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https://api.github.com/repos/pandas-dev/pandas/issues/11804 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11804/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11804/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11804/events | https://github.com/pandas-dev/pandas/issues/11804 | 121,285,183 | MDU6SXNzdWUxMjEyODUxODM= | 11,804 | DatetimeIndex with closed='left' or 'right' drop dates at the boundaries | {
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```
In [1]: pd.date_range('2015-09-12', '2015-10-30', freq='QS-MAR', closed='right')
Out[1]: DatetimeIndex([], dtype='datetime64[ns]', freq='QS-MAR')
In [2]: pd.date... | {
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```
import pandas as pd
url = 'https://raw.githubusercontent.com/cs109/2014_data/master/diamonds.csv'
diamonds = pd.read_csv(url, sep = ',', index_col=0)
diamonds.groupby('color').plot(kind='scatter', x='carat', y='price')
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This changes the definition of 'left' and 'right'. If the generated_dates are strictly within (start, end), then changing the value of closed has no impact. Before the code would always remove the leftmost date or rightmost date independently of the value of start and end.
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] | closed | false | null | [] | null | 5 | 2015-12-09T22:08:23Z | 2015-12-14T14:08:26Z | 2015-12-11T00:05:48Z | NONE | null | The following line of code prints the wrong distribution:
``` python
import matplotlib.pyplot as plt
import pandas as pd
# df is a pd.DataFrame object. Define df here...
plt.figure();
(df['x1'] - df['x2']).hist(normed=True)
plt.show();
```
It should print the green distribution, but it prints the blue instead (whi... | {
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Since the property's attribute name is neither in `_internal_names_set` nor `_info_axis`, a new exception is risen and the original stack trace is lost.
``` python
import pandas a... | {
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This contains changes to support `round()` (works in Python 3 only) for DataFrame, Series and Panel. Also includes `Panel.round()`. Note on code changes - moved code from test_format.py to test_frame.py because it made more logical sense to put the testing code for `round()` into test_frame.py
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... | 4 | 2015-12-10T05:51:39Z | 2021-03-27T13:19:34Z | 2021-03-27T13:19:34Z | NONE | null | Exception is raised when the index is duplicated
Fro example:
```
df = pd.DataFrame([['a',1],['b',1],['a',2],['b',2]],columns=['c1','c2'], index=list('ccdd'))
df.groupby('c1').apply(lambda x:x)
raise ValueError("cannot reindex from a duplicate axis")
ValueError: cannot reindex from a duplicate axis
```
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"c... | closed | false | null | [] | null | 8 | 2015-12-10T06:27:05Z | 2018-06-05T15:24:27Z | 2018-05-30T11:11:29Z | NONE | null | ``` python
>>> pd.__version__
u'0.17.1'
# Column 1 is a date column, column 0 is a text column representing a stock symbol, column 12 is a price
>>> rawdat = pd.read_csv('./data.csv', parse_dates=[1], index_col=[0, 1], usecols=[0, 1, 12])
# Get monthly change in value for each stock symbol
>>> rawdat.groupby(level=0).p... | {
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... | 6 | 2015-12-10T12:30:58Z | 2021-04-21T05:22:45Z | null | CONTRIBUTOR | null | Python (3.4) doesn't support `"%n"%9.0`, have to use `"{:n}".format(9.0)` instead.
But pandas (0.17.0) `to_native_types` uses 1st type of string formatting, so locale dependent format cannot be used with `to_csv` .
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https://api.github.com/repos/pandas-dev/pandas/issues/11813 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11813/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11813/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11813/events | https://github.com/pandas-dev/pandas/pull/11813 | 121,529,549 | MDExOlB1bGxSZXF1ZXN0NTMyODQ1MTA= | 11,813 | ENH: GH11128 add weekday_name to DatetimeIndex and .dt | {
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... | closed | false | null | [] | null | 12 | 2015-12-10T16:47:12Z | 2016-04-05T19:13:15Z | 2016-04-05T19:13:15Z | NONE | null | closes #11128
some examples:
```
In [5]: s2 = s.to_frame('value').assign(weekday=s.index.weekday_name)
In [6]: s2
Out[6]:
value weekday
2015-09-01 0 tuesday
2015-09-02 1 wednesday
2015-09-03 2 thursday
2015-09-04 3 friday
2015-09-05 4 saturday
2015-09-06 5 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11814 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11814/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11814/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11814/events | https://github.com/pandas-dev/pandas/issues/11814 | 121,583,397 | MDU6SXNzdWUxMjE1ODMzOTc= | 11,814 | BUG?: `_is_view == False` for DataFrame from DataFrame | {
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"descript... | closed | false | null | [] | null | 7 | 2015-12-10T21:40:35Z | 2020-05-11T19:44:44Z | 2015-12-11T00:38:02Z | CONTRIBUTOR | null | Constructing off a dataframe creates a _de facto_ view, but `._is_view` reports false. Seems like a bug.
```
In [1]:
df = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
df2 = pd.DataFrame(df)
df2._is_view
Out[1]:
False
In [2]:
df.loc[0,'col1'] = -88
df2
Out[2]:
col1 col2
0 -88 3... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11815 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11815/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11815/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11815/events | https://github.com/pandas-dev/pandas/issues/11815 | 121,601,831 | MDU6SXNzdWUxMjE2MDE4MzE= | 11,815 | BUG: Series.argsort with NaN | {
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"color": "0052cc"... | closed | false | null | [] | null | 3 | 2015-12-10T23:28:32Z | 2015-12-11T00:31:23Z | 2015-12-11T00:31:23Z | CONTRIBUTOR | null | Am I missing something?
```
In [178]: z = pd.Series(np.random.randn(10), index=np.arange(2, 12))
In [179]: z
Out[179]:
2 0.130294
3 1.567679
4 0.912771
5 1.097172
6 -0.259776
7 -1.546187
8 0.449616
9 0.197778
10 0.055520
11 1.170821
dtype: float64
In [180]: z.iloc[z.argsort()]
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https://api.github.com/repos/pandas-dev/pandas/issues/11816 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11816/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11816/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11816/events | https://github.com/pandas-dev/pandas/pull/11816 | 121,602,407 | MDExOlB1bGxSZXF1ZXN0NTMzMzM4NDQ= | 11,816 | ENH: add 'asm8' to NaT | {
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"followers_url": "https://api.github.com/users/jr... | 8 | 2015-12-10T23:33:14Z | 2015-12-11T13:05:35Z | 2015-12-11T13:05:31Z | CONTRIBUTOR | null | I was suprised to find that `NaT.asm8` does not exist though there is a valid nat form for `numpy.datetime64`.
This also adds tests for the asm8 behavior for a random sample of times and `NaT`
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https://api.github.com/repos/pandas-dev/pandas/issues/11817 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11817/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11817/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11817/events | https://github.com/pandas-dev/pandas/issues/11817 | 121,605,661 | MDU6SXNzdWUxMjE2MDU2NjE= | 11,817 | Specify column format by name | {
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"color": "0052c... | closed | false | null | [] | null | 2 | 2015-12-10T23:59:58Z | 2015-12-11T00:32:35Z | 2015-12-11T00:32:35Z | NONE | null | Is it possible to specify a column format by the column name?
i.e. Dataframe has two columns, both containing a array of integers. It would be great if the two columns could have different formats (i.e. one with commas, the other without, etc.)
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https://api.github.com/repos/pandas-dev/pandas/issues/11818 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11818/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11818/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11818/events | https://github.com/pandas-dev/pandas/issues/11818 | 121,611,194 | MDU6SXNzdWUxMjE2MTExOTQ= | 11,818 | ERR: between_time should raise on non-timelike objects | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-12-11T00:49:23Z | 2016-01-03T16:26:25Z | 2016-01-03T16:26:25Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/34207898/issue-selecting-data-between-time-from-dataframe-in-pandas-0-17-1/34214431#34214431)
so passing anything except `time` objects or strings should raise (might be a tad tricky as we don't have great time parsers, they all parse datetimelikes).
IIRC this was done for... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11819 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11819/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11819/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11819/events | https://github.com/pandas-dev/pandas/pull/11819 | 121,639,504 | MDExOlB1bGxSZXF1ZXN0NTMzNTQ0OTA= | 11,819 | BUG: read_excel fails when empty sheets exist and sheetname=None #11711 | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-12-11T05:13:19Z | 2015-12-15T04:34:56Z | 2015-12-12T13:49:39Z | CONTRIBUTOR | null | Fixes issue #11711.
Existing code prematurely returns an empty dataframe when an empty sheet in the source excel file is encountered.
Fix is to store an empty dataframe in the output dict and continue to the next sheet.
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https://api.github.com/repos/pandas-dev/pandas/issues/11820 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11820/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11820/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11820/events | https://github.com/pandas-dev/pandas/issues/11820 | 121,640,874 | MDU6SXNzdWUxMjE2NDA4NzQ= | 11,820 | Pandas DataFrame.to_json() does a Segmentation fault with data datetime64[ns, TimeZone] | {
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... | closed | false | null | [] | null | 1 | 2015-12-11T05:30:17Z | 2015-12-11T13:44:30Z | 2015-12-11T13:44:15Z | NONE | null | This is the working example
```
(env)vagrant@app01:~/repos/peppertap_v1/shopper_bot$ uname -a
Linux app01 3.13.0-48-generic #80-Ubuntu SMP Thu Mar 12 11:16:15 UTC 2015 x86_64 x86_64 x86_64 GNU/Linux
(env)vagrant@app01:~/repos/peppertap_v1/shopper_bot$ python --version
Python 2.7.6
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https://api.github.com/repos/pandas-dev/pandas/issues/11821 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11821/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11821/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11821/events | https://github.com/pandas-dev/pandas/pull/11821 | 121,641,778 | MDExOlB1bGxSZXF1ZXN0NTMzNTU3MTE= | 11,821 | DOC: Fix typo in example for df.merge() | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11822 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11822/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11822/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11822/events | https://github.com/pandas-dev/pandas/pull/11822 | 121,651,068 | MDExOlB1bGxSZXF1ZXN0NTMzNTk4NzE= | 11,822 | BUG: GH11808 subclasses of DataFrame did not propagate AttributeError | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11823 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11823/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11823/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11823/events | https://github.com/pandas-dev/pandas/issues/11823 | 121,691,738 | MDU6SXNzdWUxMjE2OTE3Mzg= | 11,823 | Dataframe loading with duplicated columns and usecols | {
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... | 19 | 2015-12-11T11:35:17Z | 2021-04-21T05:25:57Z | null | NONE | null | I'm using pandas 0.17.1
``` python
import pandas as pd
pd.__version__
```
`Out:'0.17.1'`
When column names are duplicated
``` python
cols = ['A', 'A', 'B']
with open('pandas.csv', 'w') as f:
f.write('1,2,3')
```
we can still load dataframe
``` python
pd.read_csv('pandas.csv',
header=None,
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https://api.github.com/repos/pandas-dev/pandas/issues/11824 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11824/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11824/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11824/events | https://github.com/pandas-dev/pandas/pull/11824 | 121,730,576 | MDExOlB1bGxSZXF1ZXN0NTM0MDY0Nzc= | 11,824 | TST: avoid int conversions on windows, xref #11816 | {
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cc @llllllllll
had to change the test as this numpy bug on window: https://github.com/numpy/numpy/issues/6812
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https://api.github.com/repos/pandas-dev/pandas/issues/11825 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11825/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11825/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11825/events | https://github.com/pandas-dev/pandas/pull/11825 | 121,786,402 | MDExOlB1bGxSZXF1ZXN0NTM0NDA2MDc= | 11,825 | ENH: Interpolate NaT (GH-11701) | {
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It does lead to precision issues when converting `to_numeric` and back `to_datetime`.
Wondering if there is a better way to do this?
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https://api.github.com/repos/pandas-dev/pandas/issues/11826 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11826/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11826/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11826/events | https://github.com/pandas-dev/pandas/pull/11826 | 121,833,346 | MDExOlB1bGxSZXF1ZXN0NTM0NjgzNjU= | 11,826 | DOC: Add example to pandas.DataFrame.applymap | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11827 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11827/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11827/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11827/events | https://github.com/pandas-dev/pandas/pull/11827 | 121,835,488 | MDExOlB1bGxSZXF1ZXN0NTM0NjkzNzc= | 11,827 | CLN: Use sentinel values instead of mutable default arguments | {
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Mutable list or dictionary used as default argument to a method or function. Python creates a single persistent object and uses it for every subsequent call in which the argument is left empty. This can cause problems if the program was expecting the function to... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11828 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11828/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11828/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11828/events | https://github.com/pandas-dev/pandas/pull/11828 | 121,836,018 | MDExOlB1bGxSZXF1ZXN0NTM0Njk2Mzk= | 11,828 | CLN: Use `is` operator for comparing with `None` (Pep8) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11829 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11829/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11829/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11829/events | https://github.com/pandas-dev/pandas/pull/11829 | 121,839,218 | MDExOlB1bGxSZXF1ZXN0NTM0NzA4NzU= | 11,829 | CLN: Fix import collisions and optimize imports | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11830 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11830/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11830/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11830/events | https://github.com/pandas-dev/pandas/issues/11830 | 121,846,659 | MDU6SXNzdWUxMjE4NDY2NTk= | 11,830 | groupby.nth lost multiindex | {
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[link](http://stackoverflow.com/questions/34237462/selecting-column-before-nth-breaks-group-indices/)
```
df = pd.DataFrame({'a': [1, 1, 2, 2], 'b': ['b', 'b', 'b', 'a'], 'c': [1, 2, 3, 4]})
print df
# a b c
#0 1 b 1
#1 1 b 2
#2 2 b 3
#3 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11831 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11831/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11831/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11831/events | https://github.com/pandas-dev/pandas/issues/11831 | 121,861,273 | MDU6SXNzdWUxMjE4NjEyNzM= | 11,831 | Performance issue with timeseries plotting on py3? | {
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"followers_url": "https://api.github.com/users/jr... | 14 | 2015-12-12T15:20:38Z | 2016-04-26T19:21:19Z | 2016-04-26T19:21:19Z | MEMBER | null | I noticed a performance issue with plotting timeseries. After some trying with different environments (different pandas, matplotlib and python versions), it seems there is a problem on python 3 -> up to 10 x slowdown compared to python 2.7:
Python 2 and pandas 0.16.2 and 0.17.1:
```
In [2]: sys.version
Out[2]: '2.7.1... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11832 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11832/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11832/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11832/events | https://github.com/pandas-dev/pandas/pull/11832 | 121,884,802 | MDExOlB1bGxSZXF1ZXN0NTM0ODcyODE= | 11,832 | ERR: between_time now checks for argument types | {
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"followers_url": "https://api.github.com/users/jr... | 14 | 2015-12-12T22:27:36Z | 2016-01-03T16:26:10Z | 2016-01-03T16:26:10Z | CONTRIBUTOR | null | Closes #11818
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https://api.github.com/repos/pandas-dev/pandas/issues/11833 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11833/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11833/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11833/events | https://github.com/pandas-dev/pandas/issues/11833 | 121,889,066 | MDU6SXNzdWUxMjE4ODkwNjY= | 11,833 | Leading space in to_string(index=False) | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-13T00:24:25Z | 2016-01-03T16:36:26Z | 2016-01-03T16:36:26Z | CONTRIBUTOR | null | #11750
``` python
In [2]: df = pd.DataFrame({'a':range(5)})
In [3]: df.to_string()
Out[3]: ' a\n0 0\n1 1\n2 2\n3 3\n4 4'
In [4]: df.to_string(index=False)
Out[4]: ' a\n 0\n 1\n 2\n 3\n 4'
```
What is the use of a leading space when index = False ?
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https://api.github.com/repos/pandas-dev/pandas/issues/11834 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11834/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11834/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11834/events | https://github.com/pandas-dev/pandas/pull/11834 | 121,896,950 | MDExOlB1bGxSZXF1ZXN0NTM0OTIyODc= | 11,834 | CLN: frequency.get_offset always return copy | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-13T04:01:11Z | 2015-12-27T06:41:58Z | 2015-12-26T00:28:22Z | MEMBER | null | Closes #11192.
Fix 2 points which should not affect to normal users (thus no release note descriptions).
- get_offset (#11192): It returns cached offset, overwriting it may leads to unexpected results. `pandas` public methods doesn't use them as it is. Thus it should not affect to almost all users, except who explici... | {
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Python 3.5.1, pandas 0.17.1, numpy 0.10.1:
```
Python 3.5.1 |Continuum Analytics, Inc.| (default, Dec 7 2015, 11:24:55)
Type "copyright", "credits" or "license" for more information.
IPyt... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11836 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11836/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11836/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11836/events | https://github.com/pandas-dev/pandas/issues/11836 | 121,944,000 | MDU6SXNzdWUxMjE5NDQwMDA= | 11,836 | int64 Index in 0.17 typcasts '0' string to integer | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-12-13T21:11:01Z | 2016-02-13T13:34:34Z | 2016-02-13T13:34:34Z | NONE | null | Here's what I got. Is this expected behavior? I could not find a reference for this functionality in the release notes.
## 0.16
`conda create -n pd pandas=0.16 python=3.4 ipython`
In 0.16 the dtype of the index changes to object when adding a row with '0'.
```
In [1]: import pandas as pd
In [2]: import numpy as np
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-12-14T16:48:38Z | 2015-12-16T13:13:06Z | 2015-12-16T13:13:06Z | NONE | null | There seem to be some incompatibilities with timezones and timestamps in 0.17.1.
For example, here I'm trying to clip a timezone-aware column with a timezone-aware timestamp:
```
In [224]: import pandas
In [225]: u = pandas.Timestamp('2015-12-01 09:30:30', tz='US/Eastern')
In [226]: print u
2015-12-01 09:30:30-05:0... | {
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df = pd.read_excel('cb2.xlsx') in the column 'Zone', in which there are strings like "APAC", "EUR", "NA"
Surprisingly, the value "NA" is interpreted by pandas as nan
[cb2.xlsx](https://github.com/pydata/pandas/files/61735/cb2.xlsx)
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https://api.github.com/repos/pandas-dev/pandas/issues/11840 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11840/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11840/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11840/events | https://github.com/pandas-dev/pandas/issues/11840 | 122,151,053 | MDU6SXNzdWUxMjIxNTEwNTM= | 11,840 | Unexpected difference between loc and ix | {
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"color": "00... | closed | false | null | [] | null | 7 | 2015-12-14T22:55:06Z | 2015-12-15T17:03:54Z | 2015-12-14T23:17:24Z | NONE | null | See my [StackOverflow post](http://stackoverflow.com/questions/34259637/unexpected-difference-between-loc-and-ix) for details. I've come to the conclusion that this is a bug -
```
import pandas as pd
# Create a dataframe
df = pd.DataFrame({'id':[10,9,5,6,8], 'x1':[10.0,12.3,13.4,11.9,7.6], 'x2':['a','a','b','c','c']... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11841 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11841/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11841/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11841/events | https://github.com/pandas-dev/pandas/pull/11841 | 122,154,149 | MDExOlB1bGxSZXF1ZXN0NTM2MzcwMzE= | 11,841 | Refactored Resample API breaking change | {
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closes #11732
closes #12072
closes #9052
closes #12140
ToDo:
- [x] rewrite/expand main docs
- [x] add aggregate section
New API
```
In [4]: np.random.seed(1234)
In [5]: df = pd.DataFrame(np.random.rand(10,4),
columns=list('ABCD'),
index=pd.date_range... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11843 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11843/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11843/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11843/events | https://github.com/pandas-dev/pandas/issues/11843 | 122,244,095 | MDU6SXNzdWUxMjIyNDQwOTU= | 11,843 | Error storing cateogory values in HDFStore: NotImplementedError: cannot store a category dtype | {
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"descript... | closed | false | null | [] | null | 2 | 2015-12-15T10:46:14Z | 2015-12-15T14:39:05Z | 2015-12-15T11:46:29Z | NONE | null | Hi,
I'm having problems storing a dataframe with categorical columns as well some float64 and datetime64 columns into a HDFStore.
The docs seems to suggest that you can store category values in HDF5 since `pandas v0.15.2` [here](https://pandas-docs.github.io/pandas-docs-travis/io.html#io-hdf5-categorical), but I got... | {
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] | closed | false | null | [] | null | 5 | 2015-12-15T12:58:44Z | 2015-12-16T22:57:36Z | 2015-12-16T17:43:13Z | NONE | null | I am attempting to style a column based on another column's data, for example:
``` python
import pandas as pd
now = pd.datetime.now()
rng = now + pd.timedelta_range("1D","10D",freq='D')
df = pd.DataFrame(rng,columns=["ExactDate"])
df['Display Date'] = df['ExactDate'].dt.strftime("%m-%Y")
style = df.style.bar(subse... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11845 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11845/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11845/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11845/events | https://github.com/pandas-dev/pandas/issues/11845 | 122,311,416 | MDU6SXNzdWUxMjIzMTE0MTY= | 11,845 | SettingWithCopyWarning false positive when using .loc | {
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"color": "00... | closed | false | null | [] | null | 2 | 2015-12-15T16:29:39Z | 2015-12-15T16:44:08Z | 2015-12-15T16:36:40Z | NONE | null | I have the following code, which produces the SettingWithCopyWarning:
```
segments = df_source1.Code.unique().tolist()
for segment in segments:
seg = segment[-1]
df_source1.loc[df_source1.Code == segment, 'Seg'] = seg
```
```
C:\Anaconda3\envs\jupyter\lib\site-packages\pandas\core\indexing.py:266: SettingWi... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11846 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11846/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11846/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11846/events | https://github.com/pandas-dev/pandas/issues/11846 | 122,319,158 | MDU6SXNzdWUxMjIzMTkxNTg= | 11,846 | pd.DataFrame converts np.uint64 greater than 2**63-1 to objects | {
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] | closed | false | null | [] | null | 3 | 2015-12-15T17:04:27Z | 2016-05-16T18:15:14Z | 2015-12-15T19:54:53Z | NONE | null | My question is based on this post I found:
http://stackoverflow.com/questions/34283319/pandas-converts-large-unsigned-integers-to-object-types
The maximum value for np.uint64 is (2^64)-1 so why does pandas convert np.uint64 types greater than 2^63-1 to objects when converting arrays to dataframes. pd.to_numeric() also... | {
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Is there any reason pandas doesn't keep the np.float32 dtype, especially since it supports missing values so even ... | {
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"de... | closed | false | null | [] | null | 1 | 2015-12-15T19:23:59Z | 2015-12-15T19:47:53Z | 2015-12-15T19:47:37Z | CONTRIBUTOR | null | One "gotcha" in pandas that's always bugged me is that you can generally access and change columns in a DataFrame by dot-messaging (attribute assignment) _except_ for adding new columns. For example, one can play with existing columns easily:
```
df = pd.DataFrame({'col1':range(10,14),
'col2':range(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11849 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11849/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11849/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11849/events | https://github.com/pandas-dev/pandas/issues/11849 | 122,394,469 | MDU6SXNzdWUxMjIzOTQ0Njk= | 11,849 | efficient read+concat of multiple HDF files? | {
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```
dfs = [pd.read_hdf(filename, 'df') for filename in filenames]
df = pd.concat(dfs)
```
Each of the read operations takes about 15 seconds but the concatenatio... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11850 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11850/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11850/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11850/events | https://github.com/pandas-dev/pandas/pull/11850 | 122,409,577 | MDExOlB1bGxSZXF1ZXN0NTM3ODkzMjY= | 11,850 | REGR: Regression in .clip with tz-aware datetimes #11838 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11851 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11851/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11851/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11851/events | https://github.com/pandas-dev/pandas/issues/11851 | 122,411,562 | MDU6SXNzdWUxMjI0MTE1NjI= | 11,851 | pandas import throws AttributeError: 'module' object has no attribute 'dtype' | {
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Compiled `numpy-1.10.2` and `pandas-0.17.1`
```
======================================================================
ERROR: Failure: AttributeError ('module' object has no attribute 'dtype')
-----------------------------------------------------------... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11852 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11852/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11852/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11852/events | https://github.com/pandas-dev/pandas/issues/11852 | 122,463,164 | MDU6SXNzdWUxMjI0NjMxNjQ= | 11,852 | xz compression in to_csv() | {
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Similar to https://github.com/pydata/pandas/issues/8654, but I think in to_csv this has more added value.
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https://api.github.com/repos/pandas-dev/pandas/issues/11853 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11853/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11853/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11853/events | https://github.com/pandas-dev/pandas/issues/11853 | 122,479,523 | MDU6SXNzdWUxMjI0Nzk1MjM= | 11,853 | ERR: better exception for converting bool to datetime with 0.17 | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-16T11:08:03Z | 2016-05-26T12:43:40Z | 2016-05-26T12:43:40Z | NONE | null | While updating pandas-qt I stumbled about the following exception. Running the same with pandas 0.16 doesn't shows any exception. I think converting a bool to a datetime should raise some exception, but not the one shown. ;-)
```
import pandas as pd
import pytest
data = [
["text", 123, 12.3, True, "2015-12-16"]
]... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11854 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11854/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11854/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11854/events | https://github.com/pandas-dev/pandas/issues/11854 | 122,529,784 | MDU6SXNzdWUxMjI1Mjk3ODQ= | 11,854 | Can't get period code with frequency alias 'minute' or 'Minute' | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-16T15:31:32Z | 2016-03-15T14:22:14Z | 2016-03-15T14:22:14Z | NONE | null | Hi,
While playing with the frequency module, I might have found a bug with the function __period_str_to_code_:
the function didn't return the code 8000 for the frequency string "minute", yet it works for frequency strings as 'T', 'Min', 'min'...
Here's the code to reproduce the 'bug':
```
>>> from pandas.tseries.fr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11855 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11855/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11855/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11855/events | https://github.com/pandas-dev/pandas/pull/11855 | 122,583,058 | MDExOlB1bGxSZXF1ZXN0NTM4ODYxMzc= | 11,855 | Change pandas to never let objects share `_data`, create views instead | {
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] | closed | false | null | [] | null | 1 | 2015-12-16T19:25:50Z | 2016-01-13T19:29:57Z | 2016-01-11T13:48:57Z | CONTRIBUTOR | null | Closes Issue #11814
Several pandas operations create objects that share `_data` objects. In particular, all of the following result in `df_new`s and `df` sharing the same `_data` (i.e. `df_new._data is df._data` returns `True`).
```
df = DataFrame({'col1':[0,1,2], 'col2':[2,3,4]})
df_new = DataFrame(df)
df_new1 = d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11856 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11856/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11856/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11856/events | https://github.com/pandas-dev/pandas/pull/11856 | 122,623,173 | MDExOlB1bGxSZXF1ZXN0NTM5MTE2NzU= | 11,856 | fixed conversion to sparse for non-numeric index | {
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this fix breaks some sparse tests,
stuck on `test_combine_first` : `ValueError: total size of new array must be unchanged`
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https://api.github.com/repos/pandas-dev/pandas/issues/11857 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11857/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11857/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11857/events | https://github.com/pandas-dev/pandas/issues/11857 | 122,714,234 | MDU6SXNzdWUxMjI3MTQyMzQ= | 11,857 | Sum a panel along multiple dimensions | {
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"followers_url": "https://api.github.com/users/jorisva... | 3 | 2015-12-17T11:11:41Z | 2018-07-06T20:21:08Z | 2017-07-10T21:08:10Z | NONE | null | numpy's `sum()` function allows specifying a tuple of axis values for higher-dimensional arrays, but when it is called this way on a Pandas `Panel` or `Panel4D` it raises a ValueError:
```
>>> import numpy as np
>>> import pandas as pd
>>> a = np.random.randn(3, 3, 3)
>>> a.sum(axis=(0, 1))
array([-1.91974326, 1.4978... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11858 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11858/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11858/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11858/events | https://github.com/pandas-dev/pandas/issues/11858 | 122,749,741 | MDU6SXNzdWUxMjI3NDk3NDE= | 11,858 | Index without 0 in xerr/yerr causes KeyError | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2015-12-17T14:51:14Z | 2016-05-13T13:19:16Z | 2016-05-13T13:19:16Z | CONTRIBUTOR | null | When creating an errorbar plot, if the data object being used for the errors (`xerr` or `yerr`) doesn't have 0 in its index, it produces a `KeyError` from matplotlib code. I can produce this with the following code sample:
``` python
import pandas as pd, numpy as np
i = np.array([1,2,3])
a = pd.DataFrame(i, index=i)
a... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11860 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11860/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11860/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11860/events | https://github.com/pandas-dev/pandas/pull/11860 | 122,827,947 | MDExOlB1bGxSZXF1ZXN0NTQwMzQ4OTM= | 11,860 | DOC: typos in DataFrame.iterrows and itertuples docstrings | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11861 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11861/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11861/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11861/events | https://github.com/pandas-dev/pandas/pull/11861 | 122,840,922 | MDExOlB1bGxSZXF1ZXN0NTQwNDM1NTE= | 11,861 | BUG: force list type for tuples from chunked sql table reads #11522 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11862 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11862/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11862/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11862/events | https://github.com/pandas-dev/pandas/issues/11862 | 122,928,536 | MDU6SXNzdWUxMjI5Mjg1MzY= | 11,862 | Plot PeriodIndex data with steps | {
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... | open | false | null | [] | null | 3 | 2015-12-18T11:04:39Z | 2021-04-21T05:26:37Z | null | NONE | null | I would find it more natural for PeriodIndexed data to be plotted by default as a stepped line. A step better conveys the fact that the y-value one is plotting corresponds to that entire x-region, whereas a (linearly or otherwise) interpolated line suggests that one is using Timestamped data (since it suggests that the... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11864 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11864/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11864/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11864/events | https://github.com/pandas-dev/pandas/issues/11864 | 123,011,711 | MDU6SXNzdWUxMjMwMTE3MTE= | 11,864 | DataFrame .duplicated() / .drop_duplicates() flagging unique rows as duplicated in 0.17.1 | {
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```
import pandas as pd, numpy as np
df = pd.DataFrame({'a': pd.Series(range(1,100000)),
'b': pd.Series(range(10,1000000)),
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... | 10 | 2015-12-18T21:28:42Z | 2021-04-21T05:28:04Z | null | NONE | null | The DataFrame.describe() method docs seem to indicate that you can pass percentiles=None to not compute any percentiles, however by default it still computes 25%, 50% and 75%. The best I can do is pass an empty list to only compute the 50% percentile. I would think that passing an empty list would return no percentile... | {
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Is this the best implementation for a common method across classes?
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https://api.github.com/repos/pandas-dev/pandas/issues/11868 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11868/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11868/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11868/events | https://github.com/pandas-dev/pandas/issues/11868 | 123,073,606 | MDU6SXNzdWUxMjMwNzM2MDY= | 11,868 | replace function broken for Series with dtype: datetime64 | {
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``` python
from datetime import datetime
import pandas as pd
ser = pd.Series([datetime.now()]*4, index=range(4))
ser.replace('a', 10)
```
This throws the following exception.
<pre>
Traceback (most recent call last):
File "/home/xxxx/anaconda/envs/py3/lib/python3.4/site-packages... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11870 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11870/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11870/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11870/events | https://github.com/pandas-dev/pandas/pull/11870 | 123,095,824 | MDExOlB1bGxSZXF1ZXN0NTQxODE0Mjk= | 11,870 | raise NotImplemented for date parsing args in read_excel #11544 | {
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The `parse_dates` and `date_parser` args are passed to `TextReader` and then to `TextFileReader` where they don't seem to have an effect. It was decided to raise the exception at the `_parse_excel` level however, following suit with the handling of `chunksize` args.
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https://api.github.com/repos/pandas-dev/pandas/issues/11871 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11871/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11871/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11871/events | https://github.com/pandas-dev/pandas/issues/11871 | 123,118,514 | MDU6SXNzdWUxMjMxMTg1MTQ= | 11,871 | BUG: to_datetime issue parsing non-zero padded month in 0.17.1 | {
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```
>>> import pandas
>>> pandas.__version__
'0.16.2'
>>> pandas.to_datetime('2005-1-13', format='%Y-%m-%d')
Timestamp('2005-01-13 00:00:00')
```
With 0.17.1, it raises a ValueError:
```
>>> import pandas
>>> pandas.__version__
u'0.1... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11872 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11872/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11872/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11872/events | https://github.com/pandas-dev/pandas/pull/11872 | 123,139,983 | MDExOlB1bGxSZXF1ZXN0NTQxOTQ1MTk= | 11,872 | DOC: Added enhancement entry for to_datetime | {
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(Issues #11276, #11745)
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I'm just wondering what your code of conduct is for this project?
Thanks,
Alison
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Please Review.
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https://api.github.com/repos/pandas-dev/pandas/issues/11875 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11875/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11875/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11875/events | https://github.com/pandas-dev/pandas/issues/11875 | 123,201,596 | MDU6SXNzdWUxMjMyMDE1OTY= | 11,875 | filtration chain for DataFrames | {
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For example if I have a group -> aggregate -> filter sequence, I'd do something like this
```
In [13]: df
Out[13]:
A B C D
0 foo one -0.193 -0.900
1 bar ... | {
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GBQ can be used with Python 3. In RTBHouse we are doing this since may. It works perfectly with this pandas upgrade.
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https://api.github.com/repos/pandas-dev/pandas/issues/11877 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11877/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11877/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11877/events | https://github.com/pandas-dev/pandas/issues/11877 | 123,288,742 | MDU6SXNzdWUxMjMyODg3NDI= | 11,877 | install pandas using pip3 not work | {
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i am trying to install pandas last commit on virtualenv and this error is raise :
```
Traceback (most recent call last):
File "/usr/lib/python3.4/distutils/core.py", line 148, in setup
dist.run_commands()
File "/usr/lib/python3.4/distutils/dist.py", line 955, in run_commands
self.run_command... | {
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"... | closed | false | null | [] | null | 2 | 2015-12-21T17:11:29Z | 2019-02-20T04:35:16Z | 2019-02-20T04:35:16Z | NONE | null | Fail to load a very large pickle data frame by read_pickle after dumping it by to_pickle .
Error message below
```
In [2]: tr_fl = pd.read_pickle('dataset/train_selected_fl.pkl')
---------------------------------------------------------------------------
ValueError Traceback (most recent... | {
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(This issue is related to https://github.com/pydata/pandas/issues/680. The example below extends the example given in that issue.)
```
In [322]: df = pandas.DataFrame({u'c/\u03c3':[1,2,3]})
In [323]: df
Out[323]:
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11880 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11880/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11880/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11880/events | https://github.com/pandas-dev/pandas/issues/11880 | 123,347,417 | MDU6SXNzdWUxMjMzNDc0MTc= | 11,880 | Msgpack - ValueError: buffer source array is read-only | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-12-21T20:00:16Z | 2016-01-15T13:46:42Z | 2016-01-15T13:46:42Z | CONTRIBUTOR | null | I get the Value error when processing data using pandas. I followed the following steps:
1. convert to msgpack format with compress flag
2. subsequently read file into a dataframe
3. push to sql table with to_sql
On the third step i get ValueError: buffer source array is read-only.
This problem does not arise if I w... | {
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It also adds some useful std out messages: progress with elapsed time and percentage ~~\+ price calculation~~.
In RTBHouse we're using this service account auth since may. It works perfectly with remote jupyter server (iPython note... | {
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"... | closed | false | null | [] | null | 24 | 2015-12-22T04:08:07Z | 2016-05-03T05:08:11Z | 2016-05-03T05:08:11Z | CONTRIBUTOR | null | Fixes #11823 .
changed `usecols` from set to list in the C parser to handle duplicated columns and `usecols`. Update python parser to be consistent with C parser.
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https://api.github.com/repos/pandas-dev/pandas/issues/11883 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11883/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11883/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11883/events | https://github.com/pandas-dev/pandas/pull/11883 | 123,418,888 | MDExOlB1bGxSZXF1ZXN0NTQzMzU1NDY= | 11,883 | Add examples for pandas.*.sample | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-12-22T05:54:01Z | 2015-12-23T15:29:25Z | 2015-12-23T14:27:42Z | CONTRIBUTOR | null | The following doc files are generated from the sample() function from
pandas/core/generic.py:
pandas.DataFrame.sample.html
pandas.Panel.sample.html
pandas.Panel4D.sample.html
pandas.Series.sample.html
Examples for each have been added to the sample() function docstring.
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-22T15:11:50Z | 2015-12-29T21:12:56Z | 2015-12-29T21:12:56Z | MEMBER | null | If you call the new `round` method on a mixed dataframe (not only numerical columns), you get an error:
```
In [1]: df = pd.DataFrame({'floats':[0.123, 0.156, 5.687], 'strings': ['a', 'b', 'c']})
In [2]: df.round(2)
TypeError: can't multiply sequence by non-int of type 'float'
In [3]: df.round()
AttributeError: 'str... | {
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You can specify the sql type using the `dtype` argument in `to_sql`. Now, this argument has to be a dict (mapping of column name -> type).
Letting `to_sql` recognize a single value as the sql type for all columns is not that d... | {
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... | [] | closed | false | null | [] | null | 1 | 2015-12-22T21:29:10Z | 2015-12-22T21:43:10Z | 2015-12-22T21:43:10Z | CONTRIBUTOR | null | Related to #11755 ,#11693. Will be fixed in #11705
``` python
In [1]: import pandas as pd
In [2]: x = pd.Series(pd.date_range('20151124 10:00', '20151124 11:00', freq = '1h', tz = "UTC") )
In [3]: y = pd.Series(['a','b'])
In [4]: print(pd.concat([x,y]))
```
AttributeError: 'numpy.ndarray' object has no attribute... | {
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The default for `hader` in `read_csv` is `'infer'`, while for `read_excel` this is `0`. In most cases this does not matter I think (as the infer will use 0 as the default). But, in the case of specifying the `names` explicitly, this makes a differ... | {
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I'm willing to edit the api.rst file, but when I build the docs from scratch ... | {
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So probably all groupby plot calls that use kwargs are broken in 0.17.x.
@shoyer it was only a very small change actually!
Still need to add tests.
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replaces #9977
ToDo:
- [x] test for packers.py
- [x] more code review
Much commentary on the original issue #9977
but in essence `RangeIndex` is a complete replacement for `Int64Index`, which all indexing semantics and interop. This is now the default indexer upon construction. It should be completely... | {
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closes #11890
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A `ValueError` was raised when trying to mask an empty DataFrame. In addition to solve the bug I've also added a simple new unit test in `test_frame.py`. I didn't add a line in `whatsnew` because this was already done in #10126
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https://api.github.com/repos/pandas-dev/pandas/issues/11896 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11896/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11896/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11896/events | https://github.com/pandas-dev/pandas/issues/11896 | 123,817,107 | MDU6SXNzdWUxMjM4MTcxMDc= | 11,896 | DataFrame to_sql fails with percentage symbol `%` in column name with SQLAlchemy and PostgreSQL | {
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``` python
import pandas as pd
from sqlalchemy import create_engine
connect_str = "mysql://{USER}:{PASSWORD}@{HOST}/{DBNAME}".format(
USER = 'root',
PASSWORD = '',
HOST = '127.0.0.1',
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https://api.github.com/repos/pandas-dev/pandas/issues/11897 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11897/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11897/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11897/events | https://github.com/pandas-dev/pandas/issues/11897 | 123,827,309 | MDU6SXNzdWUxMjM4MjczMDk= | 11,897 | API: Use different exception type when MultiIndex indexing fails due to index not being lexsorted | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2015-12-24T18:35:21Z | 2016-12-09T16:36:29Z | 2016-12-09T16:36:29Z | CONTRIBUTOR | null | I'd like to be able to separately trap errors in indexing that are due to the lack of the `MultiIndex` being lexsorted. Right now, a `KeyError` is raised, and the only way to tell if the error was due to the lack of a lexsort versus a missing key is to look at the text of the message.
So I'd like to avoid code like t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11898 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11898/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11898/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11898/events | https://github.com/pandas-dev/pandas/pull/11898 | 123,835,217 | MDExOlB1bGxSZXF1ZXN0NTQ1NjY2NDM= | 11,898 | DEPR: pandas.stats.var, pandas.stats.plm, pandas.stats.ols, pandas.stats.fama_macbeth | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-12-24T21:29:09Z | 2015-12-26T13:08:42Z | 2015-12-26T13:08:42Z | CONTRIBUTOR | null | closes #6077
this cleans ups the remainder of the `pandas.stats` modules
we are directing:
- `OLS` -> `http://statsmodels.sourceforge.net/stable/regression.html`
- `PanelOLS/MovingOLS/PooledOLS` to `http://statsmodels.sourceforge.net/stable/mixed_linear.html`
- `VAR` to `http://statsmodels.sourceforge.net/stable/vec... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11899 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11899/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11899/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11899/events | https://github.com/pandas-dev/pandas/issues/11899 | 123,868,089 | MDU6SXNzdWUxMjM4NjgwODk= | 11,899 | PERF: allow even more flexible ISO 8601 datetime parsing | {
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I noticed that there is a huge code speed difference with `to_datetime` execution when format is not given and when it's given.
I wonder if there is not some room for improvements here!
```
In [1]: %time df=pd.read_csv("AUDUSD-2014-01.csv", names=['Symbol', 'Date', 'Bid', 'Ask'])
CPU times: user 3.31 s, sys: ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11900 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11900/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11900/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11900/events | https://github.com/pandas-dev/pandas/pull/11900 | 123,884,174 | MDExOlB1bGxSZXF1ZXN0NTQ1ODgzMjY= | 11,900 | Sql | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11901 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11901/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11901/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11901/events | https://github.com/pandas-dev/pandas/issues/11901 | 123,887,645 | MDU6SXNzdWUxMjM4ODc2NDU= | 11,901 | DOC/DEV: update windows instructions for setting up dev environment for py3 | {
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... | 10 | 2015-12-25T15:59:46Z | 2016-05-12T13:05:52Z | 2016-05-12T13:05:52Z | CONTRIBUTOR | null | I am trying to set up a development environment for pandas in anaconda.
But I get the following error, while installing dependencies for windows:-
on running "conda install -n pandas_dev libpython"
Error: Unsatisfiable package specifications.
Hint: the following packages conflict with each other:
- libpython
- python ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11902 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11902/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11902/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11902/events | https://github.com/pandas-dev/pandas/issues/11902 | 123,888,673 | MDU6SXNzdWUxMjM4ODg2NzM= | 11,902 | problem setting up the pandas development environment | {
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But I get the following error, while installing dependencies for windows:-
on running "conda install -n pandas_dev libpython"
Error: Unsatisfiable package specifications.
Hint: the following packages conflict with each other:
- libpython
- python ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11903 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11903/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11903/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11903/events | https://github.com/pandas-dev/pandas/pull/11903 | 123,898,708 | MDExOlB1bGxSZXF1ZXN0NTQ1OTM3MTI= | 11,903 | fix for the issue-11833 | {
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] | closed | false | null | [] | null | 6 | 2015-12-25T21:49:01Z | 2015-12-26T19:26:50Z | 2015-12-26T19:26:50Z | CONTRIBUTOR | null | updated the to_string() function of DataFrameFormatter(TableFormatter) class ,in format.py file, of the core directory
Closes https://github.com/pydata/pandas/issues/11833
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