Update imports from pandera to pandera.pandas (#1965)

* update pandera imports and docs

Signed-off-by: cosmicBboy <[email protected]>

* update readme and index docs page

Signed-off-by: cosmicBboy <[email protected]>

* update imports in docs and tests

Signed-off-by: cosmicBboy <[email protected]>

* fix lint

Signed-off-by: cosmicBboy <[email protected]>

* fix import

Signed-off-by: cosmicBboy <[email protected]>

* fix pandera.polars import

Signed-off-by: cosmicBboy <[email protected]>

* fix mypy test

Signed-off-by: cosmicBboy <[email protected]>

---------

Signed-off-by: cosmicBboy <[email protected]>

Author: cosmicBboy

README.md

@@ -2,7 +2,7 @@
 <div align="center"><a href="https://www.union.ai/pandera"><img src="docs/source/_static/pandera-banner.png" width="400"></a></div>
 
 <h1 align="center">
-  The Open-source Framework for Precision Data Testing
+  The Open-source Framework for Validating DataFrame-like Objects
 </h1>
 
 <p align="center">
@@ -32,246 +32,93 @@
 [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pandera?style=for-the-badge)](https://anaconda.org/conda-forge/pandera)
 [![Discord](https://img.shields.io/badge/discord-chat-purple?color=%235765F2&label=discord&logo=discord&style=for-the-badge)](https://discord.gg/vyanhWuaKB)
 
-`pandera` is a [Union.ai](https://union.ai/blog-post/pandera-joins-union-ai) open
+Pandera is a [Union.ai](https://union.ai/blog-post/pandera-joins-union-ai) open
 source project that provides a flexible and expressive API for performing data
-validation on dataframe-like objects to make data processing pipelines more readable and robust.
-
-Dataframes contain information that `pandera` explicitly validates at runtime.
-This is useful in production-critical or reproducible research settings. With
-`pandera`, you can:
-
-1. Define a schema once and use it to validate
-   [different dataframe types](https://pandera.readthedocs.io/en/stable/supported_libraries.html)
-   including [pandas](http://pandas.pydata.org), [polars](https://docs.pola.rs/),
-   [dask](https://dask.org), [modin](https://modin.readthedocs.io/),
-   and [pyspark](https://spark.apache.org/docs/3.2.0/api/python/user_guide/pandas_on_spark/index.html).
-1. [Check](https://pandera.readthedocs.io/en/stable/checks.html) the types and
-   properties of columns in a `DataFrame` or values in a `Series`.
-1. Perform more complex statistical validation like
-   [hypothesis testing](https://pandera.readthedocs.io/en/stable/hypothesis.html#hypothesis).
-1. [Parse](https://pandera.readthedocs.io/en/stable/parsers.html) data to standardize
-   the preprocessing steps needed to produce valid data.
-1. Seamlessly integrate with existing data analysis/processing pipelines
-   via [function decorators](https://pandera.readthedocs.io/en/stable/decorators.html#decorators).
-1. Define dataframe models with the
-   [class-based API](https://pandera.readthedocs.io/en/stable/dataframe_models.html#dataframe-models)
-   with pydantic-style syntax and validate dataframes using the typing syntax.
-1. [Synthesize data](https://pandera.readthedocs.io/en/stable/data_synthesis_strategies.html#data-synthesis-strategies)
-   from schema objects for property-based testing with pandas data structures.
-1. [Lazily Validate](https://pandera.readthedocs.io/en/stable/lazy_validation.html)
-   dataframes so that all validation checks are executed before raising an error.
-1. [Integrate](https://pandera.readthedocs.io/en/stable/integrations.html) with
-   a rich ecosystem of python tools like [pydantic](https://pydantic-docs.helpmanual.io),
-   [fastapi](https://fastapi.tiangolo.com/), and [mypy](http://mypy-lang.org/).
-
-## Documentation
-
-The official documentation is hosted here: https://pandera.readthedocs.io
-
+validation on dataframe-like objects. The goal of Pandera is to make data
+processing pipelines more readable and robust with statistically typed
+dataframes.
 
 ## Install
 
-Using pip:
+Pandera supports [multiple dataframe libraries](https://pandera.readthedocs.io/en/stable/supported_libraries.html), including [pandas](http://pandas.pydata.org), [polars](https://docs.pola.rs/), [pyspark](https://spark.apache.org/docs/latest/api/python/index.html), and more. To validate `pandas` DataFrames, install Pandera with the `pandas` extra:
+
+**With `pip`:**
 
 ```
-pip install pandera
+pip install 'pandera[pandas]'
 ```
 
-Using conda:
+**With `uv`:**
 
 ```
-conda install -c conda-forge pandera
+uv pip install 'pandera[pandas]'
 ```
 
-### Extras
-
-Installing additional functionality:
+**With `conda`:**
 
-<details>
-
-<summary><i>pip</i></summary>
-
-```bash
-pip install 'pandera[hypotheses]' # hypothesis checks
-pip install 'pandera[io]'         # yaml/script schema io utilities
-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
 ```
-
-</details>
-
-<details>
-
-<summary><i>conda</i></summary>
-
-```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-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-pandas
 ```
 
-</details>
+## Get started
 
-## Quick Start
+First, create a dataframe:
 
 ```python
 import pandas as pd
-import pandera as pa
-
+import pandera.pandas as pa
 
 # data to validate
 df = pd.DataFrame({
-    "column1": [1, 4, 0, 10, 9],
-    "column2": [-1.3, -1.4, -2.9, -10.1, -20.4],
-    "column3": ["value_1", "value_2", "value_3", "value_2", "value_1"]
+    "column1": [1, 2, 3],
+    "column2": [1.1, 1.2, 1.3],
+    "column3": ["a", "b", "c"],
 })
+```
+
+Validate the data using the object-based API:
 
-# define schema
+```python
+# define a schema
 schema = pa.DataFrameSchema({
-    "column1": pa.Column(int, checks=pa.Check.le(10)),
-    "column2": pa.Column(float, checks=pa.Check.lt(-1.2)),
-    "column3": pa.Column(str, checks=[
-        pa.Check.str_startswith("value_"),
-        # define custom checks as functions that take a series as input and
-        # outputs a boolean or boolean Series
-        pa.Check(lambda s: s.str.split("_", expand=True).shape[1] == 2)
-    ]),
+    "column1": pa.Column(int, pa.Check.ge(0)),
+    "column2": pa.Column(float, pa.Check.lt(10)),
+    "column3": pa.Column(
+        str,
+        [
+            pa.Check.isin([*"abc"]),
+            pa.Check(lambda series: series.str.len() == 1),
+        ]
+    ),
 })
 
-validated_df = schema(df)
-print(validated_df)
-
-#     column1  column2  column3
-#  0        1     -1.3  value_1
-#  1        4     -1.4  value_2
-#  2        0     -2.9  value_3
-#  3       10    -10.1  value_2
-#  4        9    -20.4  value_1
+print(schema.validate(df))
+#    column1  column2 column3
+# 0        1      1.1       a
+# 1        2      1.2       b
+# 2        3      1.3       c
 ```
 
-## DataFrame Model
-
-`pandera` also provides an alternative API for expressing schemas inspired
-by [dataclasses](https://docs.python.org/3/library/dataclasses.html) and
-[pydantic](https://pydantic-docs.helpmanual.io/). The equivalent `DataFrameModel`
-for the above `DataFrameSchema` would be:
-
+Or validate the data using the class-based API:
 
 ```python
-from pandera.typing import Series
-
+# define a schema
 class Schema(pa.DataFrameModel):
-
-    column1: int = pa.Field(le=10)
-    column2: float = pa.Field(lt=-1.2)
-    column3: str = pa.Field(str_startswith="value_")
+    column1: int = pa.Field(ge=0)
+    column2: float = pa.Field(lt=10)
+    column3: str = pa.Field(isin=[*"abc"])
 
     @pa.check("column3")
-    def column_3_check(cls, series: Series[str]) -> Series[bool]:
-        """Check that values have two elements after being split with '_'"""
-        return series.str.split("_", expand=True).shape[1] == 2
-
-Schema.validate(df)
-```
-
-## Development Installation
-
-```
-git clone https://github.com/pandera-dev/pandera.git
-cd pandera
-export PYTHON_VERSION=...  # specify desired python version
-pip install -r dev/requirements-${PYTHON_VERSION}.txt
-pip install -e .
+    def custom_check(cls, series: pd.Series) -> pd.Series:
+        return series.str.len() == 1
+
+print(Schema.validate(df))
+#    column1  column2 column3
+# 0        1      1.1       a
+# 1        2      1.2       b
+# 2        3      1.3       c
 ```
 
-## Tests
-
-```
-pip install pytest
-pytest tests
-```
-
-## Contributing to pandera [![GitHub contributors](https://img.shields.io/github/contributors/pandera-dev/pandera.svg?style=for-the-badge)](https://github.com/pandera-dev/pandera/graphs/contributors)
-
-All contributions, bug reports, bug fixes, documentation improvements,
-enhancements and ideas are welcome.
-
-A detailed overview on how to contribute can be found in the
-[contributing guide](https://github.com/pandera-dev/pandera/blob/main/.github/CONTRIBUTING.md)
-on GitHub.
-
-## Issues
-
-Go [here](https://github.com/pandera-dev/pandera/issues) to submit feature
-requests or bugfixes.
-
-## Need Help?
-
-There are many ways of getting help with your questions. You can ask a question
-on [Github Discussions](https://github.com/pandera-dev/pandera/discussions/categories/q-a)
-page or reach out to the maintainers and pandera community on
-[Discord](https://discord.gg/vyanhWuaKB)
-
-## Why `pandera`?
-
-- [dataframe-centric data types](https://pandera.readthedocs.io/en/stable/dtypes.html),
-  [column nullability](https://pandera.readthedocs.io/en/stable/dataframe_schemas.html#null-values-in-columns),
-  and [uniqueness](https://pandera.readthedocs.io/en/stable/dataframe_schemas.html#validating-the-joint-uniqueness-of-columns)
-  are first-class concepts.
-- Define [dataframe models](https://pandera.readthedocs.io/en/stable/schema_models.html) with the class-based API with
-  [pydantic](https://pydantic-docs.helpmanual.io/)-style syntax and validate dataframes using the typing syntax.
-- `check_input` and `check_output` [decorators](https://pandera.readthedocs.io/en/stable/decorators.html#decorators-for-pipeline-integration)
-  enable seamless integration with existing code.
-- [`Check`s](https://pandera.readthedocs.io/en/stable/checks.html) provide flexibility and performance by providing access to `pandas`
-  API by design and offers built-in checks for common data tests.
-- [`Hypothesis`](https://pandera.readthedocs.io/en/stable/hypothesis.html) class provides a tidy-first interface for statistical hypothesis
-  testing.
-- `Check`s and `Hypothesis` objects support both [tidy and wide data validation](https://pandera.readthedocs.io/en/stable/checks.html#wide-checks).
-- Use schemas as generative contracts to [synthesize data](https://pandera.readthedocs.io/en/stable/data_synthesis_strategies.html) for unit testing.
-- [Schema inference](https://pandera.readthedocs.io/en/stable/schema_inference.html) allows you to bootstrap schemas from data.
-
-## How to Cite
-
-If you use `pandera` in the context of academic or industry research, please
-consider citing the **paper** and/or **software package**.
-
-### [Paper](https://conference.scipy.org/proceedings/scipy2020/niels_bantilan.html)
-
-```
-@InProceedings{ niels_bantilan-proc-scipy-2020,
-  author    = { {N}iels {B}antilan },
-  title     = { pandera: {S}tatistical {D}ata {V}alidation of {P}andas {D}ataframes },
-  booktitle = { {P}roceedings of the 19th {P}ython in {S}cience {C}onference },
-  pages     = { 116 - 124 },
-  year      = { 2020 },
-  editor    = { {M}eghann {A}garwal and {C}hris {C}alloway and {D}illon {N}iederhut and {D}avid {S}hupe },
-  doi       = { 10.25080/Majora-342d178e-010 }
-}
-```
-
-### Software Package
-
-[![DOI](https://img.shields.io/badge/DOI-10.5281/zenodo.3385265-blue?style=for-the-badge)](https://doi.org/10.5281/zenodo.3385265)
-
-
-## License and Credits
+## Next steps
 
-`pandera` is licensed under the [MIT license](license.txt) and is written and
-maintained by Niels Bantilan ([email protected])
+See the [official documentation](https://pandera.readthedocs.io) to learn more.

docs/source/checks.md

@@ -29,7 +29,7 @@ of boolean values. For the check to pass, all of the elements in the boolean
 series must evaluate to `True`, for example:
 
 ```{code-cell} python
-import pandera as pa
+import pandera.pandas as pa
 import pandas as pd
 
 check_lt_10 = pa.Check(lambda s: s <= 10)
@@ -54,7 +54,7 @@ schema = pa.DataFrameSchema({
 For common validation tasks, built-in checks are available in `pandera`.
 
 ```{code-cell} python
-import pandera as pa
+import pandera.pandas as pa
 
 schema = pa.DataFrameSchema({
     "small_values": pa.Column(float, pa.Check.less_than(100)),
@@ -75,7 +75,7 @@ you can provide the `element_wise=True` keyword argument:
 
 ```{code-cell} python
 import pandas as pd
-import pandera as pa
+import pandera.pandas as pa
 
 schema = pa.DataFrameSchema({
     "a": pa.Column(
@@ -140,7 +140,7 @@ fly.
 
 ```{code-cell} python
 import pandas as pd
-import pandera as pa
+import pandera.pandas as pa
 
 schema = pa.DataFrameSchema({
     "height_in_feet": pa.Column(
@@ -195,7 +195,7 @@ columns in a `DataFrame`. For example, if you want to make assertions about
 
 ```{code-cell} python
 import pandas as pd
-import pandera as pa
+import pandera.pandas as pa
 
 
 df = pd.DataFrame({
@@ -267,7 +267,7 @@ import warnings
 
 import numpy as np
 import pandas as pd
-import pandera as pa
+import pandera.pandas as pa
 
 from scipy.stats import normaltest
 

docs/source/dask.md

@@ -28,7 +28,7 @@ below we'll use the {ref}`class-based API <dataframe-models>` to define a
 ```{code-cell} python
 import dask.dataframe as dd
 import pandas as pd
-import pandera as pa
+import pandera.pandas as pa
 
 from pandera.typing.dask import DataFrame, Series
 

docs/source/data_format_conversion.md

@@ -23,7 +23,7 @@ type that supports this feature.
 Consider this simple example:
 
 ```{code-cell} python
-import pandera as pa
+import pandera.pandas as pa
 from pandera.typing import DataFrame, Series
 
 class InSchema(pa.DataFrameModel):

docs/source/data_synthesis_strategies.md

@@ -24,7 +24,7 @@ property-based testing library.
 Once you've defined a schema, it's easy to generate examples:
 
 ```{code-cell} python
-import pandera as pa
+import pandera.pandas as pa
 
 schema = pa.DataFrameSchema(
     {