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DOC: Fixed examples in pandas/core/window #33266

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Apr 4, 2020
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DOC: Fixed examples in pandas/core/window #33266

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cf53fc4
DOC: Fixed examples in `pandas/core/window`
Apr 3, 2020
8fc6c0e
Using simpler numbers in examples
Apr 3, 2020
004ad4e
Merge remote-tracking branch 'upstream/master' into DOC-DocTests-core…
Apr 3, 2020
a2de389
Restarting azure
Apr 3, 2020
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4 changes: 4 additions & 0 deletions ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -322,6 +322,10 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
pytest -q --doctest-modules pandas/core/tools/
RET=$(($RET + $?)) ; echo $MSG "DONE"

MSG='Doctests window' ; echo $MSG
pytest -q --doctest-modules pandas/core/window/
RET=$(($RET + $?)) ; echo $MSG "DONE"

MSG='Doctests tseries' ; echo $MSG
pytest -q --doctest-modules pandas/tseries/
RET=$(($RET + $?)) ; echo $MSG "DONE"
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35 changes: 13 additions & 22 deletions pandas/core/window/ewm.py
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Expand Up @@ -167,33 +167,24 @@ def _constructor(self):
"""
Examples
--------

>>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])
>>> df = pd.DataFrame(
... {
... "A": [0.95117561, 0.632442, 0.72290796],
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Just a suggestion, I personally find these example easier to digest with "simpler numbers" like [1, 2, 3], [4, 5, 6]

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Oh OK, no problem. I'll fix that, was just avoiding random output in the docs.

... "B": [0.3740966, -1.48032596, -0.47270373],
... "C": [0.39592762, 0.73835624, -0.06817024],
... }
... )
>>> df
A B C
0 -2.385977 -0.102758 0.438822
1 -1.004295 0.905829 -0.954544
2 0.735167 -0.165272 -1.619346
3 -0.702657 -1.340923 -0.706334
4 -0.246845 0.211596 -0.901819
5 2.463718 3.157577 -1.380906
6 -1.142255 2.340594 -0.039875
7 1.396598 -1.647453 1.677227
8 -0.543425 1.761277 -0.220481
9 -0.640505 0.289374 -1.550670
0 0.951176 0.374097 0.395928
1 0.632442 -1.480326 0.738356
2 0.722908 -0.472704 -0.068170

>>> df.ewm(alpha=0.5).mean()
A B C
0 -2.385977 -0.102758 0.438822
1 -1.464856 0.569633 -0.490089
2 -0.207700 0.149687 -1.135379
3 -0.471677 -0.645305 -0.906555
4 -0.355635 -0.203033 -0.904111
5 1.076417 1.503943 -1.146293
6 -0.041654 1.925562 -0.588728
7 0.680292 0.132049 0.548693
8 0.067236 0.948257 0.163353
9 -0.286980 0.618493 -0.694496
0 0.951176 0.374097 0.395928
1 0.738687 -0.862185 0.624213
2 0.729670 -0.639624 0.228566
"""
)

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38 changes: 15 additions & 23 deletions pandas/core/window/expanding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,8 @@ class Expanding(_Rolling_and_Expanding):

Examples
--------
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})
>>> df
B
0 0.0
1 1.0
Expand Down Expand Up @@ -98,33 +99,24 @@ def _get_window(self, other=None, **kwargs):
"""
Examples
--------

>>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])
>>> df = pd.DataFrame(
... {
... "A": [0.95117561, 0.632442, 0.72290796],
... "B": [0.3740966, -1.48032596, -0.47270373],
... "C": [0.39592762, 0.73835624, -0.06817024],
... }
... )
>>> df
A B C
0 -2.385977 -0.102758 0.438822
1 -1.004295 0.905829 -0.954544
2 0.735167 -0.165272 -1.619346
3 -0.702657 -1.340923 -0.706334
4 -0.246845 0.211596 -0.901819
5 2.463718 3.157577 -1.380906
6 -1.142255 2.340594 -0.039875
7 1.396598 -1.647453 1.677227
8 -0.543425 1.761277 -0.220481
9 -0.640505 0.289374 -1.550670
0 0.951176 0.374097 0.395928
1 0.632442 -1.480326 0.738356
2 0.722908 -0.472704 -0.068170

>>> df.ewm(alpha=0.5).mean()
A B C
0 -2.385977 -0.102758 0.438822
1 -1.464856 0.569633 -0.490089
2 -0.207700 0.149687 -1.135379
3 -0.471677 -0.645305 -0.906555
4 -0.355635 -0.203033 -0.904111
5 1.076417 1.503943 -1.146293
6 -0.041654 1.925562 -0.588728
7 0.680292 0.132049 0.548693
8 0.067236 0.948257 0.163353
9 -0.286980 0.618493 -0.694496
0 0.951176 0.374097 0.395928
1 0.738687 -0.862185 0.624213
2 0.729670 -0.639624 0.228566
"""
)

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85 changes: 30 additions & 55 deletions pandas/core/window/rolling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1039,33 +1039,24 @@ def _get_window(
"""
Examples
--------

>>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])
>>> df = pd.DataFrame(
... {
... "A": [0.95117561, 0.632442, 0.72290796],
... "B": [0.3740966, -1.48032596, -0.47270373],
... "C": [0.39592762, 0.73835624, -0.06817024],
... }
... )
>>> df
A B C
0 -2.385977 -0.102758 0.438822
1 -1.004295 0.905829 -0.954544
2 0.735167 -0.165272 -1.619346
3 -0.702657 -1.340923 -0.706334
4 -0.246845 0.211596 -0.901819
5 2.463718 3.157577 -1.380906
6 -1.142255 2.340594 -0.039875
7 1.396598 -1.647453 1.677227
8 -0.543425 1.761277 -0.220481
9 -0.640505 0.289374 -1.550670

>>> df.rolling(3, win_type='boxcar').agg('mean')
0 0.951176 0.374097 0.395928
1 0.632442 -1.480326 0.738356
2 0.722908 -0.472704 -0.068170

>>> df.rolling(3, win_type="boxcar").agg("mean")
A B C
0 NaN NaN NaN
1 NaN NaN NaN
2 -0.885035 0.212600 -0.711689
3 -0.323928 -0.200122 -1.093408
4 -0.071445 -0.431533 -1.075833
5 0.504739 0.676083 -0.996353
6 0.358206 1.903256 -0.774200
7 0.906020 1.283573 0.085482
8 -0.096361 0.818139 0.472290
9 0.070889 0.134399 -0.031308
2 0.768842 -0.526311 0.355371
"""
)

Expand Down Expand Up @@ -1904,46 +1895,30 @@ def _validate_freq(self):
"""
Examples
--------

>>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])
>>> df = pd.DataFrame(
... {
... "A": [0.95117561, 0.632442, 0.72290796],
... "B": [0.3740966, -1.48032596, -0.47270373],
... "C": [0.39592762, 0.73835624, -0.06817024],
... }
... )
>>> df
A B C
0 -2.385977 -0.102758 0.438822
1 -1.004295 0.905829 -0.954544
2 0.735167 -0.165272 -1.619346
3 -0.702657 -1.340923 -0.706334
4 -0.246845 0.211596 -0.901819
5 2.463718 3.157577 -1.380906
6 -1.142255 2.340594 -0.039875
7 1.396598 -1.647453 1.677227
8 -0.543425 1.761277 -0.220481
9 -0.640505 0.289374 -1.550670
0 0.951176 0.374097 0.395928
1 0.632442 -1.480326 0.738356
2 0.722908 -0.472704 -0.068170

>>> df.rolling(3).sum()
>>> df.rolling(2).sum()
A B C
0 NaN NaN NaN
1 NaN NaN NaN
2 -2.655105 0.637799 -2.135068
3 -0.971785 -0.600366 -3.280224
4 -0.214334 -1.294599 -3.227500
5 1.514216 2.028250 -2.989060
6 1.074618 5.709767 -2.322600
7 2.718061 3.850718 0.256446
8 -0.289082 2.454418 1.416871
9 0.212668 0.403198 -0.093924

>>> df.rolling(3).agg({'A':'sum', 'B':'min'})
1 1.583618 -1.106229 1.134284
2 1.355350 -1.953030 0.670186

>>> df.rolling(2).agg({"A": "sum", "B": "min"})
A B
0 NaN NaN
1 NaN NaN
2 -2.655105 -0.165272
3 -0.971785 -1.340923
4 -0.214334 -1.340923
5 1.514216 -1.340923
6 1.074618 0.211596
7 2.718061 -1.647453
8 -0.289082 -1.647453
9 0.212668 -1.647453
1 1.583618 -1.480326
2 1.355350 -1.480326
"""
)

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