# -*- coding: utf-8 -*-
from setuptools import setup

packages = \
['pandas_dataclasses', 'pandas_dataclasses.core']

package_data = \
{'': ['*']}

install_requires = \
['typing-extensions>=3.10,<5.0']

extras_require = \
{':python_full_version >= "3.7.1" and python_version < "3.8"': ['numpy>=1.20,<1.22',
                                                                'pandas>=1.2,<1.4',
                                                                'pandas-stubs>=1.2,<1.4'],
 ':python_version >= "3.8" and python_version < "3.11"': ['numpy>=1.20,<2.0',
                                                          'pandas>=1.2,<2.0',
                                                          'pandas-stubs>=1.2,<2.0']}

setup_kwargs = {
    'name': 'pandas-dataclasses',
    'version': '0.8.0',
    'description': 'pandas data creation made easy by dataclass',
    'long_description': '# pandas-dataclasses\n\n[![Release](https://img.shields.io/pypi/v/pandas-dataclasses?label=Release&color=cornflowerblue&style=flat-square)](https://pypi.org/project/pandas-dataclasses/)\n[![Python](https://img.shields.io/pypi/pyversions/pandas-dataclasses?label=Python&color=cornflowerblue&style=flat-square)](https://pypi.org/project/pandas-dataclasses/)\n[![Downloads](https://img.shields.io/pypi/dm/pandas-dataclasses?label=Downloads&color=cornflowerblue&style=flat-square)](https://pepy.tech/project/pandas-dataclasses)\n[![DOI](https://img.shields.io/badge/DOI-10.5281/zenodo.6127352-cornflowerblue?style=flat-square)](https://doi.org/10.5281/zenodo.6127352)\n[![Tests](https://img.shields.io/github/workflow/status/astropenguin/pandas-dataclasses/Tests?label=Tests&style=flat-square)](https://github.com/astropenguin/pandas-dataclasses/actions)\n\npandas data creation made easy by dataclass\n\n## Overview\n\npandas-dataclass makes it easy to create [pandas] data (DataFrame and Series) by specifying their data types, attributes, and names using the Python\'s dataclass:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nfrom dataclasses import dataclass\nfrom pandas_dataclasses import AsDataFrame, Data, Index\n```\n</details>\n\n```python\n@dataclass\nclass Weather(AsDataFrame):\n    """Weather information."""\n\n    year: Index[int]\n    month: Index[int]\n    temp: Data[float]\n    wind: Data[float]\n\n\ndf = Weather.new(\n    [2020, 2020, 2021, 2021, 2022],\n    [1, 7, 1, 7, 1],\n    [7.1, 24.3, 5.4, 25.9, 4.9],\n    [2.4, 3.1, 2.3, 2.4, 2.6],\n)\n```\n\nwhere `df` will become a DataFrame object like:\n\n```\n            temp  wind\nyear month\n2020 1       7.1   2.4\n     7      24.3   3.1\n2021 1       5.4   2.3\n     7      25.9   2.4\n2022 1       4.9   2.6\n```\n\n### Features\n\n- Specifying data types and names of each element in pandas data\n- Specifying metadata stored in pandas data attributes (attrs)\n- Support for hierarchical index and columns\n- Support for custom factory for data creation\n- Support for full [dataclass] features\n- Support for static type check by [mypy] and [Pyright] ([Pylance])\n\n### Installation\n\n```bash\npip install pandas-dataclasses\n```\n\n## How it works\n\npandas-dataclasses provides you the following features:\n\n- Type hints for dataclass fields (`Attr`, `Column`, `Data`, `Index`) to specify the data type and name of each element in pandas data\n- Mix-in classes for dataclasses (`As`, `AsDataFrame`, `AsSeries`) to create pandas data by a classmethod (`new`) that takes the same arguments as dataclass initialization\n\nWhen you call `new`, it will first create a dataclass object and then create a Series or DataFrame object from the dataclass object according the type hints and values in it.\nIn the example above, `df = Weather.new(...)` is thus equivalent to:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nfrom pandas_dataclasses import asdataframe\n```\n</details>\n\n```python\nobj = Weather([2020, ...], [1, ...], [7.1, ...], [2.4, ...])\ndf = asdataframe(obj)\n```\n\nwhere `asdataframe` is a conversion function.\npandas-dataclasses does not touch the dataclass object creation itself; this allows you to fully customize your dataclass before conversion by the dataclass features (`field`, `__post_init__`, ...).\n\n## Basic usage\n\n### DataFrame creation\n\nAs shown in the example above, a dataclass that has the `AsDataFrame` mix-in will create DataFrame objects:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nfrom dataclasses import dataclass\nfrom pandas_dataclasses import AsDataFrame, Data, Index\n```\n</details>\n\n```python\n@dataclass\nclass Weather(AsDataFrame):\n    """Weather information."""\n\n    year: Index[int]\n    month: Index[int]\n    temp: Data[float]\n    wind: Data[float]\n\n\ndf = Weather.new(...)\n```\n\nwhere fields typed by `Index` are *index fields*, each value of which will become an index or a part of a hierarchical index of a DataFrame object.\nFields typed by `Data` are *data fields*, each value of which will become a data column of a DataFrame object.\nFields typed by other types are just ignored in the DataFrame creation.\n\nEach data or index will be cast to the data type specified in a type hint like `Index[int]`.\nUse `Any` or `None` (like `Index[Any]`) if you do not want type casting.\nSee also [data typing rules](#data-typing-rules) for more examples.\n\nBy default, a field name (i.e. an argument name) is used for the name of corresponding data or index.\nSee also [custom naming](#custom-naming) and [naming rules](#naming-rules) if you want customization.\n\n### Series creation\n\nA dataclass that has the `AsSeries` mix-in will create Series objects:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nfrom dataclasses import dataclass\nfrom pandas_dataclasses import AsSeries, Data, Index\n```\n</details>\n\n```python\n@dataclass\nclass Weather(AsSeries):\n    """Weather information."""\n\n    year: Index[int]\n    month: Index[int]\n    temp: Data[float]\n\n\nser = Weather.new(...)\n```\n\nUnlike `AsDataFrame`, the second and subsequent data fields are ignored in the Series creation even if they exist.\nOther rules are the same as for the DataFrame creation.\n\n## Advanced usage\n\n### Metadata storing\n\nFields typed by `Attr` are *attribute fields*, each value of which will become an item of attributes of a DataFrame or a Series object:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nfrom dataclasses import dataclass\nfrom pandas_dataclasses import AsDataFrame, Attr, Data, Index\n```\n</details>\n\n```python\n@dataclass\nclass Weather(AsDataFrame):\n    """Weather information."""\n\n    year: Index[int]\n    month: Index[int]\n    temp: Data[float]\n    wind: Data[float]\n    loc: Attr[str] = "Tokyo"\n    lon: Attr[float] = 139.69167\n    lat: Attr[float] = 35.68944\n\n\ndf = Weather.new(...)\n```\n\nwhere `df.attrs` will become like:\n\n```python\n{"loc": "Tokyo", "lon": 139.69167, "lat": 35.68944}\n```\n\n### Custom naming\n\nThe name of attribute, data, or index can be explicitly specified by adding a hashable annotation to the corresponding type:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nfrom dataclasses import dataclass\nfrom typing import Annotated as Ann\nfrom pandas_dataclasses import AsDataFrame, Attr, Data, Index\n```\n</details>\n\n```python\n@dataclass\nclass Weather(AsDataFrame):\n    """Weather information."""\n\n    year: Ann[Index[int], "Year"]\n    month: Ann[Index[int], "Month"]\n    temp: Ann[Data[float], "Temperature (deg C)"]\n    wind: Ann[Data[float], "Wind speed (m/s)"]\n    loc: Ann[Attr[str], "Location"] = "Tokyo"\n    lon: Ann[Attr[float], "Longitude (deg)"] = 139.69167\n    lat: Ann[Attr[float], "Latitude (deg)"] = 35.68944\n\n\ndf = Weather.new(...)\n```\n\nwhere `df` and `df.attrs` will become like:\n\n```\n            Temperature (deg C)  Wind speed (m/s)\nYear Month\n2020 1                      7.1               2.4\n     7                     24.3               3.1\n2021 1                      5.4               2.3\n     7                     25.9               2.4\n2022 1                      4.9               2.6\n```\n\n```python\n{"Location": "Tokyo", "Longitude (deg)": 139.69167, "Latitude (deg)": 35.68944}\n```\n\nIf an annotation is a [format string], it will be formatted by a dataclass object before the data creation:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nfrom dataclasses import dataclass\nfrom typing import Annotated as Ann\nfrom pandas_dataclasses import AsDataFrame, Data, Index\n```\n</details>\n\n```python\n@dataclass\nclass Weather(AsDataFrame):\n    """Weather information."""\n\n    year: Ann[Index[int], "Year"]\n    month: Ann[Index[int], "Month"]\n    temp: Ann[Data[float], "Temperature ({.temp_unit})"]\n    wind: Ann[Data[float], "Wind speed ({.wind_unit})"]\n    temp_unit: str = "deg C"\n    wind_unit: str = "m/s"\n\n\ndf = Weather.new(..., temp_unit="deg F", wind_unit="km/h")\n```\n\nwhere units of the temperature and the wind speed will be dynamically updated (see also [naming rules](#naming-rules)).\n\n### Hierarchical columns\n\nAdding tuple annotations to data fields will create DataFrame objects with hierarchical columns:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nfrom dataclasses import dataclass\nfrom typing import Annotated as Ann\nfrom pandas_dataclasses import AsDataFrame, Data, Index\n```\n</details>\n\n```python\n@dataclass\nclass Weather(AsDataFrame):\n    """Weather information."""\n\n    year: Ann[Index[int], "Year"]\n    month: Ann[Index[int], "Month"]\n    temp_avg: Ann[Data[float], ("Temperature (deg C)", "Average")]\n    temp_max: Ann[Data[float], ("Temperature (deg C)", "Maximum")]\n    wind_avg: Ann[Data[float], ("Wind speed (m/s)", "Average")]\n    wind_max: Ann[Data[float], ("Wind speed (m/s)", "Maximum")]\n\n\ndf = Weather.new(...)\n```\n\nwhere `df` will become like:\n\n```\n           Temperature (deg C)         Wind speed (m/s)\n                       Average Maximum          Average Maximum\nYear Month\n2020 1                     7.1    11.1              2.4     8.8\n     7                    24.3    27.7              3.1    10.2\n2021 1                     5.4    10.3              2.3    10.7\n     7                    25.9    30.3              2.4     9.0\n2022 1                     4.9     9.4              2.6     8.8\n```\n\nColumn names can be (explicitly) specified by *column fields* (with hashable annotations):\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nfrom dataclasses import dataclass\nfrom typing import Annotated as Ann\nfrom pandas_dataclasses import AsDataFrame, Column, Data, Index\n```\n</details>\n\n```python\n@dataclass\nclass Weather(AsDataFrame):\n    """Weather information."""\n\n    year: Ann[Index[int], "Year"]\n    month: Ann[Index[int], "Month"]\n    temp_avg: Ann[Data[float], ("Temperature (deg C)", "Average")]\n    temp_max: Ann[Data[float], ("Temperature (deg C)", "Maximum")]\n    wind_avg: Ann[Data[float], ("Wind speed (m/s)", "Average")]\n    wind_max: Ann[Data[float], ("Wind speed (m/s)", "Maximum")]\n    meas: Ann[Column[None], "Measurement"] = None\n    stat: Ann[Column[None], "Statistic"] = None\n\n\ndf = Weather.new(...)\n```\n\nwhere `df` will become like:\n\n```\nMeasurement Temperature (deg C)         Wind speed (m/s)\nStatistic               Average Maximum          Average Maximum\nYear Month\n2020 1                      7.1    11.1              2.4     8.8\n     7                     24.3    27.7              3.1    10.2\n2021 1                      5.4    10.3              2.3    10.7\n     7                     25.9    30.3              2.4     9.0\n2022 1                      4.9     9.4              2.6     8.8\n```\n\nNote that the values of the columns fields never be used for the data creation (i.e. dummy values).\nIf a tuple annotation has [format string]s, they will also be formatted by a dataclass object (see also [naming rules](#naming-rules)).\n\n### Custom pandas factory\n\nA custom class can be specified as a factory for the Series or DataFrame creation by `As`, the generic version of `AsDataFrame` and `AsSeries`.\nNote that the custom class must be a subclass of either `pandas.Series` or `pandas.DataFrame`:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nimport pandas as pd\nfrom dataclasses import dataclass\nfrom pandas_dataclasses import As, Data, Index\n```\n</details>\n\n```python\nclass CustomSeries(pd.Series):\n    """Custom pandas Series."""\n\n    pass\n\n\n@dataclass\nclass Temperature(As[CustomSeries]):\n    """Temperature information."""\n\n    year: Index[int]\n    month: Index[int]\n    temp: Data[float]\n\n\nser = Temperature.new(...)\n```\n\nwhere `ser` will become a `CustomSeries` object.\n\nGeneric Series type (`Series[T]`) is also supported, however, only for static the type check in the current pandas versions.\nIn such cases, you can additionally give a factory that must work in runtime as a class argument:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nimport pandas as pd\nfrom dataclasses import dataclass\nfrom pandas_dataclasses import As, Data, Index\n```\n</details>\n\n```python\n@dataclass\nclass Temperature(As["pd.Series[float]"], factory=pd.Series):\n    """Temperature information."""\n\n    year: Index[int]\n    month: Index[int]\n    temp: Data[float]\n\n\nser = Temperature.new(...)\n```\n\nwhere `ser` is statically regarded as `Series[float]` but will become a `Series` object in runtime.\n\n## Appendix\n\n### Data typing rules\n\nThe data type (dtype) of data or index is determined from the first `Data` or `Index` type of the corresponding field, respectively.\nThe following table shows how the data type is inferred:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nfrom typing import Any, Annotated as Ann, Literal as L\nfrom pandas_dataclasses import Data\n```\n</details>\n\nType hint | Inferred data type\n--- | ---\n`Data[Any]` | `None` (no type casting)\n`Data[None]` | `None` (no type casting)\n`Data[int]` | `numpy.int64`\n`Data[numpy.int32]` | `numpy.int32`\n`Data[L["datetime64[ns]"]]` | `numpy.dtype("<M8[ns]")`\n`Data[L["category"]]` | `pandas.CategoricalDtype()`\n`Data[int] \\| str` | `numpy.int64`\n`Data[int] \\| Data[float]` | `numpy.int64`\n`Ann[Data[int], "spam"]` | `numpy.int64`\n`Data[Ann[int, "spam"]]` | `numpy.int64`\n\n### Naming rules\n\nThe name of attribute, column, data, or index is determined from the first annotation of the first `Attr`, `Column`, `Data`, or `Index` type of the corresponding field, respectively.\nIf the annotation is a [format string] or a tuple that has [format string]s, it (they) will be formatted by a dataclass object before the data creation.\nOtherwise, the field name (i.e. argument name) will be used.\nThe following table shows how the name is inferred:\n\n<details>\n<summary>Click to see all imports</summary>\n\n```python\nfrom typing import Any, Annotated as Ann\nfrom pandas_dataclasses import Data\n```\n</details>\n\nType hint | Inferred name\n--- | ---\n`Data[Any]` | (field name)\n`Ann[Data[Any], "spam"]` | `"spam"`\n`Ann[Data[Any], "spam", "ham"]` | `"spam"`\n`Ann[Data[Any], "spam"] \\| Ann[str, "ham"]` | `"spam"`\n`Ann[Data[Any], "spam"] \\| Ann[Data[float], "ham"]` | `"spam"`\n`Ann[Data[Any], "{.name}"` | `"{.name}".format(obj)`\n`Ann[Data[Any], ("spam", "ham")]` | `("spam", "ham")`\n`Ann[Data[Any], ("{.name}", "ham")]` | `("{.name}".format(obj), "ham")`\n\nwhere `obj` is a dataclass object that is expected to have `obj.name`.\n\n### Development roadmap\n\nRelease version | Features\n--- | ---\nv0.5 | Support for dynamic naming\nv0.6 | Support for extension array and dtype\nv0.7 | Support for hierarchical columns\nv0.8 | Support for mypy and callable pandas factory\nv1.0 | Initial major release (freezing public features until v2.0)\n\n<!-- References -->\n[dataclass]: https://docs.python.org/3/library/dataclasses.html\n[format string]: https://docs.python.org/3/library/string.html#format-string-syntax\n[mypy]: http://www.mypy-lang.org\n[NumPy]: https://numpy.org\n[pandas]: https://pandas.pydata.org\n[Pylance]: https://github.com/microsoft/pylance-release\n[Pyright]: https://github.com/microsoft/pyright\n',
    'author': 'Akio Taniguchi',
    'author_email': 'taniguchi@a.phys.nagoya-u.ac.jp',
    'maintainer': 'None',
    'maintainer_email': 'None',
    'url': 'https://github.com/astropenguin/pandas-dataclasses/',
    'packages': packages,
    'package_data': package_data,
    'install_requires': install_requires,
    'extras_require': extras_require,
    'python_requires': '>=3.7.1,<3.11',
}


setup(**setup_kwargs)
