Pandera

https://pandera.readthedocs.io/en/stable/
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### 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]'

```

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

### example(size=None, n_regex_columns=1)

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

Generates an example GeoDataFrame based on the schema definition.

```APIDOC
## example(size=None, n_regex_columns=1)

### Description
Generates a synthetic GeoDataFrame that conforms to the schema definition.

### Parameters
- **size** (int) - Optional - Number of rows to generate.
- **n_regex_columns** (int) - Optional - Number of regex columns to generate.
```

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

### Install Pandera with Ibis support

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

Install the necessary packages to enable Ibis integration with Pandera.

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

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

### Install and enable Narwhals backend

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

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

```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)
```

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

### 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 and enable Narwhals backend

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

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

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

```python
import pandera.pyspark as pa

pa.set_config(use_narwhals_backend=True)
```

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

### Install Pandera with Polars support

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

Install the necessary dependencies to use Pandera with Polars.

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

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

### Install pandera xarray support

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

Install the necessary dependencies to use pandera with xarray.

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

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

### Install Pandera extras via pip

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

Install additional functionality for Pandera using pip.

```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
```

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

### Install Narwhals-powered backend dependencies

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

Install the necessary extras for the specific backend being used with Narwhals.

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

export PANDERA_USE_NARWHALS_BACKEND=True
```

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

### 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]'
```

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

### Install Narwhals dependencies

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

Install the required extras for Narwhals support alongside the specific backend library.

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

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

### 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 extras via conda

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

Install additional functionality for Pandera 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
conda install -c conda-forge pandera-narwhals     # use the Narwhals-powered backend
```

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

### Install Pandera with pandas support

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

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
```

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

### 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

```

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

### 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 Pyspark support

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

Install the necessary dependencies to enable pyspark validation features.

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

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

### 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 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

```

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

### 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.
```

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

### Inspecting Coordinate Data

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

Examples showing the values for time, latitude, and longitude coordinates.

```python
array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])
```

```python
array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])
```

```python
array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))
```

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

### 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.
```

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

### 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


```

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

### Displaying DataArray structure

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

Example output showing the structure of the validated DataArray.

```text
<xarray.DataArray 'detailed' (x: 3, y: 4)> Size: 96B
array([[1., 1., 1., 1.],
       [1., 1., 1., 1.],
       [1., 1., 1., 1.]])
Coordinates:
  * x        (x) float64 24B 0.0 1.0 2.0
  * y        (y) float64 32B 0.0 1.0 2.0 3.0
```

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

### 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.
```

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

### Output of column name access

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

Example output showing column name retrieval and usage in indexing.

```text
Column name for 'year' is year

   year  day
0  2001  200
1  2002  156
2  2003  365
```

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

### Display Latitude Coordinate Array

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

Example of a float64 latitude coordinate array.

```python
array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])
```

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

### Schema validation error output

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

Example output when a schema validation fails.

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

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

### 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

```

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

### Generate synthetic data from a schema

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

Uses the example method on a DataFrameSchema to generate synthetic data based on defined column constraints.

```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)
```

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

### Build Documentation Locally

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

Generate the project documentation locally using the make command.

```bash
make docs

```

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

### 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

```

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

### PanderaConfig.__init__

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.config.PanderaConfig.html

Initializes the Pandera configuration object with settings for validation, caching, and backend usage.

```APIDOC
## __init__(validation_enabled=True, validation_depth=None, cache_dataframe=False, keep_cached_dataframe=False, use_narwhals_backend=False, silenced_warnings=<factory>)

### Description
Initializes a new instance of the PanderaConfig class. Settings can be automatically populated via environment variables.

### Parameters
- **validation_enabled** (bool) - Optional - Whether validation is enabled.
- **validation_depth** (str) - Optional - The depth of validation to perform.
- **cache_dataframe** (bool) - Optional - Whether to cache dataframes.
- **keep_cached_dataframe** (bool) - Optional - Whether to retain cached dataframes.
- **use_narwhals_backend** (bool) - Optional - Whether to use the Narwhals-powered validation backend.
```

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

### 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__
```

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

### Runtime Backend Re-registration

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

Shows how updating the configuration after initial registration triggers an automatic swap of the validation 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
```

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

### Quick start with DatasetSchema

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

Define a schema for an xarray Dataset and validate it.

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

schema = pa.DatasetSchema(
    data_vars={
        "temperature": pa.DataVar(dtype=np.float64, dims=("x", "y")),
        "pressure": pa.DataVar(dtype=np.float64, dims=("x", "y")),
    },
    coords={"x": pa.Coordinate(dtype=np.float64)},
)

ds = xr.Dataset(
    {
        "temperature": (("x", "y"), np.random.rand(3, 4)),
        "pressure": (("x", "y"), np.random.rand(3, 4)),
    },
    coords={"x": np.arange(3, dtype=np.float64)},
)
schema.validate(ds)
```

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

### 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__
```

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

### Enable Narwhals backend via configuration

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

Use the pandera configuration API to enable the Narwhals-powered backend programmatically.

```python
import pandera

pandera.set_config(use_narwhals_backend=True)
```

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

### Display Longitude Coordinate Array

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

Example of a float64 longitude coordinate array.

```python
array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))
```

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

### 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.
```

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

### pandera.backends.narwhals.builtin_checks.str_startswith

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.backends.narwhals.builtin_checks.str_startswith.html

Checks if all values in a Narwhals column expression start with a given string.

```APIDOC
## str_startswith(col_expr, string)

### Description
Ensure that all values in a Narwhals column expression start with a certain string.

### Parameters
- **col_expr** (Expr) - Required - Narwhals column expression to check.
- **string** (str) - Required - String all values should start with.

### Return Type
- **Expr** - Returns a Narwhals expression.
```

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

### Extract DataArray values

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

Examples of accessing the underlying numpy arrays for the DataArray and its coordinates.

```python
array([[0.75293797, 0.67744525, 0.27375578, 0.82717632],
       [0.72598622, 0.08969872, 0.5126672 , 0.82152002],
       [0.06760569, 0.45619781, 0.29093671, 0.75139479]])
```

```python
array([0., 1., 2.])
```

```python
array(['a', 'b', 'c'], dtype='<U1')
```

```python
array([0., 1., 2., 3.])
```

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

### Schema generation and validation

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

Demonstrates converting a model to a schema and running validation.

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

Temperature.validate(da)
```

```text
<class 'pandera.api.xarray.container.DataArraySchema'>
```

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

### Invalid Field Configuration Example

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

Avoid combining typing.Annotated with dtype_kwargs, as this will result in a TypeError.

```python
class SchemaFieldDatetimeTZDtype(pa.DataFrameModel):
    col: Series[Annotated[pd.DatetimeTZDtype, "ns", "est"]] = pa.Field(
        dtype_kwargs={"unit": "ns", "tz": "EST"}
    )

Schema.to_schema()
```

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

### 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.
```

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

### 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

```

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

### Displaying Array Data

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

Example of a 3D array structure with shape (12, 180, 360).

```python
array([[[0.55091548, 0.26063146, 0.56309797, ..., 0.76744558,
         0.61514793, 0.77266546],
        [0.88097842, 0.97459958, 0.74974033, ..., 0.65159498,
         0.71069364, 0.90883459],
        [0.35619986, 0.08352535, 0.35808769, ..., 0.54511502,
         0.10125885, 0.47776324],
        ...,
        [0.98286399, 0.53351816, 0.39527722, ..., 0.37206078,
         0.80390064, 0.25447213],
        [0.49329316, 0.39275057, 0.97927334, ..., 0.44795531,
         0.68900081, 0.2151628 ],
        [0.1239036 , 0.96016016, 0.04259346, ..., 0.94470246,
         0.83784414, 0.04760874]],

       [[0.70072467, 0.62220426, 0.88762475, ..., 0.48654168,
         0.87214873, 0.35206031],
        [0.42275003, 0.26856817, 0.87161316, ..., 0.61329873,
         0.21663688, 0.27431122],
        [0.68555671, 0.66620749, 0.19168131, ..., 0.58370815,
         0.60766078, 0.91425913],
...
        [0.93209247, 0.16056346, 0.57348095, ..., 0.24213368,
         0.63410552, 0.46226314],
        [0.11691463, 0.23670554, 0.58795071, ..., 0.59949185,
         0.6211805 , 0.30958333],
        [0.78104833, 0.77578356, 0.82982152, ..., 0.6666105 ,
         0.9916746 , 0.96172477]],

       [[0.20787549, 0.41437817, 0.96298421, ..., 0.72413384,
         0.51197891, 0.03781643],
        [0.39153034, 0.24359384, 0.01023182, ..., 0.12761208,
         0.48155821, 0.83618221],
        [0.1400617 , 0.35495569, 0.84348242, ..., 0.97271949,
         0.42993047, 0.99733826],
        ...,
        [0.97104186, 0.4450964 , 0.74211356, ..., 0.05297643,
         0.5822762 , 0.28009761],
        [0.14054459, 0.04021215, 0.5208903 , ..., 0.99799159,
         0.22858326, 0.91678554],
        [0.83327494, 0.66897794, 0.40590294, ..., 0.04757932,
         0.90780536, 0.46445703]]], shape=(12, 180, 360))
```

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

### Example validation error output

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

The expected output when validating a DataFrame against the custom IPAddress data type.

```text
expected series 'ips' to have type IPAddress:
failure cases:
   index failure_case
0      2      0.0.0.a
```

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

### Schema Error Output Format

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

Example of the JSON error structure returned when validation fails against multiple schemas.

```json
{
    "DATA": {
        "DATAFRAME_CHECK": [
            {
                "schema": "OnlyOnesSchema",
                "column": "a",
                "check": "equal_to(0)",
                "error": "Column 'a' failed element-wise validator number 0: equal_to(0) failure cases: 2"
            },
            {
                "schema": "OnlyOnesSchema",
                "column": "a",
                "check": "equal_to(1)",
                "error": "Column 'a' failed element-wise validator number 0: equal_to(1) failure cases: 0, 2"
            }
        ]
    }
}
```

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

### Validate attributes with regex patterns

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

Strings starting with '^' are treated as regex patterns and matched against the attribute value.

```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)
```

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

### Deprecation warning for strategy kwarg

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

Example of the warning issued when using the legacy strategy kwarg alongside built-in checks.

```text
The 'strategy' kwarg on Check(check_fn=..., strategy=<fn>) is being
invoked as a chained strategy because built-in checks are also present
on this column and now produce the merged base strategy in a single
hypothesis call. ...
```

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

### 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
```

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

### 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.
```
```

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

### 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__
```

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

### 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)
```

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

### Int.__init__()

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

Initializes the Int data type.

```APIDOC
## Int.__init__()

### Description
Initializes the Int data type.

### Method
__init__
```

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

### Validate Dataset Attributes via Regex

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

Use regex patterns starting with '^' to validate attribute values against allowed formats.

```python
schema = pa.DatasetSchema(
    data_vars={"temperature": pa.DataVar(dtype=float)},
    attrs={"units": "^(K|degC|degF)$"},
)

ds_units = xr.Dataset(
    {"temperature": (("x",), np.ones(3))},
    attrs={"units": "K"},
)
schema.validate(ds_units)
```

```python
ds_bad_units = xr.Dataset(
    {"temperature": (("x",), np.ones(3))},
    attrs={"units": "meters"},
)

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

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

### 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)
```

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

### Create Pandas DataFrame

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

Create a pandas DataFrame with sample data for state, city, and price. This DataFrame will be used for validation.

```python
import pandas as pd

data = pd.DataFrame(
    {
        'state': ['FL','FL','FL','CA','CA','CA'],
        'city': [
            'Orlando', 'Miami', 'Tampa', 'San Francisco', 'Los Angeles', 'San Diego'
        ],
        'price': [8, 12, 10, 16, 20, 18],
    }
)
data
```

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

### Define DatasetSchema with chunked DataVars

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

Use `chunked=True` in `pa.DataVar` to specify that a DataVar is expected to be Dask-backed. This example validates a Dask-backed xarray Dataset.

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

ds = xr.Dataset({
    "temperature": (("x", "y"), np.random.rand(3, 4)),
    "pressure": (("x", "y"), np.random.rand(3, 4)),
}).chunk({"x": 2})

schema = pa.DatasetSchema(
    data_vars={
        "temperature": pa.DataVar(
            dtype=np.float64, dims=("x", "y"), chunked=True,
        ),
        "pressure": pa.DataVar(
            dtype=np.float64, dims=("x", "y"), chunked=True,
        ),
    },
)
schema.validate(ds)

```

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

### Enable Narwhals backend

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

Enable the Narwhals-powered backend using either an environment variable or the Python configuration function.

```bash
export PANDERA_USE_NARWHALS_BACKEND=True
```

```python
import pandera

pandera.set_config(use_narwhals_backend=True)
```

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

### Handle non-matching standard_name with Check.cf_standard_name

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

When the `standard_name` attribute does not match the expected value, `pandera.errors.SchemaError` is raised. This example demonstrates how to catch and print the error.

```python
try:
    pa.DataArraySchema(
        checks=pa.Check.cf_standard_name("sea_surface_temperature"),
    ).validate(da)
except pa.errors.SchemaError as exc:
    print(exc)
```

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

### Using Built-in Pandera Checks

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

Demonstrates the use of common built-in pandera checks for numeric, list membership, and string pattern matching.

```python
import pandera.pandas as pa

schema = pa.DataFrameSchema({
    "small_values": pa.Column(float, pa.Check.less_than(100)),
    "one_to_three": pa.Column(int, pa.Check.isin([1, 2, 3])),
    "phone_number": pa.Column(str, pa.Check.str_matches(r'^[a-z0-9-]+$')),
})

```

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

### Duration.__init__

Source: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.engines.narwhals_engine.Duration.html

Initializes a new instance of the Duration class.

```APIDOC
## Duration.__init__(time_unit=None)

### Description
Initializes the Narwhals duration data type.

### Parameters
- **time_unit** (optional) - The time unit for the duration.
```

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

### Example Pandera Pyspark Error Report Structure

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

A sample JSON representation of the error report structure, showing SCHEMA and DATA validation categories.

```json
{
    "SCHEMA": {
        "COLUMN_NOT_IN_DATAFRAME": [
            {
                "schema": "PanderaSchema",
                "column": "product_name",
                "check": "column_in_dataframe",
                "error": "column 'product_name' not in dataframe Row(id=5, product='Bread', price=Decimal('44.40000'), description=['description of product'], meta={'product_category': 'dairy'})"
            }
        ],
        "WRONG_DATATYPE": [
            {
                "schema": "PanderaSchema",
                "column": "description",
                "check": "dtype('ArrayType(StringType(), True)')",
                "error": "expected column 'description' to have type ArrayType(StringType(), True), got ArrayType(StringType(), False)"
            },
            {
                "schema": "PanderaSchema",
                "column": "meta",
                "check": "dtype('MapType(StringType(), StringType(), True)')",
                "error": "expected column 'meta' to have type MapType(StringType(), StringType(), True), got MapType(StringType(), StringType(), False)"
            }
        ]
    },
    "DATA": {
        "DATAFRAME_CHECK": [
            {
                "schema": "PanderaSchema",
                "column": "id",
                "check": "greater_than(5)",
                "error": "<Schema Column(name=id, type=DataType(IntegerType()))> failed validation greater_than(5)"
            }
        ]
    }
}
```

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

### Decimal.__init__

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

Initializes a Decimal data type with specified precision, scale, and rounding.

```APIDOC
## Decimal.__init__()

### Description
Initializes a Decimal data type with specified precision, scale, and rounding.

### Parameters

#### Path Parameters
- **_precision** (int) - Optional - The number of significant digits that the decimal type can represent. Defaults to 28.
- **_scale** (int) - Optional - The number of digits after the decimal point. Defaults to 0.
- **_rounding** (Any) - Optional - The rounding mode supported by the Python `decimal.Decimal` class. Defaults to None.
```

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

### Example Warning Output

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

This output shows the warning message generated when a check fails and `raise_warning` is set to `True`. It indicates which column and validator failed.

```text
Column 'var2' failed series or dataframe validator 0: <Check normaltest: normality test>

```
Tokens:
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