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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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} | 0 | 2015-12-09T16:28:35Z | 2015-12-17T22:43:13Z | 2015-12-17T22:43:13Z | CONTRIBUTOR | null | When either start or end is at the boundary of a date_range, they get dropped when they shouldn't if we specify the closed argument, see example below:
```
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_range('2015-09-01', '2015-10-30', freq='QS-MAR', closed='left')
Out[2]: DatetimeInddex([], dtype='datetime64[ns]', freq='QS-MAR')
```
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} | 0 | 2015-12-09T16:28:43Z | 2015-12-28T10:08:29Z | 2015-12-28T10:08:29Z | MEMBER | null | From SO: http://stackoverflow.com/questions/34123075/pandas-scatterplot-not-working-as-intended
```
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')
```
does not give you a scatter plot anymore (as an illustration, notebook with [pandas 0.16.2](http://nbviewer.ipython.org/gist/jorisvandenbossche/db561cfcd767356c76c6) and [pandas 0.17.0](http://nbviewer.ipython.org/gist/jorisvandenbossche/606509ef281f28f97bc9) )
This worked fine in pandas 0.16.2, but not anymore in 0.17.0 (or master). With using the new accessor syntax: `diamonds.groupby('color').plot.scatter(x='carat', y='price')` it still works.
Possibly related to the accessor plotting? @shoyer
cc @TomAugspurger @sinhrks
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} | 5 | 2015-12-09T18:54:09Z | 2015-12-18T19:33:16Z | 2015-12-17T22:43:13Z | CONTRIBUTOR | null | closes #11804
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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| 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 (which is not even a proper distribution).
I'm including the exact lists I used, x1 and x2, below. These are ordered lists, so please insert them into your DataFrame accordingly.
(Pardon my presentation of the x-labels in the green figure. Just shift them 1/2-bar length to the right.)
``` python
x1 = [4, 4, 6, 4, 4, 4, 4, 5, 5, 6, 4, 4, 5, 6, 4, 4, 4, 4, 5, 5, 6, 5, 3, 6, 6, 7, 7, 7, 4, 6, 4, 5, 4, 5, 4, 3, 7, 7, 7, 5, 4, 5, 4, 3, 5, 4, 4, 5, 6, 5, 4, 4, 4, 5, 6, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 6, 7, 6, 7, 7, 7, 7, 6, 7, 7, 7, 5, 7, 7, 7, 7, 7, 5, 7, 5, 5, 7, 6, 7, 6, 4, 6, 7, 6, 7, 1, 6, 7, 2, 7, 4, 6, 7, 6, 6, 4, 6, 7, 7, 7, 5, 7, 6, 7, 4, 6, 7, 7, 6, 5, 7, 6, 6, 6, 6, 5, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 4, 7, 4, 5, 7, 6, 7, 4, 5, 7, 7, 7, 7, 7, 7, 7, 7, 4, 4, 5, 5, 7, 7, 7, 7, 6, 7]
x2 = [4, 4, 6, 3, 4, 4, 4, 5, 6, 6, 4, 4, 5, 6, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 7, 7, 7, 5, 7, 2, 4, 4, 4, 6, 7, 7, 7, 7, 4, 5, 4, 5, 3, 5, 4, 5, 5, 6, 5, 5, 5, 4, 5, 6, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 4, 7, 7, 7, 7, 7, 7, 7, 5, 5, 7, 7, 7, 5, 5, 5, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 5, 6, 6, 5, 6, 7, 7, 7, 5, 7, 7, 6, 5, 5, 6, 6, 6, 5, 7, 5, 5, 5, 5, 4, 4, 3, 4, 7, 4, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 4, 4, 6, 5, 6, 4, 5, 7, 5, 7, 7, 7, 7, 7, 7, 5, 5, 7, 7, 7, 7, 7, 7, 7, 7]
```
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} | 2 | 2015-12-09T22:15:32Z | 2015-12-11T13:07:58Z | 2015-12-11T13:07:58Z | NONE | null | When an `AttributeError` is raised inside a property in DataFrame, regular attribute access fails and `__getattr__` in `NDFrame` is called.
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 as pd
class A(pd.DataFrame):
@property
def foo(self):
return 1 / 0
@property
def bar(self):
return self.i_dont_exist
>>> a = A()
>>> a.foo
ZeroDivisionError
>>> a.bar
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-35-0eea75f4bdee> in <module>()
12 #a.foo # ZeroDivisionError
13
---> 14 a.bar # AttributeError: 'A' object has no attribute 'bar'
15
16 #a.i_dont_exist # AttributeError: 'str' object has no attribute 'isnull'
/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc in __getattr__(self, name)
2148 return self[name]
2149 raise AttributeError("'%s' object has no attribute '%s'" %
-> 2150 (type(self).__name__, name))
2151
2152 def __setattr__(self, name, value):
AttributeError: 'A' object has no attribute 'bar'
>>> a.i_dont_exist # a.bar should include this in the stack trace:
AttributeError: 'A' object has no attribute 'i_dont_exist'
```
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-39-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.6
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 8 | 2015-12-09T23:17:21Z | 2015-12-15T22:52:24Z | 2015-12-15T22:51:56Z | CONTRIBUTOR | null | closes #11763
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
```
This is a new issue affecting 0.17
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| 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).pct_change(freq='M')
<Empty>
>>> rawdat.groupby(level=0).pct_change()
<Lots of data>
>>> rawdat.loc[['AAPL']].reset_index(level=0, drop=True).pct_change(freq='M')
<Lots of data>
```
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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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| 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 sunday
2015-09-07 6 monday
2015-09-08 7 tuesday
2015-09-09 8 wednesday
2015-09-10 9 thursday
```
and
```
In [7]: s3 = pd.Series(pd.date_range('2015-09-01', '2015-09-10'))
In [8]: s3.dt.weekday_name
Out[8]:
0 tuesday
1 wednesday
2 thursday
3 friday
4 saturday
5 sunday
6 monday
7 tuesday
8 wednesday
9 thursday
dtype: object
```
Please review my code :smile:
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| 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
1 2 4
```
Suggestions on how best to fix?
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| 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()]
Out[180]:
7 -1.546187
6 -0.259776
10 0.055520
2 0.130294
9 0.197778
8 0.449616
4 0.912771
5 1.097172
11 1.170821
3 1.567679
dtype: float64
In [181]: z[2] = np.nan
In [182]: z.iloc[z.argsort()]
Out[182]:
11 1.170821
6 -0.259776
5 1.097172
9 0.197778
8 0.449616
7 -1.546187
3 1.567679
4 0.912771
10 0.055520
2 NaN
dtype: float64
In [184]: pd.__version__
Out[184]: '0.17.0'
```
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} | 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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| 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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} | 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 convenience at the time. But can lead to an unstatisfactory user experience.
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} | 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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| 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
(env)vagrant@app01:~/repos/peppertap_v1/shopper_bot$ pip list | grep num
You are using pip version 7.0.3, however version 7.1.2 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
numpy (1.10.1)
phonenumbers (6.0.0a0)
(env)vagrant@app01:~/repos/peppertap_v1/shopper_bot$ pip list | grep pandas
You are using pip version 7.0.3, however version 7.1.2 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
pandas (0.17.1)
env)vagrant@app01:~/repos/peppertap_v1/shopper_bot$ python
Python 2.7.6 (default, Mar 22 2014, 22:59:56)
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> df = pd.DataFrame({'A' : pd.date_range('20130101',periods=3), 'B' : pd.date_range('20130101',periods=3,tz='US/Eastern'), 'C' : pd.date_range('20130101',periods=3,tz='CET')})
>>> df.dtypes
A datetime64[ns]
B datetime64[ns, US/Eastern]
C datetime64[ns, CET]
dtype: object
>>> df.to_json()
Segmentation fault (core dumped)
(env)vagrant@app01:~/repos/peppertap_v1/shopper_bot$
```
The only solution I found is to remove tz_localize(None)
```
(env)vagrant@app01:~/repos/peppertap_v1/shopper_bot$ python
Python 2.7.6 (default, Mar 22 2014, 22:59:56)
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> df = pd.DataFrame({'A' : pd.date_range('20130101',periods=3), 'B' : pd.date_range('20130101',periods=3), 'C' : pd.date_range('20130101',periods=3)})
>>> df.to_json()
'{"A":{"0":1356998400000,"1":1357084800000,"2":1357171200000},"B":{"0":1356998400000,"1":1357084800000,"2":1357171200000},"C":{"0":1356998400000,"1":1357084800000,"2":1357171200000}}'
>>>
(env)vagrant@app01:~/repos/peppertap_v1/shopper_bot$
```
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} | 1 | 2015-12-11T06:58:52Z | 2015-12-11T13:08:02Z | 2015-12-11T13:07:58Z | CONTRIBUTOR | null | closes #11808
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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,
names=cols,
)
```
with explainable behaviour
```
Out:
A A B
0 2 2 3
```
Then we might want to load some of the columns with python engine
``` python
pd.read_csv('pandas.csv',
engine='python',
header=None,
names=cols,
usecols=cols
)
```
and get different but still explainable result
```
Out:
A B
0 1 3
```
But then we switch back to c-engine
``` python
pd.read_csv('pandas.csv',
engine='c',
header=None,
names=cols,
usecols=cols
)
```
and get the following
```
Out:
A A B
0 2 2 NaN
```
which is:
(a) different (which is not good in my opinion)
(b) looks like bug
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} | 0 | 2015-12-11T15:35:06Z | 2015-12-11T15:49:34Z | 2015-12-11T15:49:34Z | CONTRIBUTOR | null | xref #11816
cc @llllllllll
had to change the test as this numpy bug on window: https://github.com/numpy/numpy/issues/6812
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| null | 0 | 2015-12-11T20:32:29Z | 2015-12-11T20:32:48Z | 2015-12-11T20:32:48Z | CONTRIBUTOR | null | Implemented using the method described in the StackOverflow answer (http://stackoverflow.com/questions/33921795/fill-timestamp-nat-with-a-linear-interpolation/33922824#33922824).
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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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 return a new list or dictionary after every call. Use a sentinel value to denote an empty list or dictionary.
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| null | 1 | 2015-12-12T07:29:13Z | 2015-12-15T19:44:41Z | 2015-12-15T19:44:41Z | CONTRIBUTOR | null | {
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} | 9 | 2015-12-12T10:04:38Z | 2016-05-24T13:30:48Z | 2016-05-24T13:30:45Z | NONE | null | It is bug or not? Because in function `mean` and `first` it is OK.
[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 2 a 4
#lost multiindex
print df.groupby(['a', 'b']).c.nth(0)
#0 1
#2 3
#3 4
#Name: c, dtype: int64
print df.groupby(['a', 'b']).c.mean()
#a b
#1 b 1.5
#2 a 4.0
# b 3.0
#Name: c, dtype: float64
print df.groupby(['a', 'b']).c.first()
#a b
#1 b 1
#2 a 4
# b 3
#Name: c, dtype: int64
print df.groupby(['a', 'b']).nth(0).c
#a b
#1 b 1
#2 a 4
# b 3
#Name: c, dtype: int64
```
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} | 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]: '2.7.10 |Anaconda 1.7.0 (64-bit)| (default, Oct 21 2015, 19:35:23) [MSC
v.1500 64 bit (AMD64)]'
In [3]: pd.__version__
Out[3]: '0.16.2'
In [4]: matplotlib.__version__
Out[4]: '1.4.3'
In [6]: N = 2000
In [7]: df = pd.DataFrame(np.random.randn(N, 5), index=pd.date_range('1/1/1975', periods=N))
In [8]: %timeit df.plot()
1 loops, best of 3: 228 ms per loop
In [10]: df = pd.DataFrame(np.random.randn(N, 5))
In [11]: %timeit df.plot()
10 loops, best of 3: 110 ms per loop
```
```
In [1]: import sys
In [2]: sys.version
Out[2]: '2.7.11 |Continuum Analytics, Inc.| (default, Dec 7 2015, 14:10:42) [MS
C v.1500 64 bit (AMD64)]'
In [3]: pd.__version__
Out[3]: u'0.17.1'
In [4]: matplotlib.__version__
Out[4]: '1.5.0'
In [5]: N = 2000
In [6]: df = pd.DataFrame(np.random.randn(N, 5), index=pd.date_range('1/1/1975', periods=N))
In [7]: %timeit df.plot()
1 loops, best of 3: 269 ms per loop
In [8]: df = pd.DataFrame(np.random.randn(N, 5))
In [9]: %timeit df.plot()
10 loops, best of 3: 139 ms per loop
```
With python 3, pandas 0.16.2 and 0.17.1:
```
In [2]: sys.version
Out[2]: '3.5.0 |Continuum Analytics, Inc.| (default, Dec 1 2015, 11:46:22) [MSC
v.1900 64 bit (AMD64)]'
In [3]: pd.__version__
Out[3]: '0.16.2'
In [4]: matplotlib.__version__
Out[4]: '1.5.0'
In [5]: N = 2000
In [6]: df = pd.DataFrame(np.random.randn(N, 5), index=pd.date_range('1/1/1975', periods=N))
In [7]: %timeit df.plot()
1 loops, best of 3: 1.02 s per loop
In [9]: df = pd.DataFrame(np.random.randn(N, 5))
In [10]: %timeit df.plot()
10 loops, best of 3: 143 ms per loop
```
```
In [1]: import sys
In [2]: sys.version
Out[2]: '3.5.0 |Anaconda 2.4.0 (64-bit)| (default, Nov 7 2015, 13:15:24) [MSC v
.1900 64 bit (AMD64)]'
In [3]: pd.__version__
Out[3]: '0.17.1'
In [4]: matplotlib.__version__
Out[4]: '1.5.0'
In [5]: N = 2000
In [6]: df = pd.DataFrame(np.random.randn(N, 5), index=pd.date_range('1/1/1975', periods=N))
In [7]: %timeit df.plot()
1 loops, best of 3: 2.37 s per loop <------------ !!!! 10x slower than on py2.7
In [8]: df = pd.DataFrame(np.random.randn(N, 5))
In [9]: %timeit df.plot()
10 loops, best of 3: 132 ms per loop
```
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} | 14 | 2015-12-12T22:27:36Z | 2016-01-03T16:26:10Z | 2016-01-03T16:26:10Z | CONTRIBUTOR | null | Closes #11818
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} | 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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} | 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 explicitly uses `frequencies.get_offset`
- get_offset_name: This looks leagacy impl which raises `ValueError` for `BDay` with `n != 1` which is valid input. I've marked this as deprecated, but it can be removed immediately as non-public.
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} | 1 | 2015-12-13T19:46:54Z | 2016-01-05T00:39:03Z | 2016-01-05T00:39:03Z | NONE | null | See http://stackoverflow.com/questions/34251068/runtimeerror-from-scipy-stats-mode-on-array-of-timedelta-maximum-recursion-dept
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.
IPython 4.0.1 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import numpy as np
In [2]: np.__version__
Out[2]: '1.10.1'
In [3]: import pandas as pd
In [4]: pd.__version__
Out[4]: '0.17.1'
```
Create a numpy array of `TimeDelta` objects, and do a comparison of the array to a `TimeDelta` instance:
```
In [5]: from pandas import Timedelta
In [6]: periods = [Timedelta('0 days 01:00:00'), Timedelta('0 days 01:00:00')]
In [7]: p = np.array(periods)
In [8]: periods[0] > p
---------------------------------------------------------------------------
RecursionError Traceback (most recent call last)
<ipython-input-8-1c05a376ecc2> in <module>()
----> 1 periods[0] > p
pandas/tslib.pyx in pandas.tslib._Timedelta.__richcmp__ (pandas/tslib.c:38155)()
<SNIP>
pandas/tslib.pyx in pandas.tslib._Timedelta.__richcmp__ (pandas/tslib.c:38155)()
pandas/tslib.pyx in pandas.tslib._Timedelta.__richcmp__ (pandas/tslib.c:38155)()
RecursionError: maximum recursion depth exceeded in comparison
In [9]:
```
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} | 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
In [3]: data = np.random.random(10)
In [4]: m=pd.Series(data)
In [5]: m[0]=0.4444
In [6]: m.index
Out[6]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')
In [7]: m['0']=0.5555
In [8]: m.index
Out[8]: Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, '0'], dtype='object')
```
## 0.17.1
`conda create -n pd pandas=0.17 python=3.4 ipython`
In 0.17.1 The index dtype does not change, but typecasts to the integer 0. Repeated assignment at the integer 0 index appends more 0s to the index.
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: data = np.random.random(10)
In [4]: m=pd.Series(data)
In [5]: m[0]=0.4444
In [6]: m.index
Out[6]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')
In [7]: m['0']=0.5555
In [8]: m.index
Out [8]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0], dtype='int64')
In [9]: m['0']=0.6666
In [10]: m.index
Out[10]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 0], dtype='int64')
In [11]: m[0]
Out[11]:
0 0.4444
0 0.5555
0 0.6666
dtype: float64
```
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} | 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 09:30:30-05:00
In [227]: df = pandas.DataFrame({ 'foo' : [ pandas.Timestamp('2015-12-01 09:30:00', tz='US/Eastern'), pandas.Timestamp('2015-12-01 09:31:00', tz='US/Eastern') ] })
In [228]: print df
foo
0 2015-12-01 09:30:00-05:00
1 2015-12-01 09:31:00-05:00
In [229]: df['foo'].clip(upper=u)
TypeError: Could not compare [Timestamp('2015-12-01 09:30:30-0500', tz='US/Eastern')] with block values
```
This code used to work on 0.16.2.
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| null | 1 | 2015-12-14T20:47:59Z | 2015-12-14T22:39:51Z | 2015-12-14T22:39:01Z | NONE | null | When doing a:
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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| 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']})
df.set_index('id', inplace=True)
df.loc[[10, 9, 7]] # 7 does not exist in the index so a NaN row is returned
df.loc[[7]] # KeyError: 'None of [[7]] are in the [index]'
df.ix[[7]] # 7 does not exist in the index so a NaN row is returned
```
I think `df.loc[[7]]` should not return an error, but should return a dataframe with 1 row for id=7 with NaN values.
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} | 53 | 2015-12-14T23:14:06Z | 2016-02-02T18:00:54Z | 2016-02-02T15:16:18Z | CONTRIBUTOR | null | ~~on top of #11603~~
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('2010-01-01 09:00:00', periods=10, freq='s'))
In [6]: df
Out[6]:
A B C D
2010-01-01 09:00:00 0.191519 0.622109 0.437728 0.785359
2010-01-01 09:00:01 0.779976 0.272593 0.276464 0.801872
2010-01-01 09:00:02 0.958139 0.875933 0.357817 0.500995
2010-01-01 09:00:03 0.683463 0.712702 0.370251 0.561196
2010-01-01 09:00:04 0.503083 0.013768 0.772827 0.882641
2010-01-01 09:00:05 0.364886 0.615396 0.075381 0.368824
2010-01-01 09:00:06 0.933140 0.651378 0.397203 0.788730
2010-01-01 09:00:07 0.316836 0.568099 0.869127 0.436173
2010-01-01 09:00:08 0.802148 0.143767 0.704261 0.704581
2010-01-01 09:00:09 0.218792 0.924868 0.442141 0.909316
In [7]: df.resample('2s')
Out[7]: DatetimeIndexResampler [freq=<2 * Seconds>,axis=0,closed=left,label=left,convention=start,base=0]
In [8]: r = df.resample('2s')
In [9]: r.sum()
Out[9]:
A B C D
2010-01-01 09:00:00 0.971495 0.894701 0.714192 1.587231
2010-01-01 09:00:02 1.641602 1.588635 0.728068 1.062191
2010-01-01 09:00:04 0.867969 0.629165 0.848208 1.251465
2010-01-01 09:00:06 1.249976 1.219477 1.266330 1.224904
2010-01-01 09:00:08 1.020940 1.068634 1.146402 1.613897
In [10]: r[['A','B']].agg(['mean','sum'])
Out[10]:
A B
mean sum mean sum
2010-01-01 09:00:00 0.485748 0.971495 0.447351 0.894701
2010-01-01 09:00:02 0.820801 1.641602 0.794317 1.588635
2010-01-01 09:00:04 0.433985 0.867969 0.314582 0.629165
2010-01-01 09:00:06 0.624988 1.249976 0.609738 1.219477
2010-01-01 09:00:08 0.510470 1.020940 0.534317 1.068634
```
Upsampling
```
In [11]: s = Series(np.arange(5,dtype='int64'),
....: index=date_range('2010-01-01', periods=5, freq='Q'))
In [12]: s
Out[12]:
2010-03-31 0
2010-06-30 1
2010-09-30 2
2010-12-31 3
2011-03-31 4
Freq: Q-DEC, dtype: int64
In [13]: s.resample('M').ffill()
Out[13]:
2010-03-31 0
2010-04-30 0
2010-05-31 0
2010-06-30 1
2010-07-31 1
2010-08-31 1
2010-09-30 2
2010-10-31 2
2010-11-30 2
2010-12-31 3
2011-01-31 3
2011-02-28 3
2011-03-31 4
Freq: M, dtype: int64
In [14]: s.resample('M').asfreq()
Out[14]:
2010-03-31 0
2010-04-30 NaN
2010-05-31 NaN
2010-06-30 1
2010-07-31 NaN
2010-08-31 NaN
2010-09-30 2
2010-10-31 NaN
2010-11-30 NaN
2010-12-31 3
2011-01-31 NaN
2011-02-28 NaN
2011-03-31 4
Freq: M, dtype: float64
```
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} | 1 | 2015-12-15T05:44:49Z | 2015-12-15T12:02:51Z | 2015-12-15T12:01:56Z | CONTRIBUTOR | null | closes #11769
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| 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 a `NotImplementedError` with my data and the code below.
Did some googling but couldn't find much on this topic. Is this a bug or am I doing something wrong here? From the error message it seems to be related something that is not yet implemented?
Environment:
Anaconda 2.4.1, Python 2.7.11
Pandas 0.17.1
PyTables 3.2.2
My DataFrame looks like this:
```
quotes.info()
output:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 6295150 entries, 0 to 1474363
Data columns (total 12 columns):
price float64
broker category
g_spread float64
date datetime64[ns]
```
When I tried to store it in a HDFStore I got a **`NotImplementedError: cannot store a category dtype`** error message. Here is what I did:
```
test_store = pd.HDFStore('z:/pydata/test.h5', mode='w', format='table')
test_store.put('quotes', quotes)
```
The error trace is as follows:
```
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-39-57832b6474fe> in <module>()
----> 1 test_store.put('quotes', quotes)
C:\Developer\Anaconda2\lib\site-packages\pandas\io\pytables.pyc in put(self, key, value, format, append, **kwargs)
812 format = get_option("io.hdf.default_format") or 'fixed'
813 kwargs = self._validate_format(format, kwargs)
--> 814 self._write_to_group(key, value, append=append, **kwargs)
815
816 def remove(self, key, where=None, start=None, stop=None):
C:\Developer\Anaconda2\lib\site-packages\pandas\io\pytables.pyc in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1250
1251 # write the object
-> 1252 s.write(obj=value, append=append, complib=complib, **kwargs)
1253
1254 if s.is_table and index:
C:\Developer\Anaconda2\lib\site-packages\pandas\io\pytables.pyc in write(self, obj, **kwargs)
2787 # I have no idea why, but writing values before items fixed #2299
2788 blk_items = data.items.take(blk.mgr_locs)
-> 2789 self.write_array('block%d_values' % i, blk.values, items=blk_items)
2790 self.write_index('block%d_items' % i, blk_items)
2791
C:\Developer\Anaconda2\lib\site-packages\pandas\io\pytables.pyc in write_array(self, key, value, items)
2498
2499 if com.is_categorical_dtype(value):
-> 2500 raise NotImplementedError("cannot store a category dtype")
2501
2502 if not empty_array:
NotImplementedError: cannot store a category dtype
```
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| 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(subset='Display Date', source = rng)
html = style.render()
print html
```
Gives:
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| 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: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self.obj[key] = _infer_fill_value(value)
```
I wouldn't expect to receive this warning since I'm using .loc exactly as it suggests I should be. It did occur to me that I could probably find a much better way to do this via apply, but I still run into this warning popping up all over the place, and it always makes me think I'm doing something wrong (which in this case I may be, performance wise).
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| 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 fails to convert these values back to np.uint64.
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} | 6 | 2015-12-15T18:08:24Z | 2016-12-10T15:38:14Z | 2016-12-10T15:38:14Z | NONE | null | Related to #2929, if I unstack a dataframe with mixed dtypes they all get coerced to object and I have to recast to go back which is surprisingly slow (30 seconds for 400k rows and 400 np.float32 columns)
Is there any reason pandas doesn't keep the np.float32 dtype, especially since it supports missing values so even when there are missing index/column positions it shouldn't pose a problem?
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| 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(20,24)})
df.col1 = 3
df
Out[5]:
col1 col2
0 3 20
1 3 21
2 3 22
3 3 23
```
But you can't set new columns:
```
df.col3 = pd.Series(range(30,40))
df
Out[6]:
col1 col2
0 3 20
1 3 21
2 3 22
3 3 23
```
Seems like it wouldn't be hard to modify `__setattr__` so that if the setting value is a Series, NumPy Array, or scalar value, just call `df[key] = value`.
Seem reasonable?
(Still wrestling with copy-on-write PR, but happy to come back to this if others support and no on addresses till then)
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| null | 2 | 2015-12-15T23:44:48Z | 2018-06-14T15:48:05Z | 2015-12-15T23:54:27Z | NONE | null | I have several HDF files about 500k x 500 (same columns and dtypes, mostly floats and strings). Currently I loop through and read them and then concatenate:
```
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 concatenation takes minutes! Is there any way to speed this up? Since I know the dimensions in advance, is there any way to preallocate `df` and read into it?
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} | 0 | 2015-12-16T01:51:21Z | 2015-12-16T13:13:06Z | 2015-12-16T13:13:06Z | CONTRIBUTOR | null | closes #11838
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| null | 8 | 2015-12-16T02:12:28Z | 2015-12-16T03:32:45Z | 2015-12-16T02:34:13Z | CONTRIBUTOR | null | In https://travis-ci.org/cloudera/spark-timeseries/jobs/97127307
Compiled `numpy-1.10.2` and `pandas-0.17.1`
```
======================================================================
ERROR: Failure: AttributeError ('module' object has no attribute 'dtype')
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/cloudera/spark-timeseries/python/.eggs/nose-1.3.7-py2.7.egg/nose/loader.py", line 418, in loadTestsFromName
addr.filename, addr.module)
File "/home/travis/build/cloudera/spark-timeseries/python/.eggs/nose-1.3.7-py2.7.egg/nose/importer.py", line 47, in importFromPath
return self.importFromDir(dir_path, fqname)
File "/home/travis/build/cloudera/spark-timeseries/python/.eggs/nose-1.3.7-py2.7.egg/nose/importer.py", line 94, in importFromDir
mod = load_module(part_fqname, fh, filename, desc)
File "/home/travis/build/cloudera/spark-timeseries/python/sparkts/test/test_datetimeindex.py", line 1, in <module>
from test_utils import PySparkTestCase
File "/home/travis/build/cloudera/spark-timeseries/python/sparkts/test/test_utils.py", line 2, in <module>
from sparkts.utils import add_pyspark_path, quiet_py4j
File "/home/travis/build/cloudera/spark-timeseries/python/sparkts/utils.py", line 4, in <module>
import pandas as pd
File "/home/travis/build/cloudera/spark-timeseries/python/.eggs/pandas-0.17.1-py2.7-linux-x86_64.egg/pandas/__init__.py", line 7, in <module>
from pandas import hashtable, tslib, lib
File "pandas/src/numpy.pxd", line 157, in init pandas.hashtable (pandas/hashtable.c:38262)
AttributeError: 'module' object has no attribute 'dtype'
```
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} | 4 | 2015-12-16T09:35:21Z | 2016-03-23T21:37:06Z | 2016-03-23T20:38:17Z | NONE | null | I use compression directly in DataFrame.to_csv() to save on disk space / IO. Would be nice to have support for xz compression there.
Similar to https://github.com/pydata/pandas/issues/8654, but I think in to_csv this has more added value.
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} | 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"]
]
df = pd.DataFrame(data, columns=["text", "int", "float", "bool", "date"])
with pytest.raises(ValueError) as err:
df["text"].apply(pd.to_datetime)
assert 'Unknown string format' in str(err.value)
df["int"].apply(pd.to_datetime)
df["float"].apply(pd.to_datetime)
df["date"].apply(pd.to_datetime)
df["bool"].apply(pd.to_datetime)
Traceback (most recent call last):
File "test.py", line 16, in <module>
df["bool"].apply(pd.to_datetime)
File "/home/kaotika/.virtualenvs/pandas-qt-test/local/lib/python2.7/site-packages/pandas/core/series.py", line 2169, in apply
mapped = lib.map_infer(values, f, convert=convert_dtype)
File "pandas/src/inference.pyx", line 1059, in pandas.lib.map_infer (pandas/lib.c:62578)
File "/home/kaotika/.virtualenvs/pandas-qt-test/local/lib/python2.7/site-packages/pandas/util/decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "/home/kaotika/.virtualenvs/pandas-qt-test/local/lib/python2.7/site-packages/pandas/tseries/tools.py", line 276, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "/home/kaotika/.virtualenvs/pandas-qt-test/local/lib/python2.7/site-packages/pandas/tseries/tools.py", line 397, in _to_datetime
return _convert_listlike(np.array([ arg ]), box, format)[0]
File "/home/kaotika/.virtualenvs/pandas-qt-test/local/lib/python2.7/site-packages/pandas/tseries/tools.py", line 372, in _convert_listlike
require_iso8601=require_iso8601)
File "pandas/tslib.pyx", line 1847, in pandas.tslib.array_to_datetime (pandas/tslib.c:37155)
File "pandas/tslib.pyx", line 2041, in pandas.tslib.array_to_datetime (pandas/tslib.c:36836)
File "pandas/tslib.pyx", line 1986, in pandas.tslib.array_to_datetime (pandas/tslib.c:35842)
File "pandas/tslib.pyx", line 1939, in pandas.tslib.array_to_datetime (pandas/tslib.c:34965)
TypeError: object of type 'bool' has no len()
```
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} | 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.frequencies import _period_str_to_code
>>> _period_str_to_code('Min')
8000
>>> _period_str_to_code('T')
8000
>>> _period_str_to_code('minute')
sys:1: FutureWarning: Freq "MINUTE" is deprecated, use "Min" as alternative.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "***/site-packages/pandas/tseries/frequencies.py", line 813, in _period_str_to_code
return _period_code_map[alias]
KeyError: 'Min'
```
(in pandas/tseries/frequencies.py line 783 to line 813)
It appears that 'minute' is converted into the alias 'Min' during the function **__period_str_to_code**_ (line 807).
'Min' is then used as a key in the dictionnary **__period_code_map**_, but this dictionnary doesn't hold any value for this key (line 813).
Indeed, the key 'Min' is stored in the dictionnary **__lite_rule_alias**_ where it indexes the value 'T' (the correct frequency string for minutes).
This particuliar situation could be solved by transforming the return of the function **__period_str_to_code**_ by using a recursive call which allows to 'safely' handle the converted frequency string (_period_str_to_code('minute') > 'Min' > _period_str_to_code('Min') > 8000).
The conversion from 'Min' to 'T' occures line 799, and the conversion from 'T' to 8000 occures line 804.
But I'm not sure of the implication of such a recursive call (performance impact, possibility of an 'infinite' recursive call in some situation, and so on).
Here is the function with a recursive call (change in pandas/tseries/frequencies.py line 813):
```
def _period_str_to_code(freqstr):
# hack
if freqstr in _rule_aliases:
new = _rule_aliases[freqstr]
warnings.warn(_LEGACY_FREQ_WARNING.format(freqstr, new),
FutureWarning, stacklevel=3)
freqstr = new
freqstr = _lite_rule_alias.get(freqstr, freqstr)
if freqstr not in _dont_uppercase:
lower = freqstr.lower()
if lower in _rule_aliases:
new = _rule_aliases[lower]
warnings.warn(_LEGACY_FREQ_WARNING.format(lower, new),
FutureWarning, stacklevel=3)
freqstr = new
freqstr = _lite_rule_alias.get(lower, freqstr)
try:
if freqstr not in _dont_uppercase:
freqstr = freqstr.upper()
return _period_code_map[freqstr]
except KeyError:
try:
alias = _period_alias_dict[freqstr]
warnings.warn(_LEGACY_FREQ_WARNING.format(freqstr, alias),
FutureWarning, stacklevel=3)
except KeyError:
raise ValueError("Unknown freqstr: %s" % freqstr)
return _period_str_to_code(alias)
```
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| 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 = df.loc[:,:]
df_new2 = df.iloc[:,:]
```
Needed for PR #11500
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} | 15 | 2015-12-16T23:00:23Z | 2016-05-17T13:25:14Z | 2016-05-17T13:25:14Z | NONE | null | this fixes #11633
this fix breaks some sparse tests,
stuck on `test_combine_first` : `ValueError: total size of new array must be unchanged`
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} | 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, 1.49781967, -1.29698099])
>>> pan = pd.Panel(a)
>>> pan.sum(axis=(0, 1)) # should return the same vector
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/generic.py", line 4554, in stat_func
skipna=skipna, numeric_only=numeric_only)
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/panel.py", line 1084, in _reduce
axis_name = self._get_axis_name(axis)
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/generic.py", line 327, in _get_axis_name
.format(axis, type(self)))
ValueError: No axis named (0, 2) for object type <class 'pandas.core.panel.Panel'>
>>> np.sum(pan, axis=(0, 1)) # try it another way
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numpy/core/fromnumeric.py", line 1828, in sum
return sum(axis=axis, dtype=dtype, out=out)
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/generic.py", line 4554, in stat_func
skipna=skipna, numeric_only=numeric_only)
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/panel.py", line 1084, in _reduce
axis_name = self._get_axis_name(axis)
File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/generic.py", line 327, in _get_axis_name
.format(axis, type(self)))
ValueError: No axis named (0, 1) for object type <class 'pandas.core.panel.Panel'>
```
Right now the workaround is to call `np.asarray()` on the Pandas object first and then convert it back to `Series` (or `DataFrame`, I suppose) once the result is returned, being sure to apply the correct index to the resulting object.
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} | 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.plot(yerr=a)
```
The underlying bug (if it is a bug) in matplotlib is responsible for several other issues, including #4493 and #6127, but this case is different because it uses only Pandas API methods, rather than passing a Pandas object to a matplotlib method. So it's a little harder to justify passing this off on matplotlib to fix, as was done in e.g. #6127.
If the "proper" fix ever is implemented in matplotlib code, it should solve this as well as #4493 and #6127 and all the others of that nature, but until that point, Pandas can work around it by converting the error object (if it is a `Series` or `DataFrame`) to an `ndarray`. I'm working on this in 4b04f80c41684ba9ab05ce8c87b17b96fde87290 but I'm not sure if there's a better way to fix it, or if this breaks something. (If so, the tests don't indicate it.)
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} | 0 | 2015-12-17T19:28:02Z | 2015-12-26T00:29:36Z | 2015-12-26T00:29:36Z | NONE | null | Issue #10126 seems not fixed completely. I still get the same error when running the same example. I have looked up the code and found that _getitem_frame was not fixed yet. The fix is easy, just the same as _setitem_frame.
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} | 5 | 2015-12-17T22:55:13Z | 2015-12-19T16:16:32Z | 2015-12-19T12:58:10Z | CONTRIBUTOR | null | Closes #11522.
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| 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 y-value is different at each point in time, which for periods is impossible to know).
It is, of course, possible to do this manually with `drawstyle=steps`, but this has the annoying problem that one of the Period "bins" will be missing (the one at the end). `steps-mid` is ugly, since it cuts off half of the steps at both edges.
It would be nice if the default would be changed to this `drawstyle`, but it would be even better if this style would be extended some way to fully handle Periods. Ideally, each Period step would be plotted between the edges of the Period and irregular Periods would be supported as well.
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} | 0 | 2015-12-18T18:43:53Z | 2015-12-18T19:17:37Z | 2015-12-18T19:17:37Z | CONTRIBUTOR | null | closes #11273
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} | 3 | 2015-12-18T19:30:22Z | 2016-01-07T15:15:15Z | 2016-01-07T15:15:15Z | NONE | null | Dataframe.duplicated() is flagging rows as duplicates when they are in fact distinct. This happens when using large dataframes, and duplicated(keep=False):
```
import pandas as pd, numpy as np
df = pd.DataFrame({'a': pd.Series(range(1,100000)),
'b': pd.Series(range(10,1000000)),
'c': pd.Series(3*range(2,200000,2))})
df.head()
np.sum(df.duplicated())
```
Out[]: 0
```
np.sum(df.duplicated(keep=False))
```
Out[]:110
Changing column order results in different (but still incorrect) behavior.
```
np.sum(df[['c','b','a']].duplicated(keep=False))
```
Out[]:2138
Tested on 0.17.1. Environment details are provided below:
>> pd.util.print_versions.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
```
pandas: 0.17.1
nose: None
pip: 7.1.2
setuptools: 18.5
Cython: None
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: 4.0.0
sphinx: 1.3.3
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
Jinja2: None
```
This looks like the same kind of problem described in #11668, though the specific examples provided in that issue work properly in 0.17.1
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} | 7 | 2015-12-18T20:17:02Z | 2015-12-25T21:47:39Z | 2015-12-25T21:47:39Z | CONTRIBUTOR | null | Fixes #11614
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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 computations.
Should we allow passing an empty list to not compute any percentiles?
pandas 0.17.1
``` python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame(np.random.randn(10,5))
In [4]: df.describe(percentiles=None)
Out[4]:
0 1 2 3 4 5
count 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000
mean -0.116736 -0.160728 0.066763 -0.068867 -0.242050 0.390091
std 0.771704 0.837520 0.875747 0.955985 1.093919 0.923464
min -1.347786 -1.140541 -1.297533 -1.347824 -2.085290 -0.825807
25% -0.580527 -0.613640 -0.558291 -0.538433 -0.836046 -0.275567
50% -0.261526 -0.395307 0.007595 -0.248025 0.000515 0.314278
75% 0.329780 0.154053 0.708768 0.407732 0.366278 1.192338
max 1.285276 1.649528 1.485076 1.697162 1.551388 1.762939
In [15]: df.describe(percentiles=[])
Out[15]:
0 1 2 3 4 5
count 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000
mean -0.116736 -0.160728 0.066763 -0.068867 -0.242050 0.390091
std 0.771704 0.837520 0.875747 0.955985 1.093919 0.923464
min -1.347786 -1.140541 -1.297533 -1.347824 -2.085290 -0.825807
50% -0.261526 -0.395307 0.007595 -0.248025 0.000515 0.314278
max 1.285276 1.649528 1.485076 1.697162 1.551388 1.762939
```
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} | 8 | 2015-12-19T06:59:15Z | 2016-01-30T23:50:03Z | 2016-01-03T16:53:54Z | CONTRIBUTOR | null | ...and sys.getsizeof returns correct value. Closes https://github.com/pydata/pandas/issues/11597
Is this the best implementation for a common method across classes?
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| null | 2 | 2015-12-19T09:35:29Z | 2015-12-19T13:27:09Z | 2015-12-19T13:26:55Z | NONE | null | This bug was introduced in v0.17.1
``` 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/pandas/core/internals.py", line 2061, in _try_coerce_args
other = other.astype('i8',copy=False).view('i8')
ValueError: invalid literal for int() with base 10: 'a'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/xxxx/anaconda/envs/py3/lib/python3.4/site-packages/pandas/core/internals.py", line 594, in replace
values, _, to_replace, _ = self._try_coerce_args(self.values, to_replace)
File "/home/xxxx/anaconda/envs/py3/lib/python3.4/site-packages/pandas/core/internals.py", line 2066, in _try_coerce_args
raise TypeError
TypeError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xxxx/anaconda/envs/py3/lib/python3.4/site-packages/pandas/core/generic.py", line 3110, in replace
inplace=inplace, regex=regex)
File "/home/xxxx/anaconda/envs/py3/lib/python3.4/site-packages/pandas/core/internals.py", line 2870, in replace
return self.apply('replace', **kwargs)
File "/home/xxxx/anaconda/envs/py3/lib/python3.4/site-packages/pandas/core/internals.py", line 2823, in apply
applied = getattr(b, f)(**kwargs)
File "/home/xxxx/anaconda/envs/py3/lib/python3.4/site-packages/pandas/core/internals.py", line 607, in replace
if not mask.any():
UnboundLocalError: local variable 'mask' referenced before assignment
</pre>
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} | 0 | 2015-12-19T11:52:32Z | 2015-12-19T11:52:56Z | 2015-12-19T11:52:55Z | MEMBER | null | Typo that generates sphinx warning
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} | 8 | 2015-12-19T16:21:50Z | 2016-01-15T19:05:45Z | 2015-12-19T20:03:33Z | CONTRIBUTOR | null | Fixes #11544
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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} | 5 | 2015-12-20T00:26:29Z | 2016-01-26T14:32:58Z | 2016-01-26T14:32:58Z | NONE | null | In pandas 0.16.2, the following date (non-zero padded month) was parsing correctly:
```
>>> 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'0.17.1'
>>> pandas.to_datetime('2005-1-13', format='%Y-%m-%d')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/dpinte/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/util/decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "/Users/dpinte/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/tseries/tools.py", line 276, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "/Users/dpinte/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/tseries/tools.py", line 397, in _to_datetime
return _convert_listlike(np.array([ arg ]), box, format)[0]
File "/Users/dpinte/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/tseries/tools.py", line 383, in _convert_listlike
raise e
ValueError: time data '2005-1-13' does match format specified
```
Even if `%m` is supposed to be used for zero-padded month definitions, Python's strptime function parses them properly.
Is this a known issue?
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| null | 1 | 2015-12-20T07:22:03Z | 2015-12-20T10:19:33Z | 2015-12-20T10:19:33Z | NONE | null | Supplementary Patch for pull request #11470
(Issues #11276, #11745)
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} | 13 | 2015-12-20T10:22:16Z | 2016-06-23T12:49:38Z | 2016-06-23T12:49:30Z | NONE | null | Hi,
I'm just wondering what your code of conduct is for this project?
Thanks,
Alison
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} | 1 | 2015-12-21T00:28:23Z | 2015-12-23T14:50:55Z | 2015-12-23T14:50:41Z | CONTRIBUTOR | null | closes #11468
Please Review.
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| null | 7 | 2015-12-21T02:59:44Z | 2016-04-07T01:19:51Z | 2015-12-21T12:35:05Z | CONTRIBUTOR | null | Inspired by the R package dplyr I'd like to be able to chain together my data manipulation but can't find an elegant way to do a filter.
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 one 1.505 0.223
2 foo two 0.646 -0.025
3 bar three -0.072 1.227
4 foo two -1.367 -0.873
5 bar two 1.176 2.317
6 foo one 0.424 0.858
7 foo three 2.129 0.038
In [14]: aggd = df.groupby('A').sum()
In [15]: final = aggd[aggd['C'] > 2]
In [16]: final
Out[16]:
C D
A
bar 2.608 3.767
```
What I'd like to be able to do is....
```
In [21]: df.groupby('A').sum().apply_filter(lambda row: row['C'] > 0, axis=1)
Out[21]:
C D
A
bar 2.608 3.767
```
This should be a pretty simple addition basically calling DataFrame.apply and happy to open a PR for it but wanted to see what you guys thought about this?
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https://api.github.com/repos/pandas-dev/pandas/issues/11876 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11876/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11876/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11876/events | https://github.com/pandas-dev/pandas/pull/11876 | 123,278,467 | MDExOlB1bGxSZXF1ZXN0NTQyNTUzNjY= | 11,876 | Gbq upgrade | {
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| null | 4 | 2015-12-21T13:19:19Z | 2015-12-23T08:58:40Z | 2015-12-21T14:09:28Z | NONE | null | This adds support for service account authorization to Google Big Query API + some informative messages on std out.
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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| null | 1 | 2015-12-21T14:28:37Z | 2015-12-21T18:53:56Z | 2015-12-21T18:53:56Z | NONE | null | Hello,
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(cmd)
File "/usr/lib/python3.4/distutils/dist.py", line 974, in run_command
cmd_obj.run()
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/command/bdist_egg.py", line 185, in run
cmd = self.call_command('install_lib', warn_dir=0)
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/command/bdist_egg.py", line 171, in call_command
self.run_command(cmdname)
File "/usr/lib/python3.4/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/usr/lib/python3.4/distutils/dist.py", line 974, in run_command
cmd_obj.run()
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/command/install_lib.py", line 20, in run
self.build()
File "/usr/lib/python3.4/distutils/command/install_lib.py", line 109, in build
self.run_command('build_ext')
File "/usr/lib/python3.4/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/usr/lib/python3.4/distutils/dist.py", line 974, in run_command
cmd_obj.run()
File "/tmp/easy_install-856yra78/numpy-1.10.2/numpy/distutils/command/build_ext.py", line 257, in run
File "/usr/lib/python3.4/distutils/command/build_ext.py", line 448, in build_extensions
self.build_extension(ext)
File "/tmp/easy_install-856yra78/numpy-1.10.2/numpy/distutils/command/build_ext.py", line 355, in build_extension
File "/tmp/easy_install-856yra78/numpy-1.10.2/numpy/distutils/ccompiler.py", line 28, in <lambda>
File "/tmp/easy_install-856yra78/numpy-1.10.2/numpy/distutils/ccompiler.py", line 231, in CCompiler_compile
File "/tmp/easy_install-856yra78/numpy-1.10.2/numpy/distutils/ccompiler.py", line 195, in single_compile
File "/tmp/easy_install-856yra78/numpy-1.10.2/numpy/distutils/ccompiler.py", line 28, in <lambda>
File "/tmp/easy_install-856yra78/numpy-1.10.2/numpy/distutils/unixccompiler.py", line 52, in UnixCCompiler__compile
distutils.errors.CompileError: Command "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -Inumpy/core/include -Ibuild/src.linux-x86_64-3.4/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/include/python3.4m -I/home/username/.virtualenvs/envname/include/python3.4m -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -c build/src.linux-x86_64-3.4/numpy/core/src/multiarray/lowlevel_strided_loops.c -o build/temp.linux-x86_64-3.4/build/src.linux-x86_64-3.4/numpy/core/src/multiarray/lowlevel_strided_loops.o" failed with exit status 4
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/command/easy_install.py", line 1000, in run_setup
run_setup(setup_script, args)
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/sandbox.py", line 50, in run_setup
lambda: execfile(
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/sandbox.py", line 100, in run
return func()
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/sandbox.py", line 52, in <lambda>
{'__file__':setup_script, '__name__':'__main__'}
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/compat.py", line 78, in execfile
exec(compile(source, fn, 'exec'), globs, locs)
File "setup.py", line 263, in <module>
'pandas/parser.pyx',
File "setup.py", line 255, in setup_package
class CheckSDist(sdist_class):
File "/tmp/easy_install-856yra78/numpy-1.10.2/numpy/distutils/core.py", line 169, in setup
File "/usr/lib/python3.4/distutils/core.py", line 163, in setup
raise SystemExit("error: " + str(msg))
SystemExit: error: Command "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -Inumpy/core/include -Ibuild/src.linux-x86_64-3.4/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/include/python3.4m -I/home/username/.virtualenvs/envname/include/python3.4m -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -c build/src.linux-x86_64-3.4/numpy/core/src/multiarray/lowlevel_strided_loops.c -o build/temp.linux-x86_64-3.4/build/src.linux-x86_64-3.4/numpy/core/src/multiarray/lowlevel_strided_loops.o" failed with exit status 4
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 17, in <module>
File "/tmp/pip-o23ozwi2-build/setup.py", line 596, in <module>
**setuptools_kwargs)
File "/usr/lib/python3.4/distutils/core.py", line 108, in setup
_setup_distribution = dist = klass(attrs)
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/dist.py", line 239, in __init__
self.fetch_build_eggs(attrs.pop('setup_requires'))
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/dist.py", line 264, in fetch_build_eggs
replace_conflicting=True
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/pkg_resources.py", line 580, in resolve
dist = best[req.key] = env.best_match(req, ws, installer)
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/pkg_resources.py", line 818, in best_match
return self.obtain(req, installer) # try and download/install
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/pkg_resources.py", line 830, in obtain
return installer(requirement)
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/dist.py", line 314, in fetch_build_egg
return cmd.easy_install(req)
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/command/easy_install.py", line 593, in easy_install
return self.install_item(spec, dist.location, tmpdir, deps)
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/command/easy_install.py", line 623, in install_item
dists = self.install_eggs(spec, download, tmpdir)
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/command/easy_install.py", line 809, in install_eggs
return self.build_and_install(setup_script, setup_base)
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/command/easy_install.py", line 1015, in build_and_install
self.run_setup(setup_script, setup_base, args)
File "/home/username/.virtualenvs/envname/local/lib/python3.4/site-packages/setuptools/command/easy_install.py", line 1003, in run_setup
raise DistutilsError("Setup script exited with %s" % (v.args[0],))
distutils.errors.DistutilsError: Setup script exited with error: Command "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -Inumpy/core/include -Ibuild/src.linux-x86_64-3.4/numpy/core/include/numpy -Inumpy/core/src/private -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/usr/include/python3.4m -I/home/username/.virtualenvs/envname/include/python3.4m -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -Ibuild/src.linux-x86_64-3.4/numpy/core/src/private -c build/src.linux-x86_64-3.4/numpy/core/src/multiarray/lowlevel_strided_loops.c -o build/temp.linux-x86_64-3.4/build/src.linux-x86_64-3.4/numpy/core/src/multiarray/lowlevel_strided_loops.o" failed with exit status 4
x86_64-linux-gnu-gcc: internal compiler error: Killed (program cc1)
Please submit a full bug report,
with preprocessed source if appropriate.
See <file:///usr/share/doc/gcc-4.8/README.Bugs> for instructions.
----------------------------------------
Cleaning up...
Command python setup.py egg_info failed with error code 1 in /tmp/pip-o23ozwi2-build
Storing debug log for failure in /home/username/.pip/pip.log
```
aslo try to `pip3 install pandas` and not work
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https://api.github.com/repos/pandas-dev/pandas/issues/11878 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11878/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11878/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11878/events | https://github.com/pandas-dev/pandas/issues/11878 | 123,319,300 | MDU6SXNzdWUxMjMzMTkzMDA= | 11,878 | Fail to load a very large pickle data frame by read_pickle after dumping it by to_pickle | {
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| 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 call last)
<ipython-input-2-e2e904b8836b> in <module>()
----> 1 tr_fl = pd.read_pickle('dataset/train_selected_fl.pkl')
/usr/local/lib/python2.7/site-packages/pandas/io/pickle.pyc in read_pickle(path)
58
59 try:
---> 60 return try_read(path)
61 except:
62 if PY3:
/usr/local/lib/python2.7/site-packages/pandas/io/pickle.pyc in try_read(path, encoding)
55 except:
56 with open(path, 'rb') as fh:
---> 57 return pc.load(fh, encoding=encoding, compat=True)
58
59 try:
/usr/local/lib/python2.7/site-packages/pandas/compat/pickle_compat.pyc in load(fh, encoding, compat, is_verbose)
114 up.is_verbose = is_verbose
115
--> 116 return up.load()
117 except:
118 raise
/usr/local/lib/python2.7/pickle.pyc in load(self)
856 while 1:
857 key = read(1)
--> 858 dispatch[key](self)
859 except _Stop, stopinst:
860 return stopinst.value
/usr/local/lib/python2.7/pickle.pyc in load_build(self)
1215 setstate = getattr(inst, "__setstate__", None)
1216 if setstate:
-> 1217 setstate(state)
1218 return
1219 slotstate = None
ValueError: buffer size does not match array size
```
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} | 7 | 2015-12-21T18:06:24Z | 2017-02-27T19:44:37Z | 2017-02-27T19:44:37Z | NONE | null | The `DataFrame.to_records` method fails with a `UnicodeEncodeError` for some unicode column names.
(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]:
c/σ
0 1
1 2
2 3
In [324]: df.to_records()
---------------------------------------------------------------------------
UnicodeEncodeError Traceback (most recent call last)
<ipython-input-324-6d3142e97d2d> in <module>()
----> 1 df.to_records()
/redacted/python2.7/site-packages/pandas/core/frame.pyc in to_records(self, index, convert_datetime64)
1013 elif index_names[0] is None:
1014 index_names = ['index']
-> 1015 names = index_names + lmap(str, self.columns)
1016 else:
1017 arrays = [self[c].get_values() for c in self.columns]
UnicodeEncodeError: 'ascii' codec can't encode character u'\u03c3' in position 2: ordinal not in range(128)
```
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} | 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 wrap the read_msgpack call inside a pandas.concat
Example
``` python
import pandas as pd
import numpy as np
from sqlalchemy import create_engine
eng = create_engine("sqlite:///:memory:")
df1 = pd.DataFrame({ 'A' : 1.,
'B' : pd.Timestamp('20130102'),
'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
'D' : np.array([3] * 4,dtype='int32'),
'E' : 'foo' })
df1.to_msgpack('test.msgpack', compress='zlib')
df2 = pd.read_msgpack('test.msgpack')
df2.to_sql('test', eng, if_exists='append', chunksize=1000) # throws value error
df2 = pd.cooncat([pd.read_msgpack('test.msgpack')])
df2.to_sql('test', eng, if_exists='append', chunksize=1000) # works
```
This happens with both blosc and zlib compression. While I have found a solution, this behaviour seems very odd and for very large files there is a small performance hit.
edit: @TomAugspurger changed the sql engine to sqlite
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} | 32 | 2015-12-21T22:36:41Z | 2016-02-16T12:09:33Z | 2016-02-01T23:39:27Z | NONE | null | This adds service account authentication while still supports standard web auth method.
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 notebooks).
fixes #8489
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| 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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} | 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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} | 7 | 2015-12-22T09:59:36Z | 2015-12-24T08:55:25Z | 2015-12-24T02:03:10Z | NONE | null | It would be nice if there was a "stacked" option for Series.plot like there is for DataFrame.plot.
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} | 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' object has no attribute 'rint'
```
Would it be better to ignore these non-numerical columns instead of raising an error?
Another option would be to drop these columns (like numerical aggregations like `df.mean()` do).
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} | 3 | 2015-12-22T15:16:25Z | 2019-05-26T12:56:30Z | 2016-07-23T19:28:48Z | MEMBER | null | Idea from http://stackoverflow.com/questions/34383000/pandas-to-sql-all-columns-as-nvarchar
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 difficult to add.
So instead of this:
```
df.to_sql(...., dtype={col_name: sqlalchemy.types.NVARCHAR for col_name in df})
```
you can do this:
```
df.to_sql(...., dtype=sqlalchemy.types.NVARCHAR)
```
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| 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 'tz_localize'
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} | 5 | 2015-12-23T13:45:23Z | 2015-12-23T19:09:01Z | 2015-12-23T19:09:00Z | MEMBER | null | Closes #11686
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| null | 1 | 2015-12-23T14:59:11Z | 2020-05-07T03:42:18Z | null | MEMBER | null | From https://github.com/pydata/pandas/pull/11874#issuecomment-166915826
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 difference:
For `read_csv`, you need to explicitly pass header=0 to replace header existing in file:
```
In [35]: s = """a,b,c
....: 1,2,3
....: 4,5,6"""
In [48]: pd.read_csv(StringIO(s), names=list('ABC'))
Out[48]:
A B C
0 a b c
1 1 2 3
2 4 5 6
In [49]: pd.read_csv(StringIO(s), header=None, names=list('ABC'))
Out[49]:
A B C
0 a b c
1 1 2 3
2 4 5 6
In [51]: pd.read_csv(StringIO(s), header=0, names=list('ABC'))
Out[51]:
A B C
0 1 2 3
1 4 5 6
```
while for `read_excel`, you need to explicitly pass header=None if the file contains no header row:
```
In [31]: pd.read_excel('test_names.xlsx')
Out[31]:
a b c
0 1 2 3
1 4 5 6
In [46]: pd.read_excel('test_names.xlsx', names=['A', 'B', 'C']) <--- ignoring the data of the first row
Out[46]:
A B C
0 1 2 3
1 4 5 6
In [45]: pd.read_excel('test_names.xlsx', header=False, names=['A', 'B', 'C'])
Out[45]:
A B C
0 1 2 3
1 4 5 6
In [47]: pd.read_excel('test_names.xlsx', header=None, names=['A', 'B', 'C'])
Out[47]:
A B C
0 a b c
1 1 2 3
2 4 5 6
```
Would it make sense to change the `read_excel` behaviour to make it more consistent to the `read_csv` one?
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} | 3 | 2015-12-23T19:39:21Z | 2016-01-11T13:39:53Z | 2016-01-11T13:39:53Z | CONTRIBUTOR | null | It would be really useful to have the documentation for the methods specific to `MultiIndex` such as `.from_product()` and `.from_tuples()` to be listed with links in the API reference (http://pandas.pydata.org/pandas-docs/stable/api.html).
I'm willing to edit the api.rst file, but when I build the docs from scratch (using `python make.py html`), the resulting api.html file is incomplete. Before I make changes, I'd like to make sure the build of the docs works right. Maybe the build issue should be a different issue?
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} | 3 | 2015-12-23T21:18:33Z | 2015-12-28T10:08:29Z | 2015-12-28T10:08:29Z | MEMBER | null | Closes https://github.com/pydata/pandas/issues/11805
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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} | 85 | 2015-12-23T22:10:54Z | 2016-01-17T01:56:47Z | 2016-01-16T17:36:53Z | CONTRIBUTOR | null | closes #939
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 transparent to the end user.
It provides a constant memory footprint for any size of index. Their is a tiny penalty for < about 10 elements (which is actually trivial to fix, e.g. we could simply instantiate an `Int64Index` for these cases). But I think it is more natural to _always_ get a `RangeIndex`.
One other change here is to `assert_index_equal` the `exact` kw now takes `equiv` as the default (in addition to a boolean) to allow for exact comparisions except for `Int64Index`/`RangeIndex` are considered equivalent (as are string/unicode as inferred types, this was pre-existing).
```
In [1]: s = Series(range(5))
In [2]: s.index
Out[2]: RangeIndex(start=0, stop=5, step=1)
In [3]: s.nbytes
Out[3]: 40
In [4]: s.index.nbytes
Out[4]: 72
In [5]: s.index.astype(int).nbytes
Out[5]: 40
In [6]: s = Series(range(100))
In [7]: s.index.astype(int).nbytes
Out[7]: 800
In [8]: s.index.nbytes
Out[8]: 72
```
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} | 2 | 2015-12-23T23:09:32Z | 2016-01-22T19:08:24Z | 2016-01-11T13:39:53Z | CONTRIBUTOR | null | Doc changes only.
closes #11890
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} | 4 | 2015-12-24T01:23:09Z | 2018-05-04T12:19:59Z | 2016-01-07T15:15:02Z | CONTRIBUTOR | null | Fixes GH #11864
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} | 2 | 2015-12-24T12:00:53Z | 2015-12-26T00:29:40Z | 2015-12-26T00:29:36Z | CONTRIBUTOR | null | closes #11859
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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| null | 4 | 2015-12-24T16:04:40Z | 2020-12-09T22:50:13Z | null | NONE | null | The following call to `to_sql` fails due to the percentage (%) symbol in the column names in pandas 0.17.1.
``` 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',
DBNAME = 'test'
)
engine = create_engine(connect_str, echo = False)
# Create a dataframe with '%' in column name
df = pd.DataFrame()
df['A%'] = [0.1, 0.2, 0.3]
df['B%'] = [0.1, 0.2, 0.3]
# Save to database
df.to_sql('test_table', engine, if_exists = 'replace', index = False)
# Throws error:
# OperationalError: (_mysql_exceptions.OperationalError) (1054, "Unknown column 'A%%' in 'field list'") [SQL: u'INSERT INTO test_table (`index`, `A%%`, `B%%`) VALUES (%s, %s, %s)'] [parameters: ((0, 0.1, 0.1), (1, 0.2, 0.2), (2, 0.3, 0.3))]
```
Backtrace:
```
Traceback (most recent call last):
File "test_pandas_error.py", line 20, in <module>
df.to_sql('test_table', engine, if_exists = 'replace', index = False)
File "/usr/local/lib/python2.7/site-packages/pandas/core/generic.py", line 1003, in to_sql
dtype=dtype)
File "/usr/local/lib/python2.7/site-packages/pandas/io/sql.py", line 569, in to_sql
chunksize=chunksize, dtype=dtype)
File "/usr/local/lib/python2.7/site-packages/pandas/io/sql.py", line 1241, in to_sql
table.insert(chunksize)
File "/usr/local/lib/python2.7/site-packages/pandas/io/sql.py", line 765, in insert
self._execute_insert(conn, keys, chunk_iter)
File "/usr/local/lib/python2.7/site-packages/pandas/io/sql.py", line 740, in _execute_insert
conn.execute(self.insert_statement(), data)
File "/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.py", line 914, in execute
return meth(self, multiparams, params)
File "/usr/local/lib/python2.7/site-packages/sqlalchemy/sql/elements.py", line 323, in _execute_on_connection
return connection._execute_clauseelement(self, multiparams, params)
File "/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.py", line 1010, in _execute_clauseelement
compiled_sql, distilled_params
File "/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.py", line 1146, in _execute_context
context)
File "/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.py", line 1341, in _handle_dbapi_exception
exc_info
File "/usr/local/lib/python2.7/site-packages/sqlalchemy/util/compat.py", line 199, in raise_from_cause
reraise(type(exception), exception, tb=exc_tb)
File "/usr/local/lib/python2.7/site-packages/sqlalchemy/engine/base.py", line 1116, in _execute_context
context)
File "/usr/local/lib/python2.7/site-packages/sqlalchemy/dialects/mysql/mysqldb.py", line 95, in do_executemany
rowcount = cursor.executemany(statement, parameters)
File "/usr/local/lib/python2.7/site-packages/MySQLdb/cursors.py", line 262, in executemany
r = self._query('\n'.join([query[:p], ',\n'.join(q), query[e:]]))
File "/usr/local/lib/python2.7/site-packages/MySQLdb/cursors.py", line 354, in _query
rowcount = self._do_query(q)
File "/usr/local/lib/python2.7/site-packages/MySQLdb/cursors.py", line 318, in _do_query
db.query(q)
sqlalchemy.exc.OperationalError: (_mysql_exceptions.OperationalError) (1054, "Unknown column 'A%%' in 'field list'") [SQL: u'INSERT INTO test_table (`A%%`, `B
%%`) VALUES (%s, %s)'] [parameters: ((0.1, 0.1), (0.2, 0.2), (0.3, 0.3))]
```
Versions:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.0.1
Cython: None
numpy: 1.10.2
scipy: 0.16.0
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: 2.2.0-b1
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 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 this:
``` python
try:
subdf = df.loc['A':'D','Vals']
except KeyError as ker:
if ker.args[0].startswith("MultiIndex Slicing requires the index to be fully lexsorted"):
print ("Need to handle fact index not sorted")
else:
print ("Need to handle fact that key was missing")
```
I'd rather write something like:
``` python
try:
subdf = df.loc['A':'D','Vals']
except UnsortedIndexError:
print ("Need to handle fact index not sorted")
except KeyError:
print ("Need to handle fact that key was missing")
```
So could the designers accept a change where a different error is raised in case the index is not lexsorted? If so, I'll implement it.
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} | 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/vector_ar.html#var`
- `fama_macbeth` to `http://statsmodels.sourceforge.net/stable/index.html` (as not implemented in statsmodell, but good place to start)
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} | 5 | 2015-12-25T09:31:41Z | 2016-01-26T14:32:58Z | 2016-01-26T14:32:58Z | NONE | null | Hello,
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: 481 ms, total: 3.79 s
Wall time: 4.13 s
In [2]: df
Out[274]:
Symbol Date Bid Ask
0 AUD/USD 20140101 21:55:34.404 0.88796 0.88922
1 AUD/USD 20140101 21:55:34.444 0.88805 0.88914
2 AUD/USD 20140101 21:55:34.475 0.88809 0.88910
3 AUD/USD 20140101 21:55:48.962 0.88811 0.88908
4 AUD/USD 20140101 21:56:38.293 0.88808 0.88887
... ... ... ... ...
1947101 AUD/USD 20140131 21:59:48.048 0.87525 0.87589
1947102 AUD/USD 20140131 21:59:54.599 0.87527 0.87589
1947103 AUD/USD 20140131 21:59:56.927 0.87531 0.87588
1947104 AUD/USD 20140131 21:59:59.365 0.87531 0.87574
1947105 AUD/USD 20140131 22:00:00.038 0.87531 0.87574
[1947106 rows x 4 columns]
In [3]: %time pd.to_datetime(df['Date'])
CPU times: user 11min 44s, sys: 19.4 s, total: 12min 4s
Wall time: 12min 46s
Out[3]:
0 2014-01-01 21:55:34.404
1 2014-01-01 21:55:34.444
2 2014-01-01 21:55:34.475
3 2014-01-01 21:55:48.962
4 2014-01-01 21:56:38.293
...
1947101 2014-01-31 21:59:48.048
1947102 2014-01-31 21:59:54.599
1947103 2014-01-31 21:59:56.927
1947104 2014-01-31 21:59:59.365
1947105 2014-01-31 22:00:00.038
Name: Date, dtype: datetime64[ns]
In [4]: fmt='%Y%m%d %H:%M:%S.%f'
In [5]: %time pd.to_datetime(df['Date'], format=fmt)
CPU times: user 37.3 s, sys: 1.31 s, total: 38.6 s
Wall time: 40 s
In [6]: timedelta(minutes=12, seconds=46) / timedelta(seconds=40)
Out[6]: 19.15
```
There is x19.15 factor!!!
Sample data can be found here
https://drive.google.com/file/d/0B8iUtWjZOTqla3ZZTC1FS0pkZXc/view?usp=sharing
See also https://github.com/pydata/pandas-datareader/issues/153
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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 3.5*
is conda not compatible with python 3.5??
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} | 0 | 2015-12-25T16:20:58Z | 2015-12-26T11:21:32Z | 2015-12-25T16:21:46Z | 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 3.5*
is conda not compatible with python 3.5??
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| 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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