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https://api.github.com/repos/pandas-dev/pandas/issues/25713 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25713/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25713/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25713/events | https://github.com/pandas-dev/pandas/pull/25713 | 420,619,731 | MDExOlB1bGxSZXF1ZXN0MjYwODYwNzU4 | 25,713 | Auto backport of pr 25701 on 0.24.x | {
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} | 5 | 2019-03-13T17:13:18Z | 2020-01-16T00:34:34Z | 2019-03-18T14:45:41Z | MEMBER | null | @TomAugspurger @jorisvandenbossche @jreback want to double check I did this right. I didn't use the -m flag during the cherry-pick process as that was causing failures
0.24.3 at this point? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25714 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25714/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25714/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25714/events | https://github.com/pandas-dev/pandas/pull/25714 | 420,619,892 | MDExOlB1bGxSZXF1ZXN0MjYwODYwODg3 | 25,714 | #25707 - Fixed flakiness in stata write test | {
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} | 3 | 2019-03-13T17:13:41Z | 2019-03-13T20:18:37Z | 2019-03-13T20:10:49Z | CONTRIBUTOR | null | This is my first contribution, so please be gentle.
For future reference, this error occurs when the two files are written in different minutes. I couldn't break the comparison by feeding the test datetime objects that only changed the seconds, so I assume only the date, hours, and minutes, are encoded.
Anyway, we can't pass `False` as a keyword argument for `time_stamp`. It defaults to `None`, which internally uses the current timestamp, but otherwise expects a datetime.
For testing purposes, it suffice to pass the same arbitrary timestamp as a keyword. That way it encodes the same timestamp into each file, regardless of when the test runs and how long it takes.
Instead of `False`, we could use a keyword argument like this: `{'time_stamp': pd.to_datetime('2019-01-01 00:00')}`
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https://api.github.com/repos/pandas-dev/pandas/issues/25715 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25715/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25715/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25715/events | https://github.com/pandas-dev/pandas/issues/25715 | 420,629,087 | MDU6SXNzdWU0MjA2MjkwODc= | 25,715 | CI: Linting error | {
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} | [] | closed | false | null | [] | null | 1 | 2019-03-13T17:33:11Z | 2019-03-13T17:35:18Z | 2019-03-13T17:35:18Z | CONTRIBUTOR | null | I'm working on #25424, after rebase to upstream/master, I get [linting errors](https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=9370).
error F632, E117 are simple fix.
error F821 is type hints as flakes8 cannot detect buildin type.
I can fix as:
a. add `#noqa` at the end line
b. add build in option in setup.cfg
```
builtins =
_,
_LE,
_LW
```
```
##[error]./pandas/tests/indexes/interval/test_interval_tree.py(162,20): error F632: use ==/!= to compare str, bytes, and int literals
##[error]./pandas/tests/frame/test_alter_axes.py(204,32): error F632: use ==/!= to compare str, bytes, and int literals
##[error]./pandas/tests/frame/test_alter_axes.py(204,51): error F632: use ==/!= to compare str, bytes, and int literals
##[error]./pandas/tests/frame/test_alter_axes.py(1275,13): error E117: over-indented (comment)
##[error]./pandas/tests/io/test_pytables.py(2389,13): error E117: over-indented
##[error]./pandas/tests/series/test_rank.py(424,9): error E117: over-indented
##[error]./pandas/tests/series/test_rank.py(442,9): error E117: over-indented
##[error]./pandas/tests/series/test_rank.py(460,9): error E117: over-indented
##[error]./pandas/tests/series/test_rank.py(479,9): error E117: over-indented
##[error]./pandas/tests/series/test_rank.py(497,9): error E117: over-indented
##[error]./pandas/tests/series/test_rank.py(506,9): error E117: over-indented (comment)
##[error]./pandas/tests/series/test_rank.py(507,9): error E117: over-indented
##[error]./pandas/tests/groupby/test_grouping.py(652,13): error E117: over-indented (comment)
##[error]./pandas/tests/groupby/test_grouping.py(653,13): error E117: over-indented (comment)
##[error]./pandas/tests/groupby/test_grouping.py(654,13): error E117: over-indented (comment)
##[error]./pandas/tests/groupby/test_grouping.py(655,13): error E117: over-indented
##[error]./pandas/tests/groupby/test_apply.py(12,9): error E117: over-indented (comment)
##[error]./pandas/io/formats/format.py(443,45): error E117: over-indented (comment)
##[error]./pandas/core/frame.py(6219,33): error F821: undefined name 'Union'
##[error]./pandas/core/frame.py(6219,33): error F821: undefined name 'List'
##[error]./pandas/core/frame.py(6221,33): error F821: undefined name 'Union'
##[error]./pandas/core/frame.py(6223,9): error F821: undefined name 'Union'
##[error]./pandas/core/common.py(96,5): error F821: undefined name 'Any'
##[error]./pandas/core/base.py(789,9): error F821: undefined name 'ExtensionArray'
##[error]./pandas/core/indexes/base.py(3635,9): error F821: undefined name 'Union'
##[error]./pandas/core/dtypes/base.py(67,9): error F821: undefined name 'Optional'
##[error]./pandas/core/dtypes/base.py(67,9): error F821: undefined name 'List'
##[error]./pandas/core/dtypes/dtypes.py(940,17): error E117: over-indented
##[error]./pandas/core/arrays/sparse.py(82,9): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/sparse.py(82,9): error F821: undefined name 'Any'
##[error]./pandas/core/arrays/sparse.py(375,5): error F821: undefined name 'ndarray'
##[error]./pandas/core/arrays/sparse.py(400,5): error F821: undefined name 'Callable'
##[error]./pandas/core/arrays/sparse.py(400,5): error F821: undefined name 'Any'
##[error]./pandas/core/arrays/sparse.py(674,9): error F821: undefined name 'SparseIndex'
##[error]./pandas/core/arrays/array_.py(10,25): error F821: undefined name 'Sequence'
##[error]./pandas/core/arrays/array_.py(11,25): error F821: undefined name 'Optional'
##[error]./pandas/core/arrays/array_.py(11,25): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/array_.py(11,25): error F821: undefined name 'np'
##[error]./pandas/core/arrays/array_.py(11,25): error F821: undefined name 'ExtensionDtype'
##[error]./pandas/core/arrays/integer.py(513,9): error F821: undefined name 'ndarray'
##[error]./pandas/core/arrays/datetimelike.py(61,9): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/datetimelike.py(61,9): error F821: undefined name 'Tuple'
##[error]./pandas/core/arrays/datetimelike.py(71,9): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/datetimelike.py(71,9): error F821: undefined name 'Timestamp'
##[error]./pandas/core/arrays/datetimelike.py(71,9): error F821: undefined name 'NaTType'
##[error]./pandas/core/arrays/datetimelike.py(92,9): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/datetimelike.py(92,9): error F821: undefined name 'Timestamp'
##[error]./pandas/core/arrays/datetimelike.py(92,9): error F821: undefined name 'NaTType'
##[error]./pandas/core/arrays/datetimelike.py(112,9): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/datetimelike.py(112,9): error F821: undefined name 'Timestamp'
##[error]./pandas/core/arrays/datetimelike.py(112,9): error F821: undefined name 'NaTType'
##[error]./pandas/core/arrays/datetimelike.py(353,9): error F821: undefined name 'ndarray'
##[error]./pandas/core/arrays/datetimelike.py(463,21): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/datetimelike.py(463,21): error F821: undefined name 'Sequence'
##[error]./pandas/core/arrays/datetimelike.py(463,21): error F821: undefined name 'Sequence'
##[error]./pandas/core/arrays/datetimelike.py(464,21): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/datetimelike.py(464,21): error F821: undefined name 'NaTType'
##[error]./pandas/core/arrays/datetimelike.py(464,21): error F821: undefined name 'Scalar'
##[error]./pandas/core/arrays/datetimelike.py(464,21): error F821: undefined name 'Sequence'
##[error]./pandas/core/arrays/datetimelike.py(464,21): error F821: undefined name 'Scalar'
##[error]./pandas/core/arrays/datetimes.py(277,20): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/datetimes.py(517,9): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/period.py(183,9): error F821: undefined name 'Sequence'
##[error]./pandas/core/arrays/period.py(183,9): error F821: undefined name 'Optional'
##[error]./pandas/core/arrays/period.py(246,9): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/period.py(246,9): error F821: undefined name 'NaTType'
##[error]./pandas/core/arrays/period.py(539,22): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/period.py(539,22): error F821: undefined name 'Index'
##[error]./pandas/core/arrays/period.py(539,22): error F821: undefined name 'ExtensionArray'
##[error]./pandas/core/arrays/period.py(540,21): error F821: undefined name 'Callable'
##[error]./pandas/core/arrays/period.py(540,21): error F821: undefined name 'Any'
##[error]./pandas/core/arrays/period.py(540,21): error F821: undefined name 'Any'
##[error]./pandas/core/arrays/period.py(713,5): error F821: undefined name 'Sequence'
##[error]./pandas/core/arrays/period.py(713,5): error F821: undefined name 'Optional'
##[error]./pandas/core/arrays/period.py(713,5): error F821: undefined name 'Optional'
##[error]./pandas/core/arrays/base.py(216,9): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/base.py(216,9): error F821: undefined name 'Any'
##[error]./pandas/core/arrays/base.py(289,9): error F821: undefined name 'ExtensionDtype'
##[error]./pandas/core/arrays/base.py(297,9): error F821: undefined name 'Tuple'
##[error]./pandas/core/arrays/base.py(345,9): error F821: undefined name 'Union'
##[error]./pandas/core/arrays/base.py(368,9): error F821: undefined name 'ndarray'
##[error]./pandas/core/arrays/base.py(600,9): error F821: undefined name 'Tuple'
##[error]./pandas/core/arrays/base.py(600,9): error F821: undefined name 'ndarray'
##[error]./pandas/core/arrays/base.py(600,9): error F821: undefined name 'Any'
##[error]./pandas/core/arrays/base.py(625,9): error F821: undefined name 'Tuple'
##[error]./pandas/core/arrays/base.py(625,9): error F821: undefined name 'ndarray'
##[error]./pandas/core/arrays/base.py(728,9): error F821: undefined name 'Sequence'
##[error]./pandas/core/arrays/base.py(728,9): error F821: undefined name 'Optional'
##[error]./pandas/core/arrays/base.py(728,9): error F821: undefined name 'Any'
##[error]./pandas/core/arrays/base.py(855,9): error F821: undefined name 'Callable'
##[error]./pandas/core/arrays/base.py(855,9): error F821: undefined name 'Any'
##[error]./pandas/core/arrays/base.py(855,9): error F821: undefined name 'Optional'
##[error]./pandas/core/arrays/base.py(900,9): error F821: undefined name 'Sequence'
##[error]./pandas/core/groupby/groupby.py(1043,13): error F821: undefined name 'typing'
##[error]./pandas/core/groupby/groupby.py(1052,13): error F821: undefined name 'typing'
##[error]./pandas/core/groupby/groupby.py(1741,13): error F821: undefined name 'Optional'
##[error]./pandas/core/groupby/groupby.py(1741,13): error F821: undefined name 'typing'
##[error]./pandas/core/groupby/groupby.py(1756,13): error F821: undefined name 'Optional'
##[error]./pandas/core/groupby/groupby.py(1756,13): error F821: undefined name 'typing'
##[error]./pandas/core/internals/blocks.py(1830,9): error F821: undefined name 'Optional'
##[error]./pandas/core/internals/blocks.py(1830,9): error F821: undefined name 'BlockPlacement'
##[error]./pandas/core/internals/blocks.py(1830,9): error F821: undefined name 'Any'
##[error]./pandas/core/internals/blocks.py(1830,9): error F821: undefined name 'List'
##[error]./pandas/core/internals/managers.py(1865,5): error F821: undefined name 'List'
##[error]./pandas/core/internals/managers.py(1865,5): error F821: undefined name 'Optional'
##[error]./pandas/core/internals/managers.py(1865,5): error F821: undefined name 'Union'
##[error]./pandas/core/internals/managers.py(1865,5): error F821: undefined name 'ExtensionDtype'
Linting .py code DONE
Linting .pyx code
##[error]pandas/_libs/tslibs/nattype.pyx(356,42): error E261: at least two spaces before inline comment
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} | 1 | 2019-03-13T19:13:49Z | 2019-03-13T19:19:21Z | 2019-03-13T19:19:05Z | NONE | null | ```python
# Your code here
import pandas as pd
from numpy import nan
df = pd.DataFrame([{'sample': nan }])
df.iloc[0].sample
df.astype('str').iloc[0].sample
```
#### Problem description
When converting any datatype column to string using astype('str') null values (NaN object) is converted to literal nan string
Instead of remaining as null object value
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} | 4 | 2019-03-13T23:00:37Z | 2019-03-19T23:40:34Z | 2019-03-19T23:40:31Z | CONTRIBUTOR | null | API breaking change to `concat(List[DataFrame[Sparse]])` to
return a DataFrame with sparse values, rather than a SparseDataFrame.
Doing an outright break, rather than deprecation, because I have a
followup PR deprecating SparseDataFrame. We hit this internally in
a few places (e.g. get_dummies on all-sparse data).
Closes https://github.com/pandas-dev/pandas/issues/25702
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/25720 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25720/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25720/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25720/events | https://github.com/pandas-dev/pandas/pull/25720 | 420,778,586 | MDExOlB1bGxSZXF1ZXN0MjYwOTg1MjY5 | 25,720 | ENH: Add public start, stop, and step attributes to RangeIndex | {
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} | 3 | 2019-03-14T00:25:46Z | 2019-03-14T18:38:07Z | 2019-03-14T12:38:53Z | MEMBER | null | - [X] closes #25710
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
Comments:
- Was originally going to add the tests for this to `test_constructor` but decided to create a new test instead. I parametrized `test_constructor` to clean it up before deciding to create a separate test and kept those changes in the PR.
- Copied the `start`/`stop`/`step` docstrings from the Python docs for `range` with a small rewording to fit it on one line | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25721 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25721/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25721/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25721/events | https://github.com/pandas-dev/pandas/pull/25721 | 420,807,913 | MDExOlB1bGxSZXF1ZXN0MjYxMDA2ODUw | 25,721 | TST: assert_produces_warning works with filterwarnings | {
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} | 7 | 2019-03-14T02:51:43Z | 2019-04-18T18:31:10Z | 2019-03-19T23:40:09Z | CONTRIBUTOR | null | Previously, assert_produces_warning did not play well with pytest's
filterwarnings.
```
===================================================================================== FAILURES ======================================================================================
____________________________________________________________________ test_assert_produces_warning_honors_filter _____________________________________________________________________
@pytest.mark.filterwarnings('ignore:f1:FutureWarning')
def test_assert_produces_warning_honors_filter():
with tm.assert_produces_warning(RuntimeWarning):
> f()
pandas/tests/util/test_assert_produces_warning.py:17:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x11dd61128>, type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('FutureWarning', FutureWarning('f1'), '/Users/taugspurger/sandbox/pandas/pandas/tests/util/test_assert_produces_warni
ng.py', 10)].
/usr/local/Cellar/python/3.7.2_2/Frameworks/Python.framework/Versions/3.7/lib/python3.7/contextlib.py:119: AssertionError
============================================================================= 1 failed in 0.20 seconds ==============================================================================
```
Internally, `assert_produces_warning` sets a new `filter`, which
overrides any filters set by pytest. We allow for `filter_level=None` to
not set a filter.
This will be useful for the SparseDataFrame deprecation, where I'd like to just set a `pytest.mark.filterwarning` on the class, and not worry about catching individual warnings. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25722 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25722/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25722/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25722/events | https://github.com/pandas-dev/pandas/issues/25722 | 420,869,372 | MDU6SXNzdWU0MjA4NjkzNzI= | 25,722 | BUG: pd.to_datetime | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25723 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25723/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25723/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25723/events | https://github.com/pandas-dev/pandas/issues/25723 | 420,888,776 | MDU6SXNzdWU0MjA4ODg3NzY= | 25,723 | Excel Document Passing Kwargs to Engine | {
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"description": "read_excel, to_excel"... | closed | false | null | [] | null | 11 | 2019-03-14T08:32:27Z | 2019-05-20T16:31:05Z | 2019-05-20T16:31:05Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
xls = pandas.ExcelFile(path, on_demand = True)
sheets = xls.sheet_names
```
#### Problem description
This code used to work, since extra keyword arguments were passed to `xlrd.open_workbook`. As `pd.ExcelFile` is now decoupled from `xlrd` this behaviour was removed. It now returns an error:
```
TypeError: __init__() got an unexpected keyword argument 'on_demand'
```
In a way this is related to #25523, but this is a more general issue, and might or might not solve the problem in the other issue.
#### Expected Output
No error, and the keyword arguments respected. I understand that when other engines are implemented the `on_demand` argument might not make sense, but they might take similar arguments not explicitly made available in the interface of `pandas.ExcelFile`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.7.3
Cython: 0.29.5
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.14
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.3.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.17
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
| {
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https://api.github.com/repos/pandas-dev/pandas/issues/25724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25724/events | https://github.com/pandas-dev/pandas/issues/25724 | 420,920,830 | MDU6SXNzdWU0MjA5MjA4MzA= | 25,724 | Can't explain a "Error tokenizing data. C error: Expected.." | {
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"descript... | closed | false | null | [] | null | 3 | 2019-03-14T09:50:04Z | 2019-03-14T17:17:30Z | 2019-03-14T16:15:42Z | NONE | null |
Hi guys,
I have a curious case I can't explain.
The error :
**Error tokenizing data. C error: Expected 65 fields in line 3, saw 91**
when using
`data = pd.read_csv("/Users/me/Projects/ML/Isolated.csv", header=0, escapechar='\\' )`
#### Problem description
My csv has header and three lines.
The third line of the file is the issue. That line has 22 columns as the header.
The second line also comes in 22 columns.
I can't explain why the error, as the `,` is a delimiter.
The weird thing : **taking the third line and moving it up as second line, the error disappear... same file, same data**... just different order helps..
Of course also the header comes in 22 columns, same delimiter.
My csv file is available here:
https://send.firefox.com/download/c0992396d7/#xsEsxH6tlHbtt7edDYsaZQ
Thanks in advance for any precious help and hint. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25725 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25725/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25725/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25725/events | https://github.com/pandas-dev/pandas/issues/25725 | 420,990,732 | MDU6SXNzdWU0MjA5OTA3MzI= | 25,725 | CLN: remove PY2 | {
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} | 16 | 2019-03-14T12:28:10Z | 2019-07-23T19:52:39Z | 2019-07-23T19:52:39Z | CONTRIBUTOR | null | master issue for removing all vestigaes of Python 2.
After merging: #24942 (removing the PY2 CI jobs) and #25554 (bump numpy to 1.13.3), we will be ready to remove PY2 code, tests, and doc references.
Let's do this in bite sized pieces. IOW don't do a PR with changes for everything.
cc @pandas-dev/pandas-core | {
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} | 14 | 2019-03-14T12:36:59Z | 2019-03-18T12:23:52Z | 2019-03-18T12:23:52Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from scipy.stats import pearsonr
df = pd.DataFrame({'A': [1,2,3], 'B': [2,5,6]})
print(df.corr(method=lambda x, y: pearsonr(x, y)[1]))
A B
A 1.000000 0.178912
B 0.178912 1.000000
```
#### Problem description
I want to use the method argument of `corr` to compute p-values. However, diagonal elements are set to `1`. I would expect them to be `0`. They are set to `1` here: https://github.com/pandas-dev/pandas/blob/cb00deb94500205fcb27a33cc1d0df79a9727f8b/pandas/core/frame.py#L7025-L7026
Although I can see that for a 'normal' correlation `1` is expected, this is not the case in my example. Hence, I would suggest to remove these [two lines](https://github.com/pandas-dev/pandas/blob/cb00deb94500205fcb27a33cc1d0df79a9727f8b/pandas/core/frame.py#L7025-L7026) from `frame.py`.
#### Expected Output
```python
A B
A 0.000000 0.178912
B 0.178912 0.000000
```
#### Output of ``pd.show_versions()``
<details>
```python
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.165-81-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.1.1
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.3
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.3
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.4.0-b1
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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} | 1 | 2019-03-14T12:44:07Z | 2019-03-14T15:26:14Z | 2019-03-14T15:26:14Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from scipy.stats import pearsonr
df = pd.DataFrame({'A': [1,2,3], 'B': [2,5,6]})
df.corr(method='a')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-19-be8a09a85708> in <module>
2
3 df = pd.DataFrame({'A': [1,2,3], 'B': [2,5,6]})
----> 4 df.corr(method='a')
~/miniconda3/envs/spols190117/lib/python3.6/site-packages/pandas/core/frame.py in corr(self, method, min_periods)
7034 raise ValueError("method must be either 'pearson', "
7035 "'spearman', or 'kendall', '{method}' "
-> 7036 "was supplied".format(method=method))
7037
7038 return self._constructor(correl, index=idx, columns=cols)
ValueError: method must be either 'pearson', 'spearman', or 'kendall', 'a' was supplied
```
#### Problem description
The `ValueError` in the example above does not mention that a `callable` could be supplied to `corr` as well. I would suggest to add this to the error message.
#### Expected Output
```python
ValueError: method must be either 'pearson', 'spearman', 'kendall' or callable, 'a' was supplied
```
#### Output of ``pd.show_versions()``
<details>
```python
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.165-81-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.1.1
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.3
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.3
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.4.0-b1
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
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} | 4 | 2019-03-14T13:39:55Z | 2019-03-14T15:26:17Z | 2019-03-14T15:26:15Z | CONTRIBUTOR | null | to include the option of supplying a callable.
- [x] closes #25727
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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df
valuation_date
0 2018-06-08
1 2018-06-11
2 2018-06-12
3 2018-06-13
4 2018-06-14
5 2018-06-15
```
```
type(valuation_dates_df)
Out[16]: pandas.core.frame.DataFrame
type(valuation_dates_df['valuation_date'])
Out[15]: pandas.core.series.Series
```
To reproduce the error:
```
dict= {'valuation_date': {0: Timestamp('2018-06-08 00:00:00'),
1: Timestamp('2018-06-11 00:00:00'),
2: Timestamp('2018-06-12 00:00:00'),
3: Timestamp('2018-06-13 00:00:00'),
4: Timestamp('2018-06-14 00:00:00'),
5: Timestamp('2018-06-15 00:00:00')}}
pd.DataFrame.from_records(dict)['valuation_date'].astype(datetime.datetime)
valuation_dates_df['valuation_date'].astype(datetime.datetime)
File "C:\git\mre_x\_tcp\work\win-na-x64-release\py3\mre_venv3\lib\site-packages\pandas\core\dtypes\common.py", line 2029, in pandas_dtype
raise TypeError("dtype '{}' not understood".format(dtype))
TypeError: dtype '<class 'datetime.datetime'>' not understood
```
The issue occurs with pandas 0.24.1 and did not occur in 0.23.4. | {
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} | 5 | 2019-03-14T15:01:16Z | 2019-04-05T00:45:31Z | 2019-04-05T00:45:20Z | CONTRIBUTOR | null | In `_list_to_arrays`, `to_object_array_tuples` or `to_object_array` is called, both require the argument to be a `list` (list of tuples or list of list).
- [x] closes #25691
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2019-03-14T15:51:14Z | 2019-03-18T12:23:55Z | 2019-03-18T12:23:52Z | CONTRIBUTOR | null | fixes #25726
- [x] closes #25726
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-03-14T16:57:37Z | 2019-05-03T14:52:15Z | 2019-05-03T14:52:15Z | CONTRIBUTOR | null | The last example at https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.wide_to_long.html is incorrect.
```
>>> df = pd.DataFrame({
... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
... 'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
... 'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
... })
>>> df
>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age',
sep='_', suffix='\w')
>>> l
```
locally, `l` is an empty dataframe
```python
Empty DataFrame
Columns: [ht_two, ht_one, ht]
Index: []
```
I think the `suffix` should be `\w+`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25734 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25734/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25734/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25734/events | https://github.com/pandas-dev/pandas/issues/25734 | 421,180,975 | MDU6SXNzdWU0MjExODA5NzU= | 25,734 | Pandas 0.24.x throws an error when using freezegun with CustomBusinessDay | {
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... | closed | false | null | [] | null | 3 | 2019-03-14T18:32:54Z | 2019-04-05T17:52:24Z | 2019-04-05T17:52:24Z | NONE | null | I originally opened an issue with Freezegun:
https://github.com/spulec/freezegun/issues/284
**However, this appears to be a new quirk of Pandas 0.24's `CustomBusinessDay` class and its use of `pandas._libs.tslibs.conversion.localize_pydatetime()`.** Other offset functions don't seem to be affected, for the ones I've tried.
Everything worked just fine with Pandas 0.23.4 and earlier, with the latest freezegun-0.3.11.
#### Code Sample, a copy-pastable example if possible
Test setup: Python 2.7 and 3.6 on OS X using freezegun-0.3.11 with pandas-0.24.2
```python
import pytz
import pandas
from datetime import datetime
from pandas.tseries.offsets import (CustomBusinessDay, BusinessDay)
from freezegun import freeze_time
LOCAL_TZ = pytz.timezone('America/Los_Angeles')
@freeze_time(LOCAL_TZ.localize(datetime(2019, 3, 15, 0, 0, 0)))
def these_work():
pandas.Timestamp(datetime.now(LOCAL_TZ))
pandas.Timestamp(datetime.now(LOCAL_TZ)) + BusinessDay(n=1)
@freeze_time(LOCAL_TZ.localize(datetime(2019, 3, 15, 0, 0, 0)))
def this_fails():
pandas.Timestamp(datetime.now(LOCAL_TZ)) + CustomBusinessDay(n=1)
these_work()
this_fails()
```
#### Problem description
Failure mode:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-452eb6a5f720> in <module>
17
18 these_work()
---> 19 this_fails()
~/.virtualenvs/python3/lib/python3.6/site-packages/freezegun/api.py in wrapper(*args, **kwargs)
656 result = func(time_factory, *args, **kwargs)
657 else:
--> 658 result = func(*args, **kwargs)
659 return result
660 functools.update_wrapper(wrapper, func)
<ipython-input-2-452eb6a5f720> in this_fails()
14 @freeze_time(LOCAL_TZ.localize(datetime(2019, 3, 15, 0, 0, 0)))
15 def this_fails():
---> 16 pandas.Timestamp(datetime.now(LOCAL_TZ)) + CustomBusinessDay(n=1)
17
18 these_work()
pandas/_libs/tslibs/offsets.pyx in pandas._libs.tslibs.offsets.BaseOffset.__radd__()
pandas/_libs/tslibs/offsets.pyx in pandas._libs.tslibs.offsets._BaseOffset.__add__()
~/.virtualenvs/python3/lib/python3.6/site-packages/pandas/tseries/offsets.py in wrapper(self, other)
70
71 if self._adjust_dst:
---> 72 result = conversion.localize_pydatetime(result, tz)
73
74 result = Timestamp(result)
pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.localize_pydatetime()
pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.localize_pydatetime()
AttributeError: 'FakeDatetime' object has no attribute 'tz_localize'
```
#### Expected Output
All of these should work equally well, including `CustomBusinessDay`.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.6.3
Cython: 0.29.3
numpy: 1.16.0
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.3
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.2
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25735 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25735/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25735/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25735/events | https://github.com/pandas-dev/pandas/issues/25735 | 421,186,868 | MDU6SXNzdWU0MjExODY4Njg= | 25,735 | Links to PDF and BIBs in documentation on site are broken | {
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} | [] | closed | false | null | [] | null | 0 | 2019-03-14T18:46:25Z | 2019-03-14T20:03:48Z | 2019-03-14T20:03:48Z | NONE | null | Links to first PDF and both BIBs on https://pandas.pydata.org/talks.html in section "pandas Papers" seems to be broken (gives 404). Just went there to retrieve Bibtex entry for my bibliography list and discovered that. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25736 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25736/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25736/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25736/events | https://github.com/pandas-dev/pandas/pull/25736 | 421,194,456 | MDExOlB1bGxSZXF1ZXN0MjYxMzAxMDM5 | 25,736 | Fix incorrect example in wide_to_long docstring | {
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} | 6 | 2019-03-14T19:04:31Z | 2019-04-10T11:42:14Z | 2019-04-10T05:32:06Z | CONTRIBUTOR | null | - [x] closes #25733
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/25737 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25737/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25737/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25737/events | https://github.com/pandas-dev/pandas/issues/25737 | 421,276,810 | MDU6SXNzdWU0MjEyNzY4MTA= | 25,737 | Resample with freq less than 10T generates different column names with same command | {
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] | closed | false | null | [] | null | 3 | 2019-03-14T22:40:01Z | 2019-03-16T23:17:42Z | 2019-03-16T22:10:51Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Timings.csv (see details)
<details>
```
"Invoice Id","Created Date","Modified Date"
"154021","2019-03-08 17:12:33","2019-03-08 18:50:03"
"141105","2019-02-28 17:13:54","2019-02-28 17:24:22"
"152290","2019-03-08 07:26:37","2019-03-08 07:32:24"
"136697","2019-02-26 07:22:36","2019-02-26 07:25:49"
"142892","2019-03-01 17:01:19","2019-03-01 17:17:52"
"146085","2019-03-05 10:14:12","2019-03-05 10:26:14"
"140333","2019-02-28 12:18:34","2019-02-28 12:32:46"
"159873","2019-03-13 14:09:53","2019-03-13 14:16:10"
"156567","2019-03-12 09:12:12","2019-03-12 09:21:05"
"162058","2019-03-14 14:51:39","2019-03-14 14:59:02"
"142387","2019-03-01 13:45:42","2019-03-01 13:53:30"
"154219","2019-03-08 18:38:45","2019-03-08 19:56:57"
"155780","2019-03-11 15:00:46","2019-03-11 15:38:42"
"158857","2019-03-13 09:47:48","2019-03-13 09:51:34"
"148743","2019-03-06 12:02:09","2019-03-06 13:47:16"
"139379","2019-02-27 17:04:07","2019-02-27 17:24:31"
"154375","2019-03-09 16:31:23","2019-03-09 16:36:12"
"156033","2019-03-11 16:02:41","2019-03-11 16:56:30"
"156164","2019-03-11 17:18:56","2019-03-11 17:32:03"
"156127","2019-03-11 16:53:29","2019-03-11 17:24:55"
"160476","2019-03-13 18:37:49","2019-03-13 20:00:59"
"153955","2019-03-08 16:57:13","2019-03-08 18:23:00"
"138872","2019-02-27 13:43:00","2019-02-27 14:02:52"
"146161","2019-03-05 10:29:49","2019-03-05 11:03:36"
"160620","2019-03-14 06:54:45","2019-03-14 07:01:57"
"153812","2019-03-08 16:15:33","2019-03-08 16:22:59"
"148780","2019-03-06 12:02:13","2019-03-06 14:11:34"
"141285","2019-02-28 18:55:39","2019-02-28 19:29:14"
"157709","2019-03-12 14:00:30","2019-03-12 14:10:50"
"137192","2019-02-26 11:57:56","2019-02-26 12:06:47"
"147524","2019-03-05 16:24:33","2019-03-05 18:59:20"
"141218","2019-02-28 18:45:17","2019-02-28 18:51:15"
"158015","2019-03-12 15:18:17","2019-03-12 18:13:38"
"162111","2019-03-14 15:07:09","2019-03-14 15:12:58"
"154801","2019-03-11 09:56:23","2019-03-11 10:01:46"
"150236","2019-03-06 18:08:08","2019-03-06 23:01:01"
"149391","2019-03-06 14:07:03","2019-03-06 17:55:56"
"155924","2019-03-11 15:31:49","2019-03-11 15:37:38"
"148075","2019-03-06 09:16:12","2019-03-06 09:21:46"
"145301","2019-03-04 17:07:12","2019-03-04 17:52:05"
"142602","2019-03-01 14:58:20","2019-03-01 15:23:48"
"151617","2019-03-07 13:51:45","2019-03-07 16:42:47"
"141486","2019-03-01 05:54:25","2019-03-01 06:00:47"
"160002","2019-03-13 14:36:10","2019-03-13 17:16:32"
"161105","2019-03-14 10:37:23","2019-03-14 11:21:37"
"159895","2019-03-13 14:20:19","2019-03-13 16:24:36"
"145866","2019-03-05 08:40:06","2019-03-05 08:48:44"
"159199","2019-03-13 11:12:33","2019-03-13 11:18:28"
"161618","2019-03-14 12:32:05","2019-03-14 12:38:42"
"154061","2019-03-08 17:32:47","2019-03-08 18:59:09"
```
</details>
```python
# Your code here
import pandas as pd
df = pd.read_csv("Timings.csv")
df['Created Date'] = pd.to_datetime(df['Created Date'], errors="coerce")
df['Modified Date'] = pd.to_datetime(df['Modified Date'], errors="coerce")
df1 = df.groupby(pd.Grouper(key='Created Date', freq='5T'))['Invoice Id'].count()
df2 = df.groupby(pd.Grouper(key='Modified Date', freq='5T'))['Invoice Id'].count()
df1.subtract(df2, fill_value=0).rename('Backlog').astype(int).cumsum().reset_index().head()
# index Backlog
#0 2019-02-26 07:20:00 1
#1 2019-02-26 07:25:00 0
#2 2019-02-26 07:30:00 0
#3 2019-02-26 07:35:00 0
#4 2019-02-26 07:40:00 0
df3 = df.groupby(pd.Grouper(key='Created Date', freq='15T'))['Invoice Id'].count()
df4 = df.groupby(pd.Grouper(key='Modified Date', freq='15T'))['Invoice Id'].count()
df3.subtract(df4, fill_value=0).rename('Backlog').astype(int).cumsum().reset_index().head()
# Created Date Backlog
#0 2019-02-26 07:15:00 0
#1 2019-02-26 07:30:00 0
#2 2019-02-26 07:45:00 0
#3 2019-02-26 08:00:00 0
#4 2019-02-26 08:15:00 0
```
#### Problem description
Not sure why just changing the `freq` is causing different behavior in the output (i.e. column headers are different). The values being different is of course expected.
#### Expected Output
It would be expected to consistently result in one or the other.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
## Update:
I've been able to reproduce it. Anything below `freq=10T` produces the same results (with `index` being the name of the first column) and anything above and including `freq=10T` creates a dataframe with the first column being `"Created Date"` | {
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} | 8 | 2019-03-15T06:08:57Z | 2019-04-12T22:00:08Z | 2019-04-12T22:00:03Z | CONTRIBUTOR | null | - [x] closes #22159
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/25739 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25739/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25739/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25739/events | https://github.com/pandas-dev/pandas/issues/25739 | 421,429,262 | MDU6SXNzdWU0MjE0MjkyNjI= | 25,739 | pd.merge combine two df was so huge! | {
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] | closed | false | null | [] | null | 2 | 2019-03-15T09:36:09Z | 2019-03-15T15:23:49Z | 2019-03-15T15:23:33Z | NONE | null | My Code
```python
quote = pro.daily(ts_code=code, end_date=end_date.strftime("%Y%m%d"))
basic = pro.daily_basic(ts_code=code, end_date=end_date.strftime("%Y%m%d"))
combine_quote = pd.merge(left=quote, right=basic, on='ts_code', how='inner')
```
#### Problem description
I have two dataframe which named quote and basic,let me see two tables detail
```python
quote.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
Index: 2869 entries, 20190315 to 20070427
Data columns (total 10 columns):
ts_code 2869 non-null object
open 2869 non-null float64
high 2869 non-null float64
low 2869 non-null float64
close 2869 non-null float64
pre_close 2869 non-null float64
change 2869 non-null float64
pct_chg 2869 non-null float64
vol 2869 non-null float64
amount 2869 non-null float64
dtypes: float64(9), object(1)
memory usage: 568.8 KB
```
```python
basic.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
Index: 2869 entries, 20190315 to 20070427
Data columns (total 15 columns):
ts_code 2869 non-null object
close 2869 non-null float64
turnover_rate 2869 non-null float64
turnover_rate_f 2869 non-null float64
volume_ratio 2864 non-null float64
pe 2869 non-null float64
pe_ttm 2869 non-null float64
pb 2869 non-null float64
ps 2869 non-null float64
ps_ttm 2869 non-null float64
total_share 2869 non-null float64
float_share 2869 non-null float64
free_share 2869 non-null float64
total_mv 2869 non-null float64
circ_mv 2869 non-null float64
dtypes: float64(14), object(1)
memory usage: 680.8 KB
```
then I will merge two to one use pd.merge,this procedure is not fast,about 1.5-2 second,I was shocked when I check memory usage,see that
```python
combine_quote.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
Int64Index: 8231161 entries, 0 to 8231160
Data columns (total 26 columns):
ts_code object
trade_date_x object
open float64
high float64
low float64
close_x float64
pre_close float64
change float64
pct_chg float64
vol float64
amount float64
trade_date_y object
close_y float64
turnover_rate float64
turnover_rate_f float64
volume_ratio float64
pe float64
pe_ttm float64
pb float64
ps float64
ps_ttm float64
total_share float64
float_share float64
free_share float64
total_mv float64
circ_mv float64
dtypes: float64(23), object(3)
memory usage: 3.0 GB
```
why 568KB + 680KB = 3GB?!
but pd.concat was correct
```python
quote = pro.daily(ts_code=code, end_date=end_date.strftime("%Y%m%d"))
quote.set_index("trade_date", inplace=True)
basic = pro.daily_basic(ts_code=code, end_date=end_date.strftime("%Y%m%d"))
basic.set_index("trade_date", inplace=True)
combine2 = pd.concat([quote, basic], axis=1)
combine2.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2869 entries, 0 to 2868
Data columns (total 27 columns):
ts_code 2869 non-null object
trade_date 2869 non-null object
open 2869 non-null float64
high 2869 non-null float64
low 2869 non-null float64
close 2869 non-null float64
pre_close 2869 non-null float64
change 2869 non-null float64
pct_chg 2869 non-null float64
vol 2869 non-null float64
amount 2869 non-null float64
ts_code 2869 non-null object
trade_date 2869 non-null object
close 2869 non-null float64
turnover_rate 2869 non-null float64
turnover_rate_f 2869 non-null float64
volume_ratio 2864 non-null float64
pe 2869 non-null float64
pe_ttm 2869 non-null float64
pb 2869 non-null float64
ps 2869 non-null float64
ps_ttm 2869 non-null float64
total_share 2869 non-null float64
float_share 2869 non-null float64
free_share 2869 non-null float64
total_mv 2869 non-null float64
circ_mv 2869 non-null float64
dtypes: float64(23), object(4)
memory usage: 1.2 MB
```
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.28-1-MANJARO
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.2
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.1
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25740 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25740/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25740/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25740/events | https://github.com/pandas-dev/pandas/issues/25740 | 421,600,531 | MDU6SXNzdWU0MjE2MDA1MzE= | 25,740 | Inconsistent pd.to_datetime() behaviour dependent on dtype | {
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"description": "Duplicate... | closed | false | null | [] | null | 2 | 2019-03-15T16:22:55Z | 2019-03-15T17:39:31Z | 2019-03-15T17:39:23Z | NONE | null | I have two Series, both containing years. One series is `dtype == float64` and the other is `dtype == object`. When I apply pd.to_datetime() to these two columns, i get drastically different results.
```python
## Pandas 0.24.1
import pandas as pd
year_string = pd.Series(['1980','1985','1990','1995','2000'])
year_float = pd.Series([1980.0, 1985.0, 1990.0, 1995.0, 2000.0])
## Works as expected
pd.to_datetime(year_string, infer_datetime_format=True)
# 0 1980-01-01
# 1 1985-01-01
# 2 1990-01-01
# 3 1995-01-01
# 4 2000-01-01
# dtype: datetime64[ns]
## Automatically treats floats epoch nanoseconds
pd.to_datetime(year_float, infer_datetime_format=True)
# 0 1970-01-01 00:00:00.000001980
# 1 1970-01-01 00:00:00.000001985
# 2 1970-01-01 00:00:00.000001990
# 3 1970-01-01 00:00:00.000001995
# 4 1970-01-01 00:00:00.000002000
# dtype: datetime64[ns]
```
While I understand why this difference exists (perhaps there's an assumption being made that if dtype == float, then the value are epoch nanoseconds?), I'm not sure I agree with the inconsistency in behavior based strictly on dtype. I would favor a solution that treated the float64 series the same as the string/object series. Is it possible that the logic that infers `year_string` as containing years could also be applied to `year_float`? | {
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} | 3 | 2019-03-15T21:14:09Z | 2019-03-15T21:32:05Z | 2019-03-15T21:31:58Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
pd.DataFrame([['a', 12111450436133142102]],columns=['name', 'value']).groupby('name').first()
```
#### Problem description
Pandas casts uint64 to int64, causing overflows. This happens silently, causing bugs downstream.
This happens with other groupby operations too.
#### Expected Output
```
value
name
a 12111450436133142102
```
#### Actual Output
```
value
name
a -6335293637576409514
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-135-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C.UTF-8
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 3.2.2
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.3.1
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25742 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25742/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25742/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25742/events | https://github.com/pandas-dev/pandas/issues/25742 | 421,707,474 | MDU6SXNzdWU0MjE3MDc0NzQ= | 25,742 | DataFrame regex with dictionary will not work correctly. | {
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} | [] | closed | false | null | [] | null | 2 | 2019-03-15T21:16:26Z | 2019-03-15T21:31:41Z | 2019-03-15T21:31:40Z | NONE | null | #### Code Sample
```python
df = pd.DataFrame(
{'A': [0, 1, 2],
'B': ['ba\nt', 'foo', 'bait'],
'C': ['abc', 'ba\nr', 'xyz']}
)
df.replace(regex={'\n': '', r'^fo.$': 'xyz'}) # Neither do r'\n' or '\\n' work.
```
#### Problem description
Does not replace the newline character. Also checked with other escaped characters like spaces, tabs. However, the other regex expression works:
```python
A B C
0 0 ba\nt abc
1 1 xyz ba\nr
2 2 bait xyz
```
#### Expected Output
```python
A B C
0 0 bat abc
1 1 xyz bar
2 2 bait xyz
```
#### Additional information
Working:
```python
df.replace('\n', '', regex=True).replace('foo', 'xyz')
```
[Related SO question](https://stackoverflow.com/q/55190441/3765319).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.5
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: 0.11.3
IPython: 7.3.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.18
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 11 | 2019-03-15T22:14:51Z | 2019-04-02T00:55:10Z | 2019-03-30T23:45:26Z | CONTRIBUTOR | null | - [x] closes #25704
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
There was an issue with `groupby` when you used `groupby` on a `Series` with a list of labels that was equal to the length of the entire `Series`.
`core/groupby/grouper.py:_get_grouper` was not properly handling the case where the list of labels provided were all in the `Series` index names. The `DataFrame` case was being handled, but `Series` was previously missed in this case. | {
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} | 9 | 2019-03-16T03:26:02Z | 2021-01-03T17:10:41Z | 2021-01-03T17:10:41Z | NONE | null | ```python
cats = pandas.api.types.CategoricalDtype(['a', 'b', 'c', 'd'])
# cats = None
s1 = pandas.Series(['a', 'b', 'c'], index=[1, 2, 3], dtype=cats)
s2 = pandas.Series(['b', 'a'], index=[1, 2], dtype=cats)
s1.update(s2)
```
With the dtype=None, the `s1` series is updated to be `['b', 'a', 'c']` but with the `dtype=cats`, the update fails with error "ValueError: NumPy boolean array indexing assignment cannot assign 3 input values to the 2 output values where the mask is true"
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-36-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.3.2
pip: 18.0
setuptools: 40.5.0
Cython: 0.27.3
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.6.6
patsy: 0.5.1
dateutil: 2.7.2
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.4
feather: None
matplotlib: 3.0.1
openpyxl: 2.4.10
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml.etree: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 6 | 2019-03-16T18:10:39Z | 2019-04-12T17:08:36Z | 2019-04-12T17:08:31Z | CONTRIBUTOR | null | - [x] closes #25514
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/25746 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25746/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25746/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25746/events | https://github.com/pandas-dev/pandas/pull/25746 | 421,845,789 | MDExOlB1bGxSZXF1ZXN0MjYxNzk2MTM2 | 25,746 | BUG: Fix localize_pydatetime using meta datetimes as Timestamp (#25734) | {
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"id": ... | closed | false | null | [] | null | 19 | 2019-03-16T19:51:58Z | 2019-04-05T17:31:39Z | 2019-04-05T17:31:30Z | CONTRIBUTOR | null | - [x] closes #25734
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Proposed fix for #25734
The issue was due to localize_pydatetime assuming anything that was `not PyDateTime_CheckExact` must be a pandas `Timestamp`. However, in the rare case a `datetime` was created with a custom metaclass, that `datetime` would fail `PyDateTime_CheckExact`, but obviously was not a `Timestamp`.
Therefore, I made the `not PyDateTime_CheckExact` check more explicitly look for a `Timestamp`. As the `Timestamp` class could not be imported for a `isinstance` check due to a circular import, I took the route that many of the other `is_*` style checks took of adding a `_typ` property to `Timestamp` and inspecting that to determine identity. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25747 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25747/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25747/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25747/events | https://github.com/pandas-dev/pandas/issues/25747 | 421,856,561 | MDU6SXNzdWU0MjE4NTY1NjE= | 25,747 | Problem with pandas loc, which can't find date value in index (first row) | {
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} | 1 | 2019-03-16T21:55:30Z | 2019-03-16T22:24:30Z | 2019-03-16T22:24:22Z | NONE | null | #### Code Sample, a copy-pastable example if possible
I have next CSV sample file:
```
time,value
2018-09-17,0.0002897
2018-09-16,0.0002955
2018-09-15,0.0002862
2018-09-14,0.0003054
2018-09-13,0.000312
2018-09-12,0.0003467
2018-09-11,0.0003433
2018-09-10,0.0003287
2018-09-09,0.0003355
2018-09-08,0.0003288
```
```python
>>> import pandas as pd
>>> s1 = pd.read_csv("series1.csv")
>>> s1['time'] = pd.to_datetime(s1['time'])
>>> s1.set_index('time', inplace=True)
>>> s1
value
time
2018-09-17 0.000290
2018-09-16 0.000296
2018-09-15 0.000286
2018-09-14 0.000305
2018-09-13 0.000312
2018-09-12 0.000347
2018-09-11 0.000343
2018-09-10 0.000329
2018-09-09 0.000336
2018-09-08 0.000329
>>> s1.loc['2018-09-16']
value
time
2018-09-16 0.000296
>>> s1.loc['2018-09-17']
Traceback (most recent call last):
File "C:\Python36\lib\site-packages\pandas\core\indexing.py", line 1506, in _has_valid_type
error()
File "C:\Python36\lib\site-packages\pandas\core\indexing.py", line 1501, in error
axis=self.obj._get_axis_name(axis)))
KeyError: 'the label [2018-09-17] is not in the [index]'
```
#### Problem description
Above is sample code session which shows failure of loc to locate first row in index, which it obviously should do without problem as it finds second row (see in code session)
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
Output of pd.show_versions is too long, i can't grab it whole content.
Version of pandas is 0.22.0, version of python is 3.6.7
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25748 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25748/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25748/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25748/events | https://github.com/pandas-dev/pandas/pull/25748 | 421,864,456 | MDExOlB1bGxSZXF1ZXN0MjYxODA3NjU0 | 25,748 | TST: indexing/test_floats.py::TestFloatIndexers::test_scalar_non_numeric | {
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"d... | closed | false | null | [] | null | 5 | 2019-03-16T23:42:54Z | 2019-03-27T08:28:11Z | 2019-03-27T08:28:11Z | MEMBER | null | precursor to #25750
This test has some commented out code with a pytest.raises not used as a context manager.
The goal here is primarily to remove the commented out code.
some refactoring but not enough to allow full parametrization.
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https://api.github.com/repos/pandas-dev/pandas/issues/25749 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25749/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25749/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25749/events | https://github.com/pandas-dev/pandas/issues/25749 | 421,878,636 | MDU6SXNzdWU0MjE4Nzg2MzY= | 25,749 | Setting to multi-index np array break the data | {
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] | closed | false | null | [] | null | 1 | 2019-03-17T03:46:06Z | 2020-09-22T04:09:14Z | 2020-09-22T04:09:14Z | NONE | null | #### To re-produce
```python
# create single/multi index DataFrame
df_s = pd.DataFrame(np.arange(12).reshape(3, 4),columns=list('ABCD'),index=['bar', 'baz', 'foo'])
df_m = pd.DataFrame(np.arange(24).reshape(6, 4),columns=list('ABCD'),index=[
np.array(['bar', 'baz', 'foo','bar', 'baz', 'foo']),np.array(['one','one','one','two','two','two'])])
idx_s=df_s.index.to_numpy()
# change label to other value
idx_s[0]='qux'
# normal
assert df_s.loc['qux','A']==0
assert df_s.index.to_numpy()[0]=='qux'
idx_m=df_m.index.to_numpy()
# change label to other value
idx_m[0]=('qux','one')
# it get change from to_numpy return value
assert df_m.index.to_numpy()[0]==('qux','one')
# however indexing for ('qux','one') raise key error
assert df_m.loc('qux','one'),'A']==0 <-key error
```
#### Problem description
It is already mention from https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.to_numpy.html
**For NumPy dtypes, this will be a reference to the actual data stored in this Series or Index (assuming copy=False). Modifying the result in place will modify the data stored in the Series or Index (not that we recommend doing that).**
However it is really confusing that the label is in index.to_numpy() but it can't access using loc.
**Note**: I am new to Pandas. I start reading Getting started and User Guide from last week. It is a very good resource for newcomer. For the above issue, I don't have any suggestion, just feeling strange...
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: 4.1.0
pip: 10.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25750 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25750/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25750/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25750/events | https://github.com/pandas-dev/pandas/pull/25750 | 421,907,719 | MDExOlB1bGxSZXF1ZXN0MjYxODMzNjUy | 25,750 | [WIP] STY: activate pytest.raises context manager code check | {
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"d... | closed | false | null | [] | null | 4 | 2019-03-17T10:46:44Z | 2019-03-26T00:23:05Z | 2019-03-26T00:23:05Z | MEMBER | null | xref #24332
- [x] pandas/tests/arrays/sparse/test_libsparse.py
- [x] pandas/tests/computation/test_eval.py
- [x] pandas/tests/frame/test_nonunique_indexes.py
- [x] pandas/tests/indexes/interval/test_interval_new.py
- [x] pandas/tests/indexing/multiindex/test_partial.py
- [ ] pandas/tests/indexing/test_floats.py: seperate PR #25748
- [ ] pandas/tests/io/test_pytables.py: tbc once #25004 is merged
- [x] pandas/tests/series/test_constructors.py | {
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} | 28 | 2019-03-17T15:31:22Z | 2019-03-25T11:54:51Z | 2019-03-24T22:41:26Z | CONTRIBUTOR | null | #24942 wanted to update the PY2-CI to newer python versions, but @jreback ultimately wanted to split this off. This takes the open changes from #24942.
Quoting from the OP there:
> I kept most of the CI jobs that used to be 2.7 at the lower end of the various supported versions, with 2x 2.7->3.5, 1x 2.7->3.6 and only one real upgrade from 2.7->3.7 (first CI job for 3.7 & windows).
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} | 4 | 2019-03-17T18:05:10Z | 2019-03-26T00:28:43Z | 2019-03-26T00:28:43Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
data = DataFrame(np.arange(16).reshape((4, 4)),
index=['Ohio', 'Colorado', 'Utah', 'New York'],
columns=['one', 'two', 'three', 'four'])
data[data < 5] = 0
print(data.iloc[['Colorado', 'Utah'], [3, 0, 1]])
# .ix is deprecated. Please use
# .loc for label based indexing or
# .iloc for positional indexing
print(data.iloc[['Colorado', 'Utah'], [3, 0, 1]])
#TypeError: cannot perform reduce with flexible type
print(data.loc[['Colorado', 'Utah'], [3, 0, 1]])
#KeyError: "None of [Int64Index([3, 0, 1], dtype='int64')] are in the [columns]"
```
#### Problem description
I followed the suggesion to move the ix to iloc and this is what I have got
I have used python 3.7.2 on an elemntary os 18.04
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} | 54 | 2019-03-17T18:49:47Z | 2019-05-12T20:54:49Z | 2019-05-12T20:54:25Z | CONTRIBUTOR | null | - [x] closes N/A
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2019-03-17T19:39:47Z | 2019-03-18T15:02:00Z | 2019-03-18T12:02:36Z | MEMBER | null | It isn't clear to me why this is needed. Maybe the CI will fail, in which case I'll just add a comment in the code explaining why it is needed. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25756 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25756/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25756/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25756/events | https://github.com/pandas-dev/pandas/issues/25756 | 421,967,304 | MDU6SXNzdWU0MjE5NjczMDQ= | 25,756 | Extremely Slow `Block.setitem()` when `value` is a builtin python type. | {
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"color": "0e... | open | false | null | [] | null | 5 | 2019-03-17T20:13:49Z | 2021-06-27T19:55:18Z | null | CONTRIBUTOR | null | #### Code Sample
This code example is simply calling repeated `setitem` via a `LocIndexer` on a reasonably sized DataFrame of all bools. The key is that it takes exponentially longer to call this when you're setting a python `bool` type value versus a `np.array[bool]` value.
```python
from datetime import datetime
import pandas as pd
import numpy as np
# Create dataframe of 500 columns, 1 year of dates, all False values
df = pd.DataFrame(
False,
columns=np.arange(500).astype(str),
index=pd.date_range('2010-01-01', '2011-01-01')
)
def test(true_value):
""" Test time for assigning a slice `True` and `np.array(True)` """
tmp_df = df.copy()
start = datetime(2010, 5, 1)
end = datetime(2010, 9, 1)
tmp_df.loc[start:end, :] = true_value
print("True")
%timeit test(True)
print("\nnp.array(True)")
%timeit test(np.array(True))
```
```
Output:
True
3.4 ms ± 39.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
np.array(True)
512 µs ± 4.91 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
#### Problem description
A code profiling reveals that almost all the computation time in the longer run is due to `ensure_object` being called in `cast.py:78` from a `Block.setitem`.
In the above example, a bool `True` is passed to the `blocks.py:Block.setitem(...)` method via `LocIndexer`. Since a builtin bool has no `dtype` attribute, the dtype is set to `infer`. This causes the `cast.py:maybe_downcast_to_dtype(...)` to be called with `dtype == 'infer'`, and if the dataset is large the `ensure_object(...)` method takes a _long_ time to run. Wrapping the value in a numpy array `np.array(True)` exponentially reduces the computation since it then has a `dtype`.
This behavior did not exist in older versions of pandas (pre-20ish). Could we do some pre-defined type checking in `Block.setitem(...)` to infer builtin python dtypes instead of requiring `dtype` attributed objects?
I would be happy to code a fix for this, but I am unsure of the reasoning behind only supporting objects with a `dtype` attribute as a value.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+266.g707c7201a
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25757 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25757/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25757/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25757/events | https://github.com/pandas-dev/pandas/pull/25757 | 421,985,113 | MDExOlB1bGxSZXF1ZXN0MjYxODgwMTgz | 25,757 | Move locale code out of tm, into _config | {
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} | 7 | 2019-03-17T23:02:37Z | 2019-03-20T01:17:41Z | 2019-03-20T01:06:17Z | MEMBER | null | As discussed last week, one of ugliest runtime imports in `tslibs` is of `tm` for `tm.set_locale`. This moves `set_locale` and a couple of other locale-related functions out of `tm`.
As discussed in #25162, #25203, #25613, this moves the affected functions to a new directory `pandas/_config`, intended to be strictly "upstream" from the rest of pandas (potentially even made actually-upstream as suggested [here](https://github.com/pandas-dev/pandas/pull/25176#issuecomment-461209852)).
Following this and #25613, a bunch of other cleanup/simplification becomes feasible. | {
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} | 3 | 2019-03-17T23:41:52Z | 2020-04-03T16:18:28Z | 2020-04-03T16:18:28Z | NONE | null | #### Code Sample
```python
import pandas as pd
from datetime import datetime
start = datetime(2018, 11, 3, 12)
end = datetime(2018, 11, 5, 12)
index = pd.date_range(start, end, freq="1H")
index = index.tz_localize('UTC').tz_convert('America/Havana')
data = list(range(len(index)))
dataframe = pd.DataFrame(data, index=index)
groups = dataframe.groupby(pd.Grouper(freq='1D'))
```
#### Problem description
On a long clock-change day in Cuba, e.g 2018-11-04, midnight local time is an ambiguous timestamp. pd.Grouper does not handle this as I expect. More precisely the call to `groupby` in the code above raises an `AmbiguousTimeError`.
This issue is of a similar nature to https://github.com/pandas-dev/pandas/issues/23742 but it seems #23742 was fixed in 0.24 whereas this was not.
#### Expected Output
The call to `groupby` should return three groups (one for each day, 3rd, 4th, and 5th of november). The group for the 4th of november should be labelled as '2018-11-04 00:00:00-04:00' (that is the first midnight, before the clock change) and it should contain the 25 hourly data points for this day.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.125-linuxkit
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 3.3.2
pip: None
setuptools: 40.6.3
Cython: 0.29.6
numpy: 1.15.4
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25759 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25759/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25759/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25759/events | https://github.com/pandas-dev/pandas/pull/25759 | 421,999,239 | MDExOlB1bGxSZXF1ZXN0MjYxODg5MzAw | 25,759 | BUG: Cleaner exception when `.iloc` called with non-integer list | {
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} | 5 | 2019-03-18T01:10:07Z | 2019-03-26T00:28:47Z | 2019-03-26T00:28:43Z | CONTRIBUTOR | null | - [x] closes #25753
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Issue #25753 was raised requesting more verbose error messages when calling `loc` and `iloc` with mixed dtype arguments. In this pull request, I've added a new error message when calling `iloc` with a non-numeric dtype list-like object.
This fixes numpy ultimately raising an ambiguous exception when, for example, calling `.iloc` with a list of string column names:
```
>>> print(data.iloc[['Colorado', 'Utah'], [3, 0, 1]])
Old:
TypeError: cannot perform reduce with flexible type
New:
IndexError: .iloc requires integer indexers, got ['Colorado' 'Utah']
```
However, the current exception when calling `.loc` with integer column names when the column names are actually strings I feel is already accurate:
```
>>> print(data.loc[['Colorado', 'Utah'], [3, 0, 1]])
KeyError: "None of [Int64Index([3, 0, 1], dtype='int64')] are in the [columns]"
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25760 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25760/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25760/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25760/events | https://github.com/pandas-dev/pandas/issues/25760 | 422,003,358 | MDU6SXNzdWU0MjIwMDMzNTg= | 25,760 | RecursionError when aligning DataFrames based on MultiIndex with different order of names | {
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} | 5 | 2019-03-18T01:40:01Z | 2020-01-01T20:46:46Z | 2020-01-01T20:46:46Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
x=pd.DataFrame(np.arange(4),pd.MultiIndex.from_product([[1,2],[3,4]],names=['a','b']))
y=pd.DataFrame(np.arange(4),pd.MultiIndex.from_product([[3,4],[1,2]],names=['b','a']))
print(x+y)
```
#### Problem description
RecursionError in align()/join().
The ideal expected behavior is to calculate the sum.
If different orders of names in MultiIndex are not supported by design, then a clear error message stating that would be preferable to RecursionError.
#### Expected Output
```
0
a b
1 3 0
4 3
2 3 3
4 6
```
or
```
0
b a
3 1 0
2 3
4 1 3
2 6
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.125-linuxkit
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.15.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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May be some other issues as well | {
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"de... | closed | false | null | [] | null | 5 | 2019-03-18T05:31:54Z | 2021-07-08T01:33:09Z | 2019-03-21T03:20:26Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
data = pd.read_excel('abcd.xlsx', sheet_name='Sheet 1')
print(data.head())
```
#### Problem description
Some of the columns showing NaN, even though it contains value in excel sheet.
#### Expected Output
a b c d
0 12 NaN NaN 523.1
1 24 NaN NaN 123.1
2 24 NaN NaN 213.1
3 45 NaN NaN 231.1
4 67 NaN NaN 222.1
#### Output of ``pd.show_versions()``
a b c d
0 12 12.24 2354 523.1
1 24 122.24 2364 523.1
2 24 132.24 7354 523.1
3 45 112.24 6354 523.1
4 67 412.24 2854 523.1
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
commit: None
python: 3.7.0.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 9.0.1
setuptools: 39.1.0
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: 2.5.4
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.0
lxml.etree: None
bs4: 4.6.3
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25764 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25764/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25764/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25764/events | https://github.com/pandas-dev/pandas/pull/25764 | 422,115,579 | MDExOlB1bGxSZXF1ZXN0MjYxOTc2Mzgy | 25,764 | 25708 performance degradation when dumping large dataframe with datetime to csv | {
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} | [] | closed | false | null | [] | null | 3 | 2019-03-18T09:30:59Z | 2019-03-18T10:40:37Z | 2019-03-18T10:03:21Z | CONTRIBUTOR | null | - [x] closes #25708
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Slicing in [line 2181](https://github.com/pandas-dev/pandas/blob/707c7201a744c48c835f719421e47480beee2184/pandas/core/internals/blocks.py#L2181) only slice of `i8values` that make a whole of `values` array is processed in `_is_dates_only` function.
This change was from pull #24543
```python
values = self.values
i8values = self.values.view('i8')
if slicer is not None:
i8values = i8values[..., slicer]
from pandas.io.formats.format import _get_format_datetime64_from_values
fmt = _get_format_datetime64_from_values(values, date_format)
``` | {
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} | 18 | 2019-03-18T10:14:44Z | 2019-04-21T11:09:03Z | 2019-04-19T01:06:47Z | CONTRIBUTOR | null | - [x] closes #25708
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Slicing in [line 2181](https://github.com/pandas-dev/pandas/blob/707c7201a744c48c835f719421e47480beee2184/pandas/core/internals/blocks.py#L2181) only slice of i8values that make a whole of values array is processed in _is_dates_only function. This change was from pull #24543
```python
values = self.values
i8values = self.values.view('i8')
if slicer is not None:
i8values = i8values[..., slicer]
from pandas.io.formats.format import _get_format_datetime64_from_values
fmt = _get_format_datetime64_from_values(values, date_format)
``` | {
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} | 1 | 2019-03-18T12:44:35Z | 2019-03-31T15:35:49Z | 2019-03-31T15:35:49Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'a': range(10), 'b': range(10)})
df.to_hdf('/tmp/1.hdf', 'k1')
try:
pd.read_hdf('/tmp/1.hdf', 'k2')
except Exception as e:
print(type(e))
print(e)
df.to_hdf('/tmp/1.hdf', 'k2')
```
#### Problem description
<type 'exceptions.KeyError'>
'No object named k2 in the file'
The file '/tmp/1.hdf' is already opened, but in read-only mode. Please close it before reopening in append mode.
#### Expected Output
should not raise Exception when: df.to_hdf('/tmp/1.hdf', 'k2')
#### pandas/io/pytables.py line 408
``` python
except (ValueError, TypeError): # should also catch KeyError
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-141-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: zh_CN.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: 3.2.4
pip: 19.0.1
setuptools: 39.1.0
Cython: 0.27.3
numpy: 1.14.5
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 5.5.0
sphinx: 1.6.5
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.0
openpyxl: 2.4.9
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml.etree: 4.1.1
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.15
pymysql: None
psycopg2: 2.7.3.2 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 4 | 2019-03-18T14:29:03Z | 2019-03-19T07:21:07Z | 2019-03-19T03:58:23Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> df0 = pd.DataFrame({}, columns=['A'])
>>> df1 = pd.DataFrame({'A': []})
>>> df0.columns
Index(['A'], dtype='object')
>>> df1.columns
Index(['A'], dtype='object')
>>> df0.empty
True
>>> df1.empty
True
>>> df0['A']
Series([], Name: A, dtype: object)
>>> df1['A']
Series([], Name: A, dtype: float64)
```
#### Problem description
The dtype of `df0['A']` and `df1['A']` differ (`object` vs `float64`) which is confusing and can lead to subtle bugs.
#### Expected Output
The dtype of `df0['A']` and `df1['A']` should match.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.9-arch1-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_GB.utf8
LOCALE: en_GB.UTF-8
pandas: 0.23.4
pytest: None
pip: None
setuptools: 40.4.3
Cython: 0.28.5
numpy: 1.15.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.8.0
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details> | {
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} | 5 | 2019-03-18T14:57:07Z | 2019-03-24T08:49:52Z | 2019-03-23T02:08:53Z | CONTRIBUTOR | null | - closes #23013
- 1 test replaced
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry
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} | 23 | 2019-03-18T15:35:16Z | 2019-03-28T14:30:55Z | 2019-03-20T12:22:39Z | CONTRIBUTOR | null | - [x] closes #15086 properly
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Test that was added in prior fix was failing on my machine, where my locale was set to `CP-1251` (which apparently cannot handle some unicode symbols in the test even when using MBCS, as MBCS encoding is locale-dependent).
This PR fixes the issue removing the workaround by switching to converting UTF8 to UTF16 and using Win32 Unicode file API (much like Python >= 3.6 does internally).
P.S. This as a bonus adds possibility to use `\\?\C\long\file\name` addressing if desired to overcome 260 symbols limitation on path length.
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} | 2 | 2019-03-19T00:04:55Z | 2019-03-19T13:46:32Z | 2019-03-19T09:06:12Z | MEMBER | null | Cherry picked commit from #25761 and removed trailing whitespace.
@jorisvandenbossche if you don't mind taking a look | {
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} | 0 | 2019-03-19T04:43:43Z | 2019-03-20T01:03:21Z | 2019-03-20T01:03:21Z | MEMBER | null | As a temporary workaround for recent CI issues we pinned the version of pycodestyle in #25701. A good amount of these issues were caused by type comments which did not import what was needed from typing, but due to required support of Py27 at the time those imports could not be done.
With Py27 CI out, we should be able to fix this and remove the pycodestyle pin. The solution is intermixed between PRs I've already begun (#25622 and #25697) so just need to extract the required components out of them to make this a reality | {
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} | 8 | 2019-03-19T05:34:31Z | 2019-04-05T00:04:30Z | 2019-04-05T00:04:30Z | NONE | null | I am trying to read the panel dataset of Russian individuals in stata format. The dataset can be freely obtained at the [rlms site](https://www.hse.ru/en/rlms/downloads).
```python
ind_dta = pd.read_stata('USER_RLMS-HSE_IND_1994_2017_v2_eng.dta')
```
This results in memory error and that seems strange. Machine has 16gb of memory, the file is less than 4gb.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 4.0.0
pip: 18.1
setuptools: 40.6.2
Cython: 0.29
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.1.1
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.1
openpyxl: 2.5.9
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
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} | 12 | 2019-03-19T06:26:12Z | 2019-03-19T18:54:01Z | 2019-03-19T18:17:35Z | MEMBER | null | `3.5.1` was made available on `conda`, causing `xfail_non_writeable` tests to fail.
Note that this is just to get `master` to pass again (started failing when #25751 was merged).
This new release of `pytables`, however, brings up the question of how we want to proceed with #24839, as it appears that all of our CI builds that install `pytables` will use this version now.
It seems a little early to declare that `3.5.1` is our minimum version, but it definitely looks like we *could* drop this `xfail` from our tests at some point in the near future. | {
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} | 1 | 2019-03-19T08:28:11Z | 2020-02-18T00:15:46Z | 2020-02-18T00:15:46Z | NONE | null | #### Code Sample
```python
import numpy as np
import pandas as pd
import random
# Nothing Raises
df = pd.DataFrame(dict(A=[1, 1, 1, 1, 1], B=[1., 1., 1., 1., 1.], C=[1, 1, 1, 1, 1]))
df.index = [0, 0, 1, 3, 5]
df.index.name = 'test'
df.loc[0, 'A'] = df.loc[0, 'A']
# ValueError raises
length = 20000
df = pd.DataFrame(dict(A=[1 for _ in range(length)], B=[1. for _ in range(length)], C=[1 for _ in range(length)]))
index = []
for _ in range(length):
index.append(random.randint(0, 2000))
df.index = index
df.loc[0, 'A'] = df.loc[0, 'A']
```
#### Problem description
https://github.com/pandas-dev/pandas/blob/db6993cdd970eb13790493dba38bb637e341948b/pandas/core/indexing.py#L517
https://github.com/pandas-dev/pandas/blob/db6993cdd970eb13790493dba38bb637e341948b/pandas/core/indexing.py#L546-L562
When the `DataFrame.loc` method uses boolean array selector in the backgroud, the variable `lplane_indexer` is not the same value as the number of True value in the array but the size of DataFrame. It didn't happen in a former version.
#### Expected Output
No error raises.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.3
pytest: 3.7.3
pip: 18.1
setuptools: 36.5.0.post20170921
Cython: 0.28.4
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.5
lxml: None
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.10
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 4 | 2019-03-19T08:29:42Z | 2019-06-05T12:54:35Z | 2019-06-05T12:54:35Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
pd.np.random.seed(0)
(
pd.DataFrame(pd.np.random.rand(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
.assign(b=lambda df: (df.b*10).astype(int))
.set_index(['a', 'b', 'c'])
.sort_index(axis=0, level=['b', 'a'])
)
Out[96]:
d e
a b c
0.087129 0 0.832620 0.778157 0.870012
0.639921 1 0.944669 0.521848 0.414662
0.670638 2 0.128926 0.315428 0.363711
0.359508 4 0.697631 0.060225 0.666767
0.645894 4 0.891773 0.963663 0.383442
0.791725 5 0.568045 0.925597 0.071036
0.617635 6 0.616934 0.943748 0.681820
0.264556 7 0.456150 0.568434 0.018790
0.978618 7 0.461479 0.780529 0.118274
0.548814 7 0.602763 0.544883 0.423655
```
#### Problem description
I don't understand why the a index is not sorted (see the b=7 rows).
Indeed, `set_index` does not seem to take into account levels not aligned with dataframe indexes. Since index is `['a', 'b', 'c']` it can only sort with `levels=['a'], levels=['a', 'b'], levels=['a', 'b', 'c'], levels=['b'], levels=['b', 'c'], levels=['c']`.
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
```
pd.np.random.seed(0)
(
pd.DataFrame(pd.np.random.rand(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
.assign(b=lambda df: (df.b*10).astype(int))
.sort_values(['b', 'a'])
.set_index(['a', 'b', 'c'])
)
Out[104]:
d e
a b c
0.087129 0 0.832620 0.778157 0.870012
0.639921 1 0.944669 0.521848 0.414662
0.670638 2 0.128926 0.315428 0.363711
0.359508 4 0.697631 0.060225 0.666767
0.645894 4 0.891773 0.963663 0.383442
0.791725 5 0.568045 0.925597 0.071036
0.617635 6 0.616934 0.943748 0.681820
0.264556 7 0.456150 0.568434 0.018790
0.548814 7 0.602763 0.544883 0.423655
0.978618 7 0.461479 0.780529 0.118274
```
#### Output of ``pd.show_versions()``
<details>
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: fr_FR.UTF-8
pandas: 0.23.2
pytest: 3.6.1
pip: 18.0
setuptools: 40.4.3
Cython: None
numpy: 1.14.3
scipy: 1.0.1
pyarrow: None
xarray: None
IPython: 7.0.1
sphinx: None
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
```
</details>
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] | closed | false | null | [] | null | 2 | 2019-03-19T08:46:29Z | 2019-10-18T01:22:58Z | 2019-03-26T05:13:12Z | NONE | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
To fix the issue #25774 . | {
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} | 3 | 2019-03-19T09:12:53Z | 2020-05-18T13:03:05Z | 2020-05-18T13:03:05Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In [202]: sparse_series.dtype
Out[203]: Sparse[bool, False]
In [208]: sparse_series.value_counts()
Out[208]:
False 51386
True 13
Name: C_3D Printing, dtype: int64
In [209]: sparse_series.sum()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-209-8f6162710840> in <module>
----> 1 sparse_series.sum()
~/anaconda3/envs/idp/lib/python3.6/site-packages/pandas/core/generic.py in stat_func(self, axis, skipna, level, numeric_only, min_count, **kwargs)
10929 skipna=skipna, min_count=min_count)
10930 return self._reduce(f, name, axis=axis, skipna=skipna,
> 10931 numeric_only=numeric_only, min_count=min_count)
10932
10933 return set_function_name(stat_func, name, cls)
~/anaconda3/envs/idp/lib/python3.6/site-packages/pandas/core/series.py in _reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
3613 # dispatch to ExtensionArray interface
3614 if isinstance(delegate, ExtensionArray):
-> 3615 return delegate._reduce(name, skipna=skipna, **kwds)
3616 elif is_datetime64_dtype(delegate):
3617 # use DatetimeIndex implementation to handle skipna correctly
~/anaconda3/envs/idp/lib/python3.6/site-packages/pandas/core/arrays/sparse.py in _reduce(self, name, skipna, **kwargs)
1439 kwargs.pop('numeric_only', None)
1440 kwargs.pop('op', None)
-> 1441 return getattr(arr, name)(**kwargs)
1442
1443 def all(self, axis=None, *args, **kwargs):
~/anaconda3/envs/idp/lib/python3.6/site-packages/pandas/core/arrays/sparse.py in sum(self, axis, *args, **kwargs)
1491 sum : float
1492 """
-> 1493 nv.validate_sum(args, kwargs)
1494 valid_vals = self._valid_sp_values
1495 sp_sum = valid_vals.sum()
~/anaconda3/envs/idp/lib/python3.6/site-packages/pandas/compat/numpy/function.py in __call__(self, args, kwargs, fname, max_fname_arg_count, method)
54 validate_args_and_kwargs(fname, args, kwargs,
55 max_fname_arg_count,
---> 56 self.defaults)
57 else:
58 raise ValueError("invalid validation method "
~/anaconda3/envs/idp/lib/python3.6/site-packages/pandas/util/_validators.py in validate_args_and_kwargs(fname, args, kwargs, max_fname_arg_count, compat_args)
216
217 kwargs.update(args_dict)
--> 218 validate_kwargs(fname, kwargs, compat_args)
219
220
~/anaconda3/envs/idp/lib/python3.6/site-packages/pandas/util/_validators.py in validate_kwargs(fname, kwargs, compat_args)
154 """
155 kwds = kwargs.copy()
--> 156 _check_for_invalid_keys(fname, kwargs, compat_args)
157 _check_for_default_values(fname, kwds, compat_args)
158
~/anaconda3/envs/idp/lib/python3.6/site-packages/pandas/util/_validators.py in _check_for_invalid_keys(fname, kwargs, compat_args)
125 raise TypeError(("{fname}() got an unexpected "
126 "keyword argument '{arg}'".
--> 127 format(fname=fname, arg=bad_arg)))
128
129
TypeError: sum() got an unexpected keyword argument 'min_count'
In [210]:
```
#### Problem description
Summing a sparse boolean series throws an exception `TypeError: sum() got an unexpected keyword argument 'min_count'`
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
In [198]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.20.15-200.fc29.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: nb_NO.UTF-8
LOCALE: nb_NO.UTF-8
pandas: 0.24.1+0.g1700680.dirty
pytest: None
pip: 10.0.1
setuptools: 39.0.1.post20180504
Cython: None
numpy: 1.16.1
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.8
feather: None
matplotlib: 3.0.1
openpyxl: 2.6.1
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 8 | 2019-03-19T10:35:30Z | 2019-12-03T13:39:55Z | 2019-12-03T13:39:55Z | MEMBER | null | The geopandas downstream tests are failing because of an installation issue.
See eg https://travis-ci.org/pandas-dev/pandas/jobs/508237658, giving:
```
> from fiona.ogrext import Iterator, ItemsIterator, KeysIterator
E ImportError: libiconv.so.2: cannot open shared object file: No such file or directory
```
This environment is defined by this yml file: https://github.com/pandas-dev/pandas/blob/master/ci/deps/travis-36.yaml
It is mixing defaults and conda-forge (with defaults as higher priority one), but given the `nomkl` it is getting blas / openblas from conda-forge.
Until recently, the full geospatial stack was resolved to come from defaults (see eg https://travis-ci.org/pandas-dev/pandas/jobs/507830158#L1749), but since the last hours, it is getting fiona / gdal from conda-forge as well (but not all of the gdal dependencies, hence giving the error above).
cc @ocefpaf I know this is again an example of mixing conda-forge and defaults (like the recent one we had on the geopandas CI), which is not guaranteed to work. But it is also again something that was working until recently, and a kind of common use case: getting all from defaults, except for a few things that are not available there (and therefore adding conda-forge as the second, lower priority channel) | {
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```
col1 col2
736372420181101 736372420190314
736718220181101 736718220190314
738542020181101 738542020190314
751412120181101 751412120190314
779315420181101 779315420190314
807519420190301
800573920190115
751412120181101 751412120190314
736718220181101
738542020181101
751412120181101 751412120190314
779315420181101 779315420190314
```
First problem where there is no dtype defined all values are converted to float64
Example code:
```
import pandas as pd
test1 = pd.read_table( 'test1.txt')
test1.info()
```
Second problem when I am forcing data type to int64 it fails on NA values:
```
import pandas as pd
test1 = pd.read_table( 'test1.txt', dtype={'col1': 'int64', 'col2': 'int64'})
test1.info()
```
Error msg:
`ValueError: Integer column has NA values in column 0`
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} | 11 | 2019-03-19T11:35:28Z | 2019-03-21T13:27:38Z | 2019-03-20T14:06:44Z | CONTRIBUTOR | null | Improve performance of StataReader when converting columns
with missing values
xref #25772
- [x] closes #N/A
- [x] tests added / passed N/A, perf only
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 1 | 2019-03-19T13:23:34Z | 2021-09-28T21:00:34Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
from scipy.stats import pearsonr
df = pd.DataFrame(np.zeros((2,2)))
df.corr(method=lambda x, y: pearsonr(x, y)[0])
```
gives:
```python
0 1
0 1.0 NaN
1 NaN 1.0
```
#### Problem description
This is a follow up on #25726. In the example above the self-correlation is returned as 1.0. However, it should actually be `nan`. I would argue the diagonal elements of the correlation matrix should be calculated instead of setting them to 1 here:
https://github.com/pandas-dev/pandas/blob/cb00deb94500205fcb27a33cc1d0df79a9727f8b/pandas/core/frame.py#L7025-L7026
`df.corr()` gives the output I expected:
#### Expected Output
```python
df.corr()
```
```python
0 1
0 NaN NaN
1 NaN NaN
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.165-81-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.1.1
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.3
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.3
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.4.0-b1
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 6 | 2019-03-19T15:56:03Z | 2019-04-14T21:06:31Z | 2019-04-14T15:34:11Z | CONTRIBUTOR | null | If you go to http://pandas.pydata.org/speed/pandas/#regressions?sort=1&dir=desc or http://pandas.pydata.org/speed/pandas/#summarylist?sort=3&dir=desc and sort by descending date, you'll see no results more recent than 1/15/19. The same goes for all other benchmark suites also hosted on the speed site.
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```python
import pandas as pd
import numpy as np
data = [['A','B'],np.array([1,2])]
df = pd.DataFrame(data)
```
#### Problem description
The above code causes my kernel to crash with no error message.
I believe the reason for this can be found in the following method:
https://github.com/pandas-dev/pandas/blob/c8ba766fca0b7e91f57c64fdb1e2c2a00a643d27/pandas/core/internals/construction.py#L423
It appears this method only looks at the data type of the first entry of list and then assumes that the remainder of the list is the same type. Hence, since the second entry is a numpy array we crash the kernel.
If we change the order of the entries in the data list, (ie. make an array first) we do not get an error. eg. this does not crash
```python
import pandas as pd
import numpy as np
data2 = [np.array(['A','B']),[1,2]]
df2 = pd.DataFrame(data2)
```
#### Expected Output
If mixed type lists cannot be handled in a consistent manner, then an exception should be thrown rather than a crash with no logging.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.0
pytest: None
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.15.4
scipy: 1.2.0
pyarrow: 0.9.0
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: 0.4.0
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.2
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.2.16
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 8 | 2019-03-19T16:46:03Z | 2019-03-28T14:30:54Z | 2019-03-20T14:03:55Z | CONTRIBUTOR | null | - [x] closes: N/A
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I will update the PR when CI finishes running, as I locally tested `io.parser.test_common` only.
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} | 1 | 2019-03-19T17:01:04Z | 2019-03-19T18:35:27Z | 2019-03-19T18:35:12Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
_DEF = '../pydata-book/examples'
tips = pd.read_csv(_DEF+'/tips.csv')
party_counts = pd.crosstab(tips.day, tips.size)
print(party_counts)
#unepected result i got instead,
#col_0 1464
#day
#Fri 19
#Sat 87
#Sun 76
#Thur 62
```
#### Problem description
as explained in the 0.17 documentation crosstab should return a frequency for
each item with a different value
https://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.crosstab.html
in a more recent instead of it says that it's just a function required if you have used the values
(after testing I can assume the default is just a sum of occurrences).
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.crosstab.html
Question:
how can I reproduce the default result provided by the old version obtaining a matrix of frequencies?
tk in advance,lp | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25786 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25786/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25786/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25786/events | https://github.com/pandas-dev/pandas/pull/25786 | 422,904,266 | MDExOlB1bGxSZXF1ZXN0MjYyNTkyNDE2 | 25,786 | DOC: Docstring script to not require ending period for bullet points | {
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"description": "Continuous Integratio... | closed | false | null | [] | null | 13 | 2019-03-19T19:07:30Z | 2019-10-25T16:35:36Z | 2019-10-25T16:10:22Z | CONTRIBUTOR | null | - [X] closes #25461
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
This PR tries to solve the issue #25461 .
I thought that the validator should check that the last line of the parameter description is a part of a bullet point before raising the error PR09.
In order to do that, I had to add an extra method in the Docstring class that return a parameter description line by line (in a list). This is because Docstring.parameter_desc() returns the description as 1 string.
Please let me know how I can improve this or if I did not consider something. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25787 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25787/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25787/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25787/events | https://github.com/pandas-dev/pandas/issues/25787 | 422,918,628 | MDU6SXNzdWU0MjI5MTg2Mjg= | 25,787 | AttributeError: 'BlockManager' object has no attribute 'empty' | {
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] | closed | false | null | [] | null | 5 | 2019-03-19T19:42:36Z | 2019-03-29T02:32:24Z | 2019-03-29T02:32:24Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
concated_data_frame = concat([self, other], axis=0, ignore_index=True)
```
#### Problem description
I am trying to concat two instance of a subclass of pandas.DataFrame.
The base class looks like:
```
class BaseTestResult(DataFrame):
def __new__(cls, data_df, *args, **kwargs):
# check if an empty DataFrame is passed in; reassign the data_df
if data_df.empty:
...
return super(BaseTestResult, cls).__new__(cls, data_df, *args, **kwargs)
def __init__(self, data_df, file_paths=None, *args, **kwargs):
# pylint: disable=keyword-arg-before-vararg
super(BaseTestResult, self).__init__(data_df, *args, **kwargs)
self.validate_input_paths(file_paths)
self._file_paths = defaultdict(set)
self.file_paths = defaultdict(set) if not file_paths else file_paths
self.validate()
...
```
it raise AttributeError:
```
Traceback (most recent call last):
File "/Users/leijiang/.p2/pool/plugins/org.python.pydev.core_6.4.3.201807050139/pysrc/_pydevd_bundle/pydevd_exec.py", line 3, in Exec
exec exp in global_vars, local_vars
File "<console>", line 2, in <module>
File "/Users/leijiang/pyperf/ENV/lib/python2.7/site-packages/pandas/core/reshape/concat.py", line 229, in concat
return op.get_result()
File "/Users/leijiang/pyperf/ENV/lib/python2.7/site-packages/pandas/core/reshape/concat.py", line 431, in get_result
return (cons._from_axes(new_data, self.new_axes)
File "/Users/leijiang/pyperf/ENV/lib/python2.7/site-packages/pandas/core/generic.py", line 332, in _from_axes
return cls(data, **kwargs)
File "/Users/leijiang/pyperf/pyPerf/test_results/base_test_result.py", line 130, in __new__
if data_df.empty:
AttributeError: 'BlockManager' object has no attribute 'empty'
```
#### Expected Output
It should pass
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.0
pytest: 3.8.2
pip: 9.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.1
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: 1.8.0
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 5 | 2019-03-19T21:47:31Z | 2019-03-26T20:05:59Z | 2019-03-26T20:05:56Z | CONTRIBUTOR | null | - [ ] progress towards #24471
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This is WIP for adding a ``sort`` parameter to ``RangeIndex.union`` that behaves in a similar way to the other index types.
``sort=None`` is the default to make it consistent with the union method in the base class. When ``sort=None`` a monotonically increasing ``RangeIndex`` will be returned if possible and a sorted ``Int64Index`` if not.
The way I have implemented ``sort=False`` is that it returns an ``Int64Index`` in all cases. I have been trying to think of cases where it would make sense to still return a ``RangeIndex`` when ``sort=False``. For example, there might be a case where if we had two ``RangeIndex``s and both had the same step and the second ``RangeIndex`` overlapped with the first we would want to return a ``RangeIndex`` here even if we had ``sort=False``. But would it be better just to always return an ``Int64Index`` when ``sort=False`` as I have done here to make the return type consistent and because this particular case seems quite rare?
```
# sort=False returns an Int64Index even though we might be able to return a RangeIndex as below
In [1]: RangeIndex(0, 10, 2).union(RangeIndex(10, 12, 2), sort=False)
Out[1]: Int64Index([0, 2, 4, 6, 8, 10], dtype='int64')
In [1]: RangeIndex(0, 10, 2).union(RangeIndex(10, 12, 2), sort=None)
Out[1]: RangeIndex(start=0, stop=12, step=2)
``` | {
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} | 6 | 2019-03-19T21:50:02Z | 2019-03-20T01:23:14Z | 2019-03-20T01:03:21Z | MEMBER | null | - [X] closes #25771
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Larger change than I would have hoped for but it's tough to split this up given the typing import errors get bundled in with F821 errors during LINTing that we certainly wouldn't want to ignore
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} | 1 | 2019-03-19T22:10:28Z | 2019-03-30T19:08:52Z | 2019-03-30T19:08:52Z | MEMBER | null | This should be done after #25789
Not necessarily analyzing types yet, but I think it makes sense from a readability perspective to start switching over to Python3 syntax for types. A quick grep shows 70 of these in the code base:
<details>
```sh
pandas/core/dtypes/base.py: # type: () -> Optional[List[str]]
pandas/core/dtypes/base.py: # type: () -> bool
pandas/core/dtypes/base.py: # type: () -> bool
pandas/core/dtypes/base.py: # type: () -> Type
pandas/core/dtypes/base.py: # type: () -> str
pandas/core/groupby/groupby.py: # type: (np.ndarray) -> (np.ndarray, Type)
pandas/core/groupby/groupby.py: # type: (np.ndarray, Type) -> np.ndarray
pandas/core/groupby/groupby.py: # type: (np.ndarray) -> (np.ndarray, Optional[Type])
pandas/core/groupby/groupby.py: # type: (np.ndarray, Optional[Type]) -> np.ndarray
pandas/core/internals/blocks.py: periods, # type: int
pandas/core/internals/blocks.py: axis=0, # type: libinternals.BlockPlacement
pandas/core/internals/blocks.py: fill_value=None): # type: Any
pandas/core/internals/blocks.py: # type: (...) -> List[ExtensionBlock]
pandas/core/internals/managers.py: # type: (List[Block]) -> Optional[Union[np.dtype, ExtensionDtype]]
pandas/core/common.py: # type: (Any) -> bool
pandas/core/arrays/datetimes.py: _dtype = None # type: Union[np.dtype, DatetimeTZDtype]
pandas/core/arrays/datetimes.py: # type: () -> Union[np.dtype, DatetimeTZDtype]
pandas/core/arrays/integer.py: # type: () -> np.ndarray
pandas/core/arrays/integer.py: # type: () -> np.ndarray
pandas/core/arrays/period.py: # type: (Sequence[Optional[Period]], PeriodDtype, bool) -> PeriodArray
pandas/core/arrays/period.py: # type: (Union[Period, NaTType]) -> int
pandas/core/arrays/period.py: # type: (str) -> Period
pandas/core/arrays/period.py: other, # type: Union[ExtensionArray, np.ndarray[int]]
pandas/core/arrays/period.py: op # type: Callable[Any, Any]
pandas/core/arrays/period.py: # type: (...) -> PeriodArray
pandas/core/arrays/period.py: # type: (Sequence[Optional[Period]], Optional[Tick], bool) -> PeriodArray
pandas/core/arrays/sparse.py: # type: (Union[str, np.dtype, 'ExtensionDtype', type], Any) -> None
pandas/core/arrays/sparse.py: # type: (SparseArray) -> np.ndarray
pandas/core/arrays/sparse.py: # type: (SparseArray, SparseArray, Callable, str) -> Any
pandas/core/arrays/sparse.py: # type: (np.ndarray, SparseIndex, SparseDtype) -> 'SparseArray'
pandas/core/arrays/datetimelike.py: # type: () -> Union[type, Tuple[type]]
pandas/core/arrays/datetimelike.py: # type: (str) -> Union[Period, Timestamp, Timedelta, NaTType]
pandas/core/arrays/datetimelike.py: # type: (Union[Period, Timestamp, Timedelta, NaTType]) -> int
pandas/core/arrays/datetimelike.py: # type: (Union[Period, Timestamp, Timedelta, NaTType]) -> None
pandas/core/arrays/datetimelike.py: # type: () -> np.ndarray
pandas/core/arrays/datetimelike.py: # type: () -> int
pandas/core/arrays/datetimelike.py: key, # type: Union[int, Sequence[int], Sequence[bool], slice]
pandas/core/arrays/datetimelike.py: value, # type: Union[NaTType, Any, Sequence[Any]]
pandas/core/arrays/datetimelike.py: # type: (...) -> None
pandas/core/arrays/array_.py:def array(data, # type: Sequence[object]
pandas/core/arrays/array_.py: dtype=None, # type: Optional[Union[str, np.dtype, ExtensionDtype]]
pandas/core/arrays/array_.py: copy=True, # type: bool
pandas/core/arrays/array_.py: # type: (...) -> ExtensionArray
pandas/core/arrays/base.py: # type: (Union[int, np.ndarray], Any) -> None
pandas/core/arrays/base.py: # type: () -> int
pandas/core/arrays/base.py: # type: () -> ExtensionDtype
pandas/core/arrays/base.py: # type: () -> Tuple[int, ...]
pandas/core/arrays/base.py: # type: () -> int
pandas/core/arrays/base.py: # type: () -> int
pandas/core/arrays/base.py: # type: () -> Union[ExtensionArray, np.ndarray]
pandas/core/arrays/base.py: # type: () -> np.ndarray
pandas/core/arrays/base.py: # type: (int, object) -> ExtensionArray
pandas/core/arrays/base.py: # type: () -> Tuple[np.ndarray, Any]
pandas/core/arrays/base.py: # type: (int) -> Tuple[np.ndarray, ExtensionArray]
pandas/core/arrays/base.py: # type: (Sequence[int], bool, Optional[Any]) -> ExtensionArray
pandas/core/arrays/base.py: # type: (bool) -> ExtensionArray
pandas/core/arrays/base.py: # type: (bool) -> Callable[[Any], Optional[str]]
pandas/core/arrays/base.py: # type: () -> np.ndarray
pandas/core/arrays/base.py: # type: (Sequence[ExtensionArray]) -> ExtensionArray
pandas/core/arrays/base.py: # type: () -> np.ndarray
pandas/core/frame.py: key, # type: Union[str, List[str]]
pandas/core/frame.py: ndim, # type: int
pandas/core/frame.py: subset=None # type: Union[Series, DataFrame, None]
pandas/core/frame.py: # type: (...) -> Union[Series, DataFrame]
pandas/core/base.py: # type: () -> ExtensionArray
pandas/core/base.py: # type: () -> np.ndarray
pandas/core/indexes/period.py: _data = None # type: PeriodArray
pandas/core/indexes/datetimelike.py: _data = None # type: DatetimeLikeArrayMixin
pandas/core/indexes/datetimelike.py: # type: () -> np.ndarray
pandas/core/indexes/base.py: # type: () -> Union[ExtensionArray, Index, np.ndarray]
```
</details>
I think it makes sense to break this up into 2 PRs:
- [ ] Everything in pandas/core/arrays
- [ ] Everything else
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@jbrockmendel @jreback | {
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} | 9 | 2019-03-20T02:04:38Z | 2019-03-24T18:35:01Z | 2019-03-24T18:34:11Z | MEMBER | null | and update imports. Move `tests.test_config` to `tests.config.test_config`.
Following this there will be one more follow-up that will 1) address the TODO in `tests.config.test_localization`, 2) fully disentangle `tslibs` from its dependency on `core`. | {
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```python
df_strain=df["strain"].value_counts()
DF(df_strain)
```
## Problem description
Hello masters, thanks for your concern.
As the code and image show, I'm using the value_count function.
The function is very convient~ But I found that if I want to merge the rows which count is lower than 5, I have to do it manually like:
```python
series_dict={}
others_sum=0
for index in df_strain.index:
count=df_strain.at[index]
if count>=5:
series_dict.setdefault(index,count)
else:
others_sum+=count
series_dict.setdefault("others",others_sum)
DF(Series(series_dict))
```
## Expected Output
My question is that: Is there any more convenient way to implement the emergence according to my conditions?
Thank you for your time~
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 18.1
setuptools: 39.2.0
Cython: 0.28.5
numpy: 1.15.0
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: None
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.0
openpyxl: 2.5.9
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.1
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 2 | 2019-03-20T06:24:50Z | 2019-03-26T11:54:45Z | 2019-03-26T11:54:45Z | NONE | null | Good day!
```python
s = pd.Series(np.arange(10), name='items')
c = s.value_counts()
c.name #=> 'items'
c.index.names #=> None
```
IMHO that's very strange. Values of `c` are quantities/counts/etc. not "items". "Items" are represented in `c.index`. No?
What do you think?
Thanks! | {
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} | 4 | 2019-03-20T06:49:44Z | 2019-03-26T00:27:21Z | 2019-03-26T00:25:03Z | MEMBER | null | - [x] closes #3232
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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"color": "e102d8"... | open | false | null | [] | null | 3 | 2019-03-20T07:30:54Z | 2021-06-27T20:27:54Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
# fails in version 0.24.2 but works in 0.23.4
df = pd.DataFrame.from_dict({'Test': ['0.5', True, '0.6']})
print(df['Test'].dtype)
assert df['Test'].dtype == 'object'
df['Test'] = df['Test'].replace([True], [np.nan])
print(df['Test'].dtype)
assert df['Test'].dtype == 'object'
# fails in version 0.24.2 but works in 0.23.4
df = pd.DataFrame.from_dict({'Test': ['0.5', None, '0.6']})
print(df['Test'].dtype)
assert df['Test'].dtype == 'object'
df['Test'] = df['Test'].replace([None], [np.nan])
print(df['Test'].dtype)
assert df['Test'].dtype == 'object'
# works in both mentioned versions
df = pd.DataFrame.from_dict({'Test': ['0.5', None, '0.6']})
print(df['Test'].dtype)
assert df['Test'].dtype == 'object'
df['Test'] = df['Test'].fillna(np.nan)
print(df['Test'].dtype)
assert df['Test'].dtype == 'object'
```
#### Problem description
I have encountered this change of behavior from pandas version 0.23.4 to 0.24.2, but could not find anything in the release notes or the latest documentation, which would inform the user about this change. I am not sure whether the behavioral change of the method is an improvement or not. However, I think it should be mentioned somehow. In addition, since it seems that it is not mentioned, it may hint to a deeper problem of the to_replace method.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
#### Expected Output
# no assertion errors, dtype of column stays object for all three cases
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.12.1
xarray: None
IPython: 7.3.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: None
html5lib: None
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"de... | closed | false | null | [] | null | 1 | 2019-03-20T07:53:39Z | 2020-09-23T17:13:36Z | 2020-09-23T17:13:35Z | CONTRIBUTOR | null | Based on [review](https://github.com/pandas-dev/pandas/pull/25633#pullrequestreview-216493057) of @jreback in #25633:
> frame/test_indexing.py is actually pretty huge. so pls change according to my comments, and open an issue to split this to separate smaller files (after) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25799/events | https://github.com/pandas-dev/pandas/pull/25799 | 423,232,794 | MDExOlB1bGxSZXF1ZXN0MjYyODQ5Nzc1 | 25,799 | BUG: Respect na_rep in DataFrame.to_latex() when used with formatters (#9046) | {
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"description": "np.nan, pd.NaT, pd.NA, d... | closed | false | null | [] | null | 9 | 2019-03-20T12:47:42Z | 2019-05-29T10:08:31Z | 2019-05-29T09:35:47Z | CONTRIBUTOR | null | - [x] closes #9046
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
A very old issue so low priority!
I'd be tempted to remove the shortcut in `_format_strings()` since I'd guess in most cases it doesn't really save much time. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25800/events | https://github.com/pandas-dev/pandas/issues/25800 | 423,258,669 | MDU6SXNzdWU0MjMyNTg2Njk= | 25,800 | dataframe.to_excel openpyxl multilpe sheets | {
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"descript... | closed | false | null | [] | null | 1 | 2019-03-20T13:44:19Z | 2019-03-20T15:11:06Z | 2019-03-20T15:10:58Z | NONE | null | #### Code to test the issue, you need a csv file
```python
import os
from math import ceil
import pandas
def csv_to_xls(csvfile):
def write_ws(p_writer, p_min, p_max, p_sheetname):
df.loc[p_min:p_max].to_excel(p_writer, sheet_name=p_sheetname, index=False)
MAX_XLSX_ROWSIZE = 1024 ** 2 - 1
csvfilename = os.path.basename(csvfile)
xlsxfilename = '{0}.xlsx'.format(os.path.splitext(csvfilename)[0])
df = pandas.read_csv(filepath_or_buffer=csvfile, header=0)
if len(df.index) < MAX_XLSX_ROWSIZE:
df.to_excel(xlsxfilename, index=False)
else:
writer = pandas.ExcelWriter(xlsxfilename, engine='openpyxl')
# writer = pandas.ExcelWriter(xlsxfilename, engine='xlsxwriter')
for i in range(1, ceil(len(df.index) / MAX_XLSX_ROWSIZE)+1):
# lmin = (i-1)*(MAX_XLSX_ROWSIZE + 1)
# lmax = i*MAX_XLSX_ROWSIZE
# Debug
lmin = (i-1) * (1000 + 1)
lmax = i * 1000
# #####
sheetname = f'Sheet{i}'
# comment next 2 rows with xlsxwriter
if sheetname not in writer.book.sheetnames:
writer.book.create_sheet()
write_ws(writer, lmin, lmax, sheetname)
writer.save()
writer.close()
return xlsxfilename
source_dir = 'source'
lcsvfilename = 'smartweb.csv'
lcsvfile = os.path.join(source_dir, lcsvfilename)
lxlsxfilename = csv_to_xls(lcsvfile)
print(f"Creato file {lxlsxfilename}")
```
#### Problem description
I need to split a CSV on multiple xls worksheets.
If I use as writer **xlsxwriter** all goes fine, but if I use **openpyxl**:
- If i don't create the worksheet, the writer overrides the worksheet just created and gives it the new name and the new data
- If I create a worksheet the writer creates another new worksheet with the same name and adds to it the suffix 1
#### Output of ``pd.show_versions()``
<details>
pandas: 0.24.2
pytest: None
pip: 10.0.1
setuptools: 39.1.0
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.5
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 5 | 2019-03-20T14:38:21Z | 2019-03-21T06:20:48Z | 2019-03-20T19:14:00Z | CONTRIBUTOR | null | Fix some reStructuredText issues in *usecols* parameter of read_excel
- bulletlists must start with a space
- the deprecated/versionadded directives do only apply to the respective bullet point an thus need to be indented.
Comapre http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html for the current formatting issues.
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https://api.github.com/repos/pandas-dev/pandas/issues/25802 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25802/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25802/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25802/events | https://github.com/pandas-dev/pandas/pull/25802 | 423,329,592 | MDExOlB1bGxSZXF1ZXN0MjYyOTI1NzAy | 25,802 | Update type hints to python3 style (non-array files) | {
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} | 16 | 2019-03-20T15:50:12Z | 2019-03-31T14:46:27Z | 2019-03-30T19:08:52Z | CONTRIBUTOR | null | - [X ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Fixes #25790 (non-array part) | {
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... | open | false | null | [] | null | 3 | 2019-03-20T16:18:35Z | 2020-04-01T04:23:04Z | null | MEMBER | null | With pandas 0.24:
```
In [1]: import pandas as pd
In [2]: s = pd.Series([1, 2, 3], pd.to_datetime(['2018-01-01', '2018-02-02T01:01', '2018-02-02T02:02']))
In [3]: s.loc['2018-01-01'].size
Out[3]: 1
In [4]: s.loc['2018-01-02'].size
Out[4]: 0
In [5]: s.loc['2018-02-02'].size
Out[5]: 2
In [6]: s.loc['2018-03-03'].size
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
TypeError: an integer is required
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/miniconda3/envs/xarray-py37/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2601 try:
-> 2602 return self._engine.get_loc(key)
2603 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine._date_check_type()
KeyError: '2018-03-03'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
TypeError: an integer is required
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/miniconda3/envs/xarray-py37/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in get_loc(self, key, method, tolerance)
998 try:
--> 999 return Index.get_loc(self, key, method, tolerance)
1000 except (KeyError, ValueError, TypeError):
~/miniconda3/envs/xarray-py37/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2603 except KeyError:
-> 2604 return self._engine.get_loc(self._maybe_cast_indexer(key))
2605 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine._date_check_type()
KeyError: '2018-03-03'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 1520035200000000000
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/miniconda3/envs/xarray-py37/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2601 try:
-> 2602 return self._engine.get_loc(key)
2603 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
KeyError: Timestamp('2018-03-03 00:00:00')
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 1520035200000000000
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/miniconda3/envs/xarray-py37/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in get_loc(self, key, method, tolerance)
1011 stamp = stamp.tz_localize(self.tz)
-> 1012 return Index.get_loc(self, stamp, method, tolerance)
1013 except KeyError:
~/miniconda3/envs/xarray-py37/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2603 except KeyError:
-> 2604 return self._engine.get_loc(self._maybe_cast_indexer(key))
2605 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
KeyError: Timestamp('2018-03-03 00:00:00')
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-6-92239fa9614e> in <module>()
----> 1 s.loc['2018-03-03'].size
~/miniconda3/envs/xarray-py37/lib/python3.7/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1498
1499 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1500 return self._getitem_axis(maybe_callable, axis=axis)
1501
1502 def _is_scalar_access(self, key):
~/miniconda3/envs/xarray-py37/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1911 # fall thru to straight lookup
1912 self._validate_key(key, axis)
-> 1913 return self._get_label(key, axis=axis)
1914
1915
~/miniconda3/envs/xarray-py37/lib/python3.7/site-packages/pandas/core/indexing.py in _get_label(self, label, axis)
135 # but will fail when the index is not present
136 # see GH5667
--> 137 return self.obj._xs(label, axis=axis)
138 elif isinstance(label, tuple) and isinstance(label[axis], slice):
139 raise IndexingError('no slices here, handle elsewhere')
~/miniconda3/envs/xarray-py37/lib/python3.7/site-packages/pandas/core/generic.py in xs(self, key, axis, level, drop_level)
3573 drop_level=drop_level)
3574 else:
-> 3575 loc = self.index.get_loc(key)
3576
3577 if isinstance(loc, np.ndarray):
~/miniconda3/envs/xarray-py37/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in get_loc(self, key, method, tolerance)
1012 return Index.get_loc(self, stamp, method, tolerance)
1013 except KeyError:
-> 1014 raise KeyError(key)
1015 except ValueError as e:
1016 # list-like tolerance size must match target index size
KeyError: '2018-03-03'
```
(side note: this is quite the traceback!)
Bizarrely, whether indexing with a string raises a KeyError or returns an array of size 0 depends upon the value.
But more generally, does it ever make sense to raise an error? It's arguably more consistent to only return size 0 arrays.
xref https://github.com/pandas-dev/pandas/issues/7827 and https://github.com/pydata/xarray/issues/2825 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25804 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25804/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25804/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25804/events | https://github.com/pandas-dev/pandas/pull/25804 | 423,354,883 | MDExOlB1bGxSZXF1ZXN0MjYyOTQ1MjUw | 25,804 | PERF: Improve performance of hash sets in read_csv | {
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} | 9 | 2019-03-20T16:36:42Z | 2019-03-27T00:56:04Z | 2019-03-22T16:19:38Z | CONTRIBUTOR | null | - [x] closes N/A
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Improved performance of checking of string presence in hashsets by:
1. implemented lookup table for first symbol of entries - this speeds up negative answers
2. increased size of hash table to reduce the probability of hash collision
Hash bucket increase is needed due to khash search algorithm - it computes a hash of key being searched, uses its last N bits where N corresponds to bucket size, and then does an open ended search over the table if collision happened with calling a comparison function over keys. When keys are strings (or, more precisely, `char*` things) comparison function is `strcmp()` which complexity is O(N), thus we want for collisions to be rare.
Most lookups of those sets happen when values are checked against N/A table which by default has 17 entries. Without the change bucket size is 32, thus hash collision probability is 17/32 (~53%), after the patch bucket size is 256 and collision probability is 6.6%.
I will add `asv` results and whatsnew later. | {
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] | closed | false | null | [] | null | 2 | 2019-03-20T16:47:09Z | 2020-07-15T03:40:46Z | 2020-07-15T03:40:46Z | NONE | null | #### Code Sample
```python
df = pd.DataFrame([{'Key': 'Blue', 'Value': 101.25}, {'Key': 'Green', 'Value': 99.12}])
df.to_html()
```
#### Problem description
The code above, `to_html` has no direct way of specifying column styles. When I dump the output, it produces the table below.
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;"><th></th><th>Key</th> <th>Value</th></tr> </thead>
<tbody>
<tr><th>0</th><td>Blue</td><td>101.25</td></tr>
<tr><th>1</th><td>Green</td><td>99.12</td></tr>
</tbody>
</table>
I would like to pass in custom styles (which could be tags based on column index) for specific columns, such `Value` is right-aligned. `to_html` references `DataFrameFormatter` which reference `HTMLFormatter`. the `write_tr` method in `HTMLFormatter` supports `tags` which can be used to specify cell styles or `align` which can be used to specify row text alignment.
#### Expected Output
It would be nice to pass in align & tags values when calling `DataFrame.to_html`
#### Output of ``pd.show_versions()``
<details>
pandas: 0.24.1
pytest: 4.0.2
pip: 19.0.1
setuptools: 40.7.3
Cython: None
numpy: 1.15.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: 2.5.14
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.17
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25806 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25806/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25806/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25806/events | https://github.com/pandas-dev/pandas/issues/25806 | 423,378,159 | MDU6SXNzdWU0MjMzNzgxNTk= | 25,806 | Effect of assigning list to DataFrame cell depends on unrelated column's type | {
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"description": "Related to indexing on s... | open | false | null | [] | null | 1 | 2019-03-20T17:22:39Z | 2021-06-27T20:32:01Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame([{'foo': None, 'bar': None}], index=['a'])
df.loc['a', 'foo'] = ['123']
print(df)
print()
df = pd.DataFrame([{'foo': None, 'bar': None}], index=['a'])
df['bar'] = df['bar'].astype(float)
df.loc['a', 'foo'] = ['123']
print(df)
print()
df = pd.DataFrame([{'foo': None, 'bar': None}], index=['a'])
df.at['a', 'foo'] = ['123']
print(df)
print()
df = pd.DataFrame([{'foo': None, 'bar': None}], index=['a'])
df['bar'] = df['bar'].astype(float)
df.index = df.index.astype('category')
df.at['a', 'foo'] = ['123']
print(df)
```
#### Problem description
Output is:
```
bar foo
a None [123]
bar foo
a NaN 123
bar foo
a None [123]
bar foo
a NaN 123
```
Behavior of the operation `df.loc['a', 'foo'] = ['123']` depends on the type of the `bar` column, even though the `bar` column has nothing to do with the operation. If one writes a function using such operations, and a new column containing some unrelated data is added to the data frame, the function will break. The dependency on unrelated columns is also surprising.
If `bar` is of type `object`, then the list `['123']` is assigned to the cell. If `bar` is of type `float`, then the string `'123'` is assigned to the cell. If the list is longer than one element, then in the latter case one gets the exception `ValueError: Must have equal len keys and value when setting with an iterable`.
Instead of `loc`, using `at` works consistently for the first two examples. However, if one changes the index's type to `category` instead of `object` as in the last two examples, then one observes the same inconsistency as with `loc`. This is even more surprising, since one gets the same `ValueError: Must have equal len keys and value when setting with an iterable` exception for longer lists, and `at` is supposed to do single element assignment, but that exception suggests that it is trying to do multiple element assignment instead.
#### Expected Output
```
bar foo
a None [123]
bar foo
a NaN [123]
bar foo
a None [123]
bar foo
a NaN [123]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Darwin
OS-release: 17.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 18.1
setuptools: 39.0.1
Cython: 0.28.5
numpy: 1.15.2
scipy: 1.1.0
pyarrow: None
xarray: 0.10.9
IPython: 6.5.0
sphinx: 1.8.1
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: 0.1.6
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-03-20T19:01:47Z | 2019-03-21T11:14:06Z | 2019-03-21T11:14:06Z | NONE | null | #### Code Sample
```python
import pandas as pd
import datetime as dt
import pytz as tz
data = pd.DataFrame([{'localized_date': dt.datetime(2018, 1, 12, 8, 15, tzinfo=tz.timezone('Europe/Berlin'))}])
data
"""
localized_date
0 2018-01-12 08:22:00+01:00
"""
data.dtypes
"""
localized_date datetime64[ns, Europe/Berlin]
dtype: object
"""
data.update(data)
data
"""
localized_date
0 2018-01-12 07:22:00
"""
data.dtypes
"""
localized_date datetime64[ns]
dtype: object
"""
```
#### Problem description
Update() method drops timezone info for datetime columns and cast all values to utc without a warning.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 9.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-03-20T21:54:01Z | 2019-03-21T03:07:49Z | 2019-03-21T03:07:44Z | CONTRIBUTOR | null | Fixed a couple of minor inconsistencies in the formatting of the CheatSheet README file.
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} | 3 | 2019-03-20T23:08:11Z | 2019-03-21T11:13:32Z | 2019-03-21T11:13:32Z | CONTRIBUTOR | null | Behavior in pandas 0.23:
```python
>>> import pandas as pd
>>> pd.__version__
0.23.0
>>> x = pd.date_range(start='2019-01-01', end='2019-01-30', freq='D')
>>> x = x.tz_localize('UTC')
>>> x.to_frame().dtypes
0 datetime64[ns, UTC]
dtype: object
```
Behavior in pandas 0.24
```python
>>> import pandas as pd
>>> pd.__version__
0.24.2
>>> x = pd.date_range(start='2019-01-01', end='2019-01-30', freq='D')
>>> x = x.tz_localize('UTC')
>>> x.to_frame().dtypes
0 datetime64[ns]
dtype: object
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} | 3 | 2019-03-20T23:31:14Z | 2019-03-21T15:25:10Z | 2019-03-21T11:14:06Z | MEMBER | null | - [x] closes #25807
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2019-03-20T23:59:02Z | 2019-03-21T15:24:53Z | 2019-03-21T11:13:32Z | MEMBER | null | - [x] closes #25809
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/25812 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25812/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25812/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25812/events | https://github.com/pandas-dev/pandas/pull/25812 | 423,528,442 | MDExOlB1bGxSZXF1ZXN0MjYzMDgyOTI4 | 25,812 | PERF: Improve performance of Series.isin() on sets | {
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"default"... | closed | false | null | [] | null | 2 | 2019-03-21T00:23:25Z | 2019-03-24T14:38:39Z | 2019-03-24T14:05:39Z | CONTRIBUTOR | null | - [x] closes #25507
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Issue #25507 pointed out that using `DataFrame.isin` or `Series.isin` on a set took extremely long due to the set being first converted to a list then back to a hash table to lookup values. This fix eliminates that conversion and simply uses the set to directly test membership.
Sample code from issue:
```python
import pandas as pd
squares = set(a**2 for a in range(10000000))
series = pd.Series(range(100))
def test_isin():
_ = series.isin(squares)
%timeit test_isin()
```
Output:
```
Before:
3.89 s ± 66.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
After:
134 µs ± 11.3 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
There are already several unit tests covering this function on sets with different dtypes. Additionally, I added an additional asv benchmark for sets:
```
series_methods.IsIn.time_isin
======== =============
dtype
-------- -------------
int64 1.46±0.04ms
uint64 1.76±0.1ms
object 3.53±0.2ms
======== =============
series_methods.IsIn.time_isin_set
======== =============
dtype
-------- -------------
int64 1.58±0.03ms
uint64 1.61±0.06ms
object 3.24±0.04ms
======== =============
```
If the additional benchmark is overkill I can of course remove it. | {
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