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https://api.github.com/repos/pandas-dev/pandas/issues/8902 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8902/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8902/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8902/events | https://github.com/pandas-dev/pandas/issues/8902 | 50,225,498 | MDU6SXNzdWU1MDIyNTQ5OA== | 8,902 | Concat coerces ints to floats if empty DataFrame is present | {
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... | 5 | 2014-11-26T20:47:30Z | 2021-04-11T05:45:26Z | 2021-04-11T05:45:25Z | NONE | null | It seems that when using `concat` coerces `Series` of type `int` to type `float` if one of the `DataFrame`s being concatted is empty:
```
>>> df1 = pd.DataFrame([1],columns=['a'])
>>> df2 = pd.DataFrame(columns=['a'])
>>> df = pd.concat([df1, df2])
>>> df['a'].dtype
dtype('float64')
>>> df1['a'].dtype
dtype('int64')
`... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8903 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8903/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8903/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8903/events | https://github.com/pandas-dev/pandas/issues/8903 | 50,248,921 | MDU6SXNzdWU1MDI0ODkyMQ== | 8,903 | ENH/PERF: new kw complete to to_datetime to allow partial matches on a datetime format | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-11-27T02:48:17Z | 2014-11-27T14:08:28Z | 2014-11-27T02:48:28Z | CONTRIBUTOR | null | Clearly the default is `complete=True` for back-compat
but allows for a match starting at the beginning (has always been like this),
but doesn't require the string to ONLY match the format, IOW, can be extra stuff after the match.
Avoids having to do a regex replace first.
```
In [21]: s = Series(['19MAY11','19MAY11:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8904 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8904/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8904/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8904/events | https://github.com/pandas-dev/pandas/pull/8904 | 50,248,955 | MDExOlB1bGxSZXF1ZXN0MjUxMjY4MDg= | 8,904 | PERF: add exact kw to to_datetime to enable faster regex format parsing | {
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closes #8903
Clearly the default is exact=True for back-compat
but allows for a match starting at the beginning (has always been like this),
but doesn't require the string to ONLY match the format, IOW, can be extra stuff after the match.
Avoids having to do a regex replace first.
```
In [21]: s = Ser... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8905 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8905/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8905/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8905/events | https://github.com/pandas-dev/pandas/issues/8905 | 50,255,404 | MDU6SXNzdWU1MDI1NTQwNA== | 8,905 | Plot label None in line plot | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2014-11-27T05:24:43Z | 2015-05-01T16:05:38Z | 2015-05-01T16:05:32Z | NONE | null | The plot label appears when plotting a dataframe as a scatter plot but not as a line plot. Is this expected?
``` python
df = pd.DataFrame(np.random.rand(2, 4), columns=['a', 'b', 'c', 'd'])
ax = df.plot(kind = 'scatter', x='a', y='b',color='DarkBlue', label='Label')
ax = df.plot(kind = 'line', x='a', y='b',color='Dark... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8906 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8906/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8906/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8906/events | https://github.com/pandas-dev/pandas/issues/8906 | 50,289,524 | MDU6SXNzdWU1MDI4OTUyNA== | 8,906 | Partial indexing of a Panel | {
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"followers_url": "https://api.github.com/users/jorisva... | 11 | 2014-11-27T13:06:46Z | 2018-07-06T20:21:06Z | 2017-07-10T21:06:18Z | NONE | null | See also: http://stackoverflow.com/questions/26736745/indexing-a-pandas-panel-counterintuitive-or-a-bug
These are actually two related(?) issues.
The first is that the DataFrame is transposed, when you index the major_indexer or minor_indexer:
```
from pandas import Panel
from numpy import arange
p = Panel(arange(24)... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8907 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8907/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8907/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8907/events | https://github.com/pandas-dev/pandas/pull/8907 | 50,299,198 | MDExOlB1bGxSZXF1ZXN0MjUxNTU4MTU= | 8,907 | BUG: DatetimeIndex with time object as key | {
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on master:
```
>>> from datetime import time
>>> from pandas.index import _SIZE_CUTOFF
>>> n = _SIZE_CUTOFF + 100
>>> idx = pd.date_range('2014-11-26', periods=n, freq='S')
>>> ts = pd.Series(np.random.randn(n), index=idx)
>>> key = time(15, 0)
>>> ts[key]
TypeError:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8908 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8908/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8908/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8908/events | https://github.com/pandas-dev/pandas/issues/8908 | 50,306,945 | MDU6SXNzdWU1MDMwNjk0NQ== | 8,908 | shouldn't mangle_dupe_cols add an underscore rather than a dot in read_csv? | {
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Wouldn't it make more sense to amend them as _1, _2, etc., so that columns of a dataframe can still be accessed with the dot syntax: df.name_1 works fine, but df.name... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8909 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8909/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8909/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8909/events | https://github.com/pandas-dev/pandas/pull/8909 | 50,307,791 | MDExOlB1bGxSZXF1ZXN0MjUxNjEwNjk= | 8,909 | ENH: make Series work with map objects the same way as generators | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8910 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8910/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8910/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8910/events | https://github.com/pandas-dev/pandas/issues/8910 | 50,313,198 | MDU6SXNzdWU1MDMxMzE5OA== | 8,910 | TST/BUG: Improve error handling when parsing iso 8601 strings | {
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Parsing of datetime strings ending with `Z0`or `Z00`should raise (this is currently only the case with `Z0`).
This is due to the fact that the parser in `np_datetime_strings.c` cannot raise its own errors since in tslib.pyx, function convert_ts_tsobject(), any ValueError is catched and then a fallback exte... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8911 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8911/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8911/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8911/events | https://github.com/pandas-dev/pandas/issues/8911 | 50,317,592 | MDU6SXNzdWU1MDMxNzU5Mg== | 8,911 | Suggestion: Heatmap as part of pandas code. | {
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"description": nul... | closed | false | null | [] | null | 2 | 2014-11-27T18:56:42Z | 2014-11-28T03:26:52Z | 2014-11-28T03:26:52Z | CONTRIBUTOR | null | Here is a function that generates a heatmap:
http://nbviewer.ipython.org/github/herrfz/dataanalysis/blob/master/week3/svd_pca.ipynb
It works with numpy.arrays
Maybe you want to ask the author to contribute this useful function to pandas.
I think, its quite nice.
kind regards
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https://api.github.com/repos/pandas-dev/pandas/issues/8912 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8912/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8912/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8912/events | https://github.com/pandas-dev/pandas/pull/8912 | 50,320,958 | MDExOlB1bGxSZXF1ZXN0MjUxNjgzMTk= | 8,912 | DOC: Issue #8805 | {
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Documentation suggests other types of join, but only 'left' is implemented.
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https://api.github.com/repos/pandas-dev/pandas/issues/8913 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8913/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8913/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8913/events | https://github.com/pandas-dev/pandas/issues/8913 | 50,327,544 | MDU6SXNzdWU1MDMyNzU0NA== | 8,913 | Generate new DataFrame for two objects results in NaNs | {
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] | closed | false | null | [] | null | 5 | 2014-11-27T22:52:50Z | 2014-11-28T22:50:25Z | 2014-11-28T00:57:38Z | CONTRIBUTOR | null | Hi,
I want to generate a DataFrame out of some data x0 and an index based some dates t0.
df = pd.DataFrame(x0)
Generates the dataframe containg all data correctly.
df = pd.DataFrame(x0,t0)
Generates a dataframe with the dates as index whereas 'Date' appears as first index and all data is cleared to NaNs.
type(x0)
p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8914 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8914/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8914/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8914/events | https://github.com/pandas-dev/pandas/issues/8914 | 50,329,562 | MDU6SXNzdWU1MDMyOTU2Mg== | 8,914 | Pandas 0.15.1 df.interpolate().plot() wrong result with NaT in datelike index | {
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... | open | false | null | [] | null | 8 | 2014-11-27T23:53:11Z | 2021-04-11T05:47:24Z | null | CONTRIBUTOR | null | I am using a DataFrame df with dates as index and some data with missing values.
All dates are from the same year 1999 and the data has some missing vales.
There is one record in the dates that is NaT.
When I use df.interploate().plot() a line is plotted between 1996 and 1716.
When NaT is committed the correct plot a... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8915 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8915/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8915/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8915/events | https://github.com/pandas-dev/pandas/pull/8915 | 50,340,491 | MDExOlB1bGxSZXF1ZXN0MjUxNzczMjE= | 8,915 | ENH: adds ability to generate bq schema from df | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-11-28T04:41:41Z | 2014-12-03T22:38:40Z | 2014-12-03T22:38:33Z | CONTRIBUTOR | null | I need this function for some other work I have in a branch (to_gbq w/ a new table and to_gbq with staging in gcs). I saw issue #8325 and thought this might be a good intermediate step.
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https://api.github.com/repos/pandas-dev/pandas/issues/8916 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8916/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8916/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8916/events | https://github.com/pandas-dev/pandas/pull/8916 | 50,342,468 | MDExOlB1bGxSZXF1ZXN0MjUxNzgyOTk= | 8,916 | BUG/ENH: cleanup for Timestamp arithmetic | {
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... | closed | false | null | [] | null | 9 | 2014-11-28T05:37:15Z | 2014-12-09T23:27:02Z | 2014-12-09T23:26:55Z | MEMBER | null | Fixes #8865 (Timestamp - Timestamp -> Timedelta)
This PR cleans up and extends `Timestamp` arithmetic similarly to my treatment for `Timedelta` in #8884.
It includes a new `to_datetime64()` method, and arithmetic now works between Timestamp and ndarrays. I also ensure comparison operations work properly between all o... | {
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"description": "Timezone... | closed | false | null | [] | {
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"followers_url": "https://api.github... | 16 | 2014-11-28T11:17:53Z | 2018-10-25T11:46:30Z | 2018-10-25T11:46:30Z | NONE | null | I'm trying to use pandas to speed up timezone conversions on many datetimes and I can't get around `NonExistentTimeErrors`. Pandas `tz_localize()` seems to ignore the `ambiguous` argument for the non existent time case.
Example:
``` python
import pytz
import pandas
tz = pytz.timezone('Europe/Warsaw')
non_existent = ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8918 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8918/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8918/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8918/events | https://github.com/pandas-dev/pandas/issues/8918 | 50,405,763 | MDU6SXNzdWU1MDQwNTc2Mw== | 8,918 | Wishlist: make get_dummies() usable for train / test framework | {
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"de... | closed | false | null | [] | null | 21 | 2014-11-28T22:06:20Z | 2020-08-03T11:44:05Z | 2015-10-05T10:34:22Z | CONTRIBUTOR | null | Having get_dummies() in Pandas is really nice, but to be useful for machine learning, it would need to be usable in a train / test framework (or "fit_transform" and "transform", with the sklearn terminology). Let me know if this needs more explanations.
So, I guess this is a wishlist bug report to add that functionali... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8919 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8919/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8919/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8919/events | https://github.com/pandas-dev/pandas/issues/8919 | 50,406,811 | MDU6SXNzdWU1MDQwNjgxMQ== | 8,919 | Pandas 0.15.1 pandas.t0_datetime() sometimes returns pandas.core.series.Series and sometimes pandas.tseries.index.DatetimeIndex | {
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... | closed | false | null | [] | null | 3 | 2014-11-28T22:38:41Z | 2014-11-29T00:40:43Z | 2014-11-28T22:44:41Z | CONTRIBUTOR | null | pandas.to_datetime() should not return different types.
Otherwise, all output should have methods like .head() and .tail() and .month()
---
import pandas as pd
import numpy as np
pd.options.display.mpl_style = 'default'
import matplotlib.pyplot as plt
x0['Month']=x0['Date'].month
data=np.random.rand(5)
data[4]=NaN
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https://api.github.com/repos/pandas-dev/pandas/issues/8920 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8920/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8920/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8920/events | https://github.com/pandas-dev/pandas/issues/8920 | 50,407,511 | MDU6SXNzdWU1MDQwNzUxMQ== | 8,920 | Pandas 0.15.1 dates from a column in DataFrame cannot directly be converted to month | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8922 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8922/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8922/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8922/events | https://github.com/pandas-dev/pandas/pull/8922 | 50,423,865 | MDExOlB1bGxSZXF1ZXN0MjUyMjIyMjk= | 8,922 | CLN: move import to top of file | {
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```
Imports are always put at the top of the file, just after any module
comments and docstrings, and before module globals and constants.
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8923 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8923/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8923/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8923/events | https://github.com/pandas-dev/pandas/pull/8923 | 50,424,422 | MDExOlB1bGxSZXF1ZXN0MjUyMjI0NTY= | 8,923 | TST: harmonize testing namespace in TestCase (GH8023) | {
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Merged via https://github.com/pydata/pandas/commit/ace3c46be0ebf12f0eb4b63f22843d76b67ccfde
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statement.
Added test TestConfig.test_option_config_scope
Closes #8514
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Closes #8778
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As mentioned in the github issue its a py3 only bug. I copied the test @rmorgans made
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This closes #8420, with code that supports searchsorted in Categorical, for both side='left'/'right' and with an optional sorter. The case of side='right' was tricky, as we look to the right only when matching the category name, not when matching the codes. The docstring and unit tests cover the var... | {
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matplotlib, this functionality was accidentally removed previously.
Add tests.
Closes #8852
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https://api.github.com/repos/pandas-dev/pandas/issues/8930 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8930/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8930/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8930/events | https://github.com/pandas-dev/pandas/pull/8930 | 50,430,950 | MDExOlB1bGxSZXF1ZXN0MjUyMjQ4NDY= | 8,930 | BUG: moving the utf encoding line to the first line (before from __futur... | {
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ERROR: Failure: SyntaxError (Non-ASCII character '\xc3' in file /home/fvia/bloom/pandas/pandas/tests/test_format.py on line 825, but no encoding declared; see http://python.org/dev/peps/pep-0263/ for details (test_format.py, line 825))
```
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```
In [7]: pd.Timestamp('2000-01-01') > pd.Series(range(5))
TypeError: Cannot compare type 'Timestamp' with type 'int'
In [8]: pd.Timestamp('2000-01-01') > pd.DataFrame({'x': range(5)})
Out[8]:
x
0 True
1 True
2 True
3 True
4 True
```
Probably will fix this in the process of gett... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8933 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8933/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8933/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8933/events | https://github.com/pandas-dev/pandas/issues/8933 | 50,434,986 | MDU6SXNzdWU1MDQzNDk4Ng== | 8,933 | CLN: core.common._is_sequence -> is_sequence | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-11-29T21:13:35Z | 2016-10-12T23:05:21Z | 2015-03-11T20:46:30Z | CONTRIBUTOR | null | - Anaconda has a conda recipe for pandas, but nice to include this inline
- ideally add a binstar recipe to check builds on linux/windows/mac
- nice little binstar build badges!
will close #7127 (and obviate need for #8630)
@cpcloud
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I was also thinking of having 0.16.0 be a somewhat shorter cycle, say 2 months. Pls lmk any objections.
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] | closed | false | null | [] | null | 5 | 2014-11-29T21:17:54Z | 2015-05-09T15:58:22Z | 2015-05-09T15:58:22Z | CONTRIBUTOR | null | Hi,
This isn't ready for primetime yet, but I wanted to see if there was feedback, since it would be a new io module.
Basic usage:
```
import pandas.io.gcs as gcs
import pandas as pd
import numpy as np
df = pd.DataFrame({
'a': np.random.normal(size=1e4),
'b': np.random.normal(size=1e4)
})
gcs.to_gcs(df, "t... | {
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```
In [1]: a = pd.Series(['a','b','c'],dtype="category")
In [2]: b = pd.Series(['a','b','c'],dtype="object")
In [3]: c = pd.Series(['a','b','cc'],dtype="object")
In [5]: a==b
TypeError: Cannot compare a Categorical ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8940 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8940/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8940/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8940/events | https://github.com/pandas-dev/pandas/pull/8940 | 50,456,666 | MDExOlB1bGxSZXF1ZXN0MjUyMzQyOTY= | 8,940 | BUG: Fixed font size (set it on both x and y axis). #8765 | {
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The font size was only set on x axis if vertical or the y axis if horizontal.
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https://api.github.com/repos/pandas-dev/pandas/issues/8941 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8941/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8941/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8941/events | https://github.com/pandas-dev/pandas/pull/8941 | 50,457,958 | MDExOlB1bGxSZXF1ZXN0MjUyMzQ3OTc= | 8,941 | BUG: Resample across multiple days | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2014-11-30T13:33:57Z | 2014-11-30T23:21:01Z | 2014-11-30T23:21:01Z | CONTRIBUTOR | null | Fixes an issue where resampling over multiple days causes a ValueError
when a number of days between the normalized first and normalized last
days is not a multiple of the frequency.
Added test TestSeries.test_resample
Closes #8683
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https://api.github.com/repos/pandas-dev/pandas/issues/8943 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8943/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8943/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8943/events | https://github.com/pandas-dev/pandas/issues/8943 | 50,459,106 | MDU6SXNzdWU1MDQ1OTEwNg== | 8,943 | COMPAT: periods needs coercion to np.int64 in _generate_regular_range | {
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a simple `np.int64(periods)` will suffice
(and tests of course)
This needs to guaranteed to be int64 as a result (e.g. if this is in32 it will have an internal overflow which will make it have a valid but weird result)
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Test code:
```
df = pd.DataFrame({'animal': 'cat dog cat fish dog cat cat'.split(),
'size': list('SSMMMLL'),
'weight': [8, 10, 11, 1, 20, 12, 12],
'adult' : [False] * ... | {
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tests that need skipping on matplotlib 1.2.1
```
=====================================================================
ERROR: test_hist_df_coord (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
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https://api.github.com/repos/pandas-dev/pandas/issues/8948 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8948/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8948/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8948/events | https://github.com/pandas-dev/pandas/pull/8948 | 50,473,041 | MDExOlB1bGxSZXF1ZXN0MjUyNDA2Mzc= | 8,948 | BUG: preserve left frame order in left merge | {
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on master:
```
>>> left
dates states
0 20140101 CA
1 20140102 NY
2 20140103 CA
>>> right
stateid states
0 1 CA
1 2 NY
>>> pd.merge(left, right, how='left', on='states', sort=False)
dates states stateid
0 20140101... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8949 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8949/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8949/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8949/events | https://github.com/pandas-dev/pandas/pull/8949 | 50,474,326 | MDExOlB1bGxSZXF1ZXN0MjUyNDExNzk= | 8,949 | BLD/TST: skip mpl 1.2.1 tests (GH8947) | {
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add SLOW build (optional)
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I deleted the previous branch that was causing problems and submitted this.
This is my first attempt at a PR. I just made a little change to allow an error message. Would appreciate some feedback.
@jreback @jorisvandenbossche @hayd
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As far as I can tell, the Timestamps for the index generated by `iterrows()` are 5 hours behind where they should be in this example:
``` python
In [33]: idx = pd.date_range("2010-01-01 00:00:00-0500", freq='D', periods=3)
In [34]: df = pd.DataFrame([1,2,3], index=[idx])
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"followers_url": "https://api.github... | 48 | 2014-12-01T18:29:49Z | 2019-11-28T22:14:34Z | 2018-12-28T21:03:13Z | CONTRIBUTOR | null | I have been trying to insert ~30k rows into a mysql database using pandas-0.15.1, oursql-0.9.3.1 and sqlalchemy-0.9.4. Because the machine is as across the atlantic from me, calling `data.to_sql` was taking >1 hr to insert the data. On inspecting with wireshark, the issue is that it is sending an insert for every row, ... | {
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reports a problem with the application of max_colwidth to groupby.
The real problem is that max_colwidth is not applied to the index. This patch solves that.
I am not applying self.justify to the formatting of the index (as that would break a test). But maybe it makes more sense to change the test?
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... | 5 | 2014-12-01T22:06:13Z | 2021-01-10T14:53:04Z | null | CONTRIBUTOR | null | In a groupby/transform when some of the groups are missing, should the transformed values be set to missing (my preference), left unchanged, or should this be an error? Currently the behavior is inconsistent between Series and Frames, and between cythonized and non-cythonized transformations.
For a Series with a non-c... | {
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```
.iloc is strictly integer position based (from 0 to length-1 of ... | {
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closes #8791
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https://api.github.com/repos/pandas-dev/pandas/issues/8959 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8959/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8959/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8959/events | https://github.com/pandas-dev/pandas/pull/8959 | 50,626,559 | MDExOlB1bGxSZXF1ZXN0MjUzMjY4OTk= | 8,959 | Fixes #8933 simple renaming | {
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Refactored _is_sequence to is_sequence
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https://api.github.com/repos/pandas-dev/pandas/issues/8960 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8960/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8960/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8960/events | https://github.com/pandas-dev/pandas/issues/8960 | 50,628,908 | MDU6SXNzdWU1MDYyODkwOA== | 8,960 | bug? with `delim_whitespace` and 'skiprows' in `read_csv` | {
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] | closed | false | null | [] | null | 4 | 2014-12-02T09:29:11Z | 2014-12-03T02:16:30Z | 2014-12-03T02:16:30Z | NONE | null | Hi all,
I've updated to version 0.15.1 from 0.14.x (Python3.4) and the following code fails in the latest version if the blank line (third line) in `data` has a tab or whitespace (I think it should be ignored when `skiprows` is used):
``` Python
import io
import pandas as pd
data = """Lat=0 Lon=0
Whatever - Not ve... | {
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This was mentioned recently here https://github.com/pydata/pandas/issues/8842#issuecomment-63456052 and https://github.com/pydata/pandas/pull/8631#issuecomment-61805926
Some reasons ... | {
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```
def my_joiner(left,right):
left.a = right.a if left.a == NaN else left.a
left.b = right.b if left.cnt < 3 else left.b
...
return left
pd.merge(df1,df2,... | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-12-02T11:24:25Z | 2015-04-04T18:51:44Z | 2015-04-04T18:51:44Z | CONTRIBUTOR | null | this is the schema from astropy
https://github.com/astropy/astropy/wiki/Issue-labeling-convention
Numpy/scipy are considering this
might make issue navigation easier
making this issue for comments on these labels (I am for accepting them almost as is)
---
`package-novice` - accessible to someone who has no knowle... | {
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The bug was caused by a couple of things, firstly when specifying only a level, the axis default was None. This meant it failed to create the internal grouper breaking on line 254 of groupby.py:
``` python
ax = obj._get_axis(self.axis)
```
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```
s = pd.Series(pd.date_range('2008-09-15', periods=10, freq='m'))
s.loc[10] = pd.NaT
s.cummin()
ValueError: Could not convert object to NumPy datetime
```
- validate for `cummax` & `timedelta64[ns]`
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https://api.github.com/repos/pandas-dev/pandas/issues/8966 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8966/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8966/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8966/events | https://github.com/pandas-dev/pandas/pull/8966 | 50,647,906 | MDExOlB1bGxSZXF1ZXN0MjUzMzk1NjQ= | 8,966 | BUG: ValueError raised by cummin/cummax when datetime64 Series contains NaT. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8967 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8967/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8967/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8967/events | https://github.com/pandas-dev/pandas/issues/8967 | 50,660,573 | MDU6SXNzdWU1MDY2MDU3Mw== | 8,967 | DataReader with google source returns DataFrame with incorrect index name | {
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```
import pandas.io.data as web
import datetime
symbol = "AAPL"
end = datetime.datetime.now()
start = end - datetime.timedelta(days=10)
df = web.DataReader("AAPL", 'google', start, end)
df.index.name
```
is `'\xef\xbb\xbfDate'` it should be only `'Date'`
\xef\xbb\xbf characters are invisible !
A quick and... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8968 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8968/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8968/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8968/events | https://github.com/pandas-dev/pandas/issues/8968 | 50,667,017 | MDU6SXNzdWU1MDY2NzAxNw== | 8,968 | test_missing_value_conversion on ubuntu 13.10 32bit KeyError: 2147483647 | {
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======================================================================
ERROR: test_missing_value_conversion (pandas.io.tests.test_stata.TestStata)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.15.1+git125-ge463818/debian/tmp/us... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8969 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8969/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8969/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8969/events | https://github.com/pandas-dev/pandas/issues/8969 | 50,675,794 | MDU6SXNzdWU1MDY3NTc5NA== | 8,969 | to_stata always stores strings as str244 | {
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I am still getting this bug (pandas 0.15.1, numpy 1.9.1, Stata 13.1 on Windows 7). The written DTA file still stores the strings as str244 even though the strings themselves have length 1. This also means that when they're read back into pandas the DataFrame looks the same. I think the only way to d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8970 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8970/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8970/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8970/events | https://github.com/pandas-dev/pandas/pull/8970 | 50,703,239 | MDExOlB1bGxSZXF1ZXN0MjUzNjY2Njg= | 8,970 | docfix: add boolean array as option for indexing with .iloc | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8971 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8971/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8971/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8971/events | https://github.com/pandas-dev/pandas/pull/8971 | 50,716,415 | MDExOlB1bGxSZXF1ZXN0MjUzNzI1NTk= | 8,971 | PERF: astype(str) on object dtypes GH8732 | {
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"default"... | closed | false | null | [] | null | 5 | 2014-12-02T20:41:29Z | 2015-05-14T15:49:37Z | 2015-05-09T16:17:22Z | NONE | null | Closes #8732
In most cases it looks like, we need to iterate over array
and coerce each element. This is so that the appropriate
exception can be raised, or we can deal with nulls.
So the original case of casting ints to strings, has to
work the way it does, unless we change the underlying behaviour.
So when astype(st... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8972 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8972/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8972/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8972/events | https://github.com/pandas-dev/pandas/pull/8972 | 50,749,193 | MDExOlB1bGxSZXF1ZXN0MjUzODU2MjM= | 8,972 | Implement Categorical.searchsorted(v, side, sorter) #8420 | {
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Continuation of #8928
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https://api.github.com/repos/pandas-dev/pandas/issues/8973 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8973/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8973/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8973/events | https://github.com/pandas-dev/pandas/pull/8973 | 50,759,457 | MDExOlB1bGxSZXF1ZXN0MjUzODkzNjY= | 8,973 | ENH: Infer dtype from non-nulls when pushing to SQL | {
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"followers_url": "https://api.github.com/users/jr... | 16 | 2014-12-03T01:21:26Z | 2014-12-08T07:42:42Z | 2014-12-08T07:42:33Z | CONTRIBUTOR | null | Closes #8778
This infers dtype from non-null values for insertion into SQL database. See #8778 . I had to alter @tiagoantao test a bit.
Like @tiagoantao, I skipped writing tests for legacy MySQL. Support for this will be removed soon, right?
As a side note, `lib.infer_dtype` throws an exception for categorical da... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8974 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8974/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8974/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8974/events | https://github.com/pandas-dev/pandas/issues/8974 | 50,763,004 | MDU6SXNzdWU1MDc2MzAwNA== | 8,974 | BUG: lib.infer_dtype croaking on Categoricals | {
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In [13]: df = pd.DataFrame({'a':pd.Series(['A','B'], dtype='category')})
In [15]: lib.infer_dtype(df['a'])
TypeError: Cannot convert Categorical to numpy.ndarray
```
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... | 5 | 2014-12-03T02:45:20Z | 2020-02-11T19:00:35Z | 2020-02-11T19:00:34Z | CONTRIBUTOR | null | xref #8956
This should raise a `ValueError`.
```
In [18]: s = Series(np.arange(5,1,-1))
In [19]: s
Out[19]:
0 5
1 4
2 3
3 2
dtype: int64
In [20]: s.iloc[[True,False,True]]
Out[20]:
0 5
2 3
dtype: int64
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8977 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8977/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8977/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8977/events | https://github.com/pandas-dev/pandas/pull/8977 | 50,770,719 | MDExOlB1bGxSZXF1ZXN0MjUzOTMyMTU= | 8,977 | BUG: StataWriter uses incorrect string length | {
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a size of 244.
closes #8969
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"followers_url": "https://api.github.com/users/jr... | 4 | 2014-12-03T07:11:34Z | 2015-01-02T16:29:45Z | 2015-01-02T16:29:45Z | CONTRIBUTOR | null | Hi All!
I've been using .nth(), and found a couple issues (though it's extremely useful in general!). In particular, I couldn't find any help documentation (in python) for the function.
Also, I ran into issues with I had a two-column groupby. for example:
```
df = pd.DataFrame([[1,2,3],[1,5,6], [7,8,6]], columns =... | {
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Google Finance DataReader returns columns with object type instead of float64
```
In [112]: import pandas.io.data as web
In [113]: import datetime
In [114]: start = datetime.datetime(2010, 1, 1)
In [115]: end = datetime.datetime(2013, 1, 27)
In [116]: f=web.DataReader("F", 'google', start, end)
In [117]: ... | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-12-03T12:40:22Z | 2014-12-04T02:48:20Z | 2014-12-04T02:48:19Z | CONTRIBUTOR | null | closes #8865
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"followers_url": "https://api.github.com/users/jr... | 7 | 2014-12-03T12:40:22Z | 2015-01-18T22:42:16Z | 2014-12-06T15:10:53Z | CONTRIBUTOR | null | Force conversion to integer for missing values when
they must be integer to avoid hash errors on 32 bit
platforms.
closes #8968
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data = """A B C \nD E F \nH I J \n1 2 3 \n4 5 6 \n"""
pd.read_csv(StringIO(data), skiprows=2, delim_whitespace=True) and
pd.read_csv(StringIO(data), skiprows=[0,1], delim_whitespace=True) work a... | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2014-12-03T18:18:24Z | 2014-12-03T23:55:42Z | 2014-12-03T21:56:45Z | CONTRIBUTOR | null | Update tokenizer.c to fix a BUG in read_csv skipping rows after tokenizing a row with trailing spaces, Closes #8983
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I have a CSV whose lines may have 11 or 18 fields. I only need to read the first 6 fields, so I use "usecols=range(6)". Even with the limited number of columns, I get the exception:
ValueError: Expected 11 fields in line 776483, saw 18
The csv_reader should completely disregard the unused fields.
Small t... | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2014-12-03T19:47:26Z | 2015-01-26T00:04:38Z | 2015-01-26T00:04:28Z | CONTRIBUTOR | null | Working with the SQL code, I realized that the legacy code does not properly validate / escape passed in table and column names.
E.g.:
```
n [116]:
import pandas as pd
import pandas.io.sql as sql
import sqlite3
df = pd.DataFrame({'a':[1,2],'b':[2,3]})
con = sqlite3.connect(':memory:')
db = sql.SQLiteDatabase(con, '... | {
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Is this all we need to do?
Or am I missing something?
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In the format string, `%f` means microseconds (https://docs.python.org/2/library/datetime.html#strftime-and-strptime-behavior), but this means the nanoseconds cannot be read in using a `format` string:
```
In [11]: pd.to_datetime("2012-01-01 09:00:00.000000001", format="%Y-%m-%d %H:%M:%S.%f")
->
ValueErro... | {
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"... | closed | false | null | [] | {
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``` python
import pandas as pd
from io import StringIO
csv = """date,time,value
YYYY/MM/DD,HH:MM,Smp
2014/12/01,00:00,1
2014/12/01,01:00,2"""
pd.read_csv(StringIO(csv), header=[0,1], parse_dates=[['d... | {
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"descript... | closed | false | null | [] | null | 4 | 2014-12-04T09:09:00Z | 2014-12-07T15:36:32Z | 2014-12-07T15:36:32Z | NONE | null | Hello
i create a new store and i am trying to append it.
all is right when i did :
store.append("table", dataframe)
but when i did the whole thing with data_columns=dataframe.columns or data_columns=True
i can write the first append and then nothing ....
no more data input
tore.append("table", dataframe,data_colu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8993 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8993/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8993/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8993/events | https://github.com/pandas-dev/pandas/issues/8993 | 50,954,346 | MDU6SXNzdWU1MDk1NDM0Ng== | 8,993 | DOC: add Categorical sorting example | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-12-04T10:02:45Z | 2014-12-04T11:02:41Z | 2014-12-04T11:02:32Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/27284460/how-to-sort-categorical-data-in-a-pandas-dataframe?noredirect=1#comment43038362_27284460
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-12-04T12:13:52Z | 2014-12-07T15:15:23Z | 2014-12-07T15:15:23Z | CONTRIBUTOR | null | A student of mine ran into a confusing problem which ended up being due to an asymmetry in `DataFrame.__setattr__` and `DataFrame.__getattr__` when an attribute and a column have the same name. Here is a short example session:
``` python
import pandas as pd
print(pd.__version__) # '0.14.1'
data = pd.DataFrame({'x':[... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8995 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8995/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8995/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8995/events | https://github.com/pandas-dev/pandas/issues/8995 | 50,975,321 | MDU6SXNzdWU1MDk3NTMyMQ== | 8,995 | API: comparisons of categorical data and (scalar or list-like) | {
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... | 3 | 2014-12-04T13:31:48Z | 2020-09-27T11:52:29Z | 2020-09-27T11:52:29Z | CONTRIBUTOR | null | From #8946:
If `cat > scalar` is allowed and `cat == list` also because it basically is doing a comparison of each line as if it was the scalar case, then by that logic, `cat > list` should also be allowed: each row in that comparison would treat the element from the list as a scalar.
On the other hand a scalar comp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8996 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8996/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8996/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8996/events | https://github.com/pandas-dev/pandas/issues/8996 | 50,994,040 | MDU6SXNzdWU1MDk5NDA0MA== | 8,996 | test_to_records fails on BE boxes: (0L, 'a') != (0L, 'a') | {
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"followers_url": "https://api.github.com/users/jr... | 8 | 2014-12-04T16:11:53Z | 2015-06-27T01:51:57Z | 2015-06-25T13:45:32Z | CONTRIBUTOR | null | see e.g. https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=sparc&ver=0.15.1%2Bgit125-ge463818-1&stamp=1417703234&file=log
```
======================================================================
FAIL: test_to_records (pandas.tests.test_categorical.TestCategoricalAsBlock)
------------------------------------... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8997 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8997/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8997/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8997/events | https://github.com/pandas-dev/pandas/issues/8997 | 50,994,382 | MDU6SXNzdWU1MDk5NDM4Mg== | 8,997 | TestExpressions.test_where fails on sparc | {
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======================================================================
FAIL: test_where (pandas.tests.test_expressions.TestExpressions)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«BUILDDIR»/pandas-0.15.1+git125-ge463818/debian/tmp/usr/lib/pytho... | {
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"descript... | closed | false | null | [] | null | 2 | 2014-12-04T16:21:50Z | 2014-12-04T23:32:34Z | 2014-12-04T23:32:23Z | CONTRIBUTOR | null | The following behavior surprised me. Of course there might be a good reason for it. Thought I'd report, feel free to close if it's not appropriate.
``` Python
In [1]: from pandas import DataFrame, Series
In [2]: s = Series([1, 2, 3], name='foo')
In [3]: DataFrame(s, columns=['bar'])
Out[3]:
Empty DataFrame
Column... | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2014-12-04T17:27:03Z | 2015-10-09T13:15:29Z | 2015-10-09T13:15:29Z | CONTRIBUTOR | null | ``` python
pd.DataFrame({"A": [1]}).squeeze() # -> 1
pd.DataFrame({"A": []}).squeeze() # Noop
```
The second one is genuinely surprising; I would have expected it to return an empty Series with the name set to `A`. But it is consisent with numpy (`np.array([[], []]).squeeze()`) so I've marked is as a doc issue.
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In 0.15.1, it returns the time in UTC but tagged with xxx time zone.
Example:
In version 0.12.0:
> > > import pandas
> > > pandas.version.version
> > > '0.12.0'
> > > pandas.Timestamp('now', tz='US/Central')
> > ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9001 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9001/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9001/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9001/events | https://github.com/pandas-dev/pandas/issues/9001 | 51,028,074 | MDU6SXNzdWU1MTAyODA3NA== | 9,001 | concat sorts columns without preserving order | {
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"description": "Concat, Merge/Join, Stac... | closed | false | null | [] | {
"closed_at": "2015-03-23T10:50:37Z",
"closed_issues": 400,
"created_at": "2014-02-14T03:31:22Z",
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"events_url": "https://api.github.com/users/jreback/events{/privacy}",
"followers_url": "https://api.github.com/users/jr... | 1 | 2014-12-04T20:57:24Z | 2014-12-04T23:22:45Z | 2014-12-04T23:22:29Z | NONE | null | Although it's _my_ desired behavior, I realize perhaps this is not _the_ desired behavior, but I think it's reasonable to bring up.
If I want to concat two data frames and their column names are the same, the order is preserved:
```
>>> df1 = pd.DataFrame(columns=["A","1"])
>>> pd.concat([df1,df1])
Empty DataFrame
Co... | {
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