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https://api.github.com/repos/pandas-dev/pandas/issues/10203 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10203/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10203/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10203/events | https://github.com/pandas-dev/pandas/issues/10203 | 79,859,046 | MDU6SXNzdWU3OTg1OTA0Ng== | 10,203 | Panel.apply (2d) doesn't return a result of Series in 0.16.1 | {
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y = panel.apply(lambda df: df + 1, axis=[1, 2])
--> Returns Panel. OK.
y = panel.apply(lambda df: df['Column'] + 1, axis=[1, 2])
--> Returns DataFrame. OK.
y = panel.apply(lambda df: df['Column'].mean(), axis=[1, 2])
--> Returns Series is expected. but got... | {
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I have added the new argument to the doc too.
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https://api.github.com/repos/pandas-dev/pandas/issues/10205 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10205/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10205/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10205/events | https://github.com/pandas-dev/pandas/issues/10205 | 80,372,256 | MDU6SXNzdWU4MDM3MjI1Ng== | 10,205 | read_csv() fills empty string with nan | {
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```
>>> import pandas as pd
>>> from StringIO import StringIO
>>> pd.read_csv(StringIO('col1... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10206/events | https://github.com/pandas-dev/pandas/pull/10206 | 80,617,384 | MDExOlB1bGxSZXF1ZXN0MzYxNTE4NDg= | 10,206 | DOC: Added nlargest/nsmallest to API docs | {
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... | 2 | 2015-05-26T10:49:13Z | 2019-06-24T15:11:42Z | 2019-06-24T15:11:41Z | CONTRIBUTOR | null | these can be moved to `core.generic.NDFrame.quantile` (leave disabled for ndim > 2 though)
xref #10174
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https://api.github.com/repos/pandas-dev/pandas/issues/10208 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10208/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10208/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10208/events | https://github.com/pandas-dev/pandas/issues/10208 | 80,979,444 | MDU6SXNzdWU4MDk3OTQ0NA== | 10,208 | Regex C Engine Warning | {
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"followers_url": "https://api.github.com/users/jr... | 9 | 2015-05-26T14:12:06Z | 2019-10-14T23:37:49Z | 2016-04-03T15:41:14Z | CONTRIBUTOR | null | Using `pd.read_csv(..., sep=", ", ...)` I'm now getting a warning about falling back to the C engine because regex parsing isn't supported in the C engine. That's fine, but this isn't actually using regex.
I don't have an idea for a good transition strategy, and maybe the ship has sailed, but perhaps there should be a... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10209 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10209/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10209/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10209/events | https://github.com/pandas-dev/pandas/issues/10209 | 80,990,484 | MDU6SXNzdWU4MDk5MDQ4NA== | 10,209 | to_csv date_format including hh:mm:ss | {
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"followers_url": "https://api.github.... | 5 | 2015-05-26T14:35:19Z | 2015-06-05T21:45:56Z | 2015-06-05T21:45:56Z | NONE | null | Since 0.16.1 the to_csv output of a dataframe has started including hh:mm:ss with date_format strings like:
date_format='%d-%m-%Y'
Reverting back to 0.16.0 the date output was then formatted properly (without time).
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] | closed | false | null | [] | null | 5 | 2015-05-26T20:04:53Z | 2015-07-28T22:04:14Z | 2015-07-28T22:04:14Z | CONTRIBUTOR | null | This is a proposal for a simple API enhancement that would make merging on indices much more convenient. I've put in an initial commit to show the change needed in the library. Here's the old way of merging on indices:
``` python
In [1]: from pandas import Index, DataFrame, merge
In [2]: df1 = DataFrame({'data1':[0, ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10213 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10213/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10213/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10213/events | https://github.com/pandas-dev/pandas/issues/10213 | 81,213,512 | MDU6SXNzdWU4MTIxMzUxMg== | 10,213 | PERF: additonal GIL releasing | {
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... | 10 | 2015-05-27T01:20:42Z | 2021-04-18T06:54:24Z | 2021-04-18T06:54:24Z | CONTRIBUTOR | null | after #10199 (all of the groupbys)
these require slightly different templates as the object cannot have the `with nogil:`. These are all in `pandas/src/generate_code.py`
~~\- [ ] `groupby_count`~~
~~\- [ ] `is_monotonic`~~
- [ ] `take_1d`
- [ ] `take_2d`
- [ ] `take_2d_multi`
- [ ] `pad`
- [ ] `backfill`
- [x] `diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/10214 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10214/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10214/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10214/events | https://github.com/pandas-dev/pandas/issues/10214 | 81,423,486 | MDU6SXNzdWU4MTQyMzQ4Ng== | 10,214 | hist raises AttributeError: 'AxesSubplot' object has no attribute 'ndim' with a one row DataFrame | {
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"followers_url": "https://api.github.... | 6 | 2015-05-27T12:12:06Z | 2015-06-09T11:54:20Z | 2015-06-09T11:54:19Z | CONTRIBUTOR | null | Hello,
this code works fine:
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
bins = np.arange(80, 100 + 2, 1)
df = pd.DataFrame({"Name": ["AAA", "BBB"], "ByCol": [1, 2], "Mark": [85, 89]})
df
ByCol Mark Name
0 1 85 AAA
1 2 89 BBB
df["Mark"].hist(by=df["ByCol"], bins=... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10215 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10215/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10215/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10215/events | https://github.com/pandas-dev/pandas/issues/10215 | 81,431,085 | MDU6SXNzdWU4MTQzMTA4NQ== | 10,215 | DOC: add versionadded specifications to docstrings | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-05-27T12:38:30Z | 2015-08-12T11:37:32Z | 2015-08-12T11:37:32Z | MEMBER | null | We already use the `.. versionadded::` directive in the tutorial docs to indicate that a certain feature is only present starting from a specific pandas version.
But I propose that we also start to do this in the docstrings, when adding a new function/method or adding a new keyword to an existing function/method:
- Whe... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10216 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10216/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10216/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10216/events | https://github.com/pandas-dev/pandas/pull/10216 | 81,435,635 | MDExOlB1bGxSZXF1ZXN0MzYzMDExODk= | 10,216 | add try / except to address issue #10154 to_datetime, Inconsistent be… | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-05-27T13:04:55Z | 2015-07-06T17:41:14Z | 2015-07-06T17:41:14Z | CONTRIBUTOR | null | closes #10154 to_datetime, Inconsistent behavior with invalid dates.
results are now
```
In [2]: pd.to_datetime('2015-02-29', format="%Y-%m-%d", coerce=True)
Out[2]: NaT
In [3]: pd.to_datetime('2015-03-32', format="%Y-%m-%d", coerce=True)
Out[3]: NaT
In [4]: pd.to_datetime('2015-02-32', format="%Y-%m-%d", coerce=Tr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10217 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10217/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10217/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10217/events | https://github.com/pandas-dev/pandas/issues/10217 | 81,514,832 | MDU6SXNzdWU4MTUxNDgzMg== | 10,217 | Holidays Offset not Returning Correct Date | {
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"followers_url": "https://api.github.... | 4 | 2015-05-27T16:09:38Z | 2015-06-02T19:25:20Z | 2015-06-01T11:05:05Z | NONE | null | Why would the below return 11/2/2015 as opposed to 11/30/2015 if 11/30 would be considered the next Monday from the 4th Saturday?
```
Holiday("Cyber Monday", month=11, day=1, offset=pd.DateOffset(weekday=SA(4)), observance=next_monday)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10218 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10218/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10218/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10218/events | https://github.com/pandas-dev/pandas/issues/10218 | 81,519,720 | MDU6SXNzdWU4MTUxOTcyMA== | 10,218 | DataFrame#where broken in latest 0.16.1 release | {
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```
In [1]: df = DataFrame([1, 2]); df
Out[1]:
0
0 1
1 2
In [2]: df.where(df[0]==1)
---------------------------------------------------------------------------
TypeError Traceback (most recent... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10219 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10219/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10219/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10219/events | https://github.com/pandas-dev/pandas/issues/10219 | 81,609,836 | MDU6SXNzdWU4MTYwOTgzNg== | 10,219 | REGR: indexing with dictionary elements | {
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regression from 0.16.0
```
df = pd.DataFrame(columns=['A','B'])
df.ix['test1', 'A'] = dict({'1':'test2'})
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10220 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10220/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10220/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10220/events | https://github.com/pandas-dev/pandas/pull/10220 | 82,019,885 | MDExOlB1bGxSZXF1ZXN0MzY0MjY1NDI= | 10,220 | Update plotting.py | {
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... | 6 | 2015-05-28T17:42:12Z | 2017-03-29T01:07:27Z | 2017-03-29T01:07:27Z | NONE | null | Would it make sense to make the backref from ix objects to the dataframe be weakrefs (and presumably, loc/iloc) The reason I ask is that it seems that the refcount is incremented once you use ix, and as a result the dataframe won't get garbage collected until the cycle detection runs
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https://api.github.com/repos/pandas-dev/pandas/issues/10222 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10222/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10222/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10222/events | https://github.com/pandas-dev/pandas/issues/10222 | 82,095,104 | MDU6SXNzdWU4MjA5NTEwNA== | 10,222 | current implementation of DataFrame.apply where passed function has side effects is a real "gotcha" | {
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"de... | closed | false | null | [] | null | 6 | 2015-05-28T19:42:34Z | 2017-02-09T18:28:39Z | 2016-01-27T15:15:19Z | NONE | null | From the documentation for DataFrame.apply:
`
In the current implementation apply calls func twice on the first column/row to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first column/row.
`
I am sure there a... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10223 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10223/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10223/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10223/events | https://github.com/pandas-dev/pandas/issues/10223 | 82,112,818 | MDU6SXNzdWU4MjExMjgxOA== | 10,223 | read_csv does not apply dtype=str correctly to NA values | {
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"descript... | closed | false | null | [] | null | 1 | 2015-05-28T20:33:56Z | 2015-05-28T20:39:10Z | 2015-05-28T20:38:51Z | NONE | null | Hi,
It seems that read_csv() ignores the dtype=str when encountering NA values. For instance if the contents of file.csv is:
```
id,symbol
1,NA
NA,g2
3,g3
4,g4
```
Then `df=pandas.read_csv('file.csv', dtype={'id':float, 'symbol':str})` will return the following data frame:
```
id symbol
0 1 NaN
1 NaN g2
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10224 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10224/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10224/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10224/events | https://github.com/pandas-dev/pandas/issues/10224 | 82,113,817 | MDU6SXNzdWU4MjExMzgxNw== | 10,224 | read_csv does not apply dtype=str correctly to NA values | {
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It seems that read_csv() ignores the dtype=str when encountering NA values. For instance if the contents of file.csv is:
id,symbol
1,NA
NA,g2
3,g3
4,g4
Then df=pandas.read_csv('file.csv', dtype={'id':float, 'symbol':str}) will return the following data frame:
id symbol
0 1 NaN
1 NaN g2
2 3 g3
3... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10225 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10225/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10225/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10225/events | https://github.com/pandas-dev/pandas/pull/10225 | 82,122,574 | MDExOlB1bGxSZXF1ZXN0MzY0NTAyOTE= | 10,225 | Fix float formatting edge case | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10226 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10226/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10226/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10226/events | https://github.com/pandas-dev/pandas/pull/10226 | 82,200,173 | MDExOlB1bGxSZXF1ZXN0MzY0NjYzMDc= | 10,226 | BUG: Holiday(..) with both offset and observance raises | {
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"followers_url": "https://api.github.... | 6 | 2015-05-29T01:16:42Z | 2015-06-02T19:26:12Z | 2015-06-01T11:04:48Z | CONTRIBUTOR | null | Resolves #10217
Doesn't fix the underlying issue yet, but at least raises on the unsupported behavior.
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https://api.github.com/repos/pandas-dev/pandas/issues/10227 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10227/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10227/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10227/events | https://github.com/pandas-dev/pandas/issues/10227 | 82,202,847 | MDU6SXNzdWU4MjIwMjg0Nw== | 10,227 | 0.16.1 No columns to parse from file | {
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"descript... | closed | false | null | [] | null | 6 | 2015-05-29T01:26:55Z | 2015-09-30T16:52:04Z | 2015-05-29T23:30:39Z | NONE | null | I've updated Pandas from 0.14.1 (all was good) to 0.16.1 and now I have that exception:
```
Error
Traceback (most recent call last):
File "/home/nedr/progs/MEA-FormBuilder/formbuilder_core/tests/test_reports.py", line 618, in test_no_reports
response = self.request(request)
File "/home/nedr/progs/MEA-FormBuild... | {
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The first pass runs, and then either the 2nd or 3rd run produces:
Segmentation fault: 11
It happens on:
- Python 2.7.9 and Pandas 0.16.1
- Python 3.4.3 and Pandas 0.16.1
It does not happen on:
- Python 2.7.9 with Pandas 0.12.0
Th... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10229 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10229/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10229/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10229/events | https://github.com/pandas-dev/pandas/pull/10229 | 82,334,398 | MDExOlB1bGxSZXF1ZXN0MzY0ODQ1OTY= | 10,229 | CLN: Remove redundant self.unique_check and self._do_unique_check | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10230 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10230/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10230/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10230/events | https://github.com/pandas-dev/pandas/pull/10230 | 82,344,961 | MDExOlB1bGxSZXF1ZXN0MzY0ODY5Nzc= | 10,230 | Close mysql connection in TestXMySQL to prevent tests freezing | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10231 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10231/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10231/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10231/events | https://github.com/pandas-dev/pandas/issues/10231 | 82,502,153 | MDU6SXNzdWU4MjUwMjE1Mw== | 10,231 | Undesired UX behavior of DataFrame output in IPython Notebook | {
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"followers_url": "https://api.github.... | 15 | 2015-05-29T16:50:51Z | 2020-01-28T18:05:16Z | 2015-06-11T15:39:35Z | CONTRIBUTOR | null | From my previous line comment at https://github.com/pydata/pandas/commit/be80898920eab8f740cf61019327621a75d73947#commitcomment-11338872. cc @takluyver @jorisvandenbossche
Because DataFrame's `_repr_html_` returns hardcoded `div` tag with style `max-height:1000px`, if the height of DataFrame's output exceeds 1000px, i... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10232 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10232/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10232/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10232/events | https://github.com/pandas-dev/pandas/pull/10232 | 82,507,469 | MDExOlB1bGxSZXF1ZXN0MzY1MjU1Njg= | 10,232 | Fix undesired UX behavior of DataFrame output in IPython Notebook | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10233 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10233/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10233/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10233/events | https://github.com/pandas-dev/pandas/pull/10233 | 82,507,495 | MDExOlB1bGxSZXF1ZXN0MzY1MjU1NzY= | 10,233 | BUG: plotting grouped_hist with a single row frame #10214 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10234 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10234/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10234/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10234/events | https://github.com/pandas-dev/pandas/issues/10234 | 82,608,133 | MDU6SXNzdWU4MjYwODEzMw== | 10,234 | to_excel function unable to subtract offset-naive and offset-aware datetimes | {
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"de... | closed | false | null | [] | null | 3 | 2015-05-29T22:13:53Z | 2019-01-21T04:18:15Z | 2019-01-21T04:18:00Z | NONE | null | Pandas version: **0.15.2**
I have a dataframe object with one column of timestamps:
```
ts value
2012-01-01 00:10:00 100
2012-01-01 00:20:00 200
...
```
And when I dumped them into csv file using to_csv, they are stored properly.
While when I dumped them into xlsx file using to_excel. the data in file are miss detai... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10235 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10235/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10235/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10235/events | https://github.com/pandas-dev/pandas/issues/10235 | 82,645,722 | MDU6SXNzdWU4MjY0NTcyMg== | 10,235 | PERF: core/base/IndexOpsMixin duplicated should be changed to use same impl as frame.duplicated | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-05-30T00:40:14Z | 2016-07-25T12:03:19Z | 2016-07-25T12:03:19Z | CONTRIBUTOR | null | `DataFrame.duplicated` correctly uses `hashtable.duplicated_int64` a specialized routine, while `core.series.IndexOpsMixin.duplicated` uses `lib.duplicated` an object based one.
as its almost always better to factorize then use the fast routine, than to do object comparisons.
but YMMV, so needs a couple of perf tests.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10236 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10236/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10236/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10236/events | https://github.com/pandas-dev/pandas/pull/10236 | 82,808,102 | MDExOlB1bGxSZXF1ZXN0MzY1NjUyMDY= | 10,236 | ENH: duplicated and drop_duplicates now accept keep kw | {
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Introduce `keep` kw to handle first / last / all duplicates based on the discussion above. If there is better API/kw, please lmk.
### `duplicated`
- `keep='first'` (default): mark duplicates as `True` except for the first occurrence.
- `keep='last'`: mark duplicates as `True` except for the... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10237 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10237/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10237/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10237/events | https://github.com/pandas-dev/pandas/issues/10237 | 82,865,098 | MDU6SXNzdWU4Mjg2NTA5OA== | 10,237 | series round ? | {
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(Pdb) data['h'].round()
0 0
1 54
2 20
3 793
4 72
5 43
6 51
Name: h, dtype: float64
(Pdb) data['h'].round(1)
0 0.00000000000000000000000
1 54.10000000000000142108547
2 19.69999999999999928945726
3 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10238 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10238/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10238/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10238/events | https://github.com/pandas-dev/pandas/issues/10238 | 82,895,976 | MDU6SXNzdWU4Mjg5NTk3Ng== | 10,238 | Disable networking in tests? | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2015-05-30T19:47:19Z | 2015-05-31T09:07:38Z | 2015-05-30T20:11:59Z | NONE | null | I'd like to run pandas tests but not have it do any tests that require networking. I can get the tests to pass if I disconnect from the internet on my machine, but otherwise I'm getting SSL errors. This might be because am building pandas with nix, but presumably there are other reasons why one might want to disable an... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10239 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10239/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10239/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10239/events | https://github.com/pandas-dev/pandas/issues/10239 | 82,911,926 | MDU6SXNzdWU4MjkxMTkyNg== | 10,239 | BUG: numexpr eval doesn't hold column name via single column selection | {
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df = pd.DataFrame(np.random.randn(2, 3), columns=['A', 'B', 'C'])
numexpr.__version__
# '2.4'
# all resets column name
pd.eval('df.A')
#0 -0.312782
#1 1.204993
# dtype: float64
pd.eval('df["A"]')
#0 -0.312782
#1 1.204993
# dtype: float64
df.eval('A')
#0 -0.312782
#1 1.204993
# dtype: float... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10240 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10240/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10240/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10240/events | https://github.com/pandas-dev/pandas/pull/10240 | 82,914,553 | MDExOlB1bGxSZXF1ZXN0MzY1NzIxNTM= | 10,240 | BUG: Series arithmetic methods incorrectly hold name | {
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"followers_url": "https://api.github.... | 1 | 2015-05-30T21:14:49Z | 2015-06-02T19:26:12Z | 2015-06-02T10:38:48Z | MEMBER | null | Closes #10068.
Should handle the case `_maybe_match_name` is `None`, because of continuous `__finalize__`.
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https://api.github.com/repos/pandas-dev/pandas/issues/10241 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10241/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10241/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10241/events | https://github.com/pandas-dev/pandas/pull/10241 | 82,922,347 | MDExOlB1bGxSZXF1ZXN0MzY1NzI2MDA= | 10,241 | BUG: SparseSeries.abs() resets name | {
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# OK: name is preserved
s = pd.Series([1, 2, -3], name='a')
s.abs()
#0 1
#1 2
#2 3
# Name: a, dtype: int64
# NG: name is reset
s = pd.SparseSeries([1, 0, -3], name='a')
s.name
# 'a'
s.abs()
#0 1
#1 0
#2 3
# dtype: int64
# BlockIndex
# Block locations: array([0], dtype=int32)
# Block lengths: arr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10242 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10242/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10242/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10242/events | https://github.com/pandas-dev/pandas/issues/10242 | 82,933,958 | MDU6SXNzdWU4MjkzMzk1OA== | 10,242 | Variance is not calculated correctly in some cases + inconsistent definition | {
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"followers_url": "https://api.github.com/users/jr... | 14 | 2015-05-30T22:37:57Z | 2015-09-04T12:15:33Z | 2015-09-04T12:15:33Z | NONE | null | The var method acts inconsistently when used on the Series vs the numpy array returned from values:
``` python
dat = pd.DataFrame({'x': [1,2,3,4,0,0,0]})
print(dat['x'].values.var())
print(dat['x'].var())
```
```
Prints:
2.24489795918
2.61904761905
```
This is due to numpy using ddof=0 as its default (calculates the... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10243 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10243/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10243/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10243/events | https://github.com/pandas-dev/pandas/pull/10243 | 83,105,158 | MDExOlB1bGxSZXF1ZXN0MzY1ODM0ODg= | 10,243 | wide table support | {
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@jreback this isn't quite working yet for older arrays - the reason is because I have to address values_block_kind_0 and things like that. Currently what I'm doing is creating a group under `self.group` of the Table class, named 'attr', which has all the attributes as CArrays. However the issue is t... | {
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"followers_url": "https://api.github.... | 31 | 2015-06-01T08:05:39Z | 2015-06-08T13:28:39Z | 2015-06-08T13:28:39Z | CONTRIBUTOR | null | Consider a file of the following format:
```
week,sow,prn,rxstatus,az,elv,l1_cno,s4,s4_cor,secsigma1,secsigma3,secsigma10,secsigma30,secsigma60,code_carrier,c_cstdev,tec45,tecrate45,tec30,tecrate30,tec15,tecrate15,tec00,tecrate00,l1_loctime,chanstatus,l2_locktime,l2_cno
1765,68460.00,126,00E80000,0.00,0.00,39.38,0.118... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10246 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10246/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10246/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10246/events | https://github.com/pandas-dev/pandas/pull/10246 | 83,658,128 | MDExOlB1bGxSZXF1ZXN0MzY2NjI1NjI= | 10,246 | ENH: GH9746 DataFrame.unstack and Series.unstack now take fill_value … | {
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closes #9746
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https://api.github.com/repos/pandas-dev/pandas/issues/10247 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10247/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10247/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10247/events | https://github.com/pandas-dev/pandas/pull/10247 | 83,863,735 | MDExOlB1bGxSZXF1ZXN0MzY2OTc0NjY= | 10,247 | BUG: GH10209 fix bug where date_format in to_csv is sometimes ignored | {
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"followers_url": "https://api.github.... | 4 | 2015-06-02T06:13:50Z | 2015-06-05T21:45:43Z | 2015-06-05T21:45:43Z | CONTRIBUTOR | null | closes #10209
When datetimes are not dates only i.e. have fractional day values, date_format passed to to_csv is ignored.
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https://api.github.com/repos/pandas-dev/pandas/issues/10249 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10249/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10249/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10249/events | https://github.com/pandas-dev/pandas/pull/10249 | 83,990,269 | MDExOlB1bGxSZXF1ZXN0MzY3MjA2ODM= | 10,249 | Datetime resolution coercion | {
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Without this fix, if the `date_parse` function passed to e.g. `read_csv` outputs a `np.datetime64` array, then it must be of `ns` resolution. With this fix, all time resolutions may be used.
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https://api.github.com/repos/pandas-dev/pandas/issues/10250 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10250/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10250/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10250/events | https://github.com/pandas-dev/pandas/pull/10250 | 84,005,251 | MDExOlB1bGxSZXF1ZXN0MzY3MjM3NTk= | 10,250 | ENH: Conditional HTML Formatting | {
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closes #4315
Not close to being done, but I wanted to put this here before the meeting. Maybe someone will have a chance to check it out.
~~http://nbviewer.ipython.org/github/TomAugspurger/pandas/blob/638bd3e361633a4c446ee02534e07b8a9332258a/style.ipynb~~
~~https:/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10251 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10251/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10251/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10251/events | https://github.com/pandas-dev/pandas/pull/10251 | 84,119,177 | MDExOlB1bGxSZXF1ZXN0MzY3NTM4NjA= | 10,251 | ENH: make sure return dtypes for nan funcs are consistent | {
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"followers_url": "https://api.github.... | 2 | 2015-06-02T17:32:47Z | 2015-06-05T21:29:03Z | 2015-06-05T21:28:59Z | CONTRIBUTOR | null | this is a follow-up to https://github.com/pydata/pandas/pull/10172
In addition to `mean()`, this PR also makes sure the returned dtype is consistent for `std()`, `var()`, `skew()`, and `kurt()`
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https://api.github.com/repos/pandas-dev/pandas/issues/10252 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10252/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10252/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10252/events | https://github.com/pandas-dev/pandas/pull/10252 | 84,242,075 | MDExOlB1bGxSZXF1ZXN0MzY3ODE3NzM= | 10,252 | REGR: ensure passed binlabels to pd.cut have a compat dtype on output (#10140) | {
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```
In [1]: df = pd.DataFrame()
In [2]: df['x'] = 100 * np.random.random(100)
In [3]: df['y'] = df.x**2
In [4]: binedges = np.arange(0,110,10)
In [5]: binlabels = np.arange(5,105,10)
```
This now yields an `Int64Index`
```
In [6]: df.groupby(pd.cut(df.x,bins=binedges ,labe... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10253 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10253/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10253/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10253/events | https://github.com/pandas-dev/pandas/pull/10253 | 84,315,734 | MDExOlB1bGxSZXF1ZXN0MzY3OTMzMjY= | 10,253 | ENH: Add pipe method | {
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In the dev meeting, we settled on the following:
- `.pipe` will **not** include a check for `__pipe_func__` on the function passed in.
- To avoid messiness with lambdas when a function takes the DataFrame other than in the first position, users can pass in a `(calla... | {
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... | 26 | 2015-06-03T08:06:49Z | 2021-04-18T06:56:40Z | null | MEMBER | null | To record the issue we discussed yesterday, to be solved for 0.16.2
Plotting of categorical:
- y-axis: the 'values' itself are used
- x-axis: they are not regarded as values, but as all unique items that just gets represented by a `range(len(cat))` x data
Overview: http://nbviewer.ipython.org/gist/jorisvandenbossche/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10255 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10255/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10255/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10255/events | https://github.com/pandas-dev/pandas/issues/10255 | 84,427,618 | MDU6SXNzdWU4NDQyNzYxOA== | 10,255 | to_sql : MySQL identifier cannot be entirely numeric | {
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MySQL supports numeric identifiers (if quoted) (http://dev.mysql.com/doc/refman/5.0/en/identifiers.html)
@artemyk Was there a reason we explicitely disallowed this? (https:/... | {
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"followers_url": "https://api.github.com/users/jorisva... | 3 | 2015-06-03T11:00:38Z | 2015-06-03T16:39:31Z | 2015-06-03T11:33:18Z | NONE | null | (Hopefully I did not find this issue in the list, because there was none...) I have experienced the following issue for a while (wonder if it ever worked actually):
I create a Series or a DataFrame with well-defined, floating-point indices. Then I try to slice some rows using a list. For some valid index values I get ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10257 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10257/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10257/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10257/events | https://github.com/pandas-dev/pandas/pull/10257 | 84,520,381 | MDExOlB1bGxSZXF1ZXN0MzY4MjQ2MjQ= | 10,257 | add numba example to enhancingperf.rst | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10258 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10258/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10258/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10258/events | https://github.com/pandas-dev/pandas/pull/10258 | 84,524,831 | MDExOlB1bGxSZXF1ZXN0MzY4MjUzNTg= | 10,258 | BUG: SparseSeries constructor ignores input data name | {
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"followers_url": "https://api.github.... | 2 | 2015-06-03T12:07:26Z | 2015-06-04T13:16:14Z | 2015-06-03T21:21:22Z | MEMBER | null | When constructing `Series` from `Series`, name attribute is preserved otherwise specified.
```
import pandas as pd
s = pd.Series([1, 2, 3], name='a')
pd.Series(s)
#0 1
#1 2
#2 3
# Name: a, dtype: int64
pd.Series(s, name='x')
#0 1
#1 2
#2 3
# Name: x, dtype: int64
```
But `SparseSeries` doesn't pres... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10259 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10259/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10259/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10259/events | https://github.com/pandas-dev/pandas/issues/10259 | 84,569,878 | MDU6SXNzdWU4NDU2OTg3OA== | 10,259 | API: Support Matrix Multiplication Ops (@) | {
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"closed_at": "2018-05-16T03:09:58Z",
"closed_issues": 1645,
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- http://legacy.python.org/dev/peps/pep-0465/
- numpy fix: https://github.com/numpy/numpy/pull/5878/
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https://api.github.com/repos/pandas-dev/pandas/issues/10260 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10260/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10260/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10260/events | https://github.com/pandas-dev/pandas/issues/10260 | 84,606,346 | MDU6SXNzdWU4NDYwNjM0Ng== | 10,260 | Convert 1 dimensional iterables returned to .apply() columns to pd.Series | {
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] | closed | false | null | [] | null | 6 | 2015-06-03T14:54:47Z | 2015-06-04T12:39:01Z | 2015-06-03T17:00:34Z | NONE | null | As per http://stackoverflow.com/questions/30621887/return-a-column-vector-in-pandas-apply-with-different-length/30622209#30622209
It would be nice if an apply function automatically converted lists to pd.Series when they are returned to .apply(), in other words,
``` python
import numpy as np
import pandas as pd
df =... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10261 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10261/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10261/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10261/events | https://github.com/pandas-dev/pandas/issues/10261 | 84,673,227 | MDU6SXNzdWU4NDY3MzIyNw== | 10,261 | read_fwf() doesn't work properly when both skiprows and iterator options are used. | {
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Here is a simple bit of code to reproduce:
``` python
import pandas as pd
#Create a fixed width file to tes... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10262/events | https://github.com/pandas-dev/pandas/pull/10262 | 84,738,293 | MDExOlB1bGxSZXF1ZXN0MzY4Njk1ODQ= | 10,262 | TST: test_sql: properly drop tables with names that need to be quoted | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-06-03T19:38:14Z | 2015-07-10T23:17:58Z | 2015-07-10T23:17:52Z | CONTRIBUTOR | null | Closes #10255 .
Rewrote some of the code in `test_sql.py` so that it now properly drops tables with names that need to be quoted.
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https://api.github.com/repos/pandas-dev/pandas/issues/10263 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10263/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10263/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10263/events | https://github.com/pandas-dev/pandas/pull/10263 | 84,768,554 | MDExOlB1bGxSZXF1ZXN0MzY4NzcxMTI= | 10,263 | BUG: Should allow numeric mysql table/column names | {
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"followers_url": "https://api.github.... | 4 | 2015-06-03T20:57:06Z | 2015-06-05T09:11:32Z | 2015-06-05T09:11:25Z | CONTRIBUTOR | null | Closes #10255 , alternative to #10262 .
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https://api.github.com/repos/pandas-dev/pandas/issues/10264 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10264/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10264/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10264/events | https://github.com/pandas-dev/pandas/issues/10264 | 84,771,124 | MDU6SXNzdWU4NDc3MTEyNA== | 10,264 | view into modified dataframe of ints causes subsequent set_value to not work properly | {
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"followers_url": "https://api.github.... | 15 | 2015-06-03T21:05:16Z | 2015-06-05T21:41:05Z | 2015-06-05T21:41:05Z | NONE | null | The following code snippet produces unexpected output:
``` python
import pandas as pd
import numpy as np
x = pd.DataFrame(data=np.zeros((5,5),dtype=np.int),columns=['a','b','c','d','e'])
x['f'] = 0
x.f.values[3]=1
y = x.iloc[np.arange(2,len(x))]
x.f.values[3]=2
print x
print x.f.values
```
```
Out[130]:
a b c ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10265 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10265/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10265/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10265/events | https://github.com/pandas-dev/pandas/pull/10265 | 84,787,632 | MDExOlB1bGxSZXF1ZXN0MzY4ODIzNDY= | 10,265 | BUG: Ensure 'coerce' actually coerces datatypes | {
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ensure that data of the correct type is returned, even if all
values are null-types (NaN or NaT).
closes #9589
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https://api.github.com/repos/pandas-dev/pandas/issues/10266 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10266/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10266/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10266/events | https://github.com/pandas-dev/pandas/pull/10266 | 84,872,285 | MDExOlB1bGxSZXF1ZXN0MzY4OTc1NDU= | 10,266 | ENH: Initial pass at implementing DataFrame.asof, GH 2941 | {
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This can almost certainly be made quicker, still digging into the
internals to understand the various underlying indexers.
There are 4 distinct logics you can apply to how to deal with the
missings (as opposed to the Series asof, wheres theres just two,
either skipna or dont skipna). The default is to b... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10268 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10268/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10268/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10268/events | https://github.com/pandas-dev/pandas/pull/10268 | 84,889,773 | MDExOlB1bGxSZXF1ZXN0MzY4OTk2OTY= | 10,268 | ENH: allow as_blocks to take a copy argument (#9607) | {
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Added copy argument in as_blocks. Defaults to True as per the original code.
Wrote test cases to check the copy=True and copy=False cases work.
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https://api.github.com/repos/pandas-dev/pandas/issues/10269 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10269/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10269/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10269/events | https://github.com/pandas-dev/pandas/pull/10269 | 84,899,304 | MDExOlB1bGxSZXF1ZXN0MzY5MDA3OTQ= | 10,269 | BUG: GH10160 in DataFrame construction from dict with datetime64 index | {
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"followers_url": "https://api.github.... | 8 | 2015-06-04T04:06:49Z | 2015-06-10T20:01:05Z | 2015-06-10T20:01:04Z | CONTRIBUTOR | null | closes #10160
closes #9456
where `DataFrame/Series` construction from nested `dict` with `datetime64` index returns a `DataFrame\Series` of `NaN`s.
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https://api.github.com/repos/pandas-dev/pandas/issues/10271 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10271/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10271/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10271/events | https://github.com/pandas-dev/pandas/pull/10271 | 85,067,000 | MDExOlB1bGxSZXF1ZXN0MzY5Mjc4Nzc= | 10,271 | PERF: write basic datetimes faster | {
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```
In [2]: df = DataFrame({'A' : pd.date_range('20130101',periods=100000,freq='s')})
In [3]: %timeit df.to_csv('test.csv',mode='w')
1 loops, best of 3: 282 ms per loop
```
0.16.1
```
In [2]: %timeit df.to_csv('test.csv',mode='w')
1 loops, best of 3: 352 ms per loop
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10272 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10272/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10272/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10272/events | https://github.com/pandas-dev/pandas/pull/10272 | 85,067,673 | MDExOlB1bGxSZXF1ZXN0MzY5Mjc5NzY= | 10,272 | BUG: bug in cache updating when consolidating #10264 | {
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``` python
import pandas as pd
#This query returns zero rows
query = "SELECT * FROM [publicdata:samples.wikipedia] where timestamp=-9999999"
df = pd.io.gbq.read_gbq(query, project_id="my_bigquery_project_id")
```
```
Job not ye... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10275 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10275/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10275/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10275/events | https://github.com/pandas-dev/pandas/pull/10275 | 85,128,780 | MDExOlB1bGxSZXF1ZXN0MzY5NDExNzk= | 10,275 | Fix meantim typo | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10276 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10276/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10276/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10276/events | https://github.com/pandas-dev/pandas/pull/10276 | 85,142,528 | MDExOlB1bGxSZXF1ZXN0MzY5NDQ3ODk= | 10,276 | TST: Check series names | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10277 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10277/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10277/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10277/events | https://github.com/pandas-dev/pandas/pull/10277 | 85,143,558 | MDExOlB1bGxSZXF1ZXN0MzY5NDUxMDA= | 10,277 | PERF/CLN: Improve datetime-like index ops perf | {
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... | 14 | 2015-06-04T15:19:00Z | 2015-12-10T12:46:09Z | 2015-12-10T12:22:48Z | MEMBER | null | Add `_isnan` to `DatetimeIndexOpsMixin` to cache `NaT` mask.
This leads to some perf improvement which is noticeable in larger data.
### after fix
```
import pandas as pd
import numpy as np
np.random.seed(1)
idx = pd.DatetimeIndex(np.append(np.array([pd.tslib.iNaT]),
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"id": ... | open | false | null | [] | null | 3 | 2015-06-04T17:24:41Z | 2020-03-31T04:52:59Z | null | CONTRIBUTOR | null | The definition of the USFederalHolidayCalendar is not correct.
```
from pandas.tseries.holiday import USFederalHolidayCalendar
print(USFederalHolidayCalendar.rules)
```
> [Holiday: New Years Day (month=1, day=1, observance=<function nearest_workday at 0x10d17dcf8>), Holiday: Dr. Martin Luther King Jr. (month=1, day=... | {
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```
from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('sqlite://')
conn = engine.connect()
conn.execute("create table test (a int, `b.c` int)")
conn.execute("insert into test values (1,2)")
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converts int -> floats for the entire block.
```
In [1]: x = pd.DataFrame(data=np.zeros((5,5),dtype=np.int),columns=['a','b','c','d','e'], index=['one','two','three','four','five'])
In [3]: x.loc['three','e'] = 3.1
In [4]: x
Out[4]:
a b c d e
one 0 0 0 0 0.0
two 0 0 0 0 0.0
t... | {
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Fixing MLK day not observed before 1986, per https://github.com/pydata/pandas/issues/10278#issuecomment-109002244
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https://api.github.com/repos/pandas-dev/pandas/issues/10283 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10283/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10283/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10283/events | https://github.com/pandas-dev/pandas/pull/10283 | 85,395,767 | MDExOlB1bGxSZXF1ZXN0MzY5OTczMzk= | 10,283 | BUG: DataFrame.where does not handle Series slice correctly (#10218) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10284 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10284/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10284/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10284/events | https://github.com/pandas-dev/pandas/issues/10284 | 85,446,951 | MDU6SXNzdWU4NTQ0Njk1MQ== | 10,284 | How To Filter Pandas DataFrames Using Dynamic URL Query String | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2015-06-05T06:14:49Z | 2015-06-05T08:44:39Z | 2015-06-05T07:34:35Z | NONE | null | Currently i am having an question in python pandas. I want to filter a dataframe using url query string dynamically.
Detailed question:
http://stackoverflow.com/questions/30658017/filter-pandas-dataframes-using-dynamic-url-query-string
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https://api.github.com/repos/pandas-dev/pandas/issues/10285 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10285/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10285/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10285/events | https://github.com/pandas-dev/pandas/issues/10285 | 85,463,961 | MDU6SXNzdWU4NTQ2Mzk2MQ== | 10,285 | Add dtype keyword to read_sql_query to control per column dtypes. | {
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"followers_url": "https://api.github.com/users/j... | 3 | 2015-06-05T07:45:39Z | 2021-09-03T10:15:55Z | 2020-12-23T23:28:23Z | NONE | null | When reading from SQL queries - especially when reading chunk-wise - issues with type conversion can occur.
It would be ideal if one could specify the types of the columns, so that type conversions go right.
A very common case is reading pure float results chunk-wise from a large table. The nuisance comes in when and... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10286 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10286/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10286/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10286/events | https://github.com/pandas-dev/pandas/pull/10286 | 85,477,555 | MDExOlB1bGxSZXF1ZXN0MzcwMTI5MjM= | 10,286 | Update v0.16.2.txt | {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2015-06-05T09:04:00Z | 2015-06-05T09:16:44Z | 2015-06-05T09:16:00Z | CONTRIBUTOR | null | Added description of Pull Request 9544
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https://api.github.com/repos/pandas-dev/pandas/issues/10287 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10287/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10287/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10287/events | https://github.com/pandas-dev/pandas/issues/10287 | 85,530,491 | MDU6SXNzdWU4NTUzMDQ5MQ== | 10,287 | PERF: mutli-index selection vs repeated selections | {
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```
idx = pd.IndexSlice
n=10000000
np.random.seed(1234)
mdt = pandas.DataFrame()
mdt['A'] = np.random.choice(range(10000,45000,1000), n)
mdt['B'] = np.random.choice(range(10,400), n)
mdt['C'] ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10288 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10288/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10288/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10288/events | https://github.com/pandas-dev/pandas/issues/10288 | 85,534,978 | MDU6SXNzdWU4NTUzNDk3OA== | 10,288 | SettingWithCopyWarning: possible spurious warning after dropna() call | {
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```
In [102]: df = pd.DataFrame({'a':[1,2,np.nan], 'b':['A', 'B', 'C']})
In [103]: df
Out[103]:
a b
0 1 A
1 2 B
2 NaN C
In [104]: df = df.dropna(subset=['a'])
In [105]: df
Out[105]:
a b
0 1 A
1 2 B
In ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10289 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10289/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10289/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10289/events | https://github.com/pandas-dev/pandas/issues/10289 | 85,583,919 | MDU6SXNzdWU4NTU4MzkxOQ== | 10,289 | mixed data type to_json(orient='index') repeats only the first index value | {
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"followers_url": "https://api.github.... | 3 | 2015-06-05T17:09:53Z | 2015-07-02T19:04:02Z | 2015-06-07T23:05:24Z | NONE | null | ``` python
from pandas import DataFrame
df = DataFrame({
'one' : pandas.Series(['foo','bar','bee'],index=['a','b','c']),
'two' : pandas.Series([1,2,3],index=['a','b','c']) } )
df.to_json(orient='index')
```
Observe that the first index value 'a' is used for all the record returned, rather than for just the f... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10290 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10290/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10290/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10290/events | https://github.com/pandas-dev/pandas/pull/10290 | 85,611,800 | MDExOlB1bGxSZXF1ZXN0MzcwNjE0MzY= | 10,290 | PERF: improved performance of multiindex slicing | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-06-05T19:16:08Z | 2015-06-24T20:59:59Z | 2015-06-24T20:59:59Z | CONTRIBUTOR | null | closes #10287
master
```
In [22]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A],:].loc[idx[:,test_B-eps_B:test_B+eps_B],:].loc[idx[:,:,test_C-eps_C:test_C+eps_C],:].loc[idx[:,:,:,test_D-eps_D:test_D+eps_D],:]
10 loops, best of 3: 141 ms per loop
In [23]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A,test_B-eps_B... | {
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```
np.random.seed(seed = 99)
s = np.random.randint(1,10,200)
s = pd.Series(np.where(s > 9, np.nan, s))
s1 = s[:100]
s2 = s[100:]
pd.crosstab(s1, s2)
KeyError: '__dummy__
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10292 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10292/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10292/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10292/events | https://github.com/pandas-dev/pandas/pull/10292 | 85,656,615 | MDExOlB1bGxSZXF1ZXN0MzcwNzg4NzI= | 10,292 | BUG: TimedeltaIndex slicing may reset freq | {
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... | closed | false | null | [] | {
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"followers_url": "https://api.github.... | 1 | 2015-06-05T23:31:04Z | 2015-06-08T13:20:31Z | 2015-06-07T23:02:54Z | MEMBER | null | `DatetimeIndex` preserve freq after slicing.
```
di = pd.date_range('2001', '2005', freq='D')
di[1:5:2]
# DatetimeIndex(['2001-01-02', '2001-01-04'], dtype='datetime64[ns]', freq='2D', tz=None)
```
But `TimedeltaIndex` doesn't.
```
tdi = pd.timedelta_range('1day', '5day', freq='D')
tdi[1:5:2]
# TimedeltaIndex(['2 da... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10293 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10293/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10293/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10293/events | https://github.com/pandas-dev/pandas/issues/10293 | 85,664,591 | MDU6SXNzdWU4NTY2NDU5MQ== | 10,293 | Python quit unexpectedly while using the lib.so plug-in. | {
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"color": "00... | closed | false | null | [] | null | 3 | 2015-06-06T00:36:29Z | 2015-06-06T13:51:49Z | 2015-06-06T13:51:35Z | NONE | null | Hello All,
I am trying to replace certain pattern with empty value in my dataframe, this my snippet,
``` python
mydfv2['Values used'].replace(to_replace= r"\[.*?\].", value="",inplace = True, regex=True)
```
This column --> "Values used" will have a list of strings or list of list of strings in each cell. Every time... | {
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... | 11 | 2015-06-06T01:34:57Z | 2015-11-14T07:30:10Z | 2015-11-02T12:09:03Z | CONTRIBUTOR | null | New urls_as_links boolean paramater that will output urls as href html
links. ref #2679
Thanks for @tdas14 for initial code.
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"description": "Timedelta... | closed | false | null | [] | {
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```
import pandas as pd
idx = pd.date_range('2011-01-01', '2011-05-01', freq='M')
# freq must be preserved, because ``DatetimeIndex`` is ordered when it has freq
idx.order()
# DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-30'], dtype='datetime64[ns]', freq=None, ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10296 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10296/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10296/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10296/events | https://github.com/pandas-dev/pandas/pull/10296 | 85,695,838 | MDExOlB1bGxSZXF1ZXN0MzcwODQ4Mjk= | 10,296 | ENH: #3335 Pivot table support for setting name of margins column. | {
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"de... | closed | false | null | [] | null | 6 | 2015-06-06T04:41:00Z | 2015-11-12T07:52:30Z | 2015-11-10T01:26:51Z | CONTRIBUTOR | null | ref #3335.
Adds margin_column parameter to pivot_table so that user can set it to
something other than 'All'.
Raises ValueError exception if there is a conflict between the value of
margin_column and one of the other values appearing in the indices of
the pivot table.
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https://api.github.com/repos/pandas-dev/pandas/issues/10297 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10297/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10297/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10297/events | https://github.com/pandas-dev/pandas/issues/10297 | 85,698,841 | MDU6SXNzdWU4NTY5ODg0MQ== | 10,297 | Date Operations | {
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] | closed | false | null | [] | null | 3 | 2015-06-06T05:04:42Z | 2015-06-06T13:56:07Z | 2015-06-06T13:55:58Z | NONE | null | IMHO, two date functions from `SAS` can be very useful and straight forward. I wonder if they can be incorporated in Pandas.
### [`intck()`](http://support.sas.com/documentation/cdl/en/lefunctionsref/63354/HTML/default/viewer.htm#p1md4mx2crzfaqn14va8kt7qvfhr.htm)
This function computes the difference between 2 dates/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10298 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10298/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10298/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10298/events | https://github.com/pandas-dev/pandas/pull/10298 | 85,702,150 | MDExOlB1bGxSZXF1ZXN0MzcwODUzNDg= | 10,298 | BUG: plotting grouped_hist with a single row frame #10214 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10299 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10299/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10299/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10299/events | https://github.com/pandas-dev/pandas/issues/10299 | 85,704,919 | MDU6SXNzdWU4NTcwNDkxOQ== | 10,299 | Cannot use hex strings or named matplotlib colors with DataFrame.plot | {
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I have a pandas DataFrame where I want to plot each column as a line with the same color. This might sound useless but I am showing groups of lines from certain selections of the DataFrame. The only problem is it appears that the `DataFrame.plot` ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10300 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10300/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10300/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10300/events | https://github.com/pandas-dev/pandas/pull/10300 | 85,726,110 | MDExOlB1bGxSZXF1ZXN0MzcwODc2MTU= | 10,300 | ENH: #8750 add Series support for to_html and _repr_html_ | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10301 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10301/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10301/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10301/events | https://github.com/pandas-dev/pandas/pull/10301 | 85,761,224 | MDExOlB1bGxSZXF1ZXN0MzcwOTEzNTE= | 10,301 | DOC: #7828 Add json_normalize to api docs. | {
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Is this all we need?
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https://api.github.com/repos/pandas-dev/pandas/issues/10302 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10302/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10302/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10302/events | https://github.com/pandas-dev/pandas/pull/10302 | 85,781,211 | MDExOlB1bGxSZXF1ZXN0MzcwOTMzNDM= | 10,302 | BUG: read_csv does not set index name on an empty DataFrame | {
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"followers_url": "https://api.github.... | 1 | 2015-06-06T16:57:35Z | 2015-09-19T00:38:30Z | 2015-06-07T23:06:15Z | CONTRIBUTOR | null | Fixes GH #10184.
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