataVar(
            dtype=np.float64,
            dims=("time", "lat", "lon"),
            checks=pa.Check.ge(0),
        ),
        "uncertainty": pa.DataVar(
            dtype=np.float64,
            dims=("time", "lat", "lon"),
            required=False,
        ),
    },
    coords={
        "time": pa.Coordinate(dtype="datetime64[ns]"),
        "lat": pa.Coordinate(dtype=np.float64),
        "lon": pa.Coordinate(dtype=np.float64),
    },
    dims=("time", "lat", "lon"),
    sizes={
        "time": 100,
        "lat": 10,
        "lon": 20,
    },
    attrs={
        "source": "met_office",
        "history": lambda v: isinstance(v, str) and v.startswith("2023-")
    },
    checks=pa.Check(lambda ds: ds.dims["time"] == ds.coords["time"].size, element_wise=False),
    strict=True,
    strict_coords=True,
    strict_attrs=True,
)
```

### Response
None (This is an initializer)
```

--------------------------------

### Compiled strategy example

Source: https://github.com/unionai-oss/pandera/blob/main/specs/optimized-strategies.md

The adapter above compiles to a strategy that uses `st.sampled_from` for efficiency.

```python
strat = pandas_dtype_strategy(
    pa.Int(),
    st.sampled_from(sorted(range(5, 100_000, 5))),
)
# zero filters; every draw is valid by construction.
```

--------------------------------

### Configure Mypy plugin

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/mypy_integration.md

Enable the Pandera plugin in your Mypy configuration file.

```ini
[mypy]
plugins = pandera.mypy
```

--------------------------------

### Validate xarray Dataset with Coordinate Schema

Source: https://github.com/unionai-oss/pandera/blob/main/specs/xarray.md

Example of defining coordinate constraints using pa.Coordinate and validating an xarray Dataset.

```python
"lat": pa.Coordinate(
            dtype=np.float64,
            checks=pa.Check.in_range(-90, 90),
        ),
        "lon": pa.Coordinate(
            dtype=np.float64,
            checks=pa.Check.in_range(-180, 180),
        ),
    },
)

ds = xr.Dataset({...})
validated = schema.validate(ds)
```

--------------------------------

### Define Schemas with Format Configuration

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/data_format_conversion.md

Demonstrates how to subclass DataFrameModel to specify input and output formats using the Config class. This allows the @check_types decorator to handle serialization automatically.

```python
import pandera.pandas as pa
from pandera.typing import DataFrame, Series

class InSchema(pa.DataFrameModel):
    str_col: Series[str] = pa.Field(unique=True, isin=[*"abcd"])
    int_col: Series[int]

class OutSchema(InSchema):
    float_col: pa.typing.Series[float]

class InSchemaParquet(InSchema):
    class Config:
        from_format = "parquet"

class OutSchemaDict(OutSchema):
    class Config:
        to_format = "dict"
        to_format_kwargs = {"orient": "records"}
```

--------------------------------

### Defining the `coerce_value` Method

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/dtypes.md

Details the importance of the `coerce_value` method for individual value coercion and provides an example from the `pandera.engines.pandas_engine.Category` class.

```APIDOC
## Defining the `coerce_value` Method

### Description
This section explains the necessity of implementing the `coerce_value` method within custom Pandera data types. This method is crucial for Pandera to correctly handle and report errors during the coercion of individual values to the specified data type. It provides an example from the `pandera.engines.pandas_engine.Category` class.

### Method
Implementation of the `coerce_value` method within a custom `DataType` class.

### Endpoint
N/A (This is a library usage example)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```python
from typing import Any

# Assume Category class inherits from a base Pandera DataType
# class Category(pandera.dtypes.DataType):
#     def __init__(self, categories, ordered=False):
#         self.categories = categories
#         self.ordered = ordered
#         self.type = 'category' # Example attribute

#     def coerce_value(self, value: Any) -> Any:
#         """Coerce an value to a particular type."""
#         if value not in self.categories:  # type: ignore
#             raise TypeError(
#                 f"value {value} cannot be coerced to type {self.type}"
#             )
#         return value
```

### Response
#### Success Response (200)
Provides the source code for the `coerce_value` method in `pandera.engines.pandas_engine.Category`, demonstrating how to check if a value exists within defined categories before returning it.

#### Response Example
```python
def coerce_value(self, value: Any) -> Any:
    """Coerce an value to a particular type."""
    if value not in self.categories:  # type: ignore
        raise TypeError(
            f"value {value} cannot be coerced to type {self.type}"
        )
    return value
```
```

--------------------------------

### Runtime Backend Re-registration

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/narwhals_backend.md

Shows how updating the configuration after schema validation triggers an automatic re-registration of the backend.

```python
import pandera.polars as pa

schema = pa.DataFrameSchema({"age": pa.Column(int)})
schema.validate(df)  # uses native Polars backend (default)

pa.config.set_config(use_narwhals_backend=True)
# UserWarning: Re-registered pandera backends after use_narwhals_backend changed.

schema.validate(df)  # same schema object, now validated by the Narwhals backend
```

--------------------------------

### Using Parsers for Data Preprocessing

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/xarray_guide/checks_and_parsers.md

Demonstrates how to use Parser objects to fill missing values and rename DataArrays before schema validation. This ensures data is in the desired format prior to checks.

```python
schema = pa.DataArraySchema(
    parsers=[
        pa.Parser(lambda da: da.fillna(0)),
        pa.Parser(lambda da: da.rename("cleaned")),
    ],
    checks=pa.Check(lambda da: float(da.min()) >= 0),
)

da_messy = xr.DataArray([1.0, np.nan, 3.0], dims="x", name="raw")
validated = schema.validate(da_messy)
print(f"name: {validated.name}, values: {validated.values}")
```

--------------------------------

### Schema with unsatisfiable checks

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/data_synthesis_strategies.md

Example demonstrating an Unsatisfiable exception raised due to nonsensical constraints.

```python
import hypothesis

schema_multiple_checks = pa.DataFrameSchema({
    "column1": pa.Column(
        float, checks=[
            # nonsensical constraints
            pa.Check.gt(0),
            pa.Check.lt(-10),
        ]
    )
})

try:
    schema_multiple_checks.example(size=3)
except Exception as e:
    print(e)
```

--------------------------------

### Define Schemas using Class Inheritance

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/notebooks/try_pandera.ipynb

Demonstrates how to extend a base Pandera DataFrameModel to create a new schema with additional fields, facilitating validation across different stages of data transformation.

```python
class Schema(pa.DataFrameModel):
    item: Series[str] = pa.Field(isin=["apple", "orange"], coerce=True)
    price: Series[float] = pa.Field(gt=0, coerce=True)


class TransformedSchema(Schema):
    expiry: Series[pd.Timestamp] = pa.Field(coerce=True)
```

--------------------------------

### Define Coordinate Schema for Datetime

Source: https://github.com/unionai-oss/pandera/blob/main/specs/xarray.md

Example of creating a Coordinate schema for datetime values, specifying the expected data type as 'datetime64[ns]'.

```python
time_coord = pa.Coordinate(dtype="datetime64[ns]")
```

--------------------------------

### Built-in Check Registration Example

Source: https://github.com/unionai-oss/pandera/blob/main/specs/optimized-strategies.md

Illustrates how built-in checks are registered with their corresponding strategy functions.

```python
from pandera.api.pandas.registry import register_builtin_check
from pandera.strategies import pandas_strategies as st

@register_builtin_check(strategy=st.gt_strategy, ...)
def greater_than(...):
    ...

```

--------------------------------

### Import Pandera GeoPandas module

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/geopandas.md

Import the entry point for GeoPandas validation.

```python
import pandera.geopandas as pg
```

--------------------------------

### Defining a custom check strategy

Source: https://github.com/unionai-oss/pandera/blob/main/docs/source/data_synthesis_strategies.md

Example of defining a custom check and its corresponding strategy for in-range values.

```python
check = pa.Check(lambda x: x.between(0, 100))
```

```python
import pandera.strategies.pandas_strategies as st

def in_range_strategy(pandera_dtype, strategy=None):
    if strategy is None:
        # handle base strategy case
        return st.floats(min_value=min_val, max_value=max_val).map(
            # the map isn't strictly necessary, but shows an example of
            # using the pandera_dtype argument
            strategies.to_numpy_dtype(pandera_dtype).type
        )

    # handle chained strategy case
    return strategy.filter(lambda val: 0 <= val <= 10)

check = pa.Check(lambda x: x.between(0, 100), strategy=in_range_strategy)
```