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https://api.github.com/repos/pandas-dev/pandas/issues/9402 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9402/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9402/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9402/events | https://github.com/pandas-dev/pandas/issues/9402 | 56,397,484 | MDU6SXNzdWU1NjM5NzQ4NA== | 9,402 | DataFrame to_latex does not present or pass through column_format to formatter | {
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} | 1 | 2015-02-03T16:05:18Z | 2015-08-31T11:08:01Z | 2015-08-31T11:08:01Z | NONE | null | ## Expected Behaviour
`column_format` keyword arguments to `df.to_latex` should honour the relevant options available and implemented in `DataFrameFormatter` and it's `to_latex` method
The reason why this is annoying is that default `frmt` behaviour is to justify based on the column datatype. For cases where the index column/first column is numeric, these columns get right-aligned, which looks horrible.
## Versions
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-27-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.15.1
nose: 1.3.4
Cython: 0.20.2
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.3.1
sphinx: 1.2.2
patsy: 0.3.0
dateutil: 2.3
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: 1.8.6
xlrd: None
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.5
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 3 | 2015-02-03T16:32:51Z | 2015-07-28T21:52:18Z | 2015-07-28T21:52:18Z | NONE | null | closes #9402
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} | 2 | 2015-02-03T17:40:13Z | 2018-07-06T20:21:07Z | 2017-07-11T09:56:04Z | NONE | null | Hello,
while exporting a panel with unicode strings in the items axis `to_excel` fails with a `UnicodeEncodeError`. The following code reproduces the error:
``` python
import pandas as pd
p = pd.Panel({u'Smørrebrød': pd.DataFrame({})})
p.to_excel('nope.xlsx')
```
I suspect that changing `name = str(item)` to `name = unicode(item)` around line 440 in `pandas/core/panel.py` fixes this small issue.
Thanks!
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21.2
numpy: 1.9.1
scipy: 0.14.0
statsmodels: None
IPython: 2.3.1
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2013b
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: 0.7.5
xlsxwriter: 0.6.6
lxml: 3.4.1
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
```
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| null | 7 | 2015-02-03T17:59:17Z | 2015-02-04T13:48:39Z | 2015-02-03T22:42:55Z | NONE | null | It looks like the support for the new 'exact' keyword has borked to_datetime() for format strings other than '%Y%m%d' as it is passed to array_strptime which doesn't seem to support it:
```
ts = 201502031753
pd.to_datetime(ts, format='%Y%m%d%H%M')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-685333561e5e> in <module>()
----> 1 pd.to_datetime(ts, format='%Y%m%d%H%M')
C:\app\python\lib\site-packages\pandas\tseries\tools.py in to_datetime(arg, errors, dayfirst, utc, box, format, exact, coerce, unit, infer_datetime_format)
316 return _convert_listlike(arg, box, format)
317
--> 318 return _convert_listlike(np.array([ arg ]), box, format)[0]
319
320 class DateParseError(ValueError):
C:\app\python\lib\site-packages\pandas\tseries\tools.py in _convert_listlike(arg, box, format)
278 try:
279 result = tslib.array_strptime(
--> 280 arg, format, exact=exact, coerce=coerce
281 )
282 except (tslib.OutOfBoundsDatetime):
C:\app\python\lib\site-packages\pandas\tslib.pyd in pandas.tslib.array_strptime (pandas\tslib.c:35070)()
TypeError: array_strptime() got an unexpected keyword argument 'exact'
```
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} | 5 | 2015-02-03T18:42:11Z | 2021-05-10T21:45:32Z | 2021-05-10T21:45:32Z | CONTRIBUTOR | null | As described in https://github.com/pydata/pandas/pull/9023#issuecomment-72707057, the way `DataFrame.stack()` and `DataFrame.unstack()` treat `NaN` indices is rather odd/inconsistent. Despite passing `test_unstack_nan_index()` in `test_frame.py`, I observe the following (this is from 0.15.2, but I think it's unchanged in the current master for 0.16.0):
```
In [140]: df = pd.DataFrame(np.arange(4).reshape(2, 2),
columns=pd.MultiIndex.from_tuples([('A','a'), ('B', 'b')],
names=['Upper', 'Lower']),
index=Index([0, 1], name='Num'), dtype=np.float64)
In [141]: df_nan = pd.DataFrame(np.arange(4).reshape(2, 2),
columns=pd.MultiIndex.from_tuples([('A',np.nan), ('B', 'b')],
names=['Upper', 'Lower']),
index=Index([0, 1], name='Num'), dtype=np.float64)
In [148]: df
Out[148]:
Upper A B
Lower a b
Num
0 0 1
1 2 3
In [149]: df.stack()
Out[149]:
Upper A B
Num Lower
0 a 0 NaN
b NaN 1
1 a 2 NaN
b NaN 3
In [150]: df.T.unstack().T
Out[150]:
Upper A B
Num Lower
0 a 0 NaN
b NaN 1
1 a 2 NaN
b NaN 3
In [151]: df_nan
Out[151]:
Upper A B
Lower NaN b
Num
0 0 1
1 2 3
In [152]: df_nan.stack()
Out[152]:
Upper A B
Num Lower
0 NaN 0 1
b 0 1
1 NaN 2 3
b 2 3
In [153]: df_nan.T.unstack().T
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-153-edbcaeb64f64> in <module>()
----> 1 df_nan.T.unstack().T
C:\Python34\lib\site-packages\pandas\core\frame.py in unstack(self, level)
3486 """
3487 from pandas.core.reshape import unstack
-> 3488 return unstack(self, level)
3489
3490 #----------------------------------------------------------------------
C:\Python34\lib\site-packages\pandas\core\reshape.py in unstack(obj, level)
439 if isinstance(obj, DataFrame):
440 if isinstance(obj.index, MultiIndex):
--> 441 return _unstack_frame(obj, level)
442 else:
443 return obj.T.stack(dropna=False)
C:\Python34\lib\site-packages\pandas\core\reshape.py in _unstack_frame(obj, level)
479 else:
480 unstacker = _Unstacker(obj.values, obj.index, level=level,
--> 481 value_columns=obj.columns)
482 return unstacker.get_result()
483
C:\Python34\lib\site-packages\pandas\core\reshape.py in __init__(self, values, index, level, value_columns)
101
102 self._make_sorted_values_labels()
--> 103 self._make_selectors()
104
105 def _make_sorted_values_labels(self):
C:\Python34\lib\site-packages\pandas\core\reshape.py in _make_selectors(self)
143
144 if mask.sum() < len(self.index):
--> 145 raise ValueError('Index contains duplicate entries, '
146 'cannot reshape')
147
ValueError: Index contains duplicate entries, cannot reshape
```
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| null | 1 | 2015-02-03T20:24:32Z | 2015-02-03T22:42:29Z | 2015-02-03T22:42:17Z | NONE | null | Ravel doesn't seem to flatten lists in a series.
``` python
import pandas as pd
df = pd.DataFrame({ 'data':[[1,2],3]})
df['data'].ravel()
Out[3]: array([[1, 2], 3], dtype=object)
df.data.values.ravel()
Out[4]: array([[1, 2], 3], dtype=object)
```
Or, in iPython Notebook
Seen this issue on two versions:
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 14.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: None
numpy: 1.9.1
scipy: None
statsmodels: None
IPython: 2.4.0
sphinx: None
patsy: None
dateutil: 2.4.0
pytz: 2014.10
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
and
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.4
Cython: 0.21
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.7
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: 0.8
apiclient: 1.2
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
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| null | 1 | 2015-02-03T22:17:24Z | 2015-02-03T22:41:11Z | 2015-02-03T22:41:00Z | NONE | null | Hello - can anything be done about the performance of date_range? I've noticed a big degradation in performance from 0.11.0 (I know it's old) to 0.15.2.
_0.11.0:_
```
%timeit pandas.date_range(start='1/1/1995', end='2/3/2015', freq='B')
1000 loops, best of 3: 412 us per loop
```
_0.15.2:_
```
%timeit pandas.date_range(start='1/1/1995', end='2/3/2015', freq='B')
10 loops, best of 3: 134 ms per loop
```
Thanks
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} | 3 | 2015-02-03T23:09:29Z | 2015-07-28T21:51:06Z | 2015-07-28T21:51:06Z | NONE | null | closes issue #9404
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| null | 2 | 2015-02-04T00:00:28Z | 2015-02-04T01:37:04Z | 2015-02-04T01:11:25Z | NONE | null | I'm seeing some odd behavior from Pandas 0.15.2 on Python 3.4.2.
First, I import the data with no issues:
import pandas as pd
hourly = pd.read_csv("fremont_bridge_data.csv", index_col='Date', parse_dates=True)
weekly = hourly.resample('w','sum')
But when trying to access, I run into some odd behavior. This works fine:
In[289]: weekly['2013-12']
Out[289]:
northbound southbound total
Date
2013-12-01 5053 5480 10533
2013-12-08 5432 5836 11268
2013-12-15 5381 5760 11141
2013-12-22 5182 5455 10637
2013-12-29 3118 3567 6685
But this fails:
In[290]: weekly['2013-12-29']
Traceback (most recent call last):
File "<ipython-input-290-96e181f8ff0a>", line 1, in <module>
weekly['2013-12-29']
File "C:\Anaconda\envs\py34\lib\site-packages\pandas\core\frame.py", line 1780, in **getitem**
return self._getitem_column(key)
File "C:\Anaconda\envs\py34\lib\site-packages\pandas\core\frame.py", line 1787, in _getitem_column
return self._get_item_cache(key)
File "C:\Anaconda\envs\py34\lib\site-packages\pandas\core\generic.py", line 1068, in _get_item_cache
values = self._data.get(item)
File "C:\Anaconda\envs\py34\lib\site-packages\pandas\core\internals.py", line 2849, in get
loc = self.items.get_loc(item)
File "C:\Anaconda\envs\py34\lib\site-packages\pandas\core\index.py", line 1402, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "pandas\index.pyx", line 134, in pandas.index.IndexEngine.get_loc (pandas\index.c:3807)
File "pandas\index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas\index.c:3687)
File "pandas\hashtable.pyx", line 696, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12310)
File "pandas\hashtable.pyx", line 704, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12261)
KeyError: '2013-12-29'
Any ideas? This also fails: weekly[weekly.index[0]], and seems like it shouldn't.
Ditto with .loc. weekly.loc['2012-12-30'] works fine, but weekly.loc[['2012-12-30','2012-12-30']] fails!
The data is here: https://github.com/sergeyf/Python_data_science_class/blob/master/code/data/fremont_bridge_data.csv
Thank you.
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} | 4 | 2015-02-04T02:02:53Z | 2015-02-10T16:58:17Z | 2015-02-08T11:26:44Z | CONTRIBUTOR | null | Closes #9393
simple test for tables names that need to be quoted.
This is related to issue [here](https://github.com/pydata/pandas/issues/9393) that is fixed in current master.
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| null | 2 | 2015-02-04T16:58:14Z | 2015-02-04T17:29:32Z | 2015-02-04T17:29:32Z | NONE | null | pandas has great methods for manipulating index dates, which works well for timeseries data. But for other kinds of data, to manipulate a date column you have to use lambda (or set that column to be the index). For example, a lot of the beginners I'm training work with transactional data that looks something like this:
``` python
purchases = pd.DataFrame({
'Purchase_Date': ['9/9/2013', '9/9/2013', '9/10/2013', '9/28/2013', '10/6/2013', '10/7/2013',
'10/18/2013', '10/18/2013', '11/1/2013'],
'Delivery_Date': ['9/11/2013', '9/11/2013', '9/18/2013', '10/2/2013', '10/8/2013', '10/8/2013', '10/22/2013', '10/22/2013', '11/8/2013'],
'Size': ['Medium', 'Small', 'Large', 'Small', 'Medium', 'Small', 'Large', 'Medium', 'Small'],
'Color': ['Red', 'Blue', 'Green', 'Red', 'Red', 'Green', 'Purple', 'Purple', 'Black']})
```
Here's what you have to do now to get the delivery purchase month:
``` python
purchases["Delivery_Month"] = purchases["Delivery_Date"].apply(lambda x: pd.to_Datetime(x).month)
```
For beginners who don't have a science or programming background, using apply and lambda is pretty confusing. And since one of the first things these beginners want to do is analyze their data by dates, it's a potential stumbling block to their adopting pandas.
It would be great if date functions could be vectorized the way strings have so that for example you could instead write:
``` python
purchases["Delivery_Month"] = purchases["Delivery_Date"].month
```
I think this change might help make pandas more accessible to a wider audience. For example, as part of [Data Chefs](http://datachefs.org), I'm working with a group of folks to create an online course and cookbook on slicing & dicing data with pandas. Having vectorized date functions would remove a small but important barrier to our target audience becoming comfortable using pandas.
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} | 6 | 2015-02-04T18:10:16Z | 2017-07-10T10:15:08Z | 2017-07-10T10:15:08Z | NONE | null | i recently bumped an unexpected issue with pandas rolling funcs. rolling_quantile for example:
```
>> row = 10
>> col = 5
>> idx = pd.date_range(20100101,periods=row,freq='B')
>> a = pd.DataFrame(np.random.rand(row*col).reshape((row,-1)),index=idx)
>> a
0 1 2 3 4
2010-01-01 0.341434 0.497274 0.596341 0.259909 0.872207
2010-01-04 0.222653 0.056723 0.064019 0.936307 0.785647
2010-01-05 0.179067 0.647165 0.931266 0.557698 0.713282
2010-01-06 0.049766 0.259756 0.945736 0.380948 0.282667
2010-01-07 0.385036 0.517609 0.575958 0.050758 0.850735
2010-01-08 0.628169 0.510453 0.325973 0.263361 0.444959
2010-01-11 0.099133 0.976571 0.602235 0.181185 0.506316
2010-01-12 0.987344 0.902289 0.080000 0.254695 0.753325
2010-01-13 0.759198 0.014548 0.139858 0.822900 0.251972
2010-01-14 0.404149 0.349788 0.038714 0.280568 0.197865
>> a.quantile([0.25,0.5,0.75],axis=0)
0 1 2 3 4
0.25 0.189963 0.282264 0.094964 0.255999 0.323240
0.50 0.363235 0.503864 0.450966 0.271964 0.609799
0.75 0.572164 0.614776 0.600761 0.513510 0.777567
>> np.percentile(a,[25,50,75],axis=0)
[array([ 0.18996316, 0.28226404, 0.09496441, 0.25599853, 0.32323997]),
array([ 0.36323529, 0.50386356, 0.45096554, 0.27196429, 0.60979881]),
array([ 0.57216415, 0.61477607, 0.6007611 , 0.51351021, 0.7775667 ])]
>> pd.rolling_quantile(a,row,0.25).tail(1)
0 1 2 3 4
2010-01-14 0.179067 0.259756 0.08 0.254695 0.282667
```
looks like pandas.DataFrame.quantile member func is consistent with the numpy.percentile func. however the pandas.rolling_quantile func returns diff results. reduce the row number to 5, the problem will be gone (all three methods return the same results). any thoughts?
ps: i also tested rolling_std func which will "randomly" generate errors with 10^-7 ~ 10^-8 scales (compared to pandas.DataFrame std member func or numpy/scipy std funcs which could limit the error close to np.spacing(1) level) for long (row-wise) pandas.DataFrames
python environment:
python 3.4.2
cython 0.21.1
numpy 1.8.2
scipy 0.14.0
pandas 0.15.1
statsmodels 0.6.0
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| null | 4 | 2015-02-04T21:34:41Z | 2015-02-11T14:45:10Z | 2015-02-11T14:27:56Z | CONTRIBUTOR | null | Is it possible to write tests for setup.py?
## Setup
```
touch pandas/period.pyx
```
and put this in your `setup.py`'s ext_data:
``` python
period=dict(pyxfile='period'),
```
## Before PR
``` bash
python setup.py build_ext --inplace
rm -rf pandas/period.c
python setup.py build_ext --inplace
python setup.py clean
ls -l pandas/period.c
# -rw-rw-r-- 1 brandon brandon 48253 Feb 4 15:27 pandas/period.c
```
`period.c` still exists after cleaning.
## After PR
``` bash
python setup.py build_ext --inplace
rm -rf pandas/period.c
python setup.py build_ext --inplace
python setup.py clean
ls -l pandas/period.c
# ls: cannot access pandas/period.c: No such file or directory
```
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} | 2 | 2015-02-04T22:17:39Z | 2015-07-18T00:03:27Z | 2015-07-18T00:03:27Z | NONE | null | Not sure if this is intentional, but Series.shift() won't run with categorical dtypes:
``` python
ser = pd.Series(['a', 'b', 'c', 'd'], dtype="category")
ser.shift(1)
Traceback (most recent call last):
File "<ipython-input-15-1a7536b0af06>", line 1, in <module>
ser.shift(1)
File "/.../pandas/core/generic.py", line 3394, in shift
new_data = self._data.shift(periods=periods, axis=block_axis)
File "/.../pandas/core/internals.py", line 2533, in shift
return self.apply('shift', **kwargs)
File "/.../pandas/core/internals.py", line 2497, in apply
applied = getattr(b, f)(**kwargs)
File "/.../pandas/core/internals.py", line 893, in shift
new_values, fill_value = com._maybe_upcast(self.values)
File "/.../pandas/core/common.py", line 1218, in _maybe_upcast
new_dtype, fill_value = _maybe_promote(dtype, fill_value)
File "/.../pandas/core/common.py", line 1124, in _maybe_promote
if issubclass(np.dtype(dtype).type, compat.string_types):
TypeError: data type not understood
```
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} | 4 | 2015-02-04T22:30:22Z | 2015-08-15T12:37:55Z | 2015-02-08T13:35:47Z | CONTRIBUTOR | null | Closes #9400
`pd.Series([2,3,4]).plot("quiver")` now gives an improved error message:
```
Users/ch/miniconda/envs/pd_dev34/lib/python3.4/site-packages/pandas/tools/plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds)
2269 klass = _plot_klass[kind]
2270 else:
-> 2271 raise ValueError(invalid_ct_fmtstr % kind)
2272
2273 from pandas import DataFrame
ValueError: 'quiver' is not a valid chart type
```
instead of:
```
/Users/ch/miniconda/envs/sci33/lib/python3.3/site-packages/pandas/tools/plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds)
2267 klass = _plot_klass[kind]
2268 else:
-> 2269 raise ValueError('Invalid chart type given %s' % kind)
2270
2271 from pandas import DataFrame
ValueError: Invalid chart type given quiver
```
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| null | 4 | 2015-02-05T17:06:02Z | 2019-07-28T16:34:09Z | 2018-10-21T01:04:59Z | NONE | null | i tested `rolling_std` func with some real data (floating number with format x.xx). For a Series/DataFrame which contains let's say 5 continuous identical rows among several hundreds, the `rolling_std` could return a small number with 10^-7 scale instead exactly zero. this is probably a known issue which has not been officially documented. i'm just curious with the reason behind and why the std member func works better.
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| null | 2 | 2015-02-05T17:43:49Z | 2016-01-07T00:31:10Z | 2016-01-07T00:31:10Z | NONE | null | I have completely rewritten the bottleneck package. Since it is an optional dependency of pandas I thought you might want to test it before I release.
Release notes are here: https://github.com/kwgoodman/bottleneck/blob/master/RELEASE.rst
Branch: master
Please report results here: https://github.com/kwgoodman/bottleneck/issues/96
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} | 116 | 2015-02-05T18:12:27Z | 2017-12-08T12:16:17Z | 2017-10-10T13:17:02Z | MEMBER | null | ### Summary
The question is what the sum of a Series of all NaNs should return (which is equivalent to an empty Series after skipping the NaNs): NaN or 0?
```
In [1]: s = Series([np.nan])
In [2]: s.sum(skipna=True) # skipping NaNs is the default
Out[2]: nan or 0 <---- DISCUSSION POINT
In [3]: s.sum(skipna=False)
Out[3]: nan
```
The reason this is a discussion point has the following cause: the internal nansum implementation of pandas returns NaN. But, when bottleneck is installed, pandas will use bottlenecks implementation of nansum, which returns 0 (for the versions >= 1.0).
Bottleneck changed the behaviour from returning NaN to returning 0 to model it after numpy's nansum function.
This has the very annoying consequence that depending on whether bottleneck is installed or not (which is only an optional dependency), you get a different behaviour.
So the decision we need to make, is to either:
- adapt pandas internal implementation to return 0, so in all cases 0 is returned for all NaN/empty series.
- workaround bottlenecks behaviour or not use it for nansum, in order to consistently return NaN instead of 0
- choose one of both above as the default, but have an option to switch behaviour
---
Original title: **nansum in bottleneck 1.0 will return 0 for all NaN arrays instead of NaN**
xref https://github.com/kwgoodman/bottleneck/issues/96
xref https://github.com/pydata/pandas/issues/9421
- [ ] Tests are turned off for bottleneck >1.0 (xref https://github.com/pydata/pandas/pull/10986)
This matches a change from numpy 1.8 -> 1.9.
We should address this for pandas 0.16.
Should we work around the new behavior (probably the simplest choice) or change nansum in pandas?
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| null | 3 | 2015-02-05T19:26:18Z | 2015-02-06T00:05:34Z | 2015-02-06T00:05:34Z | NONE | null | Some strings that clearly aren't dates are being parsed as dates. This may be expected behavior, but thought I'd flag just in case.
``` python
pd.to_datetime('0067000000M2M3HAAV')
Timestamp('2015-02-05 00:02:03')
```
Not sure how this matches to 2015! But this is fine:
``` python
pd.to_datetime('0067000000M2LkvAAF')
'0067000000M2LkvAAF'
```
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} | 3 | 2015-02-05T22:20:07Z | 2016-05-23T21:43:04Z | 2016-05-23T21:43:04Z | NONE | null | xref #10577 (has test for duplicates with empty data)
I don't expect this is the correct behavior, although it's always possible I'm doing something wrong. Importing data using the `names` keyword will clobber the values of columns where the name is duplicated. For example:
``` python
from StringIO import StringIO
import pandas as pd
data = """a,1
b,2
c,3"""
names = ['field', 'field']
print pd.read_csv(StringIO(data), names=names, mangle_dupe_cols=True)
print pd.read_csv(StringIO(data), names=names, mangle_dupe_cols=False)
```
returns
```
field field
0 1 1
1 2 2
2 3 3
field field
0 1 1
1 2 2
2 3 3
```
However, this produces the correct result:
``` python
df = pd.read_csv(StringIO(data), header=None)
df.columns = names
print df
```
```
field field
0 a 1
1 b 2
2 c 3
```
Interestingly, it works if the field names are in the header:
``` python
data_with_header = "field,field\n" + data
print pd.read_csv(StringIO(data_with_header))
```
```
field field.1
0 a 1
1 b 2
2 c 3
```
Is this a bug or am I doing something wrong?
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} | 3 | 2015-02-05T22:47:55Z | 2015-06-02T19:25:19Z | 2015-05-30T21:28:14Z | NONE | null | ``` python
import pandas as pd
index = [pd.Timestamp('2014-03-31 00:00:00'),
pd.Timestamp('2014-06-30 00:00:00'),
pd.Timestamp('2015-03-30 00:00:00'),
pd.Timestamp('2015-06-29 00:00:00')]
freq = pd.infer_freq(index) # WOM-5MON
pd.datetools.to_offset(freq) # ValueError: Could not evaluate WOM-5MON
```
Instead of supporting WOM-5###, I think we can easily modify _get_wom_rule() to return None for this set of dates so it doesn't get inferred. Agree?
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} | 13 | 2015-02-05T22:54:07Z | 2015-05-20T11:59:03Z | 2015-03-06T03:18:10Z | NONE | null | Trying to perform a left merge between two dataframes using a column of type object. If I include categoricals in the right dataframe, I get the following error. Trying to reproduce with a toy dataset but no luck so far.
``` python
out = pd.merge(left, right, how='left', left_on='left_id', right_on='right_id')
Traceback (most recent call last):
File ".../pandas/tools/merge.py", line 39, in merge return op.get_result()
File ".../pandas/tools/merge.py", line 201, in get_result concat_axis=0, copy=self.copy)
File ".../pandas/core/internals.py", line 4046, in concatenate_block_managers for placement, join_units in concat_plan]
File ".../pandas/core/internals.py", line 4135, in concatenate_join_units empty_dtype, upcasted_na = get_empty_dtype_and_na(join_units)
File ".../pandas/core/internals.py", line 4074, in get_empty_dtype_and_na dtypes[i] = unit.dtype
File ".../pandas/src/properties.pyx", line 34, in pandas.lib.cache_readonly.__get__ (pandas/lib.c:40664)
File ".../pandas/core/internals.py", line 4349, in dtype self.block.fill_value)[0])
File ".../pandas/core/common.py", line 1128, in _maybe_promote if issubclass(np.dtype(dtype).type, compat.string_types):
TypeError: data type not understood
```
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} | 3 | 2015-02-05T23:47:57Z | 2015-06-02T19:26:59Z | 2015-05-30T21:28:14Z | NONE | null | closes #9425
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} | 1 | 2015-02-06T00:36:42Z | 2015-07-25T14:52:34Z | 2015-07-25T14:52:34Z | CONTRIBUTOR | null | This is discussed on Stack Overflow [here](http://stackoverflow.com/questions/28354207/why-is-an-empty-dataframe-of-dtype-str-filled-of-n/28355909).
When constructing a DataFrame with the `dtype` as `str` but not passing in any data (just an index), the resulting DataFrame is filled with the letter `n`:
``` python
In [61]: pd.DataFrame(index=range(2), columns=[0], dtype=str)
Out[61]:
0
0 n
1 n
```
This is contrary to the behaviour of Series when no data is passed in:
``` python
In [62]: pd.Series(index=range(2), dtype=str)
Out[62]:
0 NaN
1 NaN
dtype: object
```
It would be logical for the DataFrame to be populated with `NaN` in this instance too.
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} | 3 | 2015-02-06T00:38:58Z | 2015-02-07T13:05:53Z | 2015-02-07T12:44:31Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_multi_index | 1008.6970 | 1861.1110 | 0.5420 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [907e1b7] : performance improvements in multi-key groupby
Base [5dc8009] : Merge pull request #9418 from sinhrks/rn_20150205
DOC: Fix release note format
```
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} | 3 | 2015-02-06T00:52:41Z | 2015-04-07T09:07:40Z | 2015-04-07T09:07:33Z | NONE | null | When making the request
```
opt = Options('PCLN', 'yahoo')
data = opt.get_all_data()
```
I get the error:
ValueError Traceback (most recent call last)
<ipython-input-8-f49f9e288226> in <module>()
1 opt = Options('PCLN', 'yahoo')
----> 2 data = opt.get_all_data()
/usr/local/lib/python3.4/dist-packages/pandas/io/data.py in get_all_data(self, call, put)
1088 expiry_dates, _ = self._get_expiry_dates_and_links()
1089
-> 1090 return self._get_data_in_date_range(dates=expiry_dates, call=call, put=put)
1091
1092 def _get_data_in_date_range(self, dates, call=True, put=True):
/usr/local/lib/python3.4/dist-packages/pandas/io/data.py in _get_data_in_date_range(self, dates, call, put)
1102 frame = getattr(self, nam)
1103 except AttributeError:
-> 1104 frame = self._get_option_data(expiry=expiry_date, name=name)
1105 data.append(frame)
1106
/usr/local/lib/python3.4/dist-packages/pandas/io/data.py in _get_option_data(self, expiry, name)
721 frames = getattr(self, frame_name)
722 except AttributeError:
--> 723 frames = self._get_option_frames_from_yahoo(expiry)
724
725 option_data = frames[name]
/usr/local/lib/python3.4/dist-packages/pandas/io/data.py in _get_option_frames_from_yahoo(self, expiry)
653 def _get_option_frames_from_yahoo(self, expiry):
654 url = self._yahoo_url_from_expiry(expiry)
--> 655 option_frames = self._option_frames_from_url(url)
656 frame_name = '_frames' + self._expiry_to_string(expiry)
657 setattr(self, frame_name, option_frames)
/usr/local/lib/python3.4/dist-packages/pandas/io/data.py in _option_frames_from_url(self, url)
682 if not hasattr(self, 'underlying_price'):
683 try:
--> 684 self.underlying_price, self.quote_time = self._get_underlying_price(url)
685 except IndexError:
686 self.underlying_price, self.quote_time = np.nan, np.nan
/usr/local/lib/python3.4/dist-packages/pandas/io/data.py in _get_underlying_price(self, url)
700 root = self._parse_url(url)
701 underlying_price = float(root.xpath('.//*[@class="time_rtq_ticker Fz-30 Fw-b"]')[0]\
--> 702 .getchildren()[0].text)
703
704 #Gets the time of the quote, note this is actually the time of the underlying price.
ValueError: could not convert string to float: '1,044.60'
It looks like commas just need to be stripped from the text before converting to a float.
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} | 1 | 2015-02-06T02:21:56Z | 2015-08-03T22:03:11Z | 2015-08-03T22:03:11Z | CONTRIBUTOR | null | http://stackoverflow.com/a/28357483/1240268
```
In [11]: dti = pd.DatetimeIndex([pd.Timestamp('2015-02-05 22:24:00+0000', tz='UTC')])
In [12]: dti.unique()
Out[12]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-02-05 22:24:00+00:00]
Length: 1, Freq: None, Timezone: UTC
In [13]: pd.unique(dti)
Out[13]: array([1423175040000000000L], dtype=object)
In [14]: pd.unique(dti.values)
Out[14]: array([1423175040000000000L], dtype=object)
```
_This is on 0.15.2, not checked on master._
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} | 3 | 2015-02-06T09:16:11Z | 2019-10-06T01:29:03Z | 2019-10-06T01:29:03Z | NONE | null | #### Lets make a DataFrame with at least three rows (does not fail when only two rows are present).
`df = pandas.DataFrame([[1], [2], [3]])`
#### Selecting rows using query works as expected:
`selected_df = df.query('index == [1, 2]')`
#### However, performing traditional indexing fails because Pandas is trying to perform an operation on a NotImplementedError exception object:
`df.index == [1, 2]`
```
/home/david/.virtualenvs/py34/lib/python3.4/site-packages/pandas/core/index.py in wrapper(self, other)
55 # technically we could support bool dtyped Index
56 # for now just return the indexing array directly
---> 57 if com.is_bool_dtype(result):
58 return result
59 try:
/home/david/.virtualenvs/py34/lib/python3.4/site-packages/pandas/core/common.py in is_bool_dtype(arr_or_dtype)
2463
2464 def is_bool_dtype(arr_or_dtype):
-> 2465 tipo = _get_dtype_type(arr_or_dtype)
2466 return issubclass(tipo, np.bool_)
2467
/home/david/.virtualenvs/py34/lib/python3.4/site-packages/pandas/core/common.py in _get_dtype_type(arr_or_dtype)
2392 elif isinstance(arr_or_dtype, CategoricalDtype):
2393 return CategoricalDtypeType
-> 2394 return arr_or_dtype.dtype.type
2395
2396
AttributeError: 'NotImplementedType' object has no attribute 'dtype'
```
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| null | 2 | 2015-02-06T09:42:33Z | 2015-02-07T00:26:07Z | 2015-02-06T13:35:04Z | CONTRIBUTOR | null | ``` python
df1 = pd.DataFrame([-1, 0, 1])
df2 = pd.DataFrame([1, 1, 1])
df2/df1
0
0 -inf
1 inf
2 1.00000
```
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| null | 1 | 2015-02-06T13:48:33Z | 2015-02-06T13:51:34Z | 2015-02-06T13:51:16Z | NONE | null | I have datetime strings of the form `"1996-4-7 0"`, where the `0` is supposed to be equivalent to `00:00` (i.e. hour 0). I want to turn them into timestamps, but this causes ValueErrors:
``` python
pd.Timestamp('1996-4-7 0') # Result: ValueError
```
These work, on the other hand:
``` python
pd.Timestamp('1996-4-7 00') # Result: Timestamp('1996-04-07 00:00:00')
pd.Timestamp('1996-4-7') # Result: Timestamp('1996-04-07 00:00:00')
```
Maybe this is intentional behavior but looks like a bug to me, or at least an inconsistency. Note the same happens for any single-digit hour, but not for two-digit hours:
``` python
pd.Timestamp('1996-4-7 8') # Result: ValueError
pd.Timestamp('1996-4-7 11') # Result: Timestamp('1996-04-07 11:00:00')
```
Problem occurs in both stable 0.15.2 and 0.15.2-163-g671b384.
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} | 17 | 2015-02-06T16:35:48Z | 2021-03-08T12:47:12Z | null | CONTRIBUTOR | null | xref #11728 for the multi-index case
xref https://github.com/pydata/pandas/issues/14379 for converters
``` python
import pandas as pd
import numpy as np
from pandas.compat import StringIO
data = """Internets,Spaceships
01,a
02,b
03,c
04,d
05,e
06,f
"""
# No leading zeroes in the index because it interprets the column as numeric
print pd.read_csv(StringIO.StringIO(data), index_col="Internets")
# Expected to see trailing zeroes in the index still, got 1,2,3,4 instead. Index is int64.
print pd.read_csv(StringIO.StringIO(data), index_col="Internets", dtype={"Internets": np.object})
# Trailing zeroes now, index is object.
print pd.read_csv(StringIO.StringIO(data), dtype={"Internets": np.object}).set_index("Internets")
```
Version:
```
In [1]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: None
numpy: 1.9.1
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 2.1.0
sphinx: None
patsy: 0.3.0
dateutil: 2.4.0
pytz: 2011c
bottleneck: None
tables: None
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 2.0.4
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.6
pymysql: None
psycopg2: None
```
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} | 3 | 2015-02-06T21:32:39Z | 2015-06-04T20:46:54Z | 2015-02-10T14:16:52Z | CONTRIBUTOR | null | Added explation that kurtosis returned is Fisher's kurtosis, also
known as excess kurtosis.
Also cleans up the kurtosis function to remove some unused computations.
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} | 10 | 2015-02-06T21:52:45Z | 2019-08-30T06:55:04Z | 2017-07-08T22:15:23Z | NONE | null | The code:
```
import pandas
df = pandas.read_csv(u"C:/成功例Q309~Metadata.tsv")
```
does not work, and gives the output:
```
IOError: File C:/???Q309.ppt~Metadata.tsv does not exist
```
It seems similar in nature to this issue: https://github.com/pydata/pandas/issues/9315 however #9315 was reportedly fixed in 14.2 with 3.3.5. I am using 15.1 and 2.7.7.
Here is the output of `pd.show_versions()`:
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.15.2
nose: 1.3.3
Cython: 0.20.1
numpy: 1.9.1
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
Thanks,
Justin
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} | 1 | 2015-02-06T22:47:18Z | 2015-02-11T10:41:58Z | 2015-02-10T14:14:19Z | MEMBER | null | Derived from #9111.
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} | 31 | 2015-02-07T10:31:00Z | 2015-02-17T14:38:12Z | 2015-02-17T14:33:06Z | CONTRIBUTOR | null | I propose that Period should be a more basic type, like Timestamp.
I wanted to start with implementing frequencies with multipliers (see issue #7811), but the code for Period was all over the place. So, I moved all code directly related to Period to a Cython module. Changed a bit of the logic to make calls from Cython instead of Python to get rid of some Python dependencies. However, the 'meat' of Period is still there. I will be refactoring Period to use Cython over the next week or two.
I've only done a bit of performance testing, but the results from moving Period are good so far without any real Cython refactoring.
Note: First commit in this stream is from #9415 because I needed that bug fixed to make this work.
Comments most welcome!
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} | 5 | 2015-02-07T14:12:40Z | 2015-04-01T03:27:26Z | 2015-03-31T21:06:09Z | MEMBER | null | Closes #5662. Refactored a little.
Made examples to confirm 1st and 2nd row's yaxis are identical.
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(100, 3))
df[0] = df[1]
pd.tools.plotting.scatter_matrix(df)
```
```
df[1] = df[1] / 3 - 2
df[0] = df[1]
pd.tools.plotting.scatter_matrix(df)
```
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} | 6 | 2015-02-07T19:07:25Z | 2015-08-29T13:12:39Z | 2015-08-29T13:12:39Z | NONE | null | xref #6549
When a dataframe with a timedelta64 is very large, the mean() function does not work as expected. In the following code, the mean is incorrect but all the other stats are fine:
``` python
import numpy as np
import pandas as pd
dates1 = pd.date_range(start='20000101T000000', end='20150101T230000', freq='1H').values
dates2 = dates1 + np.random.randint(0*60*60*1000000000, 10*24*60*60*1000000000, len(dates1))
df = pd.DataFrame({'dates1':dates1, 'dates2':dates2})
df['tdiff'] = dates2-dates1
df['fdiff'] = df['tdiff'].apply(lambda x: float(x.item())/1000000000/3600/24)
df.describe()
```
By making the length smaller, by changing the start date in the above example:
``` python
dates1 = pd.date_range(start='20140101T000000', end='20150101T230000', freq='1H').values
```
The correct result is obtained. (The mean of the timedelta should be about 5 days.) Is this an open bug?
Version:
``` python
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: C
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21.2
numpy: 1.9.1
scipy: 0.15.1
statsmodels: None
IPython: 2.3.1
sphinx: 1.2.3
patsy: None
dateutil: 2.4.0
pytz: 2014.10
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 5 | 2015-02-08T14:50:01Z | 2015-03-08T16:13:04Z | 2015-03-08T16:13:04Z | CONTRIBUTOR | null | Right:
```
$ python -c 'import pandas; print pandas.Series([1, 2, None, 1, 1, 3, None, 3]).value_counts(dropna = False)'
1 3
3 2
NaN 2
2 1
dtype: int64
```
Wrong, because there is no row for NaN:
```
$ python -c 'import pandas; print pandas.Series([1, 2, None, 1, 1, 3, None, 3], dtype = "category").value_counts(dropna = False)'
1 3
3 2
2 1
dtype: int64
```
`pandas.show_versions()` yields:
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-30-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.18
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.3.0
sphinx: None
patsy: 0.2.1
dateutil: 2.4.0
pytz: 2014.10
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9
apiclient: None
rpy2: 2.4.2
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 5 | 2015-02-08T15:40:42Z | 2015-02-11T14:19:34Z | 2015-02-11T14:19:34Z | CONTRIBUTOR | null | Example:
```
import pandas
def geta():
i = pandas.MultiIndex.from_tuples([(1, 2, 'a', 0),
(1, 2, 'a', 1),
(1, 1, 'b', 0),
(1, 1, 'b', 1),
(2, 1, 'b', 0),
(2, 1, 'b', 1)], names=['a', 'b', 'c', 'd'])
a = pandas.Series([0, 1, 2, 3, 4, 5], index=i)
return(a)
for dosort in [True, False]:
a = geta()
b = a.groupby(level=['a', 'b'], sort=dosort).first()
a = None
print('%s, sort=%s, \n%s' % (geta.__name__, dosort, b))
```
Output:
```
geta, sort=True,
a b
1 1 2
2 0
2 1 4
dtype: int64
geta, sort=False,
a b
1 1 2
2 0
2 1 4
dtype: int64
```
Also see: https://github.com/pydata/pandas/pull/9076
Possibly related to: https://github.com/pydata/pandas/issues/8868
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} | 2 | 2015-02-08T20:10:45Z | 2015-03-05T12:48:28Z | 2015-02-16T12:33:49Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
multiindex_sortlevel_int64 | 664.1977 | 2443.2590 | 0.2718 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [dd31bae] : performance improvement in MultiIndex.sortlevel
Base [c4a996a] : Merge pull request #9411 from dashesy/add_sql_test
test sql table name
```
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} | 3 | 2015-02-09T01:18:04Z | 2015-02-10T20:46:54Z | null | NONE | null | I think that the documentation for HDFStore could be more explicit about the limitations of accessing data from an HDF file. For example, it is not at all clear from the docs that when selecting several columns of data from a saved table that the entire table needs to be read into memory. Specifically, one might be tempted to do the following to select 3 columns from a very large table:
```
df = h5.select('test', columns=['col2', 'col7', 'col128'])
```
However, that process will potentially read a very large portion (if not all) of the table into memory, which is obviously a problem if the table doesn't fit into memory. Instead, one should really do something like this:
```
columns = ['col2', 'col7', 'col128']
df = pd.concat([tmp for tmp in h5.select('test', columns=columns, chunksize=50000)])
```
I think it also might be helpful to include some sort of comparison of HDF and SQL solutions for table storage. Maybe we could solicit use-cases from the mailing list for successful applications of one or the other. I've been using HDF files for several years, though never before with tables containing many, many columns, which tripped me up in my example above. I almost turned to SQLite, because the above selection is very simple and has a reasonable memory usage footprint:
```
engine = sqlalchemy.create_engine('sqlite:///test.db')
df = pd.read_sql_query('select col2,col7,col128 from test', engine)
```
However, I profiled this and it is several times slower, so I'll probably stick with HDF because I am much more familiar with the format and prefer the access speed for large tables.
Anyway, just a couple of thoughts. I'm happy to try to put some of this into the docs, if people think it is appropriate.
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| null | 5 | 2015-02-09T10:36:16Z | 2019-10-26T04:00:31Z | 2019-10-26T04:00:31Z | MEMBER | null | At some time [last year](https://github.com/pydata/pandas/issues/4646#issue-18447522),
```
pd.DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]]).groupby(0).apply(lambda x: pd.Series(np.arange(len(x)), x.index))
```
would return
```
0 0
1 0
2 1
3 2
4 1
dtype: int64
```
In 0.14, it returns
```
0
1 0 0
2 1
3 2
dtype: int64
2 1 0
4 1
dtype: int64
dtype: object
```
while in current git snapshot, it returns
```
1 0 0
2 1
3 2
2 1 0
4 1
dtype: int64
```
No nostalgia... but would it make sense to refer to the different behavior in different versions in the documentation of GroupBy.apply()?
Moreover: the (last version of the) docs say "case 2: group DataFrame apply transform function ((f(chunk) -> DataFrame with same indexes) yield DataFrame with resulting chunks glued together", and I think this is the case which applies. However the docs omit to mention "with group axis having group labels", as they do instead for case 1 (f(chunk) -> Series).
I will/would gladly submit a PR to enhance the docs... once I myself understand how things are supposed to work.
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} | 2 | 2015-02-09T10:59:59Z | 2020-05-07T03:35:38Z | null | MEMBER | null | For now just some observations:
- The docstring of different funtions about `float_format`:
- the documentation of [`to_csv`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html) (as well as `to_excel`) states: `string, Format string for floating point numbers`
- Example of this usage: `df.to_csv(float_format='%.3f')`
- The docstring of [`to_string`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_string.html) (as well as `to_html`) says: `one-parameter function, Formatter function to apply to columns’ elements if they are floats`
- Example of this usage: `df.to_string(float_format='{:.3f}'.format)`
- The option `display.float_format` also accepts a callable following the [docs](http://pandas.pydata.org/pandas-docs/stable/options.html?highlight=float_format#list-of-options)
- The narrative docs are consistent with that:
- http://pandas.pydata.org/pandas-docs/stable/io.html#writing-to-csv-format
- http://pandas.pydata.org/pandas-docs/stable/io.html#writing-a-formatted-string
Question:
- Should we make this consistent? Eg that all methods can accept both a format string as a format function?
- Should we make the usage of `'{:.3f}'.format)` more convenient?
There are a lot of different issues already about formatting, but I didn't find anything about this in specific.
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} | 2 | 2015-02-09T11:11:11Z | 2019-10-21T19:34:16Z | null | MEMBER | null | Related to #9448, using the following example:
```
In [143]: df = pd.DataFrame(np.random.randn(5,2), columns=['A', 'B'])
In [144]: df
Out[144]:
A B
0 -1.151315 -2.174735
1 -1.031375 -1.957344
2 -0.619368 -0.835133
3 2.562381 -0.294245
4 2.432353 0.032805
```
`to_clipboard` is said to pass the `kwargs` to `to_csv` (see docs: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_clipboard.html). However, that seems not be fully true for the `float_format` kwarg:
```
df.to_clipboard(float_format='%.3f')
->
A B
0 -1.151 -2.175
1 -1.031 -1.957
2 -0.619 -0.835
3 2.562 -0.294
4 2.432 0.033
```
Now using a callable:
```
df.to_clipboard(float_format='{:.3f}'.format)
->
A B
0 -1.151 -2.175
1 -1.031 -1.957
2 -0.619 -0.835
3 2.562 -0.294
4 2.432 0.033
```
but this does not work with `to_csv`:
```
In [150]: df.to_csv(float_format='{:.3f}'.format)
---------------------------------------------------------------------------
TypeError: unsupported operand type(s) for %: 'builtin_function_or_method' and 'float'
```
Furthermore, when using a function as `float_format`, the result is no longer pastable in excel, even with `excel=True` (the default).
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} | 32 | 2015-02-09T11:53:24Z | 2015-02-24T00:52:59Z | 2015-02-23T12:52:09Z | CONTRIBUTOR | null | Enables reading of multiple excel sheets in a single API call, reducing read time substantially.
Essentially, 2O(n) becomes O(1) + O(n).
## Before
``` python
dfs = {}
for sheet in [‘Sheet1’,’Sheet2’,’Sheet3’]:
#We have to open the file 3 times = Super Slow
dfs[sheet] = pd.read_excel("TestData.xlsx",sheetname=sheet)
```
## This PR
``` python
#We open the file only 1 time = Much Faster, and it's just one python line
dfs = pd.read_excel("TestData.xlsx",sheetname=[‘Sheet1’,’Sheet2’,’Sheet3’])
```
...but as a bonus...
``` python
#the above is the same as...
dfs = pd.read_excel_sheets("TestData.xlsx",[‘Sheet1’,’Sheet2’,’Sheet3’])
#or, assuming those are there are only the 3 sheets...
dfs = pd.read_excel_sheets("TestData.xlsx")
#and this also works. But the dictionary returned has the integer keys instead of strings.
dfs = pd.read_excel_sheets("TestData.xlsx",[0,1,2])
```
## Return Objects
For the sheetname argument, specifying an int or a string, gets you a DataFrame (same as before the PR). But, specify a list of strings/int, returns a dictionary of dataframes. Specify None, and get all sheets in dictionary of dataframes, where the keys are the sheetnames.
## More Discussion
The first commit (2db986d) implements 100% of the functionality, while the next two commits (5c2304c and 1a53b01) layer on optional changes to the documentation and API (of the two, only one is actually mandatory, but keeping both is the most explicit). Since the argument, sheetname, in read_excel was not plural, and defaults to 0, to maintain backwards compatibility AND create the “read all sheets” functionality associated with the non-default of None, the documentation of the argument gets really awkward. Hence, creating read_excel_sheets with an argument sheetnames which defaults to None instead of 0.
Things I need feedback on:
- [x] Confirm print statement acceptability
- [x] Confirm if sheetname argument documentation should be changed (or not, to avoid confusion of dictionary-based return cases)
- [x] Confirm if read_excel_sheets addition to API is right approach
Things I still have to do:
- [x] Test/write tests
- [x] Update the release notes
- [x] Build the docs and check
Note: There might have been another way of doing this, but I couldn't easily figure it out, (SO/google wasn't helpful). I just wanted to read my 623 sheets without it taking hours.
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} | 5 | 2015-02-09T12:40:34Z | 2015-05-09T16:03:59Z | 2015-05-09T16:03:59Z | NONE | null | Fixes issue #9432
A very specific fix that is not anticipated to impact performance.
The test performed is safe and documents that the origin of the
problem is actually numpy. When this numpy bug is fixed it will
be easy to remove this code.
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} | 1 | 2015-02-09T13:45:35Z | 2015-03-06T03:20:52Z | 2015-03-06T03:20:52Z | CONTRIBUTOR | null | Ran into this when downloading a file from s3 with py3.
[This line](https://github.com/pydata/pandas/blob/master/pandas/io/common.py#L157) needs to pass the encoding parameter to the boto method `.get_contents_as_string`, else it returns a stream of bytes which trips up StringIO on python 3.
Simple fix. Will try to get to it tonight unless someone beats me to it.
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| null | 3 | 2015-02-09T15:05:08Z | 2019-03-08T20:26:58Z | null | MEMBER | null | ```
df = pd.DataFrame([[1, 2, 3], [2, 3, 3], [1, 4, 3], [1, 5, 4], [2, 6, 4]])
def onlycol0(group):
return group[[group.columns[0]]]
g = df.groupby(0)
g.transform(onlycol0)
```
raises an "AssertionError: invalid dtype determination in get_concat_dtype".
```
def onemorecol(group):
group['new'] = 2
return group
g.transform(onemorecol)
```
raises a "ValueError: Length mismatch: Expected axis has 4 elements, new values have 3 elements".
```
def reversecols(group):
return group[group.columns[::-1]]
g.transform(reversecols)
```
apparently works fine, but the resulting DataFrame has columns unchanged.
So: the docs should probably state that func must return a DataFrame with same index _and columns_ (confusion is possible also because the docs for apply() refer to applying "transform function" but in a more general sense). I will submit a PR.
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} | 3 | 2015-02-09T15:32:34Z | 2017-12-09T15:37:47Z | 2017-12-09T15:37:47Z | NONE | null | It's not clear from the documentation for factorize what datatype is expected for the values. But I assume that any list of hashables should work (specifically, a list of tuples).
Factorize indeed works for a list of tuples as long as the lens of all the tuples are not identical, but fails the moment all tuples have the same length. (Looks like there is some inference about the structure of the values that shouldn't be happening.)
``` python
import pandas as pd
pd.factorize([(1, 1), (1, 2), (0, 0), (1, 2), 'nonsense']) # This works
(array([0, 1, 2, 1, 3]), array([(1, 1), (1, 2), (0, 0), 'nonsense'], dtype=object))
pd.factorize([(1, 1), (1, 2), (0, 0), (1, 2), (1, 2, 3)]) # This also works.
pd.factorize([(1, 1), (1, 2), (0, 0), (1, 2)]) # <-- fails
```
```
ValueError Traceback (most recent call last)
<ipython-input-22-3ca8ec02e16c> in <module>()
1 print pd.factorize([(1, 1), (1, 2), (0, 0), (1, 2), 'nonsense'])
----> 2 print pd.factorize([(1, 1), (1, 2), (0, 0), (1, 2)])
/usr/local/lib/python2.7/dist-packages/pandas/core/algorithms.pyc in factorize(values, sort, order, na_sentinel)
132 table = hash_klass(len(vals))
133 uniques = vec_klass()
--> 134 labels = table.get_labels(vals, uniques, 0, na_sentinel)
135
136 labels = com._ensure_platform_int(labels)
/usr/local/lib/python2.7/dist-packages/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_labels (pandas/hashtable.c:8575)()
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
```
pandas 0.15.2
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} | 15 | 2015-02-09T22:11:29Z | 2016-04-25T14:38:03Z | 2016-04-25T14:38:03Z | CONTRIBUTOR | null | Consider the following:
```
> a_df
object_uid item_uid
0 0FlIRitxKX 0D1dPxep2C
1 0FLPRriacw 0DoYxRkSdr
2 0FUh6FQ4mQ 0DsOBGsYl9
3 0FH1hIfNCL 0DoYxRkSdr
4 0FZVBzi2DX 0DoYxRkSdr
5 0FAJ5vfLQs 0DoYxRkSdr
6 0F7lEk65qa 0DoYxRkSdr
7 0Fqe3TIN7Q 0DoYxRkSdr
8 0FrSgt4Vut 0DsOBGsYl9
9 0FByA7F19x 0DoYxRkSdr
```
and
```
> b_df
object_uid after before change_rate
micro_spend micro_spend
0 0F0092Ntoi 2.940774e+09 2.932917e+09 0.267909
1 0F01SZnTfs 3.000654e+07 2.887034e+07 3.935523
2 0F027Rm6rd 6.729106e+07 6.912947e+07 -2.659376
3 0F02BZeTr6 2.072908e+09 2.048737e+09 1.179802
4 0F02Vwd6Ou 1.619934e+08 1.682784e+08 -3.734909
```
I would like to do an inner join in `object_uid`. When I do:
```
pd.merge(a_df, b_df, on='object_uid')
```
I get:
`AttributeError: 'numpy.ndarray' object has no attribute 'start'`
This is with:
```
> pd.pandas.__version__
'0.15.2'
```
The full error trace is below:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-131-5e11878c8b10> in <module>()
----> 1 pd.merge(exp_setup_df.head(10).iloc[:, [0,4]].copy(), spend_diff.reset_index().head(10).copy(), on='flight_uid')
/Users/avazquez/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/tools/merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, s
uffixes, copy)
36 right_on=right_on, left_index=left_index,
37 right_index=right_index, sort=sort, suffixes=suffixes,
---> 38 copy=copy)
39 return op.get_result()
40 if __debug__:
/Users/avazquez/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/tools/merge.py in __init__(self, left, right, how, on, left_on, right_on, axis, left_index, right
_index, sort, suffixes, copy)
182 (self.left_join_keys,
183 self.right_join_keys,
--> 184 self.join_names) = self._get_merge_keys()
185
186 def get_result(self):
/Users/avazquez/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/tools/merge.py in _get_merge_keys(self)
386
387 if right_drop:
--> 388 self.right = self.right.drop(right_drop, axis=1)
389
390 return left_keys, right_keys, join_names
/Users/avazquez/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/core/generic.py in drop(self, labels, axis, level, inplace, **kwargs)
1559 new_axis = axis.drop(labels, level=level)
1560 else:
-> 1561 new_axis = axis.drop(labels)
1562 dropped = self.reindex(**{axis_name: new_axis})
1563 try:
/Users/avazquez/anaconda3/envs/py34/lib/python3.4/site-packages/pandas/core/index.py in drop(self, labels, level)
3734 inds.append(loc)
3735 else:
-> 3736 inds.extend(lrange(loc.start, loc.stop))
3737
3738 return self.delete(inds)
AttributeError: 'numpy.ndarray' object has no attribute 'start'
```
Also, the following works:
```
> result_df = pd.concat([a_df.set_index('object_uid'), b_df.set_index('object_uid'], axis=1).columns
```
but it flattens my columns:
```
> result_df.columns
Index(['object_uid', ('after','micro_spend'), ('before', 'micro_spend'), 'item_id'], dtype='object']
```
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} | 12 | 2015-02-10T13:13:29Z | 2015-08-20T15:23:45Z | 2015-06-10T20:01:43Z | CONTRIBUTOR | null | This hits a path in `Series.__init__` which I think needs some better inference
https://github.com/pydata/pandas/blob/master/pandas/core/series.py#L178
```
In [1]: d = {numpy.datetime64('2015-01-07T02:00:00.000000000+0200'): 42544017.198965244,
...: numpy.datetime64('2015-01-08T02:00:00.000000000+0200'): 40512335.181958228,
...: numpy.datetime64('2015-01-09T02:00:00.000000000+0200'): 39712952.781494237,
...: numpy.datetime64('2015-01-12T02:00:00.000000000+0200'): 39002721.453793451}
In [2]: Series(d)
Out[2]:
2015-01-07 NaN
2015-01-08 NaN
2015-01-09 NaN
2015-01-12 NaN
dtype: float64
In [3]: Series(d.values(),d.keys())
Out[3]:
2015-01-07 42544017.198965
2015-01-08 40512335.181958
2015-01-09 39712952.781494
2015-01-12 39002721.453793
dtype: float64
```
The problem is the index is already converted at this point and its not easy to get the keys/values out (except to do so explicity which is better IMHO).
Need a review of what currently hits this path (can simply put a halt in here and see what tests hit this). Then figure out a better method.
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} | 7 | 2015-02-10T16:06:04Z | 2020-06-24T22:25:21Z | 2020-06-24T22:25:21Z | CONTRIBUTOR | null | I am testing the equivalence of two large DataFrames (9084x367). The two are the same up to 1x10-13 but when `np.array_equal` fails there is a much slower code path (comparing these two frames takes upwards of 20 seconds). If I'm not mistaken, if the arrays aren't equivalent it does a more complicated version of `np.allclose`. I think a good intermediate step would be to check for array equivalence and then as a the second step call `np.allclose`--or maybe just do this on the outset. If that fails, which it will if there are any NaNs or if the tolerance is not met, then it will use the current logic. Or we could use `np.isclose` to consider NaNs as equivalent.
https://github.com/pydata/pandas/blob/master/pandas/src/testing.pyx#L85
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| null | 3 | 2015-02-10T19:23:00Z | 2015-02-10T21:13:04Z | 2015-02-10T20:40:10Z | NONE | null | I am trying to merge multiple dataframe with consecutive merge operations, I want to add a suffix to the name of the newly merged column names. A simplified version of my code looks like this:
```
from pandas import *
f0 = DataFrame(columns=['data'], data=[1,2,3], index=['a','b','c'])
f1 = DataFrame(columns=['data'], data=[4,5,6], index=['c','b','a'])
f2 = DataFrame(columns=['data'], data=[7,8,9], index=['a','c','b'])
merged = f0
merged = merged.merge(f1, left_index=True, right_index=True, suffixes=("_0", "_1"))
merged = merged.merge(f2, left_index=True, right_index=True, suffixes=("", "_2"))
print merged.columns
```
With pandas 0.15.2 on python 2.7 this returns:
```
Index([u'data_0', u'data_1', u'data'], dtype='object')
```
while I would have expected
```
Index([u'data_0', u'data_1', u'data_2'], dtype='object')
```
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} | 15 | 2015-02-10T21:09:23Z | 2015-03-08T16:12:52Z | 2015-03-08T16:12:47Z | CONTRIBUTOR | null | closes #9443
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| null | 2 | 2015-02-10T22:03:31Z | 2015-02-10T23:31:56Z | 2015-02-10T22:12:19Z | CONTRIBUTOR | null | PR as per issue 9444. One test added.
https://github.com/pydata/pandas/issues/9444
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} | 6 | 2015-02-10T22:23:29Z | 2015-02-11T14:19:38Z | 2015-02-11T14:19:34Z | CONTRIBUTOR | null | PR as per.
One test added. Test fails without the fix.
closes https://github.com/pydata/pandas/issues/9444
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} | 6 | 2015-02-10T22:46:50Z | 2018-11-03T13:12:50Z | 2018-11-03T13:12:50Z | CONTRIBUTOR | null | I was poking around in tdi.py with pdb and I noticed this code at [line 255 of current master](https://github.com/pydata/pandas/blob/0efd4b3c8d2b0308441a6960327a2447bd419a1b/pandas/tseries/tdi.py#L255):
``` python
@classmethod
def _simple_new(cls, values, name=None, freq=None, **kwargs):
--> if not getattr(values,'dtype',None): # This doesn't do what it looks like it does
values = np.array(values,copy=False)
if values.dtype == np.object_:
values = tslib.array_to_timedelta64(values)
if values.dtype != _TD_DTYPE:
values = com._ensure_int64(values).view(_TD_DTYPE)
result = object.__new__(cls)
result._data = values
result.name = name
result.freq = freq
result._reset_identity()
return result
```
This looks to me like it's testing whether `values` is already an `ndarray`, and if it isn't, it's taking a view into its values. But that's not what it's doing at all. If `values` is a `Series`, it has a dtype, so the view-making code should be skipped! Except that it isn't:
``` python
In [1]: import numpy as np
In [2]: map(np.dtype, [None, 'i8', 'i4,i4', ('m8[ns]', (2, 2)), ('S', 4),
...: [('name', 'u1', (2, 2))],
...: {'names': ['name'], 'formats': ['f8']},
...: ('i4,i4', (2, 2))])
Out[2]:
[dtype('float64'), # note that dtype(None) is float!
dtype('int64'), # fixed dtype
dtype([('f0', '<i4'), ('f1', '<i4')]), # record dtype
dtype(('<m8[ns]', (2, 2))), # array dtype
dtype('S4'), # flexible dtype
dtype([('name', 'u1', (2, 2))]), # record dtype with array fields
dtype([('name', '<f8')]), # another record dtype
dtype(([('f0', '<i4'), ('f1', '<i4')], (2, 2)))] # array dtype with record fields
In [3]: map(bool, _)
Out[3]: [False, False, True, False, False, True, True, False] # madness
```
My tentative guess is that if the outermost layer of the dtype is a record type, then it is `True`y, and otherwise it's `False`y. This behavior seems too pathological to depend on.
Unfortunately, that's exactly what's happening when constructing a `TimedeltaIndex` from a `Series` of `Timedelta`s:
``` python
In [1]: import pandas as pd
In [2]: import pdb
In [3]: pd.Series([pd.Timedelta(1, 'h')])
Out[3]:
0 01:00:00
dtype: timedelta64[ns]
In [4]: pdb.run('pd.Index(_)')
> <string>(1)<module>()
(Pdb) b pd.TimedeltaIndex._simple_new
Breakpoint 1 at /Users/afni/ActivityMonitorProcessing/sojourns/source/lib/python2.7/site-packages/pandas/tseries/tdi.py:253
(Pdb) c
> /Users/afni/ActivityMonitorProcessing/sojourns/source/lib/python2.7/site-packages/pandas/tseries/tdi.py(255)_simple_new()
-> if not getattr(values,'dtype',None):
(Pdb) p values
0 01:00:00
dtype: timedelta64[ns] # it's still a Series
(Pdb) n
> /Users/afni/ActivityMonitorProcessing/sojourns/source/lib/python2.7/site-packages/pandas/tseries/tdi.py(256)_simple_new()
-> values = np.array(values,copy=False)
(Pdb) n
> /Users/afni/ActivityMonitorProcessing/sojourns/source/lib/python2.7/site-packages/pandas/tseries/tdi.py(257)_simple_new()
-> if values.dtype == np.object_:
(Pdb) p values
array([3600000000000], dtype='timedelta64[ns]') # now it's an ndarray
```
Everything seems to _work_, but what's actually going on is so different from what I would expect to be going on that it seems like a time bomb.
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} | 2 | 2015-02-11T09:50:20Z | 2015-02-23T11:46:54Z | 2015-02-23T11:46:54Z | MEMBER | null | Suppose I have the following dataframe and series:
```
In [20]: df = pd.DataFrame(np.ones((3,4)), columns=pd.MultiIndex.from_product([['A', 'B'],['a', 'b']]))
In [21]: s = pd.Series({'a':1, 'b':2})
In [22]: df
Out[22]:
A B
a b a b
0 1 1 1 1
1 1 1 1 1
2 1 1 1 1
In [23]: s
Out[23]:
a 1
b 2
dtype: int64
```
Trying to multiply this by the columns, and matching on the second level, gives the following error:
```
In [24]: df.mul(s, axis=1, level=1)
...
File "C:\Anaconda\lib\site-packages\pandas\core\index.py", line 1786, in _join_multi
raise ValueError("cannot join with no level specified and no overlapping names")
ValueError: cannot join with no level specified and no overlapping names
```
In any case, this is a wrong error message, as I _did_ specify a level.
Further, this does work and gives the expected output with a named level instead of a integer level:
```
In [25]: df2 = df.copy()
...: df2.columns.names = ['l0', 'l1']
In [35]: df2.mul(s, axis=1, level='l1') # <-- works
Out[35]:
l0 A B
l1 a b a b
0 1 2 1 2
1 1 2 1 2
2 1 2 1 2
```
Am I correct that this should also work with integer levels?
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} | 1 | 2015-02-11T12:12:41Z | 2015-03-31T13:31:53Z | 2015-03-05T23:27:56Z | MEMBER | null | Related to #8877 and #9278. There is remaining case which shows unnecessary warning.
Occurrence condition:
- `subplots=True`.
- `layout` is specified larger than the number of required subplot axes.
In this case, `legend` is called against blank axes and results in warning.
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} | 3 | 2015-02-11T12:24:36Z | 2015-03-31T13:31:50Z | 2015-02-16T12:56:08Z | MEMBER | null | Same as #7165. I've also added some of these tests :(
One idea to prevent this is to override ``tm.TestCase.assertEquals` to raise more explicit warning or exception.
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} | 6 | 2015-02-11T14:00:43Z | 2018-10-01T12:08:59Z | 2018-10-01T12:08:59Z | MEMBER | null | ```
In [1]: import pandas, numpy
In [2]: df = pandas.DataFrame(numpy.random.random((100000, 4)))
In [3]: %timeit df.loc[55555]
10000 loops, best of 3: 118 µs per loop
In [4]: %timeit df.loc[[55555]]
1000 loops, best of 3: 324 µs per loop
```
... makes sense to me.
```
In [5]: df.index = list(range(99999)) + [55555]
In [6]: %timeit df.loc[55555]
100 loops, best of 3: 4.04 ms per loop
In [7]: %timeit df.loc[[55555]]
100 loops, best of 3: 16.8 ms per loop
```
Non-unique index, slower (the second call probably has to scan all the index): still makes sense to me. Sorting should improve things...
```
In [8]: df.sort(inplace=True)
In [9]: %timeit df.loc[55555]
1000 loops, best of 3: 239 µs per loop
In [10]: %timeit df.loc[[55555]]
100 loops, best of 3: 17.2 ms per loop
```
... here I'm lost: why this huge difference? The difference is even larger (3 orders of magnitude) in a real database I am working on. Clearly,
```
In [12]: df.loc[[55555]] == df.loc[55555]
Out[12]:
0 1 2 3
55555 True True True True
55555 True True True True
```
(As a sidenote: the reason why I'm doing calls such as df.loc[[a_label]] is that df.loc[a_label] will return sometimes a Series, sometimes a DataFrame. I currently solve this by using df.loc[df.index == a_label], which is however ~3x slower than df.loc[a_label] - but much faster than the above df.loc[[a_label]].)
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} | 0 | 2015-02-11T19:12:29Z | 2016-04-10T14:32:46Z | 2016-04-10T14:32:46Z | MEMBER | null | SparseSeries currently relies on the assumption that all non-scalar input will raise a `TypeError` (because it won't be hashable) when used as input to `Index.get_loc`:
https://github.com/pydata/pandas/blob/bb9c311f36fbf508d11f2eabe3fdbdd010abf8c0/pandas/sparse/series.py#L361
However, some non-scalar input is hashable (e.g., `Ellipsis`):
```
In [2]: import pandas as pd
In [3]: ss = pd.Series(range(10)).to_sparse()
In [4]: ss[...]
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-4-f0ccf654123b> in <module>()
----> 1 ss[...]
/Users/shoyer/dev/pandas/pandas/sparse/series.pyc in __getitem__(self, key)
369 if isinstance(key, (int, np.integer)):
370 return self._get_val_at(key)
--> 371 raise Exception('Requested index not in this series!')
372
373 except TypeError:
Exception: Requested index not in this series!
```
(this should return the entire sparse series)
This logic is also problematic because it relies on `get_loc` always throwing `TypeError` for invalid input. So this could use some cleanup, ideally checking directly if the input is hashable. And it probably shouldn't be raising a generic `Exception`, either.
xref tslib.pyx change/discussion in #9258
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} | 7 | 2015-02-11T19:56:17Z | 2015-03-11T12:10:55Z | 2015-03-11T12:10:55Z | NONE | null | The following snippet of code is causing a segfault. Here it's failing at head, but I've seen it fail in the 0.15.2 release as well, with both NumPy 1.7.1 and NumPy 1.9.1.
Interestingly this doesn't happen with Pandas 0.13.1.
```
>>> import pandas
>>> import pytz
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-30-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2-182-gbb9c311
nose: None
Cython: 0.20.1post0
numpy: 1.9.1
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.0
pytz: 2013b
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
>>> LA_TZ = pytz.timezone('America/Los_Angeles')
>>>
>>> s = pandas.Series({
... pandas.Timestamp('2010-1-1', tz=LA_TZ): 1,
... pandas.Timestamp('2011-4-1', tz=LA_TZ): 1})
>>> s
2010-01-01 00:00:00-08:00 1
2011-04-01 00:00:00-07:00 1
dtype: int64
>>> s.resample('D', how='max', fill_method='pad')
Segmentation fault (core dumped)
```
Other observations:
- The start/end need to cross the daylight savings switch. Notice utc-08:00 vs utc-07:00 above
- Only fails with how=max and fill_method=pad
- Needs a large enough resampled window. 2011-1-1 to 2011-4-1 won't do it. But 2010-1-1 to 2011-4-1 will.
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} | 15 | 2015-02-11T20:53:55Z | 2015-02-12T20:15:50Z | 2015-02-12T19:23:50Z | CONTRIBUTOR | null | I'm unable to isolate this, but maybe this rings a bell for someone. It took me a long time to find the source of error in my code. I don't think this is intentional, and if so, is pretty dangerous.
```
[~/]
[22]: y[idx].codes[:15]
[22]: array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4], dtype=int8)
[~/]
[23]: y.codes[idx][:15]
[23]: array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], dtype=int8)
[~/]
[24]: idx[:15]
[24]:
array([ 74172, 53885, 128953, 28125, 136548, 136700, 93633, 61147,
56535, 90577, 115719, 58038, 111711, 53399, 77475])
[~/]
[25]: type(y)
[25]: pandas.core.categorical.Categorical
[~/]
[27]: pd.version.version
[27]: '0.15.2'
```
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} | 0 | 2015-02-11T21:32:22Z | 2015-02-12T19:23:50Z | 2015-02-12T19:23:50Z | CONTRIBUTOR | null | closes #9469
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} | 3 | 2015-02-11T22:03:16Z | 2021-04-12T05:16:20Z | null | MEMBER | null | This should be a pretty straightforward followup on #9258 that will allow us to also support `align` with `method='nearest'`.
fillna internally relies on interpolate which already supports `method='nearest' so this _may_ just work (it will needs lots of tests).
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| null | 9 | 2015-02-12T02:29:17Z | 2015-05-09T16:00:15Z | 2015-05-09T16:00:15Z | CONTRIBUTOR | null | Added a straightforward Serie function '`idxnonzero`', similar to `idxmin`/`idxmax`
When filtering data, a `nonzero` pattern is actually very handy; yet panda’s version returns the iloc (for compatibility with numpy), which prevent to use the result on a differently-shaped dataframe, defeating the power of pandas indexing.
This simple patch propose a version of nonzero which returns an actual pandas Index.
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} | 1 | 2015-02-12T04:44:08Z | 2015-02-16T21:53:34Z | 2015-02-16T21:53:33Z | MEMBER | null | Fixes #9276
This now relies entirely on the result of calling `hash` on the argument.
Note: I had to change the test for old style classes on Python 2 -- these are
now considered hashable by `is_hashable`, because they don't fail `hash`.
CC @aevri
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} | 2 | 2015-02-12T16:21:30Z | 2016-09-08T10:08:50Z | 2016-09-08T10:08:41Z | NONE | null | I have a very simple program in python that works perfectly within Spyder or Eclipse, but fails when executed in the command line after it was pacakged with py2exe.
OS is a French Windows 7 and I am using:
Python 3.4.1 from Anaconda 2.1.0 (64-bit)
Pandas 0.15.2 np19py34_0
cython 0.21.2
The error I get is the following one:
```
Parent module '' not loaded, cannot perform relative import
Traceback (most recent call last):
File "Main.py", line 2, in <module>
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2226, in find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1191, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1161, in _load_backward_compatible
File "C:\Anaconda3\lib\site-packages\zipextimporter.py", line 86, in load_module
return zipimport.zipimporter.load_module(self, fullname)
File "C:\Anaconda3\lib\site-packages\pandas\__init__.py", line 7, in <module>
from . import hashtable, tslib, lib
File "<frozen importlib._bootstrap>", line 2237, in _find_and_load
File "<frozen importlib._bootstrap>", line 2226, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1191, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1161, in _load_backward_compatible
File "C:\Anaconda3\lib\site-packages\zipextimporter.py", line 109, in load_module self.get_data)
File "pandas\hashtable.pyx", line 9, in init pandas.hashtable (pandas\hashtable.c:23000)
SystemError: Parent module '' not loaded, cannot perform relative import
```
Here is the main.py coding:
```
# -*- coding: utf-8 -*-
import pandas
data = pandas.read_hdf ("C:/temp/test.h5", "MainData")
file = open ("C:/temp/log.txt", "a+")
strMessage = ""
for arg in data.columns:
strMessage += str (arg) + "\n"
file.write (strMessage)
file.close()
```
and the setup.py:
```
# -*- coding: utf-8 -*-
from distutils.core import setup
import py2exe
excludes = ['tkinter']
opts = {'py2exe': {'excludes':excludes,
'bundle_files': 1,
'compressed': True,
'optimize':2}}
setup(name='Core',
version='1.0',
description = "Core Engine",
console = [{"script": "Main.py",
"dest_base": "Core"}],
options = opts)
```
I'm executing the following command line to build my executable file:
python setup.py py2exe
Note that it works fine if I remove the 'bundle_files' and 'optimize' options and I downgrade pandas to version 0.14.2.
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} | 0 | 2015-02-12T16:34:48Z | 2015-02-23T11:46:54Z | 2015-02-23T11:46:54Z | MEMBER | null | Closes #9463
@jreback also related to #6682 (I removed 'join compat' introduced in that PR, as this worked only for level names, and passing `level=level` works for both level names and integer level numbers)
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| null | 3 | 2015-02-12T16:54:37Z | 2015-02-12T17:54:11Z | 2015-02-12T17:53:11Z | NONE | null | Dupe of #9474
---
Py2exe error: Parent module '' not loaded, cannot perform relative import
I am using Windows, Python 3.3.5, pandas 0.15.2, and py2exe 0.9.2.2.
I used py2exe to generate a single-file executable from a Python script that uses pandas 0.15.2. When I tried to run it, I got the following error: `Parent module '' not loaded, cannot perform relative import`.
This is how it happened. I created a file named `test.py` with the following content.
```
import pandas
print(pandas.__version__)
```
For py2exe, I created a file called `setup.py`, with the following content.
```
import py2exe
import sys
from distutils.core import setup
sys.argv.append('py2exe')
setup(
options={
'py2exe': {
'bundle_files': 0,
'excludes': [ 'tkinter']
}
},
console=['test.py'],
zipfile=None,
)
```
I ran `python setup.py`. I verified that the file `dist/test.exe` had been generated.
When I tried to run `test.exe`, I got the following traceback:
```
C:\mac\des\ide\test\panbug\dist>test
OMP: Warning #178: Function GetModuleHandleEx failed:
OMP: System error #126: No se puede encontrar el m¾dulo especificado.
Parent module '' not loaded, cannot perform relative import
Traceback (most recent call last):
File "test.py", line 1, in <module>
File "<frozen importlib._bootstrap>", line 1565, in _find_and_load
File "<frozen importlib._bootstrap>", line 1532, in _find_and_load_unlocked
File "C:\mac\des\WinPython-32bit-3.3.5.0\python-3.3.5\lib\site-packages\zipextimporter.py", line 86, in load_module
return zipimport.zipimporter.load_module(self, fullname)
File "C:\mac\des\WinPython-32bit-3.3.5.0\python-3.3.5\lib\site-packages\pandas\__init__.py", line 7, in <module>
from . import hashtable, tslib, lib
File "<frozen importlib._bootstrap>", line 1565, in _find_and_load
File "<frozen importlib._bootstrap>", line 1532, in _find_and_load_unlocked
File "C:\mac\des\WinPython-32bit-3.3.5.0\python-3.3.5\lib\site-packages\zipextimporter.py", line 109, in load_module
self.get_data)
File "pandas\hashtable.pyx", line 9, in init pandas.hashtable (pandas\hashtable.c:23000)
SystemError: Parent module '' not loaded, cannot perform relative import
```
There is a workaround that consists on downgrading to pandas 0.14.1 **and** removing the `bundle_files` option (which generates a folder with a bunch of files instead of a single-file executable). However, I need a single-file executable, and would also like to use the latest pandas version.
Also, I know that I'm not the only one facing this issue. There is a [related question](http://stackoverflow.com/questions/28433559/pandas-py2exe-error-parent-module-not-loaded-cannot-perform-relative-impo) on StackOverflow.
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} | 2 | 2015-02-12T20:27:08Z | 2015-02-24T13:49:22Z | 2015-02-24T13:49:22Z | CONTRIBUTOR | null | The .apply on DataFrame is converting my string to a date. I have posted this on StackOverflow:
http://stackoverflow.com/questions/28487105/pandas-on-apply-passing-wrong-value
I'm not sure if I'm doing something wrong or it is a bug, but it seems quite strange behavior.
Example [here](http://stackoverflow.com/questions/28550339/interesting-pandas-loc-behavior?noredirect=1#comment45415876_28550339)
```
In [8]: df = pd.DataFrame({'wing1':wing1, 'wing2':wing2, 'mat':mat}, index=belly)
In [9]: df
Out[9]:
mat wing1 wing2
216 2016-01-22 2T15 416
3T19 2019-09-07 4H19 4T20
In [17]: df.loc['3T19']
Out[17]:
mat 2019-09-07 00:00:00
wing1 2015-02-16 04:19:00
wing2 2015-04-20 00:00:00
Name: 3T19, dtype: datetime64[ns]
In [18]: df.loc['216']
Out[18]:
mat 2016-01-22 00:00:00
wing1 2T15
wing2 416
Name: 216, dtype: object
```
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} | 6 | 2015-02-12T21:07:53Z | 2015-02-13T21:02:07Z | 2015-02-13T20:22:23Z | NONE | null | I posted a 2-part answer to this question on SO: http://stackoverflow.com/questions/28482553/pandas-set-value-of-column-to-value-of-index-based-on-condtion
What I noticed is that if your index is a datetimeindex then assigning the values is not respecting the column selection and is blatting all rows.
I am using pandas 0.15.2 using numpy 1.9.1 and python 3.4 64-bit
example:
```
In [46]:
rows = 3
df = pd.DataFrame(np.random.randn(rows,2), columns=list('AB'), index=pd.date_range('1/1/2000', periods=rows, freq='1H'))
print(df)
df.loc[df.A > 0.5, 'LAST_TIME_A_ABOVE_X'] = df.loc[df.A > 0.5].index
df
A B
2000-01-01 00:00:00 -0.761643 0.969167
2000-01-01 01:00:00 0.050335 -1.346953
2000-01-01 02:00:00 0.663857 -0.272247
Out[46]:
A B \
2000-01-01 00:00:00 1970-01-01 1970-01-01 00:00:00
2000-01-01 01:00:00 1970-01-01 1969-12-31 23:59:59.999999999
2000-01-01 02:00:00 1970-01-01 1970-01-01 00:00:00
LAST_TIME_A_ABOVE_X
2000-01-01 00:00:00 NaT
2000-01-01 01:00:00 NaT
2000-01-01 02:00:00 2000-01-01 02:00:00
```
If the index is an Int64 type then this doesn't happen.
If you reset the index and then assign the values using .loc then it works correctly
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} | 0 | 2015-02-12T22:29:18Z | 2015-02-13T20:22:24Z | 2015-02-13T20:22:23Z | CONTRIBUTOR | null | closes #9478
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| null | 3 | 2015-02-12T23:44:58Z | 2015-02-21T13:03:58Z | 2015-02-18T11:27:50Z | CONTRIBUTOR | null | Closes #8868
hack. I can add tests if this passes the CI and we think this should be taken further.
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} | 20 | 2015-02-13T07:18:16Z | 2021-09-14T13:07:58Z | 2021-09-14T13:07:58Z | NONE | null | xref SO issue [here](http://stackoverflow.com/questions/29391787/rolling-argmax-in-pandas/29423104#29423104)
Im looking to set the rolling rank on a dataframe. Having posted, discussed and analysed the code it looks like the suggested way would be to use the pandas Series.rank function as an argument in rolling_apply. However on large datasets the performance is particularly poor. I have tried different implementations and using bottlenecks rank method orders of magnitude faster, but that only offers the average option for ties. It is also still some way off the performance of rolling_mean. I have previously implemented a rolling rank function which monitors changes on a moving window (in a similar way to algos.roll_mean I believe) rather that recalculating the rank from scratch on each window. Below is an example to highlight the performance, it should be possible to implement a rolling rank with comparable performance to rolling_mean.
python: 2.7.3
pandas: 0.15.2
scipy: 0.10.1
bottleneck: 0.7.0
`````` python
rollWindow = 240
df = pd.DataFrame(np.random.randn(100000,4), columns=list('ABCD'), index=pd.date_range('1/1/2000', periods=100000, freq='1H'))
df.iloc[-3:-1]['A'] = 7.5
df.iloc[-1]['A'] = 5.5
df["SER_RK"] = pd.rolling_apply(df["A"], rollWindow, rollingRankOnSeries)
#28.9secs (allows competition/min ranking for ties)
df["SCIPY_RK"] = pd.rolling_apply(df["A"], rollWindow, rollingRankSciPy)
#70.89secs (allows competition/min ranking for ties)
df["BNECK_RK"] = pd.rolling_apply(df["A"], rollWindow, rollingRankBottleneck)
#3.64secs (only provides average ranking for ties)
df["ASRT_RK"] = pd.rolling_apply(df["A"], rollWindow, rollingRankArgSort)
#3.56secs (only provides competition/min ranking for ties, not necessarily correct result)
df["MEAN"] = pd.rolling_mean(df['A'], window=rollWindow)
#0.008secs
def rollingRankOnSeries (array):
s = pd.Series(array)
return s.rank(method='min', ascending=False)[len(s)-1]
def rollingRankSciPy (array):
return array.size + 1 - sc.rankdata(array)[-1]
def rollingRankBottleneck (array):
return array.size + 1 - bd.rankdata(array)[-1]
def rollingRankArgSort (array):
return array.size - array.argsort().argsort()[-1]
```python
I think this is likely to be a common request for users looking to use pandas for analysis on large datasets and thought it would be a useful addition to the pandas moving statistics/moments suite?
``````
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} | 1 | 2015-02-13T16:41:00Z | 2015-02-24T11:14:34Z | 2015-02-24T11:14:34Z | CONTRIBUTOR | null | xref #9394
xref #7282
`numexpr/bottleneck` are ok by default
you can check with something like this:
```
[jreback-~/pandas] python -v -c 'import pandas' > & out
[jreback-~/pandas] grep 'import ' out | more
```
- [x] ~~`matplotlib` (not sure if this is possible to remove by default)~~ The converters are set, not so easy to remove.
- [x] `io/packers.py` imports `blosc` and possibly other things, #9482
- [x] `io/gbq.py` import a lot of things (e.g. `apiclient/googleapiclient/oauth2client/gflags`), #9482
- [x] ~~`xlsxwriter`~~ this is set in `core/config_init.py` for the default engine. Not easy to remove.
- [x] `html5lib/bs4` (from `io/html.py`), #9482
These should be done on first usage of the library, and not on general import
like in `io/pytables.py` (e.g. the `_tables()`) call.
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} | 11 | 2015-02-13T16:49:04Z | 2016-07-11T13:24:33Z | 2016-07-11T13:24:28Z | NONE | null | When merging HDF5 files and thus appending to a Series stored in a node, I get the following stacktrace from time to time, with no really clear reproducer. By no clear reproducer, I meaning it happens always at the same place after having looped over several 'to merge' files but it does not appear if I run a sequence of separate processes for each file.
My pseudocode is as follows:
```
def merge_stores(out_store, in_store_files):
open(out_store, 'a')
for in_store_file in in_store_files:
in_store = pd.HDFStore(in_store_file, 'r')
for key in in_store.keys():
out_store.put(key, in_store.get(key), format='t', append=True, min_itemsize=48)
```
And here is the stack trace I have at the put:
/opt/anaconda/lib/python2.7/site-packages/tables/group.py:1156: UserWarning: problems loading leaf `/PC40102D/table`::
``````
Attribute chunksize exists in node PC40102D, but can't get it.
The leaf will become an ``UnImplemented`` node.
% (self._g_join(childname), exc))
!!!!!!!!!!!!!!!!!!
Uncaught exception
!!!!!!!!!!!!!!!!!!
Traceback (most recent call last):
File "mergehdf5.py", line 100, in <module>
main()
File "mergehdf5.py", line 95, in main
merge_files(options.outputfile, files, delete=False, append=options.append)
File "mergehdf5.py", line 81, in merge_files
out_store_keys = merge_file(out_store, in_store, out_store_keys, append=append)
File "mergehdf5.py", line 52, in merge_file
out_store.put(key, to_merge_in, format='t', append=True, min_itemsize=STR_SIZE)
File "/opt/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py", line 818, in put
self._write_to_group(key, value, append=append, **kwargs)
File "/opt/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py", line 1270, in _write_to_group
s.write(obj=value, append=append, complib=complib, **kwargs)
File "/opt/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py", line 3891, in write
obj=obj, data_columns=obj.columns, **kwargs)
File "/opt/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py", line 3605, in write
**kwargs)
File "/opt/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py", line 3136, in create_axes
if self.infer_axes():
File "/opt/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py", line 2137, in infer_axes
s = self.storable
File "/opt/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py", line 2832, in storable
return getattr(self.group, 'table', None)
File "/opt/anaconda/lib/python2.7/site-packages/tables/group.py", line 811, in __getattr__
return self._f_get_child(name)
File "/opt/anaconda/lib/python2.7/site-packages/tables/group.py", line 684, in _f_get_child
return self._v_file._get_node(childpath)
File "/opt/anaconda/lib/python2.7/site-packages/tables/file.py", line 1562, in _get_node
node = self._node_manager.get_node(nodepath)
File "/opt/anaconda/lib/python2.7/site-packages/tables/file.py", line 436, in get_node
node = self.node_factory(key)
File "/opt/anaconda/lib/python2.7/site-packages/tables/group.py", line 1158, in _g_load_child
return UnImplemented(self, childname)
File "/opt/anaconda/lib/python2.7/site-packages/tables/unimplemented.py", line 69, in __init__
super(UnImplemented, self).__init__(parentnode, name)
File "/opt/anaconda/lib/python2.7/site-packages/tables/leaf.py", line 262, in __init__
super(Leaf, self).__init__(parentnode, name, _log)
File "/opt/anaconda/lib/python2.7/site-packages/tables/node.py", line 271, in __init__
self._v_objectid = self._g_open()
File "/opt/anaconda/lib/python2.7/site-packages/tables/unimplemented.py", line 72, in _g_open
(self.shape, self.byteorder, object_id) = self._open_unimplemented()
File "hdf5extension.pyx", line 2095, in tables.hdf5extension.UnImplemented._open_unimplemented (tables/hdf5extension.c:19140)
TypeError: argument 2 to map() must support iteration
DEBUG MODE
Closing remaining open files:/var/engdata/hdf5output/hdf5/M002-20150104-comp.hdf5...done/tmp/M002-201501-comp.hdf5...done```
``````
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| null | 18 | 2015-02-13T19:40:00Z | 2015-06-03T00:09:00Z | 2015-05-09T16:00:02Z | CONTRIBUTOR | null | PLEASE REVIEW: This is my commit to a major project, and would appreciate a quick once over!
As per discussion in Issue 9382, changes all HDF functions from having default of dropping all rows with NA in all non-index rows.
closes #9382
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} | 4 | 2015-02-13T21:03:29Z | 2017-07-08T22:18:32Z | 2017-07-08T22:18:26Z | NONE | null | ``` python
# coding: utf-8
import pandas as pd
import numpy as np
frame = pd.read_csv("table.csv", engine="python", parse_dates=['since'])
print frame
d = pd.pivot_table(frame, index=pd.TimeGrouper(key='since', freq='1d'), values=["value"], columns=['id'], aggfunc=np.sum, fill_value=0)
print d
print "^that is not what I expected"
frame = pd.read_csv("table2.csv", engine="python", parse_dates=['since']) # add some values to a day
print frame
d = pd.pivot_table(frame, index=pd.TimeGrouper(key='since', freq='1d'), values=["value"], columns=['id'], aggfunc=np.sum, fill_value=0)
print d
```
The following data is the contents of `table.csv`
```
"id","since","value"
"81","2015-01-31 07:00:00+00:00","2200.0000"
"81","2015-02-01 07:00:00+00:00","2200.0000"
```
This is `table2.csv`:
```
"id","since","value"
"81","2015-01-31 07:00:00+00:00","2200.0000"
"81","2015-01-31 08:00:00+00:00","2200.0000"
"81","2015-01-31 09:00:00+00:00","2200.0000"
"81","2015-02-01 07:00:00+00:00","2200.0000"
```
The output of print after pivoting `table.csv`
```
id value
<pandas.tseries.resample.TimeGrouper object at 0x7fc595f96c10> 81 2200
id 81 2200
```
I would expect something like this:
```
value
id 81
since
2015-01-31 2200
2015-02-01 2200
```
I can trace the problem to here:
https://github.com/pydata/pandas/blob/62529cca28e9c8652ddf7cca3aa6d41d4e30bc0e/pandas/tools/pivot.py#L114 the index created by groupby already has the object there.
I can't figure anything else. What is the problem, any fixes?
Thanks.
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https://api.github.com/repos/pandas-dev/pandas/issues/9486 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9486/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9486/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9486/events | https://github.com/pandas-dev/pandas/pull/9486 | 57,656,372 | MDExOlB1bGxSZXF1ZXN0MjkzMDA1OTI= | 9,486 | Updated whatsnew for 16.0 for change to hdf dropna default | {
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| null | 1 | 2015-02-13T21:27:46Z | 2015-03-13T03:49:22Z | 2015-02-13T21:37:12Z | CONTRIBUTOR | null | Document changes from issue 9382
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} | 3 | 2015-02-13T23:09:53Z | 2015-02-24T11:14:35Z | 2015-02-24T11:14:34Z | CONTRIBUTOR | null | closes #9482
so a 30% reduction in import time
```
[jreback-~/pandas] time python -c 'import pandas'
0.788u 0.120s 0:00.91 98.9% 0+0k 0+7io 0pf+0w
[jreback-~/pandas] time python -c 'import pandas'
0.782u 0.115s 0:00.91 97.8% 0+0k 0+0io 0pf+0w
[jreback-~/pandas] time python -c 'import pandas'
0.790u 0.121s 0:00.91 100.0% 0+0k 0+0io 0pf+0w
```
```
[jreback-~/pandas] time python -c 'import pandas'
0.270u 0.091s 0:00.37 97.2% 0+0k 10+20io 0pf+0w
[jreback-~/pandas] time python -c 'import pandas'
0.262u 0.087s 0:00.35 97.1% 0+0k 0+0io 0pf+0w
[jreback-~/pandas] time python -c 'import pandas'
0.264u 0.088s 0:00.36 94.4% 0+0k 0+0io 0pf+0w
```
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| null | 8 | 2015-02-13T23:49:27Z | 2015-02-16T15:18:57Z | 2015-02-16T15:02:28Z | NONE | null | I have a ndarray (between 4 or 6 dimensions), but let's talk about 3D data.
so if I have an index like:
``` python
data[i,j,k] = 30
```
How can I create a excel sheet where the first 3 colunns are the indices like:
``` python
>>> frame
i j k X
0 0 0 1023
...
0 0 255 3
...
0 1 0 34
...
255 255 255 23
```
So, if I have a 256x256x256 ndarray cube, I would like 256x256x256 rows, and 4 columns (3 for indices and 1 for value). I tried to use `Panel`, `to_frame` and `to_csv`, but the third dimension are the columns.
Thanks
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| null | 1 | 2015-02-14T12:13:15Z | 2015-02-15T23:12:35Z | 2015-02-15T23:12:35Z | MEMBER | null | If I execute the following code:
```
import pandas
df = pandas.DataFrame([['test', -2], ['b', -2], ['b', -2]], columns=['surname', 'new_id'])
def process_by_last_name(group):
if len(group) == 1:
if "%" in str(group['new_id']):
import ipdb; ipdb.set_trace()
return group['new_id']
group['colour'] = range(len(group))
useless = group.groupby('new_id').apply(lambda *x : -1)
return group['new_id']
df.groupby('surname').apply(process_by_last_name)
```
I get dropped to an ipdb shell, where curious things happen:
```
ipdb> group
surname new_id colour
0 test -2 0
-2 1
ipdb> group.new_id
0 -2
%s %s
Name: new_id, dtype: int64
ipdb> group.shape
(1, 3)
ipdb> group.values.shape
*** ValueError: could not broadcast input array from shape (2,2) into shape (2,1)
ipdb> group.new_id.shape
(1,)
ipdb> group.new_id.values.shape
(2,)
```
As far as I can judge, my code is weird but does not do "forbidden" things: in particular, the return values are coherent across groups.
Even more confusingly to me, I am unable to reproduce with a simpler example.
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} | 1 | 2015-02-14T12:42:47Z | 2021-04-12T05:18:36Z | null | NONE | null | Now it is not possible to choose method when using corrwith() function, which defaults to pearson (I assume). Would be great to see the same methods that are allowed in corr().
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} | 5 | 2015-02-14T14:21:58Z | 2015-03-06T02:53:33Z | 2015-02-16T12:30:41Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/pull/9292#discussion_r24620887
on branch:
```
>>> df
a b c
0 2014-02-01 -1 days 100
1 NaT NaT 101
2 2014-02-03 1 days 102
3 NaT 2 days 103
4 2014-02-05 NaT 104
5 2014-02-06 4 days 105
>>> df.pivot('a', 'b', 'c').fillna('-')
b NaT -1 days 1 days 2 days 4 days
a
NaT 101 - - 103 -
2014-02-01 - 100 - - -
2014-02-03 - - 102 - -
2014-02-05 104 - - - -
2014-02-06 - - - - 105
>>> df.pivot('b', 'a', 'c').fillna('-')
a NaT 2014-02-01 2014-02-03 2014-02-05 2014-02-06
b
NaT 101 - - 104 -
-1 days - 100 - - -
1 days - - 102 - -
2 days 103 - - - -
4 days - - - - 105
```
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| null | 6 | 2015-02-14T16:52:17Z | 2015-02-16T00:30:51Z | 2015-02-15T23:14:46Z | CONTRIBUTOR | null | Added a test to `test_unstack_nan_index()` for unstacking two levels simultaneously when one of them contains a `NaN`.
Should delete the `print()` statements before merging...
This is the output I get:
```
======================================================================
FAIL: test_unstack_nan_index (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\seth\github\pandas2\build\lib.win-amd64-3.4\pandas\tests\test_frame.py", line 12439, in test_unstack_nan_index
assert_series_equal(left, right)
File "c:\Users\seth\github\pandas2\build\lib.win-amd64-3.4\pandas\util\testing.py", line 674, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "das\src\testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas\src\testing.c:2745)
File "das\src\testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas\src\testing.c:1830)
File "das\src\testing.pyx", line 135, in pandas._testing.assert_almost_equal (pandas\src\testing.c:2514)
nose.proxy.AssertionError: (very low values) expected 3.00000 but got 0.00000, with decimal 5
-------------------- >> begin captured stdout << ---------------------
dfs=
C
A B
a 0 0
1 1
2 2
NaN 3
b 4 4
5 5
6 6
7 7
left=
A B
C a 1 0
2 1
4 2
0 3
b 6 4
7 5
a 0 6
1 7
dtype: int32
right=
A B
C a NaN 3
0 0
1 1
2 2
b 4 4
5 5
6 6
7 7
dtype: int32
--------------------- >> end captured stdout << ----------------------
```
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} | 16 | 2015-02-15T04:30:17Z | 2015-11-12T23:44:19Z | 2015-03-05T23:26:19Z | CONTRIBUTOR | null | This PR adds a get_chunk method to StataReader to allow files to be read incrementally. This is quite useful when processing large files that can barely fit into memory.
The interface is modeled after the analogous method in TextFileReader.
There are some limitations when incrementally converting categoricals in pre 117 version files. I have a work-around that involves reading the file through once to get the label information, then re-reading and converting using a new function called convert_categoricals.
In a fair amount of testing, I always get identical results using get_chunk and reading the file completely.
I left the existing data(...) method for reading the complete file intact. Potentially it could be changed to simply call get_chunk.
A few additional issues:
1. The PR fixes an apparent bug in _stata_elapsed_date_to_datetime_vec, in all three of the nested convert_xxx functions. In some execution paths, a new Series is created without using the index of the series passed as an argument. This can cause incorrect NA detection when the result is indexed using bad_locs.
2. A modest performance improvement (~40% for me) is obtained by using partition inside of _null_terminate in place of index/slice operations.
3. Different date object types may be returned during date conversions, according to the execution path. These can represent identical dates but test as being different using assert_frame_equal. So there's a bit of extra care required in some of the tests.
4. It would be easy to add an iterator to read the chunks, but I didn't do that. You need to call get_chunk directly.
5. Some dtypes may change from chunk-to-chunk. For example, I believe that pandas promotes integers to floats when there are missing values. In a chunk with no missing values the dtype would be int, but become float when there are missing values.
6. A comment separate from this PR, the slowest step for reading the big data files I work with is string decoding and truncation (millions of calls to _null_terminate). Other than the modest gain using partition, I tried and failed to find a good optimization for this.
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} | 11 | 2015-02-15T20:12:25Z | 2016-02-11T13:09:26Z | 2016-02-11T02:42:04Z | NONE | null | It's sometimes convenient to assign/replace the name of a Series, especially if the result is going to be a DataFrame column, for example:
```
df.groupby([]).size().set_name('GRP_SIZE').reset_index()
```
This should be as simple as:
```
def set_name(self, name):
self.name = name
return self
```
Comments?
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| null | 3 | 2015-02-15T20:26:22Z | 2015-02-16T14:16:06Z | 2015-02-16T13:17:13Z | NONE | null | I just want to flip the direction of the bars in a barplot, but currently, the orientation of a barplot cannot be set to 'horizontal':
```
pd.Series([1,2], index=pd.Index([1,2], name='x')).plot(kind='bar', orientation='horizontal')
```
yelds an:
```
AssertionError: incompatible sizes: argument 'left' must be length 1 or scalar
```
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} | 1 | 2015-02-15T21:24:45Z | 2020-05-02T04:08:38Z | 2020-05-02T04:08:37Z | CONTRIBUTOR | null | In several of the IO interfaces, we allow `chunksize=` and `iterator` to allow chunk reading. These all use sepearate non-conforming base classes.
We should create a base class `core/base.py/Iterator` that inherits from `collections.Iterator`. Then incorporate as much of this combined functionaility into this base class. To have them each have a similar interface.
- [x] `read_stata` #9493, #12173
- [ ] `read_hdf` [here](https://github.com/pydata/pandas/blob/master/pandas/io/pytables.py#L1276)
- [x] `read_csv` #12173
- [ ] `read_msgpack` [here](https://github.com/pydata/pandas/blob/master/pandas/io/packers.py#L599)
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} | 1 | 2015-02-16T00:19:15Z | 2015-03-05T23:25:33Z | 2015-03-05T23:25:33Z | CONTRIBUTOR | null | With latest master code (including #9061 and #9292), `DataFrame.unstack()` produces incorrect results when `level` contains two levels one of which contains `NaN`:
```
df = pd.DataFrame({'A': list('aaaabbbb'),'B':range(8), 'C':range(8)})
df.iloc[3, 1] = np.NaN
dfs = df.set_index(['A', 'B'])
left = dfs.unstack([0, 1)
data = [3, 0, 1, 2, 4, 5, 6, 7]
idx = MultiIndex(levels=[['C'], ['a', 'b'], [0, 1, 2, 4, 5, 6, 7]],
labels=[[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1],
[-1, 0, 1, 2, 3, 4, 5, 6]],
names=[None, 'A', 'B'])
right = Series(data, index=idx, dtype=dfs.dtypes[0])
print("dfs=")
print(dfs)
print("\nleft=")
print(left)
print("\nright=")
print(right)
assert_series_equal(left, right)
```
produces:
```
======================================================================
FAIL: test_unstack_nan_index (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\seth\github\pandas2\build\lib.win-amd64-3.4\pandas\tests\test_frame.py", line 12439, in test_unstack_nan_index
assert_series_equal(left, right)
File "c:\Users\seth\github\pandas2\build\lib.win-amd64-3.4\pandas\util\testing.py", line 674, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "das\src\testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas\src\testing.c:2745)
File "das\src\testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas\src\testing.c:1830)
File "das\src\testing.pyx", line 135, in pandas._testing.assert_almost_equal (pandas\src\testing.c:2514)
nose.proxy.AssertionError: (very low values) expected 3.00000 but got 0.00000, with decimal 5
-------------------- >> begin captured stdout << ---------------------
dfs=
C
A B
a 0 0
1 1
2 2
NaN 3
b 4 4
5 5
6 6
7 7
left=
A B
C a 1 0
2 1
4 2
0 3
b 6 4
7 5
a 0 6
1 7
dtype: int32
right=
A B
C a NaN 3
0 0
1 1
2 2
b 4 4
5 5
6 6
7 7
dtype: int32
--------------------- >> end captured stdout << ----------------------
```
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} | 0 | 2015-02-16T23:44:53Z | 2015-02-24T13:49:22Z | 2015-02-24T13:49:22Z | CONTRIBUTOR | null | closes #9477
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