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https://api.github.com/repos/pandas-dev/pandas/issues/8001 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8001/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8001/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8001/events | https://github.com/pandas-dev/pandas/issues/8001 | 40,057,868 | MDU6SXNzdWU0MDA1Nzg2OA== | 8,001 | Inconsistent indexing of mixed type indexed Series | {
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| null | 1 | 2014-08-12T14:04:51Z | 2014-08-13T12:25:36Z | 2014-08-12T14:07:22Z | NONE | null | Consider the following code:
``` python
import pandas as pd
sr = pd.Series(data=[1,2,3,4], index=[1,2, '3', '4'])
print sr[1]
print sr[[1,2]]
```
Note that the rows of the Series are both indexed with integers and strings.
This generates the following output:
1
2 2
3 3
The first print statement selects the value correponding to the key 1, i.e. 1
The second print statement should print:
1 1
2 2
However, instead it selects row 1 to 2 and prints the result above.
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https://api.github.com/repos/pandas-dev/pandas/issues/8002 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8002/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8002/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8002/events | https://github.com/pandas-dev/pandas/issues/8002 | 40,065,647 | MDU6SXNzdWU0MDA2NTY0Nw== | 8,002 | BUG: float conversion in read_csv is inaccurate for precise input | {
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} | 7 | 2014-08-12T15:17:32Z | 2014-09-19T16:07:47Z | 2014-09-19T16:07:47Z | CONTRIBUTOR | null | This is coming from #2566, which noted that the `xstrtod()` method used by `read_csv` doesn't agree with standard numpy float conversion. I see that the priority is speed of parsing over complete accuracy (within 0.5 units in the last place, or ULP), but there are two issues here that (at least to me) actually appear to be buggy:
1. Low-precision values (i.e. less than about 15 significant figures) should be guaranteed to be within 0.5 ULP of the correct result, or at least within 1 ULP. However, I have found cases in which `xstrtod()` is off by _more_ than 1 ULP, although these don't come up often.
2. High-precision values of course can't be guaranteed to be within 0.5 ULP without a costly correction loop as in the ordinary `strtod()`, but the error in conversion increases linearly as the number of supplied significant figures increases. With 30 significant figures, the error in conversion can potentially be over 7 ULP.
[Here](http://nbviewer.ipython.org/github/amras1/ascii-profiling/blob/master/fast_conversion_profile.ipynb) is an IPython notebook analyzing the accuracy of `xstrtod()`. I think there are two problems here: `xstrtod()` keeps reading digits after the 17th, none of which should matter for conversion, and the scaling step at the end produces a compounded error by repeatedly multiplying/dividing by powers of 10. I have a solution for AstroPy that seems to fix these issues, so I can open a PR if it's agreed that `xstrtod()` should be changed.
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} | 9 | 2014-08-12T15:22:40Z | 2014-08-14T17:13:13Z | 2014-08-14T17:13:13Z | CONTRIBUTOR | null | closes #7999
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} | 15 | 2014-08-12T15:29:05Z | 2019-12-18T23:26:00Z | 2019-12-18T23:26:00Z | CONTRIBUTOR | null | ```
In [1]: from pandas import Series
In [3]: import numpy as np
In [4]: Series([], dtype=np.datetime64)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-139e5646d234> in <module>()
----> 1 Series([], dtype=np.datetime64)
/users/is/whughes/pyenvs/1c4f50ee0e346415/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
218 else:
219 data = _sanitize_array(data, index, dtype, copy,
--> 220 raise_cast_failure=True)
221
222 data = SingleBlockManager(data, index, fastpath=True)
/users/is/whughes/pyenvs/1c4f50ee0e346415/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/series.py in _sanitize_array(data, index, dtype, copy, raise_cast_failure)
2564
2565 else:
-> 2566 subarr = _try_cast(data, False)
2567
2568 # scalar like
/users/is/whughes/pyenvs/1c4f50ee0e346415/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/series.py in _try_cast(arr, take_fast_path)
2509
2510 try:
-> 2511 arr = _possibly_cast_to_datetime(arr, dtype)
2512 subarr = pa.array(arr, dtype=dtype, copy=copy)
2513 except (ValueError, TypeError):
/users/is/whughes/pyenvs/1c4f50ee0e346415/lib/python2.7/site-packages/pandas-0.13.1-py2.7-linux-x86_64.egg/pandas/core/common.py in _possibly_cast_to_datetime(value, dtype, coerce)
1659 else:
1660 raise TypeError(
-> 1661 "cannot convert datetimelike to dtype [%s]" % dtype)
1662 elif is_timedelta64 and dtype != _TD_DTYPE:
1663 if dtype.name == 'timedelta64[ns]':
TypeError: cannot convert datetimelike to dtype [datetime64]
```
The following workaround works:
```
Series([], dtype=datetime)
```
but I would expect passing in the numpy type to work too.
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| null | 1 | 2014-08-12T15:39:18Z | 2014-08-12T15:43:09Z | 2014-08-12T15:43:04Z | NONE | null | Operator **in** works differently with str and int in series; please see short snippet below.
`import pandas as pd`
`df1 = pd.DataFrame({'a':['a','b','c'], 'b':['d','e','f']})`
`df2 = pd.DataFrame({'a':[1,2,3], 'b':[3,2,1]})`
`df1.a[0] in df1.a`
False
`df2.a[0] in df2.a`
True
## INSTALLED VERSIONS
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Darwin
OS-release: 13.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.1
nose: None
Cython: None
numpy: 1.6.2
scipy: 0.11.0
statsmodels: None
IPython: 2.1.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 1.5
pytz: 2012d
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.1.1
openpyxl: None
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
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} | 1 | 2014-08-12T16:18:20Z | 2014-08-12T18:15:28Z | 2014-08-12T18:14:25Z | CONTRIBUTOR | null | replaces #7768
closes #3678
Categorical([1, np.nan]) would end up with a single `1.` float level.
This commit ensures that if `values` is a list of ints and contains np.nan,
the float conversation does not take place.
Categorical: fix describe with np.nan
Categorical: ensure that one can assign np.nan
Categorical: fix assigning NaN if NaN in levels
API: change default Categorical.from_codes() to ordered=False
In the normal constructor `ordered=True` is only assumed if the levels
are given or the values are sortable (which is most of the cases), but
in `from_codes(...)` we can't asssume this so the default should be
`False`.
Categorical: add some links to Categorical in the other docs
Categorical: use s.values when calling private methods
s.values is the underlying Categorical object, s.cat will be changed
to only expose the API methods/properties.
Categorical: Change series.cat to only expose the API
Categorical: Fix order and na_position
Categorical: Fix comparison of Categoricals and Series|Categorical|np.array
Categorical can only be comapred to another Categorical with the same levels
and the same ordering or to a scalar value.
If the Categorical has no order defined (cat.ordered == False), only equal
(and not equal) are defined.
DOC: use okexcept in docs rather than try: except:
CLN: revised isnull treatement
TST: test for tab completion
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} | 52 | 2014-08-12T18:14:31Z | 2014-08-29T18:41:49Z | 2014-08-19T13:58:05Z | CONTRIBUTOR | null | replaces #7768, #8006
closes #3678
Categorical: preserve ints when NaN are present
`Categorical([1, np.nan])` would end up with a single `1.` float level.
This commit ensures that if `values` is a list of ints and contains np.nan,
the float conversation does not take place.
Categorical: fix describe with np.nan
Categorical: ensure that one can assign np.nan
Categorical: fix assigning NaN if NaN in levels
API: change default Categorical.from_codes() to ordered=False
In the normal constructor `ordered=True` is only assumed if the levels
are given or the values are sortable (which is most of the cases), but
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Categorical: add some links to Categorical in the other docs
Categorical: use s.values when calling private methods
s.values is the underlying Categorical object, s.cat will be changed
to only expose the API methods/properties.
Categorical: Change series.cat to only expose the API
Categorical: Fix order and na_position
Categorical: Fix comparison of Categoricals and Series|Categorical|np.array
Categorical can only be comapred to another Categorical with the same levels
and the same ordering or to a scalar value.
If the Categorical has no order defined (cat.ordered == False), only equal
(and not equal) are defined.
Categorical: Tab completition tests
Categorical: Fix for NA handling/float converting in levels
Categorical: make sure fillna gets both missing values and NA-levels
Categorical: add back labels as deprecated property
Categorical: Fix assigment to a Series(Categorical)) and remove Series.cat.codes
Categorical: declare most methods in Categorical NON-API
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} | 5 | 2014-08-12T21:59:27Z | 2015-05-09T21:24:25Z | 2015-05-09T16:20:05Z | CONTRIBUTOR | null | sharex and sharey should be settable and by default False in a grouped boxplot. E.g.
```
n [97]: df = DataFrame(np.random.rand(10,2), columns=['Col1', 'Col2'] )
In [98]: df['X'] =['A','A','A','A','A','B','B','B','B','B']
In [99]: df.iloc[0:5, :2] += 10000
In [100]: df.groupby('X').boxplot() # this is unreadable
```
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| null | 12 | 2014-08-12T22:30:21Z | 2014-08-13T20:26:01Z | 2014-08-13T12:43:39Z | NONE | null | I posted this on StackOverflow as a [question](http://stackoverflow.com/questions/25275009/pandas-series-filter-values-returning-different-type-than-numpy-array), but I think it may be a bug in Pandas.
These two expressions return something slightly different:
```
df.filter(regex='other').iloc[0].values
df.iloc[0].filter(regex='other').values
```
Should they? It seems strange. I found this issue by doing an apply on the dataframe and trying to use a filter on the rows in a `scipy.stats.entropy` calculation.
The two ways I have seen they are different. First, the bottom throws an error in the entropy calculation:
```
TypeError: ufunc 'xlogy' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
Second, the print representation is slightly different:
```
[12024 9643 7681 8193 8012 7846 7615 7484 5966 11484 13627 17749 9820 5336 4611 3366]
[12024 9643 7681 8193 8012 7846 7615 7484 5966 11484 13627 17749 9820 5336 4611 3366]
```
Is this a bug? Or expected and I am just using filter on the rows incorrectly?
I've also tried using `row[row.filter(regex='other').index].values` with no success.
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| null | 1 | 2014-08-12T23:31:03Z | 2014-08-13T13:57:02Z | 2014-08-13T02:04:55Z | CONTRIBUTOR | null | This pull request is in response to [issue #8000](https://github.com/pydata/pandas/issues/8000).
Changes to core/common.py add np.nan as the default value for missing values to the left and right non-missing values during interpolation. This prevents DataFrame.interpolate() from extrapolating the last non-missing value over all trailing missing values (the default).
Changes to tests/test_common.py add test coverage to the above change. A passing test is where an interpolated series with a trailing missing value maintains that trailing missing value after interpolation.
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} | 3 | 2014-08-12T23:59:27Z | 2020-05-07T22:11:35Z | 2020-05-07T22:11:34Z | NONE | null | When specifying a chunksize during the ExcelFile.parse() method, I should be back an iterable object, but instead a DataFrame is returned.
If you return just the TextParser (change parser.read() to just parser) in the code block below, then iterating over the parsed ExcelFile works and returns the specified number of chunks.
The code block is taken from pandas.io.excel.py line 334
Hopefully I didn't make that confusing.
``` python
parser = TextParser(data, header=header, index_col=index_col,
has_index_names=has_index_names,
na_values=na_values,
thousands=thousands,
parse_dates=parse_dates,
date_parser=date_parser,
skiprows=skiprows,
skip_footer=skip_footer,
chunksize=chunksize,
**kwds)
return parser.read()
```
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} | 9 | 2014-08-13T02:04:24Z | 2016-04-04T23:57:50Z | 2015-07-28T21:56:26Z | CONTRIBUTOR | null | This pull request is in response to [issue #8000](https://github.com/pydata/pandas/issues/8000).
Changes to core/common.py add np.nan as the default value for missing values to the left and right non-missing values during interpolation. This prevents DataFrame.interpolate() from extrapolating the last non-missing value over all trailing missing values (the default).
Changes to tests/test_generic.py add test coverage to the above change. A passing test is where an interpolated series with a trailing missing value maintains that trailing missing value after interpolation.
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} | 12 | 2014-08-13T04:04:40Z | 2014-08-16T00:39:45Z | 2014-08-16T00:39:45Z | NONE | null | Expected behaviour: Using appendable table stored using HDFStore summed length of DataFrames returned using an iterator with a where clause should equal the length of the DataFrame when returned using the same where clause but with iterator=False e.g. TableIterator.get_values().
The attached code generates appendable tables of size 100064, 200064, ..., 400064. It uses a where clause which is a superset of all possible values to get DataFrames with iterator=False, with and without the where clause, and with iterator=True, also with and without the where clause. In all cases except for iterator=True with the where clause the length of the returned DataFrames is correct.
For the failure cases in closer inspection in iPython it is the last 64 rows which are not being returned.
Note: in create_file() the appending of DataFrames with lengths of 58689 and 41375 was chosen specifically to reproduce the problem. I originally encountered the problem with a dataset with length 174000064 and the last append was size 41375. I attempted to reproduce the problem by creating various length tables in chunks of 100000 with a final append of 64 and wasn't able to do so.
Creating the table with the last chunk = 41375 with total length exceeding 300000 does in my tests reproduce the problem.
Output:
```
iteration: 0 PASSED
expected: 100064, df len: 100064, it (no where clause) len: 100064, it len: 100064
iteration: 1 PASSED
expected: 200064, df len: 200064, it (no where clause) len: 200064, it len: 200064
iteration: 2 FAILED
expected: 300064, df len: 300064, it (no where clause) len: 300064, it len: 300000
iteration: 3 FAILED
expected: 400064, df len: 400064, it (no where clause) len: 400064, it len: 400000
```
> > > pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-32-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: None
IPython: 1.2.1
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: 3.3.3
bs4: 4.3.2
html5lib: 0.999
bq: None
apiclient: None
``` python
import os
from dateutil.relativedelta import relativedelta
import numpy as np
randn = np.random.randn
from pandas import DataFrame, HDFStore, date_range
def create_df(beg_dt, periods=1e5):
""" Create a DataFrame containing values v. """
dr = date_range(beg_dt, periods=periods, freq='S')
df = DataFrame(index=dr, data=np.random.randn(periods, 4), columns=['bid_price','bid_vol', 'ask_price','ask_vol'])
return(df)
def create_file(iterations=1):
beg_dt = '2014-08-12 13:30:00.000000'
periods = 1e5
for i in xrange(iterations):
df = create_df(beg_dt, periods)
store_append(store, df, key="df")
beg_dt = df.index[-1:][0] + relativedelta(seconds=1)
df = create_df(beg_dt, 58689)
store_append(store, df, key="df")
beg_dt = df.index[-1:][0] + relativedelta(seconds=1)
df = create_df(beg_dt, 41375)
store_append(store, df, key="df")
beg_dt = df.index[-1:][0] + relativedelta(seconds=1)
return(df)
def store_open(fname):
return(HDFStore(fname))
def store_get(store, key="df", where=None, start=None, stop=None, iterator=False, chunksize=None):
df = None
try:
df = store.select(key, where=where, start=start, stop=stop, iterator=iterator, chunksize=chunksize)
except (KeyError, TypeError, ):
pass
return(df)
def store_append(store, df, key="df", where=""):
store.append(key, df, format='table')
path = '.'
fname = '/'.join([path, 'delme_test.h5'])
store = None
for n in xrange(0, 4):
pass
if store:
try: store.close()
except: pass
try: os.unlink(fname)
except: pass
store = store_open(fname)
create_file(n)
store.close()
store = store_open(fname)
where = None
df = store_get(store, 'df', where=where, iterator=False)
expected_ln = len(df)
beg_dt = '2014-08-12 13:30:00.000000'
end_dt = '2032-12-31 13:30:00.000000'
where = "index >= '%s' & index <= '%s'" % (beg_dt, end_dt)
# where clause, iterator=False
df = store_get(store, 'df', where=where, iterator=False)
ln_df = len(df)
# no where clause
it = store_get(store, 'df', where=None, iterator=True)
dfs = [df for df in it if not df.empty]
ln_it_no_where_clause = sum([len(df) for df in dfs])
# where clause, iterator=True
it = store_get(store, 'df', where=where, iterator=True)
dfs = [df for df in it if not df.empty]
ln_it = sum([len(df) for df in dfs])
if expected_ln == ln_df and expected_ln == ln_it:
print("iteration: %d PASSED" % n)
else:
print("iteration: %d FAILED" % n)
print("expected: %d, df len: %d, it (no where clause) len: %d, it len: %d" %
(expected_ln, ln_df, ln_it_no_where_clause, ln_it))
store.close()
```
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} | 17 | 2014-08-13T09:43:31Z | 2017-11-26T18:38:52Z | 2014-12-03T10:51:50Z | NONE | null | ```
s = pd.Series(x)
m = s.groupby().count() # does not work
m = s.groupby(s.values).count() # works
```
In `https://github.com/pydata/pandas/blob/master/pandas/core/generic.py`, `by=None` should be corrected to just `by` as `None` always leads to an error.
```
def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True,
group_keys=True, squeeze=False):
from pandas.core.groupby import groupby
axis = self._get_axis_number(axis)
return groupby(self, by, axis=axis, level=level, as_index=as_index,
sort=sort, group_keys=group_keys, squeeze=squeeze)
```
See this http://stackoverflow.com/questions/17929426/groupby-for-pandas-series-not-working
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| null | 3 | 2014-08-13T12:15:38Z | 2014-08-13T12:25:52Z | 2014-08-13T12:25:36Z | NONE | null | When I run the following code:
``` python
import pandas as pd
import numpy as np
dfe=pd.Series(np.array([ 0, 0, 0]),index=[1,2, '3'])
df=pd.Series([2],index=[1])
print dfe.loc[df.index]
print dfe.loc[df.index] + df.loc[df.index]
dfe.loc[df.index] = dfe.loc[df.index] + df.loc[df.index]
print dfe
```
I get the following output:
1 0
dtype: int32
1 2
dtype: int64
1 0
2 NaN
3 0
This is strange because slicing dfe with the index of df clearly results in row the row indexed by the integer 1 (first output).
However, when you do an assignment row nr 1 (i.e. the row you would obtain with iloc) is used to to the assignment.
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} | 18 | 2014-08-13T13:44:09Z | 2014-10-29T15:40:22Z | 2014-09-17T12:20:13Z | NONE | null | I have a `DataFrame` with a `MultiIndex` on the columns. The first level of the MultiIndex contains `str`ings. The second, `float`s (though the problem persists if the second level is `int`s). I add a column to the `DataFrame` (which should _not_ come last if the columns are sorted). I try to sort the `DataFrame`. The result does not seem to be sorted. The behavior is fine if the columns are simply an `Index` (even after adding columns). And the sort works fine in the `MultiIndex` case as long as no columns have been added since the `DataFrame` was created.
MWE:
```
import pandas as pd
import numpy as np
np.random.seed(0)
data = np.random.randn(3,4)
df_multi_float = pd.DataFrame(data, index=list('def'), columns=pd.MultiIndex.from_tuples([('red', i) for i in [1., 3., 2., 5.]]))
print df_multi_float
#OUTPUT
red
1 3 2 5
d 1.764052 0.400157 0.978738 2.240893
e 1.867558 -0.977278 0.950088 -0.151357
f -0.103219 0.410599 0.144044 1.454274
```
This sorts just fine as it isnow:
```
print df_multi_float.sort_index(axis=1)
#OUTPUT
red
1 2 3 5
d 1.764052 0.978738 0.400157 2.240893
e 1.867558 0.950088 -0.977278 -0.151357
f -0.103219 0.144044 0.410599 1.454274
```
But if I add columns to both this `DataFrame and then show it sorted, I get what looks to be a wrong result (the new column remains last, rather than being placed second-to-last as it should be):
```
df_multi_float[('red', 4.0)] = 'world'
print df_multi_float.sort_index(axis=1)
#OUTPUT
red red
1 2 3 5 4
d 1.764052 0.978738 0.400157 2.240893 world
e 1.867558 0.950088 -0.977278 -0.151357 world
f -0.103219 0.144044 0.410599 1.454274 world
```
I'm able to produce this behavior on two systems. The first runs Pandas 0.14.0 and Numpy 1.8.1 and the second runs Pandas 0.14.1 and Numpy 1.8.2. This issue is described here: http://stackoverflow.com/questions/25287130/pandas-sort-index-fails-with-multiindex-containing-floats-as-one-level-when-col?noredirect=1#comment39408150_25287130
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https://api.github.com/repos/pandas-dev/pandas/issues/8018 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8018/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8018/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8018/events | https://github.com/pandas-dev/pandas/pull/8018 | 40,167,467 | MDExOlB1bGxSZXF1ZXN0MTk3MzIzMDA= | 8,018 | ENH: Groupby.plot enhancement | {
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| null | 31 | 2014-08-13T15:39:45Z | 2017-03-20T13:55:29Z | 2017-03-20T13:49:20Z | MEMBER | null | Allow to support `DataFrameGroupBy.plot` and `SeriesGroupBy.plot` covering current `DataFrame.hist(by=XXX)` and `DataFrame.boxplot(by=XXX)` usecases. Implementation is incomplete but covering most of usecases / behaviours.
- [x] fix `FIXME`s
- [x] Split to `plot_seriesgroupby` and `plot_dataramegroupby` specifying default values.
- [x] Refactoring `plot_frame` and `plot_series`, modify `docstring`. (#8037)
- [x] Make line subplot to accept multiple colors (#9894)
- [x] secondary_y
- [x] error bars (raise `NotImplementedError`)
- [x] split some cleanups required in prior to this PR (#10717)
- [x] doc
- [x] tests
## Following gist contains all patterns of outputs.
- https://gist.github.com/sinhrks/ad805b6689140930d99a
## Rendered doc
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} | 1 | 2014-08-13T16:08:29Z | 2017-02-06T18:23:52Z | 2014-08-18T15:38:09Z | CONTRIBUTOR | null | We have some code which filters a dataframe, then shifts the result. However, this dataframe may end up being empty. In pandas 0.13, this works without problem:
```
In [61]: df = DataFrame(columns=['foo'])
In [62]: df.shift(-1)
Out[62]:
Empty DataFrame
Columns: [foo]
Index: []
[0 rows x 1 columns]
```
However, pandas 0.14 throws a division by zero error:
```
In [1]: from pandas import *
In [2]: df = DataFrame(columns=['foo'])
In [3]: df.shift(-1)
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
<ipython-input-3-6aa009807b04> in <module>()
----> 1 df.shift(-1)
/users/is/whughes/pyenvs/da497516f84bbd5b/lib/python2.7/site-packages/pandas/core/generic.pyc in shift(self, periods, freq, axis, **kwds)
3288 block_axis = self._get_block_manager_axis(axis)
3289 if freq is None and not len(kwds):
-> 3290 new_data = self._data.shift(periods=periods, axis=block_axis)
3291 else:
3292 return self.tshift(periods, freq, **kwds)
/users/is/whughes/pyenvs/da497516f84bbd5b/lib/python2.7/site-packages/pandas/core/internals.pyc in shift(self, **kwargs)
2226
2227 def shift(self, **kwargs):
-> 2228 return self.apply('shift', **kwargs)
2229
2230 def fillna(self, **kwargs):
/users/is/whughes/pyenvs/da497516f84bbd5b/lib/python2.7/site-packages/pandas/core/internals.pyc in apply(self, f, axes, filter, do_integrity_check, **kwargs)
2190 copy=align_copy)
2191
-> 2192 applied = getattr(b, f)(**kwargs)
2193
2194 if isinstance(applied, list):
/users/is/whughes/pyenvs/da497516f84bbd5b/lib/python2.7/site-packages/pandas/core/internals.pyc in shift(self, periods, axis)
789 new_values = new_values.T
790 axis = new_values.ndim - axis - 1
--> 791 new_values = np.roll(new_values, periods, axis=axis)
792 axis_indexer = [ slice(None) ] * self.ndim
793 if periods > 0:
/users/is/whughes/pyenvs/da497516f84bbd5b/lib/python2.7/site-packages/numpy/core/numeric.pyc in roll(a, shift, axis)
1145 n = a.shape[axis]
1146 reshape = False
-> 1147 shift %= n
1148 indexes = concatenate((arange(n-shift,n),arange(n-shift)))
1149 res = a.take(indexes, axis)
ZeroDivisionError: integer division or modulo by zero
```
The behaviour is correct with numpy 1.8 however.
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} | 1 | 2014-08-13T16:10:22Z | 2016-02-27T14:46:28Z | 2016-02-27T14:46:28Z | MEMBER | null | When one tries to construct a `DataFrame` where the data column count and the passed in column count differ the error message is completely unintuitive unless you know something about the internal representation of the data inside of `NDFrame`s.
I think we should at the very least make the dimensions in the error message consistent with how the frame _looks_ rather than how it's _implemented_.
this code:
``` python
df = DataFrame(np.empty(0), columns=list('abc'))
```
raises this exception:
```
ValueError: Shape of passed values is (1, 0), indices imply (3, 0)
```
see here for another example: https://github.com/ContinuumIO/blaze/issues/466
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} | 2 | 2014-08-13T19:06:19Z | 2014-08-15T22:40:58Z | 2014-08-15T22:40:58Z | CONTRIBUTOR | null | When using SQLAlchemy to create a table from a dataframe, Pandas messes up the types of the index columns. For example:
``` python
import sqlalchemy
engine = sqlalchemy.create_engine('sqlite:///:memory:')
import pandas as pd
from pandas.io.sql import PandasSQLTable, PandasSQLAlchemy
df = pd.DataFrame.from_records([{'a':1,'b':2.1,'c':'txt1','a':2,'b':1.5,'c':'txt2'}], index=['a','b'])
o = PandasSQLTable('test_df',PandasSQLAlchemy(engine),frame=df)
print o.sql_schema()
```
outputs
```
CREATE TABLE test_df (
a FLOAT,
b BIGINT,
c TEXT
)
```
when `a` should be `BIGINT` and `b` should be `FLOAT`.
This occurs because the index columns are enumerated backwards in `PandasSQLTable._create_table_statement` in `pandas.io.sql` but the types don't take into account this backward iteration:
``` python
if self.index is not None:
for i, idx_label in enumerate(self.index[::-1]):
idx_type = self._sqlalchemy_type(
self.frame.index.get_level_values(i))
columns.insert(0, Column(idx_label, idx_type, index=True))
```
I will make a pull request with a fix for this issue. Versions appended below:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-358.6.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1-200-g534784b
nose: 1.3.0
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: None
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.4.2
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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} | 12 | 2014-08-14T08:32:13Z | 2014-08-20T20:23:36Z | 2014-08-15T22:40:58Z | CONTRIBUTOR | null | When creating a new table from a `pandas` dataframe with a multi-index, the code would iterate over the index columns backwards but use their types as if counting forwards. This meant that the created table had incorrect indexes for multi-indexes.
This code factors out a function to pull column names and column types for both SQLAlchemy and legacy table objects. This code now iterates over the indices properly.
And additional test has been written. All test pass on my local machine.
Closes https://github.com/pydata/pandas/issues/8021 .
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} | 4 | 2014-08-14T08:55:53Z | 2014-12-01T00:08:05Z | 2014-12-01T00:08:05Z | CONTRIBUTOR | null | Currently these two have different places to look for the relevant equality tests: `TestCase` has `assert_numpy_array_equivalent` and `assert_numpy_array_equal` but the testing module has `assert_frame_equal` and `assert_series_equal`.
It would be nice if all substantial code is available form the testing module and `TestCase` implements a small wrapper around these methods. That way someone outside pandas can import `tm` and us ethe methods without subclassing `TestCase`.
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} | 8 | 2014-08-14T10:34:40Z | 2014-08-14T19:43:06Z | 2014-08-14T13:08:19Z | CONTRIBUTOR | null | ```
import pandas
from statsmodels import datasets
# load the data and clean it a bit
affairs = datasets.fair.load_pandas()
datas = affairs.exog
# any time greater than 0 is cheating
datas['cheated'] = affairs.endog > 0
# sort by the marriage quality and give meaningful name
# [rate_marriage, age, yrs_married, children,
# religious, educ, occupation, occupation_husb]
datas = datas.sort(['rate_marriage', 'religious'])
num_to_desc = {1: 'awful', 2: 'bad', 3: 'intermediate',
4: 'good', 5: 'wonderful'}
datas['rate_marriage'] = datas['rate_marriage'].map(num_to_desc)
num_to_faith = {1: 'non religious', 2: 'poorly religious', 3: 'religious',
4: 'very religious'}
datas['religious'] = datas['religious'].map(num_to_faith)
num_to_cheat = {False: 'faithful', True: 'cheated'}
datas['cheated'] = datas['cheated'].map(num_to_cheat)
```
part of the following test that fails on pythonxy Ubuntu testing
#
## ERROR: statsmodels.graphics.tests.test_mosaicplot.test_mosaic
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
self.test(_self.arg)
File "/usr/lib/python2.7/dist-packages/numpy/testing/decorators.py",
line 146, in skipper_func
return f(_args, **kwargs)
File "/build/buildd/statsmodels-0.6.0~ppa18~revno/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/graphics/tests/test_mosaicplot.py",
line 124, in test_mosaic
datas['cheated'] = datas['cheated'].map(num_to_cheat)
File "/usr/lib/pymodules/python2.7/pandas/core/series.py", line 1960, in map
indexer = arg.index.get_indexer(values)
File "/usr/lib/pymodules/python2.7/pandas/core/index.py", line 1460,
in get_indexer
if not self.is_unique:
File "properties.pyx", line 34, in pandas.lib.cache_readonly.**get**
(pandas/lib.c:38722)
File "/usr/lib/pymodules/python2.7/pandas/core/index.py", line 571,
in is_unique
return self._engine.is_unique
File "index.pyx", line 205, in
pandas.index.IndexEngine.is_unique.__get__ (pandas/index.c:4338)
File "index.pyx", line 234, in
pandas.index.IndexEngine._do_unique_check (pandas/index.c:4790)
File "index.pyx", line 247, in
pandas.index.IndexEngine._ensure_mapping_populated
(pandas/index.c:4995)
File "index.pyx", line 253, in pandas.index.IndexEngine.initialize
(pandas/index.c:5092)
File "hashtable.pyx", line 731, in
pandas.hashtable.PyObjectHashTable.map_locations
(pandas/hashtable.c:12440)
ValueError: Does not understand character buffer dtype format string ('?')
```
This works on '0.13.1' but not on '0.14.1-202-g7d702e9'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8025 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8025/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8025/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8025/events | https://github.com/pandas-dev/pandas/issues/8025 | 40,245,510 | MDU6SXNzdWU0MDI0NTUxMA== | 8,025 | API: Timestamp.tz_convert/tz_localize raises Exception, should be TypeError | {
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} | 2 | 2014-08-14T11:30:26Z | 2014-08-16T16:56:02Z | 2014-08-16T16:56:02Z | MEMBER | null | `Timestamp` raises normal `Exception` in case of error, and we have to catch `Exception` for error handling.
Expect `TypeError` in consistent with `DatetimeIndex`.
```
pd.Timestamp('2011-01-01').tz_convert('US/Eastern')
# Exception: Cannot convert tz-naive Timestamp, use tz_localize to localize
pd.DatetimeIndex(['2011-01-01']).tz_convert('US/Eastern')
# TypeError: Cannot convert tz-naive timestamps, use tz_localize to localize
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8026 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8026/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8026/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8026/events | https://github.com/pandas-dev/pandas/pull/8026 | 40,246,684 | MDExOlB1bGxSZXF1ZXN0MTk3ODI1MjI= | 8,026 | BUG: related to GH5080, get_indexer choking on boolean type promotion (GH8024) | {
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} | 0 | 2014-08-14T11:50:31Z | 2014-08-14T13:08:19Z | 2014-08-14T13:08:19Z | CONTRIBUTOR | null | closes #8024
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} | 2 | 2014-08-14T11:54:01Z | 2014-10-02T12:18:35Z | 2014-08-15T12:50:09Z | MEMBER | null | Derived from #7636. Area plot sets incorrect `alpha` values to legend when `stacked=True`.
#### NG: Each areas are drawn with `alpha=1.0`, but legend has `alpha=0.5`
#### After fix
```
df = pd.DataFrame(np.random.rand(20, 5), columns=['A', 'B', 'C', 'D', 'E'])
df.plot(kind='area')
```
```
# When alpha is specified
df.plot(kind='area', alpha=0.2)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8028 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8028/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8028/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8028/events | https://github.com/pandas-dev/pandas/issues/8028 | 40,247,707 | MDU6SXNzdWU0MDI0NzcwNw== | 8,028 | ENH/API: Allow get_dummies to specify return value | {
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} | 2 | 2014-08-14T12:07:32Z | 2016-05-07T09:57:38Z | 2016-05-07T09:57:38Z | MEMBER | null | Currently `get_dummies` always returns dummy values with `0` or `1`. When we want others, have to use `replace`, `apply...` etc (and it is not easy in some cases because column name can differs depending on target value).
```
s = pd.Series(['A', 'A', 'B', 'C'])
pd.get_dummies(s)
# A B C
#0 1 0 0
#1 1 0 0
#2 0 1 0
#3 0 0 1
```
Allow to specify values to be used with `values` and `na_value` kwds corresponding `True` or `False`, accepting `scalar`, `dict`, or `Series` like `fillna` does.
```
pd.get_dummies(s, value=True, na_value=False)
# A B C
#0 True False False
#1 True False False
#2 False True False
#3 False False True
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8031 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8031/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8031/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8031/events | https://github.com/pandas-dev/pandas/issues/8031 | 40,316,011 | MDU6SXNzdWU0MDMxNjAxMQ== | 8,031 | read_msgpack compress kwarg not getting passed | {
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| null | 1 | 2014-08-15T01:24:05Z | 2014-08-15T11:45:50Z | 2014-08-15T11:45:45Z | NONE | null | The compress kwarg isn't getting passed down the chain when using pd.read_msgpack(buffer).
```
def read_msgpack(path_or_buf, iterator=False, **kwargs):
...
def read(fh):
l = list(unpack(fh)) (which then goes to decode() and unconvert() )
```
Replication:
```
df = pd.DataFrame([{'foo','bar'}])
packed_df = df.to_msgpack(compress='zlib')
pd.read_msgpack(packed_df, compress='zlib')
...
obj.get('compress'))
File "/Users/briford/myPVE/workbench/lib/python2.7/site-packages/pandas/io/packers.py", line 258, in unconvert
return np.fromstring(values.encode('latin1'), dtype=dtype)
ValueError: string size must be a multiple of element size
```
Versions:
``` pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 13.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.3
Cython: 0.20.2
numpy: 1.8.1
scipy: None
statsmodels: None
IPython: 2.2.0
sphinx: 1.2.2
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
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
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8032 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8032/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8032/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8032/events | https://github.com/pandas-dev/pandas/issues/8032 | 40,318,571 | MDU6SXNzdWU0MDMxODU3MQ== | 8,032 | test_fred fails in latest release | {
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| null | 1 | 2014-08-15T02:34:19Z | 2014-08-15T11:43:29Z | 2014-08-15T11:43:23Z | NONE | null | under all pythons
``` python
FAIL: test_fred (pandas.io.tests.test_data.TestFred)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/mnt/gen2/TmpDir/portage/dev-python/pandas-0.14.1/work/pandas-0.14.1-python2_7/lib/pandas/util/testing.py", line 1346, in wrapper
return t(*args, **kwargs)
File "/mnt/gen2/TmpDir/portage/dev-python/pandas-0.14.1/work/pandas-0.14.1-python2_7/lib/pandas/io/tests/test_data.py", line 448, in test_fred
self.assertEqual(int(received), 16535)
AssertionError: 16502 != 16535
----------------------------------------------------------------------
Ran 7477 tests in 862.713s
FAILED (SKIP=267, failures=1)
```
run with
nosetests --verbosity=3 pandas
Do you require any further information? (The build log is huge)
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https://api.github.com/repos/pandas-dev/pandas/issues/8033 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8033/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8033/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8033/events | https://github.com/pandas-dev/pandas/issues/8033 | 40,318,676 | MDU6SXNzdWU0MDMxODY3Ng== | 8,033 | build issues re -Wimplicit-function-declaration | {
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| null | 2 | 2014-08-15T02:37:54Z | 2014-08-15T12:24:28Z | 2014-08-15T11:46:32Z | NONE | null | portage repeats and lists warnings for convenience at the end of an install
``` python
>>> Completed installing pandas-0.14.1 into /mnt/gen2/TmpDir/portage/dev-python/pandas-0.14.1/image/
strip: x86_64-pc-linux-gnu-strip --strip-unneeded -R .comment -R .GCC.command.line -R .note.gnu.gold-version
usr/lib64/python2.7/site-packages/pandas/algos.so
usr/lib64/python2.7/site-packages/pandas/index.so
usr/lib64/python2.7/site-packages/pandas/tslib.so
usr/lib64/python2.7/site-packages/pandas/lib.so
usr/lib64/python2.7/site-packages/pandas/parser.so
usr/lib64/python2.7/site-packages/pandas/hashtable.so
usr/lib64/python2.7/site-packages/pandas/_sparse.so
usr/lib64/python2.7/site-packages/pandas/_testing.so
usr/lib64/python2.7/site-packages/pandas/msgpack.so
usr/lib64/python2.7/site-packages/pandas/json.so
* QA Notice: Package triggers severe warnings which indicate that it
* may exhibit random runtime failures.
* pandas/tslib.c:17485:5: warning: implicit declaration of function ‘cmp_pandas_datetimestruct’ [-Wimplicit-function-declaration]
* pandas/src/ujson/python/objToJSON.c:1765:5: warning: implicit declaration of function ‘encode’ [-Wimplicit-function-declaration]
* Please do not file a Gentoo bug and instead report the above QA
* issues directly to the upstream developers of this software.
* Homepage: http://pandas.sourceforge.net/
```
Do you require any further information?
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| null | 0 | 2014-08-15T08:45:45Z | 2014-08-15T09:05:09Z | 2014-08-15T09:05:08Z | MEMBER | null | https://github.com/pydata/pandas/commit/bc610104f96699ff73ce507e79b8be070f126454 defined new `index`, but the previous was still used in later example, and suppress SettingWIthCopyWarning from https://github.com/pydata/pandas/commit/70a17da4003af0b1882deb5bcb28e188335fb9dd
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| null | 1 | 2014-08-15T13:19:00Z | 2014-08-15T13:20:50Z | 2014-08-15T13:20:18Z | CONTRIBUTOR | null | Fixing an error in the 'chunksize' parameter name for the io doc. With the typo (parameter called 'chunsize'), the sample code from the documentation fails silently / confusingly - returns an iterator over individual dataframe elements rather than dataframe chunks.
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} | 1 | 2014-08-15T13:49:49Z | 2014-08-19T13:33:55Z | 2014-08-16T16:56:02Z | MEMBER | null | Closes #8025.
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- Made `DataFrame.plot` and `Series.plot` should have consistent docstring except differences.
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} | 4 | 2014-08-15T14:53:27Z | 2015-11-18T20:12:31Z | 2015-11-18T20:12:31Z | MEMBER | null | ```
In [1]: idx = pd.Index(range(5))
In [2]: idx
Out[2]: Int64Index([0, 1, 2, 3, 4], dtype='int64')
In [3]: idx.shift(1)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-d8362983dbf7> in <module>()
----> 1 idx.shift(1)
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\index.pyc in shift(self, period
s, freq)
1096 return self
1097
-> 1098 offset = periods * freq
1099 return Index([idx + offset for idx in self], name=self.name)
1100
TypeError: unsupported operand type(s) for *: 'int' and 'NoneType'
```
Should `shift` be allowed at all when it is no `DatetimeIndex`?
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} | 14 | 2014-08-15T16:12:36Z | 2014-08-19T13:32:17Z | 2014-08-18T13:14:00Z | NONE | null | You can see the bug in the following code:
```
import pandas as pd
import datetime as dt
key = pd.MultiIndex.from_tuples([(
dt.datetime(2014,8,1,0,0,0),
'SomeColumnName',
'AnotherOne')])
data = {
'1' : 34204,
'2' : 43580,
'3' : 84329,
'5' : 23485
}
ts = pd.Series(data=data)
df = pd.DataFrame(data=ts, columns=key)
stacked = df.stack()
print stacked
```
We would expect the data to be unchanged, however the returned DataFrame is empty.
The Pandas version used was 0.11.0
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} | 14 | 2014-08-15T16:37:12Z | 2014-08-19T23:42:04Z | 2014-08-18T20:55:52Z | NONE | null | Hello, I recently upgraded to Pandas 0.14.1 from Pandas 0.13.1, and am having trouble reading Stata .dta files using StataReader. Files that used to take 20 seconds to read now take 300 seconds, and files that used to take 220 seconds are not read even after 20 minutes.
I would really like to use the newer version of Pandas for these large datasets, and import a .dta file rather than a .csv or other filetype in order to maintain my value labels from Stata.
Steps for reproduction:
First, create a large dataset in Stata 13:
```
clear
set obs 11500
forvalues i = 1/8000{
gen var`i' = 1
}
saveold bigdataset, replace
```
Second, try to read it into pandas using StataReader:
```
from pandas.io.stata import StataReader
reader = StataReader('bigdataset.dta')
data = reader.data()
```
Using pandas 0.13.1, this takes around 220 seconds, which is ok, but using pandas 0.14.1, nothing has happened even after waiting around 20 minutes.
When I test this using a smaller dataset:
```
clear
set obs 11500
forvalues i = 1/1000{
gen var`i' = 1
}
saveold smalldataset, replace
```
```
from pandas.io.stata import StataReader
reader = StataReader('smalldataset.dta')
data = reader.data()
```
Using pandas 0.13.1, this takes around 20 seconds, but using pandas 0.14.1, this takes around 300 seconds.
Thanks for reading! Here is the output from show_versions(), if relevant:
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-32-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.20.2
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 1.2.1
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
sqlalchemy: None
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
bq: None
apiclient: None
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} | 8 | 2014-08-15T17:16:33Z | 2014-08-18T18:20:52Z | 2014-08-18T15:38:09Z | CONTRIBUTOR | null | On some versions of numpy (such as 1.7, but not 1.8), this throws a
ZeroDivisionError.
Fixes #8019.
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} | 3 | 2014-08-15T18:51:02Z | 2014-08-15T19:24:07Z | 2014-08-15T19:22:28Z | CONTRIBUTOR | null | When using `assert_frame_equal` or `assert_series_equal` in the presence of a multiindex, the dtype of the index as a whole is `object`. Hence, there is no checking that the dtypes of the individual levels match. We now iterate through the levels.
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} | 1 | 2014-08-15T22:17:05Z | 2014-08-18T13:14:00Z | 2014-08-18T13:14:00Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/8039
The only change I made was to select from the MultiIndex itself, rather than the underlying `.values` attribute. The test set passed but I want to look at this a bit more closely to make sure everything is ok.
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} | 24 | 2014-08-16T14:29:32Z | 2014-09-22T16:08:17Z | 2014-09-19T16:07:47Z | CONTRIBUTOR | null | closes #8002.
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} | 11 | 2014-08-16T22:08:26Z | 2014-08-22T15:42:41Z | 2014-08-18T20:55:52Z | CONTRIBUTOR | null | Previous versions of StataReader did not correctly check for missing values.
This was fixed in a previous PR, but these checks had previously been
implemented on a value-by-value basis. This has now been changed to a
vectorized version that is orders of magnitude faster.
Additionally, a benchmark was added to monitor performance issues in the
future.
Closes #8040
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} | 4 | 2014-08-17T02:39:32Z | 2014-10-01T12:34:42Z | 2014-08-18T15:26:26Z | MEMBER | null | hi folks,
A _Python for Data Analysis_ reader noted the following issue with recent versions of pandas (as of 1 year ago):
```
import pandas as pd
from pandas import DataFrame
import numpy as np
def demean(arr):
return arr - arr.mean()
people = DataFrame(np.random.randn(5, 5),
columns=['a', 'b', 'c', 'd', 'e'],
index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis'])
key = ['one', 'two', 'one', 'two', 'one']
```
on pandas 0.14.1:
```
In [14]: people.groupby(key).transform(demean).groupby(key).mean()
Out[14]:
a b c d e
one -0.228006 0.246737 0.201117 0.250544 0.273858
two 0.342009 -0.370106 -0.301676 -0.375816 -0.410788
```
on the other hand:
```
In [15]: people.groupby(key).apply(demean).groupby(key).mean()
Out[15]:
a b c d e
one -3.700743e-17 7.401487e-17 -7.401487e-17 7.401487e-17 0.000000e+00
two 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 5.551115e-17
```
Looks like `transform` has undergone some work in recent times; any ideas? I need to look at the book text and see if I can triage by replacing `transform` with `apply`. At this point `transform` feels a little bit anachronistic.
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| null | 1 | 2014-08-17T09:01:28Z | 2014-08-17T09:32:27Z | 2014-08-17T09:09:15Z | MEMBER | null | I'm pretty sure PyData is the main mailing list for pandas now, e.g., as seen on the website:
http://pandas.pydata.org/community.html
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} | 26 | 2014-08-17T10:24:54Z | 2017-06-09T11:04:13Z | 2015-03-03T00:59:19Z | CONTRIBUTOR | null | related #4343
I have found myself on occasion writing code to convert from a pandas Series (with n-level MultiIndex) to a scipy sparse n-dim array (plus dimension labels) and back. I am not terribly familiar with the newer sparse data structure features but my impression is that it provides a totally different kind of functionality. Does this kind of functionality exist somewhere? If not, would something like this be of interest?
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} | 0 | 2014-08-17T22:03:29Z | 2014-09-30T21:35:39Z | 2014-08-18T15:26:26Z | CONTRIBUTOR | null | closes #8046
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| null | 4 | 2014-08-18T03:36:29Z | 2015-03-08T17:00:51Z | 2015-03-08T17:00:51Z | NONE | null | I am using pandas to read/write a lot of relatively large hdf5 files (hundreds MB each), hundreds of them, using HDFStore. My app acts as a service and I am facing seg faults after it runs for some time. It seems that it seg fault after some random HDF error occurs. I am able to cause some HDF errors by repeatedly read/write a relatively large HDF files:
``` python
import pandas as pd
import numpy as np
db = pd.HDFStore('test.h5','w')
for i in range(1000): db[str(i)] = pd.DataFrame(np.random.rand(2000,3),columns=['a','b','c'])
db.close()
for i in range(100):
f="test.h5"
print i
db=pd.HDFStore(f,mode='r')
db2=pd.HDFStore(f+'.new',mode='w')
for k in db.keys(): db2[k] = db[k]
db2.close()
db.close()
```
After some time the following error occurs:
TypeError Traceback (most recent call last)
<ipython-input-2-c47d3a0893ae> in <module>()
4 db=pd.HDFStore(f,mode='r')
5 db2=pd.HDFStore(f+'.new',mode='w')
----> 6 for k in db.keys(): db2[k] = db[k]
7 db2.close()
8 db.close()
/usr/lib/python2.7/site-packages/pandas/io/pytables.pyc in **getitem**(self, key)
414
415 def **getitem**(self, key):
--> 416 return self.get(key)
417
418 def **setitem**(self, key, value):
/usr/lib/python2.7/site-packages/pandas/io/pytables.pyc in get(self, key)
618 if group is None:
619 raise KeyError('No object named %s in the file' % key)
--> 620 return self._read_group(group)
621
622 def select(self, key, where=None, start=None, stop=None, columns=None,
/usr/lib/python2.7/site-packages/pandas/io/pytables.pyc in _read_group(self, group, **kwargs)
1276 s = self._create_storer(group)
1277 s.infer_axes()
-> 1278 return s.read(**kwargs)
1279
1280
/usr/lib/python2.7/site-packages/pandas/io/pytables.pyc in read(self, **kwargs)
2656 axes = []
2657 for i in range(self.ndim):
-> 2658 ax = self.read_index('axis%d' % i)
2659 axes.append(ax)
2660
/usr/lib/python2.7/site-packages/pandas/io/pytables.pyc in read_index(self, key)
2258 return index
2259 else: # pragma: no cover
-> 2260 raise TypeError('unrecognized index variety: %s' % variety)
2261
2262 def write_index(self, key, index):
TypeError: unrecognized index variety: None
It is not the necessary the same error happening in my real system but it seems the error occurred is not always the same.
In [7]: pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-30-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: None
Cython: 0.20.2
numpy: 1.8.2
scipy: None
statsmodels: None
IPython: 2.2.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.4.3
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
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| null | 2 | 2014-08-18T06:08:22Z | 2014-08-18T08:06:58Z | 2014-08-18T08:06:58Z | NONE | null | Installation instructions ( http://pandas.pydata.org/pandas-docs/stable/install.html ) mention "Released source distributions will contain the built C files." but that does not seem to be the case? For example hashtable, lib, etc are not included in the 0.14.0 or 0.14.1 tarballs.
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cc @dstephens99
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} | 0 | 2014-08-18T12:45:15Z | 2014-08-18T13:05:59Z | 2014-08-18T13:05:59Z | CONTRIBUTOR | null | xref #8052
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} | 5 | 2014-08-18T12:46:26Z | 2014-08-19T13:34:02Z | 2014-08-19T12:50:59Z | MEMBER | null | Closes #7233.
Other tests have no problem with MPL1.4.dev:
- tests\test_graphics.py
- tseries\tests\test_plotting.py
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| null | 9 | 2014-08-18T15:53:43Z | 2020-04-04T03:34:10Z | 2020-04-04T03:34:10Z | CONTRIBUTOR | null | I'm trying to read a simple file which works fine when I don't have the file open. However, when I have the file open in LibreOffice it reliably segfaults (memory steadily increases and network traffic is at 70MB/s for a good 30 seconds). When open in the TextEditor it works fine.
csv data:
ID,Start Date,End Date
1,2014-09-16 00:00:00-05:00,2014-12-31 23:00:00-05:00
2,2014-10-02 00:00:00-05:00,2014-10-10 15:00:00-05:00
```
(gdb) backtrace
#0 0x00007ffff7879d79 in realloc () from /lib/x86_64-linux-gnu/libc.so.6
#1 0x00007fffede557de in safe_realloc (buffer=0x7fff6b6e9010, size=size@entry=2) at pandas/src/parser/tokenizer.c:55
#2 0x00007fffede55858 in grow_buffer (buffer=<optimized out>, buffer@entry=0x7fff6b6e9010, length=<optimized out>, capacity=capacity@entry=0x11fe66c, space=space@entry=524288,
elsize=elsize@entry=1, error=error@entry=0x7fffffffd25c) at pandas/src/parser/tokenizer.c:115
#3 0x00007fffede558c4 in make_stream_space (self=self@entry=0x11fe630, nbytes=262144) at pandas/src/parser/tokenizer.c:294
#4 0x00007fffede56641 in tokenize_delimited (self=0x11fe630, line_limit=262143) at pandas/src/parser/tokenizer.c:670
#5 0x00007fffede58021 in _tokenize_helper (self=0x11fe630, nrows=262143, all=all@entry=0) at pandas/src/parser/tokenizer.c:1573
#6 0x00007fffede58297 in tokenize_nrows (self=<optimized out>, nrows=<optimized out>) at pandas/src/parser/tokenizer.c:1590
#7 0x00007fffede215c6 in __pyx_f_6pandas_6parser_10TextReader__tokenize_rows (__pyx_v_self=0x7fffeda38dc0, __pyx_v_nrows=<optimized out>) at pandas/parser.c:7784
#8 0x00007fffede24aef in __pyx_f_6pandas_6parser_10TextReader__read_rows (__pyx_v_self=0x7fffeda38dc0, __pyx_v_rows=0xf55488, __pyx_v_trim=0) at pandas/parser.c:7978
#9 0x00007fffede23a2e in __pyx_f_6pandas_6parser_10TextReader__read_low_memory (__pyx_v_self=0x7fffeda38dc0, __pyx_v_rows=0x920190 <_Py_NoneStruct>) at pandas/parser.c:7369
#10 0x00007fffede22474 in __pyx_pf_6pandas_6parser_10TextReader_8read (__pyx_v_rows=0x920190 <_Py_NoneStruct>, __pyx_v_self=0x7fffeda38dc0) at pandas/parser.c:7145
#11 __pyx_pw_6pandas_6parser_10TextReader_9read (__pyx_v_self=0x7fffeda38dc0, __pyx_args=<optimized out>, __pyx_kwds=<optimized out>) at pandas/parser.c:7107
#12 0x000000000052c6d5 in PyEval_EvalFrameEx ()
#13 0x000000000055c594 in PyEval_EvalCodeEx ()
#14 0x000000000052ca8d in PyEval_EvalFrameEx ()
#15 0x000000000055c594 in PyEval_EvalCodeEx ()
#16 0x000000000052ca8d in PyEval_EvalFrameEx ()
#17 0x000000000055c594 in PyEval_EvalCodeEx ()
#18 0x000000000052ca8d in PyEval_EvalFrameEx ()
#19 0x000000000055c594 in PyEval_EvalCodeEx ()
#20 0x000000000052ca8d in PyEval_EvalFrameEx ()
#21 0x000000000055c594 in PyEval_EvalCodeEx ()
#22 0x00000000005b7392 in PyEval_EvalCode ()
#23 0x0000000000469663 in ?? ()
#24 0x00000000004699e3 in PyRun_FileExFlags ()
#25 0x0000000000469f1c in PyRun_SimpleFileExFlags ()
#26 0x000000000046ab81 in Py_Main ()
#27 0x00007ffff7817ec5 in __libc_start_main () from /lib/x86_64-linux-gnu/libc.so.6
#28 0x000000000057497e in _start ()
```
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} | 0 | 2014-08-18T15:59:30Z | 2014-08-28T17:34:49Z | 2014-08-28T17:34:49Z | CONTRIBUTOR | null | This probably should have been caught in https://github.com/pydata/pandas/pull/7766, but wasn't.
```
In [1]: from pandas import Series, rolling_count, expanding_count, expanding_mean
In [2]: expanding_count(Series([]))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-16cb9f6736f0> in <module>()
----> 1 expanding_count(Series([]))
C:\Python34\lib\site-packages\pandas\stats\moments.py in expanding_count(arg, freq, center)
872 of :meth:`~pandas.Series.resample` (i.e. using the `mean`).
873 """
--> 874 return rolling_count(arg, len(arg), freq=freq, center=center)
875
876
C:\Python34\lib\site-packages\pandas\stats\moments.py in rolling_count(arg, window, freq, center, how)
188 converted = np.isfinite(values).astype(float)
189 result = rolling_sum(converted, window, min_periods=1,
--> 190 center=center) # already converted
191
192 # putmask here?
C:\Python34\lib\site-packages\pandas\stats\moments.py in f(arg, window, min_periods, freq, center, how, **kwargs)
593 return func(arg, window, minp, **kwds)
594 return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
--> 595 center=center, how=how, **kwargs)
596
597 return f
C:\Python34\lib\site-packages\pandas\stats\moments.py in _rolling_moment(arg, window, func, minp, axis, freq, center, how, args, kwa
rgs, **kwds)
345 result = np.apply_along_axis(calc, axis, values)
346 else:
--> 347 result = calc(values)
348
349 rs = return_hook(result)
C:\Python34\lib\site-packages\pandas\stats\moments.py in <lambda>(x)
339 arg = _conv_timerule(arg, freq, how)
340 calc = lambda x: func(x, window, minp=minp, args=args, kwargs=kwargs,
--> 341 **kwds)
342 return_hook, values = _process_data_structure(arg)
343 # actually calculate the moment. Faster way to do this?
C:\Python34\lib\site-packages\pandas\stats\moments.py in call_cython(arg, window, minp, args, kwargs, **kwds)
591 def call_cython(arg, window, minp, args=(), kwargs={}, **kwds):
592 minp = check_minp(minp, window)
--> 593 return func(arg, window, minp, **kwds)
594 return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
595 center=center, how=how, **kwargs)
C:\Python34\lib\site-packages\pandas\algos.pyd in pandas.algos.roll_sum (pandas\algos.c:25271)()
C:\Python34\lib\site-packages\pandas\algos.pyd in pandas.algos._check_minp (pandas\algos.c:16394)()
ValueError: min_periods (1) must be <= window (0)
In [3]: rolling_count(Series([]), window=3)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-040797e90b09> in <module>()
----> 1 rolling_count(Series([]), window=3)
C:\Python34\lib\site-packages\pandas\stats\moments.py in rolling_count(arg, window, freq, center, how)
188 converted = np.isfinite(values).astype(float)
189 result = rolling_sum(converted, window, min_periods=1,
--> 190 center=center) # already converted
191
192 # putmask here?
C:\Python34\lib\site-packages\pandas\stats\moments.py in f(arg, window, min_periods, freq, center, how, **kwargs)
593 return func(arg, window, minp, **kwds)
594 return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
--> 595 center=center, how=how, **kwargs)
596
597 return f
C:\Python34\lib\site-packages\pandas\stats\moments.py in _rolling_moment(arg, window, func, minp, axis, freq, center, how, args, kwa
rgs, **kwds)
345 result = np.apply_along_axis(calc, axis, values)
346 else:
--> 347 result = calc(values)
348
349 rs = return_hook(result)
C:\Python34\lib\site-packages\pandas\stats\moments.py in <lambda>(x)
339 arg = _conv_timerule(arg, freq, how)
340 calc = lambda x: func(x, window, minp=minp, args=args, kwargs=kwargs,
--> 341 **kwds)
342 return_hook, values = _process_data_structure(arg)
343 # actually calculate the moment. Faster way to do this?
C:\Python34\lib\site-packages\pandas\stats\moments.py in call_cython(arg, window, minp, args, kwargs, **kwds)
591 def call_cython(arg, window, minp, args=(), kwargs={}, **kwds):
592 minp = check_minp(minp, window)
--> 593 return func(arg, window, minp, **kwds)
594 return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
595 center=center, how=how, **kwargs)
C:\Python34\lib\site-packages\pandas\algos.pyd in pandas.algos.roll_sum (pandas\algos.c:25271)()
C:\Python34\lib\site-packages\pandas\algos.pyd in pandas.algos._check_minp (pandas\algos.c:16394)()
ValueError: min_periods (1) must be <= window (0)
In [5]: expanding_mean(Series([]))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-b131e71cc033> in <module>()
----> 1 expanding_mean(Series([]))
C:\Python34\lib\site-packages\pandas\stats\moments.py in f(arg, min_periods, freq, center, **kwargs)
825 return func(arg, window, minp, **kwds)
826 return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
--> 827 center=center, **kwargs)
828
829 return f
C:\Python34\lib\site-packages\pandas\stats\moments.py in _rolling_moment(arg, window, func, minp, axis, freq, center, how, args, kwa
rgs, **kwds)
345 result = np.apply_along_axis(calc, axis, values)
346 else:
--> 347 result = calc(values)
348
349 rs = return_hook(result)
C:\Python34\lib\site-packages\pandas\stats\moments.py in <lambda>(x)
339 arg = _conv_timerule(arg, freq, how)
340 calc = lambda x: func(x, window, minp=minp, args=args, kwargs=kwargs,
--> 341 **kwds)
342 return_hook, values = _process_data_structure(arg)
343 # actually calculate the moment. Faster way to do this?
C:\Python34\lib\site-packages\pandas\stats\moments.py in call_cython(arg, window, minp, args, kwargs, **kwds)
823 def call_cython(arg, window, minp, args=(), kwargs={}, **kwds):
824 minp = check_minp(minp, window)
--> 825 return func(arg, window, minp, **kwds)
826 return _rolling_moment(arg, window, call_cython, min_periods, freq=freq,
827 center=center, **kwargs)
C:\Python34\lib\site-packages\pandas\algos.pyd in pandas.algos.roll_mean (pandas\algos.c:25815)()
C:\Python34\lib\site-packages\pandas\algos.pyd in pandas.algos._check_minp (pandas\algos.c:16394)()
ValueError: min_periods (1) must be <= window (0)
```
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} | 1 | 2014-08-18T16:10:32Z | 2014-08-19T20:33:43Z | 2014-08-19T20:33:43Z | NONE | null | On pandas.pydata.org, the download link for 0.14.1 in the Official Release box on the right hand side of the page sends users to the download page for 0.14.0. Manually changing the URL to 0.14.1 will access the correct page, so the download link is just broke.
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} | 6 | 2014-08-18T17:01:11Z | 2014-08-19T15:12:53Z | 2014-08-19T15:12:53Z | CONTRIBUTOR | null | Binary operations between two series fail if the .name attribute of the first is a Timestamp and the .name of the second is a numpy.int64.
It seems a corner case, but it's pretty common when the series are slices of dataframes.
It happens independently if, say, + or .add() is used.
It doesn't happen if the order is reversed.
I guess it boils down to Timestamps not having a sufficiently wide **equal** method.
``` python
In [1]: import pandas as pd
In [2]: pd.__version
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-2d9155319574> in <module>()
----> 1 pd.__version
AttributeError: 'module' object has no attribute '__version'
In [3]: df = pd.DataFrame(randn(5,2))
In [4]: a = df[0]
In [5]: b = pd.Series(randn(5))
In [6]: b.name = pd.Timestamp('2000-01-01')
In [7]: a / b
Out[7]:
0 -1.019117
1 -1.455596
2 0.716716
3 -0.052173
4 -10.725603
dtype: float64
In [8]: b / a
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-fc1551a96997> in <module>()
----> 1 b / a
/home/ldeleo/venv/test/lib/python2.7/site-packages/pandas/core/ops.pyc in wrapper(left, right, name)
488 if isinstance(rvalues, pd.Series):
489 rindex = getattr(rvalues,'index',rvalues)
--> 490 name = _maybe_match_name(left, rvalues)
491 lvalues = getattr(lvalues, 'values', lvalues)
492 rvalues = getattr(rvalues, 'values', rvalues)
/home/ldeleo/venv/test/lib/python2.7/site-packages/pandas/core/common.pyc in _maybe_match_name(a, b)
2927 a_name = getattr(a, 'name', None)
2928 b_name = getattr(b, 'name', None)
-> 2929 if a_name == b_name:
2930 return a_name
2931 return None
/home/ldeleo/venv/test/lib/python2.7/site-packages/pandas/tslib.so in pandas.tslib._Timestamp.__richcmp__ (pandas/tslib.c:12147)()
TypeError: Cannot compare type 'Timestamp' with type 'int64'
```
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Closes https://github.com/pydata/pandas/issues/8080
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| null | 3 | 2014-08-18T17:58:26Z | 2014-10-02T12:26:12Z | 2014-10-02T12:26:12Z | CONTRIBUTOR | null | I discovered that `store.put('key', df)` takes a whopping 40X more storage than `store('key', df, format='table')` for my DataFrames of floats. (See soft-matter/trackpy#144 for one demonstration.)
1. Am I doing it wrong? Is cost difference really that high?
2. I see that the read/write speed differences (which favor fixed-format) are documented, but I can't find mention of the storage requirements.
For my purposes, and for this issue, documenting it is sufficient. But if someone understands why the difference is so large, I'd be interested in that too.
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| null | 6 | 2014-08-18T18:32:43Z | 2015-04-08T17:19:04Z | 2015-04-08T15:21:33Z | CONTRIBUTOR | null | Currently CustomBusinessDay requires a strict format for holiday definition
```
CustomBusinessDay(holidays=['2014-05-12'])
```
but the following would raise a ValueError:
```
CustomBusinessDay(holidays=['5/12/2014'])
```
this PR makes the date parsing easier and adds a unit test
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} | 8 | 2014-08-18T20:37:40Z | 2014-09-15T21:49:48Z | 2014-08-22T07:32:12Z | CONTRIBUTOR | null | Large dataframes may fail to write to a database in one go due to packet size errors . This add a `chunksize` parameter which will write the dataframe to the database in batches. See https://github.com/pydata/pandas/issues/7347 .
Notice that there is quite a bit of code duplication between the SQLAlchemy and legacy APIs. This could be factored out if desired at the cost of a few private methods.
Closes #7347
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} | 11 | 2014-08-18T21:09:16Z | 2015-01-25T23:40:01Z | 2015-01-25T23:40:01Z | NONE | null | closes: https://github.com/pydata/pandas/issues/7879
I've added a warning when the <code>NaN</code> values are dropped when calling <code>pd.Panel(dict).to_frame()</code> with the param <code>filter_observations</code> true
I've supplied a test case to be used against the example provided in the issue tracker
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} | 1 | 2014-08-18T22:17:28Z | 2014-09-17T18:03:54Z | 2014-09-17T18:03:54Z | CONTRIBUTOR | null | Though it's not mentioned in the doc strings, `expanding/rolling_std/var` support a `ddof` parameter, while `expanding/rolling_cov` do not (they effectively assume `ddof=1`).
I think it would be best if all these functions supported an explicit (i.e. documented) `ddof` parameter that defaults to `1`.
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} | 10 | 2014-08-18T22:55:38Z | 2018-07-06T22:58:28Z | 2018-07-06T22:58:12Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/25372877/python-pandas-groupby-and-qcut-doesnt-work-in-0-14-1
```
df = pd.DataFrame({'x': np.random.rand(20), 'grp': ['a'] * 10 + ['b'] * 10})
```
worked in 0.13 (but returns a 2 element series with a list)
```
df.groupby('grp')['x'].transform(pd.qcut, 3)
```
much cleaner.
```
df.groupby('grp')['x'].apply(lambda x: pd.Series(pd.qcut(x,3), index=x.index))
```
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} | 0 | 2014-08-19T08:55:56Z | 2014-09-14T15:19:01Z | 2014-09-14T14:13:25Z | CONTRIBUTOR | null | The wheel build with https://github.com/MacPython/pandas-wheels
```
# file: pandas-0.14.1.dist-info/WHEEL
Wheel-Version: 1.0
Generator: bdist_wheel (0.24.0)
Root-Is-Purelib: false
Tag: cp27-none-macosx_10_6_intel
```
Although it was renamed to `macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64`, the real
tag didn't change.
This patch would fix the `Tag` like what I did in [mistune](https://github.com/lepture/mistune)
```
# file: mistune-0.4.dist-info/WHEEL
Wheel-Version: 1.0
Generator: bdist_wheel (0.24.0)
Root-Is-Purelib: false
Tag: cp27-none-macosx_10_6_intel
Tag: cp27-none-macosx_10_9_intel
Tag: cp27-none-macosx_10_9_x86_64
```
Related: http://lepture.com/en/2014/python-on-a-hard-wheel
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| null | 1 | 2014-08-19T13:07:33Z | 2014-08-19T13:21:25Z | 2014-08-19T13:21:21Z | CONTRIBUTOR | null | {
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| null | 0 | 2014-08-19T13:32:17Z | 2014-08-22T21:31:16Z | 2014-08-19T13:36:12Z | MEMBER | null | Fix release note #8019, #8039
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} | 1 | 2014-08-19T14:10:00Z | 2014-08-22T21:31:29Z | 2014-08-19T14:18:39Z | MEMBER | null | Current doc has no explanation, and incorrectly linked to `scatter_matrix`.
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} | 0 | 2014-08-19T14:16:29Z | 2014-08-19T15:12:53Z | 2014-08-19T15:12:53Z | CONTRIBUTOR | null | closes #8058
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} | 1 | 2014-08-19T14:47:28Z | 2014-09-18T17:16:32Z | 2014-09-18T17:16:32Z | CONTRIBUTOR | null | Once https://github.com/pydata/pandas/pull/7736 is merged, we can add this option
```
df.plot(subplots=True, layout=(4, -1)
```
This will behave similar to numpy's `reshape` method, except that we won't require the number of subplots generated by layout to exactly match the number of subplots in `df`. We'll just fill in the smallest integer that' sable to hold the subplots.
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} | 3 | 2014-08-19T14:50:00Z | 2017-06-10T21:59:33Z | 2017-06-10T21:59:33Z | CONTRIBUTOR | null | Currently (in v0.14.1) `rolling_mean(s, window=-1)` produces (the somewhat inelegant) error message `ValueError: min_periods must be >= 0`. It would be better to test explicitly for negative (non-positive?) `window`, and produce a more direct error message.
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} | 6 | 2014-08-19T15:49:29Z | 2014-08-22T15:42:25Z | 2014-08-20T10:52:34Z | CONTRIBUTOR | null | StataReader does not make use of vectorized operations.
To improve performance, the following changes have been made:
- Use numpy.frombuffer to real the stored data in a single operation
- Vectorize date conversion
- Removal of unreachable private functions
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} | 58 | 2014-08-19T23:13:41Z | 2014-09-25T22:16:35Z | 2014-09-25T22:16:35Z | MEMBER | null | I finally had some time to read up on the discussions and to look at the implementation of the Categoricals integration, and have still some questions and comments. I am sorry that this is rather late to the party, but I still think this is important to discuss (and I certainly don't want to disregard the really great work @JanSchulz and @jreback put in this! Thanks a lot for that!).
It is mainly about the public interface, and not about the internals.
So below I summed some remarks. And to be clear, just some personal ideas to discuss!
## 1. The `Categorical` object vs the 'category' `Series`
Previously, there was already the `Categorical` class:
```
In [1]: pd.Categorical(["a","b","c","a"])
Out[1]:
a
b
c
a
Levels (3, object): [a < b < c]
```
Now, you can also put this in a `Series`:
```
In [2]: pd.Series(pd.Categorical(["a","b","c","a"]))
Out[2]:
0 a
1 b
2 c
3 a
dtype: category
Levels (3, object): [a < b < c]
```
To create this Series, you can either put an existing `Categorical` inside a Series (as above, or by assigning it to a column of DataFrame, like `df['cat'] = pd.Categorical(["a","b","c","a"])`), or you can convert an existing `Series` to the 'category' dtype:
```
pd.Series(["a","b","c","a"]).astype('category')
```
So basically, there are now two different main objects to deal with categorical values (the `Categorical` object and the `Series` of 'category' dtype), which are also used both and mixed in the docs.
This raises the question if this is needed? (also touced lightly here: ). Some remarks:
- The constructor `pd.Series(pd.Categorical(...))` is a bit cumbersome I think
- In the documentation both Categorical and category Series are used. But what is the difference between both? (in user facing interaction) What is the advantage of one above the other, and in what circumstances? Why should I use a `Categorical` and not a category Series?
- Going further, are both possibilities needed (in user facing API, docs, etc)? Why not just using always a Series with `category` dtype?
I'v also found in the discusion the following by @JanSchulz (sorry if I misquote you guys :-)) (https://github.com/pydata/pandas/pull/8007#issuecomment-51985421):
> I've not found a usecase which would need to touch categoricals instead of Series(Categorical(...)))
and response of @njsmith to that:
> I don't really care what the data type for holding categorical data is, but I can certainly see the advantage of having just one data type. And if so then Series seems like a good choice for that.
So **proposal**: just use `Series` with 'category' dtype in all user facing API/functions and documentation.
## 2. Naming issues (levels, labels, codes, categories, ..)
The concepts of a categorical:
- `codes`: numerical representation (previously called `labels`)
- `levels`: descriptive names
For `codes`, I think this is indeed much better, as `labels` was very confusing (and more logical the labels would be the different values inside `levels` ..), and it has the advantage of the same name as in R.
But, the name `levels` is somewhat more problematic IMHO.
`level` has already another and established meaning in pandas, namely the different levels of a hierarchical MultiIndex. In many methods, you have a `level=..` keyword, and there are a lot of index methods to handle levels like `reorder_levels`, `droplevel`.
```
In [14]: pd.MultiIndex.from_product([['a', 'b'],[1,2]], names=['A', 'B'])
Out[14]:
MultiIndex(levels=[[u'a', u'b'], [1, 2]],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
names=[u'A', u'B'])
```
Here, there are two levels, a "first" and a "second level". While in a categorical context, the "first level" from above would consist of two levels, namely 'a' and 'b' ...
**Possible outcomes**
What are the 'things' we call now 'levels'. It are the different classes, or the different categories that are possible within the `Categorical` series. So maybe 'classes' or 'categories' would be an alternative name? So in that regard I like the proposal of @immerrr (https://github.com/pydata/pandas/pull/7217#issuecomment-47496897):
> As for naming, I like the name "codes" for the numerical representation and would like to propose "categories" for the descriptive names.
@JanSchulz responded:
> I wouldn't change the name levels to keep that aspect of R's factors.
A good argument, but I personally think the possible confusion between `df.index.reorder_levels` and `df.cat.reorder_levels` is important enough to reconsider this. Certainly when we would have eg a CategoricalIndex in the future, then `reorder_levels` will become totally dubious ...
So, at least I would go with `categorical_levels` to make the distinction (as mentioned by @JanSchulz here: https://github.com/pydata/pandas/pull/7217#issuecomment-45412477), or go with another name like `categories`.
## 3. Return type of `Series.values`
At the moment, when you have a Series with 'category' dtype, `Series.values` will return the `Categorical` object, and not a numpy array:
- This seems not very consistent with the other dtypes.
- The documentation of `Series.values` is also very clear on that : "returns Series as numpy.ndarray" (and this is also how it is printed in my head)
- What is a good reason to deviate from this rule? (certainly if you can have eg the `s.cat` attribute to return it)
Of course, if we would want to return a numpy array, it would have to be decided what it should return (eg what is returned now from `np.asarray()`). You loose information with this (the levels), and I suppose this is the reason to return a Categorical?. But I personally find the consistency more important here, certainly if you can do everything with the Series what you can do with the categorical (discussion above).
@JanSchulz @jreback @jseabold @njsmith @immerrr @cpcloud @hayd
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} | 8 | 2014-08-20T00:13:51Z | 2014-08-21T21:33:10Z | 2014-08-21T21:33:10Z | CONTRIBUTOR | null | xref #8074
closes #8076
```
In [1]: Series([1,1,2,2,3,4,5],dtype='category')
Out[1]:
0 1
1 1
2 2
3 2
4 3
5 4
6 5
dtype: category
Levels (5, int64): [1 < 2 < 3 < 4 < 5]
```
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} | 2 | 2014-08-20T07:35:05Z | 2014-08-21T21:33:10Z | 2014-08-21T21:33:10Z | MEMBER | null | ```
>>> import pandas as pd
>>> pd.DataFrame({'x': pd.Series(['a', 'b', 'c'])}, index=pd.date_range('20000101', periods=3))
x
2000-01-01 NaN
2000-01-02 NaN
2000-01-03 NaN
>>> df = pd.DataFrame({'x': pd.Series(pd.Categorical(['a', 'b', 'c']))}, index=pd.date_range('20000101', periods=3))
>>> df
<repr(<pandas.core.frame.DataFrame at 0x107f09f50>) failed: IndexError: Out of bounds on buffer access (axis 0)>
>>> df.values
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-13-e8bb9a30bd4f> in <module>()
----> 1 df.values
/Users/shoyer/dev/pandas/pandas/core/generic.py in values(self)
2071 int32.
2072 """
-> 2073 return self.as_matrix()
2074
2075 @property
/Users/shoyer/dev/pandas/pandas/core/generic.py in as_matrix(self, columns)
2053 self._consolidate_inplace()
2054 if self._AXIS_REVERSED:
-> 2055 return self._data.as_matrix(columns).T
2056 return self._data.as_matrix(columns)
2057
/Users/shoyer/dev/pandas/pandas/core/internals.py in as_matrix(self, items)
2676
2677 if self._is_single_block or not self.is_mixed_type:
-> 2678 return mgr.blocks[0].get_values()
2679 else:
2680 return mgr._interleave()
/Users/shoyer/dev/pandas/pandas/core/internals.py in get_values(self, dtype)
1079 def get_values(self, dtype=None):
1080 """ need to to_dense myself (and always return a ndim sized object) """
-> 1081 values = self.values.to_dense()
1082 if values.ndim == self.ndim - 1:
1083 values = values.reshape((1,) + values.shape)
/Users/shoyer/dev/pandas/pandas/core/categorical.py in to_dense(self)
683 def to_dense(self):
684 """ Return my 'dense' repr """
--> 685 return np.asarray(self)
686
687 def fillna(self, fill_value=None, method=None, limit=None, **kwargs):
/Users/shoyer/miniconda/envs/pandas-dev/lib/python2.7/site-packages/numpy/core/numeric.pyc in asarray(a, dtype, order)
458
459 """
--> 460 return array(a, dtype, copy=False, order=order)
461
462 def asanyarray(a, dtype=None, order=None):
/Users/shoyer/dev/pandas/pandas/core/categorical.py in __array__(self, dtype)
484 dtype as categorical.levels.dtype
485 """
--> 486 ret = com.take_1d(self.levels.values, self._codes)
487 if dtype and dtype != self.levels.dtype:
488 return np.asarray(ret, dtype)
/Users/shoyer/dev/pandas/pandas/core/common.py in take_nd(arr, indexer, axis, out, fill_value, mask_info, allow_fill)
805 axis=axis, mask_info=mask_info)
806
--> 807 func(arr, indexer, out, fill_value)
808
809 if flip_order:
/Users/shoyer/dev/pandas/pandas/algos.so in pandas.algos.take_1d_object_object (pandas/algos.c:78943)()
IndexError: Out of bounds on buffer access (axis 0)
```
This is on master.
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} | 19 | 2014-08-20T07:47:55Z | 2014-09-29T13:04:23Z | 2014-09-25T22:16:08Z | MEMBER | null | xref #8074. Some issues about `pd.cut/qcut` (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html#pandas.cut):
- If the input is a Series, also return a Series?
- The produced categorical is not ordered, while the `Categorical` constructor is by default. Also in the case of `cut` it seems logical that it would be ordered?
- The docstring of cut makes use of `labels`, but I think here the individual categories are meant, and not the numerical representation (codes) (still another use of `labels`, good we changed that name! :-)), but I am not fully sure of the explanation:
- "Labels to use for bin edges" -> why the 'edges'? Are it not just the labels for the bins itself? (this sets what we now call the 'levels'):
```
In [23]: pd.cut([1,2,3,4], bins=3, labels=['a', 'b', 'c'])
Out[23]:
a
a
b
c
Levels (3, object): [a, b, c]
```
- "or False to return integer bin labels" -> this is what now is called 'codes'?
- should we rename this to `levels/categories`?
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} | 3 | 2014-08-20T13:54:44Z | 2014-09-28T11:10:48Z | 2014-09-25T22:17:05Z | CONTRIBUTOR | null | Docs:
- [x] `np.asarray(s2.cat)` -> `np.asarray(s2)`
- [x] `Series(.....dtype='categorical')` example, #8075
- [x] Add docstring about internal useage to to_dense and from_array
- [x] go through the docs and change levels -> categories
- [x] clarify docs on usage of `Categorical` (public API restricted to creation (to specify levels/ordered, or to put in dataframe) don't use its methods/properties (-> `s.cat` is for that), apart from `from_codes` and maybe in a "under the hood" section )
- [x] underline in categoriecal.rst
Code
- [x] `cut` fixups, see #8077
- [x] change `__repr__` of `Categorical` (https://github.com/pydata/pandas/issues/8074#issuecomment-52778675)
- [x] rename `levels` -> `categories`; add FutureWarning if levels is used in constructor and accessor
- [x] `reorder_levels` and `remove_unused_levels` not inplace by default (and add `inplace` keyword, default False)
- [x] `s.unique()` should return an array instead of index (-> np.asarray(self.levels))
- [x] Add `Series.cat.codes` (wrapped into a Series)
- [x] remove `compat` kwarg (+ update docs on this -> use `from_codes`)
- [x] add rename/add/remove/reorder only methods and a "takes it all" method (probably `set_levels(new_levels, rename=False)`
- [x] make assigning to `categories` strict (no additions/removals)
- [x] fix for Problem in https://github.com/pydata/pandas/issues/8138
- [x] Failing Test on python 3.4
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} | 12 | 2014-08-20T15:27:40Z | 2014-08-22T15:42:17Z | 2014-08-21T14:26:34Z | CONTRIBUTOR | null | StataWriter wrote data using scalar operations. This has been replaced using
numpy's internal binary writer (tofile).
Changes needed to improve performance include:
- Vectorized pandas date to Stata date conversion
- Vectorized null padding
- Conversion to record array
When data contain strings, the old writing paths are still used.
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| null | 0 | 2014-08-20T17:23:17Z | 2014-08-28T17:34:49Z | 2014-08-28T17:34:49Z | CONTRIBUTOR | null | `expanding/rolling_apply()` interpret `min_periods=0` as `min_periods=1`. This is _usually_ ok, as almost every function will return `NaN` when the inputs are all `NaN`. However, this is not true, for example, of functions like `len()`.
For example, I think that in the following calls, the results should be `1, 2, 2` and `1, 2, 3`, respectively.
```
In [409]: rolling_apply(Series([None, None, None]), 2, lambda x: len(x), min_periods=0)
Out[409]:
0 NaN
1 NaN
2 NaN
dtype: float64
In [411]: expanding_apply(Series([None, None, None]), lambda x: len(x), min_periods=0)
Out[411]:
0 NaN
1 NaN
2 NaN
dtype: float64
```
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} | 1 | 2014-08-20T17:33:54Z | 2014-09-05T16:16:34Z | 2014-09-05T16:16:34Z | MEMBER | null | This example works on 0.14.1 but not master:
```
%matplotlib inline
import numpy as np
import pandas as pd
np.random.seed(12345)
df = pd.DataFrame(np.random.rand(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
df.plot(x='a', y='b', xerr='c', yerr='d', kind='scatter')
```
I get:
```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-1-38e437a109be> in <module>()
4 np.random.seed(12345)
5 df = pd.DataFrame(np.random.rand(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
----> 6 df.plot(x='a', y='b', xerr='c', yerr='d', kind='scatter')
/Users/shoyer/dev/pandas/pandas/tools/plotting.pyc in plot_frame(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logx, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, layout, **kwds)
2223 secondary_y=secondary_y, layout=layout, **kwds)
2224
-> 2225 plot_obj.generate()
2226 plot_obj.draw()
2227 return plot_obj.result
/Users/shoyer/dev/pandas/pandas/tools/plotting.pyc in generate(self)
911 self._compute_plot_data()
912 self._setup_subplots()
--> 913 self._make_plot()
914 self._add_table()
915 self._make_legend()
/Users/shoyer/dev/pandas/pandas/tools/plotting.pyc in _make_plot(self)
1402
1403 errors_x = self._get_errorbars(label=x, index=0, yerr=False)
-> 1404 errors_y = self._get_errorbars(label=y, index=1, xerr=False)
1405 if len(errors_x) > 0 or len(errors_y) > 0:
1406 err_kwds = dict(errors_x, **errors_y)
/Users/shoyer/dev/pandas/pandas/tools/plotting.pyc in _get_errorbars(self, label, index, xerr, yerr)
1362 err = None
1363 elif index is not None and err is not None:
-> 1364 err = err[index]
1365
1366 if err is not None:
IndexError: index 1 is out of bounds for axis 0 with size 1
```
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} | 2 | 2014-08-20T19:46:36Z | 2014-09-13T22:47:12Z | 2014-09-13T22:47:12Z | CONTRIBUTOR | null | I'm not sure if this qualifies as a bug, but it is a change that occurred between 0.13.1 and 0.14.1. In 0.14.1, if I have a DataFrame with a column named 'labels', I get the following error when trying to access it as an attribute of the DataFrame:
```
>>> df = pandas.DataFrame(columns=['labels'])
>>> df['labels']
Series([], name: labels, dtype: object)
>>> df.labels
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/<virtual_env>/local/lib/python2.7/site-packages/pandas/core/generic.py", line 1836, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'labels'
```
In 0.13.1, this code did not throw an error. This is with Python 2.7.6.
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} | 23 | 2014-08-20T20:23:36Z | 2014-09-14T10:42:50Z | 2014-09-11T06:33:17Z | CONTRIBUTOR | null | Unfortunately my PR #8022 introduced a bug wherein database indexes are not created from DataFrame indexes. This pull request fixes it, also introduces this feature for the legacy interface, and adds tests to make sure indexes are created as appropriate.
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} | 4 | 2014-08-20T23:36:45Z | 2017-05-03T10:11:25Z | 2017-05-03T10:11:18Z | CONTRIBUTOR | null | It appears that `Series.quantile()` interpolates between the two bracketing values, whereas `expanding/rolling_quantile()` don't.
```
In [496]: x = Series([0,1])
In [497]: x.quantile(0.3)
Out[497]: 0.29999999999999999
In [498]: expanding_quantile(x, 0.3)
Out[498]:
0 0
1 0
dtype: float64
In [499]: rolling_quantile(x, 2, 0.3, min_periods=1)
Out[499]:
0 0
1 0
dtype: float64
```
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} | 17 | 2014-08-21T01:13:34Z | 2018-07-06T22:56:35Z | 2018-07-06T22:56:29Z | CONTRIBUTOR | null | Note that for a constant series, `Series.skew()` returns `0`, while `rolling/expanding_skew()` return `NaN`.
```
In [532]: s = Series([1]*4)
In [533]: s.skew()
Out[533]: 0
In [534]: expanding_skew(s)
Out[534]:
0 NaN
1 NaN
2 NaN
3 NaN
dtype: float64
In [535]: rolling_skew(s, 4)
Out[535]:
0 NaN
1 NaN
2 NaN
3 NaN
dtype: float64
```
While `rolling/expanding_kurt()` similarly return `NaN` for a constant series, `Series.kurt()` produces an error.
```
In [541]: expanding_kurt(s)
Out[541]:
0 NaN
1 NaN
2 NaN
3 NaN
dtype: float64
In [542]: rolling_kurt(s, 4)
Out[542]:
0 NaN
1 NaN
2 NaN
3 NaN
dtype: float64
In [543]: s.kurt()
---------------------------------------------------------------------------
ZeroDivisionError Traceback (most recent call last)
<ipython-input-543-e41f2ec435bd> in <module>()
----> 1 s.kurt()
C:\Python34\lib\site-packages\pandas\core\generic.py in stat_func(self, axis, skipna, level, numeric_only, **kwargs)
3783 skipna=skipna)
3784 return self._reduce(f, axis=axis,
-> 3785 skipna=skipna, numeric_only=numeric_only)
3786 stat_func.__name__ = name
3787 return stat_func
C:\Python34\lib\site-packages\pandas\core\series.py in _reduce(self, op, axis, skipna, numeric_only, filter_type, **kwds)
2007 filter_type=None, **kwds):
2008 """ perform a reduction operation """
-> 2009 return op(_values_from_object(self), skipna=skipna, **kwds)
2010
2011 def _reindex_indexer(self, new_index, indexer, copy):
C:\Python34\lib\site-packages\pandas\core\nanops.py in _f(*args, **kwargs)
46 'this dtype'.format(f.__name__.replace('nan',
47 '')))
---> 48 return f(*args, **kwargs)
49 return _f
50
C:\Python34\lib\site-packages\pandas\core\nanops.py in nankurt(values, axis, skipna)
504 D = _zero_out_fperr(D)
505
--> 506 result = (((count * count - 1.) * D / (B * B) - 3 * ((count - 1.) ** 2)) /
507 ((count - 2.) * (count - 3.)))
508 if isinstance(result, np.ndarray):
ZeroDivisionError: float division by zero
```
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} | 5 | 2014-08-21T05:42:51Z | 2014-08-21T08:08:31Z | 2014-08-21T08:07:28Z | CONTRIBUTOR | null | Example:
``` python
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
engine = create_engine('mssql+pyodbc://localhost\\sqlexpress/test')
x1 = pd.DataFrame(np.random.rand(5,3))
x2 = pd.DataFrame(np.random.rand(5,3), columns=['0', '1', '2'])
x1.to_sql('x1', con=engine)
x2.to_sql('x2', con=engine)
```
which creates these two tables with same column names (numbers as string) but different contents:
**x1**:
```
index 0 1 2
0 NULL NULL NULL
1 NULL NULL NULL
2 NULL NULL NULL
3 NULL NULL NULL
4 NULL NULL NULL
```
**x2**:
```
index 0 1 2
0 0.749669142411769 0.120300003868206 0.424437055115581
1 0.205079144551286 0.977461912401449 0.480845560482874
2 0.635489979788438 0.0149051603743131 0.469838520671857
3 0.765853984144486 0.927480038225488 0.665683952437241
4 0.0688135748993546 0.990064095395479 0.921328636417753
```
So `x1` table and `x2` table have same column names, and as expected I can change the values in `x1` with sql query, but pandas seems to failed in instructing sqlalchemy to write the data in `x1` columns.
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| null | 7 | 2014-08-21T08:23:46Z | 2014-08-21T12:31:02Z | 2014-08-21T12:21:08Z | CONTRIBUTOR | null | This issue is covered at several places, but it doesn't seem there is a solution for mssql.
Referencing https://github.com/pydata/pandas/issues/4199
Example:
``` python
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
pg_engine = create_engine('postgresql://postgres:*pass*@localhost:5432/test')
ms_engine = create_engine('mssql+pyodbc://localhost\\sqlexpress/test')
df = pd.DataFrame(np.random.rand(5,3))
df = df.where(df < .5, None)
df.to_sql('x', con=pg_engine)
df.to_sql('x', con=ms_engine)
```
This script created successfully postgres table but fails at mssql:
```
DataError: (DataError) (
'22018', '[22018] [Microsoft][ODBC SQL Server Driver][SQL Server]
Operand type clash: float is incompatible with text (206) (SQLExecDirectW);
[42000] [Microsoft][ODBC SQL Server Driver][SQL Server]
Statement(s) could not be prepared. (8180)'
)
u'INSERT INTO x ([index], [0], [1], [2]) VALUES (?, ?, ?, ?)' (
(0L, 0.30160831834724133, None, 0.3381303856312915),
(1L, 0.15675955874181347, None, None),
(2L, None, None, 0.4913946391341001),
(3L, 0.47313398459868405, 0.41395730103112083, 0.024404123661447397),
(4L, 0.02599871607241755, 0.0038944154716251678, None)
)
```
Full trace: https://gist.github.com/51dd8360a938dece9f28
pandas: 0.14.1
sqlalchemy: 0.9.7
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} | 4 | 2014-08-22T09:41:46Z | 2014-08-28T07:06:21Z | 2014-08-28T07:03:43Z | MEMBER | null | Closes #6932
Support for writing `datetime.date` and `datetime.time` object columns with `to_sql`.
Works nicely for the sqlalchemy mode, for sqlite fallback it writes OK for `datetime.date`, but comes back as object strings (this is also the case for datetime)
Remaining question:
- when reading the data back in `TIME` columns are converted back to `datetime.time`, but `DATE` columns are converted to `datetime64` and not `datetime.date`. Is this OK? Or better try to convert to `datetime.date`?
- `datetime.time` does not work with sqlite fallback for some reason (`datetime.date` does)
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} | 1 | 2014-08-22T12:10:07Z | 2014-08-25T05:47:44Z | 2014-08-25T05:47:44Z | CONTRIBUTOR | null | These are failing on 34-64 and 33-32
```
======================================================================
ERROR: test_boxplot (pandas.tests.test_graphics.TestDataFrameGroupByPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-3.3\pandas\tests\test_graphics.py", line 2629, in test_boxplot
self._check_axes_shape(axes.values(), axes_num=2, layout=(1, 2))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-3.3\pandas\tests\test_graphics.py", line 302, in _check_axes_shape
visible_axes = self._flatten_visible(axes)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-3.3\pandas\tests\test_graphics.py", line 337, in _flatten_visible
axes = [ax for ax in axes if ax.get_visible()]
TypeError: iteration over a 0-d array
======================================================================
ERROR: test_grouped_box_multiple_axes (pandas.tests.test_graphics.TestDataFrameGroupByPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-3.3\pandas\tests\test_graphics.py", line 2827, in test_grouped_box_multiple_axes
self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-3.3\pandas\tests\test_graphics.py", line 302, in _check_axes_shape
visible_axes = self._flatten_visible(axes)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-3.3\pandas\tests\test_graphics.py", line 337, in _flatten_visible
axes = [ax for ax in axes if ax.get_visible()]
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-3.3\pandas\tests\test_graphics.py", line 337, in <listcomp>
axes = [ax for ax in axes if ax.get_visible()]
AttributeError: 'ValuesView' object has no attribute 'get_visible'
----------------------------------------------------------------------
Ran 7627 tests in 1222.636s
FAILED (SKIP=368, errors=2)
C:\Users\Jeff Reback\Documents\GitHub\pandas>more versions.33-32.log
INSTALLED VERSIONS
------------------
commit: None
python: 3.3.3.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.12.0
nose: 1.3.0
Cython: 0.19.2
numpy: 1.8.0
scipy: 0.13.1
statsmodels: 0.5.0
IPython: 1.1.0
sphinx: None
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.8
bottleneck: 0.7.0
tables: 3.0.0
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.3
pymysql: 0.6.1.None
psycopg2: 2.5.2 (dt dec pq3 ext)
```
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} | 2 | 2014-08-22T12:39:14Z | 2016-10-12T23:05:07Z | 2014-08-22T15:04:25Z | NONE | null | Using .at and .iat indexing with a Float64Index produces an error:
``` python
In [0]: import pandas as pd
In [1]: s = pd.Series([1,2,3], index = [0.1,0.2,0.3])
In [2]: s.at[0.1]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-9-a5888722fbca> in <module>()
----> 1 s.at[0.1]
/ifs/apps/apps/python-2.7.1/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key)
1264
1265 key = self._convert_key(key)
-> 1266 return self.obj.get_value(*key)
1267
1268 def __setitem__(self, key, value):
/ifs/apps/apps/python-2.7.1/lib/python2.7/site-packages/pandas/core/series.pyc in get_value(self, label)
778 value : scalar value
779 """
--> 780 return self.index.get_value(self.values, label)
781
782 def set_value(self, label, value):
/ifs/apps/apps/python-2.7.1/lib/python2.7/site-packages/pandas/core/index.pyc in get_value(self, series, key)
1821 k = _values_from_object(key)
1822 loc = self.get_loc(k)
-> 1823 new_values = series.values[loc]
1824 if np.isscalar(new_values):
1825 return new_values
AttributeError: 'numpy.ndarray' object has no attribute 'values'
In [3]: s.iat[1]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-10-db1ebf8cc8e6> in <module>()
----> 1 s.iat[1]
/ifs/apps/apps/python-2.7.1/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key)
1264
1265 key = self._convert_key(key)
-> 1266 return self.obj.get_value(*key)
1267
1268 def __setitem__(self, key, value):
/ifs/apps/apps/python-2.7.1/lib/python2.7/site-packages/pandas/core/series.pyc in get_value(self, label)
778 value : scalar value
779 """
--> 780 return self.index.get_value(self.values, label)
781
782 def set_value(self, label, value):
/ifs/apps/apps/python-2.7.1/lib/python2.7/site-packages/pandas/core/index.pyc in get_value(self, series, key)
1821 k = _values_from_object(key)
1822 loc = self.get_loc(k)
-> 1823 new_values = series.values[loc]
1824 if np.isscalar(new_values):
1825 return new_values
AttributeError: 'numpy.ndarray' object has no attribute 'values'
```
I'm not sure if this is intended behavoir. The docs only talk about [], loc, iloc and ix indexing with Float64Index, but if so, perhaps a more useful error message would be in order. I wasn't even aware my index was Float64. Its gotten changed at some point without me being aware of it.
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} | 15 | 2014-08-22T13:32:06Z | 2017-11-04T14:43:07Z | 2017-11-04T14:43:07Z | NONE | null | If `pd.read_sql` is used to load a data frame with the results of an SQL query that returns no results, the columns in the data frame will be of type 'object'. That type cannot be aggregated, so a subsequent group-by operation on that empty data frame will drop all the columns. So instead of 'profit' in the below example being an empty series, an attribute error is thrown because the columns 'revenue' and 'expenses' cannot be found in the data frame.
Two things I can think of that could fix this:
1) Have `pd.read_sql` populate the data frame with empty columns of the correct type even if the SQL query returns no results. Then the group-by would not drop the columns because they are of a type that can be aggregated.
2) Have an option in `groupby` to not drop columns of types that cannot be aggregated: maybe a drop_non_agg flag. I think not dropping columns of types that cannot be aggregated should be the default behaviour. Columns with data that cannot be aggregated can just be populated with null after a group-by.
I think 1) probably should be implemented, and 2) is kind of a design decision.
You can run this code to reproduce the issue.
``` python
import pandas as pd
import sqlite3 as lite
import sys
finance = (
(2, 132, 65),
(6, 142, 86),
(3, 183, 34),
(3, 147, 46)
)
con = lite.connect('test.db')
cur = con.cursor()
cur.execute("DROP TABLE IF EXISTS finance")
cur.execute("CREATE TABLE finance(day_of_week INT, revenue FLOAT, expenses FLOAT)")
cur.executemany("INSERT INTO finance VALUES(?, ?, ?)", finance)
# remove the 'WHERE' clause, and the error won't be thrown
my_query = '''
SELECT *
FROM finance
WHERE day_of_week = 5
'''
df = pd.read_sql(my_query, con)
df_gb = df[df.day_of_week == 5].groupby('day_of_week').sum().reset_index()
profit = df_gb.revenue - df_gb.expenses # AttributeError thrown here
```
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} | 2 | 2014-08-22T14:02:19Z | 2014-08-22T15:04:27Z | 2014-08-22T15:04:25Z | MEMBER | null | closes #8092
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| null | 2 | 2014-08-22T14:15:53Z | 2014-08-26T07:14:24Z | 2014-08-26T07:14:24Z | NONE | null | When saving dataframe to Excel, and using "xlsxwriter", it cannot create a second sheet. Althought the second sheet appears in "writer.sheets", the excel file created do not show it.
Python 2.7, Windows 7
``` python
>>> import pandas as pd
>>> print(pd.__version__)
0.14.1
>>> df=pd.DataFrame({'a': [1,2,3], 'b':[4,5,6]})
>>> writer = pd.ExcelWriter('try_xlsxwriter.xlsx')
>>> df.to_excel(writer, 'sheet1')
>>> writer.save() # ok, perfect
>>> df.to_excel(writer, 'sheet2')
>>> writer.save() # nothing appears in the Excel file!
>>> print 'engine: %s' % writer.engine
engine: xlsxwriter
>>> print 'sheets: %s'% writer.sheets.keys() # strangely, sheets look to be there though
sheets: ['sheet1', 'sheet2']
```
Using the "openpyxl" engine works as expected.
``` python
>>> writer = pd.ExcelWriter('try_openpyxl.xlsx', engine='openpyxl')
>>> df.to_excel(writer, 'sheet1')
>>> writer.save() # This works
>>> df.to_excel(writer, 'sheet2')
>>> writer.save() # This works too!
>>> print 'engine: %s' % writer.engine
engine: openpyxl
>>> print 'sheets: %s'% writer.sheets.keys()
sheets: ['sheet1', 'sheet2']
```
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} | 4 | 2014-08-22T18:47:30Z | 2015-03-08T17:01:20Z | 2015-03-08T17:01:11Z | NONE | null | Noticed that the pandas.io.data doesn't return the Data Frame on tickers having a '-' in the ticker name , for example: HINDUNILVR-EQ.NS, TATA-MOTORS.NS .
Any ideas ?
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} | 2 | 2014-08-22T19:50:23Z | 2014-09-15T21:43:01Z | 2014-09-15T21:43:01Z | MEMBER | null | - using `get_schema` in sqlalchemy mode (providing an engine) does also create the table, while just getting the create table string should not have any effects.
- the `get_schema` implementation for fallback duplicates a lot of code of `PandasSQLTableLegacy._create_table_statement`
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| null | 1 | 2014-08-23T02:40:35Z | 2014-08-30T01:18:56Z | 2014-08-25T05:47:44Z | MEMBER | null | Closes #8091.
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} | 4 | 2014-08-23T02:43:18Z | 2014-08-30T01:18:38Z | 2014-08-29T20:37:06Z | MEMBER | null | Closes #7993.
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} | 7 | 2014-08-23T03:00:19Z | 2014-09-13T22:47:12Z | 2014-09-13T22:47:00Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/8082
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