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} | 4 | 2015-07-30T14:12:21Z | 2015-08-04T17:38:52Z | 2015-08-04T17:38:52Z | CONTRIBUTOR | null | Thoughts on this?
``` python
In [8]: import pandas.util.testing as tm
In [9]: s = pd.Series(tm.makeCategoricalIndex(k=100))
In [10]: s.value_counts()
Out[10]:
vcKH 40
vXR9 31
Zn8J 29
dtype: int64
In [11]: s.value_counts().index
Out[11]: Index(['vcKH', 'vXR9', 'Zn8J'], dtype='object')
```
Now that we have `CategoricalIndex` (thanks Jeff), should that type be preserved so that `Out[11]` is a `CategoricalIndex`? My use-case (not shown in this example) is when the original categories are ordered, you get your `value_counts` and then want to sort the index.
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| null | 6 | 2015-07-30T18:58:10Z | 2015-07-30T20:56:50Z | 2015-07-30T19:04:36Z | NONE | null | Hello,
I have another issue with tuples. If you export and import a DataFrame to csv the tuples headers are transformed to string.
Here is an example of test:
``` python
import pandas as pd
from StringIO import StringIO
df_out = pd.DataFrame()
df_out['A'] = ['foo']
df_out['B', 1] = ['bar']
sio_out = StringIO()
df_out.to_csv(sio_out)
sio_in = StringIO(sio_out.getvalue())
df_in = pd.read_csv(sio_in)
assert df_in.columns.tolist() == df_out.columns.tolist()
```
Cheers,
Alexis.
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} | 3 | 2015-07-30T20:49:22Z | 2017-09-10T14:08:40Z | 2017-09-10T14:08:32Z | NONE | null | The output columns from `get_dummies()` are binary by nature, changing this default from float64 to int8 would save 7 bytes per dummy.
```
df=pd.DataFrame({ 'x':[1,2,3] })
pd.get_dummies(df.x)
```
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} | 15 | 2015-07-31T05:04:05Z | 2015-11-13T17:13:48Z | 2015-11-13T17:13:47Z | CONTRIBUTOR | null | parallel_coordinates are in pandas.tools.plotting
'black' color was hard-coded for vertical lines, I suggest to make it an option
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} | 6 | 2015-07-31T07:16:36Z | 2021-05-10T21:45:32Z | 2021-05-10T21:45:32Z | MEMBER | null | ```
In [40]: cols = pd.MultiIndex.from_product([['A', 'B', 'C'],[1,2]])
In [41]: df = pd.DataFrame(np.random.randn(5,6), columns=cols)
In [42]: df.loc[:, ['B', 'A']]
Out[42]:
A B
1 2 1 2
0 -0.990868 1.577803 1.023247 -0.423165
1 -1.180170 1.236143 -1.484085 0.044929
2 1.665502 -0.711081 0.227827 0.651859
3 -0.659154 0.154327 -1.548650 -0.070550
4 -0.232819 0.100959 -0.102296 0.260816
```
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} | 0 | 2015-07-31T11:03:02Z | 2015-08-02T17:07:09Z | 2015-08-02T17:07:09Z | CONTRIBUTOR | null | I thought had done this quite a while ago. I guess should show a deprecation warning, then remove in maybe next version. These have long been deprecated in the [docs](http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecations), but should actually be deprecated in calls (and of course removed from the test suite).
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} | 0 | 2015-07-31T15:50:00Z | 2016-02-27T15:09:04Z | 2016-02-27T15:09:04Z | MEMBER | null | `DatetimeIndex.tz_localize` preserves name.
```
import pandas as pd
idx = pd.date_range('2011-01-01', periods=5, freq='M', name='x')
idx
# DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-30', '2011-05-31'],
# dtype='datetime64[ns]', name=u'x', freq='M', tz=None)
idx.tz_localize('US/Eastern')
# DatetimeIndex(['2011-01-31 00:00:00-05:00', '2011-02-28 00:00:00-05:00',
# '2011-03-31 00:00:00-04:00', '2011-04-30 00:00:00-04:00',
# '2011-05-31 00:00:00-04:00'],
# dtype='datetime64[ns]', name=u'x', freq='M', tz='US/Eastern')
```
But `Series` doesn't. I don't check all methods which can return `Series` yet.
```
s = pd.Series(idx)
s
#0 2011-01-31
#1 2011-02-28
#2 2011-03-31
#3 2011-04-30
#4 2011-05-31
# Name: x, dtype: datetime64[ns]
# NG, name is reset
s2 = s.dt.tz_localize('US/Eastern')
s2
#0 2011-01-31 00:00:00-05:00
#1 2011-02-28 00:00:00-05:00
#2 2011-03-31 00:00:00-04:00
#3 2011-04-30 00:00:00-04:00
#4 2011-05-31 00:00:00-04:00
# dtype: object
# NG, name is reset
s2.name = 'x'
s2.dt.tz_convert('US/Pacific')
#0 2011-01-30 21:00:00-08:00
#1 2011-02-27 21:00:00-08:00
#2 2011-03-30 21:00:00-07:00
#3 2011-04-29 21:00:00-07:00
#4 2011-05-30 21:00:00-07:00
# dtype: object
# NG, name is reset
s.dt.normalize()
#0 2011-01-31
#1 2011-02-28
#2 2011-03-31
#3 2011-04-30
#4 2011-05-31
# dtype: datetime64[ns]
```
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} | 4 | 2015-07-31T15:54:00Z | 2015-08-08T19:43:55Z | 2015-08-08T19:43:55Z | MEMBER | null | Creating `Categorical` from `DatetimeIndex` with tz results in GMT.
```
import pandas as pd
idx = pd.date_range('2011-01-01', periods=5, freq='M', tz='US/Eastern')
idx
# DatetimeIndex(['2011-01-31 00:00:00-05:00', '2011-02-28 00:00:00-05:00',
# '2011-03-31 00:00:00-04:00', '2011-04-30 00:00:00-04:00',
# '2011-05-31 00:00:00-04:00'],
# dtype='datetime64[ns]', freq='M', tz='US/Eastern')
pd.Categorical(idx)
# [2011-01-31 05:00:00, 2011-02-28 05:00:00, 2011-03-31 04:00:00, 2011-04-30 04:00:00, 2011-05-31 # 04:00:00]
# Categories (5, datetime64[ns]): [2011-01-31 05:00:00, 2011-02-28 05:00:00, 2011-03-31 04:00:00
# , 2011-04-30 04:00:00, 2011-05-31 04:00:00]
```
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} | 3 | 2015-08-01T00:25:57Z | 2019-10-21T19:52:15Z | null | MEMBER | null | xref https://github.com/pydata/pandas/pull/10674#issuecomment-126837519
When using `:okexcept:`, limit the traceback to one line (or as a separate option + try to push to ipython upstream afterwards)
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} | 1 | 2015-08-01T01:56:20Z | 2015-08-18T11:22:55Z | 2015-08-18T10:51:57Z | MEMBER | null | Closes #10665. Closes #10741.
I'm adding some more tests for `Index.join`, and found a skeptic behavior.
For `right` join, all levels / labels of right side remains (OK).
```
import pandas as pd
midx = pd.MultiIndex.from_product([[0, 1], [0, 1, 2]], names=['a', 'b'])
idx = pd.Index([1, 2 ,3], name='b')
idx.join(midx, how='right')
# MultiIndex(levels=[[0, 1], [0, 1, 2]],
# labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],
# names=[u'a', u'b'])
idx.join(midx, how='right').values
# array([(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2)], dtype=object)
```
For `left` join, all levels are remain but not used as levels. Thus, values only on left side is dropped (NG).
```
idx.join(midx, how='left')
# MultiIndex(levels=[[0, 1], [1, 2, 3]],
# labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
# names=[u'a', u'b'])
idx.join(midx, how='left').values
array([(0, 1), (0, 2), (1, 1), (1, 2)], dtype=object)
```
I understand the last result must be the following. If my understanding is correct, I'll include the fix to this PR.
```
idx.join(midx, how='left')
# MultiIndex(levels=[[0, 1], [1, 2, 3]],
# labels=[[0, 0, 1, 1], [0, 1, 2, 0, 1, 2]],
# names=[u'a', u'b'])
idx.join(midx, how='left').values
# array([(0, 1), (0, 2), (0, 3), (1, 1), (1, 2), (1, 3)], dtype=object)
```
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} | 4 | 2015-08-01T10:52:31Z | 2015-08-31T13:54:03Z | 2015-08-31T13:53:59Z | MEMBER | null | Related to #8018. Because it touches lots of codes, I've split some cleanups required for #8018 which does NOT changes current functionality.
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} | 3 | 2015-08-01T14:48:50Z | 2015-08-08T19:44:00Z | 2015-08-08T19:43:55Z | MEMBER | null | Closes #10713.
- [x] Check timedelta
- [x] Refactor not to import `DatetimeIndex` and `PeriodIndex` if possible.
- [x] Fix `categories` line break location
- [x] Check `Categorical` `Series` formatting
- [x] Check `Categorical` `DataFrame` formatting
- [x] Fix `CategoricalIndex` formatting
## CategoricalIndex problem (befor this PR)
```
import pandas as pd
idx = pd.period_range('2011-01-01 09:00', freq='H', periods=5)
idx = pd.Index(pd.Categorical(idx))
idx
# CategoricalIndex([359409, 359410, 359411, 359412, 359413], categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00], ordered=False, dtype='category')
```
## Format differences (after this PR)
I left ordered category representation of `CategoricalIndex` as below because it has `ordered` repr separately.
```
pd.Categorical([1, 2, 3], ordered=True)
# [1, 2, 3]
# Categories (3, int64): [1 < 2 < 3]
pd.Series(pd.Categorical([1, 2, 3], ordered=True))
#0 1
#1 2
#2 3
# dtype: category
# Categories (3, int64): [1 < 2 < 3]
pd.CategoricalIndex([1, 2, 3], ordered=True)
# CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=True, dtype='category')
```
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} | 14 | 2015-08-01T16:01:55Z | 2020-07-02T09:26:32Z | null | CONTRIBUTOR | null | exceptions that are raised on unsuccessful datetime/timedelta parsing should add this:
`you can coerce to NaT by passing errors='coerce'`
comment at the end: https://github.com/pydata/pandas/pull/10674
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| null | 7 | 2015-08-01T20:49:57Z | 2015-08-01T21:38:33Z | 2015-08-01T21:12:42Z | CONTRIBUTOR | null | I'm (consistently) getting a `value is trying to be set on a copy of a slice...` error on the following bit of code, and can't figure out why:
```
i['gender'] = i.gender.replace({'male':'m', 'female':'f'})
```
The full error:
```
See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
"""Sympy configuration"""
/Users/Nick/anaconda/lib/python3.4/site-packages/spyderlib/widgets/externalshell/start_ipython_kernel.py:16: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
```
It seems dependent on something about the `i` DataFrame, but said DataFrame is way to big to include here (143 rows, 43 columns).
When I try and recreate this with a small example dataset, I don't get the error any more:
```
df = pd.DataFrame({'col1':['a','b','c'], 'col2':[0,1,2]})
df['col1'] = df.col1.replace({'b':'z', 'c':'z'})
```
Does anyone have any idea why this is happening?
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} | 18 | 2015-08-02T11:42:29Z | 2015-09-02T11:54:22Z | 2015-09-02T11:54:14Z | CONTRIBUTOR | null | closes #10698
Let the result of 'concat' to inherit the parent Series' names. The Series' name (if present) will be used as the resulting DataFrame column name. When only one of the Series has a valid name, the resulting DataFrame will inherit the name only, and use a column name for the other columns the column index value.
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} | 5 | 2015-08-02T12:05:21Z | 2015-08-04T17:58:00Z | 2015-08-03T22:03:11Z | CONTRIBUTOR | null | To fix [GH9431](https://github.com/pydata/pandas/issues/9431).
Previously `pd.unique` would return an array of `object` dtype when passed a 1D array, Series or Index with a `datetime64` or `timedelta64` dtype. This PR should remedy that behaviour.
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} | 1 | 2015-08-02T14:25:47Z | 2021-04-18T23:08:00Z | null | CONTRIBUTOR | null | http://www.pytables.org/usersguide/libref/declarative_classes.html#the-atom-class-and-its-descendants
- would have to ensure back-compat reading.
- `order` property would have to be retained in column meta data (IIRC this is how its done now)
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closes #8239
```
Changes to sorting API
^^^^^^^^^^^^^^^^^^^^^^
The sorting API has had some longtime inconsistencies. (:issue:`9816`,:issue:`8239`).
Here is a summary of the **prior** to 0.17.0 API
- ``Series.sort`` is **INPLACE** while ``DataFrame.sort`` returns a new object.
- ``Series.order`` returned a new object
- It was possible to use ``Series/DataFrame.sort_index`` to sort by **values** by passing the ``by`` keyword.
- ``Series/DataFrame.sortlevel`` worked only on a ``MultiIndex`` for sorting by index.
To address these issues, we have revamped the API:
- We have introduced a new method, ``.sort_values()``, which is the merger of ``DataFrame.sort()``, ``Series.sort()``,
and ``Series.order``, to handle sorting of **values**.
- The existing method ``Series.sort()`` has been deprecated and will be removed in a
future version of pandas.
- The ``by`` argument of ``DataFrame.sort_index()`` has been deprecated and will be removed in a future version of pandas.
- The methods ``DataFrame.sort()``, ``Series.order()``, will not be recommended to use and will carry a deprecation warning
in the doc-string.
- The existing method ``.sort_index()`` will gain the ``level`` keyword to enable level sorting.
We now have two distinct and non-overlapping methods of sorting. A ``*`` marks items that
will show a ``PendingDeprecationWarning`` (normally suppressed by python), and a ``+`` marks items that
will show a ``FutureWarning``.
To sort by the **values**:
================================= ====================================
Previous Replacement
================================= ====================================
*``Series.order()`` ``Series.sort_values()``
+``Series.sort()`` ``Series.sort_values(inplace=True)``
``DataFrame.sort(columns=...)`` ``DataFrame.sort_values(by=...)``
================================= ====================================
To sort by the **index**:
================================= ====================================
Previous Equivalent
================================= ====================================
``Series.sort_index()`` ``Series.sort_index()``
``Series.sortlevel(level=...)`` ``Series.sort_index(level=...``)
``DataFrame.sort_index()`` ``DataFrame.sort_index()``
``DataFrame.sortlevel(level=...)`` ``DataFrame.sort_index(level=...)``
*``DataFrame.sort()`` ``DataFrame.sort_index()``
================================== ====================================
```
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} | 8 | 2015-08-02T21:58:32Z | 2015-09-06T08:52:00Z | 2015-08-21T07:33:44Z | MEMBER | null | Closes #10715
This gives you an output like
```
In [4]: s = pd.Series([1,3,5,np.nan,6,8])
In [5]: s.iloc[11]
---------------------------------------------------------------------------
IndexError: single positional indexer is out-of-bounds
```
for input
```
.. ipython:: python
:okexcept:
s = pd.Series([1,3,5,np.nan,6,8])
s.iloc[10]
```
I think we would always want such a truncated traceback in the docs in cases that exceptions are expected, so I don't think it is needed to make it an extra new option.
I only should check for cases where the actual message spans multiple lines (now it is hardcoded to give me the last line of the error traceback)
@jreback with this we don't have to use the `code-block` s in eg whatsnew for the expect behaviour of `to_datetime`
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} | 2 | 2015-08-03T00:09:22Z | 2015-08-17T11:10:19Z | 2015-08-17T11:10:19Z | CONTRIBUTOR | null | ```
data = "1,2,3\n4,5,6\n "
print(pandas.read_csv(pandas.compat.StringIO(data), skip_blank_lines=True))
1 2 3
0 4 5 6
1 NaN NaN NaN
```
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} | 7 | 2015-08-03T02:26:59Z | 2015-08-18T12:44:38Z | 2015-08-04T17:38:52Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/10704
Changes `Categorical.value_counts` to return
a Series with a CategoricalIndex. Previously
the Series and an Index.
Just some random observations on the convenience of working with categoricals:
The code here could be simplified if
1. `Categorical.categories` was always a `Categoricalndex`
2. We were consistent about whether `NaN` is allowed in `.categories`
I thought that 1. would be a good idea, but I'm going back on it now. I can see how it'd be useful for `Categorical.categories` to be a more specific type like DatetimeIndex. For 2, I'd personally I'll always keep `NaN` out of my categories, but I could be missing a use case where it's needed.
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} | 7 | 2015-08-03T07:28:59Z | 2021-10-29T02:17:14Z | null | NONE | null | Currently `DataFrame.update()` modifies the dataframe in place. I feel this is not symmetric to how most other methods work, e.g. `set_index()`, that returns a new `DataFrame`. It is probably too late to change this default behavior (isi it?), but if at the very least an `inplace` option was added (with default value `True` to support the current behaviour), it will be easier to chain a sequence of operators that includes `update()`.
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} | 14 | 2015-08-03T11:49:00Z | 2015-08-04T11:47:19Z | 2015-08-04T11:47:19Z | CONTRIBUTOR | null | eg. on linux:
```
df2 = pandas.DataFrame([['100']], index=[1], columns=['v'])
df2.to_msgpack(r"/mnt/C/temp/p2.msgpack")
```
then on windows:
```
df = pandas.read_msgpack(r"C:\temp\p2.msgpack")
ValueError: Shape of passed values is (1, 1), indices imply (1, 2)
```
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} | 2 | 2015-08-03T12:58:13Z | 2015-08-05T08:12:35Z | 2015-08-05T08:12:32Z | MEMBER | null | With the new default of `errors='raise'`, the following section in the docs is not fully correctly phrased anymore: http://pandas-docs.github.io/pandas-docs-travis/timeseries.html#invalid-data.
So I just removed that example of `to_datetime([1, '1'])` (as it now raises, which is less unexpected, and the new example already shows the three options).
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} | 17 | 2015-08-03T13:33:28Z | 2021-04-18T23:09:12Z | null | NONE | null | The documentation for DataFrame.combine claims that the method "do[es] not propagate NaN values, so if for a (column, time) one frame is missing a value, it will default to the other frame’s value". However, this does not seem to correspond to the actual behaviour of DataFrame.combine.
Sample code:
``` python
>>> import pandas as pd
>>> from operator import add
>>> a = pd.DataFrame({
... 'a': pd.Series([1, 3], index=[0, 1]),
... 'b': pd.Series([2, 3], index=[1, 2]),
... })
>>> b = pd.DataFrame({
... 'a': pd.Series([3, 5], index=[1, 2]),
... 'c': pd.Series([1, 2, 3], index=[2, 3, 4])
... })
>>> a.combine(b, add)
a b c
0 NaN NaN NaN
1 6 NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
```
In many cases (as in this one), it may be remedied by using the _fill_value_ parameter of DataFrame.combine. However, it might be a problem when there is no acceptable neutral element for the given combining function.
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22
numpy: 1.9.2
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.1.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.6
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
```
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} | 4 | 2015-08-03T14:04:59Z | 2015-08-16T08:28:53Z | 2015-08-16T08:28:53Z | MEMBER | null | On a DataFrame, we have the [`combineAdd`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.combineAdd.html) and [`combineMult`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.combineMult.html) methods:
- they are a redundant, as they are defined as `self.add(other, fill_value=0.)` and `self.mul(other, fill_value=1.)` (that is the actual implementation), which is also just cleaner to use IMHO.
- they have awkward (and non-pep8) names
- I assume they are not used a lot (I found 3 relevant references on StackOverflow for combineAdd ([SO1](http://stackoverflow.com/questions/28421137/subtract-two-dataframes-with-non-overlapping-indexes/28421255#28421255), [SO2](http://stackoverflow.com/questions/27744908/computing-sum-of-pandas-dataframes/27746836#27746836) and [SO2](http://stackoverflow.com/questions/20927701/pandas-numpy-how-to-turn-column-data-into-sparse-matrix/20929504#20929504), which is more than I expected), and none for combineMult
Therefore, I was thinking we could maybe deprecate these (as kind of clean-up)?
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} | 10 | 2015-08-03T18:06:24Z | 2015-08-19T20:52:33Z | 2015-08-19T20:52:33Z | CONTRIBUTOR | null | I've realized the `.sample()` function is sometimes returning a view rather than a copy. This will cause the "setting on a view/copy" error:
```
df = pd.DataFrame({'col1':[5,6,7], 'col2':['a','b','c']})
a_sample = df.sample(1)
a_sample['col1'] = 'z'
```
I believe the behavior is the result of it using the `.take()` function, which is what the `.sample()` command uses to pull the sample the passed frame.
I'd like to suggest either:
- replace `.take()` with `.iloc()`
- change `.take()` to `.take().copy()` if that's faster (@shoyer suggested `.take()` would be a faster -- is `.take().copy()`?)
Thoughts?
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} | 1 | 2015-08-03T18:55:39Z | 2015-08-04T12:20:59Z | 2015-08-03T18:58:49Z | CONTRIBUTOR | null | The [API documentation for pandas.date_range](http://pandas.pydata.org/pandas-docs/version/0.15.2/generated/pandas.date_range.html) does not provide a link or explanation of the acceptable values for the 'freq' parameter, e.g. 'D' for calendar day frequency.
There is a highly-upvoted Stack Overflow question entitled ["pandas resample documentation"](http://stackoverflow.com/questions/17001389/pandas-resample-documentation) which asks a related question about pandas.Series.resample.
The documentation for pandas.date_range should include a link to the ["Offset Aliases" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases) of the timeseries documentation.
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} | 15 | 2015-08-03T19:21:25Z | 2015-08-19T20:52:38Z | 2015-08-19T20:52:33Z | CONTRIBUTOR | null | Two bug fixes:
- Makes sure to return copies, not views.
- makes sure if a separate series is passed as weights that the axis are aligned before arguments get converted into numpy arrays and handled positionally
closes #10736
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} | 8 | 2015-08-03T19:25:04Z | 2015-08-06T12:17:42Z | 2015-08-06T12:17:29Z | CONTRIBUTOR | null | This fixes http://stackoverflow.com/questions/31783392/pandas-adding-columns-to-filter-will-mess-with-data-structure
The dtypes of the DataFrame returned by `read_stata` have the same order as the original stata file, even if `columns` are specified with a different order.
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} | 12 | 2015-08-03T20:05:56Z | 2015-08-28T18:17:41Z | 2015-08-28T18:17:41Z | CONTRIBUTOR | null | closes #8735
Previously, when the function argument to DataFrame.apply returned a dict, the reduction code would mistake its "values" property for the values of a Pandas Series, and return a Series of "values" instance methods. The new check ensures that the "values" property is an np.ndarray.
Previous behavior:
```
In [1]: A = DataFrame([['foo', 'bar'], ['spam', 'eggs']])
In [2]: A.apply(lambda c: c.to_dict(), reduce=True)
Out[2]:
0 <built-in method values of dict object at 0x7f...
1 <built-in method values of dict object at 0x7f...
dtype: object
```
New behavior:
```
In [1]: A = DataFrame([['foo', 'bar'], ['spam', 'eggs']])
In [2]: A.apply(lambda c: c.to_dict(), reduce=True)
Out[2]:
0 {0: u'foo', 1: u'spam'}
1 {0: u'bar', 1: u'eggs'}
dtype: object
```
If reduce=False, the result is a DataFrame (this did not change):
```
In [3]: A.apply(lambda c: c.to_dict(), reduce=False)
Out[3]:
0 1
0 foo bar
1 spam eggs
```
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} | 4 | 2015-08-03T20:39:39Z | 2015-08-18T10:51:57Z | 2015-08-18T10:51:57Z | NONE | null | When joining DataFrames where the calling frame is a multiindex DF and the input frame is a single-index DF, `how='left'` and `how='right'` produce results that should be swapped (i.e., `'left'` returns what `'right'` should return, and vice versa). To give a single example using `how='left'`:
```
df1 = pd.DataFrame([['a', 'x', 0.471780], ['a','y', 0.774908], ['a', 'z', 0.563634],
['b', 'x', -0.353756], ['b', 'y', 0.368062], ['b', 'z', -1.721840],
['c', 'x', 1], ['c', 'y', 2], ['c', 'z', 3],
],
columns=['first', 'second', 'value1']
).set_index(['first', 'second'])
df2 = pd.DataFrame([['a', 10], ['b', 20]],
columns=['first', 'value2']).set_index(['first'])
print(df1.join(df2, how='left'))
```
Expected (for `how='left'`):
```
value1 value2
first second
a x 0.471780 10
y 0.774908 10
z 0.563634 10
b x -0.353756 20
y 0.368062 20
z -1.721840 20
c x 1.000000 NaN
y 2.000000 NaN
z 3.000000 NaN
```
Actual (for `how='left'`):
```
value1 value2
first second
a x 0.471780 10
y 0.774908 10
z 0.563634 10
b x -0.353756 20
y 0.368062 20
z -1.721840 20
```
However, the correct behavior occurs if the single-index DF is the calling frame and the multiindex DF is the input frame (`df2.join(df1, how='left')`). Behavior for `how='inner'` and `how='outer'` is correct in both situations.
pandas version 0.16.2
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} | 3 | 2015-08-03T21:09:14Z | 2020-09-28T13:08:04Z | null | NONE | null | The below example code is a variant of [this comment](https://bitbucket.org/zzzeek/sqlalchemy/issues/977/float-values-infinity-infinity-and-nan#comment-18706187). The problem is that pandas can't infer the type when the columns are all null. I would like to be able to use Postgres' NaN value if possible, but seems that pandas passes `None` to SQLAlchemy instead of `float('NaN')`. I would rather not cast the column's dtype to numeric after-the-fact. From previous link I know it's possible to make Postgres NaN values from SQLAlchemy. Is it possible pandas could support this?
```
tmaloney@vm-data-1:/var/tmp$ sudo -u postgres psql -c "drop database test;"
DROP DATABASE
tmaloney@vm-data-1:/var/tmp$ cat test.sql
create database test owner tmaloney;
tmaloney@vm-data-1:/var/tmp$ sudo -u postgres psql < /var/tmp/test.sql
CREATE DATABASE
tmaloney@vm-data-1:/var/tmp$ cat test2.py
#!/usr/bin/env python
'''
Creates a test python db to play with
'''
import sqlalchemy as sa
import pandas as pd
from pandas.util.testing import assert_frame_equal
CON_URL = 'postgres://tmaloney:secret@localhost/test'
db = sa.create_engine(CON_URL)
meta = sa.MetaData()
test_table = sa.Table(
'test_float', meta,
# A table contains design id and blocks
sa.Column('id', sa.Integer, primary_key=True),
sa.Column('float_val', sa.Float())
)
meta.drop_all(db)
meta.create_all(db)
data = [
{'float_val': float('nan')},
{'float_val': float('NaN')},
]
df = pd.DataFrame(data)
engine = sa.create_engine(CON_URL)
df.to_sql('test_float', engine, if_exists='append', index=False)
returned_df = pd.read_sql_table('test_float', engine)
assert_frame_equal(df, returned_df[['float_val']])
returned_df = pd.read_sql('select * from test_float', engine)
assert_frame_equal(df, returned_df[['float_val']])
tmaloney@vm-data-1:/var/tmp$ python test2.py
Traceback (most recent call last):
File "test2.py", line 37, in <module>
assert_frame_equal(df, returned_df[['float_val']])
File "/usr/local/lib/python2.7/dist-packages/pandas/util/testing.py", line 746, in assert_frame_equal
check_names=check_names)
File "/usr/local/lib/python2.7/dist-packages/pandas/util/testing.py", line 675, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "/usr/local/lib/python2.7/dist-packages/pandas/util/testing.py", line 552, in assert_attr_equal
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "/usr/local/lib/python2.7/dist-packages/pandas/util/testing.py", line 533, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('float64') != dtype('O')
```
The first assertion works, the second one does not.
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} | 1 | 2015-08-04T03:38:57Z | 2015-08-04T11:47:20Z | 2015-08-04T11:47:19Z | CONTRIBUTOR | null | …keys
closes #10732
I wasn't able to reproduce the odd behavior. Let's see if this passes on Travis.
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} | 16 | 2015-08-04T04:31:26Z | 2015-08-19T00:50:58Z | 2015-08-13T13:07:42Z | CONTRIBUTOR | null | Addresses #10699 - allowing relative offsets like `DateOffset(years=1)` to to be added to a `Series` containing datetimes for element-wise addition.
Previously only non-relative offsets that could be mapped to a time_delta (`Tick`, `Day`, etc) worked.
Some performance numbers are below - `Tick` based offsets should be unchanged, a subset of offsets (`DateOffset`, `YearEnd/Begin`, `MonthEnd/Begin`, `QuarterEnd/Begin`, `Week`, `BusinessDay`) will be sped up significantly; and other offsets will still fallback to a slow path, but now will raise a performance warning.
### Master
```
In [1]: idx = pd.date_range(start='20140101', freq='T', periods=100000)
In [2]: %timeit idx + pd.offsets.Day()
100 loops, best of 3: 5.72 ms per loop
In [3]: %timeit idx + pd.Timedelta(1, 'D')
100 loops, best of 3: 5.67 ms per loop
In [4]: %timeit idx + pd.offsets.DateOffset(years=1, months=2, days=1)
1 loops, best of 3: 1.79 s per loop
In [5]: %timeit idx + pd.offsets.WeekOfMonth(week=2, weekday=4)
1 loops, best of 3: 22.8 s per loop
```
### PR
```
In [3]: %timeit idx + pd.offsets.Day()
100 loops, best of 3: 5.8 ms per loop
In [4]: %timeit idx + pd.Timedelta(1, 'D')
100 loops, best of 3: 5.73 ms per loop
In [5]: %timeit idx + pd.offsets.DateOffset(years=1, months=2, days=1)
10 loops, best of 3: 86.9 ms per loop
In [6]: %timeit idx + pd.offsets.WeekOfMonth(week=2, weekday=4)
1 loops, best of 3: 23.4 s per loop
c:\users\chris\documents\python-dev\pandas\pandas\tseries\index.py:695: PerformanceWarning: Non-vectorized DateOffset being applied to Series or DatetimeIndex PerformanceWarning)
```
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| null | 12 | 2015-08-04T11:44:53Z | 2015-08-04T13:37:18Z | 2015-08-04T13:37:05Z | CONTRIBUTOR | null | trying to build master in clean windows x64 miniconda environment, gcc itself is in windows path:
```
writing build\temp.win-amd64-2.7\Release\pandas\index.def
C:\Program Files\mingw-builds\x64-4.8.1-posix-seh-rev5\mingw64\bin\dllwrap.exe -DMS_WIN64 -mdll -static --entry _DllMain@12 --output-lib build\temp.win-amd64-2.7\Release\pandas\libindex.a --def build\temp.win-amd64-2.7\Release\pandas\index.def -s build\temp.win-amd64-2.7\Release\pandas\index.o build\temp.win-amd64-2.7\Release\pandas\src\datetime\np_datetime.o build\temp.win-amd64-2.7\Release\pandas\src\datetime\np_datetime_strings.o -LC:\Miniconda\envs\pandas_dev\libs -LC:\Miniconda\envs\pandas_dev\PCbuild\amd64 -lpython27 -lmsvcr90 -o c:\Dev\src\pandas\pandas\index.pyd
'gcc" "-Wl' is not recognized as an internal or external command,
operable program or batch file.
dllwrap: gcc exited with status 1
error: command 'C:\\Program Files\\mingw-builds\\x64-4.8.1-posix-seh-rev5\\mingw64\\bin\\dllwrap.exe' failed with exit status 1
```
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} | 10 | 2015-08-04T16:21:41Z | 2015-08-15T23:47:57Z | 2015-08-15T23:47:50Z | NONE | null | When creating a DataFrame with a millisecond timestamp, created with `dtype='datetime64[ms]'`, this works as expected when there are no missing values in the data:
```
import pandas as pd
df = pd.DataFrame([1036713600000], dtype='float64')
print(df[0].astype('datetime64[ms]'))
```
Output:
```
0 2002-11-08
Name: 0, dtype: datetime64[ns]
```
Adding a missing value to the data causes the values to get parsed as nanoseconds rather than microseconds, which causes an exception:
```
df = pd.DataFrame([1036713600000, None], dtype='float64')
print(df[0].astype('datetime64[ms]'))
```
Output:
```
Traceback (most recent call last):
File "./f.py", line 6, in <module>
print(df[0].astype('datetime64[ms]'))
File "/Users/dsc/.virtualenvs/p3default/lib/python3.4/site-packages/pandas/core/generic.py", line 2411, in astype
dtype=dtype, copy=copy, raise_on_error=raise_on_error, **kwargs)
File "/Users/dsc/.virtualenvs/p3default/lib/python3.4/site-packages/pandas/core/internals.py", line 2504, in astype
return self.apply('astype', dtype=dtype, **kwargs)
File "/Users/dsc/.virtualenvs/p3default/lib/python3.4/site-packages/pandas/core/internals.py", line 2459, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/dsc/.virtualenvs/p3default/lib/python3.4/site-packages/pandas/core/internals.py", line 373, in astype
values=values, **kwargs)
File "/Users/dsc/.virtualenvs/p3default/lib/python3.4/site-packages/pandas/core/internals.py", line 407, in _astype
fastpath=True, dtype=dtype, klass=klass)
File "/Users/dsc/.virtualenvs/p3default/lib/python3.4/site-packages/pandas/core/internals.py", line 2101, in make_block
placement=placement)
File "/Users/dsc/.virtualenvs/p3default/lib/python3.4/site-packages/pandas/core/internals.py", line 1795, in __init__
values = tslib.cast_to_nanoseconds(values)
File "pandas/tslib.pyx", line 2622, in pandas.tslib.cast_to_nanoseconds (pandas/tslib.c:43295)
File "pandas/tslib.pyx", line 1333, in pandas.tslib._check_dts_bounds (pandas/tslib.c:23332)
pandas.tslib.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 292278994-08-16 16:47:04
```
Expected output was something like:
```
0 2002-11-08
1 NaT
Name: 0, dtype: datetime64[ns]
```
This was using python 3.4.2, Pandas 0.16.2.
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} | 1 | 2015-08-04T17:37:50Z | 2015-08-24T11:36:49Z | 2015-08-24T11:36:49Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/issues/10746
```
In [1]: data = '''
...: [{"id": 1, "timestamp": 1036713600000},
...: {"id": 2}]
...: '''
```
after #10776 this is ok
```
In [2]: pd.read_json(StringIO(data), orient='records', dtype={'id': 'int32', 'timestamp': 'datetime64[ms]'})
Out[2]:
id timestamp
0 1 1.036714e+12
1 2 NaN
In [2]: pd.read_json(StringIO(data), orient='records', dtype={'id': 'int32', 'timestamp': 'datetime64[ms]'})
Out[10]:
id timestamp
0 1 2002-11-08
1 2 NaT
```
still open
```
In [7]: pd.read_json(StringIO(data), orient='records', date_unit='ms')
Out[7]:
id timestamp
0 1 1.036714e+12
1 2 NaN
```
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} | 10 | 2015-08-04T17:50:43Z | 2015-09-01T19:18:15Z | 2015-09-01T19:18:15Z | CONTRIBUTOR | null | This came out of work on https://github.com/pydata/pandas/pull/10729
In the [documentation](http://pandas.pydata.org/pandas-docs/version/0.16.2/categorical.html#missing-data), we mention that
> There are two ways a np.nan can be represented in categorical data: either the value is not available (“missing value”) or np.nan is a valid category.
In the first case, `NaN` is not in `.categories`, and in the second case it is. I think we should only
recommend the first.
The option of `NaN`s in the categories makes the code in #10729 less pleasant that it would be otherwise. I don't think we should error if NaNs are included, just advise against it in the docs. Perhaps a deprecation, but I worry that I'm missing some obvious reason why NaNs were allowed in `.categories`.
@JanSchulz do you remember the initial reason for allowing either representation?
Some bad things that come out of `NaN` in `.categories`:
- Can't rely on a value of `nan` mapping to a code of `-1`:
``` python
In [2]: s = pd.Categorical(['a', 'b', 'a', np.nan], categories=['a', 'b', np.nan])
In [3]: s
Out[3]:
[a, b, a, NaN]
Categories (3, object): [a, b, NaN]
In [4]: s.categories
Out[4]: Index(['a', 'b', nan], dtype='object')
In [5]: s.codes
Out[5]: array([0, 1, 0, 2], dtype=int8)
```
- potentially have to upcast the index type or mix strings and floats (`nan`) in the `.categories` Index.
- extra code if you want to generically handle Categoricals that may or may not have `NaN` in categories.
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} | 0 | 2015-08-04T18:47:49Z | 2015-08-04T21:15:44Z | 2015-08-04T21:15:44Z | MEMBER | null | This should get the sphinx doc build again warning/error free (apart from the one handled in #10602)
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} | 2 | 2015-08-04T19:07:30Z | 2015-08-12T09:31:26Z | 2015-08-12T09:31:26Z | CONTRIBUTOR | null | `pd.read_sql_table(tablename, engine)` won't be able to find `tablename` if it's actually a view.
This should be a one line change to https://github.com/pydata/pandas/blob/b281e6570bc3bb3773520eff0b6965d5691e3ba2/pandas/io/sql.py#L340
``` python
meta = MetaData(con, schema=schema)
try:
meta.reflect(only=[table_name])
except sqlalchemy.exc.InvalidRequestError:
raise ValueError("Table %s not found" % table_name)
```
just adding the keyword argument `views=True` from http://docs.sqlalchemy.org/en/rel_1_0/core/metadata.html I don't think there will be any negative consequences to this, but I don't use sqlalchemy MetaData objects that often.
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} | 1 | 2015-08-04T21:32:38Z | 2015-08-05T06:45:09Z | 2015-08-05T06:44:58Z | CONTRIBUTOR | null | "analagous" -> "analogous"
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| null | 4 | 2015-08-04T22:04:23Z | 2015-10-13T21:03:11Z | 2015-10-13T21:03:11Z | CONTRIBUTOR | null | Minor change to allow customizing the kde through the pandas api.
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| null | 1 | 2015-08-04T22:14:19Z | 2015-08-04T22:21:39Z | 2015-08-04T22:19:51Z | NONE | null | ``` python
In [1]: import pandas as pd
In [2]: i = pd.Index(range(10))
In [3]: pd.Series.count(i)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-759ac5e97e30> in <module>()
----> 1 pd.Series.count(i)
TypeError: unbound method count() must be called with Series instance as first argument (got Int64Index instance instead)
In [4]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.0.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.16.1
nose: 1.3.6
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.1.0
sphinx: 1.2.3
patsy: None
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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| null | 6 | 2015-08-05T17:43:32Z | 2016-09-10T13:42:34Z | 2016-09-10T13:42:34Z | NONE | null | https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L102
Why is this in the gbq.py? can we set this as an environmental Variable?
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| null | 2 | 2015-08-05T22:31:10Z | 2020-04-14T05:11:24Z | null | NONE | null | Writing a DataFrame with utf-16 encoding adds garbage characters to the file:
``` python
import codecs
import pandas as pd
#This works
enc = 'utf-8'
print '\n\n',enc,'\n\n'
d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
print df,'\n\n'
df.to_csv('foo.csv', encoding = enc)
df = pd.read_csv('foo.csv', encoding = enc)
print df,'\n\n'
with codecs.open('foo.csv', encoding=enc, mode='r') as f:
for line in f:
print line
#this does not work
enc = 'utf-16'
print '\n\n',enc,'\n\n'
d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
print df,'\n\n'
df.to_csv('foo.csv', encoding = enc)
df = pd.read_csv('foo.csv', encoding = enc)
print df,'\n\n'
#prints
# Unnamed: 0 one two???????????
# 0 b 2 2.0???????????
# 1 d NaN 4.0?
with codecs.open('foo.csv', encoding=enc, mode='r') as f:
for line in f:
print line
#prints
#,one,two愀Ⰰ⸀ Ⰰ⸀ ഀ
#b,2.0,2.0挀Ⰰ㌀⸀ Ⰰ㌀⸀ ഀ
#d,,4.0
```
Versions:
```
pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.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: de_DE
pandas: 0.16.1
nose: 1.3.4
Cython: 0.21.1
numpy: 1.8.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.3
pytz: 2014.10
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: 2.1.3
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.4.1
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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| null | 16 | 2015-08-06T09:17:49Z | 2017-10-30T16:38:09Z | 2017-08-22T14:18:55Z | NONE | null | I would expect the following code to produce a plot with transparent bars, but it doesn't.
``` python
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
plt.style.use('ggplot')
N = 1000
df = pd.DataFrame({
'a': np.random.randint(0, 2, N),
'b': np.random.random(N)
})
#df.groupby('a').b.hist(alpha=0.5)
df.groupby('a').b.plot(kind="hist", alpha=0.5)
plt.legend()
plt.show()
```
Neither the commented out `hist` command, nor the `plot` command works.
Version information from `pd.show_versions()`:
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: 0.20.1post0
numpy: 1.9.2
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 3.0.0
sphinx: None
patsy: 0.2.1
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.4.3
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 4 | 2015-08-06T12:27:07Z | 2015-08-06T15:17:07Z | 2015-08-06T15:16:59Z | CONTRIBUTOR | null | This fixes http://stackoverflow.com/questions/31783392/pandas-adding-columns-to-filter-will-mess-with-data-structure
The dtypes of the DataFrame returned by read_stata have the same order as the original stata file, even if columns are specified with a different order.
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} | 0 | 2015-08-06T12:28:09Z | 2016-05-20T14:16:17Z | 2016-05-20T14:16:17Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/21599633/how-to-create-a-pandas-timestamp-object)
`Timestamp(year, month, day, second, millisecond, microsecond, nanosecond)`
as optional keyword args might be nice for compat with `datetime.datetime`. We do something similar for `Timedelta` construction.
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} | 1 | 2015-08-06T18:03:52Z | 2020-04-05T01:52:31Z | null | NONE | null | I believe qcut would be much more useful if there was a convenient way to apply it to a rolling window instead of the whole Series object. Of course it's possible with rolling_apply, but that does window_size times more calculations that are necessary for the output. For something as trivial as checking how the current value compares to the ones around it, I'm forced to use this monstrosity:
pd.rolling_apply(arg,window,lambda x: pd.qcut(x,q,labels=False)[window/2],center=True)
instead of much more elegant pd.rolling_qcut(arg,window,q,center=True)
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} | 1 | 2015-08-07T04:46:12Z | 2015-08-10T11:05:43Z | 2015-08-10T11:05:43Z | NONE | null | ```
import pandas as pd
from decimal import Decimal
df = pd.DataFrame()
df['a'] = pd.Series(['a', 'b', 'a'])
df['b'] = pd.Series([Decimal(x) for x in [1, 2, 3]])
df.pivot_table(columns='a', values='b')
```
Results in:
```
DataError Traceback (most recent call last)
<ipython-input-8-158f4a12f2dd> in <module>()
5 df['a'] = pd.Series(['a', 'b', 'a'])
6 df['b'] = pd.Series([Decimal(x) for x in [1, 2, 3]])
----> 7 df.pivot_table(columns='a', values='b')
/home/user/anaconda/envs/fdb/lib/python2.7/site-packages/pandas/tools/pivot.pyc in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna)
109
110 grouped = data.groupby(keys)
--> 111 agged = grouped.agg(aggfunc)
112
113 table = agged
/home/user/anaconda/envs/fdb/lib/python2.7/site-packages/pandas/core/groupby.pyc in agg(self, func, *args, **kwargs)
683 @Appender(_agg_doc)
684 def agg(self, func, *args, **kwargs):
--> 685 return self.aggregate(func, *args, **kwargs)
686
687 def _iterate_slices(self):
/home/user/anaconda/envs/fdb/lib/python2.7/site-packages/pandas/core/groupby.pyc in aggregate(self, arg, *args, **kwargs)
2635 def aggregate(self, arg, *args, **kwargs):
2636 if isinstance(arg, compat.string_types):
-> 2637 return getattr(self, arg)(*args, **kwargs)
2638
2639 result = OrderedDict()
/home/user/anaconda/envs/fdb/lib/python2.7/site-packages/pandas/core/groupby.pyc in mean(self)
698 """
699 try:
--> 700 return self._cython_agg_general('mean')
701 except GroupByError:
702 raise
/home/user/anaconda/envs/fdb/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)
2558
2559 def _cython_agg_general(self, how, numeric_only=True):
-> 2560 new_items, new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)
2561 return self._wrap_agged_blocks(new_items, new_blocks)
2562
/home/user/anaconda/envs/fdb/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_blocks(self, how, numeric_only)
2605
2606 if len(new_blocks) == 0:
-> 2607 raise DataError('No numeric types to aggregate')
2608
2609 return data.items, new_blocks
DataError: No numeric types to aggregate
```
But works fine for ints/floats:
```
df = pd.DataFrame()
df['a'] = pd.Series(['a', 'b', 'a'])
df['b'] = pd.Series([float(x) for x in [1, 2, 3]])
df.pivot_table(columns='a', values='b')
```
Out:
```
a
a 2
b 2
Name: b, dtype: int64
```
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| null | 5 | 2015-08-07T09:58:14Z | 2021-04-18T23:10:48Z | null | NONE | null | Environment is Anaconda 2.3, using python 2.7.10. Pandas version 0.16.2.
Here's the code to reproduce this problem in IPython Notebook 3.2, only `df['col3']` is plotted.
When sharing an axis the last plot() seems to override whatever was plotted before. Only the last plotted item is shown in figure.
```
import pandas as pd
import bumpy as np
import matplotlib.pyplot as plt
%matplotlib inline
drange = pd.date_range(start='2015-07-01', end='2015-07-10', freq='D')
df = pd.DataFrame(data=np.random.random_sample((10, 3)), index=drange, columns=['col1', 'col2', 'col3'])
fig, ax = plt.subplots()
plt.hold(True) # when the index is just integer type, this will make it work.
df[['col1', 'col2']].plot(kind='bar', ax=ax, stacked=True)
df[['col3']].plot(ax=ax, color='r', marker='o', ls='-', alpha=.7) # This overrides the previous plot
# if you use this line below instead of the line above, both bars and lines will show up:
#ax.plot(df[['col3']], color='r', marker='o', ls='-', alpha=.7)
ax.grid(True)
ax.legend(loc='best')
```
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} | 3 | 2015-08-07T12:36:14Z | 2015-08-07T13:00:41Z | 2015-08-07T12:39:11Z | NONE | null | ```
import pandas as pd
ts1 = pd.Timestamp('2015-07-01 10:00:00')
ts2 = pd.Timestamp('2015-07-01 11:00:00')
df1 = pd.DataFrame([[0.1],[0.2]], index=[ts1, ts2], columns=['a'])
print(df1.index.dtype)
df1 = df1.reset_index(drop=False)
print(df1['index'].dtype)
df2 = pd.DataFrame([[0.1],[0.2]], index=[ts1, ts2], columns=['a'])
df2.index = df2.index.tz_localize('Europe/London')
print(df2.index.dtype)
df2 = df2.reset_index(drop=False)
print(df2['index'].dtype)
```
gives
datetime64[ns]
datetime64[ns]
datetime64[ns]
object
While one would expect that the last two lines are identical.
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} | 4 | 2015-08-07T12:59:54Z | 2015-09-05T16:17:23Z | 2015-09-05T16:17:23Z | NONE | null | xref #10477
It is possible with numpy, but not with pandas:
```
import numpy as np
import pandas as pd
ts1 = pd.Timestamp('2015-07-01 10:00:00').tz_localize('Europe/London')
ts2 = pd.Timestamp('2015-07-01 11:00:00').tz_localize('Europe/London')
dt = ts2 - ts1
x = np.array([ts1, ts2])
print(x)
dx = np.array([dt, dt])
print(x + dx)
s = pd.Series([ts1, ts2])
print(s)
ds = pd.Series([dt, dt])
print(s + ds)
```
`s + ds` throws an error `ufunc add cannot use operands with types dtype('O') and dtype('<m8[ns]')`
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| null | 9 | 2015-08-07T13:10:21Z | 2015-08-07T14:09:43Z | 2015-08-07T13:15:32Z | NONE | null | When upgrading to pandas 0.16.2, reindex() from string index to DatetimeIndex index returns nan values.
This test code:
```
indexInit = ["19990101","19990102","19990103"]
values = [1.0,2.5,2.1]
outSeries = Series(values,index=indexInit)
outSeries.reindex(index=DatetimeIndex(outSeries.index))
```
returns:
```
1999-01-01 nan
1999-01-02 nan
1999-01-03 nan
```
Same output on the latest Mac OS X version and also the latest linux version.
We had to downgrade the Pandas version for this to work.
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} | 7 | 2015-08-07T15:56:20Z | 2016-11-11T11:46:51Z | 2016-11-11T11:46:43Z | NONE | null | Adding cassandra support should be very similar to `to_sql()` but is different enough where I think it needs it's own call, as it wouldn't use SQLAlchemy or behave even remotely close.
Python driver is here: https://github.com/datastax/python-driver
Willing to help on this if it'll get pulled in.
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| null | 3 | 2015-08-07T16:15:34Z | 2015-08-07T16:38:13Z | 2015-08-07T16:37:40Z | NONE | null | Hi Pandas.
Just uncovered a clear bug in the interaction between pandas series and numpy isreal(). The behavior is simple: isreal() produces incorrect results on panda's series that include string types. It's not 100% clear the underlying cause but it appears to be related to np.isreal operating over the pd.Series object because isreal returns the correct result when individual elements are accessed. See example reproducing the issue in pandas 0.16.1:
```
print(np.isreal(pd.Series(['1.0'])))
print(np.isreal(np.array(pd.Series(['1.0']))))
print(np.isreal(np.array(pd.Series(['1.0']))[0]))
print(np.isreal(np.array(pd.Series(['1.0'])[0])))
print(np.isreal(np.array(['1.0'])))
```
produces
```
[ True]
[ True]
False
False
False
```
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| null | 5 | 2015-08-07T16:31:58Z | 2015-08-07T21:18:15Z | 2015-08-07T19:18:08Z | NONE | null | I attempted to use `rolling_window()` and couldn't figure out why my simple test cases weren't working. I realized there's a default value of `mean=True` to average the result out. Is this really the most common use case people have? I would have assumed most use cases would be `mean=False`
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| null | 1 | 2015-08-07T19:43:43Z | 2015-08-08T01:33:11Z | 2015-08-08T01:33:11Z | NONE | null | As part of my PhD research on code authorship, we calculated the Truck Factor (TF) of some popular GitHub repositories.
As you probably know, the [Truck (or Bus) Factor](https://en.wikipedia.org/wiki/Bus_factor) designates the minimal number of developers that have to be hit by a truck (or quit) before a project is incapacitated. In our work, we consider that a system is in trouble if more than 50% of its files become orphan (i.e., without a main author).
More details on our work in this preprint: https://peerj.com/preprints/1233
We calculated the TF for **pandas** and obtained a value of **2**.
The developers responsible for this TF are:
Wes McKinney - author of 59% of the files
Jeff Reback - author of 19% of the files
To validate our results, we would like to ask **pandas** developers the following three brief questions:
(a) Do you agree that the listed developers are the main developers of **pandas**?
(b) Do you agree that **pandas** will be in trouble if the listed developers leave the project (e.g., if they win in the lottery, to be less morbid)?
(c) Does **pandas** have some characteristics that would attenuate the loss of the listed developers (e.g., detailed documentation)?
Thanks in advance for your collaboration,
Guilherme Avelino
PhD Student
Applied Software Engineering Group (ASERG)
UFMG, Brazil
http://aserg.labsoft.dcc.ufmg.br/
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| null | 7 | 2015-08-08T14:14:29Z | 2015-08-10T11:01:04Z | 2015-08-08T16:11:19Z | NONE | null | Hello,
I've stumbled onto a behavior that causes performance problems on large datasets: When creating a multiindex with second level being a DateTime64 col, and then doing a `.loc[('val1', TimeStamp('val2'),)]` to get at particular value, pandas coerces (via _box_func, called from __iter__) all values in that particular column to TimeStamp dtype. This is done only on first access, but affect performance quite heavily since the op is O(n).
However, when chaining the same op (or calling it via multi-level `.xs`, this behavior is not exhibited. It is not a viable workaround however because doing so introduces an extra call to getitem per each level of multiindex.
Is there a workaround for this or what am i doing wrong?
In essence, given a df with unique multiindex, i want to get to a particular row given values for each level of the index.
Repro:
```
df = <dataframe with: [colA:string, colB:datetime64, colC:numeric with 1,000,000 rows>
gr = df.groupby(('colA', 'colB',))
ag = gr.aggregate({'colC':'sum'})
path = ('one', pd.TimeStamp('2015-01-01 01:02:03'),)
ag.loc[path] # << first call iterates over entire dataset doing the conversion, and is slow
ag.loc[path] # << second call is very quick
ag.xs(path, levels=('colA','colB',)) # << this takes a different code path but does an extra call to getitem, making it slower.
```
Other things:
- padding numpy.datetime64 into the path tuple instead of timestamp doesnt help
- specifiying axis for loc doesn't help
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} | 0 | 2015-08-08T19:20:35Z | 2015-10-30T22:08:05Z | 2015-10-30T22:08:05Z | CONTRIBUTOR | null | xref #9605
document http://pandas-docs.github.io/pandas-docs-travis/timeseries.html#time-date-components
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} | 2 | 2015-08-08T21:31:09Z | 2015-08-12T09:30:03Z | 2015-08-12T06:46:14Z | CONTRIBUTOR | null | closes #10750
Add ability to 'read_sql_table' to read views and implement an unit test to check its behaviour
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} | 15 | 2015-08-08T22:20:16Z | 2021-04-18T23:12:17Z | null | CONTRIBUTOR | null | If one uses `series.value_counts(dropna=False)` it includes `NaN` values, but `crosstab(series1, series2, dropna=False)` does not include `NaN` values. I think of `cross_tab` kind of like 2-dimensional `value_counts()`, so this is surprising.
``` python
In [31]: x = pd.Series(['a', 'b', 'a', None, 'a'])
In [32]: y = pd.Series(['c', 'd', 'd', 'c', None])
In [33]: y.value_counts(dropna=False)
Out[33]:
d 2
c 2
NaN 1
dtype: int64
In [34]: pd.crosstab(x, y, dropna=False)
Out[34]:
c d
row_0
a 1 1
b 0 1
```
I believe what `crosstab(..., dropna=False)` really does is just include rows/columns that would otherwise have all zeros, but that doesn't seem the same as `dropna=False` to me. I would have expected to see a row and column entry for `NaN`, something like:
``` python
c d NaN
row_0
a 1 1 1
b 0 1 0
NaN 1 0 0
```
Thoughts on this?
This is on current master (almost 0.17).
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| null | 2 | 2015-08-08T22:48:28Z | 2015-08-08T23:08:02Z | 2015-08-08T22:55:00Z | CONTRIBUTOR | null | This may very well be known/intentional, and if so I think it's worth putting in doc somewhere.
```
>>> datetime(2300,1,1)
datetime.datetime(2300, 1, 1, 0, 0)
>>> numpy.datetime64('2300-01-01')
numpy.datetime64('2300-01-01')
>>> Timestamp('2300-01-01') # pandas.tslib.OutOfBoundsDatetime
```
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} | 6 | 2015-08-09T01:16:09Z | 2021-04-18T23:16:23Z | 2021-04-18T23:16:23Z | MEMBER | null | Following slices basically result in the same `Index`.
```
import pandas as pd
pd.Index([1, 2, 3])[:, None]
# Int64Index([[1], [2], [3]], dtype='int64')
pd.MultiIndex.from_tuples([('a', 1), ('b', 2)])[:, None]
# MultiIndex(levels=[[u'a', u'b'], [1, 2]],
# labels=[[[0], [1]], [[0], [1]]])
pd.period_range('2011-01-01', freq='M', periods=3)[:, None]
# PeriodIndex(['2011-01', '2011-02', '2011-03'], dtype='int64', freq='M')
```
But `DatetimeIndex` and `TimedeltaIndex` doesn't because of following line.
- https://github.com/pydata/pandas/blob/master/pandas/tseries/base.py#L119
```
pd.date_range('2011-01-01', freq='M', periods=3)[:, None]
# array([['2011-01-31T09:00:00.000000000+0900'],
# ['2011-02-28T09:00:00.000000000+0900'],
# ['2011-03-31T09:00:00.000000000+0900']], dtype='datetime64[ns]')
pd.timedelta_range('1 days', freq='D', periods=3)[:, None]
# array([[ 86400000000000],
# [172800000000000],
# [259200000000000]], dtype='timedelta64[ns]')
```
I understand these must be `Index` also, or any reason for this?
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| null | 1 | 2015-08-09T01:35:59Z | 2015-08-09T01:56:38Z | 2015-08-09T01:56:20Z | CONTRIBUTOR | null | Here's what's going on:
```
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.16.2+308.g529288e'
In [3]: dt = pd.Timedelta(seconds=-2)
In [4]: dt
Out[4]: Timedelta('-1 days +23:59:58')
In [5]: dt.seconds
Out[5]: 86398
```
I would expect `dt.seconds == -2`.
It seems like the fix may be something like:
```
In [6]: dt.days * 3600 * 24 + dt.seconds
Out[6]: -2
```
Is it that easy? `Timedelta` is based on UTC time, so days always have 24hrs, right?
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} | 2 | 2015-08-09T06:52:20Z | 2015-08-12T13:44:37Z | 2015-08-12T13:44:32Z | CONTRIBUTOR | null | related to first of #10747
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| null | 3 | 2015-08-09T06:57:23Z | 2015-08-09T08:08:27Z | 2015-08-09T08:05:04Z | CONTRIBUTOR | null | Hello,
I noticed that size of a CSV file generated with Pandas and Python are differents depending of which version of Python was used.
I was expecting same behaviour for same code.
With Python 3
```
pc:~ scls$ cat big.py
import pandas as pd
import numpy as np
(rows, cols) = (400, 10)
a = np.random.random((rows, cols))
df = pd.DataFrame(a)
filename = "big_random.csv"
df.to_csv(filename, index=False)
pc:~ scls$ python --version
Python 3.4.3 :: Anaconda 2.3.0 (x86_64)
pc:~ scls$ python big.py
pc:~ scls$ head big_random.csv
0,1,2,3,4,5,6,7,8,9
0.7175194974125143,0.9868374217576047,0.032049602250014075,0.6289681136928122,0.8096042270600179,0.33685028982497345,0.15762455315620005,0.4691775579462879,0.4456870865050826,0.18795963399879667
0.6587841850716022,0.9138862585748279,0.725186213931711,0.8455808154946725,0.7513894749589103,0.1264561639354813,0.22313629403106283,0.7082854809639854,0.6372581410511284,0.39526526133363016
0.41655411171973145,0.82608272240786,0.39046502732419675,0.21280845299958473,0.7260928524192569,0.13413288736071716,0.6403422588148618,0.38493112678936114,0.1008469225955716,0.7569988810703301
0.4153526009936582,0.31647402611493414,0.9975731184442808,0.6426165566647016,0.09261643366931205,0.3227891182788275,0.8867457623057338,0.27223526407455145,0.3281299815210015,0.9740848774163636
0.18492378563510736,0.6467683901479606,0.040191223061303516,0.06418796210918698,0.6377758098323728,0.3015310590768058,0.35801398526272554,0.3847352145606483,0.5169639983061501,0.7688238573672432
0.12776779442246045,0.13988857304612567,0.5174730743084831,0.48860306709655155,0.6430744296754209,0.7043353997674583,0.9036918523659346,0.8363827082165963,0.10904005101984726,0.3467075055731551
0.8735436905183718,0.3094682378308442,0.3425056806446519,0.6327109907812603,0.027768508379761192,0.7572863534573687,0.013631783039836698,0.9498400284024592,0.7489006948603708,0.26146706653431384
0.00706906732485435,0.398808829510499,0.1603837067149072,0.1162434740119399,0.6308407696050173,0.38437501090290294,0.7084745025285255,0.6766732951295603,0.09640698119674629,0.16475759581133098
0.37288409337600503,0.8170980071434518,0.10346296752178363,0.22734655867481057,0.977310707692392,0.2058569589426188,0.810879704065204,0.4644448946189589,0.7872748134058031,0.21634203693609444
```
Let's do the same with Python 2
```
pc:~ scls$ source activate py2
discarding //anaconda/bin from PATH
prepending //anaconda/envs/py2/bin to PATH
(py2)pc:~ scls$ python --version
Python 2.7.10 :: Anaconda 2.3.0 (x86_64)
(py2)pc:~ scls$ python big.py
(py2)pc:~ scls$ head big_random.csv
0,1,2,3,4,5,6,7,8,9
0.5579481683,0.684701543521,0.754306080917,0.618156128389,0.172254680145,0.0174204117472,0.42003733688,0.544810598703,0.501523693218,0.254650528482
0.245211610381,0.242803787702,0.74730831067,0.902427362626,0.79284128878,0.759901967668,0.138869495692,0.409657542539,0.800543764611,0.126875692556
0.157008551856,0.196911813758,0.427114483552,0.513200703916,0.629485103457,0.158393748929,0.725090100741,0.997671387723,0.168756770968,0.307894016467
0.277986851471,0.841819960853,0.948682092484,0.0698344807858,0.843959698756,0.124105138469,0.685600301284,0.638439389501,0.153843520073,0.00693283214343
0.825322391369,0.246830314636,0.76342798427,0.588335209531,0.0639153711562,0.277168287326,0.660799511539,0.246912047114,0.525794863223,0.606527113773
0.422893634037,0.416014910374,0.0282877421175,0.479474754244,0.562079226872,0.554424129574,0.850810096081,0.980219346119,0.376727776223,0.0202092423104
0.107718832593,0.82063197471,0.293988837033,0.0741333403483,0.223505401274,0.506775135928,0.411408416805,0.828313119764,0.670612028027,0.67312260052
0.822882425742,0.0355538636782,0.0453556725915,0.483123830922,0.726536606867,0.265317264415,0.190839972237,0.63416336544,0.776559958794,0.198684003523
0.0159240676555,0.082225869763,0.9188622672,0.628898793501,0.598847602455,0.479313877636,0.830676086143,0.930886044804,0.979980325282,0.42786165221
```
Compare number of digits of
- `0.7175194974125143`
- `0.5579481683`
Same code leads to 6 digits difference.
Kind regards
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} | 5 | 2015-08-09T15:55:45Z | 2019-02-18T19:15:45Z | 2016-04-26T18:54:47Z | NONE | null | This code from the Categorical tutorial:
``` python
df = pd.DataFrame({"A":["a","b","c","a"]})
df["B"] = df["A"].astype('category')
```
Will segfault when `df.to_json` is called. Tested on:
``` bash
Python 3.4.3 |Anaconda 2.3.0 (x86_64)| (default, Mar 6 2015, 12:07:41)
[GCC 4.2.1 (Apple Inc. build 5577)] on darwin
$ conda list | grep pandas
pandas 0.16.2 np19py34_0
```
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} | 4 | 2015-08-09T18:18:52Z | 2015-08-14T18:41:14Z | 2015-08-14T18:41:14Z | NONE | null | In other words, there is no bounds checking for Series.iloc[] with a negative argument. It just accesses whatever is in the memory there. Also a security breach.
It does appear to check on write, just not on read.
Python 2.7.10 |Anaconda 2.1.0 (64-bit)| (default, May 28 2015, 17:02:03)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://binstar.org
> > > import pandas as pd
> > > pd>>> pd.**version**
> > > '0.16.2'
> > > s = pd.Series([1,2,3])
> > > s
> > > 0 1
> > > 1 2
> > > 2 3
> > > dtype: int64
> > > s.iloc[-2]
> > > 2
> > > s.iloc[-4]
> > > 33
> > > s.iloc[-12345]
> > > 0
> > > s.iloc[-123123123123123]
> > > Segmentation fault (core dumped)
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} | 3 | 2015-08-10T06:15:26Z | 2016-12-20T20:44:26Z | 2016-12-20T20:44:26Z | NONE | null | in Pandas 0.16.2:
```
grouped = a.groupby(idx)
grouped.filter(lambda x: x.mean()>0.0, dropna=False)
```
in case there was no group whose `x.mean()` was larger than zero, an `AttributeError: 'list' object has no attribute 'astype'` would be raised (rather than returning an all `NaN` dataframe/serie in previous versions).
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| null | 1 | 2015-08-10T08:22:02Z | 2015-08-10T10:50:35Z | 2015-08-10T10:50:23Z | NONE | null | For an integer MultiIndex DataFrame, the ix and loc properties should use labels rather than indices, right? However, this seems not to be the case if ix/loc is passed a list containing multiple labels refering to a single level. That is, the following example
``` python
import numpy as np
import pandas as pd
index = pd.MultiIndex.from_product((np.arange(3), np.arange(3)), names=('level0', 'level1'))
columns = pd.Index(('column',))
data = pd.DataFrame(np.arange(9), index=index, columns=columns)
print('{}\n\n{}'.format(
data.ix[0],
data.ix[[0]]))
```
outputs
```
column
level1
0 0
1 1
2 2
column
level0 level1
0 0 0
```
whereas I would expect both `data.ix[0]` and `data.ix[[0]]` to output the first three rows. If I change the levels of the MultiIndex from integers to floats, the desired behaviour is obtained. That is, the modified example
``` python
import numpy as np
import pandas as pd
index = pd.MultiIndex.from_product((np.arange(3.), np.arange(3.)), names=('level0', 'level1'))
columns = pd.Index(('column',))
data = pd.DataFrame(np.arange(9), index=index, columns=columns)
print('{}\n\n{}'.format(
data.ix[0.],
data.ix[[0.]]))
```
outputs
```
column
level1
0 0
1 1
2 2
column
level0 level1
0 0 0
1 1
2 2
```
Did I miss some subtlety or is this indeed a bug?
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} | 8 | 2015-08-10T11:56:34Z | 2015-08-11T10:35:48Z | 2015-08-11T10:35:45Z | CONTRIBUTOR | null | On this website http://pandas.pydata.org/pandas-docs/stable/install.html there are the console commands to install pandas on for example debian.
They are listed as _sudo apt-get install python-pandas_.
It would be a good idea to mention, that this will install pandas for python2 and you need to specificly write _python3-pandas_ to get the version for python3.
This might probably be completely obvious to everyone installing stuff like this every day, but for a beginner like me it was not clear at all and I struggled for quite some time to get pandas for py3.
Thank you.
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} | 4 | 2015-08-10T12:28:48Z | 2015-08-13T15:50:22Z | 2015-08-11T08:15:04Z | NONE | null | Hi,
My data frame contains unicode strings. Now when I use 'to_csv' without any quoting parameter, I get a csv file where certain fields (2 out of 11 to be exact) are in double quotes. For example:
`28,"May 8, 2013 10:22 AM","76th St, Yates Blvd",41.75717,-87.56633,3,-,1,4,-`
Now when I use csv.QUOTE_NONE i get the following error:
`Error: need to escape, but no escapechar set`
So i do the following (quoting=csv.QUOTE_NONE, escapechar='"'), and I get this:
`28,May 8", 2013 10:22 AM,76th St", Yates Blvd,41.75717,-87.56633,3,-,1,4,-`
So I get a new quote (") in those 2 fields. How to get around this problem? Thanks.
Sample code is below:
```
id = [u'1']
date = [u'Jan 12, 2013 08:30 AM']
location = [u'Ashland Avenue, Polk Street']
vehicles = [u'4']
drunken_persons = [u'-']
fatalities = [u'3']
persons = [u'4']
pedestrians = [u'-']
data = pd.DataFrame({'#':id, 'Date':date, 'Vehicles':vehicles, 'Drunken Persons':drunken_persons, 'Fatalities':fatalities, 'Persons':persons, 'Pedestrians':pedestrians, 'Location':location})
data[['#','Date','Location','Vehicles','Drunken Persons','Fatalities','Persons', 'Pedestrians']].astype(str).to_csv('Test.csv', sep=',', index = False, quoting=csv.QUOTE_NONE, escapechar='"')
```
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} | 1 | 2015-08-10T12:51:02Z | 2015-08-11T10:35:34Z | 2015-08-11T10:35:34Z | CONTRIBUTOR | null | closes #10782
- Added hint regarding pip install on low memory machines.
- Added hint to python 3 version of pandas from distributon repos.
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| null | 1 | 2015-08-10T12:55:04Z | 2015-08-10T12:57:27Z | 2015-08-10T12:56:54Z | NONE | null | Seeing an odd issue. When doing a sum and the full column or row is NAN, I expect NAN as the result. Recently my system has been giving a sum of zero if the full row is NAN, unless skipna=FALSE is specified (but of course that screws up rows with only one NAN, for example).
I would have thought skipna=TRUE, which is default, should give NAN if the full row is NAN per the docs.
This seems to be dependant on some combination of package versions but I cannot figure out why. I reinstalled completely to a fresh Anaconda 2.3.0 amd64 and the behaviour is the same.
See screenshot. For In[6] I expect index [0] to be NAN, not zero.
My package list, for reference:
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alabaster 0.7.3 py27_0
anaconda 2.3.0 np19py27_0
argcomplete 0.8.9 py27_0
astropy 1.0.3 np19py27_0
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} | 1 | 2015-08-10T14:19:14Z | 2015-08-10T14:30:57Z | 2015-08-10T14:30:33Z | CONTRIBUTOR | null | In pandas 0.15, a DataFrame's index no longer supports the modulo operator. I believe the following code used to work under pandas 0.14, but under pandas 0.15, a TypeError indicating "unsupported operand types" is raised:
```
In [1]: import pandas
In [2]: print pandas.version.version
0.15.2
In [3]: df = pandas.DataFrame({ 'i': xrange(10) })
In [4]: df.index
Out[4]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')
In [5]: r = df.index % 2
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-405b698d3a56> in <module>()
----> 1 r = df.index % 2
TypeError: unsupported operand type(s) for %: 'Int64Index' and 'int'
In [6]: r = df.index.to_series() % 2
In [7]:
```
Line 6 shows that a workaround exists, using the to_series() function.
This issue is very similar to [Issue #8608](https://github.com/pydata/pandas/issues/8608), which dealt with other arithemtic operations on an index. Pandas commit [2fcbc7f](https://github.com/pydata/pandas/pull/8634/commits) fixed that issue by adding addition, subtraction, multiplication, division, and unary operators, but not the modulo operator.
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| null | 2 | 2015-08-10T15:42:30Z | 2015-08-11T11:39:16Z | 2015-08-11T10:36:50Z | NONE | null | I'm using windows server 2003, and python2.7 with pandas 0.16.2. It takes my computer 4-5 minutes to execute the following program:
---
from pandas.io.data import Options
aapl = Options('uso', 'yahoo')
data = aapl.get_all_data()
print data.iloc[0:5, 0:5]
---
Is it ok or not?
If anyone wants to know more details, please let me know.
Thanks
L
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} | 3 | 2015-08-10T16:25:49Z | 2020-05-22T03:09:52Z | null | MEMBER | null | Derived from #10507. Consider better API for testing functions.
### 1. How to specify comparison tolerance
Current assert functions have `check_less_precise` and `check_exact` flags to specify how to compare values:
- `check_exact=True`: Use `assert_equal`, comparison using `==`.
- `check_exact=False` and `check_less_precise=False`: Use `assert_almost_equal`, comparing 5 digits after decimal points (default)
- `check_exact=False` and `check_less_precise=True`: Use `assert_almost_equal`, comparing 3 digits after decimal points
There can be single kw to specify above behaviors?
### 2. Make integrated assert function
I think it's nice to have test functions which supports all `pandas` objects for users who is starting contribution / using pandas as their dependencies. Changing `assertEquals` and `assertAlmostEquals` to internally use current `assert_index/series/frame_equal` is one idea.
### 3. Remove `assert_almost_equal`
Remove `assert_almost_equal` and use class-based validation method. Also add input type validation to `numpy_assert_array_equal`.
xref #9895.
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} | 2 | 2015-08-10T16:43:54Z | 2016-03-06T14:57:59Z | 2016-03-06T14:57:59Z | CONTRIBUTOR | null | Currently the exponential functions take one of three arguments to specify the length of backhistory. Each of these numerically converts to alpha. Outlined here: http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-moment-functions
Is there a reason we don't allow users to just pass in `alpha`? This is how I think about weighing some of the time.
I'm happy to do a PR in time; this issue is to solicit feedback.
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| null | 6 | 2015-08-10T23:09:16Z | 2015-08-11T15:53:42Z | 2015-08-11T10:36:31Z | NONE | null | This may have been intended, but this change (https://github.com/pydata/pandas/commit/3d54482bbd8086c27a01d489a0ae751e0b9c3731) released in 0.16.2 broke some of my code because `Index.to_native_types()` started converting all index types to strings, even integers. By reading the source I found I can prevent that by passing `quoting=True` to `to_native_types`, but since there's no documentation for `to_native_types` I wanted to get a little clarification: What is the expected behavior of `to_native_types` and what does the `quoting` keyword mean? Thanks!
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} | 4 | 2015-08-10T23:30:11Z | 2015-08-12T13:51:53Z | 2015-08-12T13:51:50Z | MEMBER | null | `Index.freq` should raise `AttributeError` in non-datetime-like index. But `Index.take` adds `freq=None` and broke the behavior.
```
import pandas as pd
idx = pd.Index([1, 2, 3])
idx.freq
# AttributeError: 'Int64Index' object has no attribute 'freq'
idx.take([True, False, True]).freq
# None
```
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} | 2 | 2015-08-10T23:45:43Z | 2015-08-29T00:03:43Z | 2015-08-29T00:03:43Z | CONTRIBUTOR | null | `nlargest/nsmallest` on frame should have `take_last` -> `keep`, in-line with #10236
[here](https://github.com/pydata/pandas/blob/master/pandas/core/frame.py#3187)
no deprecation is necessary as this is new in 0.17.0
cc @sinhrks
cc @cpcloud
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| null | 6 | 2015-08-11T04:22:08Z | 2015-08-11T10:36:09Z | 2015-08-11T10:36:09Z | NONE | null | I'm working on a windows computer, with python pandas 0.16.2 installed. This is what happens when I tried loading in my csv file.
According to <a href="http://www.utf8-chartable.de/unicode-utf8-table.pl?start=65024&utf8=string-literal">this page</a>, the unicode character that pandas is loading in (`\xef\xbb\xbf`) is the "zero width no-break-space" character. The first entry of the csv doesn't need to be "mouse" for this bug to occur.
Can someone try to replicate this bug on another windows computer?
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} | 1 | 2015-08-11T09:13:41Z | 2015-08-12T11:37:32Z | 2015-08-12T11:37:32Z | MEMBER | null | Closes #10215
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} | 6 | 2015-08-11T09:21:38Z | 2015-08-12T13:51:24Z | 2015-08-12T09:55:47Z | MEMBER | null | Follow-up of #10236. Suppress unnecessary warnings during the test.
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} | 5 | 2015-08-11T16:55:37Z | 2016-04-04T21:33:52Z | null | NONE | null | I ran into this bug when attempting some groupby-aggregates on data that had 64-bit floats.
```
_ids = np.array(range(201500000100,201500000110)*3).astype(float)
_df = pd.DataFrame({'id':_ids,
'value_1':np.random.randint(0,10,30),
'value_2':list('ABC')*10})
```
A simple groupby-aggregation produces the correct results, namely returning a DataFrame with an index of 10 rows.
```
_gb1 = _df.groupby('id').agg({'value_1':np.sum})
```
| value_1 | id |
| --- | --- |
| 2.015000e+11 | 14 |
| 2.015000e+11 | 4 |
| 2.015000e+11 | 8 |
| 2.015000e+11 | 25 |
| 2.015000e+11 | 12 |
| 2.015000e+11 | 16 |
| 2.015000e+11 | 15 |
| 2.015000e+11 | 21 |
| 2.015000e+11 | 8 |
| 2.015000e+11 | 7 |
However, grouping by the second value to return a multi-index returns an unexpected DataFrame.
```
_df.groupby(['id','value_2']).agg({'value_1':np.sum})
```
| id | value_2 | value_1 |
| --- | --- | --- |
| 2.015000e+11 | A | 2 |
| | B | 5 |
| | C | 3 |
| | A | 3 |
| | B | 0 |
| | C | 0 |
| | A | 4 |
| | B | 1 |
| | C | 3 |
| | A | 8 |
| | B | 7 |
| | C | 0 |
| | A | 3 |
| | B | 0 |
| | C | 2 |
| | A | 0 |
| | B | 4 |
| | C | 5 |
| | A | 4 |
| | B | 9 |
| | C | 0 |
| | A | 6 |
| | B | 6 |
| | C | 6 |
| | A | 8 |
| | B | 3 |
| | C | 4 |
| | A | 5 |
| | B | 7 |
| | C | 3 |
Casting the `id` variable to unicode appears to correct the problem and return an aggregated DF with the correct dimensions and multi-indexing.
```
_df['id_str'] = _df['id'].astype(unicode).str.slice(0,-2)
_df.groupby(['id_str','value_2']).agg({'value_1':np.sum})
```
---
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
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} | 5 | 2015-08-11T18:46:23Z | 2021-04-18T23:20:29Z | null | NONE | null | Running this code generates an error, since columns are now considered an Index and not a list. I'm not sure if the error that gets raised (set_index ends up asking for an index that is the entire length of the data frame) is actually by design. In this case, it was a bit unintuitive to figure out how to catch, since Index objects and python lists often work so similarly.
```
import string
import pandas as pd
data1 = 'x'*5 + 'y'*5
data2 = string.lowercase[:5]*2
data3 = range(10)
data_dict = {'Cat': list(data1), 'SubCat': list(data2), 'Vals':data3}
df = pd.DataFrame(data_dict)
ordered_df = df[['Cat', 'SubCat', 'Vals']]
correct_df = ordered_df.reset_index([x for x in ordered_df.columns[:2]])
error_df = ordered_df.set_index(ordered_df.columns[:2])
```
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} | 3 | 2015-08-11T19:56:02Z | 2021-06-05T18:59:49Z | null | CONTRIBUTOR | null | `PeriodIndex`s should resolve when `datetime.date` is passed into `in`, but doesn't. Example below.
Tracing this through, it looks like `parse_time_string` is expected to return a tuple in `get_loc` (which is called by `__contains__`): https://github.com/pydata/pandas/blob/master/pandas/tseries/period.py#L610
But if passed something that isn't a string, just returns it back as a single item: https://github.com/pydata/pandas/blob/master/pandas/tseries/tools.py#L447
...so in this instance the lookup raises an exception and mistakenly propagates up that the value can't be found.
What's the best way forward here? Should the return type always be the same? Or do we add a check in `get_loc` to stop it calling `parse_time_string`? Or broaden the `except` here to be any exception: https://github.com/pydata/pandas/blob/master/pandas/tseries/period.py#L612
My inclination is the first, but whether that will have some blast radius / what the standards are? The third seems like a reasonable cut through too.
``` python
In [9]:
period_index
Out[9]:
PeriodIndex(['2015-01-01', '2015-01-02', '2015-01-05', '2015-01-06',
'2015-01-07', '2015-01-08', '2015-01-09', '2015-01-12',
'2015-01-13', '2015-01-14', '2015-01-15', '2015-01-16',
'2015-01-19', '2015-01-20', '2015-01-21', '2015-01-22',
'2015-01-23', '2015-01-26', '2015-01-27', '2015-01-28',
'2015-01-29', '2015-01-30'],
dtype='int64', freq='B')
In [10]:
'2015-01-06' in period_index
Out[10]:
True
In [15]:
dt = datetime.datetime(2015,1,6)
dt
Out[15]:
datetime.datetime(2015, 1, 6, 0, 0)
In [16]:
dt in period_index
Out[16]:
False
In [17]:
d = datetime.date(2015,1,6)
d
Out[17]:
datetime.date(2015, 1, 6)
In [18]:
d in period_index
Out[18]:
False
```
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| null | 3 | 2015-08-11T20:12:01Z | 2015-11-10T01:27:28Z | 2015-11-10T01:27:28Z | NONE | null | Simple change, closes #10797
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} | 18 | 2015-08-11T20:14:58Z | 2015-08-21T20:20:10Z | 2015-08-21T19:56:29Z | CONTRIBUTOR | null | Closes #10633
This a first attempt at the error messages mentioned. Not ready to merge yet but I wanted to start the conversation...
I think I might have the error message wrong - anyone got any ideas.
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} | 17 | 2015-08-11T22:50:49Z | 2015-11-22T09:02:08Z | 2015-08-26T01:23:14Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/10798
Not sure if the testing is done in a good way - I just copy & pasted code to test `PeriodIndex` & `DatetimeIndex` (it wouldn't work for `LikeDatetimeIndex` because of `TimeDeltaIndex`) - very open to feedback on better ways to do this.
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https://api.github.com/repos/pandas-dev/pandas/issues/10802 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10802/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10802/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10802/events | https://github.com/pandas-dev/pandas/issues/10802 | 100,438,790 | MDU6SXNzdWUxMDA0Mzg3OTA= | 10,802 | Should empty strings be null for notnull() and isnull()? | {
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| null | 6 | 2015-08-12T00:10:03Z | 2020-05-03T20:09:24Z | 2020-05-03T20:09:24Z | CONTRIBUTOR | null | Is there a reason that `notnull()` and `isnull()` consider an empty string to not be a missing value?
```
pd.isnull('')
False
```
Seems like in string data, people usually think of the empty string as "missing".
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