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https://api.github.com/repos/pandas-dev/pandas/issues/11604 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11604/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11604/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11604/events | https://github.com/pandas-dev/pandas/issues/11604 | 116,948,061 | MDU6SXNzdWUxMTY5NDgwNjE= | 11,604 | pandas not found when it's supposed to be installed | {
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] | closed | true | null | [] | null | 27 | 2015-11-14T19:39:37Z | 2020-01-10T14:08:50Z | 2015-11-14T19:43:52Z | NONE | null | I got this error message. Already installed pandas.
```python
>>> import pandas as pd
Traceback (most recent call last):
File "analyze_tweets.py", line 9, in <module>
import pandas as pd
ImportError: No module named pandas
```
**SOLUTION** (by @datapythonista)
This error happens when Python can't ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11605 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11605/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11605/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11605/events | https://github.com/pandas-dev/pandas/pull/11605 | 116,951,457 | MDExOlB1bGxSZXF1ZXN0NTA3MzE5MDA= | 11,605 | CI: testing newer matplotlib with jinja | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11606 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11606/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11606/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11606/events | https://github.com/pandas-dev/pandas/pull/11606 | 116,951,648 | MDExOlB1bGxSZXF1ZXN0NTA3MzE5ODc= | 11,606 | BUG GH11600 - MultiIndex column level names lost when to_sparse() called | {
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Fixed problem with multi-index column level names not propagating into sparse frames or back out to dense on a round trip through sparse. Includes 4 new tests to cover some relevant scenarios. Problem fixed for the conventions to_sparse path, I'm not sure about other paths where something else is passed... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11607 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11607/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11607/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11607/events | https://github.com/pandas-dev/pandas/pull/11607 | 116,955,410 | MDExOlB1bGxSZXF1ZXN0NTA3MzM2Nzc= | 11,607 | BUG: loc against CategoricalIndex may results in normal Index | {
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After the PR:
```
import pandas as pd
index = pd.CategoricalIndex(list('aabbca'), categories=list('cabe'))
df = pd.DataFrame({'A' : np.arange(6,dtype='int64')}, index=index)
# OK (not changed)
df.loc[['a', 'b']].index
# CategoricalIndex([u'a', u'a', u'a', u'b', u'b'], categories=[u'c', u'a', u'b', u'e... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11608 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11608/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11608/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11608/events | https://github.com/pandas-dev/pandas/issues/11608 | 116,975,820 | MDU6SXNzdWUxMTY5NzU4MjA= | 11,608 | ValueError: unknown locale: UTF-8 | {
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] | closed | false | null | [] | null | 1 | 2015-11-15T06:09:08Z | 2015-11-18T12:08:29Z | 2015-11-18T12:08:23Z | NONE | null | I run the python script that contain Pandas (that one cause the problem) library I got this error:
```
Traceback (most recent call last):
File "test.py", line 2, in <module>
import pandas
ImportError: No module named pandas
```
But if I import from package path to make sure it detects Pandas library, I got this... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11609 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11609/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11609/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11609/events | https://github.com/pandas-dev/pandas/pull/11609 | 117,009,920 | MDExOlB1bGxSZXF1ZXN0NTA3NTI2NTk= | 11,609 | WARN: fix performance warning failure on numpy master | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11610 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11610/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11610/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11610/events | https://github.com/pandas-dev/pandas/issues/11610 | 117,030,280 | MDU6SXNzdWUxMTcwMzAyODA= | 11,610 | Followup to Conditional HTML Styling | {
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For 0.17.1
- [x] Nicer table styling for the pydata.org website
- [x] Include a visual example in 0.17.1.rst
- [x] remove `doc/source/html-styling.html` and find a way to include `doc/source/html-styling.ipynb` in the doc build (should use `--template=basic`)
- [x] update `print_versions`
- [x] update `... | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-11-16T11:05:38Z | 2015-11-20T13:55:35Z | 2015-11-20T13:55:35Z | CONTRIBUTOR | null | Applying a numpy function, such as `np.round`, to a DataFrame with duplicated column indicies can cause an unrecoverable stack overflow error (Fatal Python error: Cannot recover from stack overflow.), which causes e.g. a ipython kernel to crash. E.g. take the following example, where python crashes at line 4:
``` pyth... | {
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Pandas version : 0.17.0
``` python
import pandas
import StringIO
df1 = pandas.read_csv(StringIO.StringIO("0\t-9223372036854775295\n0\t0\n0\t9223372036854775807\n0\t\n"),names=["foo", "bar"], sep="\t")
print df1
df2 = pandas.read_csv... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11614/events | https://github.com/pandas-dev/pandas/issues/11614 | 117,150,131 | MDU6SXNzdWUxMTcxNTAxMzE= | 11,614 | bar-plots with wrong color using Matplotlib 1.5 | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-11-16T15:34:11Z | 2015-12-25T21:47:39Z | 2015-12-25T21:47:39Z | NONE | null | Hi there,
I'm not sure if this is a pandas/matplotlib or seaborn issue...but since I upgraded to Matplotlib 1.5 using anaconda I can't get the colors of my barplots right anymore, here's an example:
In ipython notebook:
```
import pandas as pd
%matplotlib inline
import seaborn as sns
sns.set_style('whitegrid')
# ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11615 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11615/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11615/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11615/events | https://github.com/pandas-dev/pandas/issues/11615 | 117,163,991 | MDU6SXNzdWUxMTcxNjM5OTE= | 11,615 | performance issue - apply with pd.Series vs tuple | {
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] | closed | false | null | [] | null | 5 | 2015-11-16T16:32:50Z | 2015-12-30T19:07:12Z | 2015-11-17T20:39:42Z | NONE | null | ERROR: type should be string, got "https://groups.google.com/forum/#!topic/pydata/8Xr7SBmCvbs\n\nBelow is 2 versions of applyshift function (if you comment out some places), in one case it returns tuple and in the other case pandas Series.\n\nThen this function is applied to a large pandas dataframe along each row (axis=1).\n\nWhy is the running time slower about 10 times with pd.Series???\n\n```\ndef applyshift(col1,col2,col3,col4):\n if abs(col1-0)<0.01:\n #return pd.Series(dict(zip(('col2','col3','col4'),\n return (\n 0,0,col4)#)))\n if abs(col1-1)<0.01:\n #return pd.Series(dict(zip(('col2','col3','col4'),\n return (\n col2,0,col4)#)))\n\n%%time\ndf2=df1.apply(lambda row: applyshift(\n row['col1'],row['col2'],row['col3'],row['col4']), axis=1)\n```\n" | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11616 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11616/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11616/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11616/events | https://github.com/pandas-dev/pandas/issues/11616 | 117,163,999 | MDU6SXNzdWUxMTcxNjM5OTk= | 11,616 | groupby UTC timestamp aggregation | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-11-16T16:32:52Z | 2015-11-27T13:40:10Z | 2015-11-27T13:40:10Z | NONE | null | Hi all,
I've found an inconsistency between pandas 0.17 and 0.16.2 when aggregating on UTC timestamps. Here is a snippet to reproduce the problem:
``` python
import numpy as np
import pandas as pd
np.random.seed(42)
data = pd.DataFrame({
'factor': np.random.randint(0, 3, size=60),
'time': pd.date_range('01/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11617 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11617/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11617/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11617/events | https://github.com/pandas-dev/pandas/issues/11617 | 117,164,757 | MDU6SXNzdWUxMTcxNjQ3NTc= | 11,617 | .loc on DataFrame returning coerced dtype for single rows | {
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The `.loc` method of `DataFrame` with different dtypes yields coerced type **even if the resulting slice does only contain elements from one type**. This happens only when selecting a single row.
I can guess that this might be intended because the implementation of `loc` seems to first lookup the row _as ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11618 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11618/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11618/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11618/events | https://github.com/pandas-dev/pandas/pull/11618 | 117,166,279 | MDExOlB1bGxSZXF1ZXN0NTA4MzQyMDU= | 11,618 | BUG: fix col iteration in DataFrame.round, #11611 | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-11-16T16:43:55Z | 2015-11-20T16:26:22Z | 2015-11-20T13:55:24Z | CONTRIBUTOR | null | Fixes #11611. The iterator now uses DataFrame.iteritems instead of direct indexing.
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https://api.github.com/repos/pandas-dev/pandas/issues/11619 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11619/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11619/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11619/events | https://github.com/pandas-dev/pandas/issues/11619 | 117,207,004 | MDU6SXNzdWUxMTcyMDcwMDQ= | 11,619 | Cannot infer dst time from Timestamp('2015-11-01 01:00:10'), try using the 'ambiguous' argument | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-11-16T20:07:23Z | 2015-11-18T16:49:58Z | 2015-11-18T16:49:58Z | NONE | null | running pandas 0.17, and getting this error when generating a csv, traceback is below. What exactly does "try using the ambigous argument mean"?
df.to_csv("VIP_data.csv", encoding="utf-8", index=False)
---
AmbiguousTimeError Traceback (most recent call last)
<ipython-input-24-222761ffc778> in ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11620 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11620/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11620/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11620/events | https://github.com/pandas-dev/pandas/pull/11620 | 117,243,535 | MDExOlB1bGxSZXF1ZXN0NTA4ODI1MTc= | 11,620 | Roll apply nonfloat dtypes | {
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"de... | closed | false | null | [] | null | 3 | 2015-11-16T23:27:04Z | 2015-11-18T22:25:16Z | 2015-11-18T22:25:16Z | NONE | null | Fixed issue [#4964](https://github.com/pydata/pandas/issues/4964) so rolling_apply works on non-numeric columns as well.
I followed instructions [here](https://github.com/pydata/pandas/blob/master/CONTRIBUTING.md) but I'm not sure where to add documentation in the [release notes](https://github.com/pydata/pandas/blob/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11621 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11621/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11621/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11621/events | https://github.com/pandas-dev/pandas/issues/11621 | 117,257,336 | MDU6SXNzdWUxMTcyNTczMzY= | 11,621 | Scalar operations over dataframe - Inconsistent results | {
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``` python
In [1]: df = pd.read_hdf(r'pct_change.h5', 'data')
In [2]: df.head()
Out[2]:
T0 T1 T2 T3 T4 T5 ... T1194 T1195 T1196 T1197 T1198 T1199
date ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11622 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11622/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11622/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11622/events | https://github.com/pandas-dev/pandas/pull/11622 | 117,273,130 | MDExOlB1bGxSZXF1ZXN0NTA4OTkxNTQ= | 11,622 | CLN/COMPAT: Remove raise StopIteration syntax in SparseArray.__iter__ | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11623 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11623/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11623/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11623/events | https://github.com/pandas-dev/pandas/issues/11623 | 117,279,457 | MDU6SXNzdWUxMTcyNzk0NTc= | 11,623 | Pandas quantile function very slow | {
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``` python
import time
import pandas as pd
import numpy as np
q = np.array([0.1,0.4,0.6,0.9])
data = np.random.randn(10000, 4)
df = pd.DataFrame(data, columns=['a', 'b', 'c', 'd'])
time1 = time.time()... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11624 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11624/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11624/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11624/events | https://github.com/pandas-dev/pandas/issues/11624 | 117,320,354 | MDU6SXNzdWUxMTczMjAzNTQ= | 11,624 | AmbiguousTimeError when retrieving Series from HDFStore in table format with DST change | {
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Here's the minimal code example to reproduce the bug:
``` python
import pandas as pd
index = pd.date_range("2013-10-26 23:00", "2013-... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11625/events | https://github.com/pandas-dev/pandas/pull/11625 | 117,332,842 | MDExOlB1bGxSZXF1ZXN0NTA5MzMzNjM= | 11,625 | PERF: speed-up DateFrame.itertuples() with namedtuples | {
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Also:
- replace bare `except:` to avoid catching `SystemExit` and `KeyboardInterrupt`
- remove the generator `return` from the `try`-clause to an `else`
- more explicit fallback to regular tuples when `name=... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11626 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11626/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11626/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11626/events | https://github.com/pandas-dev/pandas/issues/11626 | 117,334,954 | MDU6SXNzdWUxMTczMzQ5NTQ= | 11,626 | AmbiguousTimeError when constructing a Series with DST change | {
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Here's the minimal code example to reproduce the bug:
``` python
import pandas as pd
index = pd.date_range("2013-10-26 23:00", "2013-10-27 01:00",
tz="Europe/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11627 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11627/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11627/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11627/events | https://github.com/pandas-dev/pandas/pull/11627 | 117,336,755 | MDExOlB1bGxSZXF1ZXN0NTA5MzU5MTA= | 11,627 | BUG: date_range creation with an ambiguous endpoint, #11619 | {
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closes #11626
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I have create the snippet below to reproduce. It will fail sooner or later (may take a while, 100000 or more iterations).
I have reproduced the problem always in pandas 0.17.0 and windows x64 (linux x64 seems fine). Using both anaconda and winpython, u... | {
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> > > import pandas as pd
> > > p = pd.Timestamp(2015, 11, 12)
> > > p
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "pandas\tslib.pyx", line 339, in pandas.tslib.Timestamp.**repr** (pandas\tslib.c:9660)
> > > AttributeError: 'int' objec... | {
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"followers_url": "https://api.github.com/users/j... | 2 | 2015-11-17T22:45:59Z | 2020-06-15T13:20:34Z | 2020-06-15T13:20:34Z | CONTRIBUTOR | null | Particularly now that `shift` only works on datetime-like indexes (https://github.com/pydata/pandas/pull/11211)
They look almost the same, although not exactly:
``` python
In [11]: df=pd.DataFrame(pd.np.random.rand(5,2), index=pd.date_range(periods=5, start='2000'))
In [12]: df
Out[12]:
0 ... | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-11-17T23:21:52Z | 2018-09-11T02:58:11Z | 2015-11-18T11:48:36Z | CONTRIBUTOR | null | I spotted two failing tests with errors similar to what described @chris-b1 in #10631:
```
Attribute "dtype" are different
[left]: int32
[right]: int64
```
This PR fixes this on windows.
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https://api.github.com/repos/pandas-dev/pandas/issues/11633 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11633/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11633/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11633/events | https://github.com/pandas-dev/pandas/issues/11633 | 117,488,583 | MDU6SXNzdWUxMTc0ODg1ODM= | 11,633 | Pivot to SparseDataFrame: TypeError: ufunc 'isnan' not supported in sparse matrix conversion | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2015-11-18T01:10:01Z | 2016-05-18T13:22:36Z | 2016-05-18T13:22:36Z | NONE | null | I want to convert a DataFrame to SparseDataFrame before pivoting it (when it gets really sparse, see also [this discussion](http://stackoverflow.com/questions/31661604/efficiently-create-sparse-pivot-tables-in-pandas) ). I have a textual key, which I need to keep ("chr"):
```
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"followers_url": "https://api.github.com/users/jr... | 0 | 2015-11-18T03:15:27Z | 2015-11-18T11:52:12Z | 2015-11-18T11:52:12Z | CONTRIBUTOR | null | Partially closes https://github.com/pydata/pandas/issues/11610#issuecomment-157197629
- CSS update
- whatsnew example
- print_versions
- requirements_all
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... | 8 | 2015-11-18T09:31:34Z | 2017-07-20T12:31:42Z | 2017-07-20T12:31:42Z | MEMBER | null | From SO: http://stackoverflow.com/questions/33775560/how-to-group-categorical-values-in-pandas
```
import pandas as pd
df = pd.DataFrame()
df['A'] = ['C1', 'C1', 'C2', 'C2', 'C3', 'C3']
df['B'] = [1,2,3,4,5,6]
df['A'] = df.loc[:,'A'].astype('category')
df2 = df[0:3]
result = df2.groupby(by='A')['B'].nunique()
```
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| Index | IssueId | AppName | IssueStatus | IssueDescription | IssueCreatedTime |
| --- | --- | --- | --- | --- | --- |
| 9965 | 239578 | abc | New | 'Rewards' | 2015-11-15 14:25:40 |
| 9966 | 219878 | abc | Resolved | '\u0414' | 2015-10-27 13:35:03 |
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``` python
import pandas as pd
df = pd.DataFrame({0: ['a', 'b', 'c'],
1: ['aa', 'bb', 'cc']})
df['test'] = 'txt'
df.to_csv('/home/temp/df.csv', columns=['test', 0, 1])
```
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``` python
Python 2.7.10 |Continuum Analytics, Inc.| (default, Oct 19 2015, 18:04:42)
Type "copyright", "credits" or "license" for more information.
IPython 4.0.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick ... | {
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"followers_url": "https://api.github.com/users/jr... | 13 | 2015-11-18T14:27:07Z | 2016-01-29T14:40:50Z | 2016-01-29T14:40:50Z | CONTRIBUTOR | null | I guess it will be clearer with an example. First, let's prepare the dataframe:
```
In [2]: df = pd.DataFrame(columns=['a','b','c','d'], data=[[1,'b1','c1',3], [1,'b2','c2',4]])
In [3]: df = df.pivot_table(index='a', columns=['b','c'], values='d').reset_index()
In [4]: df
Out[28]:
b a b1 b2
c c1 c2
0 1 3 4
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https://api.github.com/repos/pandas-dev/pandas/issues/11641 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11641/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11641/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11641/events | https://github.com/pandas-dev/pandas/pull/11641 | 117,622,127 | MDExOlB1bGxSZXF1ZXN0NTExMDU0MTE= | 11,641 | TST: tests for outputing ambiguous times, #11619, fixed already in #11301 | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-11-18T22:49:17Z | 2017-06-01T22:12:15Z | 2017-06-01T22:12:15Z | CONTRIBUTOR | null | Sometimes it's handy to have access to a distinct integer for each group. For example, using the (internal) grouper:
```
>>> df = pd.DataFrame({"a": list("xyyzxy"), "b": list("ab"*3), "c": range(6)})
>>> df["group_id"] = df.groupby(["a","b"]).grouper.group_info[0]
>>> df
a b c group_id
0 x a 0 0
1 y... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11643 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11643/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11643/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11643/events | https://github.com/pandas-dev/pandas/pull/11643 | 117,723,771 | MDExOlB1bGxSZXF1ZXN0NTExNzA2MDE= | 11,643 | PERF: improves performance in remove_unused_categories | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-11-19T02:02:07Z | 2015-11-21T21:41:14Z | 2015-11-19T03:13:22Z | CONTRIBUTOR | null | on master:
``` python
In [1]: np.random.seed(2718281)
In [2]: n = 1 << 20
In [3]: ix = tm.makeCategoricalIndex(n, n // 10)
In [4]: %timeit ix.remove_unused_categories()
1 loops, best of 3: 527 ms per loop
```
on branch:
``` python
In [4]: %timeit ix.remove_unused_categories()
1 loops, best of 3: 216 ms per loop
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Please review.
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Please consider the following code :
```
import pandas as pd
import numpy as ny
dates = pd.date_range("2015-01-01", periods=10, freq="D")
ts = pd.TimeSeries(data=range(10), index=dates, dtype=ny.float64)
ts_mean = pd.rolling_mean(ts, 5)
print(ts)
2015-01-01 0
2015-01-02 1
2015-01-03 2
2015-01-04 3... | {
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"followers_url": "https://api.github.com/users/jr... | 0 | 2015-11-19T13:18:00Z | 2015-11-19T13:38:45Z | 2015-11-19T13:38:45Z | CONTRIBUTOR | null | change in function locations, very minor corrections for functions like `DataFrame.sum`
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```
In [0]: import pandas as pd
In [1]: my_data = [{'TICKET_NUMBER' : '001', 'DATE' : '2015-11-11 11:15'},
{'TICKET_NUMBER' : '002', 'DATE' : '2015-11-12 14:32'},
{'TICKET_NUMBER' : '003', 'DATE' : '2015-11-13 15:33'},
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] | closed | false | null | [] | null | 2 | 2015-11-19T19:31:36Z | 2015-11-19T19:54:03Z | 2015-11-19T19:53:46Z | NONE | null | ``` python
import pandas as pd
## two timepoints in UTC flanking the US/Eastern DST shift
date_strings = ['2015-11-01T05:00:00Z', '2015-11-01T07:00:00Z']
S = pd.Series(date_strings)
## Convert to US/Eastern
S = pd.to_datetime(S).apply(lambda x: x.tz_localize('UTC').tz_convert('US/Eastern'))
S.dt.dayofweek
## Attribute... | {
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Error Description : incorrect output when a mix of integer and string column names passed as columns parameter in to_csv().
[format.py](https://github.com/pydata/pandas/blob/master/pandas/core/format.py#L1330) is the cause of this discrepancy.
Please review .
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https://api.github.com/repos/pandas-dev/pandas/issues/11650 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11650/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11650/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11650/events | https://github.com/pandas-dev/pandas/pull/11650 | 117,903,073 | MDExOlB1bGxSZXF1ZXN0NTEyNzc0OTE= | 11,650 | DOC: sponsor notice | {
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cc @cpcloud
cc @shoyer
cc @wesm
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Pandas raises a ValueError when assigning multiple values to a Series (or DataFrame) using range(x) where x > 1. This error is raised only when its length is one million or larger.
``` python
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```
>>> import pandas as pd
>>> import StringIO
>>> dataframe = pd.read_csv(StringIO.StringIO('group,"this\nreally,error\ni,hope\n'), sep=',', header=None, skiprows=0, nrows=None, index_col=Fal... | {
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```
import itertools
import pandas as pd
import numpy as np
jobs = itertools.product(['a', 'b',... | {
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... | 7 | 2015-11-20T11:07:38Z | 2021-09-09T14:13:05Z | 2021-09-09T14:13:04Z | NONE | null | There's a problem in the way `.set_precision` currently works as it is being applied to all table cells indiscriminately. For instance a dataframe with means and standard errors cross validated across some parameter values the `set_precision` will easily hide a lot of detail in the results.
![screen shot 2015-11-20 at... | {
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In my case the problem manifests only in PyCharm when I try to debug some code. I've already tried to force building the c dependencies using `python setup.py build_ext --inplace -f` but that didn't help.
The error I get is:
```
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```
df = DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
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- warning in the whatsnew
- warning in the Styler docstring
- removes generated HTML version of the notebook, that's built as part of the doc build.
Building the docs now to double check, I'll merge when those are good.
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@jorisvandenbossche
@jreback
I think we're ok to merge.
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`cache_datetime=False` keyword as an addition to `read_csv` and `pd.to_datetime`
this would use a lookup cache (a dict will probably work), to map datetime strings to Timestamp objects. For repeated dates this will lead to some dramatic speedups.
Care must be taken if a `format` kw is provided (in `to... | {
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I found some extra time behind the sofa and started working on this. I made quite a few additions but by default everything should work as it did.
- just calling `.set_precision` with an `int` will truncate all columns to that many decimal places as before
- precision can be set per column as an extra `... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11668 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11668/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11668/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11668/events | https://github.com/pandas-dev/pandas/issues/11668 | 118,095,000 | MDU6SXNzdWUxMTgwOTUwMDA= | 11,668 | DataFrame .duplicated() / .drop_duplicates() flagging unique rows as duplicated in 0.17.0 | {
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] | closed | false | null | [] | null | 5 | 2015-11-20T18:11:39Z | 2015-12-18T18:14:37Z | 2015-11-20T18:17:35Z | NONE | null | Dataframe.duplicated() and .drop_duplicates() are flagging rows as duplicates when they are in fact distinct.
This was the smallest dataset I could make to recreate the issue, but I've seen this issue on DataFrames of any size:
``` python
>>> import pandas as pd
>>> json_data = '{"Col1":{"0":"S2#OaGwWII","1":")A9$... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11670 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11670/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11670/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11670/events | https://github.com/pandas-dev/pandas/issues/11670 | 118,129,358 | MDU6SXNzdWUxMTgxMjkzNTg= | 11,670 | possible bug when calculating mean of DataFrame? | {
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``` python
import pandas as pd
from decimal import Decimal
d = [
{'A': 2, 'B': None, 'C': Decimal('628.00')... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11671 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11671/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11671/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11671/events | https://github.com/pandas-dev/pandas/pull/11671 | 118,230,029 | MDExOlB1bGxSZXF1ZXN0NTE0NTQyMjA= | 11,671 | BLD: Change pytables to tables in the asv dependencies. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11672 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11672/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11672/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11672/events | https://github.com/pandas-dev/pandas/pull/11672 | 118,236,057 | MDExOlB1bGxSZXF1ZXN0NTE0NTY0MzU= | 11,672 | BUG: GH11616 fixes timezone selection error | {
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closes #11682
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https://api.github.com/repos/pandas-dev/pandas/issues/11673 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11673/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11673/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11673/events | https://github.com/pandas-dev/pandas/pull/11673 | 118,254,441 | MDExOlB1bGxSZXF1ZXN0NTE0NjE3Mjk= | 11,673 | DOC: minor fixes | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11674 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11674/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11674/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11674/events | https://github.com/pandas-dev/pandas/issues/11674 | 118,254,590 | MDU6SXNzdWUxMTgyNTQ1OTA= | 11,674 | json with multiindex and orient=split, fail to read what has been written | {
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Attempting to read a JSON file that has been written with orient="split" and a multi index is failing.
Pandas '0.17.0' on Python 3.5.0.
_Observations_
It works with orient=columns
It works when there is no multiindex
The output seems ok, the issue is probably on the decoding
_Test case_
import pandas as... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11675 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11675/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11675/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11675/events | https://github.com/pandas-dev/pandas/issues/11675 | 118,275,779 | MDU6SXNzdWUxMTgyNzU3Nzk= | 11,675 | apply loop adds an imaginary row | {
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``` python
import pandas
from pandas import Timestamp
test_data = {'A': {Timestamp('2015-05-05 19:59:09'): 90.659, Timestamp('2015-05-22 19:59:02'): 90.810},
'B': {Timestamp('2015-05-05 19:59:09'): 100.0, Timestamp('2015-05-22 19:59:02'): 100.0}}
def disp(row):
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https://api.github.com/repos/pandas-dev/pandas/issues/11676 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11676/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11676/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11676/events | https://github.com/pandas-dev/pandas/issues/11676 | 118,290,782 | MDU6SXNzdWUxMTgyOTA3ODI= | 11,676 | Can't get it to read a csv | {
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> > > import pandas as pd
> > > df= pd.read_csv('FBI-CRIME11.csv')
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "/Users/ajinkyasirsikar/anaconda/envs/py35/lib/python3.5/site-packages/pandas/io/parsers.py", line 491, in parser_f
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https://api.github.com/repos/pandas-dev/pandas/issues/11677 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11677/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11677/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11677/events | https://github.com/pandas-dev/pandas/pull/11677 | 118,298,621 | MDExOlB1bGxSZXF1ZXN0NTE0Nzg3MTY= | 11,677 | ENH: Read from compressed data sources | {
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"description": "IO issue... | closed | false | null | [] | null | 5 | 2015-11-22T23:40:42Z | 2016-01-20T14:11:40Z | 2016-01-20T14:11:40Z | CONTRIBUTOR | null | This is the start of an effort to address compression in `read_*` methods (at the moment, just `read_pickle`, as per #11666. So far, I've factored out the compression handling in the `_read` function of `pandas.io.parsers` and used that in the pickling read routine.
Tests on the way, along with actual testing that it ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11678 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11678/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11678/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11678/events | https://github.com/pandas-dev/pandas/pull/11678 | 118,314,891 | MDExOlB1bGxSZXF1ZXN0NTE0ODUzOTY= | 11,678 | BUG: style.bar results in incorrect gradient using specific browser | {
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"color": "006b7... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-11-23T03:35:51Z | 2015-12-12T00:26:48Z | 2015-12-10T20:59:56Z | MEMBER | null | It's not a bug on pandas side, but CSS `linear-gradient` with `0%` width seems not be rendered properly using specific browsers.
Below is the output using Mac OSX 10.10.5 / Firefox.
The fix s... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11679 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11679/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11679/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11679/events | https://github.com/pandas-dev/pandas/issues/11679 | 118,394,225 | MDU6SXNzdWUxMTgzOTQyMjU= | 11,679 | BUG: error in in _convert_to_indexer while using .loc with tz-aware DateTimeIndex | {
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"description": "pandas testing functions... | closed | false | null | [] | {
"closed_at": "2019-01-26T00:51:26Z",
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"followers_url": "https://api.github... | 7 | 2015-11-23T14:37:50Z | 2018-06-28T10:23:02Z | 2018-06-28T10:23:02Z | NONE | null | addtl example / repro in #13908
Hello,
I encounter some problems using the data provided here: [data.txt](https://github.com/pydata/pandas/files/41644/data.txt)
When I was trying to use **.loc** on my **DateTimeIndex**, I go t the following error
``` python
KeyError: "['2015-03-01T02:00:00.000000000+0100'] not in ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11680 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11680/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11680/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11680/events | https://github.com/pandas-dev/pandas/issues/11680 | 118,416,530 | MDU6SXNzdWUxMTg0MTY1MzA= | 11,680 | Warning issued every time Pandas is imported | {
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"followers_url": "https://api.github.com/users/jorisva... | 8 | 2015-11-23T16:19:54Z | 2017-05-31T08:17:50Z | 2017-05-31T08:17:43Z | NONE | null | I am getting the following warning every time I import Pandas in any script
```
/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
warnings.warn(self.msg_depr % (key, alt_key))
```
One example would... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11681 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11681/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11681/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11681/events | https://github.com/pandas-dev/pandas/pull/11681 | 118,432,548 | MDExOlB1bGxSZXF1ZXN0NTE1NTUwMjQ= | 11,681 | API: DataFrame.to_csv formatting parameters for float indexes | {
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"followers_url": "https://api.github.com/users/jr... | 13 | 2015-11-23T17:38:21Z | 2015-12-27T17:25:29Z | 2015-12-27T17:24:32Z | CONTRIBUTOR | null | Fix issue #11553
Two things:
1) I've created a `Float64Index._format_native_types` method which is a copy-paste of `FloatBlock.to_native_types`. I would have preferred to call the latter directly, but I'm not sure what the `placement` parameter of the `FloatBlock` constructor means. I guess I doesn't really matters... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11682 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11682/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11682/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11682/events | https://github.com/pandas-dev/pandas/issues/11682 | 118,474,145 | MDU6SXNzdWUxMTg0NzQxNDU= | 11,682 | Timezone info lost when broadcasting scalar datetime to DataFrame | {
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"description": "Related to indexing on s... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-11-23T21:17:53Z | 2015-11-27T13:40:22Z | 2015-11-27T13:40:22Z | CONTRIBUTOR | null | I've encountered a bug in pandas 0.16.2, where when using broadcasting to assign a datetime.datetime value to a whole column of a DataFrame, the timezone info is lost. Here is an example:
``` python
In [1]: import pandas, datetime, pytz
In [2]: df = pandas.DataFrame({'a': [1,2,3]})
In [3]: dt = datetime.datetime.no... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11683 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11683/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11683/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11683/events | https://github.com/pandas-dev/pandas/issues/11683 | 118,488,878 | MDU6SXNzdWUxMTg0ODg4Nzg= | 11,683 | Params not properly supported for pymssql | {
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"description": "to_sql, ... | open | false | null | [] | null | 2 | 2015-11-23T22:34:54Z | 2020-05-16T03:45:19Z | null | NONE | null | Pandas read SQL functions internally convert params to a list in pandas.io.sql._convert_params().
However on pymssql, params must be a tuple or dictionary. Stacktrace below.
```
Traceback (most recent call last):
File "/Users/simon/.virtualenvs/rfcapital/lib/python3.4/site-packages/pandas/io/sql.py", line 1539, in... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11684 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11684/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11684/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11684/events | https://github.com/pandas-dev/pandas/issues/11684 | 118,506,723 | MDU6SXNzdWUxMTg1MDY3MjM= | 11,684 | semantic is not consistent in astype(..) and read_csv(dtype=..) | {
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] | closed | false | null | [] | null | 1 | 2015-11-24T00:40:33Z | 2015-11-24T13:20:45Z | 2015-11-24T13:15:45Z | NONE | null | pandas 0.17.1
```
import pandas as pd
from io import StringIO
sio=StringIO("""
A
2
nan
""")
df = pd.read_csv(sio,dtype={'A':str})
print(type(df.A[0]),df.A[0])
print(type(df.A[1]),df.A[1])
print()
df=df.astype(str)
print(type(df.A[0]),df.A[0])
print(type(df.A[1]),df.A[1])
```
prints:
```
<class 'str'> 2
<class 'flo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11685 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11685/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11685/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11685/events | https://github.com/pandas-dev/pandas/issues/11685 | 118,543,265 | MDU6SXNzdWUxMTg1NDMyNjU= | 11,685 | Bug Report: drop_duplicates inconsistent result | {
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```
I found out one potential bug about drop_duplicates producing inconsistent result, with only a switch order of two columns. My Pandas version is 0.17.0. The data set is in Walmart - TripType classification question. https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/data?trai... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11686 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11686/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11686/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11686/events | https://github.com/pandas-dev/pandas/issues/11686 | 118,556,999 | MDU6SXNzdWUxMTg1NTY5OTk= | 11,686 | Install.rst is missing references to optional packages | {
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- rpy2
- psycopg (at least testing needs it)
- pymysql (at least testing needs it)
- matplotlib
perhaps there are more.
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... | closed | false | null | [] | null | 2 | 2015-11-24T11:14:57Z | 2015-12-06T04:24:18Z | 2015-12-06T04:23:08Z | CONTRIBUTOR | null | ```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.3.0-lh-nvidia
machine: x86_64
processor: Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz
byteorder: little
LC_ALL: None
LANG: en_GB.utf8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
num... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11688 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11688/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11688/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11688/events | https://github.com/pandas-dev/pandas/pull/11688 | 118,593,557 | MDExOlB1bGxSZXF1ZXN0NTE2NTA0MDU= | 11,688 | Use tz.gettz() instead of zoneinfo.gettz() | {
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the zoninfo tarball (e.g. Debian, Gentoo and Fedora) but rely on the system
zoneinfo files. This results in test failures (3 & 4)
tz.gettz() doesn't suffer from this problem.
1 https://github.com/dateutil/dateutil/issues/8
2 https://github.... | {
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raises `ImportError`, where as the lowercase version works fine.
Signed-off-by: Justin Lecher jlec@gentoo.org
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... | 2 | 2015-11-24T13:47:25Z | 2021-04-21T05:02:23Z | null | NONE | null | `import pandas as pd`
`import numpy as np`
`index = pd.date_range('1-Jan-2016',periods=12,freq='MS')`
`a = pd.DataFrame(np.ones((12,5)),index)`
`b = pd.Series(4.,index)`
`print a.multiply(b,axis=0)`
out:
0 1 2 3 4
2016-01-01 4 4 4 4 4
2016-02-01 4 4 4 4 4
2016-03-01 4 4 4 4 4
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Documented here:
http://stackoverflow.com/questions/33875937/apply-number-formatting-to-pandas-html-css-sty... | {
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``` python
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```
cls@clsmba ~> python3
Python 3.5.0 (default, Sep 23 2015, 04:41:33)
[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)] on darwin
Type "help", "copy... | {
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The issue prevent, when creating a new `Index` object based on a passed `Index` or `DatetimeIndex` object, to inherit the existing `Index` name if a new name is not provided.
I've also created two unit test which test the new behaviour on both cases (passing an `Index` or `Dateti... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11696 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11696/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11696/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11696/events | https://github.com/pandas-dev/pandas/pull/11696 | 118,699,138 | MDExOlB1bGxSZXF1ZXN0NTE3MTQ1NTY= | 11,696 | BUG/API: Index creation with different tz coerces DatetimeIndex | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-11-24T20:45:14Z | 2015-12-10T12:47:58Z | 2015-12-10T12:17:20Z | MEMBER | null | Closes #11488.
Based on the type check added in #11588, the change affects to fewer functions than I originally thought.
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https://api.github.com/repos/pandas-dev/pandas/issues/11697 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11697/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11697/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11697/events | https://github.com/pandas-dev/pandas/issues/11697 | 118,709,250 | MDU6SXNzdWUxMTg3MDkyNTA= | 11,697 | Rolling_median fails with large number of elements | {
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"description": "Arithmetic, Comparison, ... | closed | false | null | [] | {
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``` python
pandas\algos.pyx in pandas.algos.roll_median_c (pandas\algos.c:36408)()
MemoryError: skiplist_insert failed
```
when called with series containing large number of values. In comparison, the previous version of 0.17.0 was robust and never showed t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11698 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11698/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11698/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11698/events | https://github.com/pandas-dev/pandas/issues/11698 | 118,713,761 | MDU6SXNzdWUxMTg3MTM3NjE= | 11,698 | pandas.DataFrame.replace raises UnboundLocalError on mixed data types | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-11-24T22:07:52Z | 2015-12-02T15:04:44Z | 2015-12-02T15:04:43Z | CONTRIBUTOR | null | This is in 0.17.1. Suppose we have a frame with mixed data types. Suppose also we want to replace a string value with something else. If the frame has only string columns, or maybe string and int columns, this works fine. However, if the frame has string and datetime columns, this raises an exception. Example:
```
>>>... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11699 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11699/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11699/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11699/events | https://github.com/pandas-dev/pandas/issues/11699 | 118,817,123 | MDU6SXNzdWUxMTg4MTcxMjM= | 11,699 | group_by produces 'minlength must be positive error' when applied to empty DataFrame | {
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The following code:
```
import pandas as pd
df = pd.DataFrame({'A': [], 'B': []})
gb = df.groupby('A') .size()
```
Produces this error:
```
ValueError: minlength must be positive
```
In v 0.16.2 the same code produced an empty DataFrame... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11700 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11700/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11700/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11700/events | https://github.com/pandas-dev/pandas/issues/11700 | 118,838,334 | MDU6SXNzdWUxMTg4MzgzMzQ= | 11,700 | CLN/API: implemented to_html in terms of .style | {
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"followers_url": "https://api.github.com/users/j... | 16 | 2015-11-25T13:37:40Z | 2021-08-25T20:34:06Z | null | CONTRIBUTOR | null | Implement `to_html` / notebook repr based on `.style`.
prob need to expand this to take a `use` argument (to select the style, needs to be 'classic' for a while, to replicate the current `.to_html` one).
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https://api.github.com/repos/pandas-dev/pandas/issues/11701 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11701/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11701/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11701/events | https://github.com/pandas-dev/pandas/issues/11701 | 118,888,749 | MDU6SXNzdWUxMTg4ODg3NDk= | 11,701 | Interpolate NaT | {
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... | 7 | 2015-11-25T17:40:52Z | 2020-03-31T05:30:33Z | 2020-03-31T05:30:23Z | CONTRIBUTOR | null | Hello,
`interpolate` doesn't work with `NaT`
see http://stackoverflow.com/questions/33921795/fill-timestamp-nat-with-a-linear-interpolation/33922824#33922824
Here is a trivial example to show the situation:
```
s = pd.Series(pd.date_range('2015-01-01' , '2015-01-30'), name='t')
s[3], s[4], s[5] = pd.NaT, pd.NaT, pd... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11702 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11702/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11702/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11702/events | https://github.com/pandas-dev/pandas/pull/11702 | 118,895,356 | MDExOlB1bGxSZXF1ZXN0NTE4Mjk2MTU= | 11,702 | Doc: fix typo in style docs (collied->collide) | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-11-25T18:21:28Z | 2015-11-25T18:24:11Z | 2015-11-25T18:24:04Z | CONTRIBUTOR | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11703 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11703/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11703/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11703/events | https://github.com/pandas-dev/pandas/issues/11703 | 118,899,831 | MDU6SXNzdWUxMTg4OTk4MzE= | 11,703 | ExcelWriter won't write any row but the last when used with XlsxWriter in constant_memory mode | {
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] | closed | false | null | [] | null | 3 | 2015-11-25T18:47:47Z | 2015-11-30T01:31:06Z | 2015-11-30T01:31:06Z | NONE | null | Hi,
I'm noticing a problem with the xlsx files created by `ExcelWriter` when the engine is set to `"xlsxwriter"` and `options={'constant_memory': True})` kwarg is passed.
No exceptions are raised, but the resulting file will have all rows empty but the last one. Indexes are still written as usual, but all rows are b... | {
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