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``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.normal(size=(10000,4)))
df.index = pd.timedelta_range(start='0s', periods=10000, freq='3906250n')
df.loc['1s':,:].resample('3s',how=lambda x : len(x))
```
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How do you feel about direct URLs in the doctrings?
I saw them in a few other docstrings. Pandas maintainers, any preferences?
Still need to fix the Examples at the bottom of the page.
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``` py
import pandas as pd
str_series = pd.Series(['f', 'f', 't', 't', 't'])
bool_se... | {
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] | closed | false | null | [] | null | 15 | 2015-07-17T15:12:53Z | 2017-11-13T14:16:36Z | 2017-11-12T11:59:45Z | NONE | null | I was using Pandas version 0.16.1 on a Red Hat Enterprise Linux Server release 6.6 system and it was working fine but I wanted to update Pandas to version 0.16.2 so I uninstalled it and reinstalled it using
```
pip uninstall pandas
pip install pandas
```
The installation seemed to work fine. However, when I attempt t... | {
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Though it is not a bug, added it to the bug section because users likely to check it when they meet 404.
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``` python
from pandas.core.indexing import _maybe_convert_indices
return _maybe_convert_indices(indexer, len(self))
```
In pandas.core.indexing.py, the function is called `maybe_convert_indices`. In the 3 other places where the function is called, i... | {
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I think this happens specifically for pandas scatter plots with colorbars in ipython. The xticks are still working for:
- non-colorbar pandas scatter plots
- the same scatter plot using matplotlib
- standard python scripts using `plt.savefig`
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"... | closed | false | null | [] | null | 1 | 2015-07-17T19:57:13Z | 2015-07-17T19:59:11Z | 2015-07-17T19:59:02Z | NONE | null | If you try to read a csv file into a dataframe, and the csv file has just the header and no rows, it throws 'object of type 'bool' has no len()'. This issue does not seem to exist in 0.16.1.
The sample csv file is blank:
id,strategyId,symbol,side,target,time,reason,epochTime
Code that causes the issue:
```
import pa... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10613/events | https://github.com/pandas-dev/pandas/pull/10613 | 95,766,909 | MDExOlB1bGxSZXF1ZXN0NDAyODE0NjU= | 10,613 | ENH: add StataReader context manager to ensure closing of the path | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10614/events | https://github.com/pandas-dev/pandas/pull/10614 | 95,788,044 | MDExOlB1bGxSZXF1ZXN0NDAyODY2MzI= | 10,614 | Extended docs on numba | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10615 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10615/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10615/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10615/events | https://github.com/pandas-dev/pandas/pull/10615 | 95,844,311 | MDExOlB1bGxSZXF1ZXN0NDAyOTU1OTA= | 10,615 | PERF: Improve perf of to_datetime with ISO format | {
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Closes #8154
Using the example data from the issue:
```
In [2]: df = DataFrame({'date_text':["2015-05-18" for i in range(10**6)]})
```
Before:
```
In [3]: %timeit pd.to_datetime(df['date_text'],format="%Y-%m-%d", box=False).values.view('i8')/10**9
1 loops, best of 3: 3.14 s per loop
In [4]: %timeit p... | {
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The problem is that not all columns are getting set ... | {
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this allows for the use of temporary tables.
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"followers_url": "https://api.github.com/users/jr... | 21 | 2015-07-19T01:46:06Z | 2015-07-28T14:47:17Z | 2015-07-28T10:05:34Z | CONTRIBUTOR | null | Current behavior:
``` python
In [32]:
period_index=pd.PeriodIndex(start='2015-01-01',end='2015-01-07',freq='B')
period_index
Out[32]:
PeriodIndex(['2015-01-01', '2015-01-02', '2015-01-05', '2015-01-06',
'2015-01-07'],
dtype='int64', freq='B')
In [33]:
period_index.drop(period_index[:2])
Out[3... | {
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Basically, I want to use pandas to join two dataframes wherein the index levels of one are a subset of the index levels of the other. For example, the two DFs below ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10622 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10622/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10622/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10622/events | https://github.com/pandas-dev/pandas/pull/10622 | 95,880,111 | MDExOlB1bGxSZXF1ZXN0NDAzMDIzNzM= | 10,622 | Very minor refactoring and edits to code comments | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10623 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10623/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10623/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10623/events | https://github.com/pandas-dev/pandas/issues/10623 | 95,885,804 | MDU6SXNzdWU5NTg4NTgwNA== | 10,623 | DEPR: msgpack encode 'items' | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-07-19T04:45:58Z | 2016-02-16T14:50:50Z | 2016-02-16T14:50:50Z | CONTRIBUTOR | null | At some point deprecate `items` in `msgpack.encode()` for blocks. xref #10527
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https://api.github.com/repos/pandas-dev/pandas/issues/10624 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10624/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10624/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10624/events | https://github.com/pandas-dev/pandas/issues/10624 | 95,889,794 | MDU6SXNzdWU5NTg4OTc5NA== | 10,624 | Mismatched behavior of sorting functions for `DataFrame` vs. `Series` | {
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DF:
- [`DataFrame.sort_index`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_index.html) takes a `by` parameter, in which case the index is resorted by a specific column's values
- [`DataFrame.sort`](http://pandas.pydata.org/pandas-doc... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10625/events | https://github.com/pandas-dev/pandas/pull/10625 | 95,892,438 | MDExOlB1bGxSZXF1ZXN0NDAzMDQxNTU= | 10,625 | BUG: Fix issue with incorrect groupby handling of NaT | {
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For groupby the time stamps gets converted to integervalue `tslib.iNaT`
which is -9223372036854775808. The aggregation is then done using this
value with incorrect result as a consequence. The solution proposed here
is to replace its value by np.nan in case it is a `datetime64[ns]`
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- [x] #667 other dtypes PR #13849
- [x] #10818 `from_coo` issues (same as #10560)
- [ ] #10079 empty construction
- [x] #10560 `__repr__` broken, PR #12779
- [x] #10536 concat `SparseSeries`, PR #12844
- [x] #12174 concat `SparseDataFrame`? PR #12966
- [x] #4400 more tes... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10628 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10628/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10628/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10628/events | https://github.com/pandas-dev/pandas/pull/10628 | 95,903,800 | MDExOlB1bGxSZXF1ZXN0NDAzMDYxNTY= | 10,628 | DOC Add more examples to Series.Resample docstring | {
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flake8 doesn't do a very good job at checking code in the docstrings for PEP8 compliance, so I'm just eyeballing it at the moment and my PEP8 knowledge leaves something to be desired. Apologies if I missed something really obvious.
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https://api.github.com/repos/pandas-dev/pandas/issues/10629 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10629/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10629/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10629/events | https://github.com/pandas-dev/pandas/issues/10629 | 95,924,971 | MDU6SXNzdWU5NTkyNDk3MQ== | 10,629 | BUG: crosstab not working for integer types as columns | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2015-07-19T15:38:19Z | 2015-08-13T13:37:26Z | 2015-08-13T13:37:16Z | MEMBER | null | From the mailing list: https://groups.google.com/forum/?fromgroups#!topic/pydata/fpYfwkZvdxI
```
In [37]: tips = pd.read_csv('https://raw.github.com/pydata/pandas/master/pandas/tests/data/tips.csv')
In [38]: pd.crosstab(tips.day, tips.size)
Out[38]:
col_0 1708
day
Fri 19
Sat 87
Sun 76
Thur 62
In ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10630 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10630/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10630/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10630/events | https://github.com/pandas-dev/pandas/pull/10630 | 95,946,173 | MDExOlB1bGxSZXF1ZXN0NDAzMTQ0MjY= | 10,630 | TST: windows compat for testing / msgpack | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-07-19T20:26:50Z | 2015-07-21T12:39:44Z | 2015-07-21T12:39:44Z | CONTRIBUTOR | null | - skipping a couple of parsing routines that cause odd test failures on windows
- msgpack compat for windows (not tested before, prob not working correctly). Slight internal API change where writing the `dtype.name` rather than `dtype.num`, but back-compat is ok on this. (and forward compat is better).
- skipping a bug... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10631 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10631/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10631/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10631/events | https://github.com/pandas-dev/pandas/issues/10631 | 95,948,077 | MDU6SXNzdWU5NTk0ODA3Nw== | 10,631 | TST/BUG: some buggy windows tests | {
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... | 5 | 2015-07-19T20:46:27Z | 2019-10-07T01:13:10Z | 2019-10-07T01:13:10Z | CONTRIBUTOR | null | xref #10630
- [x] csv parsing then turning into a recarray causing segfaults, only when run with the entire parsing tests (e.g. `test_parsing.py` (2 tests) (#13373)
- [ ] in `test_libsparse`, the test_intersect is segfaulting, not really sure why.
- [ ] in `test_ujson` with small exponentials, only on win-64 with 2.7.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10632 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10632/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10632/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10632/events | https://github.com/pandas-dev/pandas/pull/10632 | 95,950,567 | MDExOlB1bGxSZXF1ZXN0NDAzMTU0MTU= | 10,632 | Remove Categorical.name | {
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"followers_url": "https://api.github.com/users/jr... | 9 | 2015-07-19T21:29:41Z | 2015-07-26T01:54:28Z | 2015-07-26T01:54:21Z | CONTRIBUTOR | null | Probably work in progress... Up to now only tests locally with 'not slow' (whoever wrote `nosetests --with-id --failed` should be praised! :-) )
See here: https://github.com/pydata/pandas/issues/10482
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https://api.github.com/repos/pandas-dev/pandas/issues/10633 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10633/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10633/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10633/events | https://github.com/pandas-dev/pandas/issues/10633 | 95,955,385 | MDU6SXNzdWU5NTk1NTM4NQ== | 10,633 | pd.Series.interpolate(method='spline') Error | {
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```
import numpy as np
import pandas as pd
np.random.seed(1)
s = pd.Series(np.arange(10)**2)
s[np.random.randint(0,9,3)] = np.nan
s.interpolate(method='spline')
# raise ValueError
ValueError: invalid method 'spline' to interpolate.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10634 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10634/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10634/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10634/events | https://github.com/pandas-dev/pandas/issues/10634 | 95,993,238 | MDU6SXNzdWU5NTk5MzIzOA== | 10,634 | Broke pdf and zip links at top of page. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10635/events | https://github.com/pandas-dev/pandas/issues/10635 | 96,024,503 | MDU6SXNzdWU5NjAyNDUwMw== | 10,635 | BUG: combine_first fails for empty Index and a MultiIndex | {
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"color": "AD7FA8"... | closed | false | null | [] | null | 2 | 2015-07-20T09:32:16Z | 2015-07-20T13:39:04Z | 2015-07-20T13:39:04Z | CONTRIBUTOR | null | Using `combine_first` on an empty frame with a single-column index fails if the other frame has a multi-index and is not empty.
This used to work for pandas version 0.14.1
``` python
import pandas as pd
midx = pd.MultiIndex.from_arrays([[-1], [1]])
a = pd.DataFrame({'a': [1]}, index=midx)
print a
b = pd.DataFrame(... | {
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xref #10615
xref #10154
The idea is that if you have completely unparseable dates with or w/o a format specified, then the default will be to raise. Further, since `coerce` is by default `False`. You will get an immediate exception rather than have strings returned.
I think some tests will need to be adj... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10637 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10637/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10637/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10637/events | https://github.com/pandas-dev/pandas/pull/10637 | 96,060,261 | MDExOlB1bGxSZXF1ZXN0NDAzNDk4NDY= | 10,637 | BUG: made behavior of operator equal for CategoricalIndex consistent,… | {
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this is a follow-up to https://github.com/pydata/pandas/pull/9947
@jreback
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https://api.github.com/repos/pandas-dev/pandas/issues/10638 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10638/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10638/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10638/events | https://github.com/pandas-dev/pandas/pull/10638 | 96,061,327 | MDExOlB1bGxSZXF1ZXN0NDAzNTAzOTQ= | 10,638 | CLN: Make ufunc works for Index | {
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I understand these are never used because `Index` is no longer the subclass of `np.array`. Correct?
- [x] Add tests for ~~all~~ almost ufuncs
- http://docs.scipy.org/doc/numpy/reference/ufuncs.html
- ~~CategoricalIndex~~: Needs to be done separately to fix `Categorical`, because number of ca... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10639 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10639/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10639/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10639/events | https://github.com/pandas-dev/pandas/pull/10639 | 96,062,224 | MDExOlB1bGxSZXF1ZXN0NDAzNTA4MDg= | 10,639 | ENH: allow duplicate column names if they are not merged upon | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-07-20T13:20:42Z | 2015-07-28T10:08:42Z | 2015-07-28T10:08:42Z | CONTRIBUTOR | null | Currently merge columns are not automatically inferred when there are duplicate column names. Instead a `MergeError` is raised. This pull request enables merging with duplicate column names as long as they are not merged upon.
```
df = DataFrame({'key': [1, 2, 3],
'v1': [4, 5, 6],
'v2':... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10640 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10640/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10640/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10640/events | https://github.com/pandas-dev/pandas/issues/10640 | 96,091,596 | MDU6SXNzdWU5NjA5MTU5Ng== | 10,640 | When transposing a datetime index without time and using to_html, time is added to column titles. | {
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... | 2 | 2015-07-20T15:41:51Z | 2021-04-18T22:30:19Z | null | NONE | null | As outlined in my [stackoverflow question](http://stackoverflow.com/questions/31520033/how-to-stop-pandas-adding-time-to-column-title-after-transposing-a-datetime-inde), pandas `to_html()` doesn't behave consistently with a datetime index after being transposed.
A simple example:
```
import pandas as pd
import numpy... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10641 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10641/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10641/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10641/events | https://github.com/pandas-dev/pandas/issues/10641 | 96,142,610 | MDU6SXNzdWU5NjE0MjYxMA== | 10,641 | Loading large STATA file throws "OSError: [Errno 22] Invalid argument" | {
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] | closed | false | null | [] | null | 7 | 2015-07-20T19:41:55Z | 2018-09-05T16:27:45Z | 2015-07-20T21:54:30Z | CONTRIBUTOR | null | Hello, this error happens when loading a large (~3.3GB) stata 13 file:
``` python
/Users/makmana/colombia/colombia/datasets.py in <lambda>()
111
112 industry4digit_department = {
--> 113 "read_function": lambda: pd.read_stata("/Users/makmana/ciddata/Subnationals/Atlas/Colombia/beta/output2008_2013.dta"),
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10642 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10642/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10642/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10642/events | https://github.com/pandas-dev/pandas/pull/10642 | 96,237,559 | MDExOlB1bGxSZXF1ZXN0NDA0MjU1NDk= | 10,642 | BUG: Fixed typo-related bug to resolve #9266 | {
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"... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-07-21T06:13:46Z | 2015-07-21T17:33:46Z | 2015-07-21T10:53:01Z | CONTRIBUTOR | null | Resubmitting pull request #10576 to resolve issue #9266 .
@jreback — This should be good to merge.
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https://api.github.com/repos/pandas-dev/pandas/issues/10644 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10644/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10644/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10644/events | https://github.com/pandas-dev/pandas/pull/10644 | 96,248,884 | MDExOlB1bGxSZXF1ZXN0NDA0MjkzMjk= | 10,644 | BUG: (GH10408, GH10412) in vectorised setting of timestamp columns | {
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closes #10412
Fix setting values with python datetime.date and numpy datetime64.
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mi = pd.MultiIndex.from_arrays([range(100),range(100)])
(1000001,0) in mi
# For a large multiindex the in operator throws an IndexError
mi = pd.MultiIndex.from_arrays([range(1000000),range(1000000)])
(1000001,0) in mi
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```
import numpy as np
import pandas as pd
df = ... | {
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"followers_url": "https://api.github.com/users/jorisva... | 14 | 2015-07-21T17:56:27Z | 2017-11-29T08:01:48Z | 2017-03-13T14:36:26Z | NONE | null | I was surprised to learn that default behavior for reading in a CSV like this:
```
%more foo.txt
x,y
"NA",NA
"foo",foo
```
is the following:
```
In [56]: pd.read_csv('foo.txt')
Out[56]:
x y
0 NaN NaN
1 foo foo
```
I realize this can be changed by using `pd.read_csv('foo.txt', keep_default_na=False)`, b... | {
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``` python
In [1]: import pandas as pd
In [2]: a = pd.SparseArray([True, False, False, False, True], fill_value=False, dtype=bool)
In [3]: a
Out[3]:
[True, False, False, False, True]
Fill: False
IntIndex
Indices: array([0, 4], dtype=int32)
In [4]: a.dtype
Out[4]: ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10650 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10650/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10650/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10650/events | https://github.com/pandas-dev/pandas/issues/10650 | 96,464,584 | MDU6SXNzdWU5NjQ2NDU4NA== | 10,650 | Memory error when plotting simple time series | {
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"description": "Du... | closed | false | null | [] | null | 2 | 2015-07-22T03:07:10Z | 2015-07-22T11:30:37Z | 2015-07-22T10:36:31Z | CONTRIBUTOR | null | This crashes:
```
import pandas as pd
index = pd.timedelta_range(start="06:00:01", end="12:00:00", freq='S')
pd.Series(range(index.size), index=index).plot()
```
Traceback at https://gist.github.com/andreabedini/074d529ff63356312ce6
So far I have figured out that `_get_default_locs` gets called with completely bogus... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10651 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10651/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10651/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10651/events | https://github.com/pandas-dev/pandas/issues/10651 | 96,509,289 | MDU6SXNzdWU5NjUwOTI4OQ== | 10,651 | Confusion around sortedness with MultiIndex | {
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"descript... | closed | false | null | [] | null | 8 | 2015-07-22T08:35:40Z | 2015-07-27T10:10:39Z | 2015-07-22T12:30:18Z | NONE | null | I find the docs around MultiIndex slicers to be quite confusing. It implies the MultiIndex needs to be lexsorted, and introduces the sortlevel() function but then has a caveat that this doesn't actually ensure sortedness.
There's some more details of my explorations and questions on StackOverflow:
http://stackoverflow... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10652 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10652/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10652/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10652/events | https://github.com/pandas-dev/pandas/issues/10652 | 96,549,470 | MDU6SXNzdWU5NjU0OTQ3MA== | 10,652 | google-api-python-client Version Check is broken | {
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"description": "pandas-gbq compatability... | closed | false | null | [] | null | 1 | 2015-07-22T11:50:00Z | 2015-09-13T20:20:52Z | 2015-09-13T20:20:52Z | NONE | null | Hi All, I cannot use io.gbq due to an issue with the version check of the installed google library.
```
>>> pkg_resources.get_distribution('google-api-python-client').version
'1.2'
```
The [offending line](https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L29) executes the version check as follows:
```
>... | {
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"followers_url": "https://api.github.com/users/jr... | 11 | 2015-07-22T14:49:38Z | 2015-08-26T01:55:25Z | 2015-08-26T01:55:25Z | MEMBER | null | From #10636
When parsing an invalid date, there are some possible different actions to take:
- ignore -> just returning the original value unparsed (`errors='ignore'`)
- raise an error (`errors='raise'`)
- coerce / convert to NaN (`coerce=True`)
AFAIK, these three options are just three different options and mutuall... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10654 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10654/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10654/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10654/events | https://github.com/pandas-dev/pandas/issues/10654 | 96,606,353 | MDU6SXNzdWU5NjYwNjM1Mw== | 10,654 | read_sql/to_sql should accept database URI as con parameter | {
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"followers_url": "https://api.github.com/users/jr... | 10 | 2015-07-22T16:14:34Z | 2015-07-27T08:32:22Z | 2015-07-27T08:32:22Z | CONTRIBUTOR | null | Hello,
it will be nice if `read_sql` (and so `read_sql_query` and `read_sql_table` could directly accept database URI as `con` parameter
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_sql.html
So we could do
```
import pandas as pd
uri = 'dialect+driver://username:password@host:port/database'
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... | 5 | 2015-07-22T17:23:09Z | 2015-11-10T01:21:15Z | 2015-11-10T01:21:15Z | NONE | null | Also fixes values and get_values.
fixes #10648
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https://api.github.com/repos/pandas-dev/pandas/issues/10656 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10656/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10656/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10656/events | https://github.com/pandas-dev/pandas/pull/10656 | 96,644,016 | MDExOlB1bGxSZXF1ZXN0NDA2MDEzNzA= | 10,656 | BUG: #10652 google-api-python-client minimum version check | {
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Here's a [short notebook](https://gist.github.com/williamsmj/12a0abb2fb98aec67c17) that rep... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10658 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10658/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10658/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10658/events | https://github.com/pandas-dev/pandas/pull/10658 | 96,677,732 | MDExOlB1bGxSZXF1ZXN0NDA2MTc5MzQ= | 10,658 | BUG: GH9428 promote string dtype to object dtype for empty DataFrame | {
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When constructing an empty DataFrame with a string dtype (e.g. `str`, `np.unicode_`, `U5`), the dtype is now promoted to the `object` dtype. This is now consistent with Series and avoids the confusing behaviour described in the original issue.
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https://api.github.com/repos/pandas-dev/pandas/issues/10659 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10659/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10659/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10659/events | https://github.com/pandas-dev/pandas/pull/10659 | 96,696,871 | MDExOlB1bGxSZXF1ZXN0NDA2MjYxODk= | 10,659 | BUG: #10565 Series.name lost in rolling_* funcions | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10660 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10660/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10660/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10660/events | https://github.com/pandas-dev/pandas/issues/10660 | 96,765,091 | MDU6SXNzdWU5Njc2NTA5MQ== | 10,660 | BUG: to_latex() output broken when the index has a name | {
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I posted this on [SO](https://stackoverflow.com/questions/31582114/groupby-to-latex-output-broken) and was asked to file a report here as well.
I'm trying to export `pandas.DataFrame.describe()` to `LaTex` using the `to_latex()`-method. This works all fine as long as I don't apply the `groupby()`-method b... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10661 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10661/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10661/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10661/events | https://github.com/pandas-dev/pandas/issues/10661 | 96,799,993 | MDU6SXNzdWU5Njc5OTk5Mw== | 10,661 | API: Add str/dt accessors to categorical | {
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```
import pandas as pd
s = pd.Series(['A', 'B'], dtype='category')
s.str
# AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas
s = pd.Series([pd.Timestamp('2011-01-01'), pd.Time... | {
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... | 5 | 2015-07-23T16:13:01Z | 2015-07-24T17:22:08Z | 2015-07-24T17:21:46Z | NONE | null | ### Code and Error description
If I try to normalize a date on daylight saving date day, the normalize_date() can put a Timestamp object into inconsistent state.
``` python
>>> import pandas as pd
>>> original_midnight = pd.Timestamp('20121104', tz='US/Eastern')
>>> original_midday = pd.Timestamp('20121104T120000', t... | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2015-07-24T09:05:20Z | 2015-07-24T09:25:11Z | 2015-07-24T09:25:11Z | CONTRIBUTOR | null | Hello,
This enhancement is quite similar to https://github.com/pydata/pandas/issues/10654
`to_sql` method should accept database URI as con parameter
So we could do
```
import pandas as pd
db_uri = 'dialect+driver://username:password@host:port/database'
query = 'SELECT * FROM table'
df.to_sql("table", db_uri)
```
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-07-24T09:21:46Z | 2015-08-18T10:51:57Z | 2015-08-18T10:51:57Z | CONTRIBUTOR | null | join='left' does a right join and 'right' does a left join
See the notebook for details
https://nbviewer.jupyter.org/gist/gdementen/fa4d388993ee981b5698
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"f... | 2 | 2015-07-24T14:04:36Z | 2019-10-13T23:57:44Z | 2019-10-13T23:57:44Z | MEMBER | null | Simple case of concatting two frames with equal (or at least partially overlapping indices) index seems to be quicker when using merge instead of concat:
```
In [98]: df1 = pd.DataFrame(np.random.randn(10000,2), columns=['a', 'b'])
In [99]: df2 = pd.DataFrame(np.random.randn(10000,2), columns=['c', 'd'])
In [100]: ... | {
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... | 8 | 2015-07-24T20:48:54Z | 2017-02-16T16:16:49Z | 2017-02-16T16:16:48Z | CONTRIBUTOR | null | xref #12898 (same fix)
(c.f. http://stackoverflow.com/questions/31617084/how-to-have-groupby-first-not-remove-timezone-info-from-datetime-columns)
Take a dataframe with a column of tz-aware datetime.datetime objects, and group it by a different column, then return the first row from each group. There are some ways to ... | {
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] | closed | false | null | [] | null | 8 | 2015-07-24T21:00:16Z | 2015-07-27T15:05:27Z | 2015-07-25T13:50:13Z | NONE | null | I have been trying for a while to save a pandas dataframe to an HDF5 file. I tried various different phrasings eg. `df.to_hdf` etc. but to no avail. I am running this in a python virtual environment see [here](http://docs.python-guide.org/en/latest/dev/virtualenvs/). Even without the use of the VE it has the same error... | {
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```
In [1]: import pandas as pd
In [2]: orig_df = pd.DataFrame({'AAA' : [4,5,6,7], '... | {
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``` python
import pandas as pd
from numpy.random import randn
df = pd.DataFrame(randn(8, 4), columns=['A', 'B', 'C', 'D'])
def function(group):
group.sort(columns = 'C', inplace=True)
group = group[['A', 'B', 'C', 'D']]
return group
df2 = df.groupby(['A']).apply(function)
```
produces t... | {
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import pandas as pd
# import dask.dataframe as dd
N = 5000
df = pd.DataFrame({'A': [1, 2] * N, 'B': [3, 4] * N, 'C': ['a', 'b'] * N})
df.to_hdf('foo.h5', '/data', format='table', mode='a')
for _ in range(3):
df.to_hdf('foo.h5', '/data', format='table', mode='a', append=True)
```
This actually works when `N`... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10673 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10673/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10673/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10673/events | https://github.com/pandas-dev/pandas/issues/10673 | 97,247,234 | MDU6SXNzdWU5NzI0NzIzNA== | 10,673 | categorical accessor (and others?) should fail if an unknown attribute is set | {
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In[10]: a = pandas.Series(pandas.Categorical(list("abc")))
In[11]: a.cat.labels = [1,2]
In[12]: a.cat.labels
Traceback (most recent call last):
File "C:\portabel\miniconda\envs\pandas_dev\lib\site-packages\IPython\core\interactiveshell.py", line 2883, in run_code
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changes `to_timedelta` API as well to similar effect.
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https://api.github.com/repos/pandas-dev/pandas/issues/10675 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10675/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10675/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10675/events | https://github.com/pandas-dev/pandas/pull/10675 | 97,322,560 | MDExOlB1bGxSZXF1ZXN0NDA4NTQwMDQ= | 10,675 | BUG: #10645 in using MultiIndex.__contains__ | {
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@sinhrks would you review my PR?
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Many functions indicate (e.g.) "New in version 0.13.0"
The addition of DataFrame.sample() in [AFAIK] 0.16.2, which bit me hard today as my dataframes all have a column "sample" so you can see how this totally hosed up all my df.sample ... | {
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In brief, it seems that there are cases that the sharex option of pandas.DataFrame.plot(kind='hexbin') defaults to True instead of False. This causes the x label and xtick labels to be invisible.
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This PR replaces the sum-of-squares algorithm used to compute the variance by a more stable algorithm. The algorithm here is essentially the same as used in numpy 1.8 and up, and I've added a TODO to replace the implementation with a direct call to numpy when that version is the default.
Somewhat counte... | {
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Further suggestions to add or make it clearer are welcome!
xref #10334
closes #7194
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http://fmwww.bc.edu/ec-p/data/wooldridge2k/401ksubs.dta
produces an error when trying to parse the header. This file is version 105. As far as I know, there are on onlin... | {
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] | closed | false | null | [] | null | 9 | 2015-07-27T08:20:52Z | 2015-07-28T16:37:16Z | 2015-07-28T16:37:16Z | NONE | null | I am using read_html to read an html table which contains timedelta information in various formats, e.g. "20s" or "20 seconds" for 20 seconds, "5 minutes", "12 hours", "4 days". The default parser incorrectly parses these in 1) converting the values to datetimes, with the time added as a timedelta to the start of the c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10685 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10685/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10685/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10685/events | https://github.com/pandas-dev/pandas/issues/10685 | 97,450,112 | MDU6SXNzdWU5NzQ1MDExMg== | 10,685 | Assignment using .ix and None incorrectly assigns NaN | {
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```
import pandas as pd
df=pd.DataFrame(1, index=['A'], columns=['x'])
df.ix[:,"Grouping"]=None
df
```
results in
```
x Grouping
A 1 NaN
```
Alternatively, if I assign a random number instead of the constant 1, I ge... | {
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Prior was in #10599 FYI
I think this has a PyTables error which I need to resolve
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When summing data, NA (missing) values will be treated as zero
=>>> If the data are all NA, the result will be NA <== This returns zero in... | {
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"c... | closed | false | null | [] | null | 2 | 2015-07-28T16:19:14Z | 2015-07-28T16:44:57Z | 2015-07-28T16:40:26Z | CONTRIBUTOR | null | This came up in #10679.
Numpy 1.8 implements `numpy.nanvar`, which can replace `pandas.core.nanops._nanvar` when the minimum requirement for Numpy is bumped to 1.8 . The implementation of `_nanvar` in #10679 is in fact based on the corresponding implementation in Numpy.
I haven't checked whether other nanops can be r... | {
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I am using formatters to control outputs, It seems to be working for int and float but not for datetime.
``` python
datetime_formatter = lambda ts: "%s" % ts.strftime("%I:%... | {
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closes #10420
**Edit: changed approach, see the rest of the thread**
Currently, interpolate() has a "limit" kwarg that, when set to n,
prevents interpolated values from propagating more than n rows forward.
This change adds a "backward_limit" kwarg that, when set to n, prevents
interpolated values from... | {
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`10**6` seems to be the exact treshold for me. That and anything bigger fails. Anything smaller works.
Example:
```
import pandas as pd
#works
X = pd.DataFrame(dict(x=range(10**6-1)))
X.loc[len(X)] = 42
#doesn't work
Y = pd.DataFrame(dic... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10693 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10693/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10693/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10693/events | https://github.com/pandas-dev/pandas/issues/10693 | 97,812,326 | MDU6SXNzdWU5NzgxMjMyNg== | 10,693 | Add doc note on memory usage of read_sql with chunksize | {
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... | 2 | 2015-07-28T22:06:54Z | 2019-10-11T11:16:22Z | null | MEMBER | null | As this typically does not give you much memory usage improvement (which is a bit unexpected from the keyword explanation), this is worth a note in the docs.
From some discussion on gitter: https://gitter.im/pydata/pandas?at=55b61bf952d85d450f404be1 (with @litaotao) and https://gitter.im/pydata/pandas?at=554609295edd8... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10694 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10694/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10694/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10694/events | https://github.com/pandas-dev/pandas/issues/10694 | 97,908,128 | MDU6SXNzdWU5NzkwODEyOA== | 10,694 | BUG: GroupBy.idxmax breaks when grouping by Categorical with unused categories | {
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... | open | false | null | [] | null | 5 | 2015-07-29T10:30:15Z | 2020-07-13T20:12:52Z | null | MEMBER | null | From SO: http://stackoverflow.com/questions/31690493/idxmax-doesnt-work-on-seriesgroupby-that-contains-nan
`idxmax` works with a normal dataframe:
```
In [60]: df = pd.DataFrame({'a':list('abab'), 'b':[0,1,2,3]})
In [61]: df.groupby('a').idxmax()
Out[61]:
b
a
a 2
b 3
```
Also when 'a' is a categorical there ar... | {
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"color": "AD... | closed | false | null | [] | null | 28 | 2015-07-29T11:25:49Z | 2021-06-07T20:37:55Z | 2018-01-18T11:35:49Z | NONE | null | I just upgraded to latest Pandas 0.16, and the new error has hit me (with Pandas 0.15) when slicing with multiple values. (df.ix[[list_of_values]]). I actually think it is more valid to return an empty DataFrame, than to throw an error.
The best I've been able to come up with to reproduce the previous behaviour (fail ... | {
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... | closed | false | null | [] | {
"closed_at": "2018-05-16T03:09:58Z",
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"followers_url": "https://api.github.com/users/j... | 8 | 2015-07-29T12:07:01Z | 2017-12-13T14:35:18Z | 2017-12-13T14:35:18Z | MEMBER | null | `s.astype('category', categories=['a', 'b', 'c'])` fails when the series is already of Categorical dtype:
> TypeError: _astype() got an unexpected keyword argument 'categories'
I am not sure if this _should_ work (it would then be equivalent to `set_categories`?), but in any case the current error message is not inf... | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2015-07-29T12:37:26Z | 2015-07-30T14:36:19Z | 2015-07-30T14:36:19Z | NONE | null | Hello,
I'm using python 2.7.9 and pandas 0.15.2.
I'm trying to concatenate two DataFrames (to_concat is a list of 2 DataFrames) and I have the following error.
```
File "/Users/alexisbenoist/Documents/CoTH/AFDS/app/core.py", line 67, in train_dataflow
df = pd.concat(to_concat)
File "/Users/alexisbenoist/.virt... | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-07-29T17:34:42Z | 2015-09-02T11:54:14Z | 2015-09-02T11:54:14Z | CONTRIBUTOR | null | `pd.concat` nicely preserves name attributes when given a list of series. However, this only works if all series have the name attribute set, otherwise all names are lost:
```
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.16.2'
In [3]: foo = pd.Series([1,2], name='foo')
In [4]: bar = pd.Series([1,2])
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10699 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10699/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10699/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10699/events | https://github.com/pandas-dev/pandas/issues/10699 | 98,017,416 | MDU6SXNzdWU5ODAxNzQxNg== | 10,699 | DateOffsets should allow element-wise addition to Series | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-07-29T19:57:58Z | 2015-08-13T13:08:11Z | 2015-08-13T13:08:11Z | MEMBER | null | Adding a DateOffset works for single Timestamps and for DatetimeIndex, but fails on a Series:
```
In [132]: s = pd.Series(pd.date_range('2012-01-01', periods=3))
In [133]: s
Out[133]:
0 2012-01-01
1 2012-01-02
2 2012-01-03
dtype: datetime64[ns]
In [134]: s[0] + pd.DateOffset(months=2)
Out[134]: Timestamp('2012... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10700 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10700/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10700/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10700/events | https://github.com/pandas-dev/pandas/issues/10700 | 98,104,618 | MDU6SXNzdWU5ODEwNDYxOA== | 10,700 | Default na values list doc is missing the empty string | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-07-30T07:45:49Z | 2015-09-02T08:52:55Z | 2015-09-02T08:52:40Z | NONE | null | Recreating my original post from here: http://stackoverflow.com/questions/26659941/what-are-the-default-na-values-when-pandas-loads-data/31705571#31705571
This documentation http://pandas.pydata.org/pandas-docs/stable/io.html#na-values states:
> The default NaN recognized values are ['-1.#IND', '1.#QNAN', '1.#IND', '... | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-07-30T09:21:07Z | 2015-07-31T07:23:04Z | 2015-07-31T07:23:04Z | MEMBER | null | @jreback I just notices some tests were not using `assert_produces_warning` as a context manager, therefore these would never fail (you could put whatever in there).
I changed it to using `with`, as I don't think there is way to use that function not as a context?
@bashtage I also changed some of your tests. I am not... | {
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Also, delete some trailing whitespaces.
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