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https://api.github.com/repos/pandas-dev/pandas/issues/13304 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13304/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13304/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13304/events | https://github.com/pandas-dev/pandas/issues/13304 | 157,207,649 | MDU6SXNzdWUxNTcyMDc2NDk= | 13,304 | order not preserved when constructing DataFrame from a list of OrderedDicts | {
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http://stackoverflow.com/questions/37484738/pandas-dataframe-construction-from-a-list-of-ordereddict-preserving-columns-ord?noredirect=1#comment62465925_37484738
workaround with constructor option "columns" works but not elegant.
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https://api.github.com/repos/pandas-dev/pandas/issues/13305 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13305/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13305/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13305/events | https://github.com/pandas-dev/pandas/issues/13305 | 157,265,935 | MDU6SXNzdWUxNTcyNjU5MzU= | 13,305 | dataframe.columns.name attribute value is missing when dataframe.style is rendered | {
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#### Code Sample, a copy-pastable example if possible
import pandas as pd
test_df = pd.DataFrame({'A':ra... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13306 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13306/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13306/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13306/events | https://github.com/pandas-dev/pandas/issues/13306 | 157,302,384 | MDU6SXNzdWUxNTczMDIzODQ= | 13,306 | Hour overflow in tz-aware datetime conversions | {
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```
# TODO: this assumed sortedness :/
```
perhaps implies this is a known problem that was never resolved, so apologies if a new issue is not appropriate.
#### Self contained example
```
import pandas as pd
import numpy as ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13307 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13307/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13307/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13307/events | https://github.com/pandas-dev/pandas/pull/13307 | 157,303,382 | MDExOlB1bGxSZXF1ZXN0NzE3ODE3NzQ= | 13,307 | Fix series comparison operators when dealing with zero rank numpy arrays | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2016-05-27T22:48:21Z | 2016-06-03T15:04:28Z | 2016-06-03T15:04:18Z | CONTRIBUTOR | null | - [x] closes #13006
- [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/13308 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13308/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13308/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13308/events | https://github.com/pandas-dev/pandas/issues/13308 | 157,303,399 | MDU6SXNzdWUxNTczMDMzOTk= | 13,308 | ImportError: cannot import name 'hashtable' | {
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... | closed | false | null | [] | null | 4 | 2016-05-27T22:48:33Z | 2016-05-27T22:55:53Z | 2016-05-27T22:52:41Z | CONTRIBUTOR | null | I cloned this repo and tried to generate docs:
``` pytb
D:\src\pandas\doc>python make.py html
[NbConvertApp] Converting notebook source/html-styling.ipynb to html
[NbConvertApp] Writing 620631 bytes to source\source/html-styling.html
Traceback (most recent call last):
File "c:\python35\lib\runpy.py", line 170, in _r... | {
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- [x] passes `git diff upstream/master | flake8 --diff`
2 changes to make tests more strict:
- `assert_numpy_array_equal` now checks input is `np.ndarray`
- `assert_almost_equal` now checks inputs are the same class.
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Found docstring example is broken:
- http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_stata.html
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Bug: tz-converting tz-aware DateTimeIndex relied on index being sorted for correct results.
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When the group selection changes, the cache for `_selected_obj` needs to be reset so that `nth`, `head`, and `tail` can return consistent results.
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My code is not pythonic enough, maybe need some refactor. Any comment is welcome.
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To use the category labels I would expect to say labels=True but instead you have to say labels=None.
It seems illogical to say labels=None when you want labels.
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2) Moved erroneous placed skiprows tests into their proper place
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https://api.github.com/repos/pandas-dev/pandas/issues/13321 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13321/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13321/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13321/events | https://github.com/pandas-dev/pandas/pull/13321 | 157,412,114 | MDExOlB1bGxSZXF1ZXN0NzE4NDEyNjI= | 13,321 | ENH: add support for na_filter in Python engine | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13322 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13322/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13322/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13322/events | https://github.com/pandas-dev/pandas/issues/13322 | 157,413,143 | MDU6SXNzdWUxNTc0MTMxNDM= | 13,322 | categories in HDFStore don't filter correctly | {
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``` python
obsids = ['ESP_012345_6789', 'ESP_987654_3210']
imgids = ['APF00006np', 'APF0001imm']
data = [4.3, 9.8]
df = pd.DataFrame(dict(obsids=obsids, imgids=imgids, data=data))
df.to_hdf('testdf_no_cats.hdf', 'df',format='t', data_columns=True)
df.obsids = df.ob... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13323 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13323/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13323/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13323/events | https://github.com/pandas-dev/pandas/pull/13323 | 157,418,392 | MDExOlB1bGxSZXF1ZXN0NzE4NDQ5MTY= | 13,323 | API: Deprecate compact_ints and use_unsigned in read_csv | {
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xref #12686 - I don't quite understand why these are marked (if at all) as internal to the C engine only, as the benefits for having these options accepted for the Python engine is quite clear based on the documentation I added as well.
Implementation simply just calls the already-written f... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13324 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13324/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13324/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13324/events | https://github.com/pandas-dev/pandas/issues/13324 | 157,479,820 | MDU6SXNzdWUxNTc0Nzk4MjA= | 13,324 | to_numeric(errors='coerce') broken when using unhashable objects | {
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``` python
python -c "import pandas as pd; s = pd.Series([[13], 1.0, 'apple']); print(pd.to_numeric(s, errors='c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13325 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13325/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13325/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13325/events | https://github.com/pandas-dev/pandas/pull/13325 | 157,510,845 | MDExOlB1bGxSZXF1ZXN0NzE5MDcwMTQ= | 13,325 | TST: remove tests_tseries.py and distribute to other tests files | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13326 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13326/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13326/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13326/events | https://github.com/pandas-dev/pandas/pull/13326 | 157,533,751 | MDExOlB1bGxSZXF1ZXN0NzE5MjMwODM= | 13,326 | BUG: Fix maybe_convert_numeric for unhashable objects | {
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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/13327 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13327/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13327/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13327/events | https://github.com/pandas-dev/pandas/issues/13327 | 157,537,294 | MDU6SXNzdWUxNTc1MzcyOTQ= | 13,327 | feature request: rolling with datetime range | {
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I would like to submit one very useful addition to rolling, namely the possibility to compute any statistics over a specific **time range.**
Indeed, my understanding is that `rolling(windows=5).mean()` computes, say, the mean over the last five observations.
Instead, it would be very useful to speci... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13328 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13328/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13328/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13328/events | https://github.com/pandas-dev/pandas/issues/13328 | 157,555,919 | MDU6SXNzdWUxNTc1NTU5MTk= | 13,328 | Parsing dates using `read_csv` is slower than parsing them manually later. | {
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Parsing the dates automatically using `read_csv` (with the keywords `parse_dates` and `date_parser`) is actually slower than reading the file without parsing any dates, manually putting the date columns together and then using `pa... | {
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... | 3 | 2016-05-30T22:11:37Z | 2018-07-14T21:51:10Z | 2018-07-14T20:18:54Z | NONE | null | Dividing a DataFrame by a `numpy.uint64` or `numpy.uint32` scalar causes python to segfault, on both 64-bit Linux and Mac OS X, and both Python 2.7.11 and 3.5.1. There appears to be a minimum DataFrame size to trigger this; the same operation with size 10, instead of 10^5, works fine.
#### Code Sample
```
import numpy... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13330 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13330/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13330/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13330/events | https://github.com/pandas-dev/pandas/issues/13330 | 157,617,611 | MDU6SXNzdWUxNTc2MTc2MTE= | 13,330 | Transpose method not working with pandas dataframes | {
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] | closed | false | null | [] | null | 3 | 2016-05-31T08:34:49Z | 2018-06-26T09:21:05Z | 2016-05-31T11:44:01Z | NONE | null | Under Ubuntu 16, I am getting the following:
```
>>> import pandas as pd
>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> a = pd.DataFrame(a)
>>> a
0 1
0 1 2
1 3 4
>>> np.transpose(a)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "....../anaconda2/lib/python2.7/si... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13331 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13331/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13331/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13331/events | https://github.com/pandas-dev/pandas/issues/13331 | 157,621,946 | MDU6SXNzdWUxNTc2MjE5NDY= | 13,331 | pd.DataFrame.describe Invalid value encountered in percentile | {
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"description": "np.nan, pd.NaT, pd.NA, d... | closed | false | null | [] | null | 1 | 2016-05-31T08:58:41Z | 2016-05-31T11:52:00Z | 2016-05-31T11:51:01Z | NONE | null | In RHEL 7.2, with python 2.7.5
```
a = pd.np.random.randn(10, 2)
a.itemset((3, 0), pd.np.NAN)
df = pd.DataFrame(a)
res = df.describe()
```
---
Output:
```
/.../function_base.py:3823: RuntimeWarning: Invalid value encountered in percentile
RuntimeWarning)
0 1
count 8.0000... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13332 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13332/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13332/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13332/events | https://github.com/pandas-dev/pandas/issues/13332 | 157,666,021 | MDU6SXNzdWUxNTc2NjYwMjE= | 13,332 | Series passed to function in DataFrame.apply on empty dataframe lack index | {
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``` python
from StringIO import StringIO
import pandas as pd
from random import random
s = "a,b,c"
df = pd.read_csv(StringIO(s))
f1 = lambda row: random()
new_col1 = df.apply(f1, axis=1)
df["d"] = new_col1
f2 = lambda row: 0 if row["a"] > 10 else 1
new_col2 = df.a... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13333 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13333/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13333/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13333/events | https://github.com/pandas-dev/pandas/issues/13333 | 157,666,144 | MDU6SXNzdWUxNTc2NjYxNDQ= | 13,333 | COMPAT: mpl 2.0.0 | {
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"followers_url": "https://api.github.com/users/jr... | 17 | 2016-05-31T13:03:15Z | 2016-08-22T20:35:12Z | 2016-08-22T20:35:12Z | CONTRIBUTOR | null | seems @tacaswell / @mdboom have been busy
https://github.com/matplotlib/matplotlib/releases/tag/v2.0.0b1
we should test with this.
is this going up on a `conda-forge` dev channel? (is there such a thing)?
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#### Code Sample, a copy-pastable example if possible
``` python
import numpy as np
import pandas as pd
from numpy.random import random
from time import time
def do_groupby(df):
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https://api.github.com/repos/pandas-dev/pandas/issues/13337 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13337/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13337/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13337/events | https://github.com/pandas-dev/pandas/pull/13337 | 157,777,447 | MDExOlB1bGxSZXF1ZXN0NzIwOTA3MjA= | 13,337 | BUG: upcasting on reshaping ops #13247 | {
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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/jr... | 1 | 2016-05-31T21:46:16Z | 2016-05-31T23:18:16Z | 2016-05-31T23:18:16Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/134204278
I think #13311 broke some of these
here's some partial fixes
```
diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py
index 4dc1e24..69b8830 100644
--- a/pandas/computation/tests/test_eval.py
+++ b/pandas/computation/tests/test_... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13339 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13339/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13339/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13339/events | https://github.com/pandas-dev/pandas/pull/13339 | 157,782,198 | MDExOlB1bGxSZXF1ZXN0NzIwOTQxNTU= | 13,339 | TST: computation/test_eval.py tests (slow) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13340 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13340/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13340/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13340/events | https://github.com/pandas-dev/pandas/pull/13340 | 157,818,834 | MDExOlB1bGxSZXF1ZXN0NzIxMTk3ODE= | 13,340 | Cleanup compression | {
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- [x] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry - not needed
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https://api.github.com/repos/pandas-dev/pandas/issues/13341 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13341/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13341/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13341/events | https://github.com/pandas-dev/pandas/issues/13341 | 157,903,103 | MDU6SXNzdWUxNTc5MDMxMDM= | 13,341 | plotting mangled with DatetimeIndex to ax with sharex and dissimilar indices | {
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```
import pylab
import pandas as pd
pylab.ion(); pylab.show()
idx1 = pd.date_range('2015-01-01', periods=3, freq='1M')
idx2 = idx1[:1] + idx1[2:]
series1 = pd.Series(range(len(idx1)), idx1)
series2 = pd.Series(range(len(idx2)), idx2)
# this doesn't work
pylab.figure()
ax1 = p... | {
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pd.to_datetime("06/2015") **# When my system date is 5/31/2016, it returns a string (06/2015)**
#### Expected Output
Timestamp('2015-06-01 00:00:00')
#### output of `pd.show_versions()`
'0.16.2'
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https://api.github.com/repos/pandas-dev/pandas/issues/13344 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13344/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13344/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13344/events | https://github.com/pandas-dev/pandas/issues/13344 | 157,945,233 | MDU6SXNzdWUxNTc5NDUyMzM= | 13,344 | groupby.agg changes the default parameter of np.std | {
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As opposed to pandas, the default value for `ddof` in standard deviation calculations is 0 in numpy. However, when I use `groupby.agg(np.std)` I get the result for `ddof=1`.
```
import numpy as np
import pandas as pd
prng = np.random.RandomState(0)
df = pd.DataFra... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13345 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13345/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13345/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13345/events | https://github.com/pandas-dev/pandas/issues/13345 | 157,968,074 | MDU6SXNzdWUxNTc5NjgwNzQ= | 13,345 | Working with pd.Period is very slow | {
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"default"... | closed | false | null | [] | null | 1 | 2016-06-01T17:52:27Z | 2016-06-01T17:59:21Z | 2016-06-01T17:58:24Z | NONE | null | I find working with `pd.Period` very slow. Much much slower than working with `pd.Timestamp` and I was wondering why that is. I have around 2 million records and I'm forced to work with full timestamps even though my data is really on the level of months. Here is an example that I think shows the discrepancy between wo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13346 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13346/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13346/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13346/events | https://github.com/pandas-dev/pandas/issues/13346 | 157,986,198 | MDU6SXNzdWUxNTc5ODYxOTg= | 13,346 | pd.Period off by hundred of years for dates past ~2263 | {
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In [1]: import pandas as pd
In [2]: pd.Period(year=2300, month=1, day=1, freq='M').end_time
Out[2]: Timestamp('1715-07-14 00:25:26.290448383')
```
Input year is '2300', output shows '1715'
#### output of `pd.show_versions()`
In [3]: pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
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https://api.github.com/repos/pandas-dev/pandas/issues/13348 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13348/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13348/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13348/events | https://github.com/pandas-dev/pandas/issues/13348 | 158,199,485 | MDU6SXNzdWUxNTgxOTk0ODU= | 13,348 | Multiindex query | {
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... | 6 | 2016-06-02T17:46:14Z | 2021-05-01T21:45:52Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
If I have a Df with a MultiIndex:
```
import numpy.random as nr
nr.seed(0)
d = DataFrame(nr.randint(0, 5, (4, 2)),
columns=pd.MultiIndex.from_product([['L1'], ['A', 'B']]))
d
''' L1
A B
0 4 0
1 3 3
2 3 1
3 3 2'''
```
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`skip_footer` : "This line <a href="https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L519">here</a> says I am the right choice."
Whatever the case is, ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13350 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13350/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13350/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13350/events | https://github.com/pandas-dev/pandas/pull/13350 | 158,260,509 | MDExOlB1bGxSZXF1ZXN0NzI0MzI3NDY= | 13,350 | BUG: DataFrame.to_string with formatters, header and index False | {
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- [x] tests added / passed - added test specific to format bug
- [x] passes `pep8radius master --diff`
- [x] whatsnew entry - not needed
Found this bug experimenting with formatters. First pull request to pandas, but I believe guidelines are quite clear. I can explain what was happening in more detail i... | {
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- [x] closed #13356
- [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/j... | 2 | 2016-06-03T00:47:45Z | 2021-05-12T13:27:56Z | 2021-05-12T13:27:56Z | NONE | null | There is a strange error happening during pandas.merge when there is a unicode column name followed by a numpy.float64 column name. The error only happens for certain numpy.float64 values. The error is: "UnicodeDecodeError: 'utf8' codec can't decode byte 0x80 in position 5: invalid start byte"
The following code repro... | {
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```
costs = pd.DataFrame({'item':['bacon','cheese','bacon','cheese'],
'cost':[2.5,4.5,3.2,3.3],
'store':['SuperS','DollarM','SuperS','DollarM'],
'day':['M','M','T','T']})
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] | closed | false | null | [] | null | 10 | 2016-06-03T10:38:00Z | 2016-06-14T14:39:18Z | 2016-06-14T14:39:18Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
`assert_numpy_array_equal`'s docstring is at odds with (and the existence of the `obj` argument clashes with) the fact that it's now strict on its arguments being ndrarrays.
There are [practical reasons](https://github.com/pydata/panda... | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2016-06-03T16:39:28Z | 2016-07-24T14:14:06Z | 2016-07-24T14:14:06Z | CONTRIBUTOR | null | the hooks already exist, just need to define the argument and pass thru to `_get_filepath_or_buffer`
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#### Code Sample, a copy-pastable example if possible
```
from pandas import _testing
_testing.assert_almost_equal(0.000011, 0.000012, check_less_precise=True)
```
#### Expected Output
Expect no output / no AssertionError
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https://api.github.com/repos/pandas-dev/pandas/issues/13358 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13358/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13358/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13358/events | https://github.com/pandas-dev/pandas/pull/13358 | 158,438,971 | MDExOlB1bGxSZXF1ZXN0NzI1NTg1NTE= | 13,358 | ENH: add pd.asof_merge | {
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xref #2941
[Here](http://nbviewer.jupyter.org/gist/jreback/5f089d308750c89b2a7d7446b790c056) is a notebook of example usage and timings
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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/13360 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13360/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13360/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13360/events | https://github.com/pandas-dev/pandas/pull/13360 | 158,483,052 | MDExOlB1bGxSZXF1ZXN0NzI1OTAzOTE= | 13,360 | DEPR, DOC: Deprecate buffer_lines in read_csv | {
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for that is a good way to combine Categoricals. That part alone is complicated enough
so I decided to do a separate PR for it.
This adds a `union_categoricals` function that takes a list of (identical dtyped, unordered)
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The same IO process on 200 MB Data Frame works perfectly, i.e. a Data Frame is returned by the function "... | {
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
…st #13354
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https://api.github.com/repos/pandas-dev/pandas/issues/13364 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13364/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13364/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13364/events | https://github.com/pandas-dev/pandas/pull/13364 | 158,527,889 | MDExOlB1bGxSZXF1ZXN0NzI2MTUyMzU= | 13,364 | BUG: resample by BusinessHour raises ValueError | {
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- [x] tests added / passed (`TestPeriodIndex.test_resample_hourly_business_hourly`)
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
I did this during pandas sprint at PyCon 2016. Hope this close #12351
Resampling with BusinessHour took much more consideration I guess.
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... | 2 | 2016-06-04T22:51:02Z | 2021-05-01T22:02:32Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [2]: c = pd.CategoricalIndex(['3z53', '3z53', 'LoJG', 'LoJG', 'LoJG', 'N503'])
In [3]: s1 = pd.Series(2, index=c)
In [4]: s2 = pd.Series(2, index=c[:-1])
In [5]: s1*s2
---------------------------------------------------------------------------
TypeError ... | {
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So, what is the trick to determine in which cases `axis=1` means functioning on rows while in other cases it means functioning on columns ?
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Alternatively, are there any recommended packages t... | {
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They are all based on pd.merge(..., how="left", ...)
The difference depends on:
##### 1) Whether we are merging using an index or a column.
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2) Added `as_recarray` functionality to Python engine
3) Fixed bug in C engine in which `usecols` was not being respected in combination with `as_recarray`
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``` python
>>> data = 'a,,b\n1,,a\n2,,"2,,b"'
>>> read_csv(StringIO(data), sep=',,', engine='python')
...
ValueError: Expected 2 fields in line 3, saw 3
```
I expect this command to work, but because ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13375 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13375/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13375/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13375/events | https://github.com/pandas-dev/pandas/pull/13375 | 158,582,426 | MDExOlB1bGxSZXF1ZXN0NzI2NDQ3MzA= | 13,375 | ENH: astype() can now take col label -> dtype mapping as arg; GH7271 | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2016-06-06T00:05:25Z | 2016-07-20T22:04:54Z | 2016-07-20T22:04:50Z | CONTRIBUTOR | null | New PR for what was started in #12086.
closes #7271
By passing a dict of {column name/column index: dtype}, multiple columns can be cast to different data types in a single command. Now users can do:
`df = df.astype({'my_bool', 'bool', 'my_int': 'int'})`
or:
`df = df.astype({0, 'bool', 1: 'int'})`
instead of:
``... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13376 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13376/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13376/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13376/events | https://github.com/pandas-dev/pandas/pull/13376 | 158,590,809 | MDExOlB1bGxSZXF1ZXN0NzI2NDk4ODM= | 13,376 | DOC: Fix wording/grammar for rolling's win_type argument. | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-06-06T02:35:12Z | 2016-06-06T12:10:51Z | 2016-06-06T12:10:41Z | CONTRIBUTOR | null | I don't know the exact intended phrasing here, but I think the writer might have meant "Provide"?
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https://api.github.com/repos/pandas-dev/pandas/issues/13377 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13377/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13377/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13377/events | https://github.com/pandas-dev/pandas/pull/13377 | 158,629,790 | MDExOlB1bGxSZXF1ZXN0NzI2NzU2Nzk= | 13,377 | DOC: actually document float_precision in read_csv | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-06-06T08:56:35Z | 2016-06-06T12:09:45Z | 2016-06-06T12:09:42Z | MEMBER | null | So I wasn't 100% correct when I said that `float_precision` was documented <a href="https://github.com/pydata/pandas/issues/12686#issuecomment-222684918">here<a/>. It was well documented internally for `TextParser` and in a section for `io.rst`, but it wasn't listed formally in the parameters for the `read_csv` docume... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13378 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13378/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13378/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13378/events | https://github.com/pandas-dev/pandas/issues/13378 | 158,644,038 | MDU6SXNzdWUxNTg2NDQwMzg= | 13,378 | BUG: in read_csv, keep_date_cols doesn't result in correct dtype | {
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... | 9 | 2016-06-06T10:13:23Z | 2019-10-21T19:52:16Z | null | NONE | null | `>>> import pandas`
`>>> import io`
`>>> data = """A`
`20150908`
`20150909`
`"""`
`>>> t=pandas.read_csv(io.StringIO(data))`
`>>> t.dtypes`
`A int64`
`dtype: object`
`>>> t=pandas.read_csv(io.StringIO(data),parse_dates={'date':['A']},keep_date_col=True)`
`>>> t.dtypes`
`date datetime64[ns]`
`A objec... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13379 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13379/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13379/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13379/events | https://github.com/pandas-dev/pandas/issues/13379 | 158,674,577 | MDU6SXNzdWUxNTg2NzQ1Nzc= | 13,379 | ENH: write Styler rendered output to file | {
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So the OP was using `to_html` but wanted conditional formatting. Therefore, I poin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13380 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13380/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13380/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13380/events | https://github.com/pandas-dev/pandas/pull/13380 | 158,674,881 | MDExOlB1bGxSZXF1ZXN0NzI3MDcwOTk= | 13,380 | CLN: extract window functions from algox.pyx and create window.pyx | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13381 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13381/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13381/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13381/events | https://github.com/pandas-dev/pandas/pull/13381 | 158,689,743 | MDExOlB1bGxSZXF1ZXN0NzI3MTcyNjU= | 13,381 | DOC, ENH: Support memory_map for Python engine | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13382 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13382/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13382/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13382/events | https://github.com/pandas-dev/pandas/issues/13382 | 158,738,996 | MDU6SXNzdWUxNTg3Mzg5OTY= | 13,382 | BUG: .extractall() throws AssertionError if capture group length > 1 | {
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```
import pandas as pd
s = pd.Series(["a13a23", "b13", "c13"], index=["A", "B", "C"])
s.str.extractall("[ab](\d\d)")
```
Note that the regex `[ab](\d)` from the documentation page works, whereas `[ab](\d\d)` above doesn't. It seems that any captured group with a length of > 1 causes this err... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13383 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13383/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13383/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13383/events | https://github.com/pandas-dev/pandas/issues/13383 | 158,803,947 | MDU6SXNzdWUxNTg4MDM5NDc= | 13,383 | Series.rolling/DataFrame.rolling don't fully check arguments, have odd behavior when used with invalid inputs | {
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In [171]: s = pd.Series(range(3))
In [172]: s.rolling(-1) # doesn't raise
Out[172]: Rolling [window=-1,center=False,axis=0]
In [173]: s.rolling(-1).mean() # Odd, indirect error
---------------------------------------------------------------------------
ValueError Traceback (most rec... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13384 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13384/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13384/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13384/events | https://github.com/pandas-dev/pandas/issues/13384 | 158,892,111 | MDU6SXNzdWUxNTg4OTIxMTE= | 13,384 | Series.nonzero() method not listed in API reference | {
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] | closed | false | null | [] | null | 3 | 2016-06-07T10:41:47Z | 2016-06-07T12:30:10Z | 2016-06-07T12:07:17Z | NONE | null | I cannot find the `Series.nonzeros()` method in the [API Reference](http://pandas.pydata.org/pandas-docs/stable/api.html#series) documentation-page for `pandas <= 0.18.1`.
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https://api.github.com/repos/pandas-dev/pandas/issues/13385 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13385/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13385/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13385/events | https://github.com/pandas-dev/pandas/issues/13385 | 158,943,103 | MDU6SXNzdWUxNTg5NDMxMDM= | 13,385 | BUG: Series.fillna(False) return 0.0(float) instead of bool | {
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"id": 31404521,
"name": "Dtype Conversions",
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"color": "005... | closed | false | null | [] | null | 1 | 2016-06-07T14:50:40Z | 2016-06-07T16:54:12Z | 2016-06-07T16:53:58Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
s1 = pd.Series([np.nan, np.nan, np.nan])
print s1.fillna(False)
```
#### Expected Output
```
0 False
1 False
2 False
dtype: bool
```
but the output is:
```
0 0.0
1 0.0
2 0.0
dtype: float64
```
#### output of `pd.show_ver... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13386 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13386/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13386/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13386/events | https://github.com/pandas-dev/pandas/pull/13386 | 158,948,020 | MDExOlB1bGxSZXF1ZXN0NzI4OTMwNDA= | 13,386 | API: Deprecate skip_footer in read_csv | {
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Closes gh-13349 and partially undoes this <a href="https://github.com/pydata/pandas/commit/9ceea2fd46e79f37269a32de8c140cddf90eda13">commit</a> back in `v0.9.0`. With such a massive API now, having duplicate arguments makes managing it way less practical.
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https://api.github.com/repos/pandas-dev/pandas/issues/13387 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13387/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13387/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13387/events | https://github.com/pandas-dev/pandas/issues/13387 | 158,971,876 | MDU6SXNzdWUxNTg5NzE4NzY= | 13,387 | describe() doesn't ignore Nan anymore | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2016-06-07T16:49:29Z | 2016-06-07T16:51:47Z | 2016-06-07T16:51:31Z | NONE | null | `.describe()` is not ignoring the `Nan` values at percentiles, so it gives `Nan`:
http://pandas.pydata.org/pandas-docs/stable/basics.html#summarizing-data-describe
Before 0.18 it was working as expected:
http://pandas.pydata.org/pandas-docs/version/0.17.0/basics.html#summarizing-data-describe
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https://api.github.com/repos/pandas-dev/pandas/issues/13388 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13388/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13388/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13388/events | https://github.com/pandas-dev/pandas/issues/13388 | 158,979,219 | MDU6SXNzdWUxNTg5NzkyMTk= | 13,388 | COMPAT: comparisons with as_recarray breaking on older numpies | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2016-06-07T17:26:20Z | 2016-06-07T22:18:39Z | 2016-06-07T22:18:39Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/135897420
cc @gfyoung
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https://api.github.com/repos/pandas-dev/pandas/issues/13389 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13389/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13389/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13389/events | https://github.com/pandas-dev/pandas/issues/13389 | 158,983,741 | MDU6SXNzdWUxNTg5ODM3NDE= | 13,389 | BUG: TypeError in merge with timedelta64 column | {
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>>> import pandas as pd
>>> df1 = pd.DataFrame({'entity_id': [101, 102]})
>>> df2 = pd.Series([None, None], index=[101, 102]).astype('timedelta64[D]').to_frame('days')
>>> print(df2); print(df2.dtypes)
days
101 NaT
102 NaT
days timedelta64[D]
dtype: object
>>> df1.merge(df2, left_on='entity_id',... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13390 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13390/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13390/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13390/events | https://github.com/pandas-dev/pandas/issues/13390 | 158,991,136 | MDU6SXNzdWUxNTg5OTExMzY= | 13,390 | [BUG] Groupby, Apply | {
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```
import pandas as pd
df = pd.DataFrame(dict(a=[0,0,0,1,1,1,2,2,2], b=[0,2,1,3,5,4,7,6,8]))
df
def f(x):
x['c'] = 1
return x.sort_values('b')
df.groupby('a', as_index=False).apply(f).reset_index(drop=True)
```
#### Expected Output
```
pd.DataFrame(dict... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13391 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13391/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13391/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13391/events | https://github.com/pandas-dev/pandas/issues/13391 | 159,007,150 | MDU6SXNzdWUxNTkwMDcxNTA= | 13,391 | Sum of all NaNs comes out zero if bottleneck is installed | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2016-06-07T19:41:05Z | 2016-06-07T19:43:28Z | 2016-06-07T19:43:11Z | CONTRIBUTOR | null | x = pandas.DataFrame(np.nan, index=['a', 'b'], columns=['c', 'd'])
print x.sum()
I expected to see all NaN's in the resulting Series. However, if i have bottleneck installed i get a Series of all zeros instead of all NaNs.
This is with pandas 0.18.1.
Thanks for any comments or suggestions
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https://api.github.com/repos/pandas-dev/pandas/issues/13392 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13392/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13392/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13392/events | https://github.com/pandas-dev/pandas/pull/13392 | 159,007,194 | MDExOlB1bGxSZXF1ZXN0NzI5MzU1MTI= | 13,392 | COMPAT, TST: allow numpy array comparisons with complex dtypes | {
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Closes #13388.
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https://api.github.com/repos/pandas-dev/pandas/issues/13393 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13393/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13393/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13393/events | https://github.com/pandas-dev/pandas/issues/13393 | 159,036,957 | MDU6SXNzdWUxNTkwMzY5NTc= | 13,393 | Pandas 0.18.1 df.groupby().count() throws "ValueError: Buffer has wrong number of dimensions" when one of the counted columns has dtype datetime64 | {
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#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
df = pd.DataFrame({'x': ['a', 'a', 'b'],
'y': [pd.Timestamp('2016-05-07 20:09:25+00:00'), pd.Timestamp('2016-05-07 20:09:29+00:00'), pd.Timestamp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13394 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13394/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13394/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13394/events | https://github.com/pandas-dev/pandas/pull/13394 | 159,050,127 | MDExOlB1bGxSZXF1ZXN0NzI5NjcxODg= | 13,394 | BUG: Fix groupby with "as_index" for categorical multi #13204 | {
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"followers_url": "https://api.github.com/users/jr... | 20 | 2016-06-07T23:50:50Z | 2016-07-03T23:28:58Z | 2016-07-03T23:28:31Z | CONTRIBUTOR | null | - [x] closes #13204
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixes a bug that returns all nan's for groupby(as_index=False) with
multiple column groupers containing a categorical one (#13204).
---
Also:
fixes an internal bug in the string representation ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13395 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13395/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13395/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13395/events | https://github.com/pandas-dev/pandas/issues/13395 | 159,059,195 | MDU6SXNzdWUxNTkwNTkxOTU= | 13,395 | Index.unique() should always return an Index object of the same type | {
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Currently, it sometimes returns numpy arrays:
```
>>> pd.DatetimeIndex(['2000-01-01', '2000-01-02']).unique()
DatetimeIndex(['2000-01-01', '2000-01-02'], dtype='datetime64[ns]', freq=None)
>>> pd.Index([0, 1, 2]).unique()
array([0, 1, 2])
```
Most of the wo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13396 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13396/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13396/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13396/events | https://github.com/pandas-dev/pandas/issues/13396 | 159,109,644 | MDU6SXNzdWUxNTkxMDk2NDQ= | 13,396 | tight_layout not working with DataFrame.hist | {
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First, relevant imports:
``` python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
```
This doesn't work:
``` python
pd.DataFrame(np.random.randn(100, 3)).hist()
plt.tight_layout()
```
It raises the following error:
```
AttributeError: ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13397 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13397/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13397/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13397/events | https://github.com/pandas-dev/pandas/pull/13397 | 159,152,587 | MDExOlB1bGxSZXF1ZXN0NzMwMzgxMTk= | 13,397 | BUG: Fix for .extractall (single group with quantifier) #13382 | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2016-06-08T12:35:01Z | 2016-06-08T15:27:11Z | 2016-06-08T15:27:07Z | CONTRIBUTOR | null | - [X] closes #13382
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
Note that I had to fix it based on this [thread](http://stackoverflow.com/questions/2111759/whats-the-best-practice-for-handling-single-value-tuples-in-python), rather than directly with `[x for ... | {
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#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
from tempfile import TemporaryFile
new_fi... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13399 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13399/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13399/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13399/events | https://github.com/pandas-dev/pandas/issues/13399 | 159,168,630 | MDU6SXNzdWUxNTkxNjg2MzA= | 13,399 | BLD: should we use tempita for cython templating | {
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might replace some of the manual code paths in `generate_code.py`
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... | open | false | null | [] | null | 1 | 2016-06-08T14:39:00Z | 2021-05-01T22:09:02Z | null | NONE | null | Instead of just histograms or kde, i would like to be able to ask scatter_matrix to put the name of the series in the diagonal panels.
Below is an example of what i did "manually". just notice how the diagonal panel is used to give the name of the series (in that case i also renamed the series to something descriptiv... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/13401 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13401/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13401/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13401/events | https://github.com/pandas-dev/pandas/issues/13401 | 159,188,812 | MDU6SXNzdWUxNTkxODg4MTI= | 13,401 | CLN: unify unicode file handle processing | {
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... | 5 | 2016-06-08T15:09:59Z | 2020-05-02T04:04:37Z | 2020-05-02T04:04:37Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/pull/13351#issuecomment-224614415
We use `io/common/_get_handle` and `io/common/get_filepath_or_buffer` in various places in most of the io modules, need to unify encoding handling to a somewhat simpler interface (1 function).
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