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https://api.github.com/repos/pandas-dev/pandas/issues/14108 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14108/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14108/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14108/events | https://github.com/pandas-dev/pandas/issues/14108 | 173,641,228 | MDU6SXNzdWUxNzM2NDEyMjg= | 14,108 | ENH: Add pd.to_period | {
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... | 5 | 2016-08-28T11:11:51Z | 2018-10-26T20:53:52Z | 2018-10-25T22:38:55Z | MEMBER | null | Would return `Period` or `PeriodIndex`, analogous to functions like `to_datetime` and `to_timedelta`.
xref #5886
xref #14048
cc @sinhrks
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- [ ] tests added / passed
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Methods such as 'hist' that take keyword arguments would now be able to be called via a Groupby object and the keyword args would be passed onwards to matplotlib as the user expects.
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I recently upgraded from pandas 0.17.1 to 0.18.1 and ran into a performance regression with DataFrame.to_csv:
#### Code Sample, a copy-pastable example if possible
import pandas
import numpy
import tempfile
fd, filename = tempfile.mkstemp(prefix='to_csv_', suffix='.csv')
test = pandas.DataFrame(numpy.random.ra... | {
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- [ x ] tests added / passed
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Not sure about the last. It's my first PR.
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Also addresses #8254.
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I'm new to configuring Travis, I just hope this will work as I expect and that the tests will fail.
- [ ] closes #12337
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I was going through the install.rst, and updated some small things, but there was actually no content from README to be moved.
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Closes #14086.
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Closes a few issues still tagged with 0.19.0 that only need a confirming test
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https://api.github.com/repos/pandas-dev/pandas/issues/14118 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14118/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14118/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14118/events | https://github.com/pandas-dev/pandas/pull/14118 | 173,817,660 | MDExOlB1bGxSZXF1ZXN0ODMxMjkwMTU= | 14,118 | BUG: series resample with timedelta values looses dtype (GH13119) | {
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In the code below:
``` python
pd.Series(range(10), index=range(10)).plot.bar(xticks=range(0, 11, 2))
```
`xticks` argument has no effect. However, there's no such problem in line() plot.
#### Expected Output
Output plot is expected to have xticks defined by `xt... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14120 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14120/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14120/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14120/events | https://github.com/pandas-dev/pandas/pull/14120 | 173,917,811 | MDExOlB1bGxSZXF1ZXN0ODMyMDAxNzc= | 14,120 | API: Warn or raise for > 1 char encoded sep | {
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Closes #14065.
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"... | open | false | null | [] | null | 19 | 2016-08-31T00:35:58Z | 2021-07-03T18:23:12Z | null | NONE | null | @pdbaines and I noticed this bug.
I want Pandas to write a CSV file so that all field data is backslash escaped if the character has a special interpretation (e.g. quotes or backslashes themselves). If a quote is backslashed, it is treated as field data, rather than a special character. This is not the behavior that I... | {
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"followers_url": "https://api.github.com/users/jorisva... | 5 | 2016-08-31T07:27:22Z | 2020-02-11T02:05:43Z | 2016-11-23T13:21:07Z | CONTRIBUTOR | null | Given a file `t.txt` like this:
```
A,B,C
1,4,7,
2,5,8,
3,6,9,
```
`pandas.read_csv('t.txt')` produces:
```
A B C
1 4 7 NaN
2 5 8 NaN
3 6 9 NaN
```
In other words, it implies a nonexistent header column for what it believes is the index. I would expect it to do the same as this code:
```
pd.read_csv('... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14125 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14125/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14125/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14125/events | https://github.com/pandas-dev/pandas/issues/14125 | 174,218,146 | MDU6SXNzdWUxNzQyMTgxNDY= | 14,125 | BUG: read_csv() crashes with engine='c' | {
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"... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-08-31T08:49:58Z | 2017-01-24T23:13:38Z | 2017-01-24T23:13:38Z | CONTRIBUTOR | null | Here's the code (input data is at the end of this message):
```
pd.read_csv('foo.csv', header=None, usecols=[0])
```
It fails with:
```
File "pandas/parser.pyx", line 805, in pandas.parser.TextReader.read (pandas/parser.c:8748)
File "pandas/parser.pyx", line 827, in pandas.parser.TextReader._read_low_memory (pandas/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14126 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14126/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14126/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14126/events | https://github.com/pandas-dev/pandas/pull/14126 | 174,239,825 | MDExOlB1bGxSZXF1ZXN0ODM0Mjc3Mjc= | 14,126 | Make show_versions not to reload modules (GH13684) | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/14128 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14128/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14128/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14128/events | https://github.com/pandas-dev/pandas/issues/14128 | 174,251,559 | MDU6SXNzdWUxNzQyNTE1NTk= | 14,128 | Possibility to draw random markers in line plots | {
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... | open | false | null | [] | null | 0 | 2016-08-31T11:41:57Z | 2016-10-04T09:10:06Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
I often have dataframes with many obervations and want to have a quick glance at the data using a line plot.
The problem is that the colors of the colormap are either repeated over X observations or hard to distinguish e.g. in case of sequential colormaps.
So my ... | {
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`DataFrame.to_sql(name, con, ...)`
It's not intuitive if `name` means name of database or name of table in database.
I think something like `table_name` or `tablename` would better explain what `name` is.
Yes, I know I can Google it every time and eventually after Googling it so man... | {
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"followers_url": "https://api.github.com/users/j... | 31 | 2016-08-31T15:58:03Z | 2019-08-05T06:26:52Z | 2019-06-21T09:37:18Z | CONTRIBUTOR | null | We have had some conversations in the past regarding an `.plot(....., engine=)` kw. in #8018
This would allow pandas to redirect the plotting to a user selectable back-end, keeping `matplotlib` as the default.
see [chartpy here](https://github.com/cuemacro/chartpy), for a way to selectively enable `matplotlib`, `boke... | {
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... | 22 | 2016-08-31T22:04:34Z | 2019-10-21T19:52:16Z | null | NONE | null | I have 5e9 rows csv file that I'm trying to load with pandas, which appears to load it only partially and produces 2852516352 rows data frame.
``` py
#Python 2.7.3 (default, Jun 22 2015, 19:33:41)
#[GCC 4.6.3] on linux2
#Type "help", "copyright", "credits" or "license" for more information.
import pandas as pd
import... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14132 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14132/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14132/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14132/events | https://github.com/pandas-dev/pandas/pull/14132 | 174,438,285 | MDExOlB1bGxSZXF1ZXN0ODM1NzE2MDM= | 14,132 | BUG: Don't print stray newline with MultiIndex | {
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Closes #6618.
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https://api.github.com/repos/pandas-dev/pandas/issues/14133 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14133/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14133/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14133/events | https://github.com/pandas-dev/pandas/issues/14133 | 174,485,625 | MDU6SXNzdWUxNzQ0ODU2MjU= | 14,133 | bug: double print statement in groupby / apply | {
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This is based on http://stackoverflow.com/questions/39268147/understand-pandas-groupby-apply-behaviour/39268538#39268538.
```
import seaborn as sns
iris = sns.load_dataset('iris')
iris.ix[1:100:10].groupby(["species"]).apply(lambda x: print(len(x), '\n***\n', x)) ## first try
iris.ix[1:10... | {
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"id": ... | closed | false | null | [] | null | 1 | 2016-09-01T11:40:02Z | 2016-09-01T11:47:46Z | 2016-09-01T11:46:34Z | NONE | null | While using a `groupby`, applying an `expanding` function duplicates the grouped dimensions. I would expect the result of this to work like `groupby.cumsum` where the `expanding` function is applied over the groups and then the result has the same index as the original data frame.
#### Code Sample, a copy-pastable exa... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14135 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14135/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14135/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14135/events | https://github.com/pandas-dev/pandas/issues/14135 | 174,662,176 | MDU6SXNzdWUxNzQ2NjIxNzY= | 14,135 | read_fwf does not use comment character if colspecs argument does not include first column | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14136 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14136/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14136/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14136/events | https://github.com/pandas-dev/pandas/issues/14136 | 174,748,520 | MDU6SXNzdWUxNzQ3NDg1MjA= | 14,136 | transpose subplots | {
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df.plot(subplots=True, layout = (15, 1),legend=False) # for a 300x15 dataframe
which gives very nice row plots like this:
However sometimes it is needed to... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14137 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14137/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14137/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14137/events | https://github.com/pandas-dev/pandas/issues/14137 | 174,772,983 | MDU6SXNzdWUxNzQ3NzI5ODM= | 14,137 | Series/DataFrame breaks truth-checking | {
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"followers_url": "https://api.github.com/users/jorisva... | 6 | 2016-09-02T14:28:39Z | 2016-09-05T22:42:56Z | 2016-09-02T21:05:50Z | CONTRIBUTOR | null | This behavior is obviously by design. Why choose to break substitutability?
```
>>> bool([])
False
>>> bool(numpy.array([]))
False
>>> bool(pandas.Series(numpy.array([])))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/si... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14138 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14138/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14138/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14138/events | https://github.com/pandas-dev/pandas/issues/14138 | 174,832,642 | MDU6SXNzdWUxNzQ4MzI2NDI= | 14,138 | Feature request: Advanced masking (e.g. `0 < pd.Series(x) < 1`) | {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2016-09-02T19:33:12Z | 2016-09-02T22:37:44Z | 2016-09-02T21:03:49Z | NONE | null | **Can there be a way to to do masking that is dependent on more than one boolean?**
```
0 < pd.Series(x) < 1
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https://api.github.com/repos/pandas-dev/pandas/issues/14139 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14139/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14139/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14139/events | https://github.com/pandas-dev/pandas/issues/14139 | 174,840,894 | MDU6SXNzdWUxNzQ4NDA4OTQ= | 14,139 | Renaming an empty MultiIndex fails | {
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s = pd.Series([0], index=pd.MultiIndex.from_tuples([('a', 1)]))
# This works:
s.rename(lambda x: x)
# This fails:
s.iloc[0:0].rename(lambda x: x)
```
Renaming an empty index should not throw an error. It should just return the original empty index. It appears to be failing because `_transform_index` calls... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14140 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14140/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14140/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14140/events | https://github.com/pandas-dev/pandas/issues/14140 | 174,860,317 | MDU6SXNzdWUxNzQ4NjAzMTc= | 14,140 | TST: windows failures | {
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======================================================================
FAIL: test_c_engine (pandas.io.tests.parser.test_unsupported.TestUnsupportedFeatures)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\conda\Documents\pandas3.5\pandas\io\... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14141 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14141/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14141/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14141/events | https://github.com/pandas-dev/pandas/pull/14141 | 174,865,430 | MDExOlB1bGxSZXF1ZXN0ODM4NzczNjc= | 14,141 | TST: sparse / dummy array comparisons on windows, xref #14140 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14142 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14142/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14142/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14142/events | https://github.com/pandas-dev/pandas/pull/14142 | 174,866,474 | MDExOlB1bGxSZXF1ZXN0ODM4NzgwOTE= | 14,142 | TST: fix blosc version | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14143 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14143/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14143/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14143/events | https://github.com/pandas-dev/pandas/issues/14143 | 174,866,590 | MDU6SXNzdWUxNzQ4NjY1OTA= | 14,143 | TST: blosc>1.4.1 failing | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2016-09-02T23:49:40Z | 2016-09-03T14:50:08Z | 2016-09-03T14:50:08Z | CONTRIBUTOR | null | xref: https://github.com/pydata/pandas/pull/14142
seems this update just hit and failing 1 tests [here](https://travis-ci.org/pydata/pandas/jobs/157218567)
worked [here](https://travis-ci.org/pydata/pandas/jobs/157068571)
seems like a back-compat issue (new blosc not reading older blosc?)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/14144 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14144/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14144/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14144/events | https://github.com/pandas-dev/pandas/pull/14144 | 174,897,660 | MDExOlB1bGxSZXF1ZXN0ODM4OTYwMDE= | 14,144 | Add the steps to setup gbq integration testing to the contributing docs | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixed a bug with assignment against block may be coerced incorrectly.
```
s = pd.Series([pd.Timestamp('2011-01-01'), pd.Timestamp('2012-01-01')])
s[1] = 1
s
#0 2011-01-01 00:00:00.000000000
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https://api.github.com/repos/pandas-dev/pandas/issues/14146 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14146/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14146/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14146/events | https://github.com/pandas-dev/pandas/pull/14146 | 174,925,764 | MDExOlB1bGxSZXF1ZXN0ODM5MTA0NjY= | 14,146 | BUG: DatetimeTZBlock can't assign values near dst boundary | {
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- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Value assignment doesn't work if data contains DST boundary because of internal `.localize(None)`.
```
s = pd.Series(pd.date_range('2016-11-06', freq='H', periods=3, tz='US/Eastern'))
s[1] = pd.Timestamp('2011-01-... | {
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flake8-ed *.pyx files and fixed errors.
Removed the E226 check because that inhibits pointers (e.g. char*). In addition, the check is not even universally accepted in Python.
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https://api.github.com/repos/pandas-dev/pandas/issues/14148 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14148/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14148/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14148/events | https://github.com/pandas-dev/pandas/issues/14148 | 174,956,962 | MDU6SXNzdWUxNzQ5NTY5NjI= | 14,148 | Datatime stored in DataFrame loses tz info and it goes to hour:minute | {
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#With this code:
#-----------------------------
from datetime import datetime
import pytz
from pandas import DataFrame
original_date = datetime(2016, 9, 2, 0, 0, 0, 0, pytz.timezone('America/Manaus'))
dataframe = DataFrame(columns = ['stored_date'])
dataframe.loc['A'] = [original_date]
print("I expect it shows '2... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14149 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14149/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14149/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14149/events | https://github.com/pandas-dev/pandas/issues/14149 | 174,964,548 | MDU6SXNzdWUxNzQ5NjQ1NDg= | 14,149 | NaT treatment in pandas groupby/nunique | {
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... | 4 | 2016-09-04T19:09:06Z | 2021-05-01T23:35:37Z | 2021-05-01T23:35:37Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Calling nunique on a column that contains NaT results in this FutureWarning from numpy:
```
python35\lib\site-packages\pandas\core\groupby.py:2854
FutureWarning: In the future, NAT != NAT will be True rather than False.
inc = np.r_[1, val[1:] != val[:-1]]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/14150 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14150/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14150/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14150/events | https://github.com/pandas-dev/pandas/issues/14150 | 174,974,632 | MDU6SXNzdWUxNzQ5NzQ2MzI= | 14,150 | SettingWithCopy dependence on reference count | {
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... | 9 | 2016-09-04T23:06:59Z | 2020-09-06T18:50:04Z | null | NONE | null | As a follow-up to my [question](http://stackoverflow.com/questions/39321914/unpredictable-pandas-slice-assignment-behavior-with-no-settingwithcopywarning), I believe the current approach is that pandas assumes that the programmer intends assignment to a child DF/S to be propagated to the parent DF/S _as long as the par... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14151 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14151/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14151/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14151/events | https://github.com/pandas-dev/pandas/pull/14151 | 174,994,705 | MDExOlB1bGxSZXF1ZXN0ODM5NDk2ODI= | 14,151 | BUG/TST: Empty input arrays in cartesian_product and MultiIndex (#12258) | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This commit:
1. fixes logic (and division by 0) in `cartesian_product` when some input arrays are empty
2. adds tests for MultiIndex empty level construction with `.from_arrays` and `.from_produ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14152 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14152/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14152/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14152/events | https://github.com/pandas-dev/pandas/issues/14152 | 175,020,132 | MDU6SXNzdWUxNzUwMjAxMzI= | 14,152 | Feature request: Access multiindex by name | {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2016-09-05T08:27:19Z | 2016-09-06T10:44:29Z | 2016-09-05T15:07:12Z | NONE | null | #### Code Sample, a copy-pastable example if possible
I have not found an easy way to access a column of a multi index by name. Consider the following code:
```
import pandas as pd
df = pd.DataFrame([[1,1,10,200],
[1,2,11,201],
[1,3,12,202],
[2,1,13,210],
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14153 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14153/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14153/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14153/events | https://github.com/pandas-dev/pandas/pull/14153 | 175,059,741 | MDExOlB1bGxSZXF1ZXN0ODM5OTM5MTg= | 14,153 | Fix typo (change 'n' to 'k' in get_dummies documentation) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14154 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14154/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14154/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14154/events | https://github.com/pandas-dev/pandas/issues/14154 | 175,092,697 | MDU6SXNzdWUxNzUwOTI2OTc= | 14,154 | ENH: allow usecols to be case insensitive | {
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``` python
import pandas as pd
from io import StringIO
data = """1,1,1,1
2,2,2,2
3,3,3,3
4,4,4,4
5,5,5,5"""
names = ['col1', 'col2', 'COL3', 'Col4']
df = pd.read_csv(StringIO(data), n... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14155 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14155/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14155/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14155/events | https://github.com/pandas-dev/pandas/issues/14155 | 175,097,488 | MDU6SXNzdWUxNzUwOTc0ODg= | 14,155 | Bug when assigning timedelta64 to a series (or dataframe) with an indexer | {
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```
In [2]: import pandas as pd
In [3]: import numpy as np
In [4]: s = pd.Series(10*[np.timedelta64(10, 'm')])
In [5]: s
Out[5]:
0 00:10:00
1 00:10:00
2 00:10:00
3 00:10:00
4 00:10:00
5 00:10:00
6 00:10:00
7 00:10:00
8 00:10:00
9 00:10:00
dt... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14156 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14156/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14156/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14156/events | https://github.com/pandas-dev/pandas/issues/14156 | 175,107,075 | MDU6SXNzdWUxNzUxMDcwNzU= | 14,156 | BUG: conversion precision on Timedeltas | {
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... | closed | false | null | [] | {
"closed_at": "2018-06-13T03:57:35Z",
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"followers_url": "https://api.github.com/users/jr... | 3 | 2016-09-05T17:09:20Z | 2018-05-21T11:06:04Z | 2018-05-21T11:06:04Z | CONTRIBUTOR | null | I think this is rounding prematurely, though not entirely sure. Could also just be loss of precision when converting from a float -> int64.
```
In [12]: pd.to_datetime(1434743731.8770001,unit='s')
Out[12]: Timestamp('2015-06-19 19:55:31.877000')
In [13]: pd.to_datetime(1434743731.8770001,unit='s').value
Out[13]: 1434... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14157 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14157/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14157/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14157/events | https://github.com/pandas-dev/pandas/issues/14157 | 175,107,755 | MDU6SXNzdWUxNzUxMDc3NTU= | 14,157 | ENH: function to take DatetimeIndex and adjust for leapseconds | {
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... | 1 | 2016-09-05T17:16:19Z | 2017-12-15T01:18:37Z | 2017-12-15T01:18:37Z | CONTRIBUTOR | null | [leapseconds list here](https://github.com/eggert/tz/blob/master/leap-seconds.list).
[SO discussion here](http://stackoverflow.com/questions/19332902/extract-historic-leap-seconds-from-tzdata). This info is actually available from the tzinfo, though I don't think `pytz` is reading it.
[astropy docs](http://docs.astro... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14158 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14158/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14158/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14158/events | https://github.com/pandas-dev/pandas/issues/14158 | 175,108,920 | MDU6SXNzdWUxNzUxMDg5MjA= | 14,158 | ENH: Reduce DF/Series to smallest possible dtype | {
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"followers_url": "https://api.github.com/users/jorisva... | 3 | 2016-09-05T17:28:46Z | 2016-09-06T14:24:32Z | 2016-09-05T17:34:42Z | CONTRIBUTOR | null | Hi,
I have been currently working with dataset mostly consisting of binary features (column has only 1, 0 or NaN). I know there is a `category` dtype, but that is not usable in some cases, e.g. when you want to store it HDF and can't use `tables` format (e.g. because of to wide tables). I have datasets with about 50k ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14159 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14159/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14159/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14159/events | https://github.com/pandas-dev/pandas/pull/14159 | 175,121,008 | MDExOlB1bGxSZXF1ZXN0ODQwMzU0NDY= | 14,159 | WIP/ENH: reading older Stata versions, #11526 | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2016-09-05T19:43:57Z | 2016-09-11T13:55:44Z | 2016-09-11T13:55:39Z | CONTRIBUTOR | null | A simple file seems to read OK if we just treat it like other "old format" (version 104-115) files. However I do not currently have any documentation for this format. The only relevant resource I can find is the file `foreign/src/stataread.c` in the R `foreign` package sources:
https://cran.r-project.org/src/contrib... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14160 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14160/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14160/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14160/events | https://github.com/pandas-dev/pandas/pull/14160 | 175,145,092 | MDExOlB1bGxSZXF1ZXN0ODQwNTAzMjA= | 14,160 | BUG : assignment of non-ns timedelta64 value (GH14155) | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/14161 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14161/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14161/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14161/events | https://github.com/pandas-dev/pandas/pull/14161 | 175,146,415 | MDExOlB1bGxSZXF1ZXN0ODQwNTExNzI= | 14,161 | TST: Make encoded sep check more locale sensitive | {
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I find it very convenient to use a DataFrame of `ufloat` from the `uncertainties` package. Each entry consists of (value, error) and could represent the result of Monte Carlo simulations or an experiment.
At present taking sums along both axes gives the expected result, but taking the mean does not.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14163 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14163/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14163/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14163/events | https://github.com/pandas-dev/pandas/pull/14163 | 175,254,488 | MDExOlB1bGxSZXF1ZXN0ODQxMjc0MzI= | 14,163 | ERR: improves message raised for bad names in usecols (GH14154) | {
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- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2016-09-06T20:51:29Z | 2016-09-06T21:06:08Z | 2016-09-06T21:05:08Z | CONTRIBUTOR | null | The following code returns all NaN's instead of "given" categories:
#### Code Sample, a copy-pastable example if possible
```
In [25]: df = pd.DataFrame({'a':['aa','bb','cc','dd']})
In [26]: df
Out[26]:
a
0 aa
1 bb
2 cc
3 dd
In [27]: df.a.astype('category', categories=[0,1,2,3])
Out[27]:
0 NaN
1 NaN
2 ... | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2016-09-06T23:45:19Z | 2016-09-07T03:30:53Z | 2016-09-07T00:15:32Z | CONTRIBUTOR | null | ``` python
>>> pd.SparseSeries([False, True], dtype=bool)[[0, 1]]
0 0.0
1 1.0
dtype: float64
BlockIndex
Block locations: array([1], dtype=int32)
Block lengths: array([1], dtype=int32)
```
#### Expected output
```
0 False
1 True
dtype: bool
BlockIndex
Block locations: array([1], dtype=int32)
Block lengths: ... | {
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``` python
>>> np.array(pd.SparseSeries([0, 1], fill_value=0))
array([1])
```
#### Expected Output
```
array([0, 1])
```
this should really be consistent with `Series` rather than just returning the non-fill values (i.e. rather than being equivalent to `np.array(... | {
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#### Expected Output
#### output of `pd.show_versions()`
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2016-09-07T06:23:45Z | 2018-05-16T13:39:45Z | 2016-09-07T10:39:47Z | NONE | null | Maybe something you want to look into:
http://stackoverflow.com/questions/35604186/convert-psycopg2-dictrow-query-to-pandas-dataframe
```
curs = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
curs.execute("SELECT * FROM mytable")
data = curs.fetchall()
print pd.DataFrame(list(data))
```
Will produce a TypeEr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14170 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14170/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14170/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14170/events | https://github.com/pandas-dev/pandas/issues/14170 | 175,426,726 | MDU6SXNzdWUxNzU0MjY3MjY= | 14,170 | Can't iterate a DataFrameGroupBy object if the group by key contains NaN | {
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```
import pandas as pd
df = pd.DataFrame({'a': [1,2,3,4], 'b':[None, None, None, None]})
chunks = df.groupby('a')
assert len([g for g, chunk in chunks]) == 4
chunks = df.groupby(['a', 'b'])
# failed here, it's emtpy
assert len([g for g, chunk in chunks]) == 4
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https://api.github.com/repos/pandas-dev/pandas/issues/14171 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14171/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14171/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14171/events | https://github.com/pandas-dev/pandas/issues/14171 | 175,447,296 | MDU6SXNzdWUxNzU0NDcyOTY= | 14,171 | Series.str.contains doesn't correct if series contains only nan values. | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2016-09-07T08:50:42Z | 2016-09-09T22:29:16Z | 2016-09-09T22:29:16Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy as np
import re
CONTAINS_REG_EXP = ur'{0}'
value = '3'
t1 = pd.Series(data=[np.nan, np.nan, np.nan], dtype=np.object)
t2 = t1.str.contains(CONTAINS_REG_EXP.format(re.escape(value)),
flag... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14172 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14172/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14172/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14172/events | https://github.com/pandas-dev/pandas/pull/14172 | 175,460,010 | MDExOlB1bGxSZXF1ZXN0ODQyNjkyNjQ= | 14,172 | DOC: cleanup build warnings | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14173 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14173/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14173/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14173/events | https://github.com/pandas-dev/pandas/issues/14173 | 175,464,412 | MDU6SXNzdWUxNzU0NjQ0MTI= | 14,173 | BUG: union_categoricals w/Series & CategoricalIndex | {
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"followers_url": "https://api.github.com/users/jr... | 8 | 2016-09-07T10:11:28Z | 2016-09-10T14:30:49Z | 2016-09-10T14:30:49Z | CONTRIBUTOR | null | ```
In [25]: from pandas.types.concat import union_categoricals
In [26]: s1 = pd.Series(['a', 'b'], dtype='category')
...: s3 = pd.Series(['a', 'b', 'c'], dtype='category')
In [27]: s1
Out[27]:
0 a
1 b
dtype: category
Categories (2, object): [a, b]
In [28]: s3
Out[28]:
0 a
1 b
2 c
dtype: categor... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14174 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14174/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14174/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14174/events | https://github.com/pandas-dev/pandas/pull/14174 | 175,467,895 | MDExOlB1bGxSZXF1ZXN0ODQyNzQ5MzE= | 14,174 | Fix trivial typo in comment | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14175 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14175/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14175/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14175/events | https://github.com/pandas-dev/pandas/issues/14175 | 175,469,317 | MDU6SXNzdWUxNzU0NjkzMTc= | 14,175 | TypeError bug when using pandas.to_timedelta (astype() unexpected argument 'copy') | {
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Running the following code:
``` python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(6,4),columns=list('ABCD'))
pd.to_timedelta(df.index,unit='s')
```
returns a `TypeError`:
```
---------------------------------------------------------------------------
TypeError ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14176 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14176/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14176/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14176/events | https://github.com/pandas-dev/pandas/pull/14176 | 175,472,034 | MDExOlB1bGxSZXF1ZXN0ODQyNzc5MzQ= | 14,176 | DOC: clean-up 0.19.0 whatsnew file | {
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**Discussion: addition of new keyword to `concat` to union categoricals with different categories**
PR #13767 proposed to add a `union_categoricals` keyword to `concat`, which lets... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14178 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14178/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14178/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14178/events | https://github.com/pandas-dev/pandas/issues/14178 | 175,523,366 | MDU6SXNzdWUxNzU1MjMzNjY= | 14,178 | DOC: source links in API reference | {
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http://docs.scipy.org/doc/numpy/reference/generated/numpy.take.html
I don't know enough about sphinx to know how hard/easy this is, but it may be nice.
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... | 3 | 2016-09-07T22:40:35Z | 2020-01-28T01:24:10Z | 2017-10-30T10:56:42Z | NONE | null | xref #6942
xref #12499
``` python
import datetime
import pandas as pd
df1 = pd.DataFrame({'a': [0,1], 'b': [datetime.datetime.now(), datetime.datetime.now()]})
# next line works as expected
df1.loc[:,'b'] = [datetime.datetime.now(), datetime.datetime.now()]
df1 = pd.DataFrame({'a': [0], 'b': [datetime.datetime.now()]}... | {
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... | 2 | 2016-09-07T22:48:20Z | 2021-05-01T23:39:07Z | null | NONE | null | I recently had to solve an issue where some code was behaving inconsistently. Effectively, I tracked this issue to a function that was being passed to apply which occasionally, under certain circumstances, mutated the input for a group - and not always the first group. Because fast_apply only checks the first group thi... | {
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module to use.
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https://api.github.com/repos/pandas-dev/pandas/issues/14182 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14182/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14182/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14182/events | https://github.com/pandas-dev/pandas/pull/14182 | 175,712,193 | MDExOlB1bGxSZXF1ZXN0ODQ0NDcxODc= | 14,182 | BUG: fix str.contains for series containing only nan values | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/14183 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14183/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14183/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14183/events | https://github.com/pandas-dev/pandas/issues/14183 | 175,724,119 | MDU6SXNzdWUxNzU3MjQxMTk= | 14,183 | TST: failing tests at pandas-wheels | {
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It are mainly issues with 32 bit linux (see eg https://travis-ci.org/MacPython/pandas-wheels/jobs/158414875), but also some tests are failing for 64bit:
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- [X] tests added / passed
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- [X] whatsnew entry
Print names of MultiIndex columns.
Added "multicol" and "multirow" flags to to_latex
which trigger the corresponding feature.
Multirow adds clines to visually separate sections.
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```
x = pd.DataFrame(index = [1,1,2,2,2], data = pd.date_range("7:00", "9:00", freq="30min", tz = 'US/Eastern'))
x:
1 2016-09-08 07:00:00-04:00
1 2016-09-08 07:30:00-04:00
2 2016-09-08 08:00:00-04:00
2 2016-09-08 08:30:00-04:00
2 2016-09-08 09:00:00-04:00
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https://api.github.com/repos/pandas-dev/pandas/issues/14187 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14187/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14187/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14187/events | https://github.com/pandas-dev/pandas/issues/14187 | 175,878,576 | MDU6SXNzdWUxNzU4Nzg1NzY= | 14,187 | date_range produces wrong values when overflowing | {
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"followers_url": "https://api.github... | 2 | 2016-09-08T22:15:00Z | 2018-10-28T03:14:14Z | 2018-10-28T03:14:14Z | MEMBER | null | In the following case, you now get a RuntimeWarning. But shouldn't we rather error on this?
```
In [23]: pd.date_range('1/1/2000', periods=100000, freq='D')
/home/joris/scipy/pandas/pandas/tseries/index.py:1944: RuntimeWarning: overflow encountered in long_scalars
e = b + np.int64(periods) * stride
/home/joris/scipy... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14188 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14188/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14188/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14188/events | https://github.com/pandas-dev/pandas/issues/14188 | 175,884,144 | MDU6SXNzdWUxNzU4ODQxNDQ= | 14,188 | reindex(fill_value=None) fills with np.NaN instead of None | {
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"color": "0052cc"... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jorisva... | 9 | 2016-09-08T22:47:30Z | 2016-09-09T10:14:33Z | 2016-09-09T00:55:57Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
In [12]: import pandas as pd
In [13]: s = pd.Series(['a', 'b'])
In [14]: s.reindex([0,1,2], fill_value=None)
Out[14]:
0 a
1 b
2 NaN
dtype: object
```
#### Expected Output
``` python
0 a
1 b
2 None
dtype: object
```
#### outp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14189 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14189/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14189/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14189/events | https://github.com/pandas-dev/pandas/issues/14189 | 175,891,207 | MDU6SXNzdWUxNzU4OTEyMDc= | 14,189 | Pandas inplace argument is not explicit True or False | {
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Is not explicit True or False like in the documentation.
Other methods tested: where, mask, sort_index
Any bool(arg) == True is accepted
Like: inplace='Fail' , inplace=3, inplace=[0]
Maybe is not a bug, but can... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14190 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14190/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14190/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14190/events | https://github.com/pandas-dev/pandas/issues/14190 | 175,893,396 | MDU6SXNzdWUxNzU4OTMzOTY= | 14,190 | BUG: Categorical constructor is not idempotent with datetime with tz | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2016-09-08T23:51:56Z | 2016-09-09T19:33:37Z | 2016-09-09T19:33:37Z | CONTRIBUTOR | null | This isn't a huge deal, but popped up adding tests for #14173
Works fine with plain datetimes
```
In [9]: dti = pd.date_range('2014-01-01', '2014-01-05')
In [10]: pd.Categorical(pd.Categorical(dti))
Out[10]:
[2014-01-01, 2014-01-02, 2014-01-03, 2014-01-04, 2014-01-05]
Categories (5, datetime64[ns]): [2014-01-01, 20... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14191 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14191/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14191/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14191/events | https://github.com/pandas-dev/pandas/pull/14191 | 175,895,382 | MDExOlB1bGxSZXF1ZXN0ODQ1NzQ2ODQ= | 14,191 | BUG: Categorical constructor not idempotent with ext dtype | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2016-09-09T00:07:11Z | 2016-09-09T19:33:37Z | 2016-09-09T19:33:37Z | CONTRIBUTOR | null | - [x] closes #14190
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/14192 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14192/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14192/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14192/events | https://github.com/pandas-dev/pandas/issues/14192 | 176,015,408 | MDU6SXNzdWUxNzYwMTU0MDg= | 14,192 | TST: Difference in drop_duplicates for numeric Series and DataFrames | {
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```
import pandas as pd
import numpy as np
df = pd.DataFrame({'a':[1,1,1,'one', 'one'],
'b': [2,2,np.nan, np.nan, np.nan],
'c': [3,3,np.nan, np.nan, 'three']
})
print df[['b']].drop_duplicates()
print df['b'].drop_duplicates()
```
#### Expecte... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14193 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14193/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14193/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14193/events | https://github.com/pandas-dev/pandas/issues/14193 | 176,042,134 | MDU6SXNzdWUxNzYwNDIxMzQ= | 14,193 | Kernel dies when exporting large dataframe to json | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2016-09-09T15:46:47Z | 2016-09-09T19:29:23Z | 2016-09-09T19:29:13Z | NONE | null | ### Problem
The kernel dies when I try to export relatively large data to json. No problems with short test data, it gets exported as it should. Also, exporting a large df to_csv works just fine, but to_json fails.
These are datasets of either tweets or newspaper articles, so at least one of the fields contains strin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14194 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14194/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14194/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14194/events | https://github.com/pandas-dev/pandas/issues/14194 | 176,069,745 | MDU6SXNzdWUxNzYwNjk3NDU= | 14,194 | ENH: DataFrame.style option for hiding the index | {
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"followers_url": "https://api.github.com/users/j... | 0 | 2016-09-09T17:56:31Z | 2017-11-19T16:15:34Z | 2017-11-19T16:15:34Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
df = pd.DataFrame(np.random.randn(10, 2))
df.style.set_include_index(False) # name TBD
```
#### Expected Output
Same output, but without the index
Would maybe want a way to hide the columns too.
---
Current workaround
``` python
df = pd.DataFrame(n... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14195 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14195/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14195/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14195/events | https://github.com/pandas-dev/pandas/issues/14195 | 176,101,662 | MDU6SXNzdWUxNzYxMDE2NjI= | 14,195 | Re: DataFrame.to_csv(quoting=csv.QUOTE_NONNUMERIC) quotes numeric values | {
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"followers_url": "https://api.github.com/users/jre... | 0 | 2016-09-09T20:27:20Z | 2016-10-06T16:04:57Z | 2016-10-06T16:04:57Z | NONE | null | As per the issue outlined in #12922, applying a float_format or having a NaN value with a float would continue to treat the value as non-numeric when quoting=csv.QUOTE_NONNUMERIC is used. The format string resulting in the float treated like a string should be documented as per @jreback in #12922.
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https://api.github.com/repos/pandas-dev/pandas/issues/14196 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14196/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14196/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14196/events | https://github.com/pandas-dev/pandas/pull/14196 | 176,150,739 | MDExOlB1bGxSZXF1ZXN0ODQ3NDIwMTg= | 14,196 | Fix minor spacing issue | {
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- [x] closes #13032
- [ ] tests added / passed
- [x] passes `pep8radius master --diff`
- [x] whatsnew entry - not necessary
See https://github.com/pydata/pandas/pull/13350 for more details, mucked up my pull request. Tests will need a lot of work.
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https://api.github.com/repos/pandas-dev/pandas/issues/14197 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14197/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14197/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14197/events | https://github.com/pandas-dev/pandas/issues/14197 | 176,169,906 | MDU6SXNzdWUxNzYxNjk5MDY= | 14,197 | BUG: numpy RuntimeWarning with Series.round() | {
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``` python
>>> import pandas as pd
>>> pd.__version__
u'0.19.0rc1'
>>> pd.Series([pd.np.nan]).round()
Warning (from warnings module):
File "C:\Program Files\Python27\lib\site-packages\pandas\core\series.py", line 1340
result = _values_from_object(self).round... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14198 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14198/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14198/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14198/events | https://github.com/pandas-dev/pandas/pull/14198 | 176,179,105 | MDExOlB1bGxSZXF1ZXN0ODQ3NTUxODU= | 14,198 | Fix: F999 dictionary key '2000q4' repeated with different values | {
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```
pandas/tseries/tests/test_tslib.py:685:18: F999 dictionary key '2000q4' repeated with different values
pandas/tseries/tests/test_tslib.py:690:18: F999 dictionary key '2000q4' repeated with different values
```
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- [ ] tests added / passed
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- [x] tests not needed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew not needed
cc @jorisvandenbossche - like you mentioned, this doesn't work for everything (properties, accessors, some generated methods)
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My blog talks more about this issue: [http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/09/pandas-dataframe-hdfstore-bug.html](http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/09/pandas-... | {
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- [x] tests added / passed. [Link to Travis-CI build with my gbq credentials](https://travis-ci.org/parthea/pandas/builds/159134242)
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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>>> from pandas.compat import StringIO
>>> from pandas import read_csv
>>> data = 'a\nfoo\n1'
>>>
>>> read_csv(StringIO(data), na_values={0: 'foo'}, engine='c')
...
TypeError: Expected list, got set
>>> read_csv(StringIO(data), na_values={0: 'foo'}, engine='python')
a
0 foo # Should be NaN
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I am running a Travis CI build in a python 2.7 min environment. Not sure why it is pulling from an unreleased version of pandas but it looks like the tempita needs to be importe... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14205 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14205/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14205/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14205/events | https://github.com/pandas-dev/pandas/issues/14205 | 176,344,670 | MDU6SXNzdWUxNzYzNDQ2NzA= | 14,205 | Datatype of Integer changes depending on indexing method on a dataframe with an integer and a float column | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2016-09-12T10:39:36Z | 2016-09-12T10:52:09Z | 2016-09-12T10:52:09Z | NONE | null | Take a dataframe that has one integer and one float column, then use various methods to get the first integer element. In certain cases the datatype will be given back correctly as int64, in other cases however it's changed to float64. Indexing using ix even does both, depending on how it is used:
```
import pandas as... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14206/events | https://github.com/pandas-dev/pandas/issues/14206 | 176,419,658 | MDU6SXNzdWUxNzY0MTk2NTg= | 14,206 | pandas.DataFrame.unstack() documentation example has errors | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2016-09-12T16:21:40Z | 2016-09-13T23:00:15Z | 2016-09-13T23:00:15Z | NONE | null | The last part of the documentation says
``` python
>>> df = s.unstack(level=0)
>>> df.unstack()
one a 1.
b 3.
two a 2.
b 4.
```
although we'd have
``` python
>>> import pandas as pd
>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
... ('two', 'a')... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/14207 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/14207/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/14207/comments | https://api.github.com/repos/pandas-dev/pandas/issues/14207/events | https://github.com/pandas-dev/pandas/issues/14207 | 176,445,314 | MDU6SXNzdWUxNzY0NDUzMTQ= | 14,207 | API: allow dependent assignment? | {
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"followers_url": "https://api.github.com/users/j... | 19 | 2016-09-12T18:14:26Z | 2018-02-10T16:20:18Z | 2018-02-10T16:20:18Z | CONTRIBUTOR | null | Since kwarg order will be [guaranteed](https://docs.python.org/3.6/whatsnew/3.6.html#pep-468-preserving-keyword-argument-order) in python 3.6, we could allow this...though would only want to try with a 3.6+ version check, otherwise code could more or less randomly work/not work, depending on dict order.
``` python
In ... | {
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