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ci/code_checks.sh

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@@ -241,8 +241,8 @@ fi
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### DOCSTRINGS ###
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if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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MSG='Validate docstrings (GL06, GL07, GL09, SS04, SS05, PR03, PR04, PR05, PR10, EX04, RT04, RT05, SA05)' ; echo $MSG
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$BASE_DIR/scripts/validate_docstrings.py --format=azure --errors=GL06,GL07,GL09,SS04,SS05,PR03,PR04,PR05,PR10,EX04,RT04,RT05,SA05
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MSG='Validate docstrings (GL03, GL06, GL07, GL09, SS04, SS05, PR03, PR04, PR05, PR10, EX04, RT04, RT05, SA05)' ; echo $MSG
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$BASE_DIR/scripts/validate_docstrings.py --format=azure --errors=GL03,GL06,GL07,GL09,SS04,SS05,PR03,PR04,PR05,PR10,EX04,RT04,RT05,SA05
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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fi

doc/source/whatsnew/v0.25.0.rst

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@@ -131,7 +131,7 @@ Performance Improvements
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Bug Fixes
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~~~~~~~~~
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- Bug in :func:`to_datetime` which would raise an (incorrect) ``ValueError`` when called with a date far into the future and the ``format`` argument specified instead of raising ``OutOfBoundsDatetime`` (:issue:`23830`)
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-
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- Bug in an error message in :meth:`DataFrame.plot`. Improved the error message if non-numerics are passed to :meth:`DataFrame.plot` (:issue:`25481`)
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-
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Categorical
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- Bug in :func:`read_json` for ``orient='table'`` when it tries to infer dtypes by default, which is not applicable as dtypes are already defined in the JSON schema (:issue:`21345`)
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- Bug in :func:`read_json` for ``orient='table'`` and float index, as it infers index dtype by default, which is not applicable because index dtype is already defined in the JSON schema (:issue:`25433`)
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- Bug in :func:`read_json` for ``orient='table'`` and string of float column names, as it makes a column name type conversion to Timestamp, which is not applicable because column names are already defined in the JSON schema (:issue:`25435`)
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- :meth:`DataFrame.to_html` now raises ``TypeError`` when using an invalid type for the ``classes`` parameter instead of ``AsseertionError`` (:issue:`25608`)
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-
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-
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pandas/_libs/tslibs/timedeltas.pyx

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@@ -1156,7 +1156,6 @@ class Timedelta(_Timedelta):
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Notes
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-----
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The ``.value`` attribute is always in ns.
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"""
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def __new__(cls, object value=_no_input, unit=None, **kwargs):
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cdef _Timedelta td_base

pandas/core/arrays/datetimes.py

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return result
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f.__name__ = name
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f.__doc__ = "\n{}\n".format(docstring)
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f.__doc__ = docstring
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return property(f)
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pandas/core/arrays/sparse.py

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@@ -541,7 +541,6 @@ class SparseArray(PandasObject, ExtensionArray, ExtensionOpsMixin):
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3. ``data.dtype.fill_value`` if `fill_value` is None and `dtype`
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is not a ``SparseDtype`` and `data` is a ``SparseArray``.
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kind : {'integer', 'block'}, default 'integer'
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The type of storage for sparse locations.
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pandas/core/base.py

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.. versionadded:: 0.24.0
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Parameters
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dtype : str or numpy.dtype, optional

pandas/core/config.py

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.format(rkey=d.rkey if d.rkey else ''))
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s += u(')')
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s += '\n\n'
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return s
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pandas/core/frame.py

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Convert URLs to HTML links.
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.. versionadded:: 0.24.0
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%(returns)s
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See Also
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--------
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columns, considered measured variables (`value_vars`), are "unpivoted" to
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the row axis, leaving just two non-identifier columns, 'variable' and
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'value'.
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%(versionadded)s
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Parameters
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----------

pandas/core/generic.py

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The indexes ``i`` and ``j`` are now optional, and default to
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the two innermost levels of the index.
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"""
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axis = self._get_axis_number(axis)
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result = self.copy()
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_shared_docs['aggregate'] = dedent("""
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Aggregate using one or more operations over the specified axis.
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%(versionadded)s
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Parameters
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----------
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func : function, str, list or dict
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* DataFrame : when DataFrame.agg is called with several functions
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Return scalar, Series or DataFrame.
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%(see_also)s
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Notes
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-----
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`agg` is an alias for `aggregate`. Use the alias.
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A passed user-defined-function will be passed a Series for evaluation.
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%(examples)s
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""")
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%(examples)s""")
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_shared_docs['transform'] = ("""
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Call ``func`` on self producing a %(klass)s with transformed values
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Returns
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-------
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%(name1)s or %(name2)s (if level specified)
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%(name1)s or %(name2)s (if level specified)\
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%(see_also)s
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%(examples)s\
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"""
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%(name2)s.cumsum : Return cumulative sum over %(name2)s axis.
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%(name2)s.cumprod : Return cumulative product over %(name2)s axis.
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%(examples)s
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"""
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%(examples)s"""
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_cummin_examples = """\
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Examples

pandas/core/groupby/base.py

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property_wrapper_template = \
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"""@property
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def %(name)s(self) :
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\"""
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%(doc)s
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\"""
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\"""%(doc)s\"""
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return self.__getattr__('%(name)s')"""
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for name in whitelist:

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