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https://api.github.com/repos/pandas-dev/pandas/issues/12804 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12804/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12804/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12804/events | https://github.com/pandas-dev/pandas/pull/12804 | 146,115,459 | MDExOlB1bGxSZXF1ZXN0NjUzNzY4NzU= | 12,804 | CLN: cleaning core/common.py | {
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- create `pandas/types`
- moved _some_ dtype to `pandas/types`
- move _some_ missing value utils to `pandas/core/missing`
- move `.take` and `.diff` to `core/algorithms`
- create `formats` with `core/formats,style` moved and `printing.py` (form `core/common.py`)
- removed some non-used functions
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https://api.github.com/repos/pandas-dev/pandas/issues/12805 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12805/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12805/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12805/events | https://github.com/pandas-dev/pandas/issues/12805 | 146,122,540 | MDU6SXNzdWUxNDYxMjI1NDA= | 12,805 | ENH: add month_name to DatetimeIndex and .dt | {
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#### Code Sample:
```
i = pd.date_range('20150112 08:34:23', periods = 4, freq = '1D')
i.month_name
d = pd.Timestamp("2016-04-04")
d.month_name
s = pd.Series(i, range(4))
s.dt.month_name
```
#### Expected Output:
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12806 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12806/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12806/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12806/events | https://github.com/pandas-dev/pandas/issues/12806 | 146,125,796 | MDU6SXNzdWUxNDYxMjU3OTY= | 12,806 | ENH: Use LocaleTime for date_field_name output | {
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Currently datename strings are pulled from a hard coded (English) array in get_date_name_field() in tslib.py, better to pull these from a Locale adjusted array.
### Code Sample:
pd.Timestamp("2016-04-06").weekday_name
### Expe... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12807 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12807/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12807/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12807/events | https://github.com/pandas-dev/pandas/issues/12807 | 146,129,460 | MDU6SXNzdWUxNDYxMjk0NjA= | 12,807 | BUG: test_clipboard fails | {
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fails on Windows 7 Professional SP1
### Code Sample
`nosetests pandas/io/tests/test_clipboard.py`
### Output
```
.EE.
======================================================================
ERROR: test_round_trip_frame (pandas.io.tests.test_clipboard.TestClipboard)
---------... | {
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] | closed | false | null | [] | null | 9 | 2016-04-05T23:27:01Z | 2016-04-06T16:47:29Z | 2016-04-05T23:39:47Z | NONE | null | Using pandas 0.18.0 and pandas-datareader 0.2.1:
```
>>> from pandas_datareader.data import Options
>>> tsla = Options('tsla', 'yahoo')
>>> data = tsla.get_all_data()
>>> data.index.levels[1]
DatetimeIndex(['2008-04-16', '2015-04-16', '2016-09-16', '2017-06-16',
'2019-01-18', '2020-01-17', '2020-05-16', '2022-0... | {
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I am using **Pandas 0.18** to open a `sas7bdat` dataset
I simply use:
```
df=pd.read_sas('P:/myfile.sas7bdat')
```
and I get the following error
```
buf[0:text_block_size].rstrip(b"\x00 ").decode())
UnicodeDecodeError: 'ascii' codec can't decode byte 0xd8 in position 0: ordinal not in range(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12810 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12810/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12810/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12810/events | https://github.com/pandas-dev/pandas/pull/12810 | 146,284,924 | MDExOlB1bGxSZXF1ZXN0NjU0NTA5MzI= | 12,810 | COMPAT: Expand compatibility with fromnumeric.py | {
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Closes #12638.
Closes #12644.
Closes #12687.
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"followers_url": "https://api.github.com/users/jr... | 1 | 2016-04-06T15:07:31Z | 2016-04-06T22:43:33Z | 2016-04-06T22:43:29Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
split series, frame and panel tests to separated files.
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https://api.github.com/repos/pandas-dev/pandas/issues/12813 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12813/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12813/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12813/events | https://github.com/pandas-dev/pandas/issues/12813 | 146,345,258 | MDU6SXNzdWUxNDYzNDUyNTg= | 12,813 | Factorize fails with read-only array | {
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"description": "pandas objects compatabi... | closed | false | null | [] | {
"closed_at": "2019-01-26T00:51:26Z",
"closed_issues": 2048,
"created_at": "2018-03-29T12:00:12Z",
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"followers_url": "https://api.github... | 2 | 2016-04-06T15:14:20Z | 2018-07-08T14:14:25Z | 2018-07-08T14:14:25Z | NONE | null | same as in #15286
Factorize raises a cython error when used with a read-only array. Seems related to #10043 and #10070. I discovered this via xarray via pydata/xarray#818.
#### Code Sample, a copy-pastable example if possible
``` python
a = np.arange(2)
a.flags.writeable = False
pd.factorize(a)
```
Rai... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12814 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12814/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12814/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12814/events | https://github.com/pandas-dev/pandas/issues/12814 | 146,359,801 | MDU6SXNzdWUxNDYzNTk4MDE= | 12,814 | BUG: merging with mixed types objects in py3 when unorderable | {
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```
df1 = dfAllStudies.copy(deep=True) #make 2 copies of the same data
df2 = dfAllStudies.copy(deep=True)
#take a subset of 1 of the identical frames by dropping rows with certain dates
df2 = df2[(df2['Reviewed on'] >= np.datetime64(DateWindowStar... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12815 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12815/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12815/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12815/events | https://github.com/pandas-dev/pandas/issues/12815 | 146,360,337 | MDU6SXNzdWUxNDYzNjAzMzc= | 12,815 | BUG: unstacking with object columns defaults to None as a fill value | {
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"description": "np.nan, pd.NaT, pd.NA, d... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/j... | 5 | 2016-04-06T16:05:34Z | 2017-11-21T00:41:13Z | 2017-11-21T00:41:13Z | CONTRIBUTOR | null | ```
In [41]: df = DataFrame({'A' : [1,1,2], 'B' : list('aba'), 'C' : ['foo', np.nan, 'bar']}).set_index(['A','B'])
In [42]: df.unstack('B')
Out[42]:
C
B a b
A
1 foo NaN
2 bar None
In [43]: df.unstack('B', fill_value=np.nan)
Out[43]:
C
B a b
A
1 foo NaN
2... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12816 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12816/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12816/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12816/events | https://github.com/pandas-dev/pandas/issues/12816 | 146,365,901 | MDU6SXNzdWUxNDYzNjU5MDE= | 12,816 | CLN: Small inconsistency in weekday property of DatetimeIndex and Timestamp | {
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... | open | false | null | [] | null | 5 | 2016-04-06T16:28:56Z | 2020-05-07T03:43:10Z | null | NONE | null | All date and time derivatives are implemented as properties of DatetimeIndex and Timestamp, except for the weekday **method** of Timestamp. This is because it is inherited from the datetime class. I don't know if it is _possible_, but it would be nice if it was consistent.
### Code Sample, a copy-pastable example if po... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12817 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12817/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12817/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12817/events | https://github.com/pandas-dev/pandas/issues/12817 | 146,415,490 | MDU6SXNzdWUxNDY0MTU0OTA= | 12,817 | ENH: Faster hashing of Period objects | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-04-06T19:53:51Z | 2016-07-20T21:23:30Z | 2016-07-20T21:23:30Z | CONTRIBUTOR | null | I've noticed that a lot of manipulations using Periods are pretty slow. It [looks like it's hashing](https://github.com/pydata/pandas/blob/48f39af49a9ae76c2c7d8f519df6069e7af00755/pandas/src/period.pyx#L789) the tuple of the `ordinal` and `freq` attributes. I'm not sure what the mapping is between `freq` and `freqstr` ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12818 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12818/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12818/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12818/events | https://github.com/pandas-dev/pandas/issues/12818 | 146,450,836 | MDU6SXNzdWUxNDY0NTA4MzY= | 12,818 | Problems with snap (closest index) indexing | {
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```
df = pd.Series(index=pd.DatetimeIndex(['2016-04-06 17:21:17', '2016-04-06 17:23:06',
'2016-04-06 17:25:50', '2016-04-06 17:26:10',
'2016-04-06 17:30:09', '2016-04-06 17:30:47',
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12819 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12819/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12819/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12819/events | https://github.com/pandas-dev/pandas/issues/12819 | 146,528,179 | MDU6SXNzdWUxNDY1MjgxNzk= | 12,819 | How to select content store in the Panel using 'item' index | {
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Out[50]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 55640 (major_axis) x 9 (minor_axis)
Items axis: 0 to 1
Major_axis axis: 0 to 55639
Minor_axis axis: TradingDay to AskVolume1
store.select('wp','items>0')
Traceback (most recent call last):
File "<ipython-input-51-79d40b638607>", line 1, in... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12820 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12820/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12820/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12820/events | https://github.com/pandas-dev/pandas/issues/12820 | 146,528,623 | MDU6SXNzdWUxNDY1Mjg2MjM= | 12,820 | How to select content store in the Panel using 'item' index | {
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"descript... | closed | false | null | [] | null | 4 | 2016-04-07T07:13:09Z | 2016-04-08T02:47:14Z | 2016-04-07T13:07:02Z | NONE | null | #### wp
```
Out[50]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 55640 (major_axis) x 9 (minor_axis)
Items axis: 0 to 1
Major_axis axis: 0 to 55639
Minor_axis axis: TradingDay to AskVolume1
store.select('wp','items>0')
Traceback (most recent call last):
File "<ipython-input-51-79d40b638607>", line 1... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12821 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12821/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12821/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12821/events | https://github.com/pandas-dev/pandas/issues/12821 | 146,602,567 | MDU6SXNzdWUxNDY2MDI1Njc= | 12,821 | agg() function on groupby dataframe changes dtype of datetime64[ns] column to float64 if all items in a single group are NaT | {
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... | closed | false | null | [] | {
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In the first example, there is a single missing (NaT) date. After groupby and agg(), the dtypes of all the columns in the aggregated dataframe are the same as the original dataframe, as expected (and as des... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12822 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12822/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12822/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12822/events | https://github.com/pandas-dev/pandas/issues/12822 | 146,612,508 | MDU6SXNzdWUxNDY2MTI1MDg= | 12,822 | pandas dataframe.drop(col,axis=1) does not drop column from column.levels in multiindex dataframe | {
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"descript... | closed | false | null | [] | null | 3 | 2016-04-07T12:53:15Z | 2019-04-23T10:30:31Z | 2016-04-07T12:58:44Z | CONTRIBUTOR | null | bug in pandas version 0.18.0 (long time ago seemingly same issue has been reported on [StackOverflow](http://stackoverflow.com/questions/16682190/pandas-dataframe-dropcol-axis-1-does-not-drop-column-from-column-levels-in-mul)):
``` python
In[1]: import pandas as pd
...: import numpy as np
...: iterables = [['bar',... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12823 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12823/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12823/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12823/events | https://github.com/pandas-dev/pandas/issues/12823 | 146,667,371 | MDU6SXNzdWUxNDY2NjczNzE= | 12,823 | Time Zone problem with tz_localize | {
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] | closed | false | null | [] | null | 10 | 2016-04-07T15:54:27Z | 2020-03-30T14:12:14Z | 2016-04-07T16:07:45Z | NONE | null | Hi All,
I'm having a issue with tz_localize, maybe is my misunderstanding.
I'm importing a dataframe and I want to se the timezone for central Europe:
So if I use **Eurpe/Rome** I get:
```
In [303]: df.index.tz_localize('Europe/Rome')
Out[303]:
DatetimeIndex(['2012-12-15 00:00:00+01:00', '2012-12-15 00:15:00+01:00... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12824 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12824/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12824/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12824/events | https://github.com/pandas-dev/pandas/issues/12824 | 146,846,474 | MDU6SXNzdWUxNDY4NDY0NzQ= | 12,824 | groupby().apply() returns different result depends on the first result is None or not. | {
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> apply can act as a reducer, transformer, or filter function, depending on exactly what is passed to apply. So depending on the path taken, and exactly what you are grouping. Thus the grouped columns(s) may be included in the output as well as set the indices.
So, I think it may... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12825 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12825/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12825/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12825/events | https://github.com/pandas-dev/pandas/issues/12825 | 146,888,250 | MDU6SXNzdWUxNDY4ODgyNTA= | 12,825 | Low performance with sum() | {
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"default"... | closed | false | null | [] | null | 1 | 2016-04-08T10:34:27Z | 2016-04-08T12:58:55Z | 2016-04-08T12:58:42Z | NONE | null | I have encountered a problem where manual addition ~25 times faster than `sum` function.
Is there a bug or bad use case?
```
In[3]: %timeit test_1()
1 loops, best of 3: 212 ms per loop
In[4]: %timeit test_2()
1 loops, best of 3: 5.66 s per loop
```
```
import pandas as pd
import numpy as np
import itertools
r, c =... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12826 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12826/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12826/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12826/events | https://github.com/pandas-dev/pandas/issues/12826 | 146,889,628 | MDU6SXNzdWUxNDY4ODk2Mjg= | 12,826 | resample() / DatetimeIndexResampler is not resampling according to the given frequency rule | {
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"id": ... | open | false | null | [] | null | 1 | 2016-04-08T10:41:54Z | 2020-05-11T18:54:04Z | null | NONE | null | When using frequency rule `2Q-SEP` it depends on the index of the time series it gives different resampled results. We should expect always March and September dates when using 2Q-SEP, however we might get June and December sometimes.
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12827 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12827/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12827/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12827/events | https://github.com/pandas-dev/pandas/issues/12827 | 146,933,234 | MDU6SXNzdWUxNDY5MzMyMzQ= | 12,827 | Indexing MultiIndex level depends on chosen level | {
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"description": "Related to indexing on s... | open | false | null | [] | null | 17 | 2016-04-08T13:45:29Z | 2021-04-23T03:04:11Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [2]: ss = pd.Series(range(4),
...: index=pd.MultiIndex.from_product([[1,2], [1,2]]))
In [3]: ss.loc[:, 1].index.__class__
Out[3]: pandas.indexes.numeric.Int64Index
In [4]: ss.loc[1,:].index.__class__
Out[4]: pandas.indexes.multi.MultiInde... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12828 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12828/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12828/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12828/events | https://github.com/pandas-dev/pandas/issues/12828 | 146,975,602 | MDU6SXNzdWUxNDY5NzU2MDI= | 12,828 | API: extend resample to handle non-datetime numeric axes? | {
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"description": "resample... | open | false | null | [] | null | 1 | 2016-04-08T16:29:21Z | 2021-04-23T03:04:54Z | null | MEMBER | null | Numeric indexes are pretty common with scientific datasets and [xarray](http://xarray.pydata.org). For such data, resampling to a new resolution is a pretty common operation.
Currently, resampling only works for dates. Any thoughts on making it work for all numeric axes? I think every argument in the [signature](http:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12829 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12829/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12829/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12829/events | https://github.com/pandas-dev/pandas/issues/12829 | 146,995,402 | MDU6SXNzdWUxNDY5OTU0MDI= | 12,829 | Bug when combining .groupby() apply with .expanding() apply | {
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In this case the strings are `['tito', 'bar', 'fee... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12830 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12830/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12830/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12830/events | https://github.com/pandas-dev/pandas/issues/12830 | 147,037,690 | MDU6SXNzdWUxNDcwMzc2OTA= | 12,830 | Datetime series cannot subtract numpy.datetime64 | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2016-04-08T20:55:41Z | 2016-04-08T21:17:27Z | 2016-04-08T21:15:40Z | CONTRIBUTOR | null | For some reason, it's not possible to subtract a `numpy.datetime64` object from a `Series`, even though subtracting a `datetime.datetime` object is possible. I found this bug when trying to work around issue #8554.
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy as np
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https://api.github.com/repos/pandas-dev/pandas/issues/12831 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12831/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12831/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12831/events | https://github.com/pandas-dev/pandas/pull/12831 | 147,049,682 | MDExOlB1bGxSZXF1ZXN0NjU4NTMwOTI= | 12,831 | BUG: SparseSeries.reindex with fill_value | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/12832 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12832/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12832/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12832/events | https://github.com/pandas-dev/pandas/pull/12832 | 147,066,756 | MDExOlB1bGxSZXF1ZXN0NjU4NjI2MTA= | 12,832 | Refactor test __tmp_* file cleanup | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12833 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12833/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12833/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12833/events | https://github.com/pandas-dev/pandas/pull/12833 | 147,071,535 | MDExOlB1bGxSZXF1ZXN0NjU4NjQ1NTM= | 12,833 | Implement Akima1DInterpolator | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12834 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12834/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12834/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12834/events | https://github.com/pandas-dev/pandas/pull/12834 | 147,089,283 | MDExOlB1bGxSZXF1ZXN0NjU4NzA4MDA= | 12,834 | BUG 12800 fixed inconsistent behavior of last_valid_index and first_valid_index | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12835 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12835/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12835/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12835/events | https://github.com/pandas-dev/pandas/pull/12835 | 147,093,660 | MDExOlB1bGxSZXF1ZXN0NjU4NzE3NjU= | 12,835 | BUG: SparseSeries.value_counts ignores fill_value | {
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- [x] tests added / passed
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Also fixed an issue which categorical `value_counts` resets name.
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https://api.github.com/repos/pandas-dev/pandas/issues/12836 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12836/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12836/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12836/events | https://github.com/pandas-dev/pandas/pull/12836 | 147,093,943 | MDExOlB1bGxSZXF1ZXN0NjU4NzE4MzA= | 12,836 | BUG: SparseSeries.to_frame results in dense | {
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- [x] tests added / passed
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This should be after #12831.
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https://api.github.com/repos/pandas-dev/pandas/issues/12837 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12837/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12837/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12837/events | https://github.com/pandas-dev/pandas/issues/12837 | 147,138,486 | MDU6SXNzdWUxNDcxMzg0ODY= | 12,837 | fill_value in pandas.DataFrame.add: Why must be float? | {
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```
fill_value : None or float value, default None
Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing
```
So its type is None or float, which causes that all dtypes in results are floats.
In practise... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12838 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12838/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12838/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12838/events | https://github.com/pandas-dev/pandas/issues/12838 | 147,144,333 | MDU6SXNzdWUxNDcxNDQzMzM= | 12,838 | BUG: fillna(inplace=True) method in DataFrame with list of column | {
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```
df = pd.DataFrame([[None,2,3], [1,None,3], [1,2,None]], columns=['one', 'two', 'three'])
df
# one two three
#0 NaN 2.0 3.0
#1 1.0 NaN 3.0
#2 1.0 2.0 NaN
#Here work as expected
df.loc[:, ['one', 'two', 'three']].fillna(0.)
# one two three
#0 0.0 2.0 3.0... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12839 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12839/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12839/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12839/events | https://github.com/pandas-dev/pandas/issues/12839 | 147,149,218 | MDU6SXNzdWUxNDcxNDkyMTg= | 12,839 | GroupBy.nth includes group key inconsistently | {
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`nth` doesn't inlcude group key as the same as `first` and `last`.
```
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [1, 2, 3, 4, 5],
'G': [1, 1, 2, 2, 1]})
g = df.groupby('G')
g.nth(1)
# A B
# G
#1 2 2
#2 4 4
```
However, callin... | {
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Because it may affect to resample perf, alternative idea is to add `TimeGropuper` path to `GroupBy.first` and `last`.
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Because dtype coercion affects to lots of functions, would like to fix in the following steps. The behavior which looks incorrect are commented with `TODO_GH12747`
1(this PR) Add numeric related tests... | {
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Add `Index.str.get_dummies` which always returns `MultiIndex`.
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Also includes some cleanup for #10723
CC: @AnkurDedania , @IamGianluca
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/12849 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12849/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12849/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12849/events | https://github.com/pandas-dev/pandas/issues/12849 | 147,223,748 | MDU6SXNzdWUxNDcyMjM3NDg= | 12,849 | pd.get_dummies(s) with s being a logical test | {
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"de... | closed | false | null | [] | null | 14 | 2016-04-10T13:07:29Z | 2016-04-10T17:12:37Z | 2016-04-10T17:12:28Z | NONE | null | Hi,
I am using Pandas more and more for preparing my dataset before feeding it to other statistical software.
I believe something really useful and, somehow, missing from the current version of Pandas is a simple way to generate dummy variables based on a logical test.
Dummy variable coding (0-1) is everywhere in... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12850 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12850/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12850/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12850/events | https://github.com/pandas-dev/pandas/issues/12850 | 147,236,911 | MDU6SXNzdWUxNDcyMzY5MTE= | 12,850 | TST/BUG: integer coercion bool with int on windows | {
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"color": "fbc... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2016-04-10T16:05:55Z | 2016-04-10T19:09:46Z | 2016-04-10T19:09:46Z | CONTRIBUTOR | null | xref #12841
this is a test in `test_coercion.py, L361`
```
In [3]: import pandas as pd
In [4]: s = pd.Series([True, False, True, False])
In [5]: s.where(s,1)
Out[5]:
0 1
1 1
2 1
3 1
dtype: int32
```
on windows the scalar is not coerced to int64 (as it is on other platforms). IIRC `.where` already has q... | {
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"description": "S... | closed | false | null | [] | null | 2 | 2016-04-10T17:28:22Z | 2019-10-11T05:00:45Z | 2019-10-11T05:00:45Z | NONE | null | It would be great if there is a support for `inplace` argument for `str.replace`, so I can do
``` python
addresses.str.replace('\s+\d{5}', '', inplace=True)
```
instead of
``` python
addresses = addresses.str.replace('\s+\d{5}', '')
```
Thank you!
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https://api.github.com/repos/pandas-dev/pandas/issues/12852 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12852/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12852/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12852/events | https://github.com/pandas-dev/pandas/pull/12852 | 147,248,385 | MDExOlB1bGxSZXF1ZXN0NjU5MTc5NTg= | 12,852 | BUG: ensure coercing scalars to when setting as numpy | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2016-04-10T18:13:02Z | 2016-04-10T19:09:46Z | 2016-04-10T19:09:46Z | CONTRIBUTOR | null | closes #12850
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https://api.github.com/repos/pandas-dev/pandas/issues/12853 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12853/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12853/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12853/events | https://github.com/pandas-dev/pandas/pull/12853 | 147,263,249 | MDExOlB1bGxSZXF1ZXN0NjU5MjE3NTQ= | 12,853 | DOC: fix code-block ipython highlighting | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2016-04-10T20:26:25Z | 2016-04-11T13:42:54Z | 2016-04-11T13:42:54Z | MEMBER | null | I noticed some warnings in the travis doc build log in the sense of "WARNING: Could not parse literal_block as "python". highlighting skipped."
This was because some of the `.. code-block:: python` directives contained code that could not be interpreted as python. And, additionally, these code blocks were also not hig... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12854 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12854/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12854/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12854/events | https://github.com/pandas-dev/pandas/issues/12854 | 147,273,483 | MDU6SXNzdWUxNDcyNzM0ODM= | 12,854 | API: remove need for CustomBusiness freqs | {
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... | 2 | 2016-04-10T22:15:38Z | 2020-09-20T23:26:57Z | 2020-09-20T23:26:56Z | CONTRIBUTOR | null | I think we can move the functionaility to the regular `Business` freqs but just accepting
the additional arguments from `CustomBusinesssDay`, IOW, expand `BusinessDay` with default args of `None` for `weekmask` and `holidays` (and just default them).
I think this can be done without any user changes at all. We can th... | {
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"followers_url": "https://api.github.com/users/jr... | 17 | 2016-04-10T22:33:40Z | 2016-12-15T11:28:26Z | 2016-12-15T11:28:26Z | MEMBER | null | The documentation of `SparseArray.cumsum` says it returns a `Series`, but that is not the case:
``` python
>>> from pandas import SparseArray, np
>>> data = np.arange(10)
>>> s = SparseArray(data, fill_value=np.nan)
>>> type(s.cumsum())
<class 'pandas.sparse.array.SparseArray'>
>>>
>>> s = SparseArray(data, fill_value... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12856 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12856/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12856/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12856/events | https://github.com/pandas-dev/pandas/issues/12856 | 147,295,089 | MDU6SXNzdWUxNDcyOTUwODk= | 12,856 | unit test test_numpy_round fail in 3.5 numpy_dev build | {
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https://travis-ci.org/pydata/pandas/jobs/122091911
```
======================================================================
ERROR: test_numpy_round (pandas.tests.frame.test_analytics.TestDataFrameAnalytics)
----------------------------------------------------------------------
Traceback (most recent ... | {
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#### Code Sample, a copy-pastable example if ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12858 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12858/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12858/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12858/events | https://github.com/pandas-dev/pandas/issues/12858 | 147,340,130 | MDU6SXNzdWUxNDczNDAxMzA= | 12,858 | Query function gives error if column names have front slash | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2016-04-11T07:32:53Z | 2016-04-11T11:21:50Z | 2016-04-11T11:21:18Z | NONE | null | ```
import pandas
df = pandas.DataFrame({'ColName / WithFrontSlash': [1, 2]})
df['ColName / WithFrontSlash']
0 1
1 2
Name: ColName / WithFrontSlash, dtype: int64
#this output is what is exptected
```
```
df.query('ColName / WithFrontSlash > 1')
pandas.computation.ops.UndefinedVariableError: name 'ColName' is n... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12859 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12859/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12859/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12859/events | https://github.com/pandas-dev/pandas/issues/12859 | 147,407,860 | MDU6SXNzdWUxNDc0MDc4NjA= | 12,859 | cross section coercion with output iterating | {
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... | 11 | 2016-04-11T12:10:21Z | 2021-05-12T13:27:55Z | 2021-05-12T13:27:55Z | NONE | null | I'm am trying to call the to_dict function on the following DataFrame:
```
import pandas as pd
data = {"a": [1,2,3,4,5], "b": [90,80,40,60,30]}
df = pd.DataFrame(data)
```
```
df
a b
0 1 90
1 2 80
2 3 40
3 4 60
4 5 30
```
```
df.reset_index().to_dict("r")
[{'a': 1, 'b': 90, 'index': 0},
{'a': 2, 'b... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12860 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12860/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12860/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12860/events | https://github.com/pandas-dev/pandas/issues/12860 | 147,453,993 | MDU6SXNzdWUxNDc0NTM5OTM= | 12,860 | ENH: support .astype('category') on DataFrame / aka co-factorization | {
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We don't allow an astype of a DataFrame to category directly
```
In [44]: df.astype('category')
NotImplementedError: > 1 ndim Categorical are not supported at this time
```
Instead you can apply the astype per-column.
```
In [35]: df = DataFrame({'A' : list('aabcda'), 'B' : list('bcdaae')})
I... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12861 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12861/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12861/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12861/events | https://github.com/pandas-dev/pandas/issues/12861 | 147,460,316 | MDU6SXNzdWUxNDc0NjAzMTY= | 12,861 | BUG: indexing with boolean array and categoricals | {
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... | 1 | 2016-04-11T15:11:43Z | 2019-10-21T19:34:17Z | null | CONTRIBUTOR | null | xref #12857
```
In [24]: df2 = df.apply(lambda x: x.astype('category',categories=(np.sort(pd.unique(df.values.ravel())))))
In [25]: df2
Out[25]:
A B
0 a b
1 a c
2 b d
3 c a
4 d a
5 a e
```
after #12564, the boolean array is ok
```
In [28]: df2=='d'
Out[28]:
A B
0 False False
1 False... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12862 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12862/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12862/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12862/events | https://github.com/pandas-dev/pandas/issues/12862 | 147,463,196 | MDU6SXNzdWUxNDc0NjMxOTY= | 12,862 | Series.__setitem__ with datetime64[ns, tz] dtype raises ValueError when setting a tz-aware timestamps | {
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"followers_url": "https://api.github.com/users/j... | 2 | 2016-04-11T15:23:43Z | 2019-07-11T16:39:34Z | 2019-07-11T16:39:34Z | NONE | null | I'm not able to construct a Series element by element if the elements are timezone localized datetimes. Any workarounds?
#### Code Sample, a copy-pastable example if possible
``` python
# This works
x = pd.Series()
x['foo'] = pd.to_datetime(42)
x['bar'] = pd.to_datetime(666)
# This doesn't
x = pd.Series()
x['foo'] = ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12863 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12863/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12863/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12863/events | https://github.com/pandas-dev/pandas/issues/12863 | 147,464,202 | MDU6SXNzdWUxNDc0NjQyMDI= | 12,863 | Series.any() and .all() don't return bool values if dtype=object | {
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```
s = pd.Series(index=range(5), data=['a', 'b', 'c', 'd', 'e'], dtype=object)
print [s.any(), s.all()]
```
#### Expected Output
`[True, True]`
#### Actual output
`['a', 'e']`
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12864 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12864/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12864/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12864/events | https://github.com/pandas-dev/pandas/pull/12864 | 147,466,098 | MDExOlB1bGxSZXF1ZXN0NjYwMTM4NzE= | 12,864 | COMPAT: .query/.eval should work w/o numexpr being installed if possible | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12865 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12865/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12865/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12865/events | https://github.com/pandas-dev/pandas/issues/12865 | 147,480,645 | MDU6SXNzdWUxNDc0ODA2NDU= | 12,865 | MetaIssue: Pandas tries too hard to be smart | {
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I had the following task: Given a csv file A and a set of identically structured files X, Y, Z [which have keys in the first two columns, and values in subsequent columns]: I wanted to create a new file which has contents MAX(A.column_val, X.column_val + Y.column_val + Z.column_val)... | {
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This might be a well-known problem but could not find a track/explaination about it. When reading a json to create a pandas object (Series or DataFrame), the index dtype is changed from int to float.
The [doc](http://pandas.pydata.org/pandas-docs/stable/io.html#data-conversion) only mentions the inverse tr... | {
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... | 0 | 2016-04-11T18:45:24Z | 2020-04-10T03:32:59Z | null | CONTRIBUTOR | null | xref #12749
xref #12864
This is a tad non-trivial as the python engine handles string operations. So this would be for numeric/boolean ops.
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`
In [7]: pd.Series([], pd.period_range(start='2000', periods=0)).resample('W').mean().index
Out[7]: DatetimeIndex([], dtype='datetime64[ns]', freq='W-SUN')
`
#### Expected Output
`
PeriodIndex([], dtype='int64', freq='W-SUN')
`
On pandas: 0.18.0
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... | 14 | 2016-04-11T21:13:09Z | 2019-10-28T17:44:13Z | null | NONE | null | Trying to investigate different subset of data frame's columns we get into trouble when 'duplicated' method is invoked for a data frame sliced to an empty subset of columns.
``` python
ValueError Traceback (most recent call last)
<ipython-input-672-9c619ea6d0ef> in <module>()
14 pr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12870 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12870/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12870/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12870/events | https://github.com/pandas-dev/pandas/issues/12870 | 147,557,735 | MDU6SXNzdWUxNDc1NTc3MzU= | 12,870 | read_excel with names keyword argument not working | {
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#### Code Sample, a copy-pastable example if possible
filename = 'test.xlsx'
column_names = ['field1', 'field2']
df = pd.read_excel... | {
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"description"... | closed | false | null | [] | null | 11 | 2016-04-11T22:05:34Z | 2020-05-11T02:08:31Z | 2020-05-11T02:08:31Z | CONTRIBUTOR | null | Various issue surrounding resampling with a `PeriodIndex`
- [x] #12884 Return PeriodIndex if possible
- [x] #12770 recursion error on existing freq (#12874)
- [x] #12774 count (#12874)
- [x] #12868 empty PeriodIndex (#12874)
- [x] #13212 empty dataframe (#13079)
- [x] #11983 with Panels?
- [ ] #7744 convention i... | {
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``` python
In[3]: pd.Index(np.arange(200))
```
```
Out[3]: Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
190, 191, 192, 193, 194, 195, 196, 197, 198, 199],
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The documentation of [itertuples](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.itertuples.html) says this function will "Iterate over DataFrame rows as namedtuples", but the resulting items are not namedtuple ... | {
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closes #12868
closes #12770
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Initial attempt at fixing some of the more urgent issues
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Cleans up a method that returned different types depending on the length of the index
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https://api.github.com/repos/pandas-dev/pandas/issues/12876 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12876/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12876/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12876/events | https://github.com/pandas-dev/pandas/issues/12876 | 147,697,196 | MDU6SXNzdWUxNDc2OTcxOTY= | 12,876 | Pandas IO to_sql extremely slow when checking for potentially case sensitivity issues | {
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Since this fix, when checking for case sensitivity issues for MySQL using InnoDB engine with large numbers of tables, Class SQLDatabase.to_sql can take a long time (I currently have ~30,000 tables and this call takes ~10 seconds).
This is due to the 'SHOW FULL TABLES' call coming from SQLAlche... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12877 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12877/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12877/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12877/events | https://github.com/pandas-dev/pandas/issues/12877 | 147,700,653 | MDU6SXNzdWUxNDc3MDA2NTM= | 12,877 | DataFrame of datetimes with NaT Comparison always evaluates to True | {
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```
>>> import pandas as pd
>>> from datetime import date
>>> df=pd.DataFrame({'a': pd.Series([date(2016,1,1)], index=[date(2016,1,1)]), 'b': pd.Series([date(2016,1,3)], index=[date(2016,1,3)])})
>>> df
a b
2016-01-01 2016-01-01 NaN
2016-01-03 NaN 2016-... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12878 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12878/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12878/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12878/events | https://github.com/pandas-dev/pandas/pull/12878 | 147,723,209 | MDExOlB1bGxSZXF1ZXN0NjYxNDMzOTU= | 12,878 | BUG: json invoke default handler for unsupported numpy dtypes | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2016-04-12T11:45:25Z | 2016-04-27T14:29:01Z | 2016-04-27T14:28:37Z | CONTRIBUTOR | null | - [x] closes #12554
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
- [x] valgrind clean
- [ ] windows tests ok
- [x] vbench ok
Recommend to merge after #12802 has been accepted. (valgrind should be clean then)
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https://api.github.com/repos/pandas-dev/pandas/issues/12879 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12879/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12879/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12879/events | https://github.com/pandas-dev/pandas/issues/12879 | 147,732,110 | MDU6SXNzdWUxNDc3MzIxMTA= | 12,879 | DOC: OrderedDict example in groupby aggregation | {
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It's me, again. ;-) I would like to submit to your attention another possible improvement your could implement.
Let me start by saying that 90% of the time, data processing in my field involves some form of groupby at the time frequency, panel frequency, etc.
So aggregating data efficiently is definitely so... | {
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- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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```
data = Index([u'0', u'1', u'2'], dtype='object')
data.astype('float64').astype('int64')
TypeError: Setting <class 'pandas.indexes.numeric.Float64Index'> dtype to anything other than float64 or object is not supported
```
this should work
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https://api.github.com/repos/pandas-dev/pandas/issues/12882 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12882/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12882/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12882/events | https://github.com/pandas-dev/pandas/pull/12882 | 147,786,050 | MDExOlB1bGxSZXF1ZXN0NjYxNzcxNDE= | 12,882 | BUG, DEP, DOC: Patch and Align Categorical's Sorting API | {
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as it is possible to call this method (as well as `sort_values`) when the `Categorical` is unordered.
Also patches a bug in `Categorical.sort_values` in which `na_position` was not being respected when
`ascendin... | {
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... | 3 | 2016-04-12T21:12:18Z | 2016-04-12T21:34:45Z | 2016-04-12T21:34:44Z | CONTRIBUTOR | null | ```
In [32]: s = pd.Series(range(2), pd.period_range('1/1/1990', periods=2, freq='M'))
In [33]: s
Out[33]:
1990-01 0
1990-02 1
Freq: M, dtype: int64
In [34]: s.resample('W').asfreq()
```
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... | 1 | 2016-04-12T22:56:01Z | 2021-04-23T05:04:34Z | null | CONTRIBUTOR | null | As discussed here: https://github.com/pydata/pandas/pull/12874, some ideas:
- ~Only return `PeriodIndex`s when resampling `PeriodIndex`~ (#16153)
- Don't use `convention` when doing simple upsampling & downsampling ('simple' meaning a one to many relationship between subperiods and superperiods)
- Define how to hand... | {
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I dont know if that is possible, but it would great to find a way to speed up the `to_csv` method in Pandas.
In my admittedly large dataframe with 20 million observations and 50 variables, it takes **literally hours** to export the data to a csv file.
Reading the `csv` in Pandas is much faster though. I won... | {
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[here](https://github.com/pydata/pandas/commit/2ba977a917ac5c092378a2f172f59d53b14de968) is the reverted commit. Note this should be rolled in the `test_resample_empty` test itself
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python -c 'import pandas;pandas.test()'
#### Expected Output
No errors/failures
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-32-generic
machine: x86_... | {
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... | 2 | 2016-04-13T09:28:07Z | 2021-04-23T05:05:13Z | null | CONTRIBUTOR | null | When creating a plot with two y-axis, I run into the following problem. When plotting using the pd.Series.plot() method on the first y-axis and then applying ax.fill_between() Python crashes. If I use fill_between on the primary y-axis also the problem disappears.
#### Code Sample, a copy-pastable example if possible
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```
python --version & python -c "import pandas as pd; print('Pandas version', pd.__version__)" & python -m timeit "import pand... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12890 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12890/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12890/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12890/events | https://github.com/pandas-dev/pandas/issues/12890 | 148,058,844 | MDU6SXNzdWUxNDgwNTg4NDQ= | 12,890 | The API to retrieve serie elements presents some inconsistencies | {
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] | closed | false | null | [] | null | 3 | 2016-04-13T13:18:01Z | 2016-04-13T15:34:39Z | 2016-04-13T15:34:39Z | NONE | null | Hello, I personally feel there is a bit of mess in the way to select elements in a Series :-)
The general idea is that .iloc and .loc have consistent behaviour for respectively demanding a position-based or a index(label)-based value, but are a bit slower than .ix and using directly [] which behaviour is not always co... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12891 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12891/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12891/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12891/events | https://github.com/pandas-dev/pandas/issues/12891 | 148,093,732 | MDU6SXNzdWUxNDgwOTM3MzI= | 12,891 | API proposal: shoudn't the "index" property (and parameter) of a df renamed "rows" ? | {
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in a DataFrame object, both the `columns` and `index` properties are actually `Index` objects.
Wouldn't be cleaner to rename `index` as `rows` ?
As you are working with n-dimensional panels, `rows` and `columns` could then be just convenient aliases for something like `ax... | {
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"color": "0052cc"... | closed | false | null | [] | null | 4 | 2016-04-13T16:21:41Z | 2016-04-13T18:19:48Z | 2016-04-13T16:26:00Z | CONTRIBUTOR | null | Depending on if the indices of two series/dataframes are aligned, floor division by zero can result in either `NaN` or `inf`:
``` python
In [68]: import pandas as pd
In [69]: l = pd.Series([1, 2, 3], index=[1, 2, 3])
In [70]: r = pd.Series([1, 1, 0], index=[0, 2, 3])
In [71]: l // r # Unaligned indices
Out[71]:
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https://api.github.com/repos/pandas-dev/pandas/issues/12893 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12893/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12893/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12893/events | https://github.com/pandas-dev/pandas/issues/12893 | 148,122,496 | MDU6SXNzdWUxNDgxMjI0OTY= | 12,893 | Related TypeErrors in multi-indexed DataFrame | {
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``` python
import pandas as pd
import numpy as np
foo = pd.DataFrame(np.arange(100).reshape((10,10)))
bar = pd.DataFrame(np.arange(100).reshape((10,10)))
df = pd.concat({
'foo': foo.stack(),
'bar': bar.stack()
}, axis=1)
df.index.names = ['fizz','buzz']
```
## Output
``` python
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] | closed | false | null | [] | null | 4 | 2016-04-13T17:41:30Z | 2016-04-17T18:54:05Z | 2016-04-13T17:49:18Z | NONE | null | #### To reproduce follow steps below
``` python
In [226]: df_test = pd.DataFrame(np.random.randn(5,5), columns=list('ABCDE'))
In [227]: df_test
Out[227]:
A B C D E
0 0.609775 0.205708 -0.015081 2.096414 0.121245
1 1.223234 -1.299398 1.238649 0.216846 -0.789828
2 0.44... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12895 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12895/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12895/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12895/events | https://github.com/pandas-dev/pandas/pull/12895 | 148,187,314 | MDExOlB1bGxSZXF1ZXN0NjYzOTQzNDA= | 12,895 | BUG: add names parameter to read_excel | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/12896 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12896/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12896/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12896/events | https://github.com/pandas-dev/pandas/issues/12896 | 148,228,431 | MDU6SXNzdWUxNDgyMjg0MzE= | 12,896 | Multiindex slicing df.loc[idx[dim1,dim2,dim3],:] not working right in some cases | {
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```
import pandas as pd
import numpy as np
np.random.seed(0)
idx = pd.IndexSlice
midx = pd.MultiIndex.from_product([['CS'], range(20), range(-1100, 6000)])
df = pd.DataFrame(np.random.randn(7100*20, 3), columns=['dat1', 'dat2', 'dat3'], index=midx)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12897 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12897/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12897/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12897/events | https://github.com/pandas-dev/pandas/issues/12897 | 148,237,645 | MDU6SXNzdWUxNDgyMzc2NDU= | 12,897 | TST: remove ResourceWarnings | {
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... | 2 | 2016-04-14T03:02:53Z | 2016-08-20T17:09:00Z | 2016-08-20T17:09:00Z | CONTRIBUTOR | null | So just enabled `ResourceWarnings` to show up (these are only on PY3), see a sample run [here](https://travis-ci.org/pydata/pandas/jobs/122942984); they are only enabled on the 3.5-dev build (you can manually enable by setting `PANDAS_TESTING_MODE="deprecate"`
These are unclosed filehandles mostly in the `read_csv` te... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12898 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12898/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12898/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12898/events | https://github.com/pandas-dev/pandas/issues/12898 | 148,296,232 | MDU6SXNzdWUxNDgyOTYyMzI= | 12,898 | bugs when groupby/aggregate on columns with datetime64[ns, timezone] | {
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... | 4 | 2016-04-14T09:00:28Z | 2016-09-20T12:34:24Z | 2016-09-20T12:34:24Z | CONTRIBUTOR | null | When aggregating a column with dtype datetime64[ns, timezone], pandas does not handle properly tz information. It looks like as if pandas was dropping the timezone info (ie doing its calculation on the UTC values) and then localizing the date to the original timezone,i.e. doing something like
`column_with_UTC_dates_in_... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/12899 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12899/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12899/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12899/events | https://github.com/pandas-dev/pandas/pull/12899 | 148,348,985 | MDExOlB1bGxSZXF1ZXN0NjY0Nzc0NDc= | 12,899 | BUG: .astype() of a Float64Index to a Int64Index | {
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closes #12866
xref #12309
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are properly skipped.
Closes #10911.
Closes #12775.
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- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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Based on the error message, I assume that groupby is bei... | {
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"closed_issues": 306,
"created_at": "2016-02-08T15:29:59Z",
"creator": {
"avatar_url": "https://avatars.githubusercontent.com/u/953992?v=4",
"events_url": "https://api.github.com/users/jreback/events{/privacy}",
"followers_url": "https://api.github.com/users/jr... | 4 | 2016-04-15T11:34:14Z | 2016-04-26T19:21:19Z | 2016-04-26T19:21:19Z | CONTRIBUTOR | null | The following imports in pandas/src/period.pyx significantly impact performance when dealing with multiple Period objects. A quick win, guys.
``` cython
def __init__(self, value=None, freq=None, ordinal=None,
year=None, month=1, quarter=None, day=1,
hour=0, minute=0, second=0):
... | {
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} | https://api.github.com/repos/pandas-dev/pandas/issues/12903/timeline | null | null | null |
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