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DOC: Fixing EX01 - Added examples (pandas-dev#53985)
* Expanding examples * change list for examples max, min * Remove replace() on Expanding examples
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+149
-23
lines changed

2 files changed

+149
-23
lines changed

‎ci/code_checks.sh

-11
Original file line numberDiff line numberDiff line change
@@ -114,17 +114,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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pandas.core.window.rolling.Window.sum \
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pandas.core.window.rolling.Window.var \
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pandas.core.window.rolling.Window.std \
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pandas.core.window.expanding.Expanding.count \
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pandas.core.window.expanding.Expanding.sum \
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pandas.core.window.expanding.Expanding.mean \
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pandas.core.window.expanding.Expanding.median \
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pandas.core.window.expanding.Expanding.min \
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pandas.core.window.expanding.Expanding.max \
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pandas.core.window.expanding.Expanding.corr \
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pandas.core.window.expanding.Expanding.cov \
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pandas.core.window.expanding.Expanding.skew \
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pandas.core.window.expanding.Expanding.apply \
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pandas.core.window.expanding.Expanding.quantile \
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pandas.core.window.ewm.ExponentialMovingWindow.mean \
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pandas.core.window.ewm.ExponentialMovingWindow.sum \
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pandas.core.window.ewm.ExponentialMovingWindow.std \

‎pandas/core/window/expanding.py

+149-12
Original file line numberDiff line numberDiff line change
@@ -183,7 +183,19 @@ def aggregate(self, func, *args, **kwargs):
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create_section_header("Returns"),
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template_returns,
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create_section_header("See Also"),
186-
template_see_also[:-1],
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template_see_also,
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create_section_header("Examples"),
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dedent(
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"""\
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>>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
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>>> ser.expanding().count()
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a 1.0
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b 2.0
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c 3.0
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d 4.0
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dtype: float64
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"""
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),
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window_method="expanding",
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aggregation_description="count of non NaN observations",
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agg_method="count",
@@ -198,7 +210,19 @@ def count(self, numeric_only: bool = False):
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create_section_header("Returns"),
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template_returns,
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create_section_header("See Also"),
201-
template_see_also[:-1],
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template_see_also,
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create_section_header("Examples"),
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dedent(
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"""\
217+
>>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
218+
>>> ser.expanding().apply(lambda s: s.max() - 2 * s.min())
219+
a -1.0
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b 0.0
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c 1.0
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d 2.0
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dtype: float64
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"""
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),
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window_method="expanding",
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aggregation_description="custom aggregation function",
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agg_method="apply",
@@ -231,7 +255,19 @@ def apply(
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create_section_header("See Also"),
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template_see_also,
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create_section_header("Notes"),
234-
numba_notes[:-1],
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numba_notes,
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create_section_header("Examples"),
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dedent(
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"""\
262+
>>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
263+
>>> ser.expanding().sum()
264+
a 1.0
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b 3.0
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c 6.0
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d 10.0
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dtype: float64
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"""
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),
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window_method="expanding",
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aggregation_description="sum",
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agg_method="sum",
@@ -258,7 +294,19 @@ def sum(
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create_section_header("See Also"),
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template_see_also,
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create_section_header("Notes"),
261-
numba_notes[:-1],
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numba_notes,
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create_section_header("Examples"),
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dedent(
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"""\
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>>> ser = pd.Series([3, 2, 1, 4], index=['a', 'b', 'c', 'd'])
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>>> ser.expanding().max()
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a 3.0
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b 3.0
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c 3.0
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d 4.0
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dtype: float64
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"""
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),
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window_method="expanding",
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aggregation_description="maximum",
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agg_method="max",
@@ -285,7 +333,19 @@ def max(
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create_section_header("See Also"),
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template_see_also,
287335
create_section_header("Notes"),
288-
numba_notes[:-1],
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numba_notes,
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create_section_header("Examples"),
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dedent(
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"""\
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>>> ser = pd.Series([2, 3, 4, 1], index=['a', 'b', 'c', 'd'])
341+
>>> ser.expanding().min()
342+
a 2.0
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b 2.0
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c 2.0
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d 1.0
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dtype: float64
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"""
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),
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window_method="expanding",
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aggregation_description="minimum",
291351
agg_method="min",
@@ -312,7 +372,19 @@ def min(
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create_section_header("See Also"),
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template_see_also,
314374
create_section_header("Notes"),
315-
numba_notes[:-1],
375+
numba_notes,
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create_section_header("Examples"),
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dedent(
378+
"""\
379+
>>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
380+
>>> ser.expanding().mean()
381+
a 1.0
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b 1.5
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c 2.0
384+
d 2.5
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dtype: float64
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"""
387+
),
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window_method="expanding",
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aggregation_description="mean",
318390
agg_method="mean",
@@ -339,7 +411,19 @@ def mean(
339411
create_section_header("See Also"),
340412
template_see_also,
341413
create_section_header("Notes"),
342-
numba_notes[:-1],
414+
numba_notes,
415+
create_section_header("Examples"),
416+
dedent(
417+
"""\
418+
>>> ser = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
419+
>>> ser.expanding().median()
420+
a 1.0
421+
b 1.5
422+
c 2.0
423+
d 2.5
424+
dtype: float64
425+
"""
426+
),
343427
window_method="expanding",
344428
aggregation_description="median",
345429
agg_method="median",
@@ -523,7 +607,20 @@ def sem(self, ddof: int = 1, numeric_only: bool = False):
523607
"scipy.stats.skew : Third moment of a probability density.\n",
524608
template_see_also,
525609
create_section_header("Notes"),
526-
"A minimum of three periods is required for the rolling calculation.\n",
610+
"A minimum of three periods is required for the rolling calculation.\n\n",
611+
create_section_header("Examples"),
612+
dedent(
613+
"""\
614+
>>> ser = pd.Series([-1, 0, 2, -1, 2], index=['a', 'b', 'c', 'd', 'e'])
615+
>>> ser.expanding().skew()
616+
a NaN
617+
b NaN
618+
c 0.935220
619+
d 1.414214
620+
e 0.315356
621+
dtype: float64
622+
"""
623+
),
527624
window_method="expanding",
528625
aggregation_description="unbiased skewness",
529626
agg_method="skew",
@@ -597,7 +694,21 @@ def kurt(self, numeric_only: bool = False):
597694
create_section_header("Returns"),
598695
template_returns,
599696
create_section_header("See Also"),
600-
template_see_also[:-1],
697+
template_see_also,
698+
create_section_header("Examples"),
699+
dedent(
700+
"""\
701+
>>> ser = pd.Series([1, 2, 3, 4, 5, 6], index=['a', 'b', 'c', 'd', 'e', 'f'])
702+
>>> ser.expanding(min_periods=4).quantile(.25)
703+
a NaN
704+
b NaN
705+
c NaN
706+
d 1.75
707+
e 2.00
708+
f 2.25
709+
dtype: float64
710+
"""
711+
),
601712
window_method="expanding",
602713
aggregation_description="quantile",
603714
agg_method="quantile",
@@ -714,7 +825,20 @@ def rank(
714825
create_section_header("Returns"),
715826
template_returns,
716827
create_section_header("See Also"),
717-
template_see_also[:-1],
828+
template_see_also,
829+
create_section_header("Examples"),
830+
dedent(
831+
"""\
832+
>>> ser1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
833+
>>> ser2 = pd.Series([10, 11, 13, 16], index=['a', 'b', 'c', 'd'])
834+
>>> ser1.expanding().cov(ser2)
835+
a NaN
836+
b 0.500000
837+
c 1.500000
838+
d 3.333333
839+
dtype: float64
840+
"""
841+
),
718842
window_method="expanding",
719843
aggregation_description="sample covariance",
720844
agg_method="cov",
@@ -782,9 +906,22 @@ def cov(
782906
columns on the second level.
783907
784908
In the case of missing elements, only complete pairwise observations
785-
will be used.
909+
will be used.\n
786910
"""
787-
).replace("\n", "", 1),
911+
),
912+
create_section_header("Examples"),
913+
dedent(
914+
"""\
915+
>>> ser1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
916+
>>> ser2 = pd.Series([10, 11, 13, 16], index=['a', 'b', 'c', 'd'])
917+
>>> ser1.expanding().corr(ser2)
918+
a NaN
919+
b 1.000000
920+
c 0.981981
921+
d 0.975900
922+
dtype: float64
923+
"""
924+
),
788925
window_method="expanding",
789926
aggregation_description="correlation",
790927
agg_method="corr",

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