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Merged
merged 17 commits into from
Apr 26, 2021
Merged

DOC: Fixed documentation for few files #40903

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9399ec6
Fix docs for algorithms.py
Apr 12, 2021
65e4ff4
Fix docs for nanops.py
Apr 12, 2021
291cae2
Fix docs for indexers.py
Apr 12, 2021
8274005
Merge remote-tracking branch 'upstream/master' into DOC-Fix-algo-inde…
Apr 19, 2021
857c110
Switching from `np.ndarray` to `np.array`
Apr 19, 2021
73d5d02
Rerunning the CI
Apr 19, 2021
0b0dd98
Merge remote-tracking branch 'upstream/master' into DOC-Fix-algo-inde…
Apr 19, 2021
4bca61f
Fixed failing tests
Apr 19, 2021
bec5b3a
Applied "black formatting" on the example code
Apr 19, 2021
6adfead
Revert "Applied "black formatting" on the example code"
Apr 26, 2021
7a03587
Merge remote-tracking branch 'upstream/master' into DOC-Fix-algo-inde…
Apr 26, 2021
72cf543
Revert "Revert "Applied "black formatting" on the example code""
Apr 26, 2021
f4d79c6
Make output on single line
Apr 26, 2021
e080f95
Revert "Make output on single line"
Apr 26, 2021
9aa2c44
Merge remote-tracking branch 'upstream/master' into DOC-Fix-algo-inde…
Apr 26, 2021
d60bc08
Merge remote-tracking branch 'upstream/master' into DOC-Fix-algo-inde…
Apr 26, 2021
c72ab22
Add the changed files to the CI test
Apr 26, 2021
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3 changes: 3 additions & 0 deletions ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -110,10 +110,13 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
pytest -q --doctest-modules \
pandas/core/accessor.py \
pandas/core/aggregation.py \
pandas/core/algorithms.py \
pandas/core/base.py \
pandas/core/construction.py \
pandas/core/frame.py \
pandas/core/generic.py \
pandas/core/indexers.py \
pandas/core/nanops.py \
pandas/core/series.py \
pandas/io/sql.py
RET=$(($RET + $?)) ; echo $MSG "DONE"
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62 changes: 38 additions & 24 deletions pandas/core/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -375,46 +375,60 @@ def unique(values):
>>> pd.unique(pd.Series([2] + [1] * 5))
array([2, 1])

>>> pd.unique(pd.Series([pd.Timestamp('20160101'),
... pd.Timestamp('20160101')]))
>>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

>>> pd.unique(pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
... pd.Timestamp('20160101', tz='US/Eastern')]))
array([Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')],
dtype=object)

>>> pd.unique(pd.Index([pd.Timestamp('20160101', tz='US/Eastern'),
... pd.Timestamp('20160101', tz='US/Eastern')]))
>>> pd.unique(
... pd.Series(
... [
... pd.Timestamp("20160101", tz="US/Eastern"),
... pd.Timestamp("20160101", tz="US/Eastern"),
... ]
... )
... )
<DatetimeArray>
['2016-01-01 00:00:00-05:00']
Length: 1, dtype: datetime64[ns, US/Eastern]

>>> pd.unique(
... pd.Index(
... [
... pd.Timestamp("20160101", tz="US/Eastern"),
... pd.Timestamp("20160101", tz="US/Eastern"),
... ]
... )
... )
DatetimeIndex(['2016-01-01 00:00:00-05:00'],
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Format here looks a bit odd?

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That's the "Black" formatting.

I personally think it's easier on the eyes, but that's not a deal breaker for me.

Can revert if you think it's best to leave it as it was before

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Sorry if this was misleading, I was referring to the lines you haven‘t touched, they look out of sync now

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I didn't managed to get the output lines to follow the "black" formatting styles.

What I could do is to make the output to appear in a single line, instead of multiple by using \. i.e

def foo():
    """
    Examples
    --------------
    >> foo()
    Line1 \
Line 2 \
Line 3 \
    """

Will result in the docs as:

>>> foo()
Line 1 Line 2 Line 3

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Sounds good to me

... dtype='datetime64[ns, US/Eastern]', freq=None)
dtype='datetime64[ns, US/Eastern]',
freq=None)

>>> pd.unique(list('baabc'))
>>> pd.unique(list("baabc"))
array(['b', 'a', 'c'], dtype=object)

An unordered Categorical will return categories in the
order of appearance.

>>> pd.unique(pd.Series(pd.Categorical(list('baabc'))))
[b, a, c]
Categories (3, object): [b, a, c]
>>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))
['b', 'a', 'c']
Categories (3, object): ['a', 'b', 'c']

>>> pd.unique(pd.Series(pd.Categorical(list('baabc'),
... categories=list('abc'))))
[b, a, c]
Categories (3, object): [b, a, c]
>>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc"))))
['b', 'a', 'c']
Categories (3, object): ['a', 'b', 'c']

An ordered Categorical preserves the category ordering.

>>> pd.unique(pd.Series(pd.Categorical(list('baabc'),
... categories=list('abc'),
... ordered=True)))
[b, a, c]
Categories (3, object): [a < b < c]
>>> pd.unique(
... pd.Series(
... pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)
... )
... )
['b', 'a', 'c']
Categories (3, object): ['a' < 'b' < 'c']

An array of tuples

>>> pd.unique([('a', 'b'), ('b', 'a'), ('a', 'c'), ('b', 'a')])
>>> pd.unique([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")])
array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)
"""
values = _ensure_arraylike(values)
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28 changes: 18 additions & 10 deletions pandas/core/indexers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -209,16 +209,24 @@ def validate_indices(indices: np.ndarray, n: int) -> None:

Examples
--------
>>> validate_indices([1, 2], 3)
# OK
>>> validate_indices([1, -2], 3)
ValueError
>>> validate_indices([1, 2, 3], 3)
IndexError
>>> validate_indices([-1, -1], 0)
# OK
>>> validate_indices([0, 1], 0)
IndexError
>>> validate_indices(np.array([1, 2]), 3) # OK

>>> validate_indices(np.array([1, -2]), 3)
Traceback (most recent call last):
...
ValueError: negative dimensions are not allowed

>>> validate_indices(np.array([1, 2, 3]), 3)
Traceback (most recent call last):
...
IndexError: indices are out-of-bounds

>>> validate_indices(np.array([-1, -1]), 0) # OK

>>> validate_indices(np.array([0, 1]), 0)
Traceback (most recent call last):
...
IndexError: indices are out-of-bounds
"""
if len(indices):
min_idx = indices.min()
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4 changes: 2 additions & 2 deletions pandas/core/nanops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1056,7 +1056,7 @@ def nanargmax(
[ 6., 7., nan],
[ 9., 10., nan]])
>>> nanops.nanargmax(arr, axis=1)
array([2, 2, 1, 1], dtype=int64)
array([2, 2, 1, 1])
"""
values, mask, _, _, _ = _get_values(values, True, fill_value_typ="-inf", mask=mask)
# error: Need type annotation for 'result'
Expand Down Expand Up @@ -1102,7 +1102,7 @@ def nanargmin(
[nan, 7., 8.],
[nan, 10., 11.]])
>>> nanops.nanargmin(arr, axis=1)
array([0, 0, 1, 1], dtype=int64)
array([0, 0, 1, 1])
"""
values, mask, _, _, _ = _get_values(values, True, fill_value_typ="+inf", mask=mask)
# error: Need type annotation for 'result'
Expand Down