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DOC: Add examples for creating an index accessor (#57867)
* DOC: Add examples for creating an index accessor * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix E501 lint errors * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix failing CI builds for Docstring * fix newline * Remove redundant code-block directives * Use variables for `Examples` content * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix docstring validation errors Co-authored-by: s1099 <46890315+s1099@users.noreply.github.com> * Fix docstring validation errors Co-authored-by: s1099 <46890315+s1099@users.noreply.github.com> * Fix CI errors * Fix CI errors * Fix CI errors * Fix CI errors * Fix CI errors (again lol) * Undo validation ignore for docstrings --------- Co-authored-by: TechnoShip123 <75491816+TechnoShip123@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: s1099 <46890315+s1099@users.noreply.github.com>
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‎pandas/core/accessor.py

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@@ -231,7 +231,7 @@ def __get__(self, obj, cls):
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return accessor_obj
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@doc(klass="", others="")
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@doc(klass="", examples="", others="")
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def _register_accessor(name: str, cls):
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"""
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Register a custom accessor on {klass} objects.
@@ -255,51 +255,26 @@ def _register_accessor(name: str, cls):
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Notes
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-----
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When accessed, your accessor will be initialized with the pandas object
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the user is interacting with. So the signature must be
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This function allows you to register a custom-defined accessor class for {klass}.
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The requirements for the accessor class are as follows:
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.. code-block:: python
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* Must contain an init method that:
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def __init__(self, pandas_object): # noqa: E999
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...
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* accepts a single {klass} object
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For consistency with pandas methods, you should raise an ``AttributeError``
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if the data passed to your accessor has an incorrect dtype.
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* raises an AttributeError if the {klass} object does not have correctly
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matching inputs for the accessor
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>>> pd.Series(["a", "b"]).dt
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Traceback (most recent call last):
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...
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AttributeError: Can only use .dt accessor with datetimelike values
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* Must contain a method for each access pattern.
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Examples
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--------
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In your library code::
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@pd.api.extensions.register_dataframe_accessor("geo")
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class GeoAccessor:
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def __init__(self, pandas_obj):
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self._obj = pandas_obj
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@property
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def center(self):
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# return the geographic center point of this DataFrame
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lat = self._obj.latitude
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lon = self._obj.longitude
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return (float(lon.mean()), float(lat.mean()))
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* The methods should be able to take any argument signature.
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def plot(self):
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# plot this array's data on a map, e.g., using Cartopy
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pass
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* Accessible using the @property decorator if no additional arguments are
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needed.
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Back in an interactive IPython session:
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.. code-block:: ipython
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In [1]: ds = pd.DataFrame({{"longitude": np.linspace(0, 10),
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...: "latitude": np.linspace(0, 20)}})
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In [2]: ds.geo.center
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Out[2]: (5.0, 10.0)
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In [3]: ds.geo.plot() # plots data on a map
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Examples
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--------
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{examples}
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"""
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def decorator(accessor):
@@ -318,21 +293,98 @@ def decorator(accessor):
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return decorator
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@doc(_register_accessor, klass="DataFrame")
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_register_df_examples = """
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An accessor that only accepts integers could
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have a class defined like this:
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>>> @pd.api.extensions.register_dataframe_accessor("int_accessor")
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... class IntAccessor:
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... def __init__(self, pandas_obj):
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... if not all(pandas_obj[col].dtype == 'int64' for col in pandas_obj.columns):
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... raise AttributeError("All columns must contain integer values only")
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... self._obj = pandas_obj
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...
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... def sum(self):
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... return self._obj.sum()
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...
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>>> df = pd.DataFrame([[1, 2], ['x', 'y']])
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>>> df.int_accessor
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Traceback (most recent call last):
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...
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AttributeError: All columns must contain integer values only.
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>>> df = pd.DataFrame([[1, 2], [3, 4]])
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>>> df.int_accessor.sum()
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0 4
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1 6
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dtype: int64"""
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@doc(_register_accessor, klass="DataFrame", examples=_register_df_examples)
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def register_dataframe_accessor(name: str):
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from pandas import DataFrame
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return _register_accessor(name, DataFrame)
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@doc(_register_accessor, klass="Series")
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_register_series_examples = """
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An accessor that only accepts integers could
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have a class defined like this:
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>>> @pd.api.extensions.register_series_accessor("int_accessor")
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... class IntAccessor:
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... def __init__(self, pandas_obj):
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... if not pandas_obj.dtype == 'int64':
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... raise AttributeError("The series must contain integer data only")
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... self._obj = pandas_obj
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...
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... def sum(self):
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... return self._obj.sum()
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...
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>>> df = pd.Series([1, 2, 'x'])
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>>> df.int_accessor
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Traceback (most recent call last):
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...
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AttributeError: The series must contain integer data only.
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>>> df = pd.Series([1, 2, 3])
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>>> df.int_accessor.sum()
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6"""
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@doc(_register_accessor, klass="Series", examples=_register_series_examples)
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def register_series_accessor(name: str):
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from pandas import Series
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return _register_accessor(name, Series)
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@doc(_register_accessor, klass="Index")
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_register_index_examples = """
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An accessor that only accepts integers could
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have a class defined like this:
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>>> @pd.api.extensions.register_index_accessor("int_accessor")
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... class IntAccessor:
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... def __init__(self, pandas_obj):
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... if not all(isinstance(x, int) for x in pandas_obj):
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... raise AttributeError("The index must only be an integer value")
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... self._obj = pandas_obj
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...
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... def even(self):
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... return [x for x in self._obj if x % 2 == 0]
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>>> df = pd.DataFrame.from_dict(
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... {"row1": {"1": 1, "2": "a"}, "row2": {"1": 2, "2": "b"}}, orient="index"
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... )
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>>> df.index.int_accessor
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Traceback (most recent call last):
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...
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AttributeError: The index must only be an integer value.
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>>> df = pd.DataFrame(
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... {"col1": [1, 2, 3, 4], "col2": ["a", "b", "c", "d"]}, index=[1, 2, 5, 8]
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... )
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>>> df.index.int_accessor.even()
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[2, 8]"""
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@doc(_register_accessor, klass="Index", examples=_register_index_examples)
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def register_index_accessor(name: str):
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from pandas import Index
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