### Example Validated DataFrame Output

Source: https://pandera.readthedocs.io/en/stable?badge=stable

This is the output of the validated DataFrame from the Quick Start example. It shows the DataFrame after successful validation against the defined schema.

```text
   column1  column2 column3
0        1      1.1       a
1        2      1.2       b
2        3      1.3       c
```

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

### example

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.pandas.components.MultiIndex.html

Generate an example of a MultiIndex with a specified size.

```APIDOC
## example

### Description
Generate an example of a MultiIndex with a specified size.

### Parameters

* **size** – Optional - number of elements in the generated DataFrame.

### Returns

`MultiIndex` - pandas DataFrame object.
```

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

### Install pandera with Modin extras

Source: https://pandera.readthedocs.io/en/stable/modin.html

Install pandera with the necessary extras for Modin integration. Choose the specific backend (ray or dask) or install both.

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

```

```bash
pip install 'pandera[modin-ray]'

```

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

```

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

### Install Pandera with Strategies Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the strategies extra for data synthesis strategies.

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

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

### Install Pandera with FastAPI Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the fastapi extra for fastapi integration.

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

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

### Install Pandera with Frictionless Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the frictionless extra for frictionless schema serialization/deserialization.

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

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

### Install Pandera with Pandas Extra using uv

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the pandas extra using uv.

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

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

### Install Pandera with Pandas Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the pandas extra using pip.

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

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

### Install Pandera with Hypothesis Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the hypothesis extra for hypothesis checks.

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

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

### Install Pandera with Conda

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with conda.

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

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

### Index.example

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.pandas.components.Index.html

Generate an example of a particular size.

```APIDOC
## example

### Description
Generate an example of a particular size.

### Parameters
#### Parameters
- **size** (UnionType[int, None]) – number of elements in the generated Index.

### Returns
`Index` - pandas Index object.
```

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

### Install Conda Packages for Extras

Source: https://pandera.readthedocs.io/en/stable/index.html

Install various pandera extras using conda.

```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
```

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

### Install Pandera with Ibis Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the ibis extra to validate ibis tables.

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

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

### Install Pandera with IO Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the io extra for yaml/script schema io utilities.

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

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

### Install Pandera with Ibis and an Ibis backend

Source: https://pandera.readthedocs.io/en/stable/ibis.html

Install pandera with the ibis extra and the desired Ibis backend, such as duckdb.

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

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

### Pandera Check Examples

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.checks.Check.html

Demonstrates various ways to define and use pandera Checks for data validation, including vectorized, element-wise, group-based, and DataFrame-level checks. This example requires pandas and pandera to be installed.

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


# column checks are vectorized by default
check_positive = pa.Check(lambda s: s > 0)

# define an element-wise check
check_even = pa.Check(lambda x: x % 2 == 0, element_wise=True)

# checks can be given human-readable metadata
check_with_metadata = pa.Check(
    lambda x: True,
    title="Always passes",
    description="This check always passes."
)

# specify assertions across categorical variables using `groupby`,
# for example, make sure the mean measure for group "A" is always
# larger than the mean measure for group "B"
check_by_group = pa.Check(
    lambda measures: measures["A"].mean() > measures["B"].mean(),
    groupby=["group"],
)

# define a wide DataFrame-level check
check_dataframe = pa.Check(
    lambda df: df["measure_1"] > df["measure_2"])

measure_checks = [check_positive, check_even, check_by_group]

schema = pa.DataFrameSchema(
    columns={
        "measure_1": pa.Column(int, checks=measure_checks),
        "measure_2": pa.Column(int, checks=measure_checks),
        "group": pa.Column(str),
    },
    checks=check_dataframe
)

df = pd.DataFrame({
    "measure_1": [10, 12, 14, 16],
    "measure_2": [2, 4, 6, 8],
    "group": ["B", "B", "A", "A"]
})

schema.validate(df)[["measure_1", "measure_2", "group"]]
```

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

### Generate Example DataFrame from Schema

Source: https://pandera.readthedocs.io/en/stable/data_synthesis_strategies.html

Use the `example` method on a pandera DataFrameSchema to generate a DataFrame of a specified size. This is useful for interactive exploration and documentation.

```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)
```

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

### Install Pandera with Mypy support

Source: https://pandera.readthedocs.io/en/stable/mypy_integration.html

Install the pandera package with the 'mypy' extra to enable Mypy integration.

```bash
pip install pandera[mypy]

```

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

### Install Fugue with Spark Support

Source: https://pandera.readthedocs.io/en/stable/fugue.html

Install Fugue with the 'spark' extra to include PySpark. Use the 'dask' extra for Dask support.

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

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

### Set Up Development Environment with uv

Source: https://pandera.readthedocs.io/en/stable/CONTRIBUTING.html

Install uv, create a virtual environment, sync dependencies, and activate the environment. This ensures a clean environment for development.

```bash
pip install uv
uv venv
uv sync --all-extras
source .venv/bin/activate

```

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

### example

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.pandas.container.DataFrameSchema.html

Generates an example pandas DataFrame based on the schema definition, with a specified number of rows.

```APIDOC
## example

### Description
Generates an example pandas DataFrame based on the schema definition, with a specified number of rows.

### Parameters

* **size** (UnionType[int, None]) – number of elements in the generated DataFrame.
* **n_regex_columns** (int) – number of regex columns to generate.

### Returns

`DataFrame` - pandas DataFrame object.
```

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

### Install Pandera Strategies with Conda

Source: https://pandera.readthedocs.io/en/stable?badge=latest

Install pandera with the strategies extra for data synthesis using conda.

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

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

### Install Pandera with Modin-Ray Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the modin-ray extra to validate modin dataframes with ray.

```bash
pip install 'pandera[modin-ray]'
```

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

### Install pandera with GeoPandas extra

Source: https://pandera.readthedocs.io/en/stable/geopandas.html

Install pandera with the geopandas extra to enable GeoPandas support.

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

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

### Install pandera with PySpark extra

Source: https://pandera.readthedocs.io/en/stable/pyspark.html

Install pandera with the necessary PySpark extra to enable pyspark.pandas integration.

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

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

### example()

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.geopandas.container.GeoDataFrameSchema.html

Generates an example GeoDataFrame based on the schema. This class method is similar to `DataFrameSchema.example()` but specifically returns a `GeoDataFrame`.

```APIDOC
## example()

### Description
Like `DataFrameSchema.example()`, but returns a `GeoDataFrame` when applicable.

### Parameters

* **size** (_int_, optional) – The number of rows to generate.
* **n_regex_columns** (_int_, optional) – The number of regex-generated columns.

### Return Type

`DataFrame`
```

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

### Install Pandera with Modin Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the modin extra to validate modin dataframes.

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

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

### Install Pandera with Polars Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the polars extra to validate polars dataframes.

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

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

### GeoDataFrameModel.example()

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.geopandas.GeoDataFrameModel.html

Generate an example of this data model specification.

```APIDOC
## GeoDataFrameModel.example()

### Description
Generate an example of this data model specification.

### Return Type
GeoDataFrameT[Self]
```

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

### Install Pandera with Xarray Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the xarray extra to validate xarray data structures.

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

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

### Generate Example Index Data

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.pandas.components.Index.html

The `example` method generates a sample pandas Index object based on the schema definition. This is useful for testing and demonstration purposes.

```python
import pandera as pa

# Define an Index schema
index_schema = pa.Index(dtype="int64", name="id")

# Generate an example Index with 5 elements
example_index = index_schema.example(size=5)

print(example_index)
```

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

### Install Pandera with Modin-Dask Extra

Source: https://pandera.readthedocs.io/en/stable/index.html

Install pandera with the modin-dask extra to validate modin dataframes with dask.

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

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

### Install pandera with Dask extra

Source: https://pandera.readthedocs.io/en/stable/dask.html

Install pandera with the necessary Dask extra to enable Dask DataFrame validation.

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

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

### Pandera DataFrame Type Checking Examples

Source: https://pandera.readthedocs.io/en/stable/mypy_integration.html

Demonstrates various ways to type hint and check pandas DataFrames using Pandera's DataFrame types and mypy. Includes examples of DataFrame assignment, piping, casting, in-place mutation, and slicing.

```python
def fn_pipe_dataframe(df: DataFrame[Schema]) -> DataFrame[SchemaOut]:
    return df.assign(age=30).pipe(DataFrame[SchemaOut])  # mypy okay


def fn_cast_dataframe(df: DataFrame[Schema]) -> DataFrame[SchemaOut]:
    return cast(DataFrame[SchemaOut], df.assign(age=30))  # mypy okay


@pa.check_types
def fn_mutate_inplace(df: DataFrame[Schema]) -> DataFrame[SchemaOut]:
    out = df.assign(age=30).pipe(DataFrame[SchemaOut])
    out.drop(columns="age", inplace=True)
    return out  # okay for mypy, pandera raises error


@pa.check_types
def fn_assign_and_get_index(df: DataFrame[Schema]) -> DataFrame[SchemaOut]:
    return df.assign(foo=30).iloc[:3]  # mypy error

```

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

### Dimension Validation Examples

Source: https://pandera.readthedocs.io/en/stable/xarray_guide/data_array_schema.html

Shows how to enforce dimension names in order, using None as a wildcard.

```python
pa.DataArraySchema(dims=("x", "y")).validate(da)
pa.DataArraySchema(dims=("x", None)).validate(da)
```

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

### DataFrameModel.example

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.dataframe.model.DataFrameModel.html

Class method to generate an example of this data model specification.

```APIDOC
## DataFrameModel.example

### Description
Generate an example of this data model specification.

### Method
`example`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **kwargs** - Keyword arguments for generating the example.

### Returns
* `DataFrameBase[Self]` - An example DataFrame instance.
```

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

### Example DataArray Coordinates

Source: https://pandera.readthedocs.io/en/stable/xarray_guide/data_models.html

Demonstrates the structure and values of coordinates for a DataArray, specifically 'lat' and 'lon'.

```python
import numpy as np

lat = np.arange(-90, 90, 1)
lon = np.arange(-180, 180, 1)

print(lat)
print(lon)
```

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

### SeriesSchema.example

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.pandas.array.SeriesSchema.html

Generates an example pandas Series based on the schema configuration.

```APIDOC
## SeriesSchema.example

### Description
Generates an example pandas Series based on the schema configuration.

### Method
example

### Parameters
#### Parameters
- **size** (None) – number of elements in the generated Series.
- **n_regex_columns** (int) – number of regex columns.

### Returns
- **Series** – pandas Series object.
```

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

### str_startswith_strategy

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.strategies.pandas_strategies.html

Generates strings that start with a specific string pattern. It supports chaining an optional hypothesis strategy and requires a string pattern.

```APIDOC
## str_startswith_strategy

### Description
Strategy to generate strings that start with a specific string pattern.

### Method
Not applicable (Python function)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **pandera_dtype** (`Union[DataType, DataType]`): `pandera.dtypes.DataType` instance.
* **strategy** (`UnionType[SearchStrategy, None]`): An optional hypothesis strategy. If specified, the pandas dtype strategy will be chained onto this strategy.
* **string** (`str`): String pattern.

### Return Type
`SearchStrategy`

### Returns
`hypothesis` strategy
```

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

### Column.example

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.pandas.components.Column.html

Generates an example pandas DataFrame based on the column's schema definition.

```APIDOC
## Column.example

### Description
Generate an example of a particular size.

### Parameters
- **size** - number of elements in the generated Index.

### Returns
- **DataFrame** - pandas DataFrame object.
```

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

### Install Pandera Mypy with Conda

Source: https://pandera.readthedocs.io/en/stable?badge=latest

Install pandera with the mypy extra to enable static type-linting of pandas using conda.

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

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

### Xarray DataArray Example

Source: https://pandera.readthedocs.io/en/stable/xarray_guide/checks_and_parsers.html

Illustrates an xarray DataArray with coordinate and attribute information.

```python
array([-90., -45.,   0.,  45.,  90.])
```

    * lon
(lon)
float64
-180.0 -140.0 ... 140.0 180.0
```
array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,  180.])
```

  * Attributes: (1)

units :
    K


```

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

### Update Dependencies from environment.yml

Source: https://pandera.readthedocs.io/en/stable/CONTRIBUTING.html

After altering the environment.yml file, run this command to sync dependencies to requirements.in. This allows installation via various package managers.

```bash
make deps-from-environment.yml

```

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

### Attribute Validation with Regex Matching

Source: https://pandera.readthedocs.io/en/stable/xarray_guide/data_array_schema.html

Use regex patterns (strings starting with `^`) to validate attributes against a set of acceptable string formats. This example checks if the 'units' attribute matches 'K', 'degC', or 'degF'.

```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)
```

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

### Build Documentation Locally

Source: https://pandera.readthedocs.io/en/stable/CONTRIBUTING.html

Generate the project documentation locally using the make command.

```bash
make docs

```

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

### Example xarray DataArray

Source: https://pandera.readthedocs.io/en/stable/xarray_guide/data_models.html

An example of an xarray DataArray with 'time', 'lat', and 'lon' dimensions.

```python
<xarray.DataArray 'temperature' (time: 12, lat: 180, lon: 360)> Size: 6MB
array([[[0.35224495, 0.23552507, 0.63530509, ..., 0.76696362,
         0.97695049, 0.89224131],
        [0.34417641, 0.6937307 , 0.55053635, ..., 0.62351941,
         0.68943517, 0.15898996],
        [0.00881282, 0.36015046, 0.52026644, ..., 0.51664449,
         0.10939061, 0.5992464 ],
        ...,
        [0.21210989, 0.70888113, 0.228842  , ..., 0.79548489,
         0.37966265, 0.70009388],
        [0.53539493, 0.27784502, 0.24944345, ..., 0.60562998,
         0.49654642, 0.34689236],
        [0.03853143, 0.02251944, 0.05700839, ..., 0.32623916,
         0.04017552, 0.55345834]],

       [[0.02220131, 0.12903081, 0.09939153, ..., 0.40078427,
         0.56776874, 0.40315637],
        [0.81293854, 0.23750217, 0.69141051, ..., 0.85707867,
         0.04011184, 0.74961657],
        [0.61291613, 0.11989162, 0.62211809, ..., 0.16181147,
         0.84414111, 0.1938169 ],
... 
        [0.20391682, 0.23525606, 0.19058816, ..., 0.58629692,
         0.42659518, 0.52615988],
        [0.24972305, 0.311079  , 0.12511614, ..., 0.05860058,
         0.77585309, 0.27618591],
        [0.19959069, 0.79446702, 0.96490449, ..., 0.79918381,
         0.29729941, 0.7445726 ]],

       [[0.91290466, 0.477176  , 0.2061919 , ..., 0.46593372,
         0.82592097, 0.81686105],
        [0.2843371 , 0.30830673, 0.5066703 , ..., 0.81571164,
         0.66502282, 0.45998696],
        [0.16127676, 0.02425449, 0.66446733, ..., 0.27367376,
         0.47944898, 0.63683746],
        ...,
        [0.97711755, 0.27625387, 0.79861479, ..., 0.10849807,
         0.88675392, 0.95296324],
        [0.42010144, 0.45662967, 0.70933448, ..., 0.43936467,
         0.85807251, 0.53692625],
        [0.08814553, 0.5614379 , 0.25692175, ..., 0.10044977,
         0.0480612 , 0.3867672 ]]], shape=(12, 180, 360))
Coordinates:
  * time     (time) float64 96B 0.0 1.0 2.0 3.0 4.0 ... 7.0 8.0 9.0 10.0 11.0
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon      (lon) float64 3kB -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5
```

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

### Enable Mypy Plugin

Source: https://pandera.readthedocs.io/en/stable/mypy_integration.html

Configure your mypy.ini or setup.cfg file to use the pandera Mypy plugin.

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

```

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

### Install Pandera Modin with Conda

Source: https://pandera.readthedocs.io/en/stable?badge=latest

Install pandera with the modin extra to validate modin dataframes using conda.

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

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

### Install Pandera PySpark with Conda

Source: https://pandera.readthedocs.io/en/stable?badge=latest

Install pandera with the pyspark extra to validate pyspark dataframes using conda.

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

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

### Object.__init__()

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.engines.numpy_engine.Object.html

Initializes the Object class.

```APIDOC
## Object.__init__()

### Description
Initializes the Object class.

### Method
__init__
```

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

### Install Pandera Modin-Ray with Conda

Source: https://pandera.readthedocs.io/en/stable?badge=latest

Install pandera with the modin-ray extra to validate modin dataframes with ray using conda.

```bash
conda install -c conda-forge pandera-modin-ray
```

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

### ArrowFloat16.__init__()

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.engines.pandas_engine.ArrowFloat16.html

Initializes the ArrowFloat16 object.

```APIDOC
## ArrowFloat16.__init__()

### Description
Initializes the ArrowFloat16 object.

### Method
__init__
```

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

### PythonDict.__init__

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.engines.pandas_engine.PythonDict.html

Initializes the PythonDict class.

```APIDOC
## Method: PythonDict.__init__

### Description
Initializes the PythonDict class.

### Parameters
- `_generic_type` (any, optional): The generic type to associate with the PythonDict instance.
```

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

### Int64.__init__()

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.engines.polars_engine.Int64.html

Initializes the Int64 data type.

```APIDOC
## Int64.__init__()

Initializes the Int64 data type.
```

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

### DataFrameSchema.example

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.polars.container.DataFrameSchema.html

Generates an example Polars DataFrame based on the schema definition.

```APIDOC
## DataFrameSchema.example

### Description
Generate an example of a particular size.

### Parameters
* **size** (UnionType[int, None]) - Number of elements in the generated DataFrame.
* **n_regex_columns** (int) - Number of regex columns to generate. Defaults to 1.

### Returns
* pandas DataFrame object.

### Warning
This method is not implemented in the Polars backend.
```

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

### List Available Nox Test Configurations

Source: https://pandera.readthedocs.io/en/stable/CONTRIBUTING.html

View all available test configurations and their parameters using the nox --list command. This helps in selecting specific test environments.

```bash
nox --list

```

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

### Generated Python Schema Script

Source: https://pandera.readthedocs.io/en/stable/schema_inference.html

This is an example of a pandera DataFrameSchema serialized into a Python script.

```python
from pandas import Timestamp
from pandera import DataFrameSchema, Column, Check, Index, MultiIndex

schema = DataFrameSchema(
    columns={
        "column1": Column(
            dtype="int64",
            checks=[
                Check.greater_than_or_equal_to(
                    min_value=5.0, raise_warning=False, ignore_na=True
                ),
                Check.less_than_or_equal_to(
                    max_value=20.0, raise_warning=False, ignore_na=True
                ),
            ],
        ),
        "column2": Column(dtype="object"),
        "column3": Column(
            dtype="datetime64[ns]",
            checks=[
                Check.greater_than_or_equal_to(
                    min_value=Timestamp("2010-01-01 00:00:00"),
                    raise_warning=False,
                    ignore_na=True,
                ),
                Check.less_than_or_equal_to(
                    max_value=Timestamp("2012-01-01 00:00:00"),
                    raise_warning=False,
                    ignore_na=True,
                ),
            ],
        ),
    },
    index=Index(
        dtype="int64",
        checks=[
            Check.greater_than_or_equal_to(
                min_value=0.0, raise_warning=False, ignore_na=True
            ),
            Check.less_than_or_equal_to(
                max_value=2.0, raise_warning=False, ignore_na=True
            ),
        ],
        nullable=False,
        coerce=False,
        name=None,
        description=None,
        title=None,
    ),
    coerce=True,
)
```

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

### Check.str_startswith

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.checks.Check.html

Verifies that all string values in a series begin with a specified prefix. This is useful for validating string formats.

```APIDOC
## Check.str_startswith

### Description
Ensure that all values start with a certain string.

### Method
classmethod

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **string** (`str`) – String all values should start with
* **kwargs** - key-word arguments passed into the Check initializer.

### Return Type
`Check`
```

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

### PydanticModel.__init__

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.engines.pandas_engine.PydanticModel.html

Initializes the PydanticModel with a given pydantic model.

```APIDOC
## PydanticModel.__init__()

### Description
Initializes the PydanticModel with a given pydantic model.

### Method
__init__

### Parameters
* **_model_**: The pydantic model to use.
```

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

### get_regex_columns

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.pyspark.components.Column.html

Get matching column names based on a regex column name pattern.

```APIDOC
## get_regex_columns

### Description
Get matching column names based on a regex column name pattern.

### Parameters
#### Parameters
- **columns** – columns to regex pattern match

### Return Type
Iterable

### Returns
matching columns
```

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

### DataFrame Model Validation Error Output

Source: https://pandera.readthedocs.io/en/stable/dataframe_models.html

Example of the error message produced when a DataFrame fails validation against a pandera schema.

```text
Column 'year' failed element-wise validator number 0: greater_than(2000) failure cases: 1999
```

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

### Column.set_name

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.polars.components.Column.html

Sets the name of the schema. If the provided name is a regex starting with '^' and ending with '$', the regex attribute is set to True.

```APIDOC
## Column.set_name

### Description
Set the name of the schema. If the name is a regex starting with ‘^’ and ending with ‘$’ set the regex attribute to True.

### Method
`set_name`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **name** (`str`) - The name to set for the schema. Can be a literal string or a regex pattern.

### Request Example
```python
# Example usage
column_with_name = column_object.set_name("my_column")
column_with_regex = column_object.set_name("^my_regex_.*$")
```

### Response
#### Success Response (200)
Returns the modified Column object.

#### Response Example
```python
# Returns the Column object with the updated name attribute.
```
```

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

### Import pandera.geopandas

Source: https://pandera.readthedocs.io/en/stable/geopandas.html

Import pandera.geopandas as a single entry point for all GeoPandas-related pandera functionalities.

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

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

### Dtype Validation Examples

Source: https://pandera.readthedocs.io/en/stable/xarray_guide/data_array_schema.html

Demonstrates validating a DataArray's dtype using Python types, NumPy dtypes, and string aliases.

```python
da_float32 = xr.DataArray(np.zeros(3, dtype=np.float32), dims="x")

pa.DataArraySchema(dtype=float).validate(da)
pa.DataArraySchema(dtype=np.float32).validate(da_float32)
pa.DataArraySchema(dtype="float32").validate(da_float32)
```

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

### Complex64.__init__()

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.dtypes.Complex64.html

Initializes the Complex64 data type.

```APIDOC
## Complex64.__init__()

### Description
Initializes the Complex64 data type.

### Method
__init__
```

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

### Validate Dataset with CF Standard Names

Source: https://pandera.readthedocs.io/en/stable/xarray_guide/cf_conventions.html

Validates if a dataset contains specified CF standard names for its variables. Requires cf_xarray to be installed.

```python
import xarray as xr
import pandera as pa
import numpy as np

# Ensure cf_xarray is installed: pip install cf_xarray
import cf_xarray  # noqa: F401

ds = xr.Dataset({
    "T": (("x",), np.ones(3), {"standard_name": "air_temperature"}),
    "P": (("x",), np.ones(3), {"standard_name": "air_pressure"}),
})

schema = pa.DatasetSchema(
    checks=pa.Check.cf_has_standard_names(
        ("air_temperature", "air_pressure"),
    ),
)
schema.validate(ds)
```

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

### Pandera Column Checks with Groupby

Source: https://pandera.readthedocs.io/en/stable/checks.html

Demonstrates using `groupby` with single columns, multiple columns, and a callable function for complex grouping logic in Pandera column checks. Ensure necessary imports are present.

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

schema = pa.DataFrameSchema({
    "height_in_feet": pa.Column(
        float, [
            # groupby as a single column
            pa.Check(
                lambda g: g[False].mean() > 6,
                groupby="age_less_than_20"),

            # define multiple groupby columns
            pa.Check(
                lambda g: g[(True, "F")].sum() == 9.1,
                groupby=["age_less_than_20", "sex"]),

            # groupby as a callable with signature:
            # (DataFrame) -> DataFrameGroupBy
            pa.Check(
                lambda g: g[(False, "M")].median() == 6.75,
                groupby=lambda df: (
                    df.assign(age_less_than_15=lambda d: d["age"] < 15)
                    .groupby(["age_less_than_15", "sex"])))
        ]),
    "age": pa.Column(int, pa.Check(lambda s: s > 0)),
    "age_less_than_20": pa.Column(bool),
    "sex": pa.Column(str, pa.Check(lambda s: s.isin(["M", "F"])))
})

df = (
    pd.DataFrame({
        "height_in_feet": [6.5, 7, 6.1, 5.1, 4],
        "age": [25, 30, 21, 18, 13],
        "sex": ["M", "M", "F", "F", "F"]
    })
    .assign(age_less_than_20=lambda x: x["age"] < 20)
)

schema.validate(df)
```

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

### Define and Validate Dataset with Dimension and Size Constraints

Source: https://pandera.readthedocs.io/en/stable/xarray_guide/dataset_schema.html

Demonstrates how to use pandera's DatasetSchema to enforce overall dataset dimensions ('x', 'y') and their sizes (x=3, y=4), independent of individual data variable specifications.

```python
schema = pa.DatasetSchema(
    data_vars={
        "temperature": pa.DataVar(dtype=float, dims=("x", "y")),
    },
    dims=("x", "y"),
    sizes={"x": 3, "y": 4},
)

ds_sized = xr.Dataset(
    {"temperature": (("x", "y"), np.random.rand(3, 4))},
)
schema.validate(ds_sized)
```

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

### Example Data Array with Longitude

Source: https://pandera.readthedocs.io/en/stable/xarray_guide/data_models.html

Demonstrates a sample DataArray representing longitude coordinates. This is a common data structure used in geospatial analysis.

```python
import numpy as np
import xarray as xr

lon = np.arange(-179.5, 180, 1)
data = np.random.rand(360)

data_array = xr.DataArray(data, coords=[lon], dims=['lon'])
print(data_array)
```