8000 BUG: Dense ranking with percent now uses 100% basis by rouzazari · Pull Request #15639 · pandas-dev/pandas · GitHub
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BUG: Dense ranking with percent now uses 100% basis #15639

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Mar 9, 2018
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Moved whatsnew to v.0.21.0
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rouzazari authored and gfyoung committed Mar 8, 2018
commit 83d76b3b9db443939458f4d6fcd81a3ede2d18c7
1 change: 0 additions & 1 deletion doc/source/whatsnew/v0.20.0.txt
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Expand Up @@ -1731,4 +1731,3 @@ Other
- Compat for 32-bit platforms for ``.qcut/cut``; bins will now be ``int64`` dtype (:issue:`14866`)
- Bug in interactions with ``Qt`` when a ``QtApplication`` already exists (:issue:`14372`)
- Avoid use of ``np.finfo()`` during ``import pandas`` removed to mitigate deadlock on Python GIL misuse (:issue:`14641`)
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can you move to 0.21.0

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to the numeric section of bug fixes. I thin this is ok as a bug fix, though if you think as a user it would warrant a bigger mention, a sub-section would be ok too.

- Bug in ``DataFrame.rank()`` and ``Series.rank()`` when ``method='dense'`` and ``pct=True`` (:issue:`15630`)
1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.23.0.txt
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Expand Up @@ -907,6 +907,7 @@ Offsets

Numeric
^^^^^^^
- Bug in ``DataFrame.rank()`` and ``Series.rank()`` when ``method='dense'`` and ``pct=True`` (:issue:`15630`)
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can you make these :func: refs, can you specify a little more what the bug is/was.

- Bug in :class:`Series` constructor with an int or float list where specifying ``dtype=str``, ``dtype='str'`` or ``dtype='U'`` failed to convert the data elements to strings (:issue:`16605`)
- Bug in :class:`Index` multiplication and division methods where operating with a ``Series`` would return an ``Index`` object instead of a ``Series`` object (:issue:`19042`)
- Bug in the :class:`DataFrame` constructor in which data containing very large positive or very large negative numbers was causing ``OverflowError`` (:issue:`18584`)
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14 changes: 6 additions & 8 deletions pandas/tests/frame/test_rank.py
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@@ -1,18 +1,16 @@
# -*- coding: utf-8 -*-
import pytest
from datetime import timedelta, datetime
from distutils.version import LooseVersion
from numpy import nan
import numpy as np
import pandas.util.testing as tm

from pandas import Series, DataFrame

import pytest
from distutils.version import LooseVersion
from datetime import timedelta, datetime
from numpy import nan

from pandas.compat import product
from pandas.util.testing import assert_frame_equal
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData
from pandas import Series, DataFrame
from pandas.compat import product


class TestRank(TestData):
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