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DOC: Fixing EX01 - Added examples #54004

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Jul 6, 2023
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DOC: Fixing EX01 - Added examples #54004

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6 changes: 0 additions & 6 deletions ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -110,12 +110,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
pandas_object \
pandas.api.interchange.from_dataframe \
pandas.DatetimeIndex.snap \
pandas.core.window.ewm.ExponentialMovingWindow.mean \
pandas.core.window.ewm.ExponentialMovingWindow.sum \
pandas.core.window.ewm.ExponentialMovingWindow.std \
pandas.core.window.ewm.ExponentialMovingWindow.var \
pandas.core.window.ewm.ExponentialMovingWindow.corr \
pandas.core.window.ewm.ExponentialMovingWindow.cov \
pandas.api.indexers.BaseIndexer \
pandas.api.indexers.VariableOffsetWindowIndexer \
pandas.io.formats.style.Styler \
Expand Down
102 changes: 88 additions & 14 deletions pandas/core/window/ewm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -502,7 +502,19 @@ def aggregate(self, func, *args, **kwargs):
create_section_header("See Also"),
template_see_also,
create_section_header("Notes"),
numba_notes.replace("\n", "", 1),
numba_notes,
create_section_header("Examples"),
dedent(
"""\
>>> ser = pd.Series([1, 2, 3, 4])
>>> ser.ewm(alpha=.2).mean()
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nice! I don't know how hard it is, but for a simple example like this one, would it be feasible to show how the result is calculated? like, to match it with just simple numpy operations?

0 1.000000
1 1.555556
2 2.147541
3 2.775068
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) mean",
agg_method="mean",
Expand Down Expand Up @@ -554,7 +566,19 @@ def mean(
create_section_header("See Also"),
template_see_also,
create_section_header("Notes"),
numba_notes.replace("\n", "", 1),
numba_notes,
create_section_header("Examples"),
dedent(
"""\
>>> ser = pd.Series([1, 2, 3, 4])
>>> ser.ewm(alpha=.2).sum()
0 1.000
1 2.800
2 5.240
3 8.192
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) sum",
agg_method="sum",
Expand Down Expand Up @@ -602,16 +626,28 @@ def sum(
template_header,
create_section_header("Parameters"),
dedent(
"""
"""\
bias : bool, default False
Use a standard estimation bias correction.
"""
).replace("\n", "", 1),
),
kwargs_numeric_only,
create_section_header("Returns"),
template_returns,
create_section_header("See Also"),
template_see_also[:-1],
template_see_also,
create_section_header("Examples"),
dedent(
"""\
>>> ser = pd.Series([1, 2, 3, 4])
>>> ser.ewm(alpha=.2).std()
0 NaN
1 0.707107
2 0.995893
3 1.277320
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) standard deviation",
agg_method="std",
Expand All @@ -632,16 +668,28 @@ def std(self, bias: bool = False, numeric_only: bool = False):
template_header,
create_section_header("Parameters"),
dedent(
"""
"""\
bias : bool, default False
Use a standard estimation bias correction.
"""
).replace("\n", "", 1),
),
kwargs_numeric_only,
create_section_header("Returns"),
template_returns,
create_section_header("See Also"),
template_see_also[:-1],
template_see_also,
create_section_header("Examples"),
dedent(
"""\
>>> ser = pd.Series([1, 2, 3, 4])
>>> ser.ewm(alpha=.2).var()
0 NaN
1 0.500000
2 0.991803
3 1.631547
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) variance",
agg_method="var",
Expand All @@ -665,7 +713,7 @@ def var_func(values, begin, end, min_periods):
template_header,
create_section_header("Parameters"),
dedent(
"""
"""\
other : Series or DataFrame , optional
If not supplied then will default to self and produce pairwise
output.
Expand All @@ -679,12 +727,25 @@ def var_func(values, begin, end, min_periods):
bias : bool, default False
Use a standard estimation bias correction.
"""
).replace("\n", "", 1),
),
kwargs_numeric_only,
create_section_header("Returns"),
template_returns,
create_section_header("See Also"),
template_see_also[:-1],
template_see_also,
create_section_header("Examples"),
dedent(
"""\
>>> ser1 = pd.Series([1, 2, 3, 4])
>>> ser2 = pd.Series([10, 11, 13, 16])
>>> ser1.ewm(alpha=.2).cov(ser2)
0 NaN
1 0.500000
2 1.524590
3 3.408836
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) sample covariance",
agg_method="cov",
Expand Down Expand Up @@ -739,7 +800,7 @@ def cov_func(x, y):
template_header,
create_section_header("Parameters"),
dedent(
"""
"""\
other : Series or DataFrame, optional
If not supplied then will default to self and produce pairwise
output.
Expand All @@ -751,12 +812,25 @@ def cov_func(x, y):
inputs. In the case of missing elements, only complete pairwise
observations will be used.
"""
).replace("\n", "", 1),
),
kwargs_numeric_only,
create_section_header("Returns"),
template_returns,
create_section_header("See Also"),
template_see_also[:-1],
template_see_also,
create_section_header("Examples"),
dedent(
"""\
>>> ser1 = pd.Series([1, 2, 3, 4])
>>> ser2 = pd.Series([10, 11, 13, 16])
>>> ser1.ewm(alpha=.2).corr(ser2)
0 NaN
1 1.000000
2 0.982821
3 0.977802
dtype: float64
"""
),
window_method="ewm",
aggregation_description="(exponential weighted moment) sample correlation",
agg_method="corr",
Expand Down