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| 14,708 |
DOC: Disambiguate 'where' in boolean indexing-10min.rst (#12661)
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| 3 | 2016-11-22T05:53:32Z | 2016-12-14T16:49:47Z | 2016-11-23T08:31:50Z |
MEMBER
| null |
- [x] closes #12661
Per @jorisvandenbossche comment in this thread (https://github.com/pandas-dev/pandas/pull/12671), remove code markdown from 'where' to not confuse the example with the `where()` method.
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MDU6SXNzdWUxOTA5Njg3MDk=
| 14,709 |
Changes in groups.items() output between Pandas 18.0.1 and Pandas 19.0.1
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| 3 | 2016-11-22T11:01:19Z | 2016-11-22T14:49:13Z | 2016-11-22T14:49:12Z |
NONE
| null |
"""The code worked with a dict, and used groupby to retrieve subdicts of lists of indexes based on length.
Then I had to apply a process on this, and I popped on the lists of indexes built by groupby through a loop.
But this morning I ran the code, it worked, then I updated Pandas to 19.0.0.1 and it started to bug with the TypeError as Int64Index has no .pop() indeed."""
```python
dictoflists={0:[0],1:[1,3],2:[2,3],3:[3,4],4:[4,5,6],5: [3,0,1,6,7,8]}
tested={0:[0,1],1:[8,1,3],2:[2,3],3:[3,4],4:[9,9,9],5: [3,0,1,6,7,8], 6:[0,0,0,1]}
transform_df=pd.DataFrame(dictoflists,dtype=object,index=dictoflists.keys(),
columns=['Sequence'])
transform_df['length']=[len(list_i) for list_i in dictoflists.values()]
transform_df2=transform_df.groupby('length', sort=False)
for key,value in transform_df2.groups.items():
group_index=value
while True:
ref=tested[group_index.pop()]
```
I easily circumvented this by turning value into a list with 'group_index=list(value)'
but I want to give this feedback as I don't know if the change is intentional or not, as I could not find a reference about it in the release notes.
Edit: updated to include reference for variables...
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Inconsistent merging for multi-indexed dataframes with datetime.date
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| 1 | 2016-11-22T11:22:33Z | 2018-07-06T20:21:13Z | 2016-11-23T11:54:16Z |
NONE
| null |
#### Code Sample
In this example, we merge multi-indexed dataframes, where one level contains `datetime.date` objects. We illustrate the inconsistent behavior by comparing the output of two merge operations.
1. Let us create the three dataframes {df1, df2, df3} such that
* df1 and df2 indexes overlap
* df1 and df3 indexes do not overlap
```python
>>> index_1 = pd.MultiIndex(levels=[['dummy'], [dt.date(2016,1,1)]],
... labels=[[0], [0]], names=['i', 'j'])
>>> index_2 = pd.MultiIndex(levels=[['dummy'], [dt.date(2016,1,1)]],
... labels=[[0], [0]], names=['i', 'j'])
>>> index_3 = pd.MultiIndex(levels=[['dummy'], [dt.date(2016,1,2)]],
... labels=[[0], [0]], names=['i', 'j'])
>>> df1 = pd.DataFrame([1], index=index_1, columns=['A'])
>>> df2 = pd.DataFrame([1], index=index_2, columns=['B'])
>>> df3 = pd.DataFrame([1], index=index_3, columns=['C'])
>>> df1
A
i j
dummy 2016-01-01 1
```
2. Let us merge using the indexes.
```python
>>> merge1 = df1.merge(df2, left_index=True, right_index=True, how='outer')
>>> merge2 = df1.merge(df3, left_index=True, right_index=True, how='outer')
>>> merge1
A B
i j
dummy 2016-01-01 1 1
>>> merge2
A C
i j
dummy 2016-01-01 1.0 NaN
2016-01-02 NaN 1.0
```
3. Let us examine the indexes
```python
>>> merge1.index[0][1]
datetime.date(2016, 1, 1)
>>> merge2.index[0][1]
Timestamp('2016-01-01 00:00:00')
```
#### Problem description
The index type of the merge output is inconsistent.
In my opinion, the merge operation should not change the types.
#### Expected Output
```python
>>> merge2.index[0][1]
datetime.date(2016, 1, 1)
```
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-229.7.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IE.UTF-8
LOCALE: en_IE.UTF-8
pandas: 0.19.1
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1a
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
#### Comment
This bug does not occur for single-index dataframes
```python
>>> df1 = pd.DataFrame([1], index=[dt.date(2016,1,1)], columns=['A'])
>>> df3 = pd.DataFrame([1], index=[dt.date(2016,1,2)], columns=['B'])
>>> df1.merge(df3, left_index=True, right_index=True, how='outer').index[0]
datetime.date(2016, 1, 1)
```
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MDU6SXNzdWUxOTEwMTY5NzA=
| 14,711 |
Make categories and ordered part of CategoricalDtype
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| 35 | 2016-11-22T14:09:25Z | 2017-09-23T18:56:12Z | 2017-09-23T18:56:12Z |
CONTRIBUTOR
| null |
This is to discuss pushing the `Categorical.categories` and
`Categorical.ordered` information into the extension type `CategoricalDtype`.
```python
pd.CategoricalDtype(categories, ordered=False)
```
Note that there is no `values` argument. This is a type constructor, that
isn't attached to any specific `Categorical` instance.
## Why?
Several times now (`read_csv(..., dtype=...)`, `.astype(...)`, `Series([], dtype=...)`)
we have places where we accept `dtype='category'` which takes the values
in the method (the series, or column from the CSV, etc.)
and hands it off to the *value* constructor, with no control over the
`categories` and `ordered` arguments.
```python
Categorical(values, categories=None, ordered=False)
```
The proposal here would add the `categories` and `ordered`
attributes / arguments to `CategoricalDtype` and provide a common API
for specifying non-default parameters for the `Categorical` constructor
in methods like `read_csv`, `astype`, etc.
```python
t = pd.CategoricalDtype(['low', 'med', 'high'], ordered=True)
pd.read_csv('foo.csv', dtype={'A': int, 'B': t)
pd.Series(['high', 'low', 'high'], dtype=t)
s = pd.Series(['high', 'low', 'high'])
s.astype(t)
```
We would continue to accept `dtype='category'`.
This becomes even more import when doing operations on larger than memory datasets with something like `dask` or even (`read_csv(..., chunksize=N)`). Right now you don't have an easy way to specify the `categories` or `ordered` for columns (assuming you know them ahead of time).
## Issues
1. `CategoricalDtype` currently isn't part of the public API. Which methods
on it do we make public?
2. Equality semantics: For backwards compat, I think all instances
of `CategoricalDtype` should compare equal with all others. You can use
identity to check if two types are the same
```python
t1 = pd.CategoricalDtype(['a', 'b'], ordered=True)
t2 = pd.CategoricalDtype(['a', 'b'], ordered=False)
t1 == t2 # True
t1 is t2 # False
t1 is t1 # True
```
3. Should the `categories` argument be required? Currently `dtype='category'`
says 1.) infer the categories based on the *values*, and 2.) it's unordered.
Would `CategoricalDtype(None, ordered=False)` be allowed?
4. Strictness? If I say
```python
pd.Series(['a', 'b', 'c'], dtype=pd.CategoricalDtype(['a', 'b']))
```
What happens? I would probably expect a `TypeError` or `ValueError` as `c`
isn't "supposed" to be there. Or do we replace `'c'` with `NA`? Should
`strict` be another parameter to `CategoricalDtype` (I don't think so).
I'm willing to work on this over the next couple weeks.
xref https://github.com/pandas-dev/pandas/issues/14676 (astype)
xref https://github.com/pandas-dev/pandas/issues/14503 (read_csv)
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MDU6SXNzdWUxOTEwMzYwNTE=
| 14,712 |
read_csv ignores dtype for empty CSV files
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| 0 | 2016-11-22T15:21:35Z | 2016-11-24T21:19:38Z | 2016-11-24T21:18:20Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import io
>>> import pandas as pd
>>> pd.read_csv(io.StringIO('foo,bar'), header=0, dtype=np.float64).values
array([], shape=(0, 2), dtype=object)
```
#### Problem description
CSV reader doesn't respect the given dtype `np.float64` but sets `object` dtype instead.
#### Expected Output
```python
array([], shape=(0, 2), dtype=float64)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.8-gnu-1
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 28.8.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.5
sqlalchemy: 1.1.4
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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DOC: update FAQ to note pandas-qt only works for python 2.x
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| 2 | 2016-11-22T16:34:29Z | 2016-11-22T16:44:06Z | 2016-11-22T16:39:16Z |
CONTRIBUTOR
| null |
- [ ] closes #14703
Here is the the pull request for the issue I raised [here](https://github.com/pandas-dev/pandas/issues/14703).
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ENH: enable pd.cut to handle i8 convertibles
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| 15 | 2016-11-22T16:58:43Z | 2016-12-03T10:11:59Z | 2016-12-03T10:11:59Z |
CONTRIBUTOR
| null |
so this should work for timedeltas AND datetimes.
Should be straight forward. detect an i8 convertible. turn into i8. do the cut. turn back to the original dtype.
```
In [15]: s = Series(pd.to_timedelta(np.random.randint(0,100,size=10),unit='ms')).sort_values()
In [16]: s
Out[16]:
3 00:00:00.005000
5 00:00:00.007000
7 00:00:00.010000
4 00:00:00.017000
9 00:00:00.023000
1 00:00:00.043000
0 00:00:00.045000
6 00:00:00.047000
8 00:00:00.065000
2 00:00:00.090000
dtype: timedelta64[ns]
In [18]: pd.cut(s, 5)
TypeError: unsupported operand type(s) for +: 'Timedelta' and 'float'
# works when converted
In [17]: pd.cut(s.astype('timedelta64[ms]'), 5)
Out[17]:
3 (4.915, 22]
5 (4.915, 22]
7 (4.915, 22]
4 (4.915, 22]
9 (22, 39]
1 (39, 56]
0 (39, 56]
6 (39, 56]
8 (56, 73]
2 (73, 90]
dtype: category
Categories (5, object): [(4.915, 22] < (22, 39] < (39, 56] < (56, 73] < (73, 90]]
```
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| 191,100,506 |
MDU6SXNzdWUxOTExMDA1MDY=
| 14,715 |
BUG: resample Grouper in a list grouping on a column with NaT throws an error
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| 6 | 2016-11-22T19:24:30Z | 2018-08-28T12:22:28Z | 2018-08-28T12:22:28Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'completed':pd.date_range('1/1/2011', periods=365, freq='D')})
# In the real case i have nulls in my data
df.iloc[-1] = pd.NaT
# This works
df.groupby(pd.Grouper(key='completed', freq='AS'))
# This doesn't and throws an error
df.groupby([pd.Grouper(key='completed', freq='AS')])
```
#### Problem description
Second form throws the following error:
```python
/anaconda/lib/python2.7/site-packages/pandas/indexes/base.pyc in _assert_take_fillable(self, values, indices, allow_fill, fill_value, na_value)
1537 taken[mask] = na_value
1538 else:
-> 1539 taken = values.take(indices)
1540 return taken
1541
IndexError: index 364 is out of bounds for size 364
```
#### Expected Output
A proper grouper
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 0.6
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTQ4ODU4NTk=
| 14,716 |
Fix num_days in PandasAutoDateLocator
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| 7 | 2016-11-22T19:52:37Z | 2017-03-21T08:53:14Z | 2017-03-21T08:52:38Z |
CONTRIBUTOR
| null |
Prior to this commit, one year is worth 12 days, not 12 * 31 days.
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MDExOlB1bGxSZXF1ZXN0OTQ4OTczMjc=
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BUG: Respect the dtype parameter for empty CSV
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| 5 | 2016-11-22T21:01:49Z | 2016-11-24T21:18:46Z | 2016-11-24T21:18:20Z |
MEMBER
| null |
Title is self-explanatory. Closes #14712.
Should be merged in before #14295 because the bug could also exist for the Python parser.
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DOC: fix typo in merge_asof docstring examples
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| 2 | 2016-11-23T00:20:44Z | 2016-11-23T08:35:09Z | 2016-11-23T08:35:09Z |
CONTRIBUTOR
| null |
Just a trivial fix to docs - incorrect function name in doctests.
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TST: test_transactions for sqlite fallback fails on python 3.6
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| 2 | 2016-11-23T08:57:34Z | 2019-09-15T13:14:00Z | 2019-09-15T13:14:00Z |
MEMBER
| null |
Skipped on travis for now: https://github.com/pandas-dev/pandas/commit/880de3054c7ce3a6c48e295837207787dc73350f
See also comment here: https://github.com/pandas-dev/pandas/issues/14679#issuecomment-261533205 (I can reproduce it locally by running the test, but not with a simple example mimicking the test)
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Feature Request: Circular bins with pd.cut
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[] | null | 5 | 2016-11-23T13:05:53Z | 2019-12-28T05:36:42Z | 2019-12-28T05:36:42Z |
NONE
| null |
pandas.cut and similar operate on the principle that bins start at one end of a line and finish at another. However, sometimes it is necessary to work with data that operates on a circular scale, for example compass directions.
It would be a great enhancement if pd.cut could deal with these cases.
I do not know of anything in numpy or pandas that currently deals with this situation, but it would be very useful.
#### Current workaround
Currently this is accomplished by having a bin covering half of the real bin at each end of the scale, for example the bin 345° to15° would be covered by a bin of 0° to 15° and a bin of 345° to 360°. It is then necessary to assign the members of one category to the other, rename it, and then delete the unused category.
```python
data = pd.DataFrame(np.random.random(400) * 360, columns=["Direction"])
bins = np.array([-15, 15, 45, 75, 105, 135, 165, 195, 225, 255, 285, 315, 345, 375])
c = pd.cut(data.Direction, bins)
c[c=="(-15, 15]"] = "(345, 375]"
c = c.cat.remove_unused_categories()
cats = list(c.cat.categories)
cats[-1] = '(345, 15]'
c = c.cat.set_categories(cats)
```
#### Desired behaviour
Better would be accepting a parameter indicating at what point the scale loops, e.g. zeroat=360. Other solutions may be possible, such as taking the structure of the list of bins to indicate that the scale loops.
One issue is the order. How can the fact that 345 is both more than and less than 0 be dealt with, depending on what direction you are looking in? There is an order, but it isn't straight forward.
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| 191,313,614 |
MDU6SXNzdWUxOTEzMTM2MTQ=
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Series.unique converts uint64 to int64 (with overflow)
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| 16 | 2016-11-23T16:03:30Z | 2016-12-20T13:46:00Z | 2016-12-20T13:46:00Z |
NONE
| null |
```
>>> import pandas as pd
>>> import numpy as np
>>> s = pd.Series([1,2,2**63], dtype=np.dtype('uint64'))
>>> s
0 1
1 2
2 9223372036854775808
dtype: uint64
>>> s.unique()
array([ 1, 2, -9223372036854775808])
```
Series.unique should preserve whatever type the values are. But instead it changes from uint64 to int64.
INSTALLED VERSIONS
------------------
...
python: 3.4.3.final.0
python-bits: 64
...
...
pandas: 0.17.1
numpy: 1.10.4
...
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doc: comverted --> converted
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| 1 | 2016-11-23T18:49:28Z | 2016-11-24T09:22:31Z | 2016-11-23T20:35:31Z |
CONTRIBUTOR
| null |
Just fixing a spelling error =)
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MAINT: Ignore .pxi files
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| 12 | 2016-11-23T19:44:39Z | 2016-12-15T09:15:05Z | 2016-11-25T13:19:12Z |
MEMBER
| null |
All `.pxi` files have the warning to not edit directly, so why not have Git ignore any changes made there?
In addition, this PR deletes all of the `.pxi` files from the codebase because they are automatically generated by the `.pxi.in` files
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Minor typo in frame.py
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| 1 | 2016-11-23T21:15:38Z | 2016-11-24T09:22:27Z | 2016-11-23T21:23:32Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
typo "explicitly"
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MDU6SXNzdWUxOTE0MDAwMzc=
| 14,725 |
Cannot add TimedeltaIndex to tz-aware DatetimeIndex (but can to non-tz-aware DatetimeIndex)
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[] | null | 2 | 2016-11-23T23:12:39Z | 2016-11-24T03:09:20Z | 2016-11-24T03:09:19Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
Under 0.19.1 and latest master:
```python
In [19]: dr = pd.date_range("20161123 00:00", "20161123 02:00", freq="H")
In [20]: tdr = pd.timedelta_range("1 minute", "3 minutes", freq="T")
In [21]: dr + tdr
Out[21]:
DatetimeIndex(['2016-11-23 00:01:00', '2016-11-23 01:02:00',
'2016-11-23 02:03:00'],
dtype='datetime64[ns]', freq='61T')
In [22]: dr_tz = pd.date_range("20161123 00:00", "20161123 02:00", freq="H", tz="America/New_York")
...:
In [23]: dr_tz + tdr
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-23-c10d4a537816> in <module>()
----> 1 dr_tz + tdr
C:\Anaconda\envs\bleeding\lib\site-packages\pandas\tseries\base.pyc in __add__(self, other)
617 from pandas.tseries.offsets import DateOffset
618 if isinstance(other, TimedeltaIndex):
--> 619 return self._add_delta(other)
620 elif isinstance(self, TimedeltaIndex) and isinstance(other, Index):
621 if hasattr(other, '_add_delta'):
C:\Anaconda\envs\bleeding\lib\site-packages\pandas\tseries\index.pyc in _add_delta(self, delta)
787 new_values = self._add_delta_td(delta)
788 elif isinstance(delta, TimedeltaIndex):
--> 789 new_values = self._add_delta_tdi(delta)
790 # update name when delta is Index
791 name = com._maybe_match_name(self, delta)
C:\Anaconda\envs\bleeding\lib\site-packages\pandas\tseries\base.pyc in _add_delta_tdi(self, other)
704 mask = (self._isnan) | (other._isnan)
705 new_values[mask] = tslib.iNaT
--> 706 return new_values.view(self.dtype)
707
708 def isin(self, values):
TypeError: data type not understood
```
#### Problem description
Given a DatetimeIndex that is not tz-aware, adding a TimedeltaIndex of the same length adds the values elementwise, as one would expect. Given a tz-aware DatetimeIndex, however, adding a TimedeltaIndex results in TypeError. Performing the same operation by iterating over the tz-aware index and adding the values directly works in the expected manner. This behavior did not occur under pandas 0.16.2; however, the above behavior occurs under at least 0.19.1 and the latest master.
#### Expected Output
Equivalent to the elementwise operation below, which works on latest master:
```python
In [25]: pd.Index([dr_tz[i] + tdr[i] for i in xrange(len(dr_tz))])
Out[25]:
DatetimeIndex(['2016-11-23 00:01:00-05:00', '2016-11-23 01:02:00-05:00',
'2016-11-23 02:03:00-05:00'],
dtype='datetime64[ns, America/New_York]', freq=None)
```
This also worked under 0.16.2:
```python
In [14]: dr_tz = pd.date_range("20161123 00:00", "20161123 02:00", freq="H", tz="America/New_York")
In [15]: tdr = pd.timedelta_range("1 minute", "3 minutes", freq="T")
In [16]: dr_tz + tdr
Out[16]:
DatetimeIndex(['2016-11-23 00:01:00-05:00', '2016-11-23 01:02:00-05:00',
'2016-11-23 02:03:00-05:00'],
dtype='datetime64[ns]', freq='61T', tz='America/New_York')
```
#### Output of ``pd.show_versions()``
<details>
In [26]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0+98.gc045e1d
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: None
lxml: 3.6.4
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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Segfault on Linux with 0.19.1
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| 1 | 2016-11-24T05:31:29Z | 2016-11-24T09:11:02Z | 2016-11-24T09:11:02Z |
CONTRIBUTOR
| null |
I'm getting a segfault on my Linux machines with the following:
```
from datetime import datetime
import pandas.tseries.offsets as offsets
from pandas.tslib import Timestamp
dt = datetime(2011, 1, 1, 9, 0)
tz = 'dateutil/Asia/Tokyo'
offset_s = offsets.LastWeekOfMonth(n=1, weekday=5)
expected = Timestamp('2011-01-29 09:00:00')
expected_localize = expected.tz_localize(tz)
result = Timestamp(dt, tz=tz) + offset_s
expected_localize = expected.tz_localize(tz)
```
Here's a script to reproduce on a clean debian-stable docker container:
```
docker run --rm -ti -v $PWD:/io debian:stable /bin/bash
```
```
apt-get update
apt-get install -y wget python-minimal
wget https://bootstrap.pypa.io/get-pip.py
python get-pip.py
pip install pandas
cat > pandas_segfault.py << EOF
from datetime import datetime
import pandas.tseries.offsets as offsets
from pandas.tslib import Timestamp
dt = datetime(2011, 1, 1, 9, 0)
tz = 'dateutil/Asia/Tokyo'
offset_s = offsets.LastWeekOfMonth(n=1, weekday=5)
expected = Timestamp('2011-01-29 09:00:00')
expected_localize = expected.tz_localize(tz)
result = Timestamp(dt, tz=tz) + offset_s
expected_localize = expected.tz_localize(tz)
EOF
python pandas_segfault.py
```
Segfault from manylinux wheel up on pypi, and install from source of 0.19.1 . No segfault from current master.
Worth a point release to fix?
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UnicodeWarning when using `DataFrame.xs`
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[] | null | 7 | 2016-11-24T05:53:27Z | 2019-07-09T14:47:41Z | 2019-07-09T14:47:40Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
charset = [u'a', u'b', u'c', u'd', u'e']
list_dt = [ {u'a': x, u'b': y, u'c': z, u'd': w, u'e': k, u'value': 1} for x in charset for y in charset for z in charset for w in charset for k in charset]
dt_this = pd.DataFrame(list_dt).set_index(['a','b','c','d','e'])
print('try c')
a=dt_this.xs(u'c', level='a')
print('try b')
a=dt_this.xs(u'b', level='a')
print('try c')
a=dt_this.xs(u'c', level='a')
level = u'a'
drop_level = True
key = u'c'
axis = 0
axis = dt_this._get_axis_number(axis)
labels = dt_this._get_axis(axis)
if level is not None:
loc, new_ax = labels.get_loc_level(key, level=level,
drop_level=drop_level)
# convert to a label indexer if needed
if isinstance(loc, slice):
lev_num = labels._get_level_number(level)
if labels.levels[lev_num].inferred_type == 'integer':
loc = labels[loc]
# create the tuple of the indexer
indexer = [slice(None)] * dt_this.ndim
indexer[axis] = loc
indexer = tuple(indexer)
print('try c, using indexing')
print(indexer)
result = a.ix[indexer]
indexer2 = (slice(1250, 1875, None), slice(None, None, None))
print(indexer2)
print('try c, using indexing, naive integer')
result = a.ix[indexer2]
```
##### output
You can run this snippet (say, saved as `test_bug.py`), with `python -W always test_bug.py`, then get the following output.
~~~
try c
/home/yimengzh/miniconda2/envs/theano-tf-test/lib/python2.7/site-packages/pandas/indexes/base.py:2134: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
return self._engine.get_loc(key)
/home/yimengzh/miniconda2/envs/theano-tf-test/lib/python2.7/site-packages/pandas/indexes/base.py:2136: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
return self._engine.get_loc(self._maybe_cast_indexer(key))
try b
try c
/home/yimengzh/miniconda2/envs/theano-tf-test/lib/python2.7/site-packages/pandas/indexes/base.py:2134: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
return self._engine.get_loc(key)
/home/yimengzh/miniconda2/envs/theano-tf-test/lib/python2.7/site-packages/pandas/indexes/base.py:2136: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
return self._engine.get_loc(self._maybe_cast_indexer(key))
try c, using indexing
(slice(1250, 1875, None), slice(None, None, None))
/home/yimengzh/miniconda2/envs/theano-tf-test/lib/python2.7/site-packages/pandas/indexes/base.py:2134: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
return self._engine.get_loc(key)
/home/yimengzh/miniconda2/envs/theano-tf-test/lib/python2.7/site-packages/pandas/indexes/base.py:2136: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
return self._engine.get_loc(self._maybe_cast_indexer(key))
(slice(1250, 1875, None), slice(None, None, None))
try c, using indexing, naive integer
~~~
#### Problem description
For some reason, when slicing those rows with column `a` equal to `c`, there are `UnicodeWarning` spit out. For some other rows, say those with `a` equal to `b`, there's no such warning (well, actually I'm not sure whether this is really a warning, as when I use `python -W error` to run the script, no warning appears.
I did some debugging myself on this, and seems that when passing a slice, or a tuple of slices into `DataFrame.ix`, where some elements in the slice are `np.int64`, this can **possibly** happen (some `np.int64` numbers won't trigger the warning). When replacing all `np.int64` attributes (`start`, `stop`, `step`) of the slice with native Python integers, there's no warning. This is based on running the source code of `DataFrame.xs` line by line.
#### Output of ``pd.show_versions()``
<details>
~~~
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-279.14.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1.post20160901
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
~~~
</details>
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BUG: Ensure min_itemsize is always a list (#11412)
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| 13 | 2016-11-24T11:39:41Z | 2016-12-05T23:44:36Z | 2016-12-05T23:44:36Z |
MEMBER
| null |
- [x] closes #11412
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] tests added / passed
- [x] whatsnew entry
(I plan to fix #10381 and #12154 next, but first we need coherence on ``type(data_columns)`` - and ``Index`` seems to me the best option.)
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ENH: add data hashing routines
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| 52 | 2016-11-24T15:21:19Z | 2016-11-28T16:31:04Z | 2016-11-28T16:19:06Z |
CONTRIBUTOR
| null |
xref https://github.com/dask/dask/pull/1807
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| 9 | 2016-11-24T15:47:13Z | 2017-05-19T09:27:51Z | 2017-01-04T00:00:09Z |
NONE
| null |
#### Code Sample
```python
import pandas as pd
index = pd.Index([1,2,3])
x = pd.Series(index=index, data=range(3))
y = pd.Series([1,3])
x.loc[[1,3]] # can index series with list
x.loc[y] # can index series with another series
index = pd.MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C']])
x = pd.Series(index=index, data=range(9))
y = pd.Series([1,3])
x.loc[[1,3]] # can index series with list
x.loc[y] # cannot index series with another series
```
#### Problem description
In pandas you can index a DataFrame or Series with a list, ndarray, series, etc. But this fails when using a muliIndex.
#### Expected Output
No failures
#### Error message
<details>
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/core/indexing.py in _get_label(self, label, axis)
94 try:
---> 95 return self.obj._xs(label, axis=axis)
96 except:
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/core/generic.py in xs(self, key, axis, level, drop_level)
1776 loc, new_index = self.index.get_loc_level(key,
-> 1777 drop_level=drop_level)
1778 else:
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in get_loc_level(self, key, level, drop_level)
1795 else:
-> 1796 return partial_selection(key)
1797 else:
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in partial_selection(key, indexer)
1761 if indexer is None:
-> 1762 indexer = self.get_loc(key)
1763 ilevels = [i for i in range(len(key))
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in get_loc(self, key, method)
1671 start, stop = (self.slice_locs(lead_key, lead_key)
-> 1672 if lead_key else (0, len(self)))
1673
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in slice_locs(self, start, end, step, kind)
1577 # happens in get_slice_bound method), but it adds meaningful doc.
-> 1578 return super(MultiIndex, self).slice_locs(start, end, step, kind=kind)
1579
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/base.py in slice_locs(self, start, end, step, kind)
3175 if start is not None:
-> 3176 start_slice = self.get_slice_bound(start, 'left', kind)
3177 if start_slice is None:
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in get_slice_bound(self, label, side, kind)
1548 label = label,
-> 1549 return self._partial_tup_index(label, side=side)
1550
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in _partial_tup_index(self, tup, side)
1591
-> 1592 if lab not in lev:
1593 if not lev.is_type_compatible(lib.infer_dtype([lab])):
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/base.py in __contains__(self, key)
1392 def __contains__(self, key):
-> 1393 hash(key)
1394 # work around some kind of odd cython bug
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/core/generic.py in __hash__(self)
830 raise TypeError('{0!r} objects are mutable, thus they cannot be'
--> 831 ' hashed'.format(self.__class__.__name__))
832
TypeError: 'Series' objects are mutable, thus they cannot be hashed
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-7-5927dd14b2bf> in <module>()
4 y = pd.Series([1,3])
5 x.loc[[1,3]] # can index series with list
----> 6 x.loc[y] # cannot index series with another series
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1309 return self._getitem_tuple(key)
1310 else:
-> 1311 return self._getitem_axis(key, axis=0)
1312
1313 def _getitem_axis(self, key, axis=0):
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1480 # fall thru to straight lookup
1481 self._has_valid_type(key, axis)
-> 1482 return self._get_label(key, axis=axis)
1483
1484
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/core/indexing.py in _get_label(self, label, axis)
95 return self.obj._xs(label, axis=axis)
96 except:
---> 97 return self.obj[label]
98 elif isinstance(label, tuple) and isinstance(label[axis], slice):
99 raise IndexingError('no slices here, handle elsewhere')
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/core/series.py in __getitem__(self, key)
640 key = check_bool_indexer(self.index, key)
641
--> 642 return self._get_with(key)
643
644 def _get_with(self, key):
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/core/series.py in _get_with(self, key)
653 if isinstance(key, tuple):
654 try:
--> 655 return self._get_values_tuple(key)
656 except:
657 if len(key) == 1:
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/core/series.py in _get_values_tuple(self, key)
701
702 # If key is contained, would have returned by now
--> 703 indexer, new_index = self.index.get_loc_level(key)
704 return self._constructor(self._values[indexer],
705 index=new_index).__finalize__(self)
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in get_loc_level(self, key, level, drop_level)
1794 return partial_selection(key)
1795 else:
-> 1796 return partial_selection(key)
1797 else:
1798 indexer = None
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in partial_selection(key, indexer)
1760 def partial_selection(key, indexer=None):
1761 if indexer is None:
-> 1762 indexer = self.get_loc(key)
1763 ilevels = [i for i in range(len(key))
1764 if key[i] != slice(None, None)]
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in get_loc(self, key, method)
1670 lead_key, follow_key = key[:i], key[i:]
1671 start, stop = (self.slice_locs(lead_key, lead_key)
-> 1672 if lead_key else (0, len(self)))
1673
1674 if start == stop:
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in slice_locs(self, start, end, step, kind)
1576 # This function adds nothing to its parent implementation (the magic
1577 # happens in get_slice_bound method), but it adds meaningful doc.
-> 1578 return super(MultiIndex, self).slice_locs(start, end, step, kind=kind)
1579
1580 def _partial_tup_index(self, tup, side='left'):
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/base.py in slice_locs(self, start, end, step, kind)
3174 start_slice = None
3175 if start is not None:
-> 3176 start_slice = self.get_slice_bound(start, 'left', kind)
3177 if start_slice is None:
3178 start_slice = 0
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in get_slice_bound(self, label, side, kind)
1547 if not isinstance(label, tuple):
1548 label = label,
-> 1549 return self._partial_tup_index(label, side=side)
1550
1551 def slice_locs(self, start=None, end=None, step=None, kind=None):
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/multi.py in _partial_tup_index(self, tup, side)
1590 section = labs[start:end]
1591
-> 1592 if lab not in lev:
1593 if not lev.is_type_compatible(lib.infer_dtype([lab])):
1594 raise TypeError('Level type mismatch: %s' % lab)
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/indexes/base.py in __contains__(self, key)
1391
1392 def __contains__(self, key):
-> 1393 hash(key)
1394 # work around some kind of odd cython bug
1395 try:
/home/owl/miniconda3/envs/p3/lib/python3.5/site-packages/pandas/core/generic.py in __hash__(self)
829 def __hash__(self):
830 raise TypeError('{0!r} objects are mutable, thus they cannot be'
--> 831 ' hashed'.format(self.__class__.__name__))
832
833 def __iter__(self):
TypeError: 'Series' objects are mutable, thus they cannot be hashed
```
</details>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-47-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.19.1
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTUyMTcwNjE=
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CLN: move assignment from header into cython
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| 3 | 2016-11-24T18:18:28Z | 2016-11-25T12:52:16Z | 2016-11-25T12:52:11Z |
CONTRIBUTOR
| null |
an insignificant refactoring that allows PyPy2.7 to build and import Pandas. The original code uses functions that are macros in CPython but actual c-functions in PyPy and one cannot assign to a function result
Note that while PyPy imports Pandas, it very quickly segfaults, Apparently PyPy does not yet play well with enough of Cython, I suspect strange interactions with tp_new and tp_deallocate
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Default example of plotting a time series fails in v0.19.1
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| 4 | 2016-11-24T19:56:28Z | 2016-11-24T23:37:36Z | 2016-11-24T23:37:27Z |
NONE
| null |
Simply using the example of the [documentation](http://pandas.pydata.org/pandas-docs/stable/visualization.html):
#### Code Sample, a copy-pastable example if possible
```python
ts = ps.Series(np.random.randn(1000), index=ps.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
ts.plot()
```
#### Problem description
fails with the error:
TypeError: float() argument must be a string or a number
Note: I confirmed that downgrading to Pandas version 0.19.0 this issue goes away.
#### Expected Output
I would expect a nicely formatted graph with date labels on the x-axis.
#### ``traceback``
<details>
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-0d06e21bb979> in <module>()
2 ap.columns = [["Month", "Passengers"]]
3 apts = ps.Series(ap.Passengers.values.astype('float'), index=ap.Month)
----> 4 ax = apts.plot(marker='.', label='Airline Passengers')
5 ax.legend()
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/pandas/tools/plotting.pyc in __call__(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
3597 colormap=colormap, table=table, yerr=yerr,
3598 xerr=xerr, label=label, secondary_y=secondary_y,
-> 3599 **kwds)
3600 __call__.__doc__ = plot_series.__doc__
3601
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/pandas/tools/plotting.pyc in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
2671 yerr=yerr, xerr=xerr,
2672 label=label, secondary_y=secondary_y,
-> 2673 **kwds)
2674
2675
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _plot(data, x, y, subplots, ax, kind, **kwds)
2467 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
2468
-> 2469 plot_obj.generate()
2470 plot_obj.draw()
2471 return plot_obj.result
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/pandas/tools/plotting.pyc in generate(self)
1041 self._compute_plot_data()
1042 self._setup_subplots()
-> 1043 self._make_plot()
1044 self._add_table()
1045 self._make_legend()
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _make_plot(self)
1724 stacking_id=stacking_id,
1725 is_errorbar=is_errorbar,
-> 1726 **kwds)
1727 self._add_legend_handle(newlines[0], label, index=i)
1728
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _ts_plot(cls, ax, x, data, style, **kwds)
1762 ax._plot_data.append((data, cls._kind, kwds))
1763
-> 1764 lines = cls._plot(ax, data.index, data.values, style=style, **kwds)
1765 # set date formatter, locators and rescale limits
1766 format_dateaxis(ax, ax.freq)
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _plot(cls, ax, x, y, style, column_num, stacking_id, **kwds)
1739 cls._initialize_stacker(ax, stacking_id, len(y))
1740 y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label'])
-> 1741 lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
1742 cls._update_stacker(ax, stacking_id, y)
1743 return lines
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _plot(cls, ax, x, y, style, is_errorbar, **kwds)
1359 else:
1360 args = (x, y)
-> 1361 return ax.plot(*args, **kwds)
1362
1363 def _get_index_name(self):
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/matplotlib/__init__.pyc in inner(ax, *args, **kwargs)
1817 warnings.warn(msg % (label_namer, func.__name__),
1818 RuntimeWarning, stacklevel=2)
-> 1819 return func(ax, *args, **kwargs)
1820 pre_doc = inner.__doc__
1821 if pre_doc is None:
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc in plot(self, *args, **kwargs)
1381
1382 for line in self._get_lines(*args, **kwargs):
-> 1383 self.add_line(line)
1384 lines.append(line)
1385
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/matplotlib/axes/_base.pyc in add_line(self, line)
1701 line.set_clip_path(self.patch)
1702
-> 1703 self._update_line_limits(line)
1704 if not line.get_label():
1705 line.set_label('_line%d' % len(self.lines))
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/matplotlib/axes/_base.pyc in _update_line_limits(self, line)
1723 Figures out the data limit of the given line, updating self.dataLim.
1724 """
-> 1725 path = line.get_path()
1726 if path.vertices.size == 0:
1727 return
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/matplotlib/lines.pyc in get_path(self)
936 """
937 if self._invalidy or self._invalidx:
--> 938 self.recache()
939 return self._path
940
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/matplotlib/lines.pyc in recache(self, always)
623 x = ma.asarray(xconv, np.float_).filled(np.nan)
624 else:
--> 625 x = np.asarray(xconv, np.float_)
626 x = x.ravel()
627 else:
/Users/willem/dev/ml/keras/mlpy/lib/python2.7/site-packages/numpy/core/numeric.pyc in asarray(a, dtype, order)
480
481 """
--> 482 return array(a, dtype, copy=False, order=order)
483
484 def asanyarray(a, dtype=None, order=None):
TypeError: float() argument must be a string or a number
```
</details>
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
</details>
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read_sas with chunksize/iterator raises ValueError
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| 9 | 2016-11-25T04:17:11Z | 2017-05-02T14:52:13Z | 2016-11-28T09:48:59Z |
CONTRIBUTOR
| null |
`read_sas` doesn't work well with `chunksize` or `iterator` parameters.
#### Code Sample and Problem Description
The following data test file in the repository have 32 lines.
```python
sasfile = 'pandas/io/tests/sas/data/airline.sas7bdat'
pd.read_sas(sasfile).shape
Out[18]: (32, 6)
```
When we carefully read the file with `chunksize`/`iterator`, all's well:
```python
reader = pd.read_sas(sasfile, chunksize=16)
df = reader.read()
df.shape
Out[31]: (16, 6)
df = reader.read()
df.shape
Out[33]: (16, 6)
```
or
```python
reader = pd.read_sas(sasfile, iterator=True)
df = reader.read(30)
df.shape
Out[37]: (30, 6)
df = reader.read(2)
df.shape
Out[39]: (2, 6)
df = reader.read(2)
type(df)
Out[41]: NoneType
```
But if we don't know the length of the data, we'll easily stumble on an exception and won't read the whole data, which is painful with large files.
```python
reader = pd.read_sas(sasfile, chunksize=20)
df = reader.read()
df.shape
Out[45]: (20, 6)
df = reader.read()
Traceback (most recent call last):
File "/usr/local/lib64/python3.5/site-packages/IPython/core/interactiveshell.py", line 2885, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-46-c5d811b93ac1>", line 1, in <module>
df = reader.read()
File "/home/users/piotr/workspace/pandas-pijucha/pandas/io/sas/sas7bdat.py", line 604, in read
rslt = self._chunk_to_dataframe()
File "/home/users/piotr/workspace/pandas-pijucha/pandas/io/sas/sas7bdat.py", line 646, in _chunk_to_dataframe
dtype=self.byte_order + 'd')
File "/home/users/piotr/workspace/pandas-pijucha/pandas/core/frame.py", line 2419, in __setitem__
self._set_item(key, value)
File "/home/users/piotr/workspace/pandas-pijucha/pandas/core/frame.py", line 2485, in _set_item
value = self._sanitize_column(key, value)
File "/home/users/piotr/workspace/pandas-pijucha/pandas/core/frame.py", line 2656, in _sanitize_column
value = _sanitize_index(value, self.index, copy=False)
File "/home/users/piotr/workspace/pandas-pijucha/pandas/core/series.py", line 2793, in _sanitize_index
raise ValueError('Length of values does not match length of ' 'index')
ValueError: Length of values does not match length of index
```
or
```python
reader = pd.read_sas(sasfile, iterator=True)
reader.read(30).shape
Out[51]: (30, 6)
reader.read(30).shape
Traceback (most recent call last):
File "/usr/local/lib64/python3.5/site-packages/IPython/core/interactiveshell.py", line 2885, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-52-5d757f713808>", line 1, in <module>
reader.read(30).shape
File "/home/users/piotr/workspace/pandas-pijucha/pandas/io/sas/sas7bdat.py", line 604, in read
rslt = self._chunk_to_dataframe()
File "/home/users/piotr/workspace/pandas-pijucha/pandas/io/sas/sas7bdat.py", line 646, in _chunk_to_dataframe
dtype=self.byte_order + 'd')
File "/home/users/piotr/workspace/pandas-pijucha/pandas/core/frame.py", line 2419, in __setitem__
self._set_item(key, value)
File "/home/users/piotr/workspace/pandas-pijucha/pandas/core/frame.py", line 2485, in _set_item
value = self._sanitize_column(key, value)
File "/home/users/piotr/workspace/pandas-pijucha/pandas/core/frame.py", line 2656, in _sanitize_column
value = _sanitize_index(value, self.index, copy=False)
File "/home/users/piotr/workspace/pandas-pijucha/pandas/core/series.py", line 2793, in _sanitize_index
raise ValueError('Length of values does not match length of ' 'index')
ValueError: Length of values does not match length of index
```
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: 75b606abad51762b3faace1bbfa0a8d2241dc297
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.20-1
machine: x86_64
processor: Intel(R)_Core(TM)_i5-2520M_CPU_@_2.50GHz
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0+112.g75b606a
nose: 1.3.7
pip: 9.0.1
setuptools: 21.0.0
Cython: 0.24
numpy: 1.11.0
</details>
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| 191,627,596 |
MDExOlB1bGxSZXF1ZXN0OTUyNTE5MjQ=
| 14,735 |
Cut enhance for issue #14714
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| 1 | 2016-11-25T05:30:07Z | 2016-11-25T08:31:00Z | 2016-11-25T05:37:18Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
Initial iteration, primarily for code review
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DOC: warning section on memory overflow when joining/merging dataframes on index with duplicate keys
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| 8 | 2016-11-25T05:51:00Z | 2016-12-11T20:26:51Z | 2016-12-11T20:26:35Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
http://stackoverflow.com/questions/32750970/python-pandas-merge-causing-memory-overflow
# coding: utf-8
import pandas as pd
data = pd.read_csv('https://gist.githubusercontent.com/xgdgsc/8671a22136e1da937f1046a5f211c0ff/raw/d261706a6e7d1d7014e45e47122ead71e7159ef4/small.csv', index_col='<Date>')
print(data.shape)
another = data[[ ' <Open>']]
joined = data.join([another])
print(joined.shape)
#### Problem description
Currently having index with duplicate keys when joining dataframes would cause severe memory overflow, sometimes freezes the computer and user has to hard reboot, which can be disastrous for unsaved work.
#### Expected Output
Adding a simple checking before joining/merging , stop the operation and warn the user would be enough.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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ENH: support cut/qcut for datetime/timedelta (GH14714)
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| 23 | 2016-11-25T06:43:10Z | 2016-12-04T22:35:52Z | 2016-12-03T10:11:59Z |
CONTRIBUTOR
| null |
- [x] closes #14714
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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DOC: Correct uniqueness of index for Series (#7808)
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| 3 | 2016-11-25T07:31:56Z | 2017-12-20T01:57:46Z | 2016-11-25T10:07:26Z |
MEMBER
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- [X] closes #7808
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| 4 | 2016-11-25T07:59:20Z | 2016-11-25T22:48:02Z | 2016-11-25T22:47:50Z |
MEMBER
| null |
- [x] closes #9032
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| 191,665,170 |
MDExOlB1bGxSZXF1ZXN0OTUyNzc2NzM=
| 14,740 |
MAINT: Cleanup pandas/src/parser
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| 26 | 2016-11-25T10:02:26Z | 2016-12-06T22:31:59Z | 2016-12-06T18:43:13Z |
MEMBER
| null |
Remove dead code and reformat for style using Google's C++ style guide (found <a href="https://google.github.io/styleguide/cppguide.html">here</a>).
Also adds line of code to install fork of Google's C++ style checker (source code <a href="https://github.com/cpplint/cpplint">here</a>) when doing style checks in Travis.
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MDU6SXNzdWUxOTE2OTIxMjk=
| 14,741 |
groupby agg with rank and parameter return does not reduce
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| 2 | 2016-11-25T12:28:09Z | 2020-01-26T06:26:22Z | null |
NONE
| null |
Sample with **Multiindex** in columns:
df = pd.DataFrame({('r', 'c'): {(1, '2016-11-01 00:00:00+00:00', 3121): 143, (1, '2016-11-01 00:00:00+00:00', 4880): 12, (1, '2016-11-01 00:00:00+00:00', 3953): 4, (1, '2016-11-01 00:00:00+00:00', 3923): 11}})
df.index.names = ['z','x','y']
print (df)
r
c
z x y
1 2016-11-01 00:00:00+00:00 3121 143
3923 11
3953 4
4880 12
x = 'x'
y = 'y'
#works perfect
print (df.groupby(level=[x, y]).agg({('r', 'c'): 'rank'}))
print (df.groupby(level=[x, y]).agg({('r', 'c'): lambda x: x.rank(ascending=False)}))
#ValueError: Function does not reduce
Problem is with **non MultiIndex** columns also:
df = pd.DataFrame({'A':[1,1,3,3],
'B':[4,5,6,1]})
print (df)
A B
0 1 4
1 1 5
2 3 6
3 3 1
print (df.groupby('A').agg({'B': 'rank'}))
B
0 1.0
1 2.0
2 2.0
3 1.0
print (df.groupby('A').agg({'B': lambda x: x.rank()}))
#Exception: Must produce aggregated value
print (df.groupby('A').agg({'B': lambda x: x.rank(ascending=False)}))
#Exception: Must produce aggregated value
Problem is how can I use function with parameter in `agg` function? [SO question](http://stackoverflow.com/questions/40785476/pandas-rank-specify-order)
print (pd.show_versions())
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: sk_SK
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
None
Thank you for pandas and for your perfect documentation.
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MDExOlB1bGxSZXF1ZXN0OTUzMDE2NTM=
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Added query_config parameter to read_gbq
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| 26 | 2016-11-25T13:05:31Z | 2017-01-03T11:28:59Z | 2017-01-03T11:20:54Z |
CONTRIBUTOR
| null |
Now more complicated queries could be processed.
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| 191,720,978 |
MDExOlB1bGxSZXF1ZXN0OTUzMTc4MDI=
| 14,743 |
SAS chunksize / iteration issues
|
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| 6 | 2016-11-25T15:10:12Z | 2016-11-28T09:49:08Z | 2016-11-28T09:48:59Z |
CONTRIBUTOR
| null |
closes #14734
closes #13654
- [X] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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Revert "TST/TEMP: fix pyqt to 4.x for plotting tests"
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| 2 | 2016-11-25T16:20:12Z | 2018-09-13T14:19:38Z | 2016-11-26T17:18:54Z |
MEMBER
| null |
Reverts pydata/pandas#14240
For when https://github.com/ContinuumIO/anaconda-issues/issues/1068 is solved
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DOC: missing ref in timeseries.rst
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| 0 | 2016-11-25T17:00:46Z | 2016-11-25T20:39:47Z | 2016-11-25T17:00:50Z |
CONTRIBUTOR
| null |
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MDU6SXNzdWUxOTE3NTE1NDc=
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pandas dataframe to rpy2 dataframe gives me unwanted data
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| 2 | 2016-11-25T18:29:10Z | 2017-02-17T22:32:52Z | 2017-02-17T22:32:52Z |
NONE
| null |
#### Code Sample
```python
df.to_csv("output_df.csv", sep=',', encoding='utf-8')
df2 = pandas2ri.py2ri(df)
df2.to_csvfile("output_df2.csv")
```
#### Problem description
I want to convert my pandas data.frame (df) into a rpy2 data.frame.
But unfortunately, it generates me a massive amount of unwanted data.
Data which isn't present in my original data.frame
#### Expected Output - ``which is the print out of df``
```xlsx
,AssociatedTaxa,BasisofRecord,Behavior,CanonicalName,CatalogNumber,CollectionCode,Conf,CoordinateUncertaintyInMeters,Country,CountryCode,DataGeneralizations,DataSetID,DataSetName,DateIdentified,Day,DecimalLatitude,DecimalLongitude,DeterminationID,EstablishmentMeans,EventDate,EventID,EventRemarks,Family,FieldDate,FilenameID,GBIFtaxoMatch,Genus,GeodeticDatum,IdentificationRemarks,IdentificationVerificationStatus,IdentifiedBy,IndividualCount,InstitutionCode,LifeStage,Locality,LocationRemarks,Modified,Month,Municipality,OccurrenceRemarks,Ordre,OrganismsQuantity,OrganismsQuantityType,ProviderID,RecordNumber,RecordedBy,SamplingProtocol,ScientificName,Sex,SpecificEpithet,StateProvince,TaxonRank,VerbatimCoordinates,VerbatimCoordinatesystem,VerbatimElevation,VerbatimEventDate,VerbatimLatitude,VerbatimLongitude,VerbatimScientificName,Year,cond_id,fips,ifbl,occurrenceID,spec_id,spec_id_orig,stat_id,uuid
0,None,HumanObservation,None,Ailanthus altissima,None,None,4,10.0,Belgium,BE,Subselection of priority classified invasive species within the compiled datasets,17,Fusion BDs invasives,2013-09-26,26,55.73,-3.9049,None,Invasive,2013-09-26,None,None,Simaroubaceae,2013-09-26,demma_invasives2.sqlite,EXACT,Ailanthus,WGS84,None,5,"Muir, Malcolm",1,DEMNA-SPW,None,Lanarkshire,None,None,09,None,Presence,Sapindales,None,None,demma,2,"Muir, Malcolm",None,Ailanthus altissima (Miller) Swingle,None,altissima ,None,species,deg 55.73 -3.9049,WGS84,None,20130926,55.73,-3.9049,Ailanthus altissima,2013,2,None,,17:2,2,None,2,d73298b8-b32c-11e6-a3ef-0a1c6976a2db
1,None,HumanObservation,None,Ailanthus altissima,None,None,4,15.0,Belgium,BE,Subselection of priority classified invasive species within the compiled datasets,17,Fusion BDs invasives,2014-10-19,19,51.4591,-2.6021,None,Invasive,2014-10-19,None,None,Simaroubaceae,2014-10-19,demma_invasives2.sqlite,EXACT,Ailanthus,WGS84,None,5,"Anonymous, Anonymous",1,DEMNA-SPW,None,West Gloucestershire,None,None,10,None,Presence,Sapindales,None,None,demma,3,"Anonymous, Anonymous",None,Ailanthus altissima (Miller) Swingle,None,altissima ,None,species,deg 51.4591 -2.6021,WGS84,None,20141019,51.4591,-2.6021,Ailanthus altissima,2014,3,None,,17:3,3,None,3,d73298b9-b32c-11e6-a3ef-0a1c6976a2db
2,None,HumanObservation,None,Ailanthus altissima,None,None,4,nan,Belgium,BE,Subselection of priority classified invasive species within the compiled datasets,17,Fusion BDs invasives,2015-08-08,08,50.8102,3.3562,None,Invasive,2015-08-08,None,None,Simaroubaceae,2015-08-08,demma_invasives2.sqlite,EXACT,Ailanthus,WGS84,None,5,"vanhee, dave",1,DEMNA-SPW,None,None,None,None,08,None,Presence,Sapindales,None,None,demma,4,"vanhee, dave",None,Ailanthus altissima (Miller) Swingle,None,altissima ,None,species,deg 50.8102 3.3562,WGS84,None,20150808,50.8102,3.3562,Ailanthus altissima,2015,4,None,E23433,17:4,4,None,4,d73298ba-b32c-11e6-a3ef-0a1c6976a2db
```
#### Output of ``df2`` - if you scroll to the right, you'll notice the problem.
```xlsx
"AssociatedTaxa","BasisofRecord","Behavior","CanonicalName","CatalogNumber","CollectionCode","Conf","CoordinateUncertaintyInMeters","Country","CountryCode","DataGeneralizations","DataSetID","DataSetName","DateIdentified","Day","DecimalLatitude","DecimalLongitude","DeterminationID","EstablishmentMeans","EventDate","EventID","EventRemarks","Family","FieldDate","FilenameID","GBIFtaxoMatch","Genus","GeodeticDatum","IdentificationRemarks","IdentificationVerificationStatus","IdentifiedBy","IndividualCount","InstitutionCode","LifeStage","Locality","LocationRemarks","Modified","Month","Municipality","OccurrenceRemarks","Ordre","OrganismsQuantity","OrganismsQuantityType","ProviderID","RecordNumber","RecordedBy","SamplingProtocol","ScientificName","Sex","SpecificEpithet","StateProvince","TaxonRank","VerbatimCoordinates","VerbatimCoordinatesystem","VerbatimElevation","VerbatimEventDate","VerbatimLatitude","VerbatimLongitude","VerbatimScientificName","Year","cond_id","fips","ifbl.0","ifbl.1","ifbl.2","ifbl.3","ifbl.4","ifbl.5","ifbl.6","ifbl.7","ifbl.8","ifbl.9","ifbl.10","ifbl.11","ifbl.12","ifbl.13","ifbl.14","ifbl.15","ifbl.16","ifbl.17","ifbl.18","ifbl.19","ifbl.20","ifbl.21","ifbl.22","ifbl.23","ifbl.24","ifbl.25","ifbl.26","ifbl.27","ifbl.28","ifbl.29","ifbl.30","ifbl.31","ifbl.32","ifbl.33","ifbl.34","ifbl.35","ifbl.36","ifbl.37","ifbl.38","ifbl.39","ifbl.40","ifbl.41","ifbl.42","ifbl.43","ifbl.44","ifbl.45","ifbl.46","ifbl.47","ifbl.48","ifbl.49","ifbl.50","ifbl.51","ifbl.52","ifbl.53","ifbl.54","ifbl.55","ifbl.56","ifbl.57","ifbl.58","ifbl.59","ifbl.60","ifbl.61","ifbl.62","ifbl.63","ifbl.64","ifbl.65","ifbl.66","ifbl.67","ifbl.68","ifbl.69","ifbl.70","ifbl.71","ifbl.72","ifbl.73","ifbl.74","ifbl.75","ifbl.76","ifbl.77","ifbl.78","ifbl.79","ifbl.80","ifbl.81","ifbl.82","ifbl.83","ifbl.84","ifbl.85","ifbl.86","ifbl.87","ifbl.88","ifbl.89","ifbl.90","ifbl.91","ifbl.92","ifbl.93","ifbl.94","ifbl.95","ifbl.96","ifbl.97","ifbl.98","ifbl.99","ifbl.100","ifbl.101","ifbl.102","ifbl.103","ifbl.104","ifbl.105","ifbl.106","ifbl.107","ifbl.108","ifbl.109","ifbl.110","ifbl.111","ifbl.112","ifbl.113","ifbl.114","ifbl.115","ifbl.116","ifbl.117","ifbl.118","ifbl.119","ifbl.120","ifbl.121","ifbl.122","ifbl.123","ifbl.124","ifbl.125","ifbl.126","ifbl.127","ifbl.128","ifbl.129","ifbl.130","ifbl.131","ifbl.132","ifbl.133","ifbl.134","ifbl.135","ifbl.136","ifbl.137","ifbl.138","ifbl.139","ifbl.140","ifbl.141","ifbl.142","ifbl.143","ifbl.144","ifbl.145","ifbl.146","ifbl.147","ifbl.148","ifbl.149","ifbl.150","ifbl.151","ifbl.152","ifbl.153","ifbl.154","ifbl.155","ifbl.156","ifbl.157","ifbl.158","ifbl.159","ifbl.160","ifbl.161","ifbl.162","ifbl.163","ifbl.164","ifbl.165","ifbl.166","ifbl.167","ifbl.168","ifbl.169","ifbl.170","ifbl.171","ifbl.172","ifbl.173","ifbl.174","ifbl.175","ifbl.176","ifbl.177","ifbl.178","ifbl.179","ifbl.180","ifbl.181","ifbl.182","ifbl.183","ifbl.184","ifbl.185","ifbl.186","ifbl.187","ifbl.188","ifbl.189","ifbl.190","ifbl.191","ifbl.192","ifbl.193","ifbl.194","ifbl.195","ifbl.196","ifbl.197","ifbl.198","ifbl.199","ifbl.200","ifbl.201","ifbl.202","ifbl.203","ifbl.204","ifbl.205","ifbl.206","ifbl.207","ifbl.208","ifbl.209","ifbl.210","ifbl.211","ifbl.212","ifbl.213","ifbl.214","ifbl.215","ifbl.216","ifbl.217","ifbl.218","ifbl.219","ifbl.220","ifbl.221","ifbl.222","ifbl.223","ifbl.224","ifbl.225","ifbl.226","ifbl.227","ifbl.228","ifbl.229","ifbl.230","ifbl.231","ifbl.232","ifbl.233","ifbl.234","ifbl.235","ifbl.236","ifbl.237","ifbl.238","ifbl.239","ifbl.240","ifbl.241","ifbl.242","ifbl.243","ifbl.244","ifbl.245","ifbl.246","ifbl.247","ifbl.248","ifbl.249","ifbl.250","ifbl.251","ifbl.252","ifbl.253","ifbl.254","ifbl.255","ifbl.256","ifbl.257","ifbl.258","ifbl.259","ifbl.260","ifbl.261","ifbl.262","ifbl.263","ifbl.264","ifbl.265","ifbl.266","ifbl.267","ifbl.268","ifbl.269","ifbl.270","ifbl.271","ifbl.272","ifbl.273","ifbl.274","ifbl.275","ifbl.276","ifbl.277","ifbl.278","ifbl.279","ifbl.280","ifbl.281","ifbl.282","ifbl.283","ifbl.284","ifbl.285","ifbl.286","ifbl.287","ifbl.288","ifbl.289","ifbl.290","ifbl.291","ifbl.292","ifbl.293","ifbl.294","ifbl.295","ifbl.296","ifbl.297","ifbl.298","ifbl.299","ifbl.300","ifbl.301","ifbl.302","ifbl.303","ifbl.304","ifbl.305","ifbl.306","ifbl.307","ifbl.308","ifbl.309","ifbl.310","ifbl.311","ifbl.312","ifbl.313","ifbl.314","ifbl.315","ifbl.316","ifbl.317","ifbl.318","ifbl.319","ifbl.320","ifbl.321","ifbl.322","ifbl.323","ifbl.324","ifbl.325","ifbl.326","ifbl.327","ifbl.328","ifbl.329","ifbl.330","ifbl.331","ifbl.332","ifbl.333","ifbl.334","ifbl.335","ifbl.336","ifbl.337","ifbl.338","ifbl.339","ifbl.340","ifbl.341","ifbl.342","ifbl.343","ifbl.344","ifbl.345","ifbl.346","ifbl.347","ifbl.348","ifbl.349","ifbl.350","ifbl.351","ifbl.352","ifbl.353","ifbl.354","ifbl.355","ifbl.356","ifbl.357","ifbl.358","ifbl.359","ifbl.360","ifbl.361","ifbl.362","ifbl.363","ifbl.364","ifbl.365","ifbl.366","ifbl.367","ifbl.368","ifbl.369","ifbl.370","ifbl.371","ifbl.372","ifbl.373","ifbl.374","ifbl.375","ifbl.376","ifbl.377","ifbl.378","ifbl.379","ifbl.380","ifbl.381","ifbl.382","ifbl.383","ifbl.384","ifbl.385","ifbl.386","ifbl.387","ifbl.388","ifbl.389","ifbl.390","ifbl.391","ifbl.392","ifbl.393","ifbl.394","ifbl.395","ifbl.396","ifbl.397","ifbl.398","ifbl.399","ifbl.400","ifbl.401","ifbl.402","ifbl.403","ifbl.404","ifbl.405","ifbl.406","ifbl.407","ifbl.408","ifbl.409","ifbl.410","ifbl.411","ifbl.412","ifbl.413","ifbl.414","ifbl.415","ifbl.416","ifbl.417","ifbl.418","ifbl.419","ifbl.420","ifbl.421","ifbl.422","ifbl.423","ifbl.424","ifbl.425","ifbl.426","ifbl.427","ifbl.428","ifbl.429","ifbl.430","ifbl.431","ifbl.432","ifbl.433","ifbl.434","ifbl.435","ifbl.436","ifbl.437","ifbl.438","ifbl.439","ifbl.440","ifbl.441","ifbl.442","ifbl.443","ifbl.444","ifbl.445","ifbl.446","ifbl.447","ifbl.448","ifbl.449","ifbl.450","ifbl.451","ifbl.452","ifbl.453","ifbl.454","ifbl.455","ifbl.456","ifbl.457","ifbl.458","ifbl.459","ifbl.460","ifbl.461","ifbl.462","ifbl.463","ifbl.464","ifbl.465","ifbl.466","ifbl.467","ifbl.468","ifbl.469","ifbl.470","ifbl.471","ifbl.472","ifbl.473","ifbl.474","ifbl.475","ifbl.476","ifbl.477","ifbl.478","ifbl.479","ifbl.480","ifbl.481","ifbl.482","ifbl.483","ifbl.484","ifbl.485","ifbl.486","ifbl.487","ifbl.488","ifbl.489","ifbl.490","ifbl.491","ifbl.492","ifbl.493","ifbl.494","ifbl.495","ifbl.496","ifbl.497","ifbl.498","ifbl.499","occurrenceID","spec_id","spec_id_orig","stat_id","uuid"
"0","None","HumanObservation","None","Ailanthus altissima","None","None","4","10.0","Belgium","BE","Subselection of priority classified invasive species within the compiled datasets","17","Fusion BDs invasives","2013-09-26","26","55.73","-3.9049","None","Invasive","2013-09-26","None","None","Simaroubaceae","2013-09-26","demma_invasives2.sqlite","EXACT","Ailanthus","WGS84","None","5","Muir, Malcolm","1","DEMNA-SPW","None","Lanarkshire","None","None","09","None","Presence","Sapindales","None","None","demma","2","Muir, Malcolm","None","Ailanthus altissima (Miller) Swingle","None","altissima ","None","species","deg 55.73 -3.9049","WGS84","None","20130926","55.73","-3.9049","Ailanthus altissima","2013","2","None",NA,NA,"E23433","E24213",NA,"?71423",NA,NA,"C72332","C14541",NA,"C44624","C14814","C14832","C24144","C04843","C04843","C04843","C04843","C04843","C35234","C35234","E11641","F32812",NA,"H74232",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"C22243","C24143","C04843","C25121","C04843","C04843","C15814","C35243","C35234","D21131","D21133","D31143","D11743","E31821","E22511","F45223","F45241","H74232",NA,NA,NA,NA,"F45241","F45232",NA,NA,NA,"?71522","B74223",NA,NA,NA,NA,NA,NA,"C11444","C72332","C22331","C32633","C32633","C32633","C32633","C32633","C22144","C14611","C23142","C23132","C23211","C24332","C24332","C14632","C24143","C04843","C04843","E31821","E41133","E25111","E35114","E45413","F43411",NA,NA,NA,NA,NA,NA,"C24142","C04843","C04843","C04843","C04843","C04843","C35522","C04843","C04843","C04843","C04843","C04843","C04843","C04843","C75722","C75722","C15612","C35241","C35234","C35234","C35234","D31143","D11732","D21133",NA,"D14834","D14744","E31821","E23413","E13833","E35114","F35521","F45331","H74232","J54113",NA,NA,NA,"C35241","F44341",NA,NA,NA,NA,NA,"D21132","D13833","E31823","F55342","E41133","J54113",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"<55643","<55544","<55623",NA,NA,">62723",NA,NA,"E23433","E24213",NA,"?71423",NA,NA,"C72332","C14541",NA,"C44624","C14814","C14832","C24144","C04843","C04843","C04843","C04843","C04843","C35234","C35234","E11641","F32812",NA,"H74232",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"C22243","C24143","C04843","C25121","C04843","C04843","C15814","C35243","C35234","D21131","D21133","D31143","D11743","E31821","E22511","F45223","F45241","H74232",NA,NA,NA,NA,"F45241","F45232",NA,NA,NA,"?71522","B74223",NA,NA,NA,NA,NA,NA,">62723",">62723",">75821","?71414",NA,"@71523",NA,NA,NA,NA,NA,NA,"@45434",NA,"E31821","E31823","E31823","C11444","C72332","C22331","C32633","C32633","C32633","C32633","C32633","C22144","C14611","C23142","C23132","C23211","C24332","C24332","C14632","C24143","C04843","C04843","E31821","E41133","E25111","E35114","E45413","F43411",NA,NA,NA,NA,NA,NA,"C24142","C04843","C04843","C04843","C04843","C04843","C35522","C04843","C04843","C04843","C04843","C04843","C04843","C04843","C75722","C75722","C15612","C35241","C35234","C35234","C35234","D31143","D11732","D21133",NA,"D14834","D14744","E31821","E23413","E13833","E35114","F35521","F45331","H74232","J54113",NA,NA,NA,"C35241","F44341",NA,NA,NA,NA,NA,"D21132","D13833","E31823","F55342","E41133","J54113",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"E31823","E31823","E31823","E31823","E31823","E31824","E31823","E41133","E22322","E22411","E22511","E12831","E23412","E23213","E13724","E23213","E23413","E23433","E23344","E14811","F35521",NA,"A33112",NA,NA,NA,"B41544",NA,NA,NA,NA,NA,"B74212",NA,NA,NA,NA,NA,NA,NA,NA,"<55643","<55544","<55623",NA,NA,">62723",">62723",">62723",">75821","?71414",NA,"@71523",NA,NA,NA,NA,NA,NA,"@45434",NA,"E31821","E31823","E31823","E31823","E31823","E31823","E31823","E31823","E31824","E31823","E41133","E22322","E22411","E22511","E12831","E23412","E23213","E13724","E23213","E23413","E23433","E23344","E14811","F35521",NA,"A33112",NA,NA,NA,"B41544",NA,NA,NA,NA,NA,"B74212",NA,NA,"B74211","C11533","C04843","C04843","C35243","C04843","C04843","C35243",NA,"B74211","C11533","C04843","C04843","C35243","C04843","C04843","C35243","17:2","2","None","2","d73298b8-b32c-11e6-a3ef-0a1c6976a2db"
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| 191,758,195 |
MDExOlB1bGxSZXF1ZXN0OTUzNDMwMTU=
| 14,747 |
BUG: Corrects stopping logic when nrows argument is supplied (#7626)
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| 14 | 2016-11-25T19:44:27Z | 2016-12-06T01:48:21Z | 2016-12-06T01:48:05Z |
CONTRIBUTOR
| null |
- closes #7626 - Subsets of tabular files with different "shapes" will now load when a valid skiprows/nrows is given as an argument
- [x] tests added / passed - Test case added to io/tests/parser/common.py. nosetests suite passed successfully.
- [x] passes ``git diff upstream/master | flake8 --diff``
Conditions for error:
1) There are different "shapes" within a tabular data file, i.e. different numbers of columns.
2) A "narrower" set of columns is followed by a "wider" (more columns) one, and the narrower set is laid out such that the end of a 262144-byte block occurs within it.
Issue summary:
The C engine for parsing files reads in 262144 bytes at a time. Previously, the "start_lines" variable in tokenizer.c/tokenize_bytes() was set incorrectly to the first line in that chunk, rather than the overall first row requested. This lead to incorrect logic on when to stop reading when nrows is supplied by the user. This always happened but only caused a crash when a wider set of columns followed in the file. In other cases, extra rows were read in but then harmlessly discarded.
This pull request always uses the first requested row for comparisons, so only nrows will be parsed when supplied.
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MDU6SXNzdWUxOTE3NzAzMTg=
| 14,748 |
RLS: v0.19.2
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| 24 | 2016-11-25T22:19:43Z | 2017-01-03T14:32:01Z | 2016-12-24T22:50:34Z |
MEMBER
| null |
Issue to follow-up 0.19.2 release.
0.19.2 milestoned issues/PRs: https://github.com/pandas-dev/pandas/milestone/45
0.19.1 was released Nov 3. In the meantime already quite some work for 0.19.2 is done (mainly thanks to Jeff!). Main things for 0.19.2 (besides general bug fixes):
- Python 3.6 compatibility (https://github.com/pandas-dev/pandas/issues/14679).
- This is already done I think thanks to Jeff
- fix segfault with latest dateutil (done in #14631)
- possible regressions (I don't think we have any urgent reported at the moment, only https://github.com/pandas-dev/pandas/issues/14687)
The Python 3.6 release schedule is a rc1 at 2016-12-05, and final release at 2016-12-16.
Regarding timeline for pandas 0.19.2, the question is maybe whether we have to wait until after the final Python 3.6 release, or if we can do it earlier (eg after rc1, with the risk there are still some changes)
@pandas-dev/pandas-core
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BUG: Improve error message for skipfooter malformed rows in Python engine
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| 3 | 2016-11-26T00:53:25Z | 2016-11-29T21:18:35Z | 2016-11-29T21:18:28Z |
MEMBER
| null |
Python's native CSV library does not respect the `skipfooter` parameter, so if one of those skipped
rows is malformed, it will still raise an error. Append some useful information to the error message regarding that if that situation is potentially applicable.
Closes #13879.
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| 14,750 |
Boolean slicing on MultiIndex only works if bool index is same length as MultiIndex
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| 14 | 2016-11-26T05:56:53Z | 2016-11-30T00:02:16Z | 2016-11-30T00:02:04Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
from __future__ import print_function
import numpy as np
import pandas as pd
idx = pd.MultiIndex.from_product([['foo', 'bar'], ['a', 'b']],
names=['first', 'second'])
df = pd.DataFrame(np.random.randn(4, 2), index=idx).sort_index()
bool_idx = pd.Series([True, False], index=['foo', 'bar'])
print ('dataframe')
print(df)
print('\nbool idx')
print(bool_idx)
try:
df.loc[(bool_idx, slice(None)), :]
except ValueError as e:
import traceback
print('\nbool index in MultiIndex failed')
traceback.print_exc()
no_multi_idx = df.reset_index(level=1)
print('\nno multi index')
print(no_multi_idx)
print('successful indexing')
print(no_multi_idx.loc[bool_idx])
```
#### Problem description
A level of a MultiIndex cannot be sliced by a boolean array shorter than the level. A single level of a MultiIndex can contain repeated values (as one level isn't the entire index), so slicing by a smaller array can be necessary.
A single Index with repeated values can currently be sliced by a boolean array without repeated Index values. It would be a smoother user experience if the same feature existed for MultiIndex, especially since this is less of a corner case in with MultiIndex (because it doesn't require repeating index values). I would guess that the same or very similar code to the single Index case could be used to handle the MultiIndex case.
#### Expected Output
```
0 1
first second
foo a -0.569935 -1.146863
b -2.087799 -0.506962
```
This can currently be computed by calling ``reset_index``, slicing, and then calling ``set_index`` with append=True.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-15-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.4
pip: 9.0.1
setuptools: 18.0.1
Cython: 0.21.1
numpy: 1.11.2
scipy: 0.14.1
statsmodels: None
xarray: None
IPython: 3.2.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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BUG: Patch read_csv NA values behaviour
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| 5 | 2016-11-26T06:03:07Z | 2016-12-16T23:32:49Z | 2016-12-16T23:32:49Z |
MEMBER
| null |
Patches the following behaviour when `na_values` is passed in as a dictionary:
1. Prevent aliasing in case `na_values` was defined in a broader scope.
2. Respect column indices as keys when doing NA conversions.
Closes #14203.
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TST: add test to confirm GH14606 (specify category dtype for empty)
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| 0 | 2016-11-26T11:41:54Z | 2016-12-10T11:02:54Z | 2016-12-10T11:02:54Z |
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Closes #14606
Issue #14606 was fixed by PR #14717, adding one more specific test to confirm this
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ENH: Add the ability to have a separate title for each subplot when plotting
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| 11 | 2016-11-26T20:18:16Z | 2016-12-09T08:32:47Z | 2016-12-09T08:32:33Z |
NONE
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- [] tests added / passed
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TST: Same return values in drop_duplicates for Series and DataFrames(#…
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| 3 | 2016-11-27T00:49:49Z | 2016-12-15T09:02:59Z | 2016-12-15T09:02:51Z |
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TST: Correct results with np.size and crosstab (#4003)
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| 4 | 2016-11-27T01:45:54Z | 2016-12-11T13:57:19Z | 2016-12-10T21:35:41Z |
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BF: (re)raise the exception always unless returning
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| 1 | 2016-11-28T02:48:34Z | 2016-11-28T17:30:19Z | 2016-11-28T17:30:16Z |
CONTRIBUTOR
| null |
coding logic flaw is obvious (leaves unser used later undefined), so no dedicated test was found
otherwise leads atm to masking of this error while testing on i386 and then failling with
UnboundLocalError: local variable unser referenced before assignment
More detail: https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=i386&ver=0.19.1-1&stamp=1479504883
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ENH: Added more options for formats.style.bar
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[] | null | 35 | 2016-11-28T16:02:35Z | 2017-07-04T10:20:42Z | 2017-05-02T01:46:44Z |
CONTRIBUTOR
| null |
- [x] 4 tests added and passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] Update documentation
You can now have the df.style.bar be centered on zero or midpoint value (in
addition to the already existing way of having the min value at the left side of the cell)
----
## Documentation:
I have produced the following example that I think does a pretty good job of explaining the new capabilities
```
import pandas as pd
from IPython.display import HTML
# Case 1: s.min() < 0:
test1 = pd.Series([-100,-60,-30,-20], name='All Negative')
# Case 2: s.max() < 0:
test2 = pd.Series([10,20,50,100], name='All Positive')
# Case 3:
test3 = pd.Series([-10,-5,0,90], name='Both Pos and Neg')
head = """
<table>
<thead>
<th>Align</th>
<th>All Negative</th>
<th>All Positive</th>
<th>Both Neg and Pos</th>
</thead>
</tbody>
"""
aligns = ['left','zero','mid']
for align in aligns:
row = "<tr><th>{}</th>".format(align)
for serie in [test1,test2,test3]:
s = serie.copy()
s.name=''
row += "<td>{}</td>".format(s.to_frame().style.bar(align=align,
color=['#d65f5f', '#5fba7d'],
width=100).render()) #testn['width']
row += '</tr>'
head += row
head+= """
</tbody>
</table>"""
HTML(head)
```
What do you think?
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BUG: invalid coercion raises in groupby uniques
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| 6 | 2016-11-28T18:46:12Z | 2017-06-12T10:46:53Z | 2017-06-12T10:46:53Z |
CONTRIBUTOR
| null |
```
df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], dtype=object) + 9223372036854775807
df.groupby(0).sum()
...
C:\Anaconda\envs\bmc3\lib\site-packages\pandas\core\groupby.py in labels(self)
2252 def labels(self):
2253 if self._labels is None:
-> 2254 self._make_labels()
2255 return self._labels
2256
C:\Anaconda\envs\bmc3\lib\site-packages\pandas\core\groupby.py in _make_labels(self)
2264 if self._labels is None or self._group_index is None:
2265 labels, uniques = algos.factorize(self.grouper, sort=self.sort)
-> 2266 uniques = Index(uniques, name=self.name)
2267 self._labels = labels
2268 self._group_index = uniques
C:\Anaconda\envs\bmc3\lib\site-packages\pandas\indexes\base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
234 if inferred == 'integer':
235 from .numeric import Int64Index
--> 236 return Int64Index(subarr.astype('i8'), copy=copy,
237 name=name)
238 elif inferred in ['floating', 'mixed-integer-float']:
OverflowError: int too big to convert
```
We are inferring int when the this should be 'object' (or just not integer):
```
In [36]: pd.lib.infer_dtype(np.array([9223372036854775808, 9223372036854775810, 9223372036854775812], dtype=object))
Out[36]: 'integer'
```
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DOC: document groupby.resample/rolling
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| 1 | 2016-11-28T23:57:54Z | 2016-12-10T11:12:01Z | 2016-12-10T11:12:01Z |
CONTRIBUTOR
| null |
examples are in the whatsnew: http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#groupby-syntax-with-window-and-resample-operations
but I think a section in groupby.rst with some xrefs in timeseries.rst (resample) and computation.rst (rolling) would be helpful. I wrote this and can barely find it!
plus the API reference is missing these functions
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| 14,760 |
No TimeGrouper docs, Day-of-Week resampling?
|
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[] | null | 4 | 2016-11-29T02:48:26Z | 2016-11-30T16:01:35Z | 2016-11-29T23:25:21Z |
NONE
| null |
TimeGrouper isn't really mentioned in the docs at all. In particular, it'd be nice to know what the grouping options are. I assume they're the same as resample's options? I also can't find a simple list of those.
In particular, weekly grouping is doable via `'W'`, but I can't find that listed in the docs anywhere.
It would also be really nice to have a `'DoW'` (Day of Week) grouping option - this can be really useful for any data with a human signal in it.
|
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| 192,252,395 |
MDU6SXNzdWUxOTIyNTIzOTU=
| 14,761 |
DOC: update docs for .astype with dict
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| 1 | 2016-11-29T11:08:02Z | 2016-12-22T13:37:19Z | 2016-12-22T13:37:19Z |
CONTRIBUTOR
| null |
in #13375 added dict argument to .astype
need to add a doc examp:
http://pandas.pydata.org/pandas-docs/stable/basics.html#
add versionadded tag to .astype docstring (0.19.0)
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| 192,309,650 |
MDExOlB1bGxSZXF1ZXN0OTU3MTIwNDk=
| 14,762 |
ENH: Introduce UnsortedIndexError GH11897
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| 21 | 2016-11-29T15:18:34Z | 2016-12-09T16:36:30Z | 2016-12-09T16:36:30Z |
CONTRIBUTOR
| null |
closes #11897
closes #14491
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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MDU6SXNzdWUxOTIzMTU0NzI=
| 14,763 |
Period Index plotting method, non standard intervals
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| 7 | 2016-11-29T15:38:27Z | 2019-05-29T00:27:04Z | 2019-05-28T20:55:59Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np, pandas as pd
idx = pd.period_range('2000-01-01', '2000-01-05', freq='6H')
s = pd.Series(np.random.randn(len(idx)), index=idx)
s.plot()
```
#### Problem description
Plotting a period index with 'D' or 'H' works fine, but changing the interval to 6-hr throws an error: `IncompatibleFrequency: Input has different freq=6H from PeriodIndex(freq=H)`
#### Expected Output
Should make a nice plot. Using the same example above with alternative frequencies of 'D' or 'H' plots as expected, but 3H, 6H, and even 24H throw an error.
#### Output
<details>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tseries\period.py in _from_arraylike(cls, data, freq, tz)
263 try:
--> 264 data = com._ensure_int64(data)
265 except (TypeError, ValueError):
pandas\src\generated.pyx in pandas.algos.ensure_int64 (pandas\algos.c:64659)()
pandas\src\generated.pyx in pandas.algos.ensure_int64 (pandas\algos.c:64550)()
TypeError: int() argument must be a string, a bytes-like object or a number, not 'pandas._period.Period'
During handling of the above exception, another exception occurred:
IncompatibleFrequency Traceback (most recent call last)
<ipython-input-103-18275fd04456> in <module>()
2 idx = ps.period_range('2000-01-01', '2000-01-05', freq='6H')
3 s = ps.Series(np.random.randn(len(idx)), index=idx)
----> 4 s.plot()
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tools\plotting.py in __call__(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
3564 colormap=colormap, table=table, yerr=yerr,
3565 xerr=xerr, label=label, secondary_y=secondary_y,
-> 3566 **kwds)
3567 __call__.__doc__ = plot_series.__doc__
3568
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tools\plotting.py in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
2643 yerr=yerr, xerr=xerr,
2644 label=label, secondary_y=secondary_y,
-> 2645 **kwds)
2646
2647
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tools\plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds)
2439 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
2440
-> 2441 plot_obj.generate()
2442 plot_obj.draw()
2443 return plot_obj.result
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tools\plotting.py in generate(self)
1026 self._compute_plot_data()
1027 self._setup_subplots()
-> 1028 self._make_plot()
1029 self._add_table()
1030 self._make_legend()
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tools\plotting.py in _make_plot(self)
1705 stacking_id=stacking_id,
1706 is_errorbar=is_errorbar,
-> 1707 **kwds)
1708 self._add_legend_handle(newlines[0], label, index=i)
1709
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tools\plotting.py in _ts_plot(cls, ax, x, data, style, **kwds)
1743 ax._plot_data.append((data, cls._kind, kwds))
1744
-> 1745 lines = cls._plot(ax, data.index, data.values, style=style, **kwds)
1746 # set date formatter, locators and rescale limits
1747 format_dateaxis(ax, ax.freq)
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tools\plotting.py in _plot(cls, ax, x, y, style, column_num, stacking_id, **kwds)
1720 cls._initialize_stacker(ax, stacking_id, len(y))
1721 y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label'])
-> 1722 lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
1723 cls._update_stacker(ax, stacking_id, y)
1724 return lines
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tools\plotting.py in _plot(cls, ax, x, y, style, is_errorbar, **kwds)
1340 else:
1341 args = (x, y)
-> 1342 return ax.plot(*args, **kwds)
1343
1344 def _get_index_name(self):
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\matplotlib\__init__.py in inner(ax, *args, **kwargs)
1816 warnings.warn(msg % (label_namer, func.__name__),
1817 RuntimeWarning, stacklevel=2)
-> 1818 return func(ax, *args, **kwargs)
1819 pre_doc = inner.__doc__
1820 if pre_doc is None:
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\matplotlib\axes\_axes.py in plot(self, *args, **kwargs)
1381
1382 for line in self._get_lines(*args, **kwargs):
-> 1383 self.add_line(line)
1384 lines.append(line)
1385
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\matplotlib\axes\_base.py in add_line(self, line)
1701 line.set_clip_path(self.patch)
1702
-> 1703 self._update_line_limits(line)
1704 if not line.get_label():
1705 line.set_label('_line%d' % len(self.lines))
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\matplotlib\axes\_base.py in _update_line_limits(self, line)
1723 Figures out the data limit of the given line, updating self.dataLim.
1724 """
-> 1725 path = line.get_path()
1726 if path.vertices.size == 0:
1727 return
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\matplotlib\lines.py in get_path(self)
936 """
937 if self._invalidy or self._invalidx:
--> 938 self.recache()
939 return self._path
940
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\matplotlib\lines.py in recache(self, always)
619 def recache(self, always=False):
620 if always or self._invalidx:
--> 621 xconv = self.convert_xunits(self._xorig)
622 if ma.isMaskedArray(self._xorig):
623 x = ma.asarray(xconv, np.float_).filled(np.nan)
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\matplotlib\artist.py in convert_xunits(self, x)
188 if ax is None or ax.xaxis is None:
189 return x
--> 190 return ax.xaxis.convert_units(x)
191
192 def convert_yunits(self, y):
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\matplotlib\axis.py in convert_units(self, x)
1442 return x
1443
-> 1444 ret = self.converter.convert(x, self.units, self)
1445 return ret
1446
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tseries\converter.py in convert(values, units, axis)
120 return values.map(lambda x: get_datevalue(x, axis.freq))
121 if com.is_period_arraylike(values):
--> 122 return PeriodIndex(values, freq=axis.freq).values
123 if isinstance(values, (list, tuple, np.ndarray, Index)):
124 return [get_datevalue(x, axis.freq) for x in values]
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tseries\period.py in __new__(cls, data, ordinal, freq, start, end, periods, copy, name, tz, **kwargs)
188 freq, kwargs)
189 else:
--> 190 ordinal, freq = cls._from_arraylike(data, freq, tz)
191 data = np.array(ordinal, dtype=np.int64, copy=False)
192
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tseries\period.py in _from_arraylike(cls, data, freq, tz)
265 except (TypeError, ValueError):
266 data = com._ensure_object(data)
--> 267 data = _get_ordinals(data, freq)
268
269 return data, freq
C:\Users\jaq\AppData\Local\Continuum\Anaconda\envs\py35\lib\site-packages\pandas\tseries\period.py in _get_ordinals(data, freq)
42 f = lambda x: Period(x, freq=freq).ordinal
43 if isinstance(data[0], Period):
---> 44 return period.extract_ordinals(data, freq)
45 else:
46 return lib.map_infer(data, f)
pandas\src\period.pyx in pandas._period.extract_ordinals (pandas\src\period.c:7030)()
IncompatibleFrequency: Input has different freq=6H from PeriodIndex(freq=H)
</details>
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MDU6SXNzdWUxOTIzMzM0MTc=
| 14,764 |
DOC: pd.DataFrame(dtype) arg cannot be list, dict, Series. And None will infer wider type than necessary.
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| 8 | 2016-11-29T16:39:10Z | 2017-09-15T18:47:10Z | 2017-05-31T08:54:26Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# This is a DOCbug simply to document what pd.DataFrame(dtype) currently does.
# It is non-obvious to new users, gives non-obvious error messages, and also behaves differently to read_csv(dtype)
# a) Leaving dtype=None in constructor will infer a wider type than necessary
df_cols = {'year':np.int32, 'month':np.int8}
df = pd.DataFrame(columns=df_cols.keys(), dtype=None, index=range(10), data=-1)
>>> df.dtypes
month int64
year int64
# b) The doc doesn't explicitly say a list/dict/Series/array-like is not allowed (and if you
# pass in one the error is not very friendly). Also behaves differently to read_csv(dtype)
df = pd.DataFrame(columns=df_cols.keys(), dtype=np.int32, index=range(10), data=-1)
# df.dtypes shows they're all np.int32
# Fix up dtypes after declaration
for col,coltype in df_cols.items():
df[col] = df[col].astype(coltype)
```
#### Problem description
The DataFrame() doc doesn't explicitly say a list/dict/Series/array-like is not allowed (and if you pass in one the error is not very friendly). Also behaves differently to read_csv(dtype).
Leaving dtype=None in constructor will infer a wider type than necessary.
So in general you either set dtype=widest_necessary_type, or dtype=None and then manually fix them up after declaration, by casting with `astype()`
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
python: 2.7.10.final.0
python-bits: 64
machine: x86_64
processor: i386
byteorder: little
pandas: 0.19.1
numpy: 1.11.2
scipy: 0.18.1
</details>
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MDExOlB1bGxSZXF1ZXN0OTU3ODMwMzM=
| 14,765 |
added read_msgpack() to index
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| 1 | 2016-11-29T21:50:28Z | 2016-12-05T19:11:28Z | 2016-11-29T23:33:47Z |
CONTRIBUTOR
| null |
- [x] closes #14702
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
First time contributing to pandas, let me know if there's something I should do differently.
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BLD: clean .pxi when cleaning
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| 0 | 2016-11-29T23:37:09Z | 2016-11-29T23:37:29Z | 2016-11-29T23:37:29Z |
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| 5 | 2016-11-29T23:53:45Z | 2016-11-30T11:02:04Z | 2016-11-30T11:02:04Z |
CONTRIBUTOR
| null |
xref #14729
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MDExOlB1bGxSZXF1ZXN0OTU4MTA5OTA=
| 14,768 |
DOC/TST: dtype param in read_fwf
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| 3 | 2016-11-30T01:29:13Z | 2016-11-30T15:11:14Z | 2016-11-30T15:11:05Z |
CONTRIBUTOR
| null |
- [x] closes #7141
- [ ] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
#14295 actually implemented the behavior, this is just adding docs and tests.
|
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| 14,769 |
DOC: Remove SparseSeries from SparseArray doc
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| 1 | 2016-11-30T06:43:32Z | 2016-11-30T15:04:50Z | 2016-11-30T11:03:10Z |
MEMBER
| null |
Title is self-explanatory. Looks like a failed copy-paste.
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BUG: Error upon Series.Groupby.nunique with empty Series (#12553)
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| 3 | 2016-11-30T06:45:08Z | 2017-12-20T01:58:14Z | 2016-12-04T17:42:07Z |
MEMBER
| null |
- [x] closes #12553
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Similar to #12560, but it has appeared to stall. Wanted to push it along.
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API: Return sparse objects always for cumsum
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| 5 | 2016-11-30T10:47:11Z | 2016-12-15T11:28:37Z | 2016-12-15T11:28:26Z |
MEMBER
| null |
Always return `SparseArray` and `SparseSeries` for `SparseArray.cumsum()` and `SparseSeries.cumsum()` respectively, regardless of `fill_value`.
Close #12855.
|
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MDU6SXNzdWUxOTI1NDExOTE=
| 14,772 |
ENH: Timestamp/DTI. to epoch time
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| 13 | 2016-11-30T11:26:01Z | 2021-05-02T00:49:53Z | null |
CONTRIBUTOR
| null |
add a ``.to_epoch(unit='s')`` method to ``Timestamp`` and ``DatetimeIndex`` that returns the epoch for that unit. I think would default this to ``s`` as that seems pretty common, but allow any of our units.
```
In [19]: s = Series(pd.date_range('20160101',periods=3))
In [20]: s
Out[20]:
0 2016-01-01
1 2016-01-02
2 2016-01-03
dtype: datetime64[ns]
In [21]: ((s-Timestamp(0)) / Timedelta('1s')).astype('i8')
Out[21]:
0 1451606400
1 1451692800
2 1451779200
dtype: int64
```
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BUG: diff with multiple columns of type np.int8 fails
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| 4 | 2016-11-30T15:14:12Z | 2021-05-19T12:53:58Z | 2021-05-19T12:53:58Z |
NONE
| null |
```
df = pd.DataFrame(np.random.randint(9, size=(4,5)), columns=list("abcde"))
df = df.astype(np.int8)
print(df.astype(int).diff())
print(df.a.diff())
df.diff()
```
diff works fine on a dataframe with int columns; or on a single column of type np.int8. However if there are multiple columns of np.int8 then it fails
```
a b c d e
0 NaN NaN NaN NaN NaN
1 6.0 -5.0 -7.0 -7.0 -1.0
2 -5.0 7.0 -1.0 5.0 -7.0
3 2.0 -6.0 8.0 0.0 3.0
0 NaN
1 6.0
2 -5.0
3 2.0
Name: a, dtype: float64
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-145-2724544318c3> in <module>()
3 print(df.astype(int).diff())
4 print(df.a.diff())
----> 5 df.diff()
C:\Users\s\Anaconda3\lib\site-packages\pandas\core\frame.py in diff(self, periods, axis)
4068 """
4069 bm_axis = self._get_block_manager_axis(axis)
-> 4070 new_data = self._data.diff(n=periods, axis=bm_axis)
4071 return self._constructor(new_data)
4072
C:\Users\s\Anaconda3\lib\site-packages\pandas\core\internals.py in diff(self, **kwargs)
3172
3173 def diff(self, **kwargs):
-> 3174 return self.apply('diff', **kwargs)
3175
3176 def interpolate(self, **kwargs):
C:\Users\s\Anaconda3\lib\site-packages\pandas\core\internals.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
3054
3055 kwargs['mgr'] = self
-> 3056 applied = getattr(b, f)(**kwargs)
3057 result_blocks = _extend_blocks(applied, result_blocks)
3058
C:\Users\s\Anaconda3\lib\site-packages\pandas\core\internals.py in diff(self, n, axis, mgr)
1040 def diff(self, n, axis=1, mgr=None):
1041 """ return block for the diff of the values """
-> 1042 new_values = algos.diff(self.values, n, axis=axis)
1043 return [self.make_block(values=new_values, fastpath=True)]
1044
C:\Users\s\Anaconda3\lib\site-packages\pandas\core\algorithms.py in diff(arr, n, axis)
1196 if arr.ndim == 2 and arr.dtype.name in _diff_special:
1197 f = _diff_special[arr.dtype.name]
-> 1198 f(arr, out_arr, n, axis)
1199 else:
1200 res_indexer = [slice(None)] * arr.ndim
pandas\src\algos_common_helper.pxi in pandas.algos.diff_2d_int8 (pandas\algos.c:62940)()
ValueError: Buffer dtype mismatch, expected 'float32_t' but got 'double'
```
######################################################################
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 28.7.1
Cython: 0.24
numpy: 1.11.2
scipy: 0.17.1
statsmodels: 0.8.0rc1
xarray: None
IPython: 4.2.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
time: 5.55 s
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| 192,608,455 |
MDU6SXNzdWUxOTI2MDg0NTU=
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AssertionError in FY5253.rollback()
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| 1 | 2016-11-30T16:14:53Z | 2017-12-06T11:15:31Z | 2017-12-06T11:15:31Z |
NONE
| null |
Hi,
I'm trying to do North American (Year,WeekOfYear) <-> Period (W-SAT) conversions, for calculations involving fiscal year week numbers, etc. From what I understand pandas.offsets.FY5253 can be used for this, but I'm getting a weird assertion error than should probably not happen.
#### Code
```python
import pandas as pd
fy = pd.offsets.FY5253(n=1, weekday=5, startingMonth=12, variation='nearest')
fy.rollback(pd.Timestamp('2004-01-01'))
```
#### Expected Output
I think the output should be equal to the one you get with
```python
fy.rollback(pd.Timestamp('2003-12-31'))
```
which is
```python
Timestamp('2002-12-28 00:00:00')
```
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-35-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 28.7.1
Cython: 0.23.4
numpy: 1.11.2
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.4.2
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.7.3
boto: 2.38.0
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTI2MDk5NTc=
| 14,775 |
Severe performance problems with multi-index joins
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[] | null | 4 | 2016-11-30T16:20:07Z | 2021-05-02T00:52:41Z | null |
NONE
| null |
#### Code Sample (microbenchmark)
```python
from __future__ import division
import pandas as pd
import numpy as np
import functools
import operator as op
import time
def join_raw(df1, df2, *args, **kwargs):
start_time = time.time()
result = df1.join(df2, *args, **kwargs)
end_time = time.time()
return result, end_time - start_time
def join_with_workaround(df1, df2, *args, **kwargs):
index1 = list(df1.index.names)
index2 = list(df2.index.names)
index1common = [level for level in index1 if level in index2]
index2common = [level for level in index2 if level in index1]
index1remove = [level for level in index1 if level not in index2]
index2remove = [level for level in index2 if level not in index1]
if index1remove:
df1 = df1.reset_index(index1remove)
if index2remove:
df2 = df2.reset_index(index2remove)
# The indices should be the same now, except that levels might be in a
# different order, which causes "join" to fail as well.
if df1.index.names != df2.index.names:
df2 = df2.reorder_levels(df1.index.names)
start_time = time.time()
result = df1.join(df2, *args, **kwargs)
end_time = time.time()
# Add all removed levels to the index of the result.
if index1remove:
result.set_index(index1remove, append=True, inplace=True)
# Make sure the levels are ordered the same way as in df1.
# Additional levels only appearing in df2 will be added at the
# end, though.
if result.index.names != index1:
result = result.reorder_levels(index1)
if index2remove:
result.set_index(index2remove, append=True, inplace=True)
return result, end_time - start_time
def join_with_reset_index(df1, df2, *args, **kwargs):
index1 = list(df1.index.names)
index2 = list(df2.index.names)
index2common = [level for level in index2 if level in index1]
index2remove = [level for level in index2 if level not in index1]
df1 = df1.reset_index()
if index2remove:
df2 = df2.reset_index(index2remove)
start_time = time.time()
result = df1.join(df2, on=index2common, *args, **kwargs)
end_time = time.time()
result.set_index(index1 + index2remove, inplace=True)
return result, end_time - start_time
def join_via_concat(df1, df2, how='left', lsuffix='', rsuffix=''):
index1 = list(df1.index.names)
index2 = list(df2.index.names)
index1remove = [level for level in index1 if level not in index2]
index2remove = [level for level in index2 if level not in index1]
# Remove levels from both indices as necessary.
if index1remove:
df1 = df1.reset_index(index1remove)
if index2remove:
df2 = df2.reset_index(index2remove)
# The indices should be the same now, except that levels might be in a
# different order.
if df1.index.names != df2.index.names:
df2 = df2.reorder_levels(df1.index.names)
if lsuffix:
df1 = df1.rename(columns=lambda c: c + lsuffix if c in df2.columns.values else c, copy=False)
if rsuffix:
df2 = df2.rename(columns=lambda c: c + rsuffix if c in df1.columns.values else c, copy=False)
overlapping_columns = [column for column in df1.columns.values if column in df2.columns.values]
if overlapping_columns:
raise Exception('Overlapping column names when joining data frames: %s (specify lsuffix or rsuffix)' % overlapping_columns)
# Use concat to simulate join.
start_time = time.time()
if how == 'left':
result = pd.concat([df1, df2], axis=1, join_axes=[df1.index])
elif how == 'right':
result = pd.concat([df1, df2], axis=1, join_axes=[df2.index])
else:
result = pd.concat([df1, df2], axis=1, join=how)
end_time = time.time()
# Add all removed levels to the index of the result.
if index1remove:
result.set_index(index1remove, append=True, inplace=True)
# Make sure the levels are ordered the same way as in df1.
# Additional levels only appearing in df2 will be added at the
# end, though.
if result.index.names != index1:
result = result.reorder_levels(index1)
if index2remove:
result.set_index(index2remove, append=True, inplace=True)
return result, end_time - start_time
result_row_count = 10000000
random_numbers = np.random.randn(result_row_count)
def create_dataframe(index_columns, index_sizes):
index_ranges = [list(range(size)) for size in index_sizes]
index = pd.MultiIndex.from_product(index_ranges, names=index_columns)
row_count = functools.reduce(op.mul, index_sizes, 1)
return pd.DataFrame(random_numbers[:row_count], columns=['V'], index=index)
def test_join_fn(fn, index1, index2, index_sizes):
df1 = create_dataframe(index1, [index_sizes[c] for c in index1])
df2 = create_dataframe(index2, [index_sizes[c] for c in index2])
try:
start_time = time.time()
result, main_time_1 = fn(df1, df2, lsuffix='l', rsuffix='r')
mid_time = time.time()
result, main_time_2 = fn(df1, df2, lsuffix='l', rsuffix='r')
end_time = time.time()
print(" %.3f (%.3f), then %.3f (%.3f)" % (mid_time - start_time, main_time_1, end_time - mid_time, main_time_2))
return True
except:
print(" ERROR")
return False
def test_joins(*args):
print(" Raw join on index:")
if not test_join_fn(join_raw, *args):
print(" Join on index with workaround (partial reset_index):")
test_join_fn(join_with_workaround, *args)
print(" Join after reset_index:")
test_join_fn(join_with_reset_index, *args)
print(" Join via concat:")
test_join_fn(join_via_concat, *args)
print("")
def test_index_combination(index1, index2):
result_index = index1 + [c for c in index2 if c not in index1]
print("%s + %s -> %s:" % (index1, index2, result_index))
remaining_row_count = result_row_count
index_sizes = {}
for c in result_index[:-1]:
if remaining_row_count >= 10:
index_sizes[c] = 10
remaining_row_count = (remaining_row_count + 9) // 10
else:
index_sizes[c] = 1
index_sizes[result_index[-1]] = remaining_row_count
test_joins(index1, index2, index_sizes)
test_index_combination(list('A'), list('A'))
test_index_combination(list('AB'), list('AB'))
test_index_combination(list('ABC'), list('ABC'))
test_index_combination(list('ABCDE'), list('ABCDE'))
test_index_combination(list('ABCDEFG'), list('ABCDEFG'))
test_index_combination(list('A'), list('AB'))
test_index_combination(list('B'), list('AB'))
test_index_combination(list('A'), list('ABC'))
test_index_combination(list('B'), list('ABC'))
test_index_combination(list('AB'), list('A'))
test_index_combination(list('AB'), list('B'))
test_index_combination(list('ABC'), list('A'))
test_index_combination(list('ABC'), list('B'))
test_index_combination(list('ABC'), list('AB'))
test_index_combination(list('ABCDEF'), list('ABCDE'))
test_index_combination(list('AB'), list('ABC'))
test_index_combination(list('ABCDE'), list('ABCDEF'))
test_index_combination(list('ABC'), list('ABD'))
test_index_combination(list('ABC'), list('BC'))
test_index_combination(list('BC'), list('ABC'))
test_index_combination(list('ABC'), list('AC'))
test_index_combination(list('AC'), list('ABC'))
test_index_combination(list('AB'), list('BA'))
test_index_combination(list('ABCEF'), list('ACDBF'))
```
#### Problem description
We are getting severe performance bottlenecks in our code due to join operations on multiple index levels.
To analyze and illustrate the problem, we have created a microbenchmark which executes joins on data frames with different indices and one data column. The benchmark is constructed such that the resulting data frame always has the same number of rows (10000000 in this case, though we also have to deal with larger data sets). We would therefore expect all join operations to take a similar amount of time, perhaps somewhat linear in the number of index levels. Instead, we see up to 200x longer execution times in some cases (see output).
The benchmark works as follows:
* First, we create two data frames with unique, hierarchically sorted multi-indices. The data frames are built so that after they are joined, all index levels except the last have 10 values each, and the last level has however many values are necessary to get 10000000 rows.
* Then these data frames are joined twice in a row, measuring the time each join operation takes.
* Moreover, we perform the same join in several different ways that (as far as we can tell) yield the same result but are sometimes faster, and/or work in cases where the native join operation fails with a "currently not implemented" exception. In particular, using concat instead of join sometimes helps a lot. Unfortunately, even the fastest method is often too slow for our purposes.
The numbers show the times of both joins that are executed on the same data frames. Interestingly, in some cases, the first join often takes a lot longer than the second. For the alternative implementations, the numbers in parentheses show the number of seconds spent in the main inner operation (either join or concat).
We would be grateful for answers to any of the following questions:
* What is causing these large differences in performance?
* Is there anything we can do on our side to speed things up (other than selecting the fastest option in each case, which we already do)?
* Are there any plans to improve the performance of joins in Pandas, in general?
* Could we assist with this, e.g. with a bug bounty?
#### Output
[test_join_results.txt](https://github.com/pandas-dev/pandas/files/622316/test_join_results.txt)
#### Expected Output
We would expect all execution times to be less than a second, usually much less.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 20.10.1
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
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
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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BUG: group.apply with non-lexsorted levels and sort=True
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| 0 | 2016-11-30T21:56:38Z | 2016-12-04T17:35:29Z | 2016-12-04T17:34:59Z |
CONTRIBUTOR
| null |
```
In [1]: df = pd.DataFrame({'x': ['a', 'a', 'b', 'a'], 'y': [1, 1, 2, 2], 'z': [1, 2,3, 4]}).set_index(['x', 'y'])
In [2]: df
Out[2]:
z
x y
a 1 1
1 2
b 2 3
a 2 4
In [3]: df.index
Out[3]:
MultiIndex(levels=[['a', 'b'], [1, 2]],
labels=[[0, 0, 1, 0], [0, 0, 1, 1]],
names=['x', 'y'])
In [5]: df.index.is_lexsorted()
Out[5]: False
In [6]: df.groupby(level=[0,1]).sum()
Out[6]:
z
x y
a 1 3
2 4
b 2 3
In [7]: df.groupby(level=[0,1]).apply(pd.DataFrame.drop_duplicates)
Exception: cannot handle a non-unique multi-index!
In [8]: df.sort_index().groupby(level=[0,1]).apply(pd.DataFrame.drop_duplicates)
Out[8]:
z
x y
a 1 1
1 2
2 4
b 2 3
# this is ok though
In [9]: df.groupby(level=[0,1], sort=False).apply(pd.DataFrame.drop_duplicates)
Out[9]:
z
x y
a 1 1
1 2
b 2 3
a 2 4
```
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| 1 | 2016-11-30T22:37:38Z | 2016-12-04T17:34:59Z | 2016-12-04T17:34:59Z |
CONTRIBUTOR
| null |
closes #14776
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ENH/DOC: wide_to_long performance and docstring clarification
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| 2 | 2016-11-30T23:31:15Z | 2016-12-13T23:30:36Z | 2016-12-13T23:30:35Z |
CONTRIBUTOR
| null |
I had to massage some messy data recently, and a big bottleneck turned out to be [wide_to_long](https://github.com/pandas-dev/pandas/blob/4814823903b862c411caf527271e384df0d0d7e7/pandas/core/reshape.py#L878)
Some test data with many id variables and time variables
```python
import pandas as pd
import numpy as np
import string
vars = string.ascii_uppercase[0:4]
nyrs = 20
nidvars = 20
N = 5000
yrvars = []
for var in vars:
for yr in range(1, nyrs + 1):
yrvars.append( var + str(yr))
yearobs = dict(zip(yrvars, np.random.randn(len(yrvars), N)))
idobs = dict(zip(range(nidvars), np.random.rand(nidvars, N)))
frame = pd.concat([pd.DataFrame(idobs), pd.DataFrame(yearobs)], axis=1)
frame.shape
#(5000, 91)
```
Reshaping with `wide_to_long` takes around 2 secs.
```python
%%time
frame['id'] = frame.index
res1 = pd.wide_to_long(frame, list(vars), i='id', j='year')
#CPU times: user 1.89 s, sys: 210 ms, total: 2.1 s
#Wall time: 2.13 s
```
I modified `wide_to_long` slightly (regex on categorical column / avoid copying many "idvariables", postpone type coercion) and the runtime is now
```python
%%time
res2 = wide_to_long2(frame, list(vars), i='id', j='year')
#CPU times: user 112 ms, sys: 10.8 ms, total: 123 ms
#Wall time: 125 ms
```
The result is the same
```python
res1.equals(res2)
#True
```
#### Docstring clarification
The `wide_to_long` docstring also contains an unused last parameter `stubend : str`, which should be removed.
A docstring `Note` addtion about escaping special characters (with for example `re.escape`) in `stubnames` could also perhaps be informative, since if the user passes a dataframe with messy stubnames, the function fails with a pretty uninformative error message.
I can send a PR for this if that is wanted?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 06f26b51e97a0e81e8bd7fca4bba18e57659d963
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0+124.g06f26b5.dirty
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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| 24 | 2016-12-01T00:43:09Z | 2016-12-13T23:32:07Z | 2016-12-13T23:30:36Z |
CONTRIBUTOR
| null |
- [x] closes #14778
- [x] tests passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Please see #14778 for details.
I make `wide_to_long` a bit faster (avoid slow regex search on long columns by first converting to Categorical, avoid melting all dataframes with all the id variables, and wait with trying to convert the "time" variable to `int` until last), and clear up the docstring.
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ENH: Pandas Series provide to_excel method
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| 7 | 2016-12-01T07:51:08Z | 2016-12-05T14:39:26Z | 2016-12-05T14:39:14Z |
CONTRIBUTOR
| null |
- [x] closes #8825
- [x] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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MDU6SXNzdWUxOTI5MDQ3NzI=
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API: Consider porting extension accessor from xarray
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| 7 | 2016-12-01T17:03:24Z | 2018-01-16T00:43:17Z | 2018-01-16T00:43:16Z |
CONTRIBUTOR
| null |
`xarray` provides a decorator to register a custom accessor on a `Dataset` - see docs [here](http://xarray.pydata.org/en/stable/internals.html#extending-xarray).
It's a bit of a blunt tool, in that it applies to all instances, but outside of that, I found it to be a nice alternative to subclassing or a composition class.
I haven't looked at the implementation, so not sure how hard it would be retrofit onto `DataFrame`
@shoyer - any thoughts or feedback from people using it?
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read_csv crash if second input line is malformed
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| 28 | 2016-12-01T17:20:12Z | 2021-03-16T14:37:46Z | 2021-03-15T20:16:25Z |
NONE
| null |
#### Problem description
**Added 2020/04/21**:
Similar issue as OP but segfaults (from [below](https://github.com/pandas-dev/pandas/issues/14782#issuecomment-617203992))!
~~~
import pandas
import io
pandas.read_csv(io.StringIO("\na\nb\n"), skip_blank_lines=False, header=1)
~~~
===
I'm usign read_csv to import files containing data in 9 columns.
Some lines have a 'time tag' composed by two columns (the first line is always a 'time tag').
Some lines are malformed in various ways.
I use read_csv to read data and skip bad lines just with a warn and usually it works.
Recently I found a file that crash my program and it seems caused by a bad formed second line (not 9 fields).
The code:
```python
import pandas as pd
print("before read_csv")
d = pd.read_csv("inputfile.txt",
header=0,
names=(11,22,33,44,55,66,77,88,99),
compression='infer',
error_bad_lines=False, warn_bad_lines=True)
print("after read_csv")
print(d)
```
works on input like this:
```
1,2
1,2,3,4,5,6,7,8,9
yunk
1,2,3,4,5,6,7,8,9
```
with output:
```
before read_csv
after read_csv
11 22 33 44 55 66 77 88 99
0 1 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
1 yunk NaN NaN NaN NaN NaN NaN NaN NaN
2 1 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
```
But fails on input like this:
```
1,2
yunk
1,2,3,4,5,6,7,8,9
1,2,3,4,5,6,7,8,9
```
with output:
```
before read_csv
b'Skipping line 3: expected 2 fields, saw 9\nSkipping line 4: expected 2 fields, saw 9\n'
Traceback (most recent call last):
File ".../bug_pandas.read_scv.py", line 42, in <module>
error_bad_lines=False, warn_bad_lines=True)
File ".../Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/io/parsers.py", line 645, in parser_f
return _read(filepath_or_buffer, kwds)
File ".../Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/io/parsers.py", line 400, in _read
data = parser.read()
File ".../Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/io/parsers.py", line 938, in read
ret = self._engine.read(nrows)
File ".../Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/io/parsers.py", line 1507, in read
data = self._reader.read(nrows)
File "pandas/parser.pyx", line 846, in pandas.parser.TextReader.read (pandas/parser.c:10364)
File "pandas/parser.pyx", line 868, in pandas.parser.TextReader._read_low_memory (pandas/parser.c:10640)
File "pandas/parser.pyx", line 945, in pandas.parser.TextReader._read_rows (pandas/parser.c:11677)
File "pandas/parser.pyx", line 1007, in pandas.parser.TextReader._convert_column_data (pandas/parser.c:12627)
pandas.io.common.CParserError: Too many columns specified: expected 9 and found 2
Process finished with exit code 1
```
The problem arises both on OSX and on CentOS 7.
I tried to find if it's an already known bug but I found nothing.
Sorry if it's already known.
Carlo.
#### Output of ``pd.show_versions()`` on OSX 10.11.6
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
#### Output of ``pd.show_versions()`` on CentOS 7
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.36.3.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 29.0.1
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTYxNTIwNjY=
| 14,783 |
ENH: merge_asof() has type specializations and can take multiple 'by' parameters (#13936)
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| 27 | 2016-12-01T19:18:27Z | 2016-12-17T17:32:48Z | 2016-12-16T23:10:34Z |
CONTRIBUTOR
| null |
- [x] closes #13936
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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na_position doesn't work for sort_index() with MultiIndex
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| 7 | 2016-12-01T23:28:41Z | 2017-04-20T08:30:48Z | 2017-04-19T22:29:29Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: pd.__version__
Out[3]: u'0.19.0'
In [4]: mi = pd.MultiIndex.from_tuples([[1, 1, 3], [1, 1, np.nan]], names=list('ABC'))
In [5]: df = pd.DataFrame([[1, 2], [3, 4]], mi)
In [6]: df.sort_index(na_position="first")
Out[6]:
0 1
A B C
1 1 NaN 3 4
3 1 2
In [7]: df.sort_index(na_position="last")
Out[7]:
0 1
A B C
1 1 NaN 3 4
3 1 2
```
#### Problem description
The `na_position` argument isn't used in `DataFrame.sort_index()` or `Series.sort_index()` due to the way we sort the `MultiIndex`. Whenever we create a `MultiIndex`, we store the labels as relative values. For instance, if we have the following `MultiIndex`:
```python
MultiIndex.from_tuples([[1, 1, 3], [1, 1, np.nan]], names=list('ABC'))
```
the values get stored as
```python
MultiIndex(levels=[[1], [1], [3]],
labels=[[0, 0], [0, 0], [0, -1]],
names=[u'A', u'B', u'C'])
```
with a `NaN` placeholder of -1.
These label values are what get passed to the sorting algorithm for both [DataFrames](https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L3334-L3337) and [Series](https://github.com/pandas-dev/pandas/blob/master/pandas/core/series.py#L1782). Since the sorting only happens on the `labels`, it has no notion of the `NaN`.
This has been discussed in #14015 and #14672 .
My original naive solution was to change [these lines](https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L3337-L3338) from:
```python
indexer = _lexsort_indexer(labels.labels, orders=ascending,
na_position=na_position)
```
to
```python
index_values_list = np.dstack(labels.get_values())[0].tolist()
indexer = _lexsort_indexer(index_values_list, orders=ascending,
na_position=na_position)
```
This didn't break any tests, but it isn't necessarily the best approach.
#### Expected Output
```python
In [7]: df.sort_index(na_position="last")
Out[7]:
0 1
A B C
1 1 3 1 2
NaN 3 4
```
#### Output of ``pd.show_versions()``
<details>
In [3]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-77-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.4
pip: 9.0.0
setuptools: 27.2.0
Cython: 0.21
numpy: 1.11.2
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.5.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.5
sqlalchemy: 0.9.7
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.7.3
boto: 2.32.1
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTYyMDQzOTg=
| 14,785 |
Fix a simple typo
|
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| 1 | 2016-12-02T01:21:12Z | 2016-12-15T09:38:06Z | 2016-12-02T12:52:55Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
Insignificant PR, it's just fixing a single typo on an exception.
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API: add dtype param to read_excel
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| 1 | 2016-12-02T02:02:22Z | 2016-12-03T12:37:32Z | 2016-12-03T12:37:32Z |
CONTRIBUTOR
| null |
- [x] closes #8212
- [ ] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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Fix typo at pandas/core/generic.py
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| 1 | 2016-12-02T04:11:03Z | 2016-12-15T09:37:59Z | 2016-12-02T12:51:50Z |
CONTRIBUTOR
| null |
`indentifier` -> `identifier`
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| 14,788 |
DOC: warning section on memory overflow when joining/merging dataframes on index with duplicate keys
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| 2 | 2016-12-02T09:41:58Z | 2016-12-11T20:26:55Z | 2016-12-11T20:26:35Z |
CONTRIBUTOR
| null |
- [ ] closes #14736
|
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Small typos
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pd.read_sql throws TypeError
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[] | null | 1 | 2016-12-03T12:07:07Z | 2016-12-03T12:23:59Z | 2016-12-03T12:08:46Z |
NONE
| null |
Trying to run a postgres query through `pd.read_sql` which throws a vexing `TypeError: 'dict' object does not support indexing`
Pandas version: 0.18.1
```
pd.read_sql('select extract(year from creationdate) as Year, count(id) as Count, \'c\' as Language from posts where "tags" like \'%<c>%\' group by year;', engine)
```
#### Expected Output
####
year | count | language
------+-------+----------
2008 | 1175 | C
2009 | 6812 | C
2010 | 15347 | C
2011 | 22726 | C
2012 | 31295 | C
2013 | 41057 | C
2014 | 42285 | C
2015 | 40960 | C
2016 | 24481 | C
<details>
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-189-c4bd24872e35> in <module>()
1 # pd.read_sql(pl_count_by_yr.format(tag).strip(), engine, chunksize=1000)
----> 2 pd.read_sql('select extract(year from creationdate) as Year, count(id) as Count, \'c\' as Language from posts where "tags" like \'%<c>%\' group by year;', engine)
c:\Anaconda\lib\site-packages\pandas\io\sql.pyc in read_sql(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)
513 sql, index_col=index_col, params=params,
514 coerce_float=coerce_float, parse_dates=parse_dates,
--> 515 chunksize=chunksize)
516
517
c:\Anaconda\lib\site-packages\pandas\io\sql.pyc in read_query(self, sql, index_col, coerce_float, parse_dates, params, chunksize)
1188 args = _convert_params(sql, params)
1189
-> 1190 result = self.execute(*args)
1191 columns = result.keys()
1192
c:\Anaconda\lib\site-packages\pandas\io\sql.pyc in execute(self, *args, **kwargs)
1079 def execute(self, *args, **kwargs):
1080 """Simple passthrough to SQLAlchemy connectable"""
-> 1081 return self.connectable.execute(*args, **kwargs)
1082
1083 def read_table(self, table_name, index_col=None, coerce_float=True,
c:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in execute(self, statement, *multiparams, **params)
1989
1990 connection = self.contextual_connect(close_with_result=True)
-> 1991 return connection.execute(statement, *multiparams, **params)
1992
1993 def scalar(self, statement, *multiparams, **params):
c:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in execute(self, object, *multiparams, **params)
904 """
905 if isinstance(object, util.string_types[0]):
--> 906 return self._execute_text(object, multiparams, params)
907 try:
908 meth = object._execute_on_connection
c:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_text(self, statement, multiparams, params)
1052 statement,
1053 parameters,
-> 1054 statement, parameters
1055 )
1056 if self._has_events or self.engine._has_events:
c:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
1144 parameters,
1145 cursor,
-> 1146 context)
1147
1148 if self._has_events or self.engine._has_events:
c:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _handle_dbapi_exception(self, e, statement, parameters, cursor, context)
1342 )
1343 else:
-> 1344 util.reraise(*exc_info)
1345
1346 finally:
c:\Anaconda\lib\site-packages\sqlalchemy\engine\base.pyc in _execute_context(self, dialect, constructor, statement, parameters, *args)
1137 statement,
1138 parameters,
-> 1139 context)
1140 except Exception as e:
1141 self._handle_dbapi_exception(
c:\Anaconda\lib\site-packages\sqlalchemy\engine\default.pyc in do_execute(self, cursor, statement, parameters, context)
448
449 def do_execute(self, cursor, statement, parameters, context=None):
--> 450 cursor.execute(statement, parameters)
451
452 def do_execute_no_params(self, cursor, statement, context=None):
TypeError: 'dict' object does not support indexing
</details>
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BUG: multi-index HDFStore data_columns=True
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| 4 | 2016-12-03T14:31:51Z | 2016-12-04T17:22:52Z | 2016-12-04T17:22:51Z |
CONTRIBUTOR
| null |
- [x] closes #14435
- [ ] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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read_csv() doesn't parse correctly when `usecols` and `parse_dates` are both used
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| 3 | 2016-12-03T17:19:21Z | 2016-12-30T19:27:40Z | 2016-12-30T19:27:40Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [22]: s = """a,b,c,d,e,f,g,h,i,j
...: 2016/09/21,1,1,2,3,4,5,6,7,8"""
In [23]: pd.read_csv(StringIO(s), parse_dates=[0], usecols=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']).info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1 entries, 0 to 0
Data columns (total 10 columns):
a 1 non-null datetime64[ns]
b 1 non-null int64
c 1 non-null int64
d 1 non-null int64
e 1 non-null int64
f 1 non-null int64
g 1 non-null int64
h 1 non-null int64
i 1 non-null object <- !!
j 1 non-null int64
dtypes: datetime64[ns](1), int64(8), object(1)
memory usage: 160.0+ bytes
In [24]: pd.read_csv(StringIO(s), parse_dates=[[0, 1]], usecols=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']).info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1 entries, 0 to 0
Data columns (total 9 columns):
a_b 1 non-null object
c 1 non-null int64
d 1 non-null int64
e 1 non-null int64
f 1 non-null int64
g 1 non-null int64
h 1 non-null object <- !!
i 1 non-null object <- !!
j 1 non-null int64
dtypes: int64(6), object(3)
memory usage: 152.0+ bytes
```
#### Problem description
Since v0.18.1 `pd.read_csv()` doesn't parse correctly, and it occurs randomly at every run. It occurs only when `usecols` and `parse_dates` are both used.
#### Expected Output
All the columns parsed as int64 and not some randomly as object.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 29.0.1.post20161201
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
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
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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can not run pandas unit tests without x11 connection
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| 6 | 2016-12-03T19:23:50Z | 2016-12-05T16:33:57Z | 2016-12-04T23:36:28Z |
NONE
| null |
I am working on automating the testing of conda environments that include pandas. I am unable to run the tests if I do not have a x11 connection setup correctly. Example:
```python
psdev105: ~ $ python -c "import pandas; pandas.test('fast',verbose=10)"
Running unit tests for pandas
pandas version 0.19.1
numpy version 1.11.2
pandas is installed in /reg/g/psdm/sw/conda/inst/miniconda2-dev-rhel6/envs/ana-1.0.6/lib/python2.7/site-packages/pandas
Python version 2.7.12 |Continuum Analytics, Inc.| (default, Jul 2 2016, 17:42:40) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
nose version 1.3.7
nose.config: INFO: Ignoring files matching ['^\\.', '^_', '^setup\\.py$']
X11 connection rejected because of wrong authentication.
: cannot connect to X server localhost:15.0
```
Are there a set of tests I can run that do not include a x11 connection?
#### Output of ``pd.show_versions()``
<details>
(ana-1.0.6) psdev105: ~ $ python -c "import pandas; pandas.show_versions()"
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-504.16.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.8
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
</details>
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Incorrect sequence handling with MultiIndex.from_tuples
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| 3 | 2016-12-03T20:45:48Z | 2019-10-21T19:52:17Z | null |
CONTRIBUTOR
| null |
```python
T = tuple(tuple(range(k)) for k in range(1, 8))
#Note: min(map(len, T)) == 1 and max(map(len, T)) == 7
bad = pd.MultiIndex.from_tuples(T)
bad.nlevels # == 1 ???
good = pd.MultiIndex.from_tuples(list(T))
good.nlevels # == 7
```
#### Problem description
The docstring leaves some ambiguity as to whether a tuple or list can be passed in. It does say a list of tuples at the top, but in the argument specification, it hints that tuples can be a list/sequence of tuple-likes. A tuple of tuples is a sequence of tuple-likes. I find it strange that my tuple of tuples was truncated to a single level MultiIndex, while my list of tuples was not.
Changing https://github.com/groutr/pandas/blob/master/pandas/indexes/multi.py#L983
to call `itertools.zip_longest` would fix the truncation issue.
I also don't understand why sometimes the arrays are transposed and every other case we don't transpose.
https://github.com/groutr/pandas/blob/master/pandas/indexes/multi.py#L980-L983
#### Expected Output
I would expect lists and tuples and other sequences of tuple-likes (sets, for example) to be treated the same. Do lists have a special meaning here?
This is in the latest master (588e29d23fa289916a8b057d093af62f35c6dcf6) and pandas 0.19.1.
I'd be happy to submit a PR if this is unintended behavior
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| 1 | 2016-12-03T23:27:28Z | 2016-12-04T16:50:27Z | 2016-12-04T16:50:12Z |
NONE
| null |
In 0.19.1 appending a DateTimeIndex to another DateTimeIndex where they have different time zones now returns an Index object. In 0.18.1 it would return a DateTimeIndex with a UTC time zone.
In 0.19.1 (and in 0.18.1) if the two DateTimeIndex have the same time zone a DateTimeIndex is returned. I believe the behavior in 0.18.1 of returning a DateTimeIndex with time zone UTC should be the correct behavior.
--- Example of the problem ---
```
import pandas as pd
df1 = pd.DataFrame({'a': [1, 2, 3]}, index=pd.date_range('1990-02-01 09:00:00', periods=3, freq='1min', tz='America/New_York'))
df2 = pd.DataFrame({'a': [4, 5, 6]}, index=pd.date_range('1990-02-01 08:03:00', periods=3, freq='1min', tz='America/Chicago'))
print(df1.index.tzinfo) # America/New_York
print(df2.index.tzinfo) # America/Chicago
result = df1.index.append(df2.index)
print(type(new_index))
print(type(result))
>> pandas 0.18.1 returns <class 'pandas.tseries.index.DatetimeIndex'>
>> pandas 0.19.1 returns <class 'pandas.indexes.base.Index'>
```
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| 14,796 |
ENH: Pandas `DataFrame.append` and `Series.append` methods should get an `inplace` kwag
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| 6 | 2016-12-04T05:50:35Z | 2020-11-16T05:32:49Z | 2016-12-04T16:15:17Z |
NONE
| null |
#### Problem description
Currently to append to a DataFrame, the following is the approach:
```
df = pd.DataFrame(np.random.rand(5,3), columns=list('abc'))
df = df.append(pd.DataFrame(np.random.rand(5,3), columns=list('abc')))
```
`append` is a DataFrame or Series method, and as such should be able to modify the DataFrame or Series in place. If in place modification is not required, one may use `concat` or set `inplace` kwag to `False`. It will avoid an explicit assignment operation which is quite slow in Python, as we all know. Further, it will make the expected behavior similar to Python lists, and avoid questions such as these: [1](http://stackoverflow.com/questions/16597265/appending-to-an-empty-data-frame-in-pandas), [2](http://stackoverflow.com/questions/37009287/using-pandas-append-within-for-loop)...
Additionally at present, `append` is full subset of `concat`, and as such it need not exist at all. Given the vast number of functions to append a DataFrame or Series to another in Pandas, it makes sense that each has it's merits and demerits. Gaining an `inplace` kwag will clearly distinguish `append` from `concat`, and simplify code.
I understand that this issue was raised in #2801 a long time ago. However, the conversation in that deviated from the simplification offered by the `inplace` kwag to performance enhancement. I (and many like me) are looking for ease of use, and not so much at performance. Also, we expect the data to fit in memory (which is a limitation even with current version of `append`).
#### Expected Code
```
df = pd.DataFrame(np.random.rand(5,3), columns=list('abc'))
df.append(pd.DataFrame(np.random.rand(5,3), columns=list('abc')), inplace=True)
```
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BLD: doc-build not updating
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| 1 | 2016-12-04T17:41:20Z | 2016-12-12T01:32:05Z | 2016-12-12T01:32:05Z |
CONTRIBUTOR
| null |
https://travis-ci.org/pandas-dev/pandas/jobs/180943536
```
remote: Invalid username or password.
fatal: Authentication failed for 'https://[email protected]/pandas-docs/pandas-docs-travis/'
```
this is after I restored the original credentials.........
cc @wesm
cc @jorisvandenbossche
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allowing datetime and timedelta datatype in pd cut bins
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| 5 | 2016-12-04T22:52:49Z | 2016-12-22T11:37:03Z | 2016-12-22T11:37:01Z |
CONTRIBUTOR
| null |
xref #14714, follow-on to #14737
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ENH: add timedelta as valid type for interpolate with method='time'
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| 12 | 2016-12-05T00:24:22Z | 2016-12-10T11:20:03Z | 2016-12-10T11:19:49Z |
CONTRIBUTOR
| null |
- [x] closes #6424
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
|
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DEPR: relocate exceptions in pandas.core.common
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| 7 | 2016-12-05T00:36:52Z | 2017-04-03T21:52:14Z | 2017-04-03T21:52:14Z |
CONTRIBUTOR
| null |
move these to ``pandas.api.exceptions``
using ``pandas.util.depr_module`` this should be straightforward
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| 5 | 2016-12-05T02:52:52Z | 2016-12-10T11:21:49Z | 2016-12-10T11:12:01Z |
CONTRIBUTOR
| null |
- [x] closes #14759
This is my first PR here. Not sure about the missing entries in API reference. Need some guidance on that. The same doubt about the best place to put the xref in computation.rst and timeseries.rst
Thanks.
|
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DOC: minor format fix
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| 1 | 2016-12-05T04:28:29Z | 2016-12-05T08:57:14Z | 2016-12-05T08:57:01Z |
CONTRIBUTOR
| null |
in the original version.
dflow is not displayed.
see: http://pandas.pydata.org/pandas-docs/stable/cookbook.html#splitting
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DOC: Fix grammar and formatting typos
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| 9 | 2016-12-05T07:00:13Z | 2016-12-08T16:42:00Z | 2016-12-08T16:41:52Z |
MEMBER
| null |
One grammatical typo, and the rest address small formatting typos when rendering online.
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BUG: This should be a 64-bit int, not an 8-bit int
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[] | null | 0 | 2016-12-06T00:02:47Z | 2016-12-06T13:15:08Z | 2016-12-06T13:15:08Z |
CONTRIBUTOR
| null |
See #14711
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BUG: fix hash collisions for from int overflow
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| 2 | 2016-12-06T01:18:20Z | 2016-12-06T13:15:08Z | 2016-12-06T13:15:08Z |
CONTRIBUTOR
| null |
hash collisions brought up: https://github.com/pandas-dev/pandas/issues/14711#issuecomment-264885726
closes #14804
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BUG: MultiIndex truncated for some non-list arguments
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| 2 | 2016-12-06T07:46:37Z | 2016-12-11T20:41:27Z | 2016-12-08T04:37:38Z |
CONTRIBUTOR
| null |
closes #14794.
WIP. Discussion/suggestions welcome.
|
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| 193,708,475 |
MDExOlB1bGxSZXF1ZXN0OTY2Nzg1NzE=
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TST: Test pivot with categorical data
|
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| 5 | 2016-12-06T08:14:35Z | 2017-12-20T01:59:00Z | 2016-12-06T19:10:40Z |
MEMBER
| null |
closes #8860
closes #8731
closes #9534
Looks like the examples in these issues pass in the master branch. Added test to confirm.
|
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