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Revert "remove code for ptp()"
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This reverts commit d9e955e.
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hasnain2808 committed Dec 24, 2019
1 parent 0c19cd3 commit ab8343a
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Showing 3 changed files with 68 additions and 68 deletions.
72 changes: 36 additions & 36 deletions pandas/core/generic.py
Original file line number Diff line number Diff line change
Expand Up @@ -10184,42 +10184,42 @@ def mad(self, axis=None, skipna=None, level=None):
_min_examples,
)

# @classmethod
# def _add_series_only_operations(cls):
# """
# Add the series only operations to the cls; evaluate the doc
# strings again.
# """

# axis_descr, name, name2 = _doc_parms(cls)

# def nanptp(values, axis=0, skipna=True):
# nmax = nanops.nanmax(values, axis, skipna)
# nmin = nanops.nanmin(values, axis, skipna)
# warnings.warn(
# "Method .ptp is deprecated and will be removed "
# "in a future version. Use numpy.ptp instead."
# "if you are already using numpy.ptp and still getting this message,"
# "please call to_numpy() to avoid this message in future calls."
# "For example: np.ptp(pd.Series([1, 2, 3]).to_numpy())",
# FutureWarning,
# stacklevel=4,
# )
# return nmax - nmin

# cls.ptp = _make_stat_function(
# cls,
# "ptp",
# name,
# name2,
# axis_descr,
# """Return the difference between the min and max value.
# \n.. deprecated:: 0.24.0 Use numpy.ptp instead
# \nReturn the difference between the maximum value and the
# minimum value in the object. This is the equivalent of the
# ``numpy.ndarray`` method ``ptp``.""",
# nanptp,
# )
@classmethod
def _add_series_only_operations(cls):
"""
Add the series only operations to the cls; evaluate the doc
strings again.
"""

axis_descr, name, name2 = _doc_parms(cls)

def nanptp(values, axis=0, skipna=True):
nmax = nanops.nanmax(values, axis, skipna)
nmin = nanops.nanmin(values, axis, skipna)
warnings.warn(
"Method .ptp is deprecated and will be removed "
"in a future version. Use numpy.ptp instead."
"if you are already using numpy.ptp and still getting this message,"
"please call to_numpy() to avoid this message in future calls."
"For example: np.ptp(pd.Series([1, 2, 3]).to_numpy())",
FutureWarning,
stacklevel=4,
)
return nmax - nmin

cls.ptp = _make_stat_function(
cls,
"ptp",
name,
name2,
axis_descr,
"""Return the difference between the min and max value.
\n.. deprecated:: 0.24.0 Use numpy.ptp instead
\nReturn the difference between the maximum value and the
minimum value in the object. This is the equivalent of the
``numpy.ndarray`` method ``ptp``.""",
nanptp,
)

@classmethod
def _add_series_or_dataframe_operations(cls):
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2 changes: 1 addition & 1 deletion pandas/core/series.py
Original file line number Diff line number Diff line change
Expand Up @@ -4390,7 +4390,7 @@ def to_period(self, freq=None, copy=True):
["index"], docs={"index": "The index (axis labels) of the Series."},
)
Series._add_numeric_operations()
# Series._add_series_only_operations()
Series._add_series_only_operations()
Series._add_series_or_dataframe_operations()

# Add arithmetic!
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62 changes: 31 additions & 31 deletions pandas/tests/series/test_analytics.py
Original file line number Diff line number Diff line change
Expand Up @@ -861,37 +861,37 @@ def test_ptp(self):
assert np.ptp(ser) == np.ptp(arr)

# GH11163
# s = Series([3, 5, np.nan, -3, 10])
# with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# assert s.ptp() == 13
# assert pd.isna(s.ptp(skipna=False))

# mi = pd.MultiIndex.from_product([["a", "b"], [1, 2, 3]])
# s = pd.Series([1, np.nan, 7, 3, 5, np.nan], index=mi)

# expected = pd.Series([6, 2], index=["a", "b"], dtype=np.float64)
# with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# tm.assert_series_equal(s.ptp(level=0), expected)

# expected = pd.Series([np.nan, np.nan], index=["a", "b"])
# with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# tm.assert_series_equal(s.ptp(level=0, skipna=False), expected)

# msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>"
# with pytest.raises(ValueError, match=msg):
# with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# s.ptp(axis=1)

# s = pd.Series(["a", "b", "c", "d", "e"])
# msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"
# with pytest.raises(TypeError, match=msg):
# with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# s.ptp()

# msg = r"Series\.ptp does not implement numeric_only\."
# with pytest.raises(NotImplementedError, match=msg):
# with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# s.ptp(numeric_only=True)
s = Series([3, 5, np.nan, -3, 10])
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
assert s.ptp() == 13
assert pd.isna(s.ptp(skipna=False))

mi = pd.MultiIndex.from_product([["a", "b"], [1, 2, 3]])
s = pd.Series([1, np.nan, 7, 3, 5, np.nan], index=mi)

expected = pd.Series([6, 2], index=["a", "b"], dtype=np.float64)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
tm.assert_series_equal(s.ptp(level=0), expected)

expected = pd.Series([np.nan, np.nan], index=["a", "b"])
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
tm.assert_series_equal(s.ptp(level=0, skipna=False), expected)

msg = "No axis named 1 for object type <class 'pandas.core.series.Series'>"
with pytest.raises(ValueError, match=msg):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
s.ptp(axis=1)

s = pd.Series(["a", "b", "c", "d", "e"])
msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"
with pytest.raises(TypeError, match=msg):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
s.ptp()

msg = r"Series\.ptp does not implement numeric_only\."
with pytest.raises(NotImplementedError, match=msg):
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
s.ptp(numeric_only=True)

def test_repeat(self):
s = Series(np.random.randn(3), index=["a", "b", "c"])
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