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BUG: DatetimeIndex indexing fails at nanosecond precision if timezone is used
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| 4 | 2016-08-04T17:46:26Z | 2017-06-29T14:50:22Z | 2016-08-04T20:30:19Z |
CONTRIBUTOR
| null |
`DatetimeIndex` fails to find its own values if a timezone is used:
```
index = pd.DatetimeIndex(['2016-06-28 08:30:00.123456789'], dtype='datetime64[ns, America/Chicago]')
df = pd.DataFrame({'a':[10]}, index=index)
df.loc[df.index[0]]
```
It fails with:
```
*** KeyError: 'the label [2016-06-28 08:30:00.123456789-05:00] is not in the [index]'
```
This is impossible.
It works if you use only microseconds rather than nanoseconds, and it works if you don't set a timezone.
Pandas 0.18.1.
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BUG: pd.to_datetime doesn't raises AttributeError with specific input…
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| 3 | 2016-08-04T19:38:56Z | 2016-08-05T19:52:09Z | 2016-08-05T19:52:05Z |
CONTRIBUTOR
| null |
- [x] closes #12424
- [x] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
…s when errors='ignore'(#12424)
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Type inferencing makes assignment of datetime to an object Series non-idempotent
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| 7 | 2016-08-04T22:27:45Z | 2019-11-02T18:22:16Z | 2019-11-02T18:09:16Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
>>> import pandas as pd
>>> s = pd.Series()
>>> ts = pd.Timestamp('2016-01-01')
>>> s['a'] = None
>>> s['b'] = ts
>>> s
a None
b 1451606400000000000
dtype: object
```
OK, no worries, we got coerced to integer. Now let's just redo the **same assignment**:
```
>>> s['b'] = ts
>>> s
a None
b 2016-01-01 00:00:00
dtype: object
```
That's ... suprising. This is probably just an unfortunate feature of a type inference algorithm, but it's awfully shocking.
#### Related examples for testing
#18410
#21143
#### Expected Output
The two outputs above would be identical; I'd prefer that they were both the second form (with timestamp information preserved), but anything consistent would be better than the current state.
#### output of `pd.show_versions()`
```
>>> pd.show_versions()
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: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 25.1.4
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
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
```
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BUG: na_rep given precedence in to_html
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| 10 | 2016-08-04T23:06:54Z | 2017-02-27T21:05:28Z | 2017-02-27T16:00:58Z |
CONTRIBUTOR
| null |
- [x] closes #13828
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
~~I know new tests are appreciated from the get-go, but it's the end of the day for me. I'll add new tests and the appropriate whatsnew entry tomorrow.~~
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BUG: Columns used in sum on 1 row dataframe depend on values instead of dtype
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| 3 | 2016-08-04T23:55:20Z | 2021-05-17T15:17:30Z | 2021-05-17T15:17:30Z |
CONTRIBUTOR
| null |
It seems that somehow the columns used in `sum` when applied to a 1 row dataframe depend on the values in the row instead of just the dtypes. Observe:
``` python
import pandas as pd
import numpy as np
# Frame with some non-numeric dtypes
df = pd.DataFrame({'a': [1], 'b': [1.1], 'c': ['foo'], 'd': [pd.Timestamp('2000-01-01')]})
# Only change here is that `d` is `NaT`
df2 = pd.DataFrame({'a': [1], 'b': [1.1], 'c': ['foo'], 'd': [pd.NaT]})
# This is just the first one twice
df3 = pd.concat([df, df])
# I'd expect all 3 to use the same columns in the reduction
df_sum = df.sum()
df2_sum = df2.sum()
df3_sum = df3.sum()
```
Loading that in an ipython session:
``` python
In [1]: df_sum
Out[1]:
a 1
b 1.1
c foo
d 2000-01-01 00:00:00
dtype: object
In [2]: df2_sum
Out[2]:
a 1.0
b 1.1
dtype: float64
In [3]: df3_sum
Out[3]:
a 2.0
b 2.2
dtype: float64
In [4]: pd.__version__
Out[4]: u'0.18.1'
In [5]: np.__version__
Out[5]: '1.11.1'
```
I'd expect all 3 to only use the columns `['a', 'b']`, as these are the only numeric columns. Strangely, `_get_numeric_data` does return just `['a', 'b']` in all cases, so it's not that.
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MDU6SXNzdWUxNjk1MDIwNzA=
| 13,913 |
Groupby apply using a custom function displays incorrect arguments (DF)
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| 8 | 2016-08-05T00:07:55Z | 2016-08-05T17:57:07Z | 2016-08-05T00:12:24Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
def func3(df):
print(df)
df['Mean'] = np.mean(df['Data'].values)
out = df[['Mean', 'Group']].iloc[0, :].copy(deep=True)
return out
var1 = [1, 2, 3]
grp_var = ['Group1', 'Group2', 'Group2']
df_test = pd.DataFrame.from_dict({'Data': var1, 'Group': grp_var})
# Groupby test
df_grp = df_test.groupby(['Group'])
df_out = df_grp.apply(func3)
```
#### Surprising Print Output
```
Data Group
0 1 Group1
Data Group
0 1 Group1
Data Group Mean
1 2 Group2 1.0
2 3 Group2
Data Group
0 1 Group1
Data Group
1 2 Group2
2 3 Group2
In [31]: df_out
Out[31]:
Mean Group
Group
Group1 1.0 Group1
Group2 2.5 Group2
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.6.1
matplotlib: 1.5.2
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: 2.40.0
pandas_datareader: None
```
#### Summary
The final output is correct, but the intermediate argument calls appear to be referencing incorrect data frames. There are two groups to iterate over, but 5 statements are printed, one of which contains a new column called 'Mean' and is printed under Group2, but the mean value is for Group1. This seems to occur when a pandas series object is returned, and makes it difficult to interactively evaluate/debug groupby calls using custom arguments. The printed DF's should match the DF for the given group being processed. Is this known behavior?
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BUG: resampling a MultiIndex and specified keys are tuples in .agg
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| 5 | 2016-08-05T04:16:31Z | 2021-05-01T23:04:46Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
You can reproduce the error by pasting this chunk of code, but this error is ONLY with pandas 0.18.0 or higher. This chunk of code worked fine under 0.17.1 and earlier, and is related to the change in how resampling is done.
The error I get is "TypeError: other must be a MultiIndex or a list of tuples"
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randint(0, 100, (10, 3)), columns=list('ABC'),
index=pd.date_range('2016-01-01', freq='1400T', periods=10))
df['D'] = ['x', 'x', 'x', 'x', 'x', 'y', 'y', 'y', 'y', 'y']
df.index.name = "timestamp"
df_pivot = pd.pivot_table(df.reset_index(), values=['A', 'B'], index="timestamp",
columns=['C', 'D'])
resample_methods = map({'A': 'sum', 'B': 'mean'}.get, [x[0] for x in df_pivot.columns])
resample_method_tuple_list = zip(df_pivot.columns, resample_methods)
resample_dict = dict(resample_method_tuple_list)
df_pivot.resample('D').agg(resample_dict)
```
Note that this works just fine if I only pivot on one column
#### Expected Output
This should resample the dataframe, without error
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: 0.8.0
IPython: 5.0.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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MDExOlB1bGxSZXF1ZXN0ODAyMDQyMTc=
| 13,915 |
DOC: Add missing word in docstring
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| 2 | 2016-08-05T08:04:06Z | 2016-08-05T10:28:44Z | 2016-08-05T10:24:34Z |
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sum in pandas can concatenate strings
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| 1 | 2016-08-05T08:21:10Z | 2021-11-13T17:32:15Z | null |
MEMBER
| null |
Possibly related: #13912
This looks wrong to me -- probably a bug?
```
In [36]: pd.Series(['a', 'b', 'c']).sum()
Out[36]: 'abc'
In [37]: pd.__version__
Out[37]: '0.18.1'
```
This happens on DataFrames, as well:
```
In [8]: pd.Series(['a', 'b', 'c']).to_frame().sum()
Out[8]:
0 abc
dtype: object
```
Note, of course, that summing strings:
```
In [2]: sum(['a', 'b', 'c'])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-eba0487fc411> in <module>()
----> 1 sum(['a', 'b', 'c'])
TypeError: unsupported operand type(s) for +: 'int' and 'str'
```
Interestingly, NumPy has the same (buggy?) behavior on `dtype=object` arrays:
```
In [11]: pd.Series(['a', 'b', 'c']).values.sum()
Out[11]: 'abc'
In [12]: pd.Series(['a', 'b', 'c']).values.astype(str).sum()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-12-4e25bf0067a8> in <module>()
----> 1 pd.Series(['a', 'b', 'c']).values.astype(str).sum()
/Users/shoyer/conda/envs/xarray-dev/lib/python3.5/site-packages/numpy/core/_methods.py in _sum(a, axis, dtype, out, keepdims)
30
31 def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
---> 32 return umr_sum(a, axis, dtype, out, keepdims)
33
34 def _prod(a, axis=None, dtype=None, out=None, keepdims=False):
TypeError: cannot perform reduce with flexible type
```
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BUG: multi-type SparseDataFrame fixes and improvements
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| 13 | 2016-08-05T12:28:58Z | 2016-08-10T10:42:54Z | 2016-08-10T10:38:24Z |
CONTRIBUTOR
| null |
- [X] 1 tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Types were incorrectly determined when slicing SparseDataFrames with
multiple dtypes (such as float and object) into SparseSeries.
``` python
import pandas as pd
sdf = pd.SparseDataFrame([[1, 2, 'a'], [4, 5, 'b']])
sdf.iloc[[0]] # OK
sdf.iloc[0] # ValueError: could not convert string to float: 'a'
```
No existing issue covers this.
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Pandas.items in Python2 vs Python3
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| 5 | 2016-08-05T13:07:22Z | 2017-08-19T21:59:20Z | 2017-08-19T21:59:20Z |
CONTRIBUTOR
| null |
Inconsistency between python2 and 3 - python2 has no `items` method (but does have `iteritems`). Shouldn't `items` and `iteritems` be the same?
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import string
ser = pd.Series(np.arange(10), index=list(string.ascii_letters[:10]))
print(ser.items())
```
#### Expected Output
`<zip object at 0x11dc1f6c8>`
python 2:
`AttributeError: 'Series' object has no attribute 'items'`
#### output of `pd.show_versions()` (python2)
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 15.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
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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MDU6SXNzdWUxNjk2MDM4Mjk=
| 13,919 |
ERR: Categoricals should not allow non-strings when an object dtype is passed
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| 11 | 2016-08-05T13:11:06Z | 2020-05-16T20:31:21Z | null |
CONTRIBUTOR
| null |
HDF Store (with `table`) doesn't support categories with mixed type inside a category
Even though it is possible to store category type using `to_hdf` with `table`, you can't do that when you have mixed types inside the category. It would be nice to mention this at least on docs.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
df = pd.DataFrame({'a':[1, 'string']})
df['a'] = df['a'].astype('category')
df.to_hdf('play.hdf', 'main', format='table')
```
the error:
```
TypeError: Cannot serialize the column [values] because
its data contents are [mixed-integer] object dtype
```
#### Expected Output
No error, saved file.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.6.4-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.4.1
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
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removed compat check for Series.items method
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[] | null | 20 | 2016-08-05T15:55:49Z | 2017-02-27T16:04:05Z | 2017-02-27T16:04:05Z |
CONTRIBUTOR
| null |
Should I add something in the whatsnew?
I couldn't find a relevant test, so this is to check if this breaks any tests in CI.
- [x] closes #13918
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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MDU6SXNzdWUxNjk2ODI1NDk=
| 13,921 |
Split-out Cython modules from algos.pyx
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| 0 | 2016-08-05T19:46:41Z | 2016-08-06T21:44:36Z | 2016-08-06T21:44:17Z |
CONTRIBUTOR
| null |
This is a broad discussion, following some observations from @jreback.
With the impending addition of `joins_func_helper.pxi.in`, does it make sense to split Cython modules so that `algos.pyx` isn't so massive?
CC @sinhrks
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MDU6SXNzdWUxNjk3NTUwNDI=
| 13,922 |
memory leak in reindex
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[] | null | 3 | 2016-08-06T16:44:28Z | 2016-08-06T18:06:27Z | 2016-08-06T18:06:12Z |
CONTRIBUTOR
| null |
When I reindex, I see memory usage permanently go up.
Path I traced was from pd.DataFrame.assign -> pd.Series.reindex -> pd.Index.reindex
Not sure what the ultimate source of the issue is
``` python
import psutil
import pandas as pd
K = 10
N = 1000 * 1000
for i in range(K):
# any of these will cause the issue
#df = (pd.DataFrame(pd.np.random.rand(N, 2))
# .assign(series=pd.Series(range(N+1))))
#series = pd.Series(pd.np.random.rand(N)).reindex(range(N+1))
index = pd.Index(range(N)).reindex(range(N-1))
print('finished reindex iter {:2d}, using {:3.1f} percent memory'
.format(i, psutil.virtual_memory().percent))
```
prints this
```
finished reindex iter 0, using 45.6 percent memory
finished reindex iter 1, using 45.9 percent memory
finished reindex iter 2, using 46.1 percent memory
finished reindex iter 3, using 46.4 percent memory
finished reindex iter 4, using 46.7 percent memory
finished reindex iter 5, using 47.0 percent memory
finished reindex iter 6, using 47.2 percent memory
finished reindex iter 7, using 47.5 percent memory
finished reindex iter 8, using 47.7 percent memory
finished reindex iter 9, using 48.0 percent memory
```
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.14.32-pv-ts1
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24.1
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.2
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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| 169,757,142 |
MDU6SXNzdWUxNjk3NTcxNDI=
| 13,923 |
Stata read categoricals gives ValueError: Categorical categories must be unique
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| 13 | 2016-08-06T17:30:47Z | 2016-08-10T22:37:45Z | 2016-08-10T22:37:44Z |
NONE
| null |
I am trying to open some Stata files generated in [IPUMS International](https://international.ipums.org/international/), but I am getting a `ValueError: Categorical categories must be unique`. I opened in Stata and could not find a repeated category for the column I am trying to import. I had similar issues with other datasets from the same source, which seemed to be generated by missing values, but that does not seem to be the case here. Here's the [link to the file](https://drive.google.com/open?id=0By-h7HPv1NhVVllUUEFkc1JucGs) I am trying to read.
#### Code Sample, a copy-pastable example if possible
```
df = pd.read_stata('ipumsi_00014.dta', columns=['ethnicsn'])
```
#### Expected Output
```
df.shape = (1694761,1)
```
#### output of `pd.show_versions()`
```
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 18.2
Cython: 0.22.1
numpy: 1.11.1
scipy: 0.15.1
statsmodels: 0.6.1
xarray: None
IPython: 3.2.1
sphinx: None
patsy: 0.2.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 0.8.0
tables: None
numexpr: 2.4
matplotlib: 1.4.3
openpyxl: 2.1.3
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.4
lxml: 3.3.5
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
jinja2: 2.7.3
boto: 2.34.0
pandas_datareader: None
```
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MDExOlB1bGxSZXF1ZXN0ODAzNDg3MTQ=
| 13,924 |
CLN/PERF: remove ndarray.take and platform int conversions
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| 10 | 2016-08-06T19:32:59Z | 2016-08-17T10:19:08Z | 2016-08-15T23:10:03Z |
CONTRIBUTOR
| null |
I was looking into #13745 (GIL on merge), and there's some low hanging fruit in the non-parallel case.
- a couple python `any` calls
- unnecessary (?) platform int checks / conversions. I'm testing on Windows - I'm assuming on
Linux 64 this doesn't really make a difference.
``` python
In [1]: right = pd.DataFrame({'key': range(10), 'val': range(10)})
...: left = pd.DataFrame({'key': range(1,11) * 100000})
# master
In [2]: %timeit left.merge(right, how='inner')
10 loops, best of 3: 104 ms per loop
In [3]: %timeit left.merge(right, how='left')
10 loops, best of 3: 141 ms per loop
In [4]: %timeit left.merge(right, how='outer')
10 loops, best of 3: 125 ms per loop
# PR
In [2]: %timeit left.merge(right, how='inner')
10 loops, best of 3: 79.8 ms per loop
In [3]: %timeit left.merge(right, how='left')
10 loops, best of 3: 110 ms per loop
In [4]: %timeit left.merge(right, how='outer')
10 loops, best of 3: 100 ms per loop
```
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BLD: split join.pyx from algos.pyx
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| 1 | 2016-08-06T19:48:36Z | 2016-08-06T21:44:17Z | 2016-08-06T21:44:17Z |
CONTRIBUTOR
| null |
closes #13921
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BUG: DatetimeTz shift raises AmbiguousTimeError near DST
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| 2 | 2016-08-06T20:40:44Z | 2016-08-06T22:42:17Z | 2016-08-06T22:42:17Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
As the same as #13650, caused by `.tz_localize(None)` which can be removed.
```
import pandas as pd
s = pd.Series(pd.date_range('2016-11-06', freq='H', periods=10, tz='US/Eastern'))
s.shift(1)
# pytz.exceptions.AmbiguousTimeError: Cannot infer dst time from Timestamp('2016-11-06 01:00:00'), try using the 'ambiguous' argument
```
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COMPAT/TST: windows sparse int comparisons
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| 0 | 2016-08-07T01:08:27Z | 2016-08-07T01:24:12Z | 2016-08-07T01:24:12Z |
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CI: re-enable 3.4 build / updated conda-build on appeveyor
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| 1 | 2016-08-07T14:48:37Z | 2017-01-20T13:56:41Z | 2017-01-19T18:00:21Z |
CONTRIBUTOR
| null |
[fix conda-build version](https://github.com/pydata/pandas/commit/cfbb24f2c0e33ee221c54fd4944458cb0c6f4e84)
Builds are consecutive, 1 at a time.
[disable 3.4 build](https://github.com/pydata/pandas/commit/7e15923d908a42008fb7fecde58be626277fd82e)
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Possible bug when parsing from string to datetime when slicing
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| 6 | 2016-08-07T22:40:11Z | 2020-03-31T05:54:57Z | null |
NONE
| null |
From [this question in SO](http://stackoverflow.com/questions/38774044/unexpected-behavior-in-pandas-timestamp-slicing?noredirect=1#comment64980593_38774044)
Please consider the following example
```
data = pd.DataFrame(np.random.random((72000,3)), columns=list('uvw'),
index=pd.date_range('2013-11-08 10:00:00', periods=72000, freq='50L'))
data.loc['2013-11-08 10:15:00.000':'2013-11-08 10:17:00.000']
```
This outputs
```
u v w
2013-11-08 10:15:00.000 0.569030 0.850393 0.600106
2013-11-08 10:15:00.050 0.679713 0.933720 0.041018
2013-11-08 10:15:00.100 0.503491 0.142397 0.841705
2013-11-08 10:15:00.150 0.171248 0.545567 0.247094
2013-11-08 10:15:00.200 0.149745 0.149588 0.935516
2013-11-08 10:15:00.250 0.039780 0.097837 0.087254
... ... ... ...
2013-11-08 10:17:00.700 0.001165 0.020971 0.197322
2013-11-08 10:17:00.750 0.003923 0.722930 0.312988
2013-11-08 10:17:00.800 0.941241 0.600529 0.479640
2013-11-08 10:17:00.850 0.272536 0.738084 0.486551
2013-11-08 10:17:00.900 0.060388 0.606207 0.359640
2013-11-08 10:17:00.950 0.464268 0.965543 0.699740
[2420 rows x 3 columns]
```
This is weird for me because I expected the last row to be `2013-11-08 10:17:00.000`, since that's the end-point I defined. Indeed when I define the endpoint as `datetime(2013,11,8,10,17,0,0)`, which should be identical, it works as I would expect:
```
In [13]: data.loc['2013-11-08 10:15:00.000':datetime(2013,11,8,10,17,0,0)]
Out[13]:
u v w
2013-11-08 10:15:00.000 0.569030 0.850393 0.600106
2013-11-08 10:15:00.050 0.679713 0.933720 0.041018
2013-11-08 10:15:00.100 0.503491 0.142397 0.841705
2013-11-08 10:15:00.150 0.171248 0.545567 0.247094
2013-11-08 10:15:00.200 0.149745 0.149588 0.935516
2013-11-08 10:15:00.250 0.039780 0.097837 0.087254
... ... ... ...
2013-11-08 10:16:59.750 0.652168 0.606795 0.901583
2013-11-08 10:16:59.800 0.868184 0.249873 0.517637
2013-11-08 10:16:59.850 0.917543 0.303403 0.980257
2013-11-08 10:16:59.900 0.118191 0.032437 0.580734
2013-11-08 10:16:59.950 0.093644 0.017865 0.080326
2013-11-08 10:17:00.000 0.770234 0.310025 0.065127
[2401 rows x 3 columns]
```
I'm submitting this as a suggestion from an SO user because this seems like a bug.
To complete:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-21-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
IPython: 2.4.1
sphinx: 1.4.5
patsy: None
dateutil: 2.4.2
pytz: 2014.10
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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| 7 | 2016-08-08T00:06:47Z | 2016-08-13T22:44:08Z | 2016-08-13T22:44:01Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Invalid freq like below doesn'traise `ValueError` properly. The PR makes all the below raise.
```
pd.tseries.frequencies.to_offset('U1')
# <Micro>
pd.tseries.frequencies.to_offset('3U1')
# <3 * Micros>
pd.tseries.frequencies.to_offset('-33-22U')
# <-22 * Micros>
```
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ERR: improved pd.Period date parsing and formatting
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| 9 | 2016-08-08T13:43:02Z | 2020-05-11T02:12:08Z | null |
NONE
| null |
It seems that pandas.Period has a few bugs w.r.t. date parsing and formatting, or at least poorly documented behavior.
- The documentation makes no mention of the possible range of dates/years.
- Formatting of years in strftime using the %Y format seems inconsistent.
- Parsing of what is commonly assumed to be ISO-8601 dates seems wrong.
#### Code Sample, a copy-pastable example if possible
This seems reasonable, assuming American-centered times (m/d/y):
`>>> pd.Period("1/2/3")`
`Period('2003-01-02', 'D')`
This seems wrong, as the common interpretation of dates using hyphens as separators is (in my experience), ISO-8601 (though, I will grant that not specifying the necessary leading zeroes makes the input suspect):
`>>> pd.Period("1-2-3")`
`Period('2003-01-02', 'D')`
Hard to see how either of these is correct. I'm not sure quite what to expect for the first example, but the second example clearly reads like year=3, month=1, day=2 to me. Despite the presence or lack of leading zeroes, I would think when presented with a date containing hyphens as separators, %Y-%m-%d would be assumed. I also think that %Y, %m, and %d should also zero-pad their arguments to the correct widths (4, 2, 2, respectively). (The same could be said for other normally zero-padded timestamp fields.)
`>>> pd.Period("01-02-0003")`
`Period('3-01-02', 'D')`
`>>> pd.Period("0003-01-02")`
`Period('2-03-01', 'D')`
`>>> pd.Period("0003-01-02").strftime("%Y")`
`u'2'`
Given that date formats differ so widely, to eliminate ambiguity perhaps the Period constructor should take an (optional keyword) argument which specifies a format string as understood by strptime(). It would clearly have to be extended somewhat to accommodate the quarter notation.
#### output of `pd.show_versions()`
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.4.63-2.44-desktop
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
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: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
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| 13,932 |
CLN: resource warnings
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| 4 | 2016-08-08T13:43:09Z | 2016-08-11T10:37:50Z | 2016-08-09T21:53:08Z |
CONTRIBUTOR
| null |
https://travis-ci.org/pydata/pandas/jobs/150623749
so we have warnings enabled on the 3.5/dev build. Lots of resource warnings....
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BUG: loffset isn't applied when using resample() with a TimedeltaIndex
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| 2 | 2016-08-08T14:05:21Z | 2016-08-08T14:22:31Z | 2016-08-08T14:15:39Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```
In [3]: from pandas import *
In [4]: rng = timedelta_range(start='0s', periods=10, freq='s')
In [5]: df = DataFrame({'A' : range(10)}, index=rng)
In [6]: df.resample('2S', loffset='2h').count()
Out[6]:
A
00:00:00 2
00:00:02 2
00:00:04 2
00:00:06 2
00:00:08 2
```
#### Expected Output
```
A
02:00:00 2
02:00:02 2
02:00:04 2
02:00:06 2
02:00:08 2
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: cff1f5595888becee0e58dedf014a12e8131b352
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-21-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.18.1+327.gcff1f55
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
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
```
I'm working on #13218 and can't proceed with writing the tests due to this bug. I will try to see if I can fix it, but I'm not totally sure if I will be able to.
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| 169,936,133 |
MDU6SXNzdWUxNjk5MzYxMzM=
| 13,934 |
Column reappears after drop() when working on a reference of that column
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| 3 | 2016-08-08T14:25:13Z | 2021-05-17T15:17:30Z | 2021-05-17T15:17:30Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
``` python
import pandas
df = pandas.DataFrame()
df['a'] = [1,2,3]
a = df.a
df.drop(['a'], axis=1, inplace=True)
print df.columns
a -= a.mean()
print df.columns
```
#### Current Output
``` python
Index([], dtype='object')
Index([u'a'], dtype='object')
```
#### Expected Output
``` python
Index([], dtype='object')
Index([], dtype='object')
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-65-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 1.5.4
setuptools: 20.9.0
Cython: 0.23.4
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.0
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
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MDExOlB1bGxSZXF1ZXN0ODA0NzA4NjM=
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BUG: Right result for DatetimeIndex + TimeDelta when timezone is set(…
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[] | null | 8 | 2016-08-08T15:27:52Z | 2016-12-30T23:13:50Z | 2016-12-30T21:46:00Z |
CONTRIBUTOR
| null |
- [x] closes #13905
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
…#13905)
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| 13,936 |
Expand type specializations and multiple "by" parameters in merge_asof()
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| 7 | 2016-08-08T15:35:12Z | 2016-12-16T23:10:34Z | 2016-12-16T23:10:34Z |
CONTRIBUTOR
| null |
`pd.merge_asof()` can take an integer or floating-point number in the `on` parameter, and it can take an integer or an object in the `by` parameter. Specifically, the user's types are promoted to `int64_t`, `double`, or `object` as needed. That means, for example, that an `int32_t` is permitted, but we'll have to create a copy of the user's column to promote to `int64_t`.
This brings a question of whether we should add type specializations for every integer and floating-point number for better performance.
A second issue to consider is that only one column is permitted in the `by` parameter. But the user may wish to match on both ticker symbol and stock exchange, for example. To allow for this, the implementation logic would need to allow arrays of objects/integers for our grouping.
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| 13,937 |
BUG: DataFrame.to_records() bug in converting datetime64 index with timezone
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| 2 | 2016-08-08T15:45:10Z | 2017-03-02T13:45:02Z | 2017-03-02T13:44:58Z |
NONE
| null |
#### Fix
in to_records(), use pandas.core.common.is_datetime64_any_dtype instead of pandas.core.common.is_datetime64_dtype to check to see if the index is in fact of the datetime64 type.
#### Code Sample, a copy-pastable example if possible
```
import datetime
import pandas as pd
import pytz
data = [datetime.datetime.now(pytz.timezone('UTC')) for _ in range(10)]
df = pd.DataFrame({'datetime': data})
df.set_index('datetime', inplace=True)
df.to_records()
```
#### Expected Output
data
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
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: 2016.2
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: 2.39.0
pandas_datareader: None
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DOC: Added example of using OrderedDict for agg.
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| 1 | 2016-08-08T15:53:41Z | 2016-08-08T16:10:54Z | 2016-08-08T16:10:54Z |
CONTRIBUTOR
| null |
- closes #12879
- [ ] tests added / passed (doc only), but builds on my machine
- passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry (doc only)
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Reading SAS files from AWS S3
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[] | null | 6 | 2016-08-08T17:19:10Z | 2019-12-11T22:06:12Z | null |
NONE
| null |
Currently the `read_sas` method doesn't support reading SAS7BDAT files from AWS S3 like `read_csv`.
Can it be added?
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BUG: properly close files opened by parsers
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| 9 | 2016-08-08T21:42:25Z | 2016-08-11T10:37:42Z | 2016-08-11T10:34:41Z |
CONTRIBUTOR
| null |
- [x] closes #13932
- [x] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Addresses all ~~most~~ of the `ResourceWarning`s from #13932. There were three tests that opened files but did not close them. The rest were due to parsers (`CParserWrapper` and `PythonParser`) opening files or streams but never closing them. There was one case — the `mmap` one — that was tricky because there was a file handle going into it and immediately reaching a refcount of zero.
I am at a loss about writing new tests for this. Is it even possible?
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ENH: PeriodIndex now has period dtype
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| 14 | 2016-08-08T22:14:19Z | 2016-08-17T23:07:22Z | 2016-08-17T22:18:51Z |
MEMBER
| null |
- [x] split from #13755.
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
splitted `dtype` related part from #13755, to clarify the differences related to `PeriodIndex`
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MDExOlB1bGxSZXF1ZXN0ODA1NDA1Mjk=
| 13,942 |
BLD: Fix sparse warnings
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| 2 | 2016-08-08T23:44:42Z | 2016-08-09T21:31:41Z | 2016-08-09T21:31:09Z |
MEMBER
| null |
- [x] split from #13849 (comment)
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
Because sparse `IntIndex` and `BlockIndex` store value locations as `int32`, its size / block numbers can't exceed `int32` also.
warnings has been disappeared in appveyor.
- https://ci.appveyor.com/project/sinhrks/pandas-2dhxb/build/1.0.669/job/2s24fpuxk3d2nrvq
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| 13,943 |
TST: Removed regex warning for read_csv
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| 2 | 2016-08-09T01:44:10Z | 2016-08-09T21:53:08Z | 2016-08-09T21:53:08Z |
MEMBER
| null |
Follow-up to #13481 and partially fixes #13932. Regex mistakenly matches to the empty string, which will cause Python 3.x to issue a warning. In addition, not sure why this test was placed in `python_parsers_only.py`...
|
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| 13,944 |
DOC: Design drafts to assist with next-gen pandas internals discussion
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| 23 | 2016-08-09T03:07:06Z | 2016-08-24T03:45:31Z | 2016-08-24T03:45:31Z |
MEMBER
| null |
Sprinted on my accumulated design ideas. There's more to do, but I'm going to take a break for a bit.
I published this as a sphinx site temporarily to https://wesm.github.io/pandas2-design/ so it's easier to read. This needs to be a team effort, so depending on where the discussions go perhaps we can build a set of documents that we can make PRs to and discuss specific matters either in GitHub issues or on the mailing list. Let me know what everybody thinks!
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Msgpack roundtrips python 3 strings to bytes
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| 4 | 2016-08-09T05:39:58Z | 2016-08-09T11:44:52Z | 2016-08-09T10:06:07Z |
CONTRIBUTOR
| null |
Possibly related to https://github.com/pydata/pandas/issues/13591. Causes https://github.com/dask/dask/issues/1452
#### Code Sample, a copy-pastable example if possible
``` Python
import pandas as pd
pd.msgpack.unpackb(pd.msgpack.packb("a"))
```
#### Expected Output
```
"a"
```
instead, we get
```
b"a"
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-31-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 25.1.6
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
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: None
pandas_datareader: None
```
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| 13,946 |
pd.read_clipboard in Jupyter makes python 3.5 crash on windows
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[] | null | 10 | 2016-08-09T10:40:27Z | 2016-11-15T01:21:57Z | 2016-08-09T11:10:32Z |
CONTRIBUTOR
| null |
Disclaimer: I'm not quite sure if the problem is really on pandas' side or on jupyter's.
I have seen #4561 but this isn't the same problem.
#### Code Sample, a copy-pastable example if possible
Given the following code:
```
import pandas as pd
pd.read_clipboard()
```
If I use it in a python file, it works fine.
If I use ipython in cmd.exe, it works fine.
Now if I use it in a Jupyter notebook, it consistently makes my kernel die.
#### output of `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 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.1
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: 0.2.1
##### Jupyter info:
jupyter 1.0.0 py35_3
jupyter_client 4.3.0 py35_0
jupyter_console 5.0.0 py35_0
jupyter_core 4.1.0 py35_0
ipython 5.0.0 py35_0
ipython-notebook 4.0.4 py35_3
ipython-qtconsole 4.0.1 py35_4
ipython_genutils 0.1.0 py35_0
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Unified common dtype discovery
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| 2 | 2016-08-09T12:10:33Z | 2016-08-12T15:44:55Z | 2016-08-12T15:44:54Z |
CONTRIBUTOR
| null |
Common dtype discovery (like `np.find_common_type`) should be unified into an internal function, with proper handling of `pandas` dtypes. As of #13917, there are two such implementations:
1. `pandas/types/common.py:_lcd_dtypes`, which works on `numpy` dtypes only, but differently than `np.find_common_type`.
2. `pandas/types/cast.py:_find_common_type`, which just uses `np.find_common_type` internally.
Exchanging the first one with the second (which is, judging by the code, more proper), breaks some tests.
Proposed tasks:
- [ ] Convert usages of `_lcd_dtypes` into `_find_common_types`.
- [ ] Extend `_find_common_types` to properly evaluate `pandas` dtypes.
Occurences of `_lcd_dtypes`, the one in `internals.py` is removed by #13917:
```
[jreback-~/pandas] grin _lcd_dtype pandas
pandas/core/frame.py:
47 : _lcd_dtypes,
3705 : new_dtype = _lcd_dtypes(this_dtype, other_dtype)
pandas/core/internals.py:
4438 : def _lcd_dtype(l):
4473 : lcd = _lcd_dtype(counts[IntBlock])
4489 : return _lcd_dtype(counts[FloatBlock] + counts[SparseBlock])
pandas/types/common.py:
389 : def _lcd_dtypes(a_dtype, b_dtype):
```
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Allow different comment characters in one file
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| 7 | 2016-08-09T13:52:38Z | 2021-07-08T19:22:40Z | null |
NONE
| null |
Unfortunately, I need to work with files containing both `#comments` and `!warnings` aside of useful numbers. Thus, it would be very convenient to load them (in pandas 0.17.1) using
`df = pd.read_csv(inputfile, comment='#!')`
however, this command now allows one comment-identifying character at most, so I need to parse the lines manually and save them into a temporary file first.
I guess it would involve rather a minor change to _search_replace_num_columns() in [parsers.py](https://github.com/pydata/pandas/blob/a292c13a7f831145f3daac9881813aeb7ff08138/pandas/io/parsers.py#L2329) and perhaps somewhere in [parser.pyx](https://github.com/pydata/pandas/blob/a292c13a7f831145f3daac9881813aeb7ff08138/pandas/parser.pyx).
Before I write a patch, I would like to read your opinion.
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ENH: Improve error message for repeated Stata categories
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| 2 | 2016-08-09T13:55:44Z | 2016-08-10T22:37:58Z | 2016-08-10T22:37:45Z |
CONTRIBUTOR
| null |
- [x] closes #13923
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Improve the error message to be more explicit when attempting to read Stata
files containing repeated categories.
closes #13923
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MDU6SXNzdWUxNzAxNzUwMTQ=
| 13,950 |
DOC: more documentation that pandas by-default aligns when setting
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| 2 | 2016-08-09T14:05:39Z | 2021-08-23T04:03:20Z | null |
CONTRIBUTOR
| null |
Hi,
I'm not sure this is a bug but the behaviour is far from intuitive. I'm trying to assign the values of a slice of a `DataFrame` into another slice (of same size) of the same `DataFrame`.
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: a = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [6, 7, 8, 9, 10]})
In [4]: a
Out[4]:
a b
0 1 6
1 2 7
2 3 8
3 4 9
4 5 10
In [5]: a.iloc[0:2,:] = a.iloc[3:5, :]
In [6]: a
Out[6]:
a b
0 NaN NaN
1 NaN NaN
2 3.0 8.0
3 4.0 9.0
4 5.0 10.0
```
Specifically, in the current implementation the data type is not preserved (`int` to `float`) and the value copied are `np.NaN` instead of the correct values.
The expected output can be obtained using this counter-intuitive command:
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: a = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [6, 7, 8, 9, 10]})
In [4]: a.iloc[0:2,:] = (np.array(a.iloc[3:5, :])).astype(int)
In [5]: a
Out[5]:
a b
0 4 9
1 5 10
2 3 8
3 4 9
4 5 10
```
This is the environment I'm working on:
``` pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-31-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: 8.1.2
setuptools: 24.0.2
Cython: None
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: 1.4.5
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.1.0b2
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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| 170,218,948 |
MDExOlB1bGxSZXF1ZXN0ODA2NTc0MTI=
| 13,951 |
DOC: NDFrame.to_hdf(data_columns) documented (#13061).
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| 3 | 2016-08-09T17:03:01Z | 2016-08-19T10:50:01Z | 2016-08-19T10:49:54Z |
CONTRIBUTOR
| null |
- [x] closes #13061
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
I'm aware there's another PR for the same issue, but it hasn't had activity in a while so this is my attempt. I took the opportunity to remove the unusable "mode=r" option from the same docstring, and make the title clearer.
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DataFrame sum method for all NAs does not return NA
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| 1 | 2016-08-09T19:53:00Z | 2016-08-09T20:03:18Z | 2016-08-09T20:02:58Z |
NONE
| null |
Output of code:
`pd.DataFrame([[np.NaN, np.NaN], [np.NaN, np.NaN]]).sum(skipna=True)`
is:
```
0 0.0
1 0.0
dtype: float64
```
What in my opinion contradicts what is written in documentation (and what by the way is more intuitve outcome):
> Exclude NA/null values. If an entire row/column is NA, the result will be NA
#### output of `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 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
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: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
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DOC: add whatsnew for #11920
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| 1 | 2016-08-10T10:22:03Z | 2016-08-10T12:27:14Z | 2016-08-10T12:27:13Z |
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| null |
#11920 (forgot to add to commit when merging)
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DOC: remove rplot seaborn equivalent example figures
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| 1 | 2016-08-10T10:47:29Z | 2016-08-10T14:16:10Z | 2016-08-10T14:16:10Z |
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CLN: Period cleanup related to array like meth
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| 2 | 2016-08-10T11:26:06Z | 2016-08-10T22:12:48Z | 2016-08-10T22:12:48Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
removed unused / duplicated internal method returning array-likes. Also added some tests to guarantee existing API before fixing its dtype (see #13941).
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tz_localize, conversion from dst-affected timezone on sorted index
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[] | null | 8 | 2016-08-10T12:53:51Z | 2019-07-01T16:42:52Z | 2019-07-01T16:42:52Z |
NONE
| null |
Polish government provides [data on power demand](http://www.pse.pl/index.php?dzid=78) with local timestamps. DST switches are unfortunately not accounted for, so some timestamps are duplicated. Thankfully, the differences aren't big to matter in some applications, but it seems the way these timestamps are provided in the file do not work with `tz_localize`, which seems to be the tool to fix this problem.
#### Code Sample
```
from pandas import Series, Timestamp
Series([14603.9, 14559.700000000001, 14239.0, 14371.0, 13914.5, 14427.200000000001, 14300.5, 14267.5, 14162.200000000001, 14148.5, 14028.1, 14083.1, 14122.9], index=[Timestamp('2015-10-25 01:30:00'), Timestamp('2015-10-25 01:45:00'), Timestamp('2015-10-25 02:00:00'), Timestamp('2015-10-25 02:00:00'), Timestamp('2015-10-25 02:15:00'), Timestamp('2015-10-25 02:15:00'), Timestamp('2015-10-25 02:30:00'), Timestamp('2015-10-25 02:30:00'), Timestamp('2015-10-25 02:45:00'), Timestamp('2015-10-25 02:45:00'), Timestamp('2015-10-25 03:00:00'), Timestamp('2015-10-25 03:15:00'), Timestamp('2015-10-25 03:30:00')]).tz_localize('Europe/Warsaw', ambiguous='infer')
```
#### Current Output
```
[…]
pandas/tslib.pyx in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:69085)()
AmbiguousTimeError: There are 4 dst switches when there should only be 1.
```
#### Expected Output
Obviously, we don't know what should be the exact result. My understanding of `ambiguous='infer'` is that at this point, I'm just letting `pandas` find some timezone assignment that is not totally wrong, because in some applications this will still be better than NaT. For example:
```
2015-10-25 01:30:00+02:00 14603.9
2015-10-25 01:45:00+02:00 14559.7
2015-10-25 02:00:00+02:00 14239.0
2015-10-25 02:00:00+01:00 14371.0
2015-10-25 02:15:00+02:00 13914.5
2015-10-25 02:15:00+01:00 14427.2
2015-10-25 02:30:00+02:00 14300.5
2015-10-25 02:30:00+01:00 14267.5
2015-10-25 02:45:00+02:00 14162.2
2015-10-25 02:45:00+01:00 14148.5
2015-10-25 03:00:00+01:00 14028.1
2015-10-25 03:15:00+01:00 14083.1
2015-10-25 03:30:00+01:00 14122.9
dtype: float64
```
#### output of `pd.show_versions()`
```
In [4]: pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 4.6.0-0.bpo.1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.18.1
nose: None
pip: 1.5.6
setuptools: 24.0.3
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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DataFrame.update() changes type of boolean column to Object.
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| 2 | 2016-08-10T13:06:14Z | 2016-08-10T22:16:41Z | 2016-08-10T22:16:26Z |
NONE
| null |
After applying DataFrame.update() on a DataFrame that has boolean column the type of that column changes to Object and filtering stops working as negate operator (~) produces '-1' series instead of 'True'.
#### Code Sample, a copy-pastable example if possible
``` python
df = pd.DataFrame({'a': [1, 2, 3], 'b': ['notset'] * 3, 'c': [False] * 3})
df.set_index('a', inplace=True)
df_update_from = pd.DataFrame({'a': [2, 3], 'b': ['updated2', 'updated3'], 'c': [False, False]})
df_update_from.set_index('a', inplace=True)
df.update(df_update_from)
~df.c
```
#### Expected Output
> a
> 1 True
> 2 True
> 3 True
> Name: c, dtype: bool
### Actual Output
> a
> 1 -1
> 2 -1
> 3 -1
> Name: c, dtype: object
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-238.9.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.16.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.0.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2016.3
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
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MDU6SXNzdWUxNzA0MzU4MTg=
| 13,958 |
newey west weight is always 0
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| 3 | 2016-08-10T14:47:06Z | 2016-08-10T22:42:19Z | 2016-08-10T18:27:35Z |
NONE
| null |
In the `newey_west` function in `pandas.stats.math`, the weight variable is always gonna be 0, since max_lags and lag are both integers and lag < max_lags+1. So the newey-west lag has no effect on the Xeps. Is that right?
```
Xeps = np.dot(m.T, m)
for lag in range(1, max_lags + 1):
auto_cov = np.dot(m[:-lag].T, m[lag:])
weight = lag / (max_lags + 1)
if nw_overlap:
weight = 0
bb = auto_cov + auto_cov.T
dd = (1 - weight) * bb
Xeps += dd
Xeps *= nobs / (nobs - df)
```
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kdb-like window join for pandas
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| 7 | 2016-08-10T20:54:04Z | 2021-05-01T23:13:47Z | null |
CONTRIBUTOR
| null |
I would like a time-based aggregating function that also acts as a join:
http://code.kx.com/wiki/Reference/wj
In pandas, this might look something like:
```
ms = pd.Timedelta(1, 'ms')
merge_window(df1, df2, on='time', by='ticker', time_range=(-100*ms, 100*ms), how=(np.sum, 'volume'))
```
This would compute the total volume from df2 whose timestamps are within (-100, 100) ms of df1’s timestamps and whose tickers match.
I imagine I could specify different columns:
```
merge_window(df1, df2, left_on='trade_time', right_on='exchange_time', time_range=(0*ms, 500*ms),
how={'total_volume':(np.sum, 'volume'), 'mean_volume':(np.mean, 'volume')})
```
By the way, this is a more general form of the `.rolling()` functions @jreback [wrote recently](https://github.com/pydata/pandas/pull/13513) since I can just use one DataFrame as both parameters:
```
merge_window(df, df, on='time', time_range=(-5*ms, 0*ms), how=[(np.median, 'price'), (np.sum, 'volume')])
```
My firm has custom business calendars for things like market hours as well as exchange holidays. The kdb version takes arrays of timestamps directly for the begin and end, which handles the general-purpose case of custom business calendars. So I imagine could get the five-day average of volume with:
```
# internal functions
cal = get_our_internal_business_calendar()
begin = adjust_our_time(df.time, -5, cal)
end = adjust_our_time(df.time, 0, cal)
# use values directly
merge_window(df1, df2, on='time', begin_times=begin, end_times=end, how=(np.mean, 'volume'))
```
I can get started on this right away if my proposal makes sense.
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BLD: add tempita files to depends
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| 5 | 2016-08-10T23:58:16Z | 2016-08-11T10:13:53Z | 2016-08-11T10:13:49Z |
CONTRIBUTOR
| null |
This change makes it so that if a `.pxi.in` file is modified, the appropriate module will be rebuilt.
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API: Expanded resample
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| 25 | 2016-08-11T00:52:53Z | 2016-08-31T13:14:30Z | 2016-08-31T13:13:55Z |
CONTRIBUTOR
| null |
- [x] closes #13500
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
My only question here is if `on=` is the keyword name for picking a column - in a `TimeGrouper` that's called `key`. But `on` is what was used in the rolling selection (and elsewhere) so seems consistent.
```
In [63]: df = pd.DataFrame({'date': pd.date_range('2015-01-01', freq='W', periods=5),
....: 'a': np.arange(5)},
....: index=pd.MultiIndex.from_arrays([
....: [1,2,3,4,5],
....: pd.date_range('2015-01-01', freq='W', periods=5)],
....: names=['v','d']))
....:
In [64]: df
Out[64]:
a date
v d
1 2015-01-04 0 2015-01-04
2 2015-01-11 1 2015-01-11
3 2015-01-18 2 2015-01-18
4 2015-01-25 3 2015-01-25
5 2015-02-01 4 2015-02-01
In [65]: df.resample('M', on='date').sum()
Out[65]:
a
date
2015-01-31 6
2015-02-28 4
In [66]: df.resample('M', level='d').sum()
Out[66]:
a
d
2015-01-31 6
2015-02-28 4
```
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TST: fail test suite on ResourceWarnings
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| 6 | 2016-08-11T10:40:52Z | 2018-09-18T16:52:01Z | 2018-09-18T16:52:01Z |
CONTRIBUTOR
| null |
see comments [here](https://github.com/pydata/pandas/pull/13940#issuecomment-239126585)
should just require a setting in
`pandas.util.testing.TestCase.setUpClass` to set when the suite is running.
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Feature request: pandas.DataFrame.setdefault()
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[] | null | 5 | 2016-08-11T13:32:10Z | 2021-05-01T23:14:10Z | null |
NONE
| null |
We often find ourselves checking for column existence before assigning a value to it, e.g.:
``` python
df = pd.DataFrame()
col = 'ColumnA'
val = 'Value1'
if col not in df.columns:
df[col] = val
```
And we were thinking it would be nice if there was a way to tell pandas: add this column with that value(s) but only if it does not exist already - somewhat similar to dict.setdefault() so we could say, for instance:
``` python
df.setdefault(col, val)
```
Doesn't necessarily have to be that syntax obviously - just a suggestion. What do you guys think?
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Pandas 0.18.1 groupby and resample with multilevel aggregation error
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| 4 | 2016-08-11T14:45:51Z | 2020-01-20T07:34:12Z | 2020-01-20T07:34:11Z |
NONE
| null |
Initially posted here: http://stackoverflow.com/questions/38861244/pandas-0-18-1-groupby-and-resample-with-multilevel-aggregation-error
From the documentation at the below links, both df3_resample and df4_resample should return the same dataframe, however df4_resample throws an exception.
http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-0180-breaking-resample
http://pandas.pydata.org/pandas-docs/version/0.18.1/whatsnew.html#groupby-syntax-with-window-and-resample-operations
```
df = pd.DataFrame(np.random.rand(10,4),
columns=list('ABCD'),
index=pd.date_range('2010-01-01 09:00:00', periods=10, freq='s'))
df['item'] = 'item_a' # add column for groupby
df3_resample = df.groupby('item').apply(lambda x: x.resample('2s').agg({'A': {'A_mean': np.mean, 'A_max': np.max}}))
df4_resample = df.groupby('item').resample('2s').agg({'A': {'A_mean': np.mean, 'A_max': np.max}})
```
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Exact matches in time-based .rolling()
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| 9 | 2016-08-11T15:50:49Z | 2017-04-13T11:39:15Z | 2017-04-13T11:39:14Z |
CONTRIBUTOR
| null |
I would like to `allow_exact_matches` in time-based `.rolling()`. Using an example similar to the one found in the documentation:
```
df = pd.DataFrame({'B': [1, 1, 2, np.nan, 4]},
index = [pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130101 09:00:02'),
pd.Timestamp('20130101 09:00:03'),
pd.Timestamp('20130101 09:00:05'),
pd.Timestamp('20130101 09:00:06')])
```
I can compute the three-second rolling sum:
```
In [200]: df
Out[200]:
B
2013-01-01 09:00:00 1.0
2013-01-01 09:00:02 1.0
2013-01-01 09:00:03 2.0
2013-01-01 09:00:05 NaN
2013-01-01 09:00:06 4.0
In [201]: df.rolling('3s').sum()
Out[201]:
B
2013-01-01 09:00:00 1.0
2013-01-01 09:00:02 2.0
2013-01-01 09:00:03 3.0
2013-01-01 09:00:05 2.0
2013-01-01 09:00:06 4.0
```
But note that `09:00:03` does not include `09:00:00`, `09:00:05` does not include `09:00:02`, and `09:00:06` does not include `09:00:03`. If I were to include these timestamps, then I would have:
```
B
2013-01-01 09:00:00 1.0
2013-01-01 09:00:02 2.0
2013-01-01 09:00:03 4.0
2013-01-01 09:00:05 3.0
2013-01-01 09:00:06 6.0
```
A quick-and-dirty way of getting these values is with:
```
from itertools import starmap
window = pd.Timedelta('3s')
begins = np.searchsorted(df.index, df.index - window)
ends = np.arange(len(df)) + 1
ranges = starmap(range, zip(begins, ends))
pd.concat([df.ix[r].sum() for r in ranges])
```
`pd.merge_asof()` has the parameter `allow_exact_matches` to permit this. Would it be possible to add this to `.rolling()`?
I can try this one myself.
|
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MDU6SXNzdWUxNzA3MDAwOTU=
| 13,966 |
Time-based .rolling() fails with .groupby()
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| 19 | 2016-08-11T17:26:11Z | 2021-02-08T20:53:19Z | 2017-04-22T01:28:28Z |
CONTRIBUTOR
| null |
Starting with [this example](http://pandas.pydata.org/pandas-docs/version/0.18.1/whatsnew.html#groupby-syntax-with-window-and-resample-operations):
```
df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
'B': np.arange(40)})
```
I can easily compute the rolling mean by identifier:
```
In [20]: df.groupby('A').rolling(4).B.mean()
Out[20]:
A
1 0 NaN
1 NaN
2 NaN
3 1.5
4 2.5
5 3.5
6 4.5
7 5.5
8 6.5
9 7.5
...
2 30 28.5
31 29.5
3 32 NaN
33 NaN
34 NaN
35 33.5
36 34.5
37 35.5
38 36.5
39 37.5
Name: B, dtype: float64
```
Now I want to add a timestamp column:
```
dates = pd.date_range(start='2016-01-01 09:30:00', periods=20, freq='s')
df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
'B': np.concatenate((dates, dates)),
'C': np.arange(40)})
```
The timestamps are ordered within each identifier, but pandas complains:
```
In [25]: df.groupby('A').rolling('4s', on='B').C.mean()
...
ValueError: B must be monotonic
```
Re-sorting leads to a different error:
```
In [26]: df.sort_values('B', inplace=True)
In [27]: df.groupby('A').rolling('4s', on='B').C.mean()
...
ValueError: invalid on specified as B, must be a column (if DataFrame) or None
```
But we know that these column names are valid:
```
n [28]: df.rolling('4s', on='B').C.mean()
Out[28]:
0 0.000000
20 10.000000
1 7.000000
21 10.500000
2 8.800000
22 11.000000
3 9.857143
23 11.500000
4 10.857143
24 12.500000
...
35 24.714286
15 23.500000
36 25.714286
16 24.500000
37 26.714286
17 25.500000
38 27.714286
18 26.500000
19 25.857143
39 27.500000
Name: C, dtype: float64
```
It seems like a bug that time-based `.rolling()` does not work with `.groupby()`.
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DOC: read_excel() documentation for na_values should show default na values
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| 3 | 2016-08-11T18:45:43Z | 2016-08-20T13:25:01Z | 2016-08-20T13:25:01Z |
NONE
| null |
I'd like to see read_excel show the same valid information for na_values as read_csv does. This ended up taking a lot of digging to find because it was unclear what the defaults were with read_excel().
#### read_csv() documentation shows the following for na_values
na_values : str or list-like or dict, default None
Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘nan’.
#### read_excel() documentation does not include default NaN values
na_values : list-like, default None
List of additional strings to recognize as NA/NaN
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ENH: allow exact matches in time-based .rolling()
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[] | null | 53 | 2016-08-11T19:25:51Z | 2017-03-23T22:38:29Z | 2017-03-23T22:38:28Z |
CONTRIBUTOR
| null |
- [x] closes #13965
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
TODO: update docs.
|
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Handling a CustomBusinessDay in time-based .rolling()
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| 6 | 2016-08-11T19:49:40Z | 2020-10-05T00:19:16Z | 2020-10-05T00:19:15Z |
CONTRIBUTOR
| null |
Starting with 10 business days following Christmas Eve:
```
from pandas.tseries.offsets import CustomBusinessDay
from pandas.tseries.holiday import USFederalHolidayCalendar
days = CustomBusinessDay(calendar=USFederalHolidayCalendar())
df = pd.DataFrame({'value': np.arange(n)},
index=pd.date_range('2015-12-24', periods=n, freq=days))
```
I can compute the three-day sum of the values with just:
```
In [21]: df.rolling('3d').sum()
Out[21]:
value
2015-12-24 0.0
2015-12-28 1.0
2015-12-29 3.0
2015-12-30 6.0
2015-12-31 9.0
2016-01-04 5.0
2016-01-05 11.0
2016-01-06 18.0
2016-01-07 21.0
2016-01-08 24.0
```
But this is purely in terms of _Gregorian_ calendar days, not the _business_ calendar days that I had created the DataFrame with.
I can easily compute
```
df.index - 3*days
```
though I get a performance warning:
> Non-vectorized DateOffset being applied to Series or DatetimeIndex
But I can't just pass this offset directly:
```
In [23]: df.rolling(3*days).sum()
...
ValueError: <3 * CustomBusinessDays> is a non-fixed frequency
```
I would like to be able to handle a `CustomBusinessDay` in `.rolling()`. (Because the DataFrame may come from any source, it would be easier to just pass the `DateOffset` object to `.rolling()` instead of using anything specific to the DataFrame's index.)
I know that the `freq` parameter was deprecated in 0.18, though 0.19 kinda brings this back in `window`. Is there an intrinsic reason `window` can't handle a `CustomBusinessDay`?
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BUG/DEPR: combine dtype fixes
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| 8 | 2016-08-11T21:25:52Z | 2016-08-12T15:45:02Z | 2016-08-12T15:44:55Z |
MEMBER
| null |
- [x] closes #7630, closes #10567 (fix `combine_first` dtype)
- [x] closes #13947 (`_find_common_type` to support extension types)
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
- `_find_common_type` also needs to handle numpy datetimelike to be compat with pandas because:
```
np.find_common_type([np.dtype('datetime64[ns]'), np.dtype('datetime64[ns]')], [])
# dtype('O')
```
- another `comibine_first` related issue #7509 is NOT fixed because it is related to filling with `align`
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BUG: unstack(fill_value) does nothing when unstacking multiple columns
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| 2 | 2016-08-11T22:00:56Z | 2017-10-16T14:25:24Z | 2017-10-16T14:25:24Z |
CONTRIBUTOR
| null |
In #9746 we got a `fill_value` argument for `pd.unstack()`. But it does nothing if we unstack multiple levels at once:
```
df = pd.DataFrame({'x':['a', 'a', 'b'], 'y':['j', 'k', 'j'], 'z':[0, 1, 2], 'w':[0, 1, 2]})
df.set_index(['x', 'y', 'z']).unstack(['x', 'y'], fill_value=0)
```
That gives:
```
w
x a b
y j k j
z
0 0.0 NaN NaN
1 NaN 1.0 NaN
2 NaN NaN 2.0
```
It should give zeros instead of NaN.
Pandas 0.18.1.
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PERF/COMPAT: define platform int to np.intp
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| 10 | 2016-08-12T00:42:13Z | 2016-08-17T10:21:40Z | 2016-08-17T10:21:24Z |
CONTRIBUTOR
| null |
AFAIK this only affects 64 bit python on Windows.
`numpy` wants an `np.intp` (i8 on Windows) as a indexer for `take`, but pandas defines a "platform int" as a `np.int_` (i4 on Windows). This hits performance twice, because we often start with i8, cast to i4, then numpy will cast back to i8 in its `take`.
This is an alternative to #13924 - there I explored replacing `ndarray.take` with our `take_nd` fully, but this approach solves the perf problem without much pain or risking new segfaults.
I'd still need to adjust a bunch of tests here to pass on Windows. This is an API change for "advanced" methods like `get_indexer`, but I don't think anything is necessary beyond a doc note?
ASV:
```
before after ratio
[4a80521 ] [3ced5d5 ]
3.49ms 2.86ms 0.82 indexing.series_take_dtindex.time_series_take_dtindex
3.28ms 3.03ms 0.92 indexing.series_take_intindex.time_series_take_intindex
15.16ms 14.57ms 0.96 join_merge.join_dataframe_index_single_key_bigger.time_join_dataframe_index_single_key_bigger
19.81ms 16.62ms 0.84 join_merge.join_dataframe_index_single_key_bigger_sort.time_join_dataframe_index_single_key_bigger_sort
14.05ms 13.63ms 0.97 join_merge.join_dataframe_index_single_key_small.time_join_dataframe_index_single_key_small
4.83ms 4.64ms 0.96 join_merge.join_dataframe_integer_2key.time_join_dataframe_integer_2key
1.80ms 1.63ms 0.90 join_merge.join_dataframe_integer_key.time_join_dataframe_integer_key
633.31us 570.37us 0.90 join_merge.join_non_unique_equal.time_join_non_unique_equal
2.39s 1.36s 0.57 join_merge.left_outer_join_index.time_left_outer_join_index
```
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| 170,798,180 |
MDU6SXNzdWUxNzA3OTgxODA=
| 13,973 |
df.sort_values by 2 columns does not work in py3.5.1 but work in py2?
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| 4 | 2016-08-12T04:44:36Z | 2018-02-13T14:09:50Z | 2018-02-13T12:01:21Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
The code above works in py2 but not works in py3.
#### Expected Output
should get sorted dataframe.
#### output of `pd.show_versions()`
0.18.0
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| 13,974 |
Is it normal that iloc is very slow when iterating?
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| 1 | 2016-08-12T09:45:24Z | 2016-08-12T13:54:44Z | 2016-08-12T12:01:10Z |
NONE
| null |
I'm honestly not sure if that is to be expected or if it's a general issue. It is a problem when doing row comparisons that can't be done with existing row-wise methods like diff.
#### Consider this example showing three functions doing row-wise differences on a randomly generated dataframe:
```
import pandas as pd
import random as rd
ITER = 1000
rd.seed()
D = {'A':[]}
for i in range(ITER):
D['A'].append(rd.randint(0,100))
P = pd.DataFrame.from_dict(D)
def Test1(P):
K = zip(P.A)
S = [0]
for i in range(ITER-1):
S.append(K[i+1][0] - K[i][0])
return pd.merge(P,pd.DataFrame(S),left_index=True,right_index=True)
def Test2(P):
S = [0]
for i in range(ITER-1):
S.append(P.iloc[i+1][0] - P.iloc[i][0])
return pd.merge(P,pd.DataFrame(S),left_index=True,right_index=True)
def Test3(P):
return pd.merge(P,P.A.diff().to_frame(),left_index=True,right_index=True)
```
Now of course Test3 is the way to do it correctly in this special case, but this is just meant as an example.
Here's the output of doing a timeit on all three methods:
```
%timeit(Test1(P))
1000 loops, best of 3: 2 ms per loop
%timeit(Test2(P))
1 loop, best of 3: 315 ms per loop
%timeit(Test3(P))
1000 loops, best of 3: 1.3 ms per loop
```
And it shows how extremely slow iterating over iloc is compared to iterating over a list that has been created using zip on the dataframe column.
#### output of `pd.show_versions()`
python: 2.7.12.final.0
pandas: 0.18.1
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| 170,841,172 |
MDExOlB1bGxSZXF1ZXN0ODEwOTY5MjM=
| 13,975 |
CLN: PeriodIndex to use nan related cache
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| 3 | 2016-08-12T10:16:44Z | 2016-08-12T13:18:22Z | 2016-08-12T12:59:52Z |
MEMBER
| null |
- [x] no tests added (all cases look to be covered) / passed
- [x] passes `git diff upstream/master | flake8 --diff`
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BUG: Kernel crashing when using shift with groupby when there are NaNs in group column
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| 4 | 2016-08-12T17:49:21Z | 2017-03-23T19:40:13Z | 2016-08-12T18:16:42Z |
NONE
| null |
Sometimes when shifting a variable by groups, if there are NaNs in the group column, it crashes my kernel. Sometimes the operation completes successfully, though it crashes over half the time.
#### Code Sample, a copy-pastable example if possible
```
from numpy import nan
import pandas as pd
from pandas import Timestamp
df = pd.DataFrame(data = [
(Timestamp('2003-01-15 00:00:00'), 1, 1),
(Timestamp('2003-01-15 00:00:00'), nan, nan),
(Timestamp('2003-02-14 00:00:00'), 1, 2),
], columns=['Date','ID','var'])
test.groupby('ID')['var'].shift(1) #crashes kernel sometimes
test.dropna(subset=['ID']).groupby('ID')['var'].shift(1) #does not crash kernel
test.groupby('ID')['var'].apply(lambda x: x) #does not crash kernel
```
#### Expected Output
```
0 NaN
1 NaN
2 1.0
Name: var, dtype: float64
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 24.0.2
Cython: 0.23.4
numpy: 1.11.1
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
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MDU6SXNzdWUxNzA5MzYwMjU=
| 13,977 |
df.iterrows() constructs Series instead of its proper subclass
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| 0 | 2016-08-12T18:54:42Z | 2016-08-26T20:04:20Z | 2016-08-26T20:04:20Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
``` py
import pandas as pd
import numpy as np
class SubclassedSeries(pd.Series):
pass
class SubclassedDataFrame(pd.DataFrame):
_constructor_sliced = SubclassedSeries
df = SubclassedDataFrame(np.random.random((3, 2))
i, row = next(df.iterrows())
assert isinstance(row, SubclassedSeries) # fails
```
#### Expected Output
`row` would be of proper overriden `_constructor_sliced` type (i.e. `SubclassedSeries`).
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MDExOlB1bGxSZXF1ZXN0ODExNjY4NTg=
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BUG: yield correct Series subclass in df.iterrows() (#13977)
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| 16 | 2016-08-12T19:00:44Z | 2016-08-26T20:04:20Z | 2016-08-26T20:04:20Z |
CONTRIBUTOR
| null |
- [x] closes #13977
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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API: change unique to return Index
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| 9 | 2016-08-13T01:39:54Z | 2016-08-29T12:29:13Z | 2016-08-29T12:29:04Z |
MEMBER
| null |
- [x] closes #13395, closes #13565
- [x] tests added / passed
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- [x] whatsnew entry
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BUG: ufunc with PeriodIndex may raise IncompatibleFrequency
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| 2 | 2016-08-13T01:49:45Z | 2016-08-18T22:27:51Z | 2016-08-18T22:27:43Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
`PeriodIndex` supportds addition / subtraction with integer.
```
# OK
pi = pd.PeriodIndex(['2011-01'], freq='M')
pi + 1
# PeriodIndex(['2011-02'], dtype='int64', freq='M')
pi - 1
# PeriodIndex(['2010-12'], dtype='int64', freq='M')
```
but raises if op is performed via ufunc.
```
# NG
np.add(pi, 1)
# pandas._period.IncompatibleFrequency: Input has different freq from PeriodIndex(freq=M)
np.subtract(pi, 1)
# pandas._period.IncompatibleFrequency: Input has different freq from PeriodIndex(freq=M)
```
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MDExOlB1bGxSZXF1ZXN0ODEyMDQzNzA=
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BUG: Index may ignore specified datetime/timedelta dtypes
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| 6 | 2016-08-13T02:18:54Z | 2016-08-15T16:06:57Z | 2016-08-13T22:34:12Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
`Index` ignores specified `dtype` if it is `datetime64` (normal and tz) or `timedelta64`. This PR makes it consistent with `DatetimeIndex` and `TimedeltaIndex`.
```
pd.Index([1, 2, 3], dtype='datetime64[ns, US/Eastern]')
# Index([1, 2, 3], dtype='object')
pd.Index([1, 2, 3], dtype='datetime64[ns]')
# Index([1, 2, 3], dtype='object')
pd.Index([1, 2, 3], dtype='timedelta64[ns]')
# Int64Index([1, 2, 3], dtype='int64')
```
Also, fixed `MultiIndex.get_level_values` not to pass unnecessary `tz` and `freq`.
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MDExOlB1bGxSZXF1ZXN0ODEyMDczMTA=
| 13,982 |
TST: Cleanup test_indexing
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| 4 | 2016-08-13T05:00:19Z | 2016-08-13T22:08:31Z | 2016-08-13T22:08:18Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
cleaned up `test_indexing` which is bit large, splitting `@slow` tests to a separate file.
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CLN: Datetimelike._can_hold_na
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| 11 | 2016-08-13T09:20:07Z | 2016-09-01T07:27:44Z | 2016-09-01T07:27:38Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] no whatsnew, as it is internal fix
datetimelike `Index._can_hold_na` is all `False` even though these can store `NaT`.
```
idx = pd.DatetimeIndex(['2011-01-01', 'NaT'])
idx.hasnans
# True
idx._can_hold_na
# False
idx._nan_idxs
# array([], dtype=int64)
```
Because of `test_base.py` condition below, datetime-like Indexes had skipped some tests until now.
- https://github.com/pydata/pandas/blob/master/pandas/tests/test_base.py#L219
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| 0 | 2016-08-13T09:23:29Z | 2021-05-01T23:14:34Z | null |
MEMBER
| null |
`Series/Index.nunique` has `dropna` kw to exclude `NaN`. Add the same kw to `.unique` also.
As #13979 changes `.unique` to always return `Index`, we can replace `Index._get_unique_index(dropna)` if this option is added.
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BUG: Sparse indexing with bool sparse may be incorrect
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| 2 | 2016-08-13T09:29:32Z | 2016-08-13T22:13:40Z | 2016-08-13T22:13:38Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
indexing with `SparseArray` with `bool` dtype may return incorrect result because of some internal conversions.
```
arr = pd.SparseArray([1, 2, 3])
indexer = pd.SparseArray([True, False, True], fill_value=False, dtype=bool)
# NG
arr[indexer]
# [2.0, 2.0]
# Fill: nan
# IntIndex
# Indices: array([0, 1], dtype=int32)
# Expected (after this PR)
arr[indexer]
# [1.0, 3.0]
# Fill: nan
# IntIndex
# Indices: array([0, 1], dtype=int32)
```
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| 7 | 2016-08-13T10:02:03Z | 2016-09-03T15:31:07Z | 2016-09-03T15:31:03Z |
MEMBER
| null |
- [x] closes #13107
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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CLN: move PeriodIndex.__getitem__ to DatetimeIndexOpsMixin
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| 2 | 2016-08-13T11:27:25Z | 2016-08-18T22:25:40Z | 2016-08-18T22:25:37Z |
MEMBER
| null |
- [x] related to #6469
- [x] tests added / passed
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merge `PeriodIndex.__getitem__` to `DatetimeIndexOpsMixin`
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| 8 | 2016-08-13T12:51:59Z | 2016-08-24T23:36:04Z | 2016-08-24T23:36:02Z |
MEMBER
| null |
- [x] split from #13941 (comment)
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This change itself needs more tests / perf testing, split from the original PR.
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DOC: suppress warnings for Panel4D deprecation
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| 3 | 2016-08-13T13:29:36Z | 2016-08-13T20:14:09Z | 2016-08-13T20:14:08Z |
MEMBER
| null |
Other options is to just remove those sections in the latest docs ... (they are always available in the older ones).
The example in the HDF5 docs with a legacy file will have to be updated to not include Panel4D, or the reading of such files will have to be adapted in the future to handle those when Panel4D is actually removed (@jreback)
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Use DeprecationWarning for core.common deprecations (GH13634)
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| 4 | 2016-08-13T13:30:16Z | 2016-08-13T22:28:26Z | 2016-08-13T22:22:28Z |
MEMBER
| null |
Related to second question in #13634 (whether to use FutureWarning or DeprecationWarning in deprecating the public pandas.core.common functions).
As those functions are mostly used in library code, and less directly by users in their own code, I think a DeprecationWarning is more appropriate in this case.
For example, in our own docs, we started to get warnings due to an example with a statsmodels regression that uses patsy using one of those functions. Note that recent IPython also shows DeprecationWarnings when using a deprecated function interactively.
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RLS: 0.19.0
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| 22 | 2016-08-13T13:57:52Z | 2016-10-03T08:51:55Z | 2016-10-03T08:51:55Z |
MEMBER
| null |
Issue to track the progress towards the 0.19.0 release.
cc @jreback @shoyer @sinhrks @TomAugspurger @wesm
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SeriesGroupby.cumsum raises on object dtype
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| 2 | 2016-08-13T17:50:13Z | 2021-05-01T23:15:15Z | null |
CONTRIBUTOR
| null |
```
import pandas as pd
s = pd.Series(list('ABCDEF'))
grouper = pd.Series([0]*3+[1]*3)
```
The obvious attempt to obtain
```
0 A
1 AB
2 ABC
3 D
4 DE
5 DEF
dtype: object
```
using
```
s.groupby(grouper).cumsum()
```
raises a `DataError: No numeric types to aggregate`. A workaround is available via
```
s.groupby(grouper).apply(pd.Series.cumsum)
```
`SeriesGroupby.cumsum` should follow the behavior of `SeriesGroupby.sum`, where both `s.groupby(grouper).apply(pd.Series.sum)` and `s.groupby(grouper).sum()` produce the correct output:
```
0 ABC
1 DEF
dtype: object
```
This was introduced some time between 0.15.2 and 0.18.1, as observed [here](http://stackoverflow.com/a/38935108/509824).
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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| 171,021,648 |
MDU6SXNzdWUxNzEwMjE2NDg=
| 13,993 |
groupby cumsum with axis=1 computes cumprod
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| 0 | 2016-08-13T18:36:50Z | 2016-08-14T00:15:47Z | 2016-08-14T00:15:47Z |
CONTRIBUTOR
| null |
#### Code sample
``` python
import pandas as pd
df = pd.DataFrame({'A': range(6), 'B': range(6)})
df.groupby('A').cumsum(axis=1)
```
#### Output
```
A B
0 0 0
1 1 1
2 2 4
3 3 9
4 4 16
5 5 25
```
#### Expected Output
```
A B
0 0 0
1 1 2
2 2 4
3 3 6
4 4 8
5 5 10
```
#### The culprit
A [single line](https://github.com/pydata/pandas/blob/29d9e24f4c778b0c9ebe9288bfc217808d2c6edb/pandas/core/groupby.py#L1400) in `pandas.core.groupby`:
``` python
def cumsum(self, axis=0, *args, **kwargs):
"""Cumulative sum for each group"""
nv.validate_groupby_func('cumsum', args, kwargs)
if axis != 0:
return self.apply(lambda x: x.cumprod(axis=axis)) # <==== should be `cumsum`
return self._cython_transform('cumsum')
```
(the result of a copy-paste oversight in [this commit](https://github.com/pydata/pandas/commit/b07dd0cbd6d18c55aaa0043d85f42a483eab7dbb#diff-720d374f1a709d0075a1f0a02445cd65))
#### output of `pd.show_versions()`
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INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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BUG: groupby cumsum with axis=1 computes cumprod
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| 6 | 2016-08-13T21:35:11Z | 2016-08-14T00:15:50Z | 2016-08-14T00:15:47Z |
CONTRIBUTOR
| null |
- [x] closes #13993
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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CLN: Move PeriodIndex.repeat to DatetimeIndexOpsMixin
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| 2 | 2016-08-13T21:49:47Z | 2016-08-14T00:10:54Z | 2016-08-14T00:10:51Z |
MEMBER
| null |
- [x] related to #6469
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
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TST: Fix unused tests in tseries/test_base.py
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| 2 | 2016-08-13T21:51:40Z | 2016-08-14T00:12:21Z | 2016-08-14T00:12:21Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- enabled unused patterns left as comments
- made `tz` related tests more comprehensive
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CLN: move _na_value to DatetimeIndexOpsMixin
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| 3 | 2016-08-13T21:54:19Z | 2016-08-15T12:29:13Z | 2016-08-15T12:29:13Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
Because `PeriodIndex` now shares the same `NaT` repr (#13609), can use the same definition on `DatetimeIndexOpsMixin`
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Fixed to_string with line_width and without index
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| 5 | 2016-08-14T04:02:31Z | 2016-08-15T17:53:50Z | 2016-08-15T17:53:24Z |
CONTRIBUTOR
| null |
When calling to_string on a DataFrame with line_width, this calls
_join_multiline internally which only sets the idx variable when
index is True, otherwise idx is never set resulting in the error:
UnboundLocalError: local variable 'idx' referenced before assignment
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API: SparseSeries comparison now returns sparse
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| 2 | 2016-08-14T13:38:35Z | 2016-08-20T13:32:42Z | 2016-08-20T13:32:42Z |
MEMBER
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- [x] tests added / passed
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- [x] whatsnew entry
Because #13985 has been fixed, `SparseSeries` comparison op now can return `SparseSeries` (it returns normal `Series` on current master).
also fixed a bug when `SparseArray` created from `SparseSeries` may not inherit `dtype`.
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ENH: bool sparse now supports logical op
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| 2 | 2016-08-14T13:43:22Z | 2016-08-16T09:57:03Z | 2016-08-16T09:56:41Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
implemented bool (logical and / or) op for bool `SparseArray`. Currently it raises if `sp_index` are different.
```
s1 = pd.SparseArray([True, True, True, True, True], dtype=np.bool, fill_value=False)
s2 = pd.SparseArray([False, True, False, True, True], dtype=np.bool, fill_value=False)
s1 & s2
# ValueError: operands could not be broadcast together with shapes (5,) (3,)
```
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| 14,001 |
BUG/API: Data assignment dtype inference inconsistencies
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| 2 | 2016-08-14T23:54:18Z | 2017-07-21T11:05:20Z | 2017-07-21T11:05:20Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
Assuming we have int `DataFrame` and assign float values.
- assigning 2-d float array keeps `int` columns if possible.
```
df = pd.DataFrame(np.zeros((3, 2), dtype=np.int64))
df.iloc[0:2, 0:2] = np.array([[1, 2.1], [2, 2.2]], dtype=np.float64)
df
# 0 1
#0 1 2.1
#1 2 2.2
#2 0 0.0
```
- but assigning 1-d array coerces to `float`, not keeps `int` .
```
df = pd.DataFrame(np.zeros((3, 2), dtype=np.int64))
df.iloc[0:2, 0] = np.array([1, 2], dtype=np.float64)
df
# 0 1
#0 1.0 0
#1 2.0 0
#2 0.0 0
```
#### Expected Output
I think 1st one should be all float, as we're assigning float dtype.
```
df = pd.DataFrame(np.zeros((3, 2), dtype=np.int64))
df.iloc[0:2, 0:2] = np.array([[1, 2.1], [2, 2.2]], dtype=np.float64)
df
# 0 1
#0 1.0 2.1
#1 2.0 2.2
#2 0.0 0.0
```
The inference occurs around [here](https://github.com/pydata/pandas/blob/master/pandas/core/internals.py#L744), and it should be something like:
```
if hasattr(value, 'dtype'):
if is_dtype_equal(values.dtype, value.dtype):
dtype = value.dtype
else:
dtype = _find_common_type(values.dtpye, value.dtype)
elif is_scalar(value):
dtype, _ = _infer_dtype_from_scalar(value)
else:
# not sure can reach here...
dtype = 'infer'
...
```
#### output of `pd.show_versions()`
0.18.1
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GH13347 ENH Allow true_values and false_values options in read_excel
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| 14 | 2016-08-15T00:47:17Z | 2017-12-12T15:39:45Z | 2016-09-13T23:07:26Z |
MEMBER
| null |
- [ X] closes #13347
- [ X] tests added / passed
- [ X] passes `git diff upstream/master | flake8 --diff`
- [ X] whatsnew entry
<img width="1016" alt="asv_tests" src="https://cloud.githubusercontent.com/assets/609873/17653246/3a3f842a-6260-11e6-8ed2-1d21cc2fccc8.png">
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DOC: doc/make.py uses IPython promptManager, which is removed from IPython 5.x
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[] | null | 1 | 2016-08-15T06:25:54Z | 2016-08-18T20:50:14Z | 2016-08-18T20:50:14Z |
CONTRIBUTOR
| null |
When I build document as described in contributing guideline below,
http://pandas-docs.github.io/pandas-docs-travis/contributing.html#building-the-documentation
it makes an error like this.
```
AttributeError: "'InteractiveShell' object has no attribute 'prompt_manager'"
```
This file uses removed class, from IPython 5.x (promptManager)
http://ipython.readthedocs.io/en/stable/whatsnew/version5.html
file: doc/sphinxext/ipython_sphinxext/ipython_directive.py
``` python
805 if not self.state.document.current_source in self.seen_docs:
806 self.shell.IP.history_manager.reset()
807 self.shell.IP.execution_count = 1
808 self.shell.IP.prompt_manager.width = 0
809 self.seen_docs.add(self.state.document.current_source)
810
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: a0d05dbf7477df863486d82a0cdd4e3e93023864
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: ko_KR.UTF-8
LOCALE: ko_KR.UTF-8
pandas: 0.18.1+356.ga0d05db.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
I'm currently on pandas sprint at PyCon APAC, so i will send pull request today if possible.
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| 4 | 2016-08-15T08:03:05Z | 2016-08-18T20:50:26Z | 2016-08-18T20:50:14Z |
CONTRIBUTOR
| null |
- [x] closes #14003, #13639
- [ ] tests added / passed (there's no test on building docs)
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry (it's not user facing bug fix)
this PR was made during PyCon APAC, pandas sprint
thanks
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BUG: No affect both index and column(GH 13522)
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| 4 | 2016-08-15T15:40:29Z | 2016-08-17T22:04:03Z | 2016-08-17T22:03:59Z |
CONTRIBUTOR
| null |
- [x] closes #13522
- [x] tests added / passed
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BUG: Avoid sentinel-infinity comparison problems (#13445)
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| 2 | 2016-08-16T01:59:10Z | 2016-08-16T10:32:47Z | 2016-08-16T10:15:30Z |
CONTRIBUTOR
| null |
The problem causing #13445 ultimately traces to the fact that our Infinity/NegInfinity objects were greater than/less than themselves, which violates an assumption numpy makes when sorting. This was separately reported as https://github.com/numpy/numpy/issues/7934, but we can fix and test downstream as well.
- [X] closes #13445
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
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DEPR: Deprecate SparseList
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| 2 | 2016-08-16T02:24:36Z | 2016-08-16T09:57:42Z | 2016-08-16T09:57:38Z |
MEMBER
| null |
Title is self-explanatory. Closes #13784.
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