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| 14,908 |
List required for `percentiles` kwarg in `DataFrame.describe` when median is not present as opposed to array-like
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| 1 | 2016-12-18T19:30:38Z | 2016-12-19T20:30:21Z | 2016-12-19T20:30:21Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame(np.random.random((1000, 4)))
In [4]: percentiles = np.linspace(0, 0.99, 10)
In [5]: df.describe(percentiles=percentiles)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-83616b318eba> in <module>()
----> 1 df.describe(percentiles=percentiles)
C:\Users\pbreach\Anaconda3\lib\site-packages\pandas\core\generic.py in describe(self, percentiles, include, exclude)
5194 # median should always be included
5195 if 0.5 not in percentiles:
-> 5196 percentiles.append(0.5)
5197 percentiles = np.asarray(percentiles)
5198 else:
AttributeError: 'numpy.ndarray' object has no attribute 'append'
```
#### Problem description
In the documentation the kwarg `percentiles` is expecting an array-like input, however when passing in a numpy array, an attribute error is thrown as if it were expecting a list. If a list is being expected in the case that the median is not found should there be an explicit conversion to `list` before the median is appended?
#### Expected Output
```
In [6]: df.describe(percentiles=list(percentiles))
Out[6]:
0 1 2 3
count 1000.000000 1000.000000 1000.000000 1000.000000
mean 0.500730 0.501185 0.498594 0.498648
std 0.289616 0.286023 0.290509 0.292264
min 0.001290 0.000822 0.000459 0.001975
0% 0.001290 0.000822 0.000459 0.001975
11% 0.119319 0.124990 0.107683 0.114136
22% 0.211321 0.232740 0.209913 0.227046
33% 0.331405 0.325820 0.336409 0.311294
44% 0.439314 0.446085 0.443036 0.431923
50% 0.500374 0.505759 0.499125 0.491579
55.0% 0.553634 0.552899 0.552896 0.544990
66% 0.666159 0.647926 0.661797 0.661387
77% 0.777984 0.774892 0.776067 0.773342
88% 0.883761 0.874293 0.872860 0.884350
99% 0.985795 0.989234 0.991083 0.993646
max 0.998623 0.999924 0.999723 0.999185
```
#### Output of ``pd.show_versions()``
<details>
In [7]: pd.show_versions()
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 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 4.2.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
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: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTg0ODU5MTE=
| 14,909 |
CLN: remove simple _DATELIKE_DTYPES test and replace with is_datetimelike accessor
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| 0 | 2016-12-18T20:17:55Z | 2016-12-18T20:29:36Z | 2016-12-18T20:29:36Z |
CONTRIBUTOR
| null |
this was some older code
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BUG: select_dtypes now allows 'datetimetz' for generically selecting datetimes with timezones
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| 1 | 2016-12-18T20:19:13Z | 2016-12-19T11:02:27Z | 2016-12-19T11:02:27Z |
CONTRIBUTOR
| null |
was missing a generic way to use select_dtypes to select *any* datetime with tz.
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| 21 | 2016-12-18T20:35:30Z | 2017-01-19T20:58:17Z | 2016-12-31T15:55:21Z |
MEMBER
| null |
1) Update documentation about how the `dialect` parameter is handled.
2) Verify that the `dialect` parameter passed in is valid before accessing the dialect attributes.
Closes #14898.
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CLN: Resubmit of GH14700. Fixes GH14554. Errors other than Indexing…
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| 3 | 2016-12-18T21:40:40Z | 2016-12-19T23:47:01Z | 2016-12-19T23:46:27Z |
CONTRIBUTOR
| null |
…Error and KeyError now bubble up appropriately.
- [x] closes #14554
- [ ] tests added / passed (Not required per GH14700 discussion)
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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TST:Test to_sparse with nan dataframe (#10079)
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| 3 | 2016-12-18T23:04:24Z | 2016-12-19T17:51:02Z | 2016-12-19T11:20:43Z |
MEMBER
| null |
- [x] closes #10079
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
Possibly fixed with a PR that fixed one of #10627. Look like `to_dense` is fixed for 0.20.0, but should be unrelated to this.
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BUG: Fixed DataFrame.describe percentiles are ndarray w/ no median
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| 5 | 2016-12-19T05:33:48Z | 2016-12-19T20:30:24Z | 2016-12-19T20:30:21Z |
CONTRIBUTOR
| null |
Explicit conversion to list for `percentiles`. Fixes the case where `percentiles` is passed as a numpy with no median (0.5) present. Closes #14908.
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BUG: Prevent uint64 overflow in Series.unique
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| 9 | 2016-12-19T05:43:48Z | 2016-12-20T13:46:51Z | 2016-12-20T13:46:00Z |
MEMBER
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Introduces a `UInt64HashTable` class to hash `uint64` elements and prevent overflow in functions like `Series.unique`.
Closes #14721.
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| 2 | 2016-12-19T06:42:33Z | 2016-12-20T13:49:33Z | 2016-12-20T13:49:05Z |
MEMBER
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Adds handling for `uint64` objects during conversion. When negative numbers and `uint64` are detected, we then convert the result to `object`.
Picks up where #4845 left off. Closes #4471.
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| 4 | 2016-12-19T07:46:35Z | 2016-12-19T16:59:51Z | 2016-12-19T16:59:50Z |
MEMBER
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The hack used to resolve #2355 is no longer needed. Removes the hack and patches several tests that relied on this hacky (and buggy) behavior.
Closes #14881.
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| 10 | 2016-12-19T09:20:50Z | 2016-12-21T11:28:06Z | 2016-12-21T11:28:03Z |
MEMBER
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Patches bug in `read_msgpack` in which `Series` categoricals were accidentally being constructed with a non-categorical dtype, resulting in an error.
Closes #14901.
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CLN: move unique1d to algorithms from nanops
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| 0 | 2016-12-19T11:31:18Z | 2016-12-19T14:03:00Z | 2016-12-19T14:03:00Z |
CONTRIBUTOR
| null |
TST: consolidate hashtable testing to test_algos.py
|
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| 196,425,783 |
MDExOlB1bGxSZXF1ZXN0OTg1NzM3ODg=
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TST: Test empty input for read_csv (#14867)
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| 3 | 2016-12-19T14:25:24Z | 2016-12-20T16:03:26Z | 2016-12-20T16:03:24Z |
CONTRIBUTOR
| null |
- [x ] closes #14867
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
LMK if it would be more suitable in a different test file.
|
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Series.expanding not working with ndarray
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| 1 | 2016-12-19T15:29:54Z | 2016-12-19T15:37:45Z | 2016-12-19T15:37:12Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> df
e q w
0 4 1 2
1 5 2 3
2 6 3 4
3 7 4 5
4 8 5 6
>>>
>>> def f1(ser):
... return pd.expanding_max(ser)
...
>>> def f2(ser):
... return Series.expanding(ser).max()
...
>>> df["e"].rolling(min_periods=1,center=False,window=1).apply(func=f1)
0 4.0
1 5.0
2 6.0
3 7.0
4 8.0
Name: e, dtype: float64
>>> df["e"].rolling(min_periods=1,center=False,window=1).apply(func=f2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/window.py", line 1084, in apply
return super(Rolling, self).apply(func, args=args, kwargs=kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/window.py", line 805, in apply
center=False)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/window.py", line 740, in _apply
result = calc(values)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/window.py", line 734, in calc
return func(x, window, min_periods=self.min_periods)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/window.py", line 802, in f
kwargs)
File "/pandas/window.pyx", line 1382, in pandas._window.roll_generic (pandas/window.c:35122)
File "<stdin>", line 2, in f2
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/generic.py", line 5503, in expanding
axis = self._get_axis_number(axis)
AttributeError: 'numpy.ndarray' object has no attribute '_get_axis_number'
```
#### Problem description
Hi,
I'm wondering why expanding_max() can support ndarray, but expanding() can't ?
And how can I use expanding in this condition?
def f2(ser):
... return Series.expanding(ser).max()
change function f2 to this?
def f2(ser):
... return Series.expanding(Series(ser)).max()
It looks like a little stupid....
|
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MDU6SXNzdWUxOTY0NDM3MTc=
| 14,922 |
BUG: sorting with large float and multiple columns incorrect
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| 5 | 2016-12-19T15:37:14Z | 2016-12-23T21:04:14Z | 2016-12-23T21:04:14Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
int_values = (2, -9223372036854775808)
float_values = (2.0, -1.797693e308)
df = pd.DataFrame(dict(int=int_values,
float=float_values), columns=("int", "float"))
print("df")
print(df)
print()
print("sort by int")
print(df.sort_values(["int"]))
print()
print("sort by float")
print(df.sort_values(["float"]))
print()
print("sort by (int, float)")
print(df.sort_values(["int", "float"]))
```
produces
```
$ python wrong.py
df
int float
0 2 2.000000e+00
1 -9223372036854775808 -1.797693e+308
sort by int
int float
1 -9223372036854775808 -1.797693e+308
0 2 2.000000e+00
sort by float
int float
1 -9223372036854775808 -1.797693e+308
0 2 2.000000e+00
sort by (int, float)
int float
0 2 2.000000e+00
1 -9223372036854775808 -1.797693e+308
```
As you can see the first two calls (only sorting by `int` or by `float`) sort correctly whereas `df.sort_values(["int", "float"])` does not seem to sort at all. Row with index `1` should appear first, and then row with index `0`.
Using `pandas 0.17.0` I get the correct result:
```
int float
1 -9223372036854775808 -1.797693e+308
0 2 2.000000e+00
```
####
This issue was discussed at http://stackoverflow.com/questions/41225286/sort-by-broken-in-pandas-0-18-0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS [1/193]
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 7.1.2
setuptools: 18.2
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
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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MDU6SXNzdWUxOTY0NDg1MjU=
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why pandas.expanding_max support adarray
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[] | null | 1 | 2016-12-19T15:55:15Z | 2016-12-19T16:01:56Z | 2016-12-19T16:01:50Z |
NONE
| null |
#### Problem description
I'm sorry, I'm a beginner with pandas.
In the document of 0.17.0, I found this,
pandas.expanding_max(arg, min_periods=1, freq=None, **kwargs)
arg : Series, DataFrame
It seems that expanding_max only support Series and DataFrame, but why it also works with adarray?
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MDExOlB1bGxSZXF1ZXN0OTg1OTMwMzg=
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MAINT: Only output errors in C style check
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| 3 | 2016-12-19T16:12:12Z | 2016-12-19T19:20:52Z | 2016-12-19T19:20:52Z |
MEMBER
| null |
Title is self-explanatory. Follow-up to #14814.
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PERF: make all inference routines cpdef bint
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| 1 | 2016-12-19T23:33:29Z | 2016-12-20T14:11:33Z | 2016-12-20T14:11:33Z |
CONTRIBUTOR
| null |
going to rebase after #14827
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| 3 | 2016-12-20T06:36:18Z | 2016-12-20T20:44:31Z | 2016-12-20T20:44:26Z |
MEMBER
| null |
- [x] closes #10780
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
Didn't find a PR in 0.20 or 0.19.2 that closed this, but works on master 0.19.1
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DataFrame.groupby.apply returns different results with copy lambda functions.
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| 3 | 2016-12-20T07:31:50Z | 2021-06-11T13:08:53Z | null |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [32]: df = pd.DataFrame({"g":[1, 2, 2, 2], "a":[1, 2, 3, 4], "b":[5, 6, 7, 8]})
...:
In [33]: df.groupby('g').apply(lambda x: x)
Out[33]:
a b g
0 1 5 1
1 2 6 2
2 3 7 2
3 4 8 2
In [34]: df.groupby('g').apply(lambda x: x[:])
Out[34]:
a b g
g
1 0 1 5 1
2 1 2 6 2
2 3 7 2
3 4 8 2
In [35]: df.groupby('g').apply(lambda x: x.copy(deep=False))
Out[35]:
a b g
0 1 5 1
1 2 6 2
2 3 7 2
3 4 8 2
In [36]: df.groupby('g').apply(lambda x: x.copy(deep=True))
Out[36]:
a b g
g
1 0 1 5 1
2 1 2 6 2
2 3 7 2
3 4 8 2
```
#### Problem description
xref #9867
Issue #9946 (In[36]) no longer raises a `ValueError` on master 0.19.1, but I am curious why these 4 cases return 2 different results.
#### Expected Output
Not sure since I don't have a personal use-case for this operation, but I'd expect these operations to return the same result unless I am missing something with using a deepcopy.
#### Output of ``pd.show_versions()``
<details>
```
commit: 3ccb50131b698bccee21780c660a70bed87396d0
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0+228.g3ccb501
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: None
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: 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
s3fs: None
pandas_datareader: None
```
</details>
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| 196,721,411 |
MDExOlB1bGxSZXF1ZXN0OTg3ODQwMDQ=
| 14,928 |
BUG: bug in Series construction from UTC
|
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| 0 | 2016-12-20T16:44:32Z | 2016-12-20T17:48:14Z | 2016-12-20T17:48:14Z |
CONTRIBUTOR
| null |
xref #14918
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| 14,929 |
Group by using time grouper and 1 other column does not work
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| 1 | 2016-12-20T17:04:40Z | 2016-12-20T17:24:29Z | 2016-12-20T17:24:13Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
list_of_dicts = [{
'time': pandas.to_datetime('2016-12-03 18:00:00'),
'building': 'tall',
'type': 'steel'
}, {
'time': pandas.to_datetime('2016-12-03 18:00:00'),
'building': 'tall',
'type': 'brick'
}]
df = pandas.DataFrame(list_of_dicts)
building time type
0 tall 2016-12-03 18:00:00 steel
1 tall 2016-12-03 18:00:00 brick
df = df.groupby(['building', pandas.Grouper(key = 'time', freq = '1D')])
[(<pandas.tseries.resample.TimeGrouper at 0x2b1a5978aed0>,
building time type
1 tall 2016-12-03 18:00:00 brick),
('building',
building time type
0 tall 2016-12-03 18:00:00 steel)]
```
#### Problem description
Apparently this was fixed in https://github.com/pandas-dev/pandas/issues/3794, but I am still seeing this issue. However, this goes away if I add 1 more grouper column to it, which is strange.
```python
df.groupby(['building', pandas.Grouper(key = 'time', freq = '1D'), 'type'])
[(('tall', Timestamp('2016-12-03 00:00:00', offset='D'), 'brick'),
building time type
1 tall 2016-12-03 18:00:00 brick),
(('tall', Timestamp('2016-12-03 00:00:00', offset='D'), 'steel'),
building time type
0 tall 2016-12-03 18:00:00 steel)]
```
#### Expected Output
If I give in the explicit list, it works correctly.
```python
df.groupby([df['building'], pandas.Grouper(key = 'time', freq = '1D')])
[(('tall', Timestamp('2016-12-03 00:00:00', offset='D')),
building time type
0 tall 2016-12-03 18:00:00 steel
1 tall 2016-12-03 18:00:00 brick)]
```
This should be the expected output by just using 'building' or even pandas.Grouper(key='building').
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-164.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: None
Cython: None
numpy: 1.9.0.dev-Unknown
scipy: 0.15.1
statsmodels: None
IPython: 0.13.2
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2013d
bottleneck: 0.8.0
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
</details>
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| 196,740,101 |
MDU6SXNzdWUxOTY3NDAxMDE=
| 14,930 |
DataFrame __getitem__ ~100X slower on Pandas 0.19.1 vs 0.18.1 possibly getitem caching?
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| 7 | 2016-12-20T18:05:10Z | 2016-12-21T11:09:55Z | 2016-12-21T11:09:54Z |
NONE
| null |
```python
# Run on Pandas 0.19.1
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(1000,5000), columns=['C'+str(c) for c in range(5000)])
%%prun
for col in df:
df[col]
65011 function calls (65010 primitive calls) in 2.012 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
5000 1.935 0.000 1.955 0.000 base.py:1362(__contains__)
5000 0.025 0.000 2.002 0.000 frame.py:2033(__getitem__)
1 0.011 0.011 2.012 2.012 <string>:2(<module>)
5000 0.010 0.000 0.016 0.000 frame.py:2059(_getitem_column)
5001 0.007 0.000 0.007 0.000 {method 'view' of 'numpy.ndarray' objects}
5001 0.004 0.000 0.012 0.000 base.py:492(values)
5000 0.004 0.000 0.007 0.000 generic.py:1381(_get_item_cache)
5000 0.004 0.000 0.018 0.000 base.py:1275(<lambda>)
5000 0.003 0.000 0.014 0.000 base.py:874(_values)
5000 0.002 0.000 0.002 0.000 {built-in method builtins.isinstance}
5000 0.002 0.000 0.003 0.000 common.py:438(_apply_if_callable)
5000 0.002 0.000 0.002 0.000 {method 'get' of 'dict' objects}
5000 0.001 0.000 0.001 0.000 {built-in method builtins.hash}
5000 0.001 0.000 0.001 0.000 {built-in method builtins.callable}
1 0.000 0.000 2.012 2.012 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 generic.py:833(__iter__)
1 0.000 0.000 0.000 0.000 generic.py:401(_info_axis)
2/1 0.000 0.000 0.000 0.000 {built-in method builtins.iter}
1 0.000 0.000 0.000 0.000 {built-in method builtins.getattr}
1 0.000 0.000 0.000 0.000 base.py:1315(__iter__)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
%time df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Columns: 5000 entries, C0 to C4999
dtypes: float64(5000)
memory usage: 38.1 MB
Wall time: 4.39 s
```
#### Problem description
It appears that ```get_item_cache()``` or ```__contains__``` may have something to do with it. This affects other functionality such as ```df.info()``` which is now also ~100X slower.
#### Expected Output
```python
# Run on Pandas 0.18.1
In [6]: %%prun
...: for col in df:
...: df[col]
...:
45011 function calls (45010 primitive calls) in 0.032 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
5000 0.012 0.000 0.028 0.000 frame.py:1973(__getitem__)
5000 0.005 0.000 0.008 0.000 frame.py:1999(_getitem_column)
5000 0.004 0.000 0.005 0.000 base.py:1233(__contains__)
1 0.004 0.004 0.032 0.032 <string>:2(<module>)
5000 0.002 0.000 0.003 0.000 generic.py:1345(_get_item_cache)
5000 0.001 0.000 0.002 0.000 common.py:1846(_apply_if_callable)
5000 0.001 0.000 0.001 0.000 {built-in method builtins.isinstance}
5000 0.001 0.000 0.001 0.000 {method 'get' of 'dict' objects}
5000 0.001 0.000 0.001 0.000 {built-in method builtins.hash}
5000 0.000 0.000 0.000 0.000 {built-in method builtins.callable}
1 0.000 0.000 0.032 0.032 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 generic.py:808(__iter__)
1 0.000 0.000 0.000 0.000 base.py:440(values)
2/1 0.000 0.000 0.000 0.000 {built-in method builtins.iter}
1 0.000 0.000 0.000 0.000 {method 'view' of 'numpy.ndarray' objects}
1 0.000 0.000 0.000 0.000 generic.py:381(_info_axis)
1 0.000 0.000 0.000 0.000 base.py:1186(__iter__)
1 0.000 0.000 0.000 0.000 {built-in method builtins.getattr}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
%time df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Columns: 5000 entries, C0 to C4999
dtypes: float64(5000)
memory usage: 38.1 MB
Wall time: 40 ms
```
#### 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 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.10.1
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.7.1
IPython: 4.2.0
sphinx: 1.3.6
patsy: 0.4.0
dateutil: 2.5.0
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.2.5
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.6.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTg4MDM4MDE=
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PERF: PeriodIndex speed up
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| 13 | 2016-12-20T18:46:30Z | 2016-12-21T01:58:23Z | 2016-12-21T01:58:20Z |
CONTRIBUTOR
| null |
- [x] closes #https://github.com/pandas-dev/pandas/issues/14822
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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pd.read_pickle, pd.DataFrame.to_pickle do not accept pathlib.Path objects
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| 5 | 2016-12-20T18:51:13Z | 2017-05-12T13:30:12Z | 2017-05-12T13:30:00Z |
NONE
| null |
#### Code Sample
```python
In [1]: import pandas as pd
...: import pathlib as pl
...: path = pl.Path.home() / 'tmp.pickle' # I have prepared a pickle in this path
...:
In [2]: df = pd.read_pickle(str(path)) # This works, no problem
...:
In [3]: df2 = pd.read_pickle(path) # This breaks
...:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/ntaw/anaconda3/lib/python3.5/site-packages/pandas/io/pickle.py in try_read(path, encoding)
50 try:
---> 51 with open(path, 'rb') as fh:
52 return pkl.load(fh)
TypeError: invalid file: PosixPath('/home/ntaw/tmp.pickle')
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/home/ntaw/anaconda3/lib/python3.5/site-packages/pandas/io/pickle.py in try_read(path, encoding)
55 try:
---> 56 with open(path, 'rb') as fh:
57 return pc.load(fh, encoding=encoding, compat=False)
TypeError: invalid file: PosixPath('/home/ntaw/tmp.pickle')
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/home/ntaw/anaconda3/lib/python3.5/site-packages/pandas/io/pickle.py in read_pickle(path)
64 try:
---> 65 return try_read(path)
66 except:
/home/ntaw/anaconda3/lib/python3.5/site-packages/pandas/io/pickle.py in try_read(path, encoding)
60 except:
---> 61 with open(path, 'rb') as fh:
62 return pc.load(fh, encoding=encoding, compat=True)
TypeError: invalid file: PosixPath('/home/ntaw/tmp.pickle')
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/home/ntaw/anaconda3/lib/python3.5/site-packages/pandas/io/pickle.py in try_read(path, encoding)
50 try:
---> 51 with open(path, 'rb') as fh:
52 return pkl.load(fh)
TypeError: invalid file: PosixPath('/home/ntaw/tmp.pickle')
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/home/ntaw/anaconda3/lib/python3.5/site-packages/pandas/io/pickle.py in try_read(path, encoding)
55 try:
---> 56 with open(path, 'rb') as fh:
57 return pc.load(fh, encoding=encoding, compat=False)
TypeError: invalid file: PosixPath('/home/ntaw/tmp.pickle')
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-3-d33de9a00518> in <module>()
----> 1 df2 = pd.read_pickle(path)
/home/ntaw/anaconda3/lib/python3.5/site-packages/pandas/io/pickle.py in read_pickle(path)
66 except:
67 if PY3:
---> 68 return try_read(path, encoding='latin1')
69 raise
70
/home/ntaw/anaconda3/lib/python3.5/site-packages/pandas/io/pickle.py in try_read(path, encoding)
59 # compat pickle
60 except:
---> 61 with open(path, 'rb') as fh:
62 return pc.load(fh, encoding=encoding, compat=True)
63
TypeError: invalid file: PosixPath('/home/ntaw/tmp.pickle')
```
#### Problem description
`pandas` breaks at `df = pd.read_pickle(path)`. It seems to me that this should have been solved along with issue [#11033](https://github.com/pandas-dev/pandas/issues/11033), but it evidently doesn't work for my setup.
I encountered the same issue with `pd.DataFrame.to_pickle`.
The usecase appears to be related to open issue [#14705](https://github.com/pandas-dev/pandas/issues/14705) and closed pull request [#12930](https://github.com/pandas-dev/pandas/pull/12930).
#### Expected Output
I would expect the `pandas.read_*` functions and `pandas.DataFrame.to_*` methods to recognise `pathlib` objects, and handle the conversion `str(path)` under the hood.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-53-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: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: 1.1.3
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTg4Mjc0Njg=
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PERF: fix getitem unique_check / initialization issue
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| 8 | 2016-12-20T21:19:03Z | 2016-12-21T11:09:55Z | 2016-12-21T11:09:55Z |
CONTRIBUTOR
| null |
closes #14930
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BUG, TST: Check uint64 behaviour in algorithms.py
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| 5 | 2016-12-20T22:40:55Z | 2016-12-23T11:15:30Z | 2016-12-23T11:15:24Z |
MEMBER
| null |
First of a series of PR's to patch and test `uint64` behaviour in `core/algorithms.py`. In this PR, the following functions are checked:
1. `duplicated()` : robust but now has test to confirm
2. `mode()` : robust but now has test to confirm
3. `unique()` : non-robust but patched and tested
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BUG: Patch rank() uint64 behavior
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| 6 | 2016-12-21T04:25:45Z | 2016-12-24T22:04:49Z | 2016-12-24T22:04:45Z |
MEMBER
| null |
Adds `uint64` ranking functions to `algos.pyx` to allow for proper ranking with `uint64`.
Also introduces partial patch for `factorize()` by adding `uint64` hashtables and vectors for
usage. However, this patch is only partial because the larger bug of non-support for `uint64`
in `Index` has not been fixed (**UPDATE**: tackled in #14937):
~~~python
>>> from pandas import Index, np
>>> Index(np.array([2**63], dtype=np.uint64))
Int64Index([-9223372036854775808], dtype='int64')
~~~
Also patches a bug in `UInt64HashTable` from #14915 that had an erroneous null condition that was caught during testing and was hence removed.
Note there is overlap with #14934 with the implementation of `is_signed_integer_dtype` and `is_unsigned_integer_dtype`. That PR should be merged before this one.
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TST: Concat MultiIndex dataframes with deepcopy (#9967)
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| 3 | 2016-12-21T06:34:45Z | 2017-12-20T02:03:39Z | 2016-12-22T11:25:52Z |
MEMBER
| null |
- [x] closes #9967
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
Potentially related to #10638? Otherwise, don't think this was fixed recently
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ENH: Create and propagate UInt64Index
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| 35 | 2016-12-21T08:03:45Z | 2017-01-18T05:09:47Z | 2017-01-17T13:53:58Z |
MEMBER
| null |
1) Introduces and propagates `UInt64Index`, an index specifically for `uint`. xref #14935
2) <strike> Patches bug from #14916 that makes `maybe_convert_objects` robust against the known `numpy` bug that `uint64` cannot be compared to `int64`. This bug was caught during testing of `UInt64Index`. </strike>
**UPDATE**: Patched in #14951
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| 14,938 |
ERR: No Error when values argument in pivot_table is not in df.columns
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| 2 | 2016-12-21T09:28:04Z | 2016-12-23T20:49:57Z | 2016-12-23T20:49:56Z |
CONTRIBUTOR
| null |
#### Code Sample
```python
import pandas as pd
test = pd.DataFrame({'Date':pd.date_range(pd.datetime.today(), periods=20,freq='M').tolist(),
'occurrenceID':range(0,20)})
test['year'] = test.set_index('eventDate').index.year
test['month'] = test.set_index('eventDate').index.month
heatmap_prep = test.reset_index().pivot_table(index='year',columns='month',values='occurenceID',aggfunc='count')
```
#### Problem description
The above code gives no error, while 'occurenceID' is not a dataframe column (as there is a typo, needs to be 'occurrenceID', with two r's). It just produces a dataframe with only an index. Maybe a KeyError should be thrown?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 32.1.0.post20161217
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
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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MDU6SXNzdWUxOTY4OTE3NDk=
| 14,939 |
read_csv parse error on tab-delimited
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| 5 | 2016-12-21T10:02:28Z | 2016-12-23T15:02:24Z | 2016-12-23T07:37:50Z |
NONE
| null |
I'm getting a `CParserError` on a large (25M rows) tab-delimited file. I've narrowed the problem to the lines in the example attached:
[test.txt](https://github.com/pandas-dev/pandas/files/665986/test.txt)
When reading this file I get weird behaviour when displaying some of the lines.
```
➜ cat -vet test.txt
125403116848943104^IThey stay away from you for a reason stupid SMH$
125402641516859394^I"Women Would Sell Their Soul Jus To Buy Some Attention" #Fact$
125402461803515904^I@FFHIPHOP wats gd boii$
125402355914129408^I#TripleKrown$
125402323110461441^IWhat's ya purpose ??$
125402099134631936^IWhen one person says something about you you can fight it but when multiple people say the same thing then you lose out$
125402077856927744^IBlammer$
125386739329150976^IBitch you ignorant haaaaaaaaa$
```
```
In [109]: a = pandas.read_csv(root_dir + 'test.txt', sep='\t', header=None)
In [110]: a
Out[110]:
0 1
0 125403116848943104 They stay away from you for a reason stupid SMH
1 125402641516859394 Women Would Sell Their Soul Jus To Buy Some At...
2 125402461803515904 @FFHIPHOP wats gd boii
3 125402355914129408 #TripleKrown
4 125402323110461441 What's ya purpose ??
5 125402099134631936 When one person says something about you you c...
6 125402077856927744 Blammer
7 125386739329150976 Bitch you ignorant haaaaaaaaa
In [111]: a.iloc[1, 1]
Out[111]: 'Women Would Sell Their Soul Jus To Buy Some Attention #Fact'
In [112]: a.iloc[2, 1]
Out[112]: '@FFHIPHOP wats gd boii'
In [113]: a.iloc[3, 1]
Out[113]: '#TripleKrown'
```
#### Output of ``pd.show_versions()``
```
In [114]: pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 28.6.1
Cython: 0.25.2
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.4
lxml: 3.7.0
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
```
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| 14,940 |
BUG: pandas.Series.dt.round inconsistent behaviour on NAT's with different arguments?
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| 3 | 2016-12-21T10:57:08Z | 2017-01-14T18:24:27Z | 2017-01-14T18:24:27Z |
NONE
| null |
#### Code Sample
```python
# Your code here
import pandas as pd
from datetime import datetime
d=pd.DataFrame(data=[[datetime(2010,1,1,23,14,12,599), 2], [None, 4]], columns=['dtime', 'int'])
print d['dtime'].dt.round('s')
print d['dtime'].dt.round('5s')
print d['dtime'].dt.round('min')
```
#### Problem description
This is the output I get:
```python
0 2010-01-01 23:14:12
1 NaT
Name: dtime, dtype: datetime64[ns]
0 2010-01-01 23:14:10.000000000
1 2262-04-10 00:12:44.999999488
Name: dtime, dtype: datetime64[ns]
0 2010-01-01 23:14:00
1 2262-04-10 00:13:00
Name: dtime, dtype: datetime64[ns]
```
In the first case (freq argument 's') NaT are preserved (I would say, as I expect). However, in the second and third case, NaT's are converted to some apparently weird date time. If I don't miss some particular information (which in case after I googled and browsed the docs shouldn't be that hidden), this seems to be a bug
#### Expected Output
0 2010-01-01 23:14:12
1 NaT
Name: dtime, dtype: datetime64[ns]
0 2010-01-01 23:14:10.000000000
1 NaT
Name: dtime, dtype: datetime64[ns]
0 2010-01-01 23:14:00
1 NaT
Name: dtime, dtype: datetime64[ns]
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 9.0.1
setuptools: 24.0.2
Cython: None
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 196,914,637 |
MDU6SXNzdWUxOTY5MTQ2Mzc=
| 14,941 |
pd.to_numeric(series, downcast='integer') does not prpoerly handle floats over 10,000
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| 8 | 2016-12-21T11:54:27Z | 2016-12-31T15:45:30Z | 2016-12-31T15:45:30Z |
NONE
| null |
Hi - I came across this issue in [stackoverflow ](http://stackoverflow.com/a/38983440/6041010)while testing pd.to_numeric()
> If all floats in column are over 10000 it loses precision and converts them to integers.
```
tst_df = pd.DataFrame({'colA':['a','b','c','a','z', 'q'],
'colB': pd.date_range(end=datetime.datetime.now() , periods=6),
'colC' : ['a1','b2','c3','a4','z5', 'q6'],
'colD': [10000.0, 20000, 3000, 40000.36, 50000, 50000.00]})
pd.to_numeric(tst_df['colD'], downcast='integer')
```
This doesn't seem like the desired behavior.
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MDU6SXNzdWUxOTY5NTY3NDI=
| 14,942 |
groupby with category column and two additional columns eats up all main memory
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| 9 | 2016-12-21T15:14:01Z | 2018-05-01T15:09:10Z | 2018-05-01T15:09:10Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({
'cat': np.random.randint(0, 255, size=3000000),
'int_id': np.random.randint(0, 255, size=3000000),
'other_id': np.random.randint(0, 10000, size=3000000),
'foo': 0
})
df['cat'] = df.cat.astype(str).astype('category')
# killed after 6 minutes of 100% cpu and 90G maximum main memory usage
grouped = df.groupby(['cat', 'int_id', 'other_id']).count()
```
#### Problem description
The problem occurs only when I try to group by at least three columns. For two and one columns, it works.
If I replace the categorical column with an integer one, the groupby only takes about 2 seconds and does not use so much memory. This is also the workaround I use currently when I have to group by columns where one of the columns has the type category. But this is kind of ugly.
#### Expected Output
```python
from collections import defaultdict
class CategoryColGrouper:
def __init__(self):
self.lookups = defaultdict(dict)
self.max_num = {}
self.seen_cat_cols = set()
def get_data_type(self, max_num):
if max_num < 2 ** 8:
return np.uint8
elif max_num < 2 ** 16:
return np.uint16
elif max_num < 2 ** 32:
return np.uint32
else:
return np.uint64
def get_cat_cols(self, df):
cat_cols = []
for col in df.columns:
if str(df[col].dtype) == 'category':
cat_cols.append(col)
return cat_cols
def get_lookup_for_col(self, df, col):
col_lookup = self.lookups[col]
max_num = self.max_num.get(col, 0)
missing = []
for value in df[col].unique():
if value not in col_lookup:
missing.append(value)
for num, value in enumerate(missing):
new_num = num + max_num
col_lookup[value] = new_num
self.max_num[col] = new_num
return col_lookup
def make_groupable(self, df):
cat_cols = self.get_cat_cols(df)
self.seen_cat_cols = self.seen_cat_cols.union(cat_cols)
for cat_col in cat_cols:
col_lookup = self.get_lookup_for_col(df, cat_col)
df[cat_col] = df[cat_col].apply(lambda x: col_lookup[x])
data_type = self.get_data_type(self.max_num[cat_col])
df[cat_col] = df[cat_col].astype(data_type)
return df
def restore_category_cols(self, df):
for cat_col in self.seen_cat_cols:
col_lookup = self.lookups[cat_col]
rcol_lookup = {v: k for k, v in col_lookup.items()}
df[cat_col] = df[cat_col].apply(lambda x: rcol_lookup[x])
df[cat_col] = df[cat_col].astype('category')
return df
ccg = CategoryColGrouper()
df = ccg.make_groupable(df)
grouped = df.groupby(['cat', 'int_id', 'other_id']).count()
df = ccg.restore_category_cols(df)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: C
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 32.1.0.post20161217
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0rc2
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: None
pandas_datareader: None
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| 196,980,690 |
MDExOlB1bGxSZXF1ZXN0OTg5NjcyMjc=
| 14,943 |
DOC: Pandas Cheat Sheet (GH13202)
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| 5 | 2016-12-21T16:50:47Z | 2016-12-21T17:24:42Z | 2016-12-21T17:23:05Z |
CONTRIBUTOR
| null |
- [x ] closes #13202
Pandas Cheat Sheet. Someone needs to figure out (or tell me the best way to do it!) how the PDF version gets integrated into the documentation tree.
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BUG: sorting with large float and multiple columns incorrect
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| 6 | 2016-12-21T17:36:44Z | 2016-12-23T21:04:14Z | 2016-12-23T21:04:14Z |
CONTRIBUTOR
| null |
Fixes https://github.com/pandas-dev/pandas/issues/14922
Having the `int` equivalent of `NaT` in an `int64` column caused wrong sorting because this special value was considered as "missing value".
- [ ] closes #xxxx
- [X] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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| 14,945 |
Column inference in read_fwf() has unexpected outcome for sparse files
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[] | null | 2 | 2016-12-21T19:24:50Z | 2020-04-05T01:27:24Z | null |
NONE
| null |
#### Code Sample
```python
import io
import pandas as pd
filecontent = u"""
Qty Value Parts Description MAN_PART_NO VENDOR
1 200kOhm R5 Resistor MCR01MRTF2003 Digikey
5 FACE_BOARD_CONNECTOR U$10, U$11, U$12, U$13, U$14 1-84981-0 Digikey
1 I2C_VOLTAGE_LEVEL_TRANSLATOR U$27 PCA9306DCUR Digikey
2 LED GREEN, ORGNE Small inidicator LED SML-P11MTT86 Digikey
1 MEGA164A/PA/324A/PA/644A/PA/1284/PAU U1 TQFP44 package of Atmega1284 ATMEGA1284-AUR Digikey
1 POWER_REG5.0V-SOT23 VR5.0 Linear Power Regulator MIC5219-5.0YM5 TR Digikey
2 RESISTOR_ARRAY RA1, RA2 CRA04S08310K0JTD Digikey
"""
print pd.read_fwf(io.StringIO(filecontent), colspecs='infer', skiprows=1)
```
#### Problem description
In electronics, some older CAD softwares export the Bill of Materials (BOM) document as fixed width text file. Importing such files using `pd.read_fwf(..., colspec='infer')` often results in unexpected outcomes. This is because the colspec inference isn't written with files in mind that exhibit the following two properties:
* high variance in content length of cells in a column
* space (or other delimiter) characters within the cell content
The code sample uses a simplified example BOM document from an open source electronics project that exhibits these properties: Note that the "Parts" column is split up into multiple columns.
#### Actual Output
```
Qty Value Parts Unnamed: 3 \
0 1 200kOhm R5 NaN
1 5 FACE_BOARD_CONNECTOR U$10, U$11, U$12, U$13,
2 1 I2C_VOLTAGE_LEVEL_TRANSLATOR U$27 NaN
3 2 LED GREEN, ORGNE NaN
4 1 MEGA164A/PA/324A/PA/644A/PA/1284/PAU U1 NaN
5 1 POWER_REG5.0V-SOT23 VR5.0 NaN
6 2 RESISTOR_ARRAY RA1, RA2 NaN
Unnamed: 4 Description MAN_PART_NO VENDOR
0 NaN Resistor MCR01MRTF2003 Digikey
1 U$14 NaN 1-84981-0 Digikey
2 NaN NaN PCA9306DCUR Digikey
3 NaN Small inidicator LED SML-P11MTT86 Digikey
4 NaN TQFP44 package of Atmega1284 ATMEGA1284-AUR Digikey
5 NaN Linear Power Regulator MIC5219-5.0YM5 TR Digikey
6 NaN NaN CRA04S08310K0JTD Digikey
```
#### Expected Output
```
Qty Value Parts \
0 1 200kOhm R5
1 5 FACE_BOARD_CONNECTOR U$10, U$11, U$12, U$13, U$14
2 1 I2C_VOLTAGE_LEVEL_TRANSLATOR U$27
3 2 LED GREEN, ORGNE
4 1 MEGA164A/PA/324A/PA/644A/PA/1284/PAU U1
5 1 POWER_REG5.0V-SOT23 VR5.0
6 2 RESISTOR_ARRAY RA1, RA2
Description MAN_PART_NO VENDOR
0 Resistor MCR01MRTF2003 Digikey
1 NaN 1-84981-0 Digikey
2 NaN PCA9306DCUR Digikey
3 Small inidicator LED SML-P11MTT86 Digikey
4 TQFP44 package of Atmega1284 ATMEGA1284-AUR Digikey
5 Linear Power Regulator MIC5219-5.0YM5 TR Digikey
6 NaN CRA04S08310K0JTD Digikey
```
#### Possible Improvements
Right now any set of neighboring columns of the text file that contain delimiter characters only across all rows is considered a boundary. This is prone to include false positives. Possible alternative strategies for colspec inference:
* Require more than one column to transition from delimiter to non-delimiter character. A possible cutoff might be the median number of occurrences of this transition across all columns of the file.
* Consider the header row (if present) separately from the content rows and give it additional weight.
I realize that this use case falls outside the usual/common use cases of pandas and wouldn't be surprised if this gets triaged as #wontfix or if my suggested improvements interfere with more common use cases that I am not aware of. If maintainers agree that my suggestions would add value, I'd be happy to contribute the PR.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 32.1.2
Cython: None
numpy: 1.11.3
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
</details>
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ValueError when using loc with TimedeltaIndex
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| 1 | 2016-12-21T21:26:42Z | 2017-01-26T15:01:14Z | 2017-01-26T15:01:14Z |
CONTRIBUTOR
| null |
This snippet will work correctly:
```python
import pandas as pd
df = pd.DataFrame({'x':range(10)})
cond = df['x'] > 3
df.loc[cond, 'x'] = 10 # Works properly
```
But when using a TimedeltaIndex:
```python
import pandas as pd
df = pd.DataFrame({'x':range(10)})
df.index = pd.to_timedelta(range(10), unit='s')
cond = df['x'] > 3
df.loc[cond, 'x'] = 10 # ValueError is raised
```
pandas 0.18.1 has a different problem, but there is a workaround. Simple use the underlying numpy array:
```python
df.loc[cond.values, 'x'] = 10 # Works as intended
df.loc[cond, 'x'] = 10 # No error, wrong results
```
### Ouput
---------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-8732af012025> in <module>()
----> 1 df.loc[cond, 'x'] = 10
~/pandas/pandas/core/indexing.py in __setitem__(self, key, value)
138 else:
139 key = com._apply_if_callable(key, self.obj)
--> 140 indexer = self._get_setitem_indexer(key)
141 self._setitem_with_indexer(indexer, value)
142
~/pandas/pandas/core/indexing.py in _get_setitem_indexer(self, key)
120
121 if isinstance(key, tuple) and not self.ndim < len(key):
--> 122 return self._convert_tuple(key, is_setter=True)
123 if isinstance(key, range):
124 return self._convert_range(key, is_setter=True)
~/pandas/pandas/core/indexing.py in _convert_tuple(self, key, is_setter)
182 else:
183 for i, k in enumerate(key):
--> 184 idx = self._convert_to_indexer(k, axis=i, is_setter=is_setter)
185 keyidx.append(idx)
186 return tuple(keyidx)
~/pandas/pandas/core/indexing.py in _convert_to_indexer(self, obj, axis, is_setter)
1149 # if we are a label return me
1150 try:
-> 1151 return labels.get_loc(obj)
1152 except LookupError:
1153 if isinstance(obj, tuple) and isinstance(labels, MultiIndex):
~/pandas/pandas/tseries/tdi.py in get_loc(self, key, method, tolerance)
675 """
676
--> 677 if isnull(key):
678 key = tslib.NaT
679
~/pandas/pandas/core/generic.py in __nonzero__(self)
915 raise ValueError("The truth value of a {0} is ambiguous. "
916 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
--> 917 .format(self.__class__.__name__))
918
919 __bool__ = __nonzero__
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
#### Versions
Here is my pd.show_versions(). It also has been tested on windows7 64 bits with version 0.19
<details>
INSTALLED VERSIONS
--------------------------------
commit: f79bc7a9d128187f3a93a3dae84ff03f4f4a62f4
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
pandas: 0.19.0+243.gf79bc7a
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
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: 2016.10
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
s3fs: None
pandas_datareader: None
</details>
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Clarified error in read_sas method when buffer object provided withou…
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CONTRIBUTOR
| null |
…t format
- [x] closes #14947
- [x] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
Added three lines to sasreader.py immediately following line 33 (if format==None:) to handle the case when a buffer object is provided without a format='sas7bdat' or format='xport' situation. Method otherwise works splendidly when a filepath is provided, but a buffer object fails. This is an issue when using sasreader directly on SFTP file objects. I am unaware of any bug request (and am happy to open one), but I came across this issue when using the library.
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BUG: Timezones are lost in pivot_table
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| 6 | 2016-12-21T22:35:02Z | 2019-07-01T11:41:24Z | 2019-07-01T11:41:24Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
# table to pivot
df = pd.DataFrame([{'uid': u'aa',
'ts': pd.Timestamp('2016-08-12 13:00:00-0700', tz='US/Pacific')},
{'uid': u'aa',
'ts': pd.Timestamp('2016-08-12 08:00:00-0700', tz='US/Pacific')},
{'uid': u'aa',
'ts': pd.Timestamp('2016-08-12 14:00:00-0700', tz='US/Pacific')},
{'uid': u'aa',
'ts': pd.Timestamp('2016-08-25 11:00:00-0700', tz='US/Pacific')},
{'uid': u'aa',
'ts': pd.Timestamp('2016-08-25 13:00:00-0700', tz='US/Pacific')}])
# not sure why I need this, but it doesn't work without it....
df = df.set_index('ts').reset_index()
# Series of day timestamps we want to pivot over
mins = df.ts.map(lambda x: x.replace(hour=0, minute=0,
second=0, microsecond=0))
# pivot
pd.pivot_table(df.set_index('ts').reset_index(),
values='ts', index=['uid'], columns=[mins], aggfunc=np.min)
# prints in UTC time and discards timezones
# do the aggregation portion of the pivot
df.groupby(mins).agg(np.min) # works as expected where timezones are maintained
```
#### Problem description
`pivot_table` strips timezone information from the aggregated values.
I traced back in the code and it seems that the culprit is `unstack` (https://github.com/pandas-dev/pandas/blob/4814823903b862c411caf527271e384df0d0d7e7/pandas/tools/pivot.py#L138).
Specifically, either of these numpy operations will strip the tz: https://github.com/pandas-dev/pandas/blob/86233e15193c3bcd0f646915891ca6c7892335d9/pandas/core/reshape.py#L75-L76
#### Expected Output
Would expect aggregated values to maintain timezone like they do with groupby.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.11-23.53.amzn1.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: 9.0.1
setuptools: 32.1.0.post20161217
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
</details>
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added 'separator' argument to json_normalize
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[] | null | 2 | 2016-12-21T23:39:58Z | 2016-12-22T00:06:02Z | 2016-12-21T23:53:19Z |
NONE
| null |
- [x] closes #14883
- [ ] tests added / passed (added 3 tests: `test_simple_normalize_with_{default, user_specified, user_specified_unicode}_separator`)
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry (v0.20.0)
However, this doesn't work, even after making the fixes suggested in https://github.com/pandas-dev/pandas/pull/14891. I thought replacing `'.'` in `meta_keys = ['.'.join(val) for val in meta]` with `sep` would do the trick. It doesn't, so I'm a little puzzled. Happy to take a suggestion.
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ENH: GH14883: json_normalize now takes a user-specified separator
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| 13 | 2016-12-22T00:05:26Z | 2017-03-28T22:07:01Z | 2017-03-28T21:50:38Z |
NONE
| null |
- [x] closes #14883
- [ ] tests added / passed (added 3 tests: `test_simple_normalize_with_{default, user_specified, user_specified_unicode}_separator`)
- [x] passes ``git diff upstream/master | flake8 --diff``
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| 2 | 2016-12-22T06:07:36Z | 2016-12-22T11:27:34Z | 2016-12-22T11:27:30Z |
MEMBER
| null |
Makes method robust against known `numpy` bug that you can't compare `uint64` against `int64`
because they are casted to `float64` during the comparison, causing truncation.
xref #14937.
Follow-up to #14916.
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TST: Groupby.groups of datetimeindex (#11442)
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| 2 | 2016-12-22T06:45:33Z | 2017-12-20T02:03:40Z | 2016-12-23T11:16:25Z |
MEMBER
| null |
- [x] closes #11442
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
Doesn't look a PR in 0.19.2 and 0.20.0 addressed this, but it works on master 0.19.1
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Original numpy array getting altered when changes made to dataframe
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| 1 | 2016-12-22T10:16:44Z | 2016-12-22T11:08:48Z | 2016-12-22T11:08:30Z |
NONE
| null |
#### Code Sample
```python
a = np.array([1,2,3, np.nan])
b = pd.DataFrame(a)
b.fillna(4, inplace=True)
print b
print a
```
Output
```python
0
0 1.0
1 2.0
2 3.0
3 4.0
[ 1. 2. 3. 4.]
```
#### Problem description
When a dataframe is created from a numpy array the changes to the dataframe are altering the original numpy array. I did not expect this to happen and I'm not sure if this is an expected behaviour or a known issue.
I do know how to work around this, but my question is whether I have to.
#### Expected Output
```python
0
0 1.0
1 2.0
2 3.0
3 4.0
[ 1. 2. 3. nan]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.8-040408-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
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: 2016.6.1
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: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
</details>
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Object columns strangely exportet to csv
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| 1 | 2016-12-22T10:17:41Z | 2018-07-06T20:21:13Z | 2016-12-22T11:19:30Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'year':[1990.0, 1975.0,np.nan, 1990.0]})
print(df['year'].astype(object))
0 1990
1 1975
2 NaN
3 1990
Name: year, dtype: object
df['year']=df['year'].astype(object)
df.to_csv('test.csv')
```
Then the csv file looks like
```
,year
0,1990.0
1,1975.0
2,
3,1990.0
```
#### Problem description
Converting a dataframe containing years (written as floats appended by .0 and some NaN) to object type. The pandas output, as well as its representation then neglects the decimal point and only returns the years as int and NaN where necessary.
However, the csv output still contains the decimal point followed by 0
#### Expected Output
A csv file of the similar to the first output shown above:
```
,year
0,1990
1,1975
2,
3,1990
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
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
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
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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| 197,161,349 |
MDU6SXNzdWUxOTcxNjEzNDk=
| 14,955 |
DataFrameGroupBy fillna swallows exceptions.
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| 3 | 2016-12-22T12:10:25Z | 2020-01-21T01:06:10Z | 2020-01-21T01:06:10Z |
NONE
| null |
#### Problem description
Doing a fillna on DataFrameGroupBy fails silently - returning an empty dataframe, rather than raising what the error is.
This often masks the actual problem (e.g. errors like
```ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True``` when handling tz-aware data.)
#### Code Sample, a copy-pastable example if possible
```python
>>> df = pd.DataFrame({'a':[1,2],'b':[1,1]})
>>> df.groupby('b').fillna()
Empty DataFrame
Columns: []
Index: []
```
#### Expected Output
```ValueError: must specify a fill method or value```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 1.5.6
setuptools: 25.1.1
Cython: 0.23.2
numpy: 1.11.1
scipy: 0.14.0
statsmodels: 0.6.1
xarray: None
IPython: 2.3.1
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9
apiclient: 1.1
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.7.3
boto: 2.26.0
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTcxNjkxNjE=
| 14,956 |
BUG: Series.ffill() of Series with mixed dtypes containing tz-aware datetimes, fails.
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| 3 | 2016-12-22T12:57:27Z | 2016-12-23T20:59:22Z | 2016-12-23T20:59:22Z |
NONE
| null |
#### Problem description
Doing a ffill/bfill on a mixed dtype series fails if there are tz-aware datetime.datetime instances.
It does work with timestamps, but as it's mixed type column anyway, it probably should just work with non-native types, leaving them as it is. Furthermore, it used to work in 0.18.1.
#### Code Sample, a copy-pastable example if possible
```python
>>> s = pd.Series([datetime.datetime(2015, 1, 1, tzinfo=pytz.utc), 1])
>>> s.ffill()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/core/generic.py", line 3302, in ffill
limit=limit, downcast=downcast)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/core/series.py", line 2368, in fillna
**kwargs)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/core/generic.py", line 3250, in fillna
downcast=downcast)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/core/internals.py", line 3177, in interpolate
return self.apply('interpolate', **kwargs)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/core/internals.py", line 3056, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/core/internals.py", line 918, in interpolate
downcast=downcast, mgr=mgr)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/core/internals.py", line 960, in _interpolate_with_fill
return self._maybe_downcast(blocks, downcast)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/core/internals.py", line 1917, in _maybe_downcast
for b in blocks])
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/core/internals.py", line 1876, in convert
self.values.ravel(), **fn_kwargs).reshape(self.values.shape)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/types/cast.py", line 641, in _soft_convert_objects
values = lib.maybe_convert_objects(values, convert_datetime=datetime)
File "pandas/src/inference.pyx", line 842, in pandas.lib.maybe_convert_objects (pandas/lib.c:56711)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/util/decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/tseries/index.py", line 319, in __new__
yearfirst=yearfirst)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/util/decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/tseries/tools.py", line 428, in to_datetime
return _convert_listlike(arg, box, format)
File "/Users/charon/.virtualenvs/pandas/lib/python2.7/site-packages/pandas/tseries/tools.py", line 413, in _convert_listlike
raise e
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
```
#### Expected Output
(Output returned in 0.18.1)
```python
>>> s = pd.Series([datetime.datetime(2015, 1, 1, tzinfo=pytz.utc), 1])
>>> s.ffill()
0 2015-01-01 00:00:00+00:00
1 1
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS ------------------ commit: None python: 2.7.10.final.0 python-bits: 64 OS: Darwin OS-release: 15.6.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 1.5.6
setuptools: 25.1.1
Cython: 0.23.2
numpy: 1.11.1
scipy: 0.14.0
statsmodels: 0.6.1
xarray: None
IPython: 2.3.1
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9
apiclient: 1.1
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.7.3
boto: 2.26.0
pandas_datareader: None
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MDExOlB1bGxSZXF1ZXN0OTkxMDk2MjA=
| 14,957 |
TST: matplotlib 2.0 fix in log limits for barplot (GH14808)
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| 4 | 2016-12-22T14:06:45Z | 2016-12-24T14:04:07Z | 2016-12-24T10:50:47Z |
MEMBER
| null |
Closes #14808
|
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| 197,188,746 |
MDU6SXNzdWUxOTcxODg3NDY=
| 14,958 |
some markers missing from legend
|
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| 18 | 2016-12-22T14:40:49Z | 2020-01-04T21:33:41Z | 2020-01-04T21:33:41Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.randn(8, 3),
columns=['A', 'B', 'C'])
ax = df.plot (y=['A'], marker='x', linestyle='solid')
df.plot (y=['B'], marker='o', linestyle='dotted', ax=ax)
df.plot (y=['C'], marker='<', linestyle='dotted', ax=ax)
plt.grid()
plt.show()
```
#### Problem description
The legend is missing some markers.
If I add
plt.legend()
then they show up.
See attached
[figure1.pdf](https://github.com/pandas-dev/pandas/files/668980/figure1.pdf)
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.14-300.fc25.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: 31.0.0
Cython: 0.25.2
numpy: 1.12.0rc1
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0rc2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 14,959 |
Unnecessary pytz.exceptions.AmbiguousTimeError
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| 3 | 2016-12-22T17:07:53Z | 2016-12-22T23:44:19Z | 2016-12-22T23:44:01Z |
NONE
| null |
pd.Timestamp() raises what seems to me an unnecessary error.
```python
import pandas as pd
# this throws error:
>>> pd.Timestamp(pd.Timestamp('2016-10-30 02:44:04.663323'), tz='Europe/Zurich')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/tslib.pyx", line 405, in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:9932)
File "pandas/tslib.pyx", line 1446, in pandas.tslib.convert_to_tsobject (pandas/tslib.c:26009)
File "pandas/tslib.pyx", line 1624, in pandas.tslib._localize_pydatetime (pandas/tslib.c:28710)
File "pandas/tslib.pyx", line 620, in pandas.tslib.Timestamp.tz_localize (pandas/tslib.c:13016)
File "pandas/tslib.pyx", line 4297, in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:70327)
pytz.exceptions.AmbiguousTimeError: Cannot infer dst time from Timestamp('2016-10-30 02:44:04.663323'), try using the 'ambiguous' argument
# this works though
>>> pd.Timestamp(str(pd.Timestamp('2016-10-30 02:44:04.663323')), tz='Europe/Zurich')
Timestamp('2016-10-30 02:44:04.663323+0100', tz='Europe/Zurich')
```
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-66-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 1.5.4
setuptools: 3.3
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: None
xlrd: None
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xlsxwriter: None
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bs4: None
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httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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BUG: Series.ffill() with mixed dtypes containing tz-aware datetimes f…
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| 6 | 2016-12-22T17:12:15Z | 2016-12-23T20:59:55Z | 2016-12-23T20:59:22Z |
NONE
| null |
…ails. (GH14956)
Seems to work with all the datetime classes usually encountered, although 'tz' seems to be the idiom in the codebase (not sure why?). If both need to be supported I can replace `getattr(val, 'tzinfo', None)` with `getattr(val, 'tz', None) or getattr(val, 'tzinfo', None)`, thus also giving precedence to the former (if available).
(breaking commit was 4de83d25d751d8ca102867b2d46a5547c01d7248)
- [ x] closes #14956
- [x] tests added / passed
- [x ] passes ``git diff upstream/master | flake8 --diff``
- [x ] whatsnew entry
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Quantile result errors.
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| 3 | 2016-12-22T17:50:56Z | 2016-12-22T23:40:49Z | 2016-12-22T23:40:30Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
>>> loc1series4 = pd.Series([6,8,10,12,14,9,11,7,13,11])
```
#### Problem description
>>> loc1series4.quantile(0.25)
8.25
>>> loc1series4.quantile(0.75)
11.75
Wrong result.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
#### Expected Output
>>> loc1series4.quantile(0.25)
7.75
>>> loc1series4.quantile(0.75)
12.25
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
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: None
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTcyMzA1MTQ=
| 14,962 |
Inconsistent return type for downsampling on resample of empty DataFrame
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| 7 | 2016-12-22T17:55:07Z | 2017-06-13T23:16:03Z | 2017-06-13T23:16:03Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> df1 = pd.DataFrame(dict(a=range(100)), index=pd.date_range('1/1/2000', periods=100, freq="M"))
>>> df2 = df1[df1.a < 0]
>>> df1.shape
(100, 1)
>>> df2.shape
(0, 1)
>>> df2.empty
True
>>> type(df1.resample("Q").size())
<class 'pandas.core.series.Series'>
>>> type(df2.resample("Q").size())
<class 'pandas.core.frame.DataFrame'>
```
#### Problem description
Code that is resampling a DataFrame that also has some filtering should be able to expect consistent types. My specific case of this causing a problem was that the `size()` output was being forwarded on to other code using `Series.tolist()`, which obviously fails when the output is a DataFrame.
#### Expected Output
Expectation is that `.size()` should always return a `Series`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_CA.utf8
LOCALE: en_CA.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
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: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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DOC :Cheatsheet update.
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| 2 | 2016-12-22T18:32:58Z | 2016-12-23T00:01:12Z | 2016-12-23T00:00:28Z |
CONTRIBUTOR
| null |
Update to cheatsheet, from suggestions from a colleague.
1. Fix bug in example for outer join
2. Add head() and tail() and use space for nsmallest()
3. Formatting of logic table.
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MDU6SXNzdWUxOTcyNTA4Njk=
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Get progress information with DataFrame.apply()
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| 3 | 2016-12-22T19:35:06Z | 2018-07-06T20:21:13Z | 2016-12-22T23:34:26Z |
NONE
| null |
I'm looking into the code to see if this change would be a major or minor undertaking. I don't know what your opinion is about it.
I have a large data frame and a complicated map function I want to apply to all rows. Calling apply along axis 1 has been running for an hour now, and I have no idea if this will take until an hour from now or until tomorrow or until next week. I think a some progress reporting would be instrumental in helping a user decide quickly if its reasonable to use pandas or if moving to another library / stack is worthwhile.
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MDExOlB1bGxSZXF1ZXN0OTkxNjM1Mjg=
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ERR: raise on missing values in pd.pivot_table
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| 4 | 2016-12-22T20:15:28Z | 2016-12-23T20:50:00Z | 2016-12-23T20:49:57Z |
CONTRIBUTOR
| null |
- [x] closes #14938
- [x] tests added - for #14938
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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[DOC] typo correction
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| 1 | 2016-12-22T20:21:10Z | 2016-12-23T00:18:05Z | 2016-12-23T00:18:02Z |
CONTRIBUTOR
| null |
Follow-up to #14856. I believe that the word *timestamp* here should be de-capitalized at is not a name of object (like `Timestamp`), just a common noun.
|
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[Depr] raise_on_error kwarg with errors kwarg in astype#14878
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| 7 | 2016-12-22T20:43:29Z | 2017-01-03T20:54:09Z | 2017-01-03T20:53:59Z |
CONTRIBUTOR
| null |
- [x] closes #14878
- [x] tests passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Please check that the entry in `whatsnew/v0.20.0.txt`. Unsure that the update was in the
`_whatsnew_0200.deprecations` or `_whatsnew_0200.prior_deprecations` so put it in the
former.
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MDU6SXNzdWUxOTcyNjUzODU=
| 14,968 |
DEPR:change DataFrame.where(..,raise_on_error) -> errors
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| 4 | 2016-12-22T21:00:05Z | 2017-10-05T18:36:29Z | 2017-10-05T18:36:29Z |
CONTRIBUTOR
| null |
xref #14878
#14878 deprecated the `raise_on_error` kwarg in favour of the `errors` kwarg
for `DataFrame.astype()`.
This issue addresses the same deprecation of the `raise_on_error` kwarg for
`DataFrame.where()`. Again this should be replaced with the `errors` kwarg.
The `errors` kwarg can have the value `raise` or `ignore` with a default
value of `raise`.
This issue can be assigned to me - @m-charlton
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MDU6SXNzdWUxOTcyNzY1NTM=
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Inconsistent output when using integer labels in multiindex on both column and index
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| 5 | 2016-12-22T22:06:59Z | 2017-03-23T13:35:36Z | null |
NONE
| null |
#### Description of problem
Forgive me if I'm missing a sublety when using integers for multiindexing, but I seem to be getting inconsistent behavior when using multiindexing. Using loc to index both column and index simultaneously doesn't always give the same result. This seems to depend on the datatype of the innermost index.
#### Example of the expected behavior
The following example works as I'd expect, giving me a dataframe representing the (0,0) label for the outermost index level:
```python
>>>ind = pd.MultiIndex.from_product([[0,1],['A','B','C','D','E']])
>>>df = pd.DataFrame(np.random.rand(10,10), index=ind, columns=ind)
>>>print(df.loc[0,0])
A B C D E
A 0.392093 0.167340 0.292854 0.138955 0.575715
B 0.495728 0.062870 0.733270 0.889761 0.141171
C 0.973444 0.518498 0.648546 0.448096 0.383729
D 0.987809 0.697177 0.601228 0.094184 0.986927
E 0.950939 0.109866 0.151390 0.173802 0.855105
```
#### Example of the unexpected behavior
However, if I change the second index level dataype to, for example, floats or ints, loc uses positional indexing rather than label based indexing for the second label. Thus, the same syntax returns a series of a single column, rather than a dataframe.
```python
>>>ind = pd.MultiIndex.from_product([[0,1],np.linspace(0,1,5)])
>>>df = pd.DataFrame(np.random.rand(10,10), index=ind, columns=ind)
>>>print(df.loc[0,0])
0 0.00 0.666874
0.25 0.023773
0.50 0.799715
0.75 0.752675
1.00 0.935531
1 0.00 0.510080
0.25 0.845125
0.50 0.410635
0.75 0.067144
1.00 0.658522
```
#### Problem description
The problem is that the output is inconsistent. My code breaks depending upon the datatypes used for the indices in a non-obvious way. I would expect things to work as in my first example, with the str dtype used for the second index level. At a minimum, I'd prefer it if the behavior was consistent, regardless of the datatype of the second index level.
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
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: 9.0.1
setuptools: 23.1.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.1.2
sphinx: 1.4
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 197,294,643 |
MDExOlB1bGxSZXF1ZXN0OTkxOTA1Njg=
| 14,970 |
DOC: Fix typo in docstring
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| 1 | 2016-12-23T00:35:04Z | 2017-04-22T21:40:05Z | 2016-12-23T01:13:26Z |
CONTRIBUTOR
| null |
scaler->scalar
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MDU6SXNzdWUxOTczNTE1ODI=
| 14,971 |
Series.sort_index does not() implement optional argument kind
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| 2 | 2016-12-23T11:07:26Z | 2016-12-26T23:15:17Z | 2016-12-23T11:23:02Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> a = pd.Series(range(10))
>>> a.sort_index(kind='mergesort')
```
#### Problem description
The documentation present the 'kind' optional argument:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_index.html#pandas.Series.sort_index
But it is not implemented (while it is for depreciated _sort()_ )
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: fr_FR.utf8
LOCALE: fr_FR.UTF-8
pandas: 0.19.0
nose: 1.3.4
pip: 1.5.6
setuptools: 5.5.1
Cython: 0.21.1
numpy: 1.8.2
scipy: 0.14.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: 2.38.0
pandas_datareader: None
</details>
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Categorical Series.describe() returns wrong dtype
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| 4 | 2016-12-23T11:47:16Z | 2016-12-23T16:08:46Z | 2016-12-23T11:56:09Z |
NONE
| null |
`Series.describe()` seems to return the wrong dtype for series with categorical data. See example below:
```python
import pandas as pd
series = pd.Series(pd.Categorical(values=[1], categories=[1, 2]))
print(series.describe())
# prints: ... dtype: int64
print(series.dtype)
# prints: category
```
This is at least confusing. Example has been produced using pandas 0.19.1 and Python 3.5.2.
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| 197,402,133 |
MDU6SXNzdWUxOTc0MDIxMzM=
| 14,973 |
Using the python power operator on numerical Index objects yields unexpected results
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| 3 | 2016-12-23T17:12:08Z | 2016-12-30T19:14:33Z | 2016-12-30T19:14:33Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
index = pd.Float64Index(range(1, 11))
expected_pow_values = 2.0**index.values
actual_pow_values = 2.0**index
similar_pow_values = 2.0**np.arange(len(index))
expected_mul_values = 2.0*index.values
actual_mul_values = 2.0*index
other_way_around = index**2.0
same_as = index.values**2.0
```
#### Problem description
Using the pow '**' operator on an index object results in a behaviour different than when using the '*' operator. The pow result implies that the values being used are not index.values, which one would expect given the behaviour of other operators (*, -, +, / ...).
This problem only arises when the index object is the second argument.
#### Expected Output
expected output would be the result of 2.0**index.values
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 2.7.13.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: 32.2.0
Cython: None
numpy: 1.12.0rc1
scipy: 0.18.1
statsmodels: 0.8.0rc1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: None
lxml: 3.7.0
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
</details>
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MDU6SXNzdWUxOTc0MDUzNTY=
| 14,974 |
ENH: ExcelWriter class should implement context manager interface (__enter__ and __exit__)
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| 2 | 2016-12-23T17:37:06Z | 2016-12-23T20:57:13Z | 2016-12-23T20:41:14Z |
NONE
| null |
Is there any downside to adding this? This would allow one to avoid having to save/close an Excel file deliberately.
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MDExOlB1bGxSZXF1ZXN0OTkyODQ5NTk=
| 14,975 |
BUG: GH14882 Incorrect index label displayed on MultiIndex DataFrame
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| 15 | 2016-12-23T22:09:35Z | 2017-01-21T23:08:02Z | 2017-01-21T23:07:44Z |
CONTRIBUTOR
| null |
- [x] closes #14882
- [x] tests added / passed
Add `tests/formats/test_format.py:TestDataFrameFormatting.test_to_html_multiindex_odd_even_truncate`
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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MDU6SXNzdWUxOTc0MzQyNDE=
| 14,976 |
ENH: New short indexer for operating on values
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| 6 | 2016-12-23T22:44:45Z | 2018-07-06T20:21:13Z | 2016-12-28T11:22:29Z |
CONTRIBUTOR
| null |
First of all, if I missed a point, please feel free to comment.
Using arithmetic operations on pd.DataFrames is sometimes a mouthful. Take the following example, where columns `a` and `b` should be multiplied by the column `c`:
```python
import numpy as np
import pandas as pd
np.random.seed(0)
df = pd.DataFrame(np.random.randn(3, 3), columns=list('abc'))
df[['a', 'b']] * df['c']
```
Apparently this doesn't work as expected. Instead one has to use either `pd.Dataframe.mul()`, which brings up poor legibility, or `pd.Dataframe.values`, which yields long lines and therefore also results in poor legibility:
```python
# using pd.DataFrame.mul()
df[['a', 'b']].mul(df['c'], axis='index')
# This is quite short, but does not work...
df[['a', 'b']] * df[['c']].values
# .. you have to use numpy arrays instead
df[['a', 'b']].values * df[['c']].values
```
Surely, the last call in this example returns a numpy array, but in my case thats the only thing I'm interested in, since I'm rewrapping my data at a later stage.
I'm proposing a new short indexer for operating on values, sth like:
```python
df.v[['a', 'b']] * df.v[['c']]
# which returns the same as
df[['a', 'b']].values * df[['c']].values
```
Or even more sophisticated:
```python
df[['a', 'b']] * df.v[['c']]
# which returns the same as
df[['a', 'b']].mul(df['c'], axis='index')
```
Btw the same goes for all other arithmetic operators.
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MDExOlB1bGxSZXF1ZXN0OTkyOTQ4MzA=
| 14,977 |
BUG: applymap on empty DataFrame returns Series (#8222)
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| 3 | 2016-12-24T04:18:10Z | 2017-12-20T02:03:40Z | 2016-12-24T20:53:51Z |
MEMBER
| null |
- [x] closes #8222
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry for 0.20.0
Within `applymap`, the data is converted into a numpy array with `asobject` [here](https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L4291) and evaluated with the `apply` method `_apply_empty_result` since this frame is empty. The numpy array is compared to an `Series` [here](https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L4112) which proceeds to get returned as a `Series` [here](https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L4118).
Found that passing in the data without using `asobject` allows the logic to return a copy of the empty dataframe [here](https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L4120).
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NAs in mixed-type columns
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| 6 | 2016-12-24T14:17:21Z | 2016-12-25T13:29:55Z | 2016-12-24T14:31:48Z |
NONE
| null |
**File:** [sample.txt](https://github.com/pandas-dev/pandas/files/671790/sample.txt)
```python
import pandas as pd
# This does not work as expected
w = pd.read_csv("sample.txt", dtype={"a": str, "b": float}, sep="\t")
print(type(w["a"][0]))
print(type(w["a"][1]))
print(type(w["a"][2]))
print(type(w["b"][0]))
print(type(w["b"][1]))
print(type(w["b"][2]))
print(w)
print("======")
# This is cumbersome work-around
w = pd.read_csv("sample.txt", dtype={"a": str, "b": str}, sep="\t", na_values=[], keep_default_na=False)
w.b = w.b.apply(pd.to_numeric, args=('coerce',))
print(type(w["a"][0]))
print(type(w["a"][1]))
print(type(w["a"][2]))
print(type(w["b"][0]))
print(type(w["b"][1]))
print(type(w["b"][2]))
print(w)
```
#### Output
```
<class 'str'>
<class 'float'>
<class 'str'>
<class 'numpy.float64'>
<class 'numpy.float64'>
<class 'numpy.float64'>
a b
0 sampletext 1.0
1 NaN 2.0
2 sampletext NaN
3 NaN NaN
======
<class 'str'>
<class 'str'>
<class 'str'>
<class 'numpy.float64'>
<class 'numpy.float64'>
<class 'numpy.float64'>
a b
0 sampletext 1.0
1 2.0
2 sampletext NaN
3 NaN NaN
```
#### Problem description
Documentation states:
> Use str or object to preserve and not interpret dtype.
This is somewhat confusing behaviour itself, and has consequences. One could expect that if pandas finds out that some column is string, it will coerce all values within the column to string. It's not the case.
Given the fact that NA can only be float, it is possible to get mixed-type column (which is fun by itself).
Imagine that you have two columns, str and float. Both can have empty spaces and "NaN"s (and other possible stuff). You want to preserve empty spaces and NaNs as string in first column, yet want other column to have proper NAs.
The only solution here seems to be setting na_values to [], and coercing column to float manually. I work with relatively large tables (40 columns, half strings, half floats, ~2..10M rows), so it's very time-memory consuming (floats stored as strings) and error-prone to manually cast everything to proper types.
#### Expected behaviour
Everything works as stated in documentation. However, this has unpleasant consequences. I cannot fathom a scenario where one would need a mixed-type column, string mixed with float-NAs. For me, it seems obvious to expect column to contain strings. Also, it seems obvoius that if I have parameter dtype to force column types, I can force column to contain strings (but it's not the case - it doesn't even throw a warning! why it's designed this way? It's puzzling me).
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 32
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 20.1.1
Cython: None
numpy: 1.11.3
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
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: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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Clean up py36 build
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| 0 | 2016-12-24T17:59:37Z | 2016-12-24T20:40:25Z | 2016-12-24T20:40:25Z |
CONTRIBUTOR
| null |
- remove some deprecation warnings
- fix use of StopIteration in a generator
- add scipy to build
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| 2 | 2016-12-24T19:26:05Z | 2017-01-02T10:14:11Z | 2017-01-02T10:12:44Z |
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Fixed power operation on indicies
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[] | null | 5 | 2016-12-25T00:28:57Z | 2016-12-27T11:39:31Z | 2016-12-27T05:38:47Z |
CONTRIBUTOR
| null |
Fixed power operation on indicies to allow for power on rows. See #14973
- [ ] closes #14973
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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| 1 | 2016-12-25T07:30:19Z | 2016-12-30T18:53:09Z | 2016-12-30T18:53:08Z |
MEMBER
| null |
`master` at <a href="https://github.com/pandas-dev/pandas/commit/aba7d255a165bbc221be106839c59a876655c0c2">aba7d2</a>:
~~~python
>>> from pandas import lib, np
>>> arr = np.array([2**63], dtype=object)
>>> lib.maybe_convert_numeric(arr, set())
...
OverflowError: Python int too large to convert to C long
~~~
At first, I thought the patch would be similar to #14916, but there are several complications here:
* Handling an array with negative and `uint64` values: in `maybe_convert_objects`, we would just return `object` to avoid data truncation. However, this function currently only returns numeric arrays. And if we do decide to return non-numeric, should we then raise if `coerce_numeric=True` then?
* `uint64` has no convenient `na_value`, so what happens if a `uint64` is in `na_values`?
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BUG: read_csv fails with uint64
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| 2 | 2016-12-25T07:44:45Z | 2017-01-02T19:50:02Z | 2017-01-02T19:50:02Z |
MEMBER
| null |
`master` at <a href="https://github.com/pandas-dev/pandas/commit/aba7d255a165bbc221be106839c59a876655c0c2">aba7d2</a>:
~~~python
>>> from pandas import read_csv
>>> from pandas.compat import StringIO
>>> data = 'a\n' + str(2**63)
>>>
>>> read_csv(StringIO(data), engine='c').info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1 entries, 0 to 0
Data columns (total 1 columns):
a 1 non-null object
dtypes: object(1)
memory usage: 88.0+ bytes
>>>
>>> read_csv(StringIO(data), engine='python').info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1 entries, 0 to 0
Data columns (total 1 columns):
a 1 non-null object
dtypes: object(1)
memory usage: 88.0+ bytes
~~~
We should be able to handle `uint64`, and tests like this one <a href="https://github.com/pandas-dev/pandas/blob/aba7d255a165bbc221be106839c59a876655c0c2/pandas/io/tests/parser/common.py#L937">here</a> should not be enforcing buggy behavior.
The buggy behavior for the C engine traces to <a href="https://github.com/pandas-dev/pandas/blob/master/pandas/parser.pyx#L1126">here</a>, where we attempt to cast according to this order defined <a href="https://github.com/pandas-dev/pandas/blob/master/pandas/parser.pyx#L417">here</a>. Note for starters that `uint64` is not in that list. This `try-except` is due to `OverflowError` with `int64`, after which we immediately convert to an `object` array of strings. At first, I thought inserting `uint64` to the list would be good, but that can cause bad casting in the other direction, i.e. negative numbers get converted to their `uint64` equivalents.
The buggy behavior for the Python engine traces to <a href="https://github.com/pandas-dev/pandas/blob/master/pandas/io/parsers.py#L1345">here</a>, where we attempt to infer the `dtype` <a href="https://github.com/pandas-dev/pandas/blob/master/pandas/io/parsers.py#L1392">here</a>. However, as I pointed out in #14982, this function fails with `uint64` with a similar (and non-sensical) `try-except` for `OverflowError` in `int64`.
The questions that I posed in #14982 are also relevant here, since they should be consistent across both engines that also is performant. Patching the Python engine probably requires fixing #14982 first, and patching the C engine probably requires adding new functions to `parser.pyx` to parse `uint64` and `tokenizer.c`. However, in light of the questions that I posed in #14982, I'm not really sure what is best.
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MDExOlB1bGxSZXF1ZXN0OTkzMjgzMDI=
| 14,984 |
BUG: Avoid flaky usecols set in C engine
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| 3 | 2016-12-25T19:42:36Z | 2016-12-30T19:27:51Z | 2016-12-30T19:27:40Z |
MEMBER
| null |
Explanation of the bug can be found <a href="https://github.com/pandas-dev/pandas/issues/14792#issuecomment-269115098">here</a>.
Closes #14792.
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DOC: Refactor numeric index docs
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| 6 | 2016-12-25T23:48:25Z | 2016-12-30T19:05:46Z | 2016-12-30T19:05:34Z |
MEMBER
| null |
Refactor `NumericIndex` docs to avoid duplicate documentation. xref #14937.
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ENH: Added 'sum' to the set of statistics returned by df.field.describe()
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| 4 | 2016-12-26T00:25:01Z | 2016-12-26T17:13:01Z | 2016-12-26T17:12:46Z |
CONTRIBUTOR
| null |
I've added ``sum`` to the set of statistics returned by the ``describe`` method.
I believe it should be added because in my experience, the sum is just as commonly needed as the other values already returned by ``describe``. I have often found myself writing an additional line of code after ``describe`` to calculate the sum of the same series. Adding sum to the mix would remove that requirement and make life easier for myself and others.
I believe I am not alone. For instance, ``sum`` is already included in the descriptive statistics methods of other programs, like Microsoft Excel.
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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to_datetime() fails with Python 3.6
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| 2 | 2016-12-26T02:18:34Z | 2016-12-26T17:11:19Z | 2016-12-26T17:11:04Z |
CONTRIBUTOR
| null |
#### Code Sample
pd.to_datetime('22/12/2016')
#### Problem description
This fails on Python 3.6.0, but works with previous versions like 3.5.2. An exception is raised with 3.6:
ValueError: Month out of range in datetime string "22/12/2016"
The error also occurs if `dayfirst=True` is passed to the function.
#### Expected Output
2016-12-22 00:00:00
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0
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: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
</details>
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CLN: used np.__version__ and removed instantiation
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| 2 | 2016-12-26T03:42:19Z | 2016-12-26T17:16:23Z | 2016-12-26T17:16:23Z |
CONTRIBUTOR
| null |
@rkern and others [recommend](http://stackoverflow.com/questions/1520234/how-to-check-which-version-of-numpy-im-using) using `np.__version__` instead of `np.version`. However I realize this is pandas "internals" so there may be some reason for using `np.version` over `np.__version__`. I could not find any such reason. I also removed a duplicate `LooseVersion(_np_version)` and reused the existing `_nlv` instance.
- [x] doesn't close any issue, that I'm aware of
- [x] I ran `nosetests .\pandas\tests\test_compat.py` and 5 tests passed
- [x] couldn't get the `git` command to work, but `flake8 .\compat\numpy\__init__.py` passes fine
- [x] whatsnew entry should be covered by `several new features, enhancements, and performance improvements` in the v0.20.0.txt file
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| 2 | 2016-12-26T10:24:53Z | 2016-12-26T17:17:59Z | 2016-12-26T17:17:57Z |
MEMBER
| null |
Title is self-explanatory. xref #14937.
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| 5 | 2016-12-26T11:45:50Z | 2017-11-19T09:13:28Z | 2017-02-27T15:58:48Z |
NONE
| null |
- [ ] closes #14833
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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BLD: move + update build script
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| 3 | 2016-12-26T13:47:02Z | 2016-12-26T14:34:35Z | 2016-12-26T13:47:45Z |
MEMBER
| null |
Only one source dist format to upload since https://www.python.org/dev/peps/pep-0527/
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| 1 | 2016-12-26T16:59:31Z | 2016-12-30T19:41:22Z | 2016-12-30T19:41:21Z |
CONTRIBUTOR
| null |
#### Code Sample
```python
>>> df = pd.DataFrame(data=[[11, 12, 13], [21, 22, 23], [31, 32, 33]],
index=[1, 3, 5],
columns=[1, 3, 5],
dtype=float)
>>> print(df)
1 3 5
1 11.0 12.0 13.0
3 21.0 22.0 23.0
5 31.0 32.0 33.0
# Applying the reindex function to the columns, with the "ffill" method:
>>> print(df.reindex(columns=range(7), method='ffill'))
0 1 2 3 4 5 6
1 NaN 11.0 NaN 12.0 NaN 13.0 NaN
3 NaN 21.0 NaN 22.0 NaN 23.0 NaN
5 NaN 31.0 NaN 32.0 NaN 33.0 NaN
# Workaround to get the expected output:
>>> print(df.T.reindex(index=range(7), method='ffill').T)
0 1 2 3 4 5 6
1 NaN 11.0 11.0 12.0 12.0 13.0 13.0
3 NaN 21.0 21.0 22.0 22.0 23.0 23.0
5 NaN 31.0 31.0 32.0 32.0 33.0 33.0
```
#### Problem description
I want to apply the `reindex` function to the columns using the `"ffill"` method, but the holes are filled with NaN. The workaround is to transpose the DataFrame, apply the function and then transpose again.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
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
pandas: 0.18.1
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
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: 2.8
boto: 2.38.0
pandas_datareader: None
</details>
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BUG: Reindex with columns and method
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| 3 | 2016-12-26T20:47:20Z | 2017-09-21T15:17:33Z | 2016-12-30T19:41:22Z |
CONTRIBUTOR
| null |
- [x] closes #14992
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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Bug: Raise ValueError with interpolate & fillna limit = 0 (#9217)
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| 21 | 2016-12-26T22:50:36Z | 2017-12-20T02:04:10Z | 2017-02-14T13:34:23Z |
MEMBER
| null |
- [x] closes #9217
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Add similar logic to `fillna`
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DOC: Clarified and expanded describe documentation
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| 7 | 2016-12-27T04:49:17Z | 2017-01-02T09:05:55Z | 2017-01-02T09:05:47Z |
CONTRIBUTOR
| null |
- [x] passes ``git diff upstream/master | flake8 --diff``
As discussed in #14483.
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BUG: fixed index power operation
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| 3 | 2016-12-27T05:37:58Z | 2016-12-30T19:14:38Z | 2016-12-30T19:14:33Z |
CONTRIBUTOR
| null |
* The power operation on a range of indexes was fixed. See issue #14973
- [ ] closes #14973
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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DOC: consistent import timedeltas docs
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| 2 | 2016-12-27T15:17:38Z | 2016-12-27T16:25:23Z | 2016-12-27T16:24:52Z |
CONTRIBUTOR
| null |
Added consistent import for timedeltas docs, i.e.: `import pandas as pd`, `import datetime`.
See: https://github.com/pandas-dev/pandas/issues/9886
Didn't touch the `pandas.tseries.offsets` since no consensus has been made how to import those.
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DataFrame.to_html with max_rows and index=False raises IndexError
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| 2 | 2016-12-27T21:32:16Z | 2016-12-30T19:43:12Z | 2016-12-30T19:43:12Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [4]: import pandas as pd
In [5]: import numpy as np
In [6]: df = pd.DataFrame(np.random.randn(100, 2))
In [7]: df.to_html(max_rows=10, index=False)
```
raises
```pytb
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-7-c16b7151f39c> in <module>()
----> 1 df.to_html(max_rows=10, index=False)
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas-0.19.0+265.gaba7d255a-py3.6-macosx-10.12-x86_64.egg/pandas/core/frame.py in to_html(self, buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, bold_rows, classes, escape, max_rows, max_cols, show_dimensions, notebook, decimal, border)
1553 decimal=decimal)
1554 # TODO: a generic formatter wld b in DataFrameFormatter
-> 1555 formatter.to_html(classes=classes, notebook=notebook, border=border)
1556
1557 if buf is None:
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas-0.19.0+265.gaba7d255a-py3.6-macosx-10.12-x86_64.egg/pandas/formats/format.py in to_html(self, classes, notebook, border)
698 border=border)
699 if hasattr(self.buf, 'write'):
--> 700 html_renderer.write_result(self.buf)
701 elif isinstance(self.buf, compat.string_types):
702 with open(self.buf, 'w') as f:
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas-0.19.0+265.gaba7d255a-py3.6-macosx-10.12-x86_64.egg/pandas/formats/format.py in write_result(self, buf)
1022 indent += self.indent_delta
1023 indent = self._write_header(indent)
-> 1024 indent = self._write_body(indent)
1025
1026 self.write('</table>', indent)
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas-0.19.0+265.gaba7d255a-py3.6-macosx-10.12-x86_64.egg/pandas/formats/format.py in _write_body(self, indent)
1184 else:
1185 for i in range(len(self.frame)):
-> 1186 row = [fmt_values[j][i] for j in range(len(self.columns))]
1187 self.write_tr(row, indent, self.indent_delta, tags=None)
1188
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas-0.19.0+265.gaba7d255a-py3.6-macosx-10.12-x86_64.egg/pandas/formats/format.py in <listcomp>(.0)
1184 else:
1185 for i in range(len(self.frame)):
-> 1186 row = [fmt_values[j][i] for j in range(len(self.columns))]
1187 self.write_tr(row, indent, self.indent_delta, tags=None)
1188
IndexError: list index out of range
```
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0+265.gaba7d255a
nose: None
pip: 9.0.1
setuptools: 32.3.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.8.0.dev0+fea7e82
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.4.1
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: None
numexpr: 2.6.1
matplotlib: 2.0.0rc2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: None
None
</details>
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BUG: Fixed to_html with index=False and max_rows
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| 6 | 2016-12-27T21:54:58Z | 2016-12-31T10:06:20Z | 2016-12-30T19:43:12Z |
CONTRIBUTOR
| null |
closes https://github.com/pandas-dev/pandas/issues/14998
Previously raised an IndexError by assuming that
`len(fmt_values)` was always the same length as `len(self.data)`.
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| 197,768,760 |
MDExOlB1bGxSZXF1ZXN0OTk0OTExNjY=
| 15,000 |
ERR: qcut uniquess checking (try 2)
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| 6 | 2016-12-27T22:56:15Z | 2016-12-30T19:02:08Z | 2016-12-30T19:02:01Z |
CONTRIBUTOR
| null |
- [x] closes #7751
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Add option to drop non-unique bins.
|
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MDU6SXNzdWUxOTc3OTczNzk=
| 15,001 |
CLN/DEPR: Consistent naming for isnull, notnull, fillna, dropna
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| 4 | 2016-12-28T05:16:41Z | 2017-07-25T10:18:24Z | 2017-07-25T10:18:24Z |
CONTRIBUTOR
| null |
In 2.0, these functions could be named more consistently, either all `*na` or `*null`. Or, to preserve backwards compatibility, `isna`, `notna`, `fillnull` and `dropnull` could be added as aliases. Either way, it would be helpful to take the guess work out of this.
Let me know if this is not the correct forum for such proposals.
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MDU6SXNzdWUxOTc4MzEwNjI=
| 15,002 |
Different behavior of DataFrame.resample() between pandas 0.16.2 and pandas 0.18.1
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| 1 | 2016-12-28T10:28:36Z | 2016-12-31T16:54:16Z | 2016-12-28T10:56:53Z |
NONE
| null |
I just upgraded my projects from pandas 0.16.2 to pandas 0.18.1, but I have a difference on some `DataFrame.resample` operations, with Weekly frequencies.
For instance, I want to resample daily data to weekly data (`W-SUN`), and apply an offset to the results, so each row will be a **MONDAY** and not a SUNDAY.
#### Code Sample
```python
import pandas as pd
from datetime import timedelta
offset = +timedelta(days=1)
dates = pd.date_range("2001-01-01", freq="D", periods=18)]
a=pd.DataFrame(data=list(range(18)), columns=["value"], index=[dates])
a.resample("1W", how={"value": "sum"}, loffset=offset, closed="right", label="right")
```
#### Problem description
With `pandas 0.16.2`, I got this correct output:
```
value
2001-01-08 21
2001-01-15 70
2001-01-22 62
```
ie, each row is a weekly aggregation of the data, and each date is a **MONDAY**
Resulting index is as follow:
```
a.index[0]
Timestamp('2001-01-08 00:00:00', offset='W-MON')
```
However, with `pandas 0.18.1`, I got this output:
```
value
2001-01-07 21
2001-01-14 70
2001-01-21 62
```
each row is correctly aggregated, but the dates are **SUNDAY**s
Resulting index is as follow:
```
a.index[0]
Timestamp('2001-01-07 00:00:00', offset='W-SUN')
```
<observations>
It's like the `loffset` parameter is not correctly taken into account in the new `DataFrame.resample()` implementation.
ie, the output with `pandas 0.18.1` is the same as the one using `pandas 0.16.2`, but calling `a.resample("1W", how={"value": "sum"}, `**loffset=None**`, closed="right", label="right")`
</observations>
#### Output of ``pd.show_versions()``
pandas `0.16.1` installation:
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-106-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
bottleneck: 1.0.0
tables: 3.2.1
numexpr: 2.4.3
matplotlib: 1.5.0
openpyxl: 2.3.2
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.7
pymysql: 0.6.6.None
psycopg2: None
```
</details>
pandas `0.18.2` installation:
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-57-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.22.1
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: 1.0.0
tables: 3.2.1
numexpr: 2.6.0
matplotlib: 1.4.3
openpyxl: 2.3.2
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.7
pymysql: 0.6.6.None
psycopg2: None
jinja2: 2.7.3
boto: None
pandas_datareader: None
```
</details>
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MDU6SXNzdWUxOTc4MzY2NzM=
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CLN/API: wide_to_long or lreshape
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| 10 | 2016-12-28T11:11:41Z | 2021-05-02T01:04:34Z | 2021-05-02T01:04:34Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/issues/2567
```
In [27]: data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526],
...: 'team': ['Red Sox', 'Yankees'],
...: 'year1': [2007, 2008], 'year2': [2008, 2008]})
...:
In [28]: data
Out[28]:
hr1 hr2 team year1 year2
0 514 545 Red Sox 2007 2008
1 573 526 Yankees 2008 2008
In [29]: pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']})
Out[29]:
team year hr
0 Red Sox 2007 514
1 Yankees 2008 573
2 Red Sox 2008 545
3 Yankees 2008 526
In [30]: pd.wide_to_long(data, ['hr', 'year'], 'team', 'index')
Out[30]:
hr year
team index
Red Sox 1 514 2007
Yankees 1 573 2008
Red Sox 2 545 2008
Yankees 2 526 2008
```
So we should drop one of these.
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DOC: Fix df.resample docstring example
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| 2 | 2016-12-28T16:56:10Z | 2016-12-28T23:43:59Z | 2016-12-28T23:43:47Z |
CONTRIBUTOR
| null |
- [x] passes ``git diff upstream/master | flake8 --diff``
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BUG: Convert uint64 in maybe_convert_numeric
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| 10 | 2016-12-28T19:54:53Z | 2016-12-30T18:54:11Z | 2016-12-30T18:53:09Z |
MEMBER
| null |
Add handling for `uint64` elements in an array with the follow behavior specifications:
1) If `uint64` and `NaN` are both detected, the original input will be returned if `coerce_numeric`
is `False`. Otherwise, an `Exception` is raised.
2) If `uint64` and negative numbers are both detected, the original input be returned if `coerce_numeric` is `False`. Otherwise, an `Exception` is raised.
Closes #14982.
Partial fix for #14983.
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Importing pandas automatically opens multiple threads
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| 1 | 2016-12-29T07:47:27Z | 2016-12-29T11:10:08Z | 2016-12-29T11:09:54Z |
NONE
| null |
When I run the following script on my macbookpro, I see that 8 threads are spawn. Similar behavior (opening multiple threads) happens on my centos servers.
```python
import time
import pandas
print 'aaa'
time.sleep(30)
```
I would expect pandas as a pure computing library will only use 1 thread automatically, not 8 (my mac has 4 cores). Wondering if this is a bug.
The output of <code>pd.show_versions()</code> is:
```bash
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 28.8.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
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: 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
```
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| 198,007,931 |
MDExOlB1bGxSZXF1ZXN0OTk2NTYwODY=
| 15,007 |
TST: fix tests catching unorderable errors for python 3.6
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| 3 | 2016-12-29T13:33:22Z | 2016-12-30T18:46:02Z | 2016-12-30T18:46:02Z |
MEMBER
| null |
See also https://github.com/MacPython/pandas-wheels/pull/8, seems that the order of int and str is switched in some cases (eg https://travis-ci.org/MacPython/pandas-wheels/jobs/187223139). Therefore making the test here more robust.
Related to https://github.com/pandas-dev/pandas/pull/14684
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