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CLN: Removed the kwds param in to_csv
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| 5 | 2016-07-26T15:21:51Z | 2016-07-29T07:56:37Z | 2016-07-29T07:56:20Z |
MEMBER
| null |
We aren't using it anymore with the removal of parameters like `engine`. Closes #8206.
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ASV: fix params to be strings (for better repr)
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| 1 | 2016-07-26T18:29:31Z | 2016-07-27T10:19:32Z | 2016-07-27T10:19:31Z |
MEMBER
| null |
Small fix to using params in asv (otherwise the name was huge, list of 500000 elements)
cc @gfyoung (just so you know I changed this for future reference)
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MDU6SXNzdWUxNjc2Nzk3NjY=
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"Or" does not have commutative property (possibly because it catches exceptions silently?)
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| 13 | 2016-07-26T18:37:57Z | 2016-07-27T12:28:06Z | 2016-07-27T11:25:41Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
import pandas as pd
```
#Create sample dataframe
test = pd.DataFrame({"a":["12", float("nan"), "3", "meow" ]})
# Select rows that are null or numeric:
print len(test[ test["a"].isnull() | test["a"].str.isnumeric()])
# Select rows that are numeric or null
print len(test[ test["a"].str.isnumeric() | test["a"].isnull() ])
# These two should return the same result, right? Unfortunately they don't
```
Notice that `Series.str.isnumeric` throws an exception on null entries. However, I believe that this may introduce bugs silently
#### Expected Output
Expected output is:
2
2
Instead, I get:
3
2
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
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DOC: contributing: explain how to set the asv reporting threshold
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| 2 | 2016-07-26T19:41:47Z | 2016-07-27T09:37:44Z | 2016-07-27T09:37:43Z |
CONTRIBUTOR
| null |
The default `asv continuous` reporting threshold is 100% change (2x), which probably is far too large for pandas use case, so instruct how to set it lower. (Probably the asv default should be changed, but that's a different issue.)
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| 13,808 |
Initializing DataFrom from list of OrderedDicts does not preserve Column order
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| 2 | 2016-07-26T19:49:47Z | 2016-07-26T22:52:55Z | 2016-07-26T22:20:56Z |
NONE
| null |
``` python
>>> import pandas as pd
>>> from collections import OrderedDict
>>> od = [OrderedDict([('z', 0), ('a', 1), ('*', 2)])]
>>> pd.DataFrame(od)
* a z
0 2 1 0
>>> pd.DataFrame.from_dict(od)
* a z
0 2 1 0
```
If OrderedDicts are used, I would expect the column order of the first OrderDict that is encountered to be preserved, as the following:
``` python
>>> pd.DataFrame(od, columns=od[0].keys())
z a *
0 0 1 2
>>>
```
If different orders occur in the list, the earlier would overwrite the later. If new columns are introduced with subsequent list entries, I would expect all new columns to be appended to the original columns, with their relative order preserved.
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Why is there no mode method for groupby objects?
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| 2 | 2016-07-26T21:52:06Z | 2016-07-26T22:24:47Z | 2016-07-26T22:24:38Z |
NONE
| null |
There is `mean`, `max`, `median`, `min` and `mad` yet no `mode` for `groupby` objects. Is there a good reason? What if I add and issue pull-request?
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MDExOlB1bGxSZXF1ZXN0Nzg5NDk0MzE=
| 13,810 |
DOC: Fix groupby nth
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| 4 | 2016-07-27T00:48:19Z | 2016-07-29T00:28:27Z | 2016-07-29T00:28:21Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
Current API doc looks incorrect. Fixed it to meet #11039.
- http://pandas-docs.github.io/pandas-docs-travis/generated/pandas.core.groupby.GroupBy.nth.html
cc @behzadnouri
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specific-column-is-last
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| 7 | 2016-07-27T02:25:54Z | 2016-07-27T21:37:10Z | 2016-07-27T02:36:45Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
df.columns
Index([u'a', u'b', u'c', u'd'], dtype='object')
Dont do this, looks like a bug.
df.columns.drop(["b"]).insert(-1, 'b')
Index([u'a', u'c', u'b', u'd'], dtype='object')
df.columns.drop(["b"]).insert(-1, 'x')
Index([u'a', u'c', u'x', u'd'], dtype='object')
WORK Around:
df.columns.drop(["b"]).insert(99999, 'b')
Index([u'a', u'c', u'd', u'b'], dtype='object')
#### Expected Output
df.columns.drop(["b"]).insert(-1, 'b')
Index([u'a', u'c', u'd', u'b'], dtype='object')
#### output of `pd.show_versions()`
pandas 18.1
http://stackoverflow.com/questions/38601841/what-is-the-quickest-way-to-ensure-a-specific-column-is-last-or-first-in-a-dat/38602601#38602601
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.12-1-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 24.0.2
Cython: None
numpy: 1.11.1
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.1
sphinx: 1.4.4
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: 1.2.8
bottleneck: None
tables: None
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.2.3
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html5lib: 0.9999999
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apiclient: 1.4.2
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.1
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| 13,812 |
BUG, COMPAT: Fix multi-char sep + non-utf8 in read_csv for Python 2
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| 5 | 2016-07-27T04:07:34Z | 2016-07-28T23:43:44Z | 2016-07-28T23:43:39Z |
MEMBER
| null |
Title is self-explanatory. Closes #3404.
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Groupby and shift causing coredump
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| 3 | 2016-07-27T08:23:37Z | 2016-07-29T10:22:23Z | 2016-07-29T10:22:23Z |
NONE
| null |
Hi,
I have attempted to reduce this to the smallest example that exhibits this issue - rather than a useful example. The problem is that the operation causes python to core dump.
In the original case in which I discovered this the core dump would only occur sometimes (and when I put it in a loop it would occur on different iterations). This code seems to core dump on the third iteration every time I have run it.
Code example:
```
import os
import pandas as pd
df = pd.read_csv(os.path.join(os.getcwd(), 'error_report.txt'), sep='\t')
for i in range(0, 1000):
print "Pre shift {}".format(i)
df['shift_F'] = df.groupby(['B', 'C'])['F'].shift(-1)
print "Post shift {}".format(i)
```
With the attached data file (tab separated) - code assumes in the same directory:
[error_report.txt](https://github.com/pydata/pandas/files/385604/error_report.txt)
This the output I get:
```
python pandas_test.py
Pre shift 0
Post shift 0
Pre shift 1
Post shift 1
Pre shift 2
Segmentation fault (core dumped)
```
If I modify the code to use apply and then add the shifted column inside the apply function then there is no error. Similarly if I use .shift(0) I do not get the error.
Version info:
```
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: 1.3.1
pip: 8.1.2
setuptools: 20.2.2
Cython: None
numpy: 1.11.1
scipy: 0.13.3
statsmodels: 0.5.0
xarray: None
IPython: 1.2.1
sphinx: None
patsy: 0.2.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.6.1
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: 0.7.2.None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
Regards
Stephen
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TST: AmbiguousTimeError with set_index()
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| 3 | 2016-07-27T11:03:16Z | 2016-07-27T21:31:58Z | 2016-07-27T21:31:55Z |
MEMBER
| null |
- [x] closes #12920
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
this has been fixed by #13750. added tests.
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| 4 | 2016-07-27T11:06:35Z | 2016-07-28T23:42:08Z | 2016-07-28T23:41:41Z |
MEMBER
| null |
- [x] closes #13786
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
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| 167,835,617 |
MDU6SXNzdWUxNjc4MzU2MTc=
| 13,816 |
pd.Series.min(skipna=True) is not working on a slice
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[] | null | 2 | 2016-07-27T11:52:32Z | 2016-07-27T21:29:47Z | 2016-07-27T21:29:36Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
``` Python
s = pd.Series(data=[1, 3, datetime(2016,7,27).date(), datetime(2015,7,27).date(), datetime(2017,7,27).date(), np.nan])
s
0 1
1 3
2 2016-07-27
3 2015-07-27
4 2017-07-27
5 NaN
dtype: object
s.iloc[2:]
2 2016-07-27
3 2015-07-27
4 2017-07-27
5 NaN
dtype: object
s.iloc[2:].min(skipna=True)
# ...
# TypeError: can't compare datetime.date to float
```
#### Expected Output
`'2015-07-27'`
#### output of `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_GB.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.1
setuptools: 21.2.1
Cython: 0.24
numpy: 1.10.2
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.6.7.None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
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MDU6SXNzdWUxNjc4NDE5OTk=
| 13,817 |
BUG: df.to_dict('record') casts ints to floats
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[] | null | 2 | 2016-07-27T12:31:03Z | 2016-07-27T13:07:15Z | 2016-07-27T13:07:10Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame({"a": [1, 2], "b": [42, 65], "c": [0.5, 0.4]})
df.to_dict("records")
>>> [{'a': 1.0, 'b': 42.0, 'c': 0.5},
{'a': 2.0, 'b': 65.0, 'c': 0.40000000000000002}]
```
#### Expected Output
```
[{'a': 1, 'b': 42, 'c': 0.5},
{'a': 2, 'b': 65, 'c': 0.40000000000000002}]
```
#### comment
Same bug for `df.to_dict("index")` and `df.to_dict("split")`, but not for the rest. If all data is integer, the function works properly.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-38-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: pl_PL.UTF-8
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
pandas_datareader: None
```
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MDU6SXNzdWUxNjc4NDgzNzg=
| 13,818 |
DataFrame - Add chunksize to from_records()
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[] | null | 17 | 2016-07-27T13:03:50Z | 2021-05-01T22:47:08Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Currently the from_records does not take a chunksize.
```
import pandas as pd
df = pd.DataFrame.from_records(data, index=None, exclude=None,
columns=None,
coerce_float=False,
nrows=None)
```
#### Enhancement
I would like to see the chunksize options like in read_csv()
```
import pandas as pd
dfs = pd.DataFrame.from_records(data, index=None, exclude=None,
columns=None,
coerce_float=False,
nrows=None, chunksize=10000)
res = []
for df in dfs:
df['col'] = 'blah'
res.append(df)
df = pd.concat(res)
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 32
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.1
nose: 1.3.7
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: None
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext)
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| 13,819 |
BUG: group_shift_indexer checks for null group keys
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| 9 | 2016-07-27T13:07:58Z | 2016-07-29T10:23:06Z | 2016-07-29T10:22:23Z |
CONTRIBUTOR
| null |
- [x] closes #13813
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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ERR: better error message on invalid return from .apply
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| 4 | 2016-07-27T15:37:16Z | 2020-01-07T23:45:41Z | 2020-01-07T23:45:41Z |
NONE
| null |
#### Code Sample
``` python
def apply_list (row):
return [2*row['A'], 4*row['C'], 3*row['B']]
df = pd.DataFrame(np.random.randn(6,4), columns=list('ABCD'))
df['E'] = pd.Timestamp('20130102')
df['L'] = df.apply(apply_list, axis=1)
```
#### (Wrong) Error Output
```
ValueError: Shape of passed values is (6, 3), indices imply (6, 7)
```
#### Expected Result & Use case
Expected: a Serie containing a list for each row which are **in my real case the result of a matrix multiplication processed with values coming from other columns** (see a complete example in the next section)
Note: It works with a DataFrame without a datetime column (but not with a DataFrame with it):
```
0 [0.513057023122, -0.473155481431, -4.51039058299]
1 [-0.331758428452, 3.92166465759, 2.75920806524]
2 [-2.07257568656, 1.22070341071, 0.809676040678]
3 [3.38079201699, 2.77074189984, 1.4351938036]
4 [2.27838740024, 3.04253558763, 3.57504651688]
5 [1.55497385554, 8.01021203173, -2.22893240905]
```
#### Complete example with matrix multiplication
``` python
from numpy import pi, mat, cos, sin
def rotation(lon_rad, lat_rad):
c_lat = cos(lat_rad); s_lat = sin(lat_rad)
c_lon = cos(lon_rad); s_lon = sin(lon_rad)
R = mat([[-s_lon, -s_lat*c_lon, c_lat*c_lon],
[ c_lon, -s_lat*s_lon, c_lat*s_lon],
[ 0, c_lat, s_lat]])
return R.T
def row_rotation(row):
return rotation_ECEF_to_ENU(row['A']*pi/180.0, row['B']*pi/180).tolist()
df = pd.DataFrame(np.random.randn(6,4), columns=list('ABCD'))
# If I don't add the line below, I get what I want
df['E'] = pd.Timestamp('20130102')
df['M'] = df.apply(row_rotation, axis=1)
```
It works as I want if there is no datetime column in df:
```
A B C D \
0 0.498796 0.209267 -0.551132 -0.066941
1 -0.576161 -0.160802 0.895587 -0.355315
2 -0.604028 0.959712 -1.388824 -0.356734
3 -0.351012 0.242736 0.339068 -0.531425
4 -1.355900 0.058885 1.458610 1.891502
5 -0.560297 0.750459 1.288340 0.904650
M
0 [[-0.008705521998618918, 0.9999621062253967, 0...
1 [[0.010055737814803208, 0.9999494397903326, 0....
2 [[0.010542084651556644, 0.9999444306816251, 0....
3 [[0.006126282255403784, 0.9999812341567851, 0....
4 [[0.023662708414555287, 0.99971999891494, 0.0]...
5 [[0.00977887456308289, 0.9999521856630343, 0.0...
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-92-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 22.0.5
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.1
xlsxwriter: 0.8.9
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
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MDU6SXNzdWUxNjc4ODc2NDI=
| 13,821 |
'groupby' multiple columns and 'sum' multiple columns with different types
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| 7 | 2016-07-27T15:45:39Z | 2018-11-08T15:09:15Z | 2018-11-08T15:08:41Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
from decimal import *
import pandas as pd
df = pd.DataFrame(
{'name': ['foo', 'bar', 'foo', 'bar'],
'title': ['boo', 'far', 'boo', 'far'],
'id': [123, 456, 123, 456],
'int_column': [1, 2, 3, 4],
'dec_column1': [Decimal('0.50'), Decimal('0.15'), Decimal('0.25'), Decimal('0.40')],
'dec_column2': [Decimal('0.20'), Decimal('0.30'), Decimal('0.55'), Decimal('0.60')]
},
columns=['name','title','id','int_column','dec_column1','dec_column2']
)
df.groupby(['name', 'title', 'id'], as_index=False).sum()
```
#### Expected Output
i have dataframe that looks something like this...
| **name** | **title** | **id** | **int_column** | **dec_column1** | **dec_column2** |
...that has multiple rows with the same **name**, **title**, and **id**, but different values for the 3 number columns (**int_column**, **dec_column1**, **dec_column2**).
**int_column** == column of integers
**dec_column1** == column of decimals
**dec_column2** == column of decimals
I would like to be able to groupby the first three columns, and sum the last 3. I would expect to be able to do the following:
df = df.groupby(['name', 'title', 'id'], as_index=False).sum()
however, the only column that gets summed and ends up in the final dataframe is the int_column.
| **name** | **title** | **id** | **int_column** |
if i explicitly name the columns, i can get the statement to target the decimal columns either on their own or together....
df = df.groupby(['name', 'title', 'id'], as_index=False)['dec_column1'].sum()
returns...
| **name** | **title** | **id** | **dec_column1** |
and...
df = df.groupby(['name', 'title', 'id'], as_index=False)['dec_column1', 'dec_column2'].sum()
returns...
| **name** | **title** | **id** | **dec_column1** | **dec_column1** |
however...
df = df.groupby(['name', 'title', 'id'], as_index=False)['dec_column1', 'dec_column2', 'user_num'].sum()
or...
df = df.groupby(['name', 'title', 'id'], as_index=False)['dec_column1', 'user_num', 'dec_column2'].sum()
or...
df = df.groupby(['name', 'title', 'id'], as_index=False)['user_num', 'dec_column1', 'dec_column2'].sum()
returns...
| **name** | **title** | **id** | **int_column** |
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.11.1
scipy: None
statsmodels: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.3.5
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: 0.7.5.None
psycopg2: 2.5.5 (dt dec pq3 ext)
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MDU6SXNzdWUxNjc5MzA1NTc=
| 13,822 |
KeyError shows incorrect column name when DataFrame has duplicate columns
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| 3 | 2016-07-27T19:02:37Z | 2020-02-14T10:34:14Z | 2016-08-02T10:50:50Z |
CONTRIBUTOR
| null |
``` python
>>> import pandas as pd
>>> df = pd.DataFrame({'x': [1.], 'y': [2.], 'z': [3.]})
>>> df.columns = ['x', 'x', 'z']
>>> df[['x', 'y', 'z']]
KeyError: "['z'] not in index"
```
I expected to see `KeyError: "['y'] not in index"`.
I've tested this on the latest code in master (and on 0.16):
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-400.1.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB
LOCALE: None.None
pandas: 0.18.1+279.g31f8e4d
nose: None
pip: 1.3.1
setuptools: 0.6
Cython: 0.22
numpy: 1.9.2
scipy: None
statsmodels: None
xarray: None
IPython: 3.2.0-1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.6
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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| 13,823 |
API: Add support for PEP 519
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| 3 | 2016-07-27T23:24:55Z | 2017-05-18T21:06:27Z | 2017-05-18T21:06:27Z |
NONE
| null |
File System Path Protocol has been accepted and implemented into Python 3.6, a backwards compatibility layer to support the introduced PEP 519 for python version < 3.6.
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Mixing bars and lines in a time series plot clobbers the xaxis tick values?
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| 1 | 2016-07-28T05:52:17Z | 2016-07-28T06:13:20Z | 2016-07-28T06:12:38Z |
NONE
| null |
All rendered in a jupter notebook with `%matplotlib inline`
The following non-time series plot works fine
``` python
dd = pd.DataFrame({'a':[-1,1,2], 'b':[3,2,3]})
fig, ax = plt.subplots()
dd.a.plot.bar(ax=ax)
dd.b.plot(ax=ax)
```
The following timeseries variant results in the bars not appearing on the plot, but the y_lim range showing it formed part of the rendering.
``` python
rng = pd.period_range('1/1/2000', '3/30/2000', freq='M')
dd = pd.DataFrame({'a':[-1,1,2], 'b':[3,2,3]}, index=rng)
fig, ax = plt.subplots()
dd.a.plot.bar(ax=ax)
dd.b.plot(ax=ax)
```
And swapping the plot order shows that the dates for the bars are being set to the epoch - with the line not being rendered - but the line tick labels are (along with the epoch dates for the bars).
``` python
rng = pd.period_range('1/1/2000', '3/30/2000', freq='M')
dd = pd.DataFrame({'a':[-1,1,2], 'b':[3,2,3]}, index=rng)
fig, ax = plt.subplots()
dd.b.plot(ax=ax)
dd.a.plot.bar(ax=ax)
```
I'm guessing the xaxis tick values for the bar variable are being set to commence at zero, resulting in the the rendered dates to commence at the epoch.
But the xaxis values for the variable plotted as a line do not change, but are now at a different value to the bar variable.
Condsequently, only the line or the bar is rendered, depending on the plotting order.
I'm guessing the issue is in the time series converter.py module.
With some guidance, I'm happy to try and pin it down further. I do have spare time at the moment.
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Feature suggestion: flexible merge by function
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[] | null | 4 | 2016-07-28T09:09:11Z | 2019-10-09T05:12:40Z | 2019-10-09T05:12:40Z |
NONE
| null |
It would be really convenient to allow the **on**, **left_on** and **right_on** arguments of **pandas.merge** being a function, with the following suggested signatures:
Arguments:
- **index** (pandas.Index)
- **row** (pandas.Series)
They will return an hashable object (of any dtype) to be compared against the other dataframe when doing the merge.
For example, you will be able to the the following:
`pd.merge(df1, df2, on_left = 'first_name', on_right = lambda index, row: parse_first_name(row['full_name']))`
This way, although **df2** doesn't have a **first_name** column (only **full_name**), it will be mergable with **df1** without any preprocessing step.
I would also like to suggest that leaving **on**, **left_on** and **right_on** with **None** would result the full Cartesian product of the two dataframes.
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| 13,826 |
Feature suggestion: custom DataFrame HTML display (for IPython notebook)
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[] | null | 8 | 2016-07-28T09:44:51Z | 2017-08-10T10:49:28Z | 2017-08-10T10:49:28Z |
NONE
| null |
I looked briefly at your new custom styling feature from version 0.17.1 (http://pandas.pydata.org/pandas-docs/stable/style.html). I'm excited about the possibilities of custom styling and all the benefits it would allow researchers, but I feel that the current interface is somewhat non-intuitive and limited.
I would like to suggest a single generic feature that has the potential to include almost all other existing features, and allow a lot more flexibility. The idea is to allow overriding the default function that "draws" the elements in the dataframe as HTML table cells.
Let's say that this drawing function is `df.style.draw_element` and it receives the following arguments:
- **value** (any object/dtype): the value of the element to display
- **column** (str): the name of the value's column
- **row** (pandas.Series): the value of the entire element's row (if one wants to make a context-dependent drawing)
Additionally you can also add **index** and **df** as arguments, if you want to allow even more flexible context-dependent drawing.
The default built-in implementation of this function is just:
`def draw_element(value, column, row): return str(value)`
(i.e. every element is just displayed as it is)
But now, it will allow users to override it and add their own cool functionality. For example, one could do the following:
`df.style.draw_element = lambda: value, column, row: str(value) if value >= 0 else '<div style = "background-color: red;">%s</div>' % str(value)`
This will highlight in red all the negative values in the table.
This trick is already achievable with the current interface, but here's another example for some cool application that I don't think can be achieved with the current interface. Suppose that a bioinformatician working with DNA sequences (made of the four letters A, C, G, T) is interested in drawing each DNA letter with a different color (although all the letters are part of a single sequence within the same element in the dataframe). Using the new interface she will be able to simply do the following:
`df.style.draw_element = lambda: value, column, row: ''.join(['<span style = "color: %s;">%s</span>' % (get_dna_letter_color(letter), letter) for letter in value]) if 'dna' in column.lower() else str(value)`
How cool is that? :)
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COMPAT: Categorical Subclassing
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| 5 | 2016-07-28T12:52:08Z | 2016-08-04T10:36:57Z | 2016-08-04T10:36:54Z |
MEMBER
| null |
- [x] related to #8640
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
It allows to subclass `Categorical` related to #8640. Shoud it use `_simple_new` or `_constructor`?
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MDU6SXNzdWUxNjgxMDExNjc=
| 13,828 |
to_html ignores na_rep when float_format set
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| 1 | 2016-07-28T13:53:34Z | 2020-10-24T02:49:08Z | 2020-10-24T02:49:08Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
http://stackoverflow.com/questions/23700835/pandas-to-html-no-value-representation
df = pd.DataFrame([['A',1.2225],['A',]], columns=['Group','Data'])
df.to_html(na_rep="Ted",float_format='{0:.2f}'.format)
#### Expected Output
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Group</th>
<th>Data</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td> A</td>
<td>1.22</td>
</tr>
<tr>
<th>1</th>
<td> A</td>
<td> Ted</td>
</tr>
</tbody>
</table>
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.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: 0.18.0
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
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Incorrect timedelta type coercion when doing in-place .loc expansion
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| 2 | 2016-07-28T15:00:13Z | 2019-10-21T19:52:16Z | null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
``` python
In [1]: import pandas as pd
In [2]: d = pd.DataFrame(columns=list('abc'))
In [3]: d.loc[0] = dict(a=pd.to_timedelta(5, unit='s'),
b=pd.to_timedelta(72, unit='s'),
c='23')
In [4]: d
Out[4]:
a b c
0 00:00:05 00:01:12 00:00:00.000000
In [5]: d.c
Out[5]:
0 00:00:00.000000
Name: c, dtype: timedelta64[ns]
```
#### Expected Output
```
a b c
0 00:00:05 00:01:12 23
```
The `c` column should have type `object` and the value at `loc[0]` should be of type string.
Note that this does _not_ happen when only one of the `a` or `b` are timedeltas:
``` python
In [9]: d2 = pd.DataFrame(columns=list('abc'))
In [10]: d2.loc[0] = dict(a=5, b=pd.to_timedelta(72, unit='s'), c='23')
In [11]: d2
Out[11]:
a b c
0 5.0 00:01:12 23
In [12]: d3 = pd.DataFrame(columns=list('abc'))
In [13]: d3.loc[0] = dict(a=pd.to_timedelta(5, unit='s'), b=72, c='23')
In [14]: d3
Out[14]:
a b c
0 00:00:05 72.0 23
```
In these two cases, the `c` column retains the correct type.
#### output of `pd.show_versions()`
```
In [6]: 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: 1.3.7
pip: 8.1.2
setuptools: 20.1.1
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.2
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.0
matplotlib: 1.5.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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| 13,830 |
Inconsistent types in output of series.to_dict() and DataFrame([series]).loc[0].to_dict()
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[] | null | 3 | 2016-07-28T15:36:10Z | 2016-07-28T16:41:28Z | 2016-07-28T16:40:17Z |
NONE
| null |
`to_dict()` extracts the elements from a Series as different types depending on whether or not the series was accessed by, e.g. `loc[0]` on a DataFrame or not:
``` python
>>> s = pd.Series({'a': None, 'b': 99, 'c': 'hello'})
>>> df = pd.DataFrame([s])
>>> [type(v) for k, v in s.to_dict().items()]
[NoneType, str, int]
>>> [type(v) for k, v in df.loc[0].to_dict().items()]
[NoneType, str, numpy.int64]
```
Note that the number is a base int when extracted with `s.to_dict()`, but it's a `numpy.int64` when extracted from `df.loc[0]`. The same inconsistency applies to `tolist()`.
Is this inconsistency a feature or a bug? And if it's a feature, does anyone know how do I reliably extract the values of a row from a DataFrame in base python types, using either `to_dict()` or `tolist()`?
#### output of `pd.show_versions()`
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-56-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 1.5.6
setuptools: 12.2
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 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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Series.loc/iloc[x, y] does not raise exception
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| 4 | 2016-07-28T23:21:52Z | 2020-03-30T18:49:31Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame({'a': [10]})
# df['a'] is a series, can be indexed with 1 index only
# will raise IndexingError, as expected
df['a'].iloc[0, 0]
df['a'].loc[0, 0]
# will raise nothing, not as expected
df['a'].iloc[0, 0] = 1000 # equivalent to pass
df['a'].loc[0, 0] = 1000 # equivalent to df['a'].loc[0] = 1000
```
See [this SO post](http://stackoverflow.com/a/38644472/336527)
#### Expected Output
Expected an IndexingError exception in every case where two values are used to index a Series.
#### output of `pd.show_versions()`
pandas version 0.18.1, python 3.5
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MDExOlB1bGxSZXF1ZXN0NzkzMjQyNzI=
| 13,832 |
BUG: Don't error in pd.to_timedelta when errors=ignore
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| 35 | 2016-07-29T08:32:20Z | 2016-08-15T22:27:42Z | 2016-08-15T22:27:20Z |
MEMBER
| null |
Title is self-explanatory. Closes #13613.
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TST: A test to cover fault in issue #5291
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| 2 | 2016-07-29T11:56:44Z | 2017-11-06T02:01:14Z | 2016-08-02T05:37:04Z |
CONTRIBUTOR
| null |
The mentioned issue faulted under similar conditions as issue #13703, and was also fixed by PR #13788. This PR just adds a test to replicate the fault, as discussed in the issue's thread.
- [x] closes #5291
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This PR also corrects accidental use of **float("nan")** instead of **np.nan** in [`test_parse_trim_buffers`](https://github.com/pydata/pandas/blob/master/pandas/io/tests/parser/c_parser_only.py#L442).
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[BUG]: Wrong unix timestamp parsing with floating point using pd.to_datetime
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| 3 | 2016-07-29T12:48:05Z | 2016-08-09T21:48:25Z | 2016-08-09T21:48:25Z |
NONE
| null |
Hello!
I'm trying to parse floating point timestamps with `pd.to_datetime` but the only way that it works is using the **deprecated parameter** `coerce=True`
For example, without `coerce=True` the result is wrong.
``` python
>>> pd.to_datetime(1.1, unit='s') # Expected result Timestamp('1970-01-01 00:00:01.100000')
Timestamp('1970-01-01 00:00:01')
```
However, with `coerce=True` the result is correct.
``` python
>>> pd.to_datetime(1.1, unit='s', coerce=True) # Expected result Timestamp('1970-01-01 00:00:01.100000')
Timestamp('1970-01-01 00:00:01.100000')
```
I'm happy to help if you need more details.
#### Code Sample (copy-pastable example)
``` python
import pandas as pd
expected_result = pd.Timestamp('1970-01-01 00:00:01.100000')
timestamp1 = pd.to_datetime(1.1, unit='s')
timestamp2 = pd.to_datetime(1.1, unit='s', coerce=True)
print("Result without coerce: {}".format(timestamp1 == expected_result))
print("Result with coerce: {}".format(timestamp2 == expected_result))
```
#### pd.show_versions() output
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.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: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 19.2
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: None
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
```
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| 168,357,108 |
MDExOlB1bGxSZXF1ZXN0NzkzODI3Mjg=
| 13,836 |
BUG: Fix edge cases in merge_asof() by comparing factorized keys (#13709)
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| 16 | 2016-07-29T15:57:25Z | 2016-08-01T20:56:59Z | 2016-08-01T20:56:54Z |
CONTRIBUTOR
| null |
closes #13709
Also removes unnecessary check_duplicates.
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MDU6SXNzdWUxNjgzNjE0Nzc=
| 13,839 |
Problem reading one-column files with guessed separator
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[] | null | 9 | 2016-07-29T16:19:20Z | 2016-08-01T15:09:12Z | 2016-08-01T14:47:52Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Create a simple csv file:
----- C:\myfile.csv
name
1
2
3
4
----- C:\myfile.csv
And run:
pd.read_csv("C:\myfile.csv", encoding = "ISO-8859-1", sep = None, engine = "python")
This triggers an error:
ValueError: Expected 2 fields in line 2, saw 1
If a specific separator is entered, it works.
#### Expected Output
The file should be imported correctly.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 8.1
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: fr_FR
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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MDU6SXNzdWUxNjgzNjcyNDM=
| 13,841 |
Arithmetic Broadcasting
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[] | null | 3 | 2016-07-29T16:49:07Z | 2016-07-29T17:06:41Z | 2016-07-29T16:52:00Z |
NONE
| null |
Okay, so the following "new" syntax is fine.
import pandas as pd
import random as randy
df = pd.DataFrame({'A': range(1000), 'B': randy.sample(range(1000),1000)},\
index=randy.sample(pd.date_range('2013-02-01 09:00:00.000000',periods=1e6,freq='U'),1000)).sort_index()
df.sub(df['A'],axis=0)
But, at some point, this has been broken:
df - df['A']
When did this overload change?
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| 168,386,177 |
MDU6SXNzdWUxNjgzODYxNzc=
| 13,842 |
.loc on Hierarchical Index with single-valued index level can drop that index level in place
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| 32 | 2016-07-29T18:31:41Z | 2018-11-17T22:55:26Z | 2018-11-17T22:55:25Z |
NONE
| null |
### Small Example
``` python
In [13]: import pandas as pd
...:
...: df1 = pd.DataFrame(data=dict(A=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
...: B=[1, 1, 2, 2, 2, 3, 1, 1, 1, 2, 3, 4],
...: C=[1, 2, 1, 2, 3, 2, 1, 2, 3, 2, 1, 4],
...: X=[1, 5, 2, 3, 8, 3, 3, 3, 1, 2, 1, 4],
...: Y=[7, 3, 4, 1, 3, 9, 9, 3, 1, 9, 3, 7]))
...: df1 = df1.set_index(['A', 'B', 'C'])
...:
In [14]: df1.loc[pd.IndexSlice[1, :, :]]
Out[14]:
X Y
B C
1 1 1 7
2 5 3
2 1 2 4
2 3 1
3 8 3
.. .. ..
1 2 3 3
3 1 1
2 2 2 9
3 1 1 3
4 4 4 7
[12 rows x 2 columns]
In [15]: df1
Out[15]:
X Y
B C
1 1 1 7
2 5 3
2 1 2 4
2 3 1
3 8 3
.. .. ..
1 2 3 3
3 1 1
2 2 2 9
3 1 1 3
4 4 4 7
[12 rows x 2 columns]
```
### Expected Output
The output of the slice `Out[14]` is correct, but `df1` should not be modified inplace. So the expected `Out[15]` is the original `df1`:
``` python
In [17]: df1
Out[17]:
X Y
A B C
1 1 1 1 7
2 5 3
2 1 2 4
2 3 1
3 8 3
... .. ..
1 2 3 3
3 1 1
2 2 2 9
3 1 1 3
4 4 4 7
[12 rows x 2 columns]
```
---
I'm still not good at submitting issues here with code and print out, so I appreciate your patience. Also, thank you guys for making pandas as amazing as it is!!
Anyhow...
I have dataframes that sometimes have up to 5 levels on their multiindex. It's not uncommon for me to want to just grab a subset containing only one value on a certain level. If one level of that index has only one value, then .loc can drop that level inplace. I'd say this is highly undesirable.
First the normal behavior. Here's my input:
```
X Y
A B C
1 1 1 1 7
2 5 3
2 1 2 4
2 3 1
3 8 3
3 2 3 9
2 1 1 3 9
2 3 3
3 1 1
2 2 2 9
3 1 1 3
4 4 4 7
```
When I have a multi-indexed dataframe, and I do:
df.loc[1]
I get:
```
X Y
B C
1 1 1 7
2 5 3
2 1 2 4
2 3 1
3 8 3
3 2 3 9
```
I personally expect it to return the original multi-index where the first level has only that value. Sadly, it drops it entirely ( I think this is terrible, since if you plan on resetting the index or concatenating later, you've just unwittingly lost information).
Anyhow, I recognize now that you need to provide an index for all levels, e.g., the way I expected it to work can actually be achieved by (for a three level index):
df.loc[pd.IndexSlice[1, :, :]]
```
X Y
A B C
1 1 1 1 7
2 5 3
2 1 2 4
2 3 1
3 8 3
3 2 3 9
```
Here's the rub... If the level that I indexed above has more than one unique value, this works fine. If **it has only one, then once again that level gets dropped, but worse, the index is modified in place during the .loc operation.**
Here's the dataframe showing the bad behavior:
```
X Y
A B C
1 1 1 1 7
1 3 9
2 5 3
2 3 3
3 1 1
2 1 2 4
2 3 1
2 2 9
3 8 3
3 1 1 3
2 3 9
4 4 4 7
```
df.loc[pd.IndexSlice[1, :, :]] gives:
```
X Y
B C
1 1 1 7
1 3 9
2 5 3
2 3 3
3 1 1
2 1 2 4
2 3 1
2 2 9
3 8 3
3 1 1 3
2 3 9
4 4 4 7
```
Same syntax as the other case, but it dropped index A. Worse is that this is now df.
print(df)
```
X Y
B C
1 1 1 7
1 3 9
2 5 3
2 3 3
3 1 1
2 1 2 4
2 3 1
2 2 9
3 8 3
3 1 1 3
2 3 9
4 4 4 7
```
If I modify the syntax slightly. I.e., df.loc[pd.IndexSlice[1, :, :], :] (with the original not modifed frame, I get the expected result:
```
X Y
A B C
1 1 1 1 7
1 3 9
2 5 3
2 3 3
3 1 1
2 1 2 4
2 3 1
2 2 9
3 8 3
3 1 1 3
2 3 9
4 4 4 7
```
I've tried to provide a code sample with comments that demonstrates the problem.
### Code Sample, a copy-pastable example if possible
```
import pandas as pd
df1 = pd.DataFrame(data=dict(A=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
B=[1, 1, 2, 2, 2, 3, 1, 1, 1, 2, 3, 4],
C=[1, 2, 1, 2, 3, 2, 1, 2, 3, 2, 1, 4],
X=[1, 5, 2, 3, 8, 3, 3, 3, 1, 2, 1, 4],
Y=[7, 3, 4, 1, 3, 9, 9, 3, 1, 9, 3, 7]))
df2 = df1.copy(deep=True)
df2['A'] = [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2]
df1 = df1.set_index(['A', 'B', 'C']).sortlevel()
df2 = df2.set_index(['A', 'B', 'C']).sortlevel()
df1_copy = df1.copy()
print("Here's df2, with more than 1 unique value for the index A:")
print(df2)
# already annoyed by this, I don't think this is how it should work, but I understand it
print("\nHere's what df2.loc[1] returns")
print(df2.loc[1])
# understand how to get around it at least
print("\nCan get around this annoyance by df2.loc[pd.IndexSlice[1, :, :]]")
print(df2.loc[pd.IndexSlice[1, :, :]])
# BUT! If it's the only one...
print("\nHere's df1, with only a single value for the index A")
print(df1)
print("\nNow let's do the same thing we did for df2, namely display df1.loc[pidx[1, :, :]]")
print(df1.loc[pd.IndexSlice[1, :, :]])
# and holy crap it's an inplace operation!
print("\nDamnit.. it dropped by index again! And... ruh roh! It has a side effect! Here's df1 again:")
print(df1)
print("\nDoing df1.loc[pidx[1, :, :], :] (using the original df1) works as expected.")
print(df1_copy.loc[pd.IndexSlice[1, :, :], :])
```
#### Here's what I get from running the code
```
Here's df2, with more than 1 unique value for the index A:
X Y
A B C
1 1 1 1 7
2 5 3
2 1 2 4
2 3 1
3 8 3
3 2 3 9
2 1 1 3 9
2 3 3
3 1 1
2 2 2 9
3 1 1 3
4 4 4 7
Here's what df2.loc[1] returns
X Y
B C
1 1 1 7
2 5 3
2 1 2 4
2 3 1
3 8 3
3 2 3 9
Can get around this annoyance by df2.loc[pd.IndexSlice[1, :, :]]
X Y
A B C
1 1 1 1 7
2 5 3
2 1 2 4
2 3 1
3 8 3
3 2 3 9
Here's df1, with only a single value for the index A
X Y
A B C
1 1 1 1 7
1 3 9
2 5 3
2 3 3
3 1 1
2 1 2 4
2 3 1
2 2 9
3 8 3
3 1 1 3
2 3 9
4 4 4 7
Now let's do the same thing we did for df2, namely display df1.loc[pidx[1, :, :]]
X Y
B C
1 1 1 7
1 3 9
2 5 3
2 3 3
3 1 1
2 1 2 4
2 3 1
2 2 9
3 8 3
3 1 1 3
2 3 9
4 4 4 7
Damnit.. it dropped by index again! And... ruh roh! It has a side effect! Here's df1 again:
X Y
B C
1 1 1 7
1 3 9
2 5 3
2 3 3
3 1 1
2 1 2 4
2 3 1
2 2 9
3 8 3
3 1 1 3
2 3 9
4 4 4 7
Doing df1.loc[pidx[1, :, :], :] (using the original df1) works as expected.
X Y
A B C
1 1 1 1 7
1 3 9
2 5 3
2 3 3
3 1 1
2 1 2 4
2 3 1
2 2 9
3 8 3
3 1 1 3
2 3 9
4 4 4 7
```
#### Expected Output
What I expect from all of the examples above, is:
```
X Y
A B C
1 1 1 1 7
2 5 3
2 1 2 4
2 3 1
3 8 3
3 2 3 9
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.6.3-300.fc24.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.0.3
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.10
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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MDU6SXNzdWUxNjg0MTA4ODM=
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BUG/API Series/Index integer floordiv/mod inconsistency
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| 3 | 2016-07-29T20:45:39Z | 2019-07-11T14:32:15Z | 2019-07-11T14:32:15Z |
MEMBER
| null |
`Series` looks to be coerced to `float` if the result contains `NaN` or `inf`. Otherwise `Index` doesn't. Make it to be consistent in either way.
#### Code Sample, a copy-pastable example if possible
```
pd.Series([1]) // pd.Series([0])
#0 inf
# dtype: float64
pd.Index([1]) // pd.Index([0])
# Int64Index([0], dtype='int64')
np.array([1]) // np.array([0])
# array([0])
```
```
pd.Series([1]) % pd.Series([0])
#0 NaN
# dtype: float64
pd.Index([1]) % pd.Index([0])
# Int64Index([0], dtype='int64')
np.array([1]) % np.array([0])
# array([0])
```
#### output of `pd.show_versions()`
on current master.
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Empty dataset: typerror ufunc add cannot use operands with types dtype('<M8[ns]') and dtype('O')
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| 4 | 2016-07-29T21:03:30Z | 2016-08-26T20:43:20Z | 2016-08-26T20:43:20Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
``` python
from pandas import DataFrame, to_datetime, to_timedelta
data = {'Item': [1], 'Date_Time': ['2/8/2015 6:00:30'], 'Relative_Time': 20}
dtf = DataFrame(data=data)
print(dtf)
dtf = dtf[dtf.Item == 2] # if the filter returns empty dataset expected results will be that the same
dtf.Date_Time = to_datetime(dtf.Date_Time, infer_datetime_format=True)
dtf.Relative_Time = to_timedelta(dtf.Relative_Time, unit='s')
dtf.Date_Time = dtf.Date_Time + dtf.Relative_Time
print(dtf)
```
#### Current Output
```
Date_Time Item Relative_Time
0 2/8/2015 6:00:30 1 20
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/ops.py:477: PerformanceWarning: Adding/subtracting array of DateOffsets to Series not vectorized
"Series not vectorized", PerformanceWarning)
Traceback (most recent call last):
File "bug.py", line 12, in <module>
dtf.Date_Time = dtf.Date_Time + dtf.Relative_Time
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/ops.py", line 641, in wrapper
arr = na_op(lvalues, rvalues)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/ops.py", line 481, in <lambda>
self.na_op = lambda x, y: getattr(x, self.name)(y)
TypeError: ufunc add cannot use operands with types dtype('<M8[ns]') and dtype('O')
```
#### Expected Output
```
Date_Time Item Relative_Time
0 2/8/2015 6:00:30 1 20
Empty DataFrame
Columns: [Date_Time, Item, Relative_Time]
Index: []
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 24.0.3
Cython: None
numpy: 1.11.1
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
None
statsmodels: None
xarray: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
None
```
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| 168,416,363 |
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| 13,845 |
BUG: fixes 13822, incorrect KeyError string with non-unique columns w…
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| 2 | 2016-07-29T21:17:14Z | 2016-08-02T10:50:51Z | 2016-08-02T10:50:50Z |
CONTRIBUTOR
| null |
- [x] closes #13822
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Made changes to fix the bug related to accessing columns in a df with duplicate columns where the KeyError string was returning the wrong column.
The test that I added passed, but there seems to be a test that won't pass and I wasn't sure what's going on. Even on a clean master, when I run "nosetests pandas/tests/indexing/test_indexing.py" I get "ERROR: test_float64index_slicing_bug (pandas.tests.indexing.test_indexing.TestIndexing)".
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ENH: add sort_categories argument to union_categoricals
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| 6 | 2016-07-29T21:48:04Z | 2016-08-03T10:24:16Z | 2016-08-03T10:24:10Z |
CONTRIBUTOR
| null |
- needed for #13406, follow-up to #13763
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
cc: @janschulz, @sinhrks
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BUG: Fixed float parsing with unit when using pd.to_datetime (GH13834)
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| 8 | 2016-07-29T23:40:31Z | 2016-08-09T21:48:40Z | 2016-08-09T21:48:25Z |
CONTRIBUTOR
| null |
- [x] closes #13834
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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ENH: add sparse op for int64 dtypes
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| 9 | 2016-07-30T10:02:48Z | 2016-08-04T10:40:28Z | 2016-08-03T22:48:47Z |
MEMBER
| null |
- [x] related to #667
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
As a first step for #667, numeric op can now preserve `int64` dtype. On current master, dtype is reset to `float64` after op.
```
# current master
a = pd.SparseArray([1, 2], dtype=np.int64)
a.dtype
# dtype('int64')
(a + a).dtype
# dtype('float64')
```
**NOTE:** `int64` `SparseSeries.__floordiv__` test is skipped because dense `Series` also has inconsistency in `nan/inf` handling (#13843). Currently it outputs the same result as `float64`.
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MDExOlB1bGxSZXF1ZXN0Nzk0NTQwNTA=
| 13,849 |
ENH: Sparse int64 and bool dtype support enhancement
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| 19 | 2016-07-30T10:26:34Z | 2016-09-01T01:33:05Z | 2016-08-31T07:57:34Z |
MEMBER
| null |
- [x] closes #667, closes #8292, closes #13001, closes #8276, closes #13110
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Currently, sparse doesn't support `int64` and `bool` dtypes actually. When `int` or `bool` values are passed, it is coerced to `float64` if `dtype`kw is not explicitly specified.
**on current master**
```
pd.SparseArray([1, 2, 0, 0 ])
# [1.0, 2.0, 0.0, 0.0]
# Fill: nan
# IntIndex
# Indices: array([0, 1, 2, 3], dtype=int32)
pd.SparseArray([True, False, True])
# [1.0, 0.0, 1.0]
# Fill: nan
# IntIndex
# Indices: array([0, 1, 2], dtype=int32)
```
**after this PR**
The created data should have the `dtype` of passed values (as the same as normal `Series`).
```
pd.SparseArray([1, 2, 0, 0 ])
# [1, 2, 0, 0]
# Fill: 0
# IntIndex
# Indices: array([0, 1], dtype=int32)
pd.SparseArray([True, False, True])
# [True, False, True]
# Fill: False
# IntIndex
# Indices: array([0, 2], dtype=int32)
```
Also, `fill_value` is automatically specified according to the following rules (because `np.nan` cannot appear in `int` or `bool` dtype):
Basic rule: sparse `dtype` must not be changed when it is converted to dense.
- If `sparse_index` is specified and data has a hole (missing values):
- `fill_value` is np.nan
- `dtype` is `float64` or `object` (which can store both `data` and `fill_value`)
- If `sparse_index` is None (all values are provided via `data`, no missing values)
- if `fill_value` is not explicitly passed, following default will be used depending on its dtype.
- `float`: `np.nan`
- `int`: `0`
- `bool`: `False`
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MDExOlB1bGxSZXF1ZXN0Nzk0NjA2NTU=
| 13,850 |
Allow users to specify whether gbq should use standard SQL
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| 10 | 2016-07-30T15:54:14Z | 2016-08-03T13:10:49Z | 2016-08-03T10:30:31Z |
CONTRIBUTOR
| null |
- [ x ] closes #13615
- [ x ] tests added / passed
- [ x ] passes `git diff upstream/master | flake8 --diff`
- [ x ] whatsnew entry
All gbq tests passed locally
```
tony@tonypc:~/parthea-pandas/pandas/io/tests$ nosetests test_gbq.py -v
test_import_google_api_python_client (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_read_gbq_with_corrupted_private_key_json_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_read_gbq_with_empty_private_key_file_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_read_gbq_with_empty_private_key_json_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_read_gbq_with_invalid_private_key_json_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_read_gbq_with_no_project_id_given_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_read_gbq_with_private_key_json_wrong_types_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_return_bigquery_booleans_as_python_booleans (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_return_bigquery_floats_as_python_floats (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_return_bigquery_integers_as_python_floats (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_return_bigquery_strings_as_python_strings (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_return_bigquery_timestamps_as_numpy_datetime (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_that_parse_data_works_properly (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_to_gbq_should_fail_if_invalid_table_name_passed (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_to_gbq_with_no_project_id_given_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_be_able_to_get_a_bigquery_service (pandas.io.tests.test_gbq.TestGBQConnectorIntegration) ... ok
test_should_be_able_to_get_results_from_query (pandas.io.tests.test_gbq.TestGBQConnectorIntegration) ... ok
test_should_be_able_to_get_schema_from_query (pandas.io.tests.test_gbq.TestGBQConnectorIntegration) ... ok
test_should_be_able_to_get_valid_credentials (pandas.io.tests.test_gbq.TestGBQConnectorIntegration) ... ok
test_should_be_able_to_make_a_connector (pandas.io.tests.test_gbq.TestGBQConnectorIntegration) ... ok
test_should_be_able_to_get_a_bigquery_service (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyContentsIntegration) ... ok
test_should_be_able_to_get_results_from_query (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyContentsIntegration) ... ok
test_should_be_able_to_get_schema_from_query (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyContentsIntegration) ... ok
test_should_be_able_to_get_valid_credentials (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyContentsIntegration) ... ok
test_should_be_able_to_make_a_connector (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyContentsIntegration) ... ok
test_should_be_able_to_get_a_bigquery_service (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyPathIntegration) ... ok
test_should_be_able_to_get_results_from_query (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyPathIntegration) ... ok
test_should_be_able_to_get_schema_from_query (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyPathIntegration) ... ok
test_should_be_able_to_get_valid_credentials (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyPathIntegration) ... ok
test_should_be_able_to_make_a_connector (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyPathIntegration) ... ok
test_bad_project_id (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_bad_table_name (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_column_order (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_column_order_plus_index (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_download_dataset_larger_than_200k_rows (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_index_column (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_malformed_query (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_arbitrary_timestamp (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_empty_strings (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_false_boolean (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_null_boolean (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_null_floats (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_null_integers (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_null_strings (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_null_timestamp (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_timestamp_unix_epoch (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_true_boolean (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_valid_floats (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_valid_integers (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_valid_strings (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_read_as_service_account_with_key_contents (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_read_as_service_account_with_key_path (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_standard_sql_query (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_unicode_string_conversion_and_normalization (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_zero_rows (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_create_dataset (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_create_table (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_dataset_does_not_exist (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_dataset_exists (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_delete_dataset (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_delete_table (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_generate_schema (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_google_upload_errors_should_raise_exception (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_list_dataset (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_list_table (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_list_table_zero_results (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_table_does_not_exist (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_upload_data (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_upload_data_if_table_exists_append (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_upload_data_if_table_exists_fail (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_upload_data_if_table_exists_replace (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_upload_data_as_service_account_with_key_contents (pandas.io.tests.test_gbq.TestToGBQIntegrationServiceAccountKeyContents) ... ok
test_upload_data_as_service_account_with_key_path (pandas.io.tests.test_gbq.TestToGBQIntegrationServiceAccountKeyPath) ... ok
pandas.io.tests.test_gbq.test_requirements ... ok
pandas.io.tests.test_gbq.test_generate_bq_schema_deprecated ... ok
----------------------------------------------------------------------
Ran 75 tests in 404.905s
OK
```
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MDU6SXNzdWUxNjg0NzY3OTU=
| 13,851 |
DataFrame.rolling.apply does not appear to support kwargs
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[] | null | 1 | 2016-07-30T17:00:25Z | 2016-07-30T17:31:05Z | 2016-07-30T17:18:13Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy as np
def subtract_custom_value(x, custom_value, debug=False):
return x-custom_value
# this works (no rolling)
series = pd.Series([20, 21, 12])
series.apply(subtract_custom_value, args=(5,), debug=True)
# this complains "TypeError: apply() got an unexpected keyword argument 'debug'"
rows, cols = 20, 1
df = pd.DataFrame(np.random.randint(0, rows, size=(rows, cols)), columns=map(str,range(cols)))
df.rolling(10).apply(subtract_custom_value, args=(5,), debug=True)
```
#### Expected Output
Would expect the rolling case to be the same as non-rolling.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.0.2
setuptools: 23.0.0
Cython: 0.23.1
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.8.0rc1
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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| 168,481,230 |
MDU6SXNzdWUxNjg0ODEyMzA=
| 13,852 |
Series.replace and DataFrame.replace have same docstring?
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| 19 | 2016-07-30T18:46:14Z | 2018-02-04T16:32:53Z | 2018-02-04T16:32:53Z |
NONE
| null |
```
In [4]: pd.__version__
Out[4]: u'0.18.1'
```
The docstring for Series.replace refers to looking up column names, which AFAIK doesn't make sense for the Series version of `replace`. It seems like the Series function is just blindly inheriting the DataFrame docstring?
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.replace.html?highlight=replace#pandas.Series.replace
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.replace.html?highlight=replace#pandas.DataFrame.replace
For example, the following trivial example of `Series.replace()` does not agree with the docstring:
```
import pandas as pd
x = pd.DataFrame([dict(A=1, B=2, C=3), dict(A=2, B=3, C=4), dict(A=3, B=4, C=5)])
print(x.A.replace({1:0}))
print(x.A)
0 0
1 2
2 3
Name: A, dtype: int64
0 1
1 2
2 3
Name: A, dtype: int64
```
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BUG: ufunc is not applied to sparse.fill_value
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| 4 | 2016-07-30T22:01:42Z | 2016-08-03T22:42:32Z | 2016-08-03T22:42:32Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
When ufunc is applied to sparse, it is not applied to `fill_value`. Thus results are incorrect.
**on current master:**
```
np.abs(pd.SparseArray([1, -2, -1], fill_value=-2))
# [1.0, -2, 1.0]
# Fill: -2
# IntIndex
# Indices: array([0, 2], dtype=int32)
np.add(pd.SparseArray([1, -2, -1], fill_value=-2), 1)
# [2.0, -2, 0.0]
# Fill: -2
# IntIndex
# Indices: array([0, 2], dtype=int32)
```
cc @gfyoung
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| 13,854 |
BUG: Preserve categorical dtypes in MultiIndex levels (#13743)
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| 17 | 2016-07-31T00:08:00Z | 2016-09-07T13:24:30Z | 2016-09-02T22:08:10Z |
CONTRIBUTOR
| null |
- [x] closes #13743
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This commit modifies `MultiIndex.from_array` and `MultiIndex.from_product`.
Example:
``` python
cat = pd.Categorical(['a', 'b'], categories=list("bac"), ordered=True)
mi = pd.MultiIndex.from_arrays([cat, cat])
mi.levels[0]
Out[55]: CategoricalIndex(['b', 'a', 'c'], categories=['b', 'a', 'c'], ordered=True, dtype='category')
mi.get_level_values(0)
Out[56]: CategoricalIndex(['a', 'b'], categories=['b', 'a', 'c'], ordered=True, dtype='category')
```
Previously, the results were:
``` python
mi.levels[0]
Out[345]: Index(['b', 'a', 'c'], dtype='object')
mi.get_level_values(0)
Out[346]: Index(['a', 'b'], dtype='object')
```
This modification makes `groupby`, `pivot`, and `set_index` preserve categorical types in indexes.
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CLN: Removed trellis rplot
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| 12 | 2016-07-31T04:30:11Z | 2016-08-02T10:46:40Z | 2016-08-02T10:46:38Z |
MEMBER
| null |
xref https://github.com/pydata/pandas/pull/9357.
Title is self-explanatory.
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TST: Use pytest
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| 53 | 2016-07-31T13:32:45Z | 2017-04-05T02:07:20Z | 2017-02-10T15:52:09Z |
CONTRIBUTOR
| null |
- [x] closes https://github.com/pydata/pandas/issues/13097
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Just a WIP for now, will need to iterate on the CI stuff to ensure that's all good.
What can I provide to make this easier to review? Refactoring tests is scary.
I'll give verbose outputs of nose on master and pytest on this branch.
junit.xml output too. Anything else?
Here are some notes I took along the way
### Kinds of Changes
- Nosetests Generators to fixtures
See TestPickle, TestMsgPack and [this](http://stackoverflow.com/questions/6689537/nose-test-generators-inside-class) SO post. We switched to using parametrized fixtures (yay) and dependency injection instead of setUp + a for loop inside the test.
- Different Discover Rules
See the sql tests. `py.test` discovers `_Test*` class (if they inherit from `TestCase`?), while unittest does not.
Solution: move TestCase inheritance down a level
- Assert Rewriting
See e.g. pandas/src/testing.py
- solution: rewrite `assert cond, msg` as `if not cond; raise AssertionError(msg)`
- looking into just excluding these files... They aren't actual tests, just test helpers.
- CI work:
- updating nose to pytest
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CLN: Removed colSpace parameter
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| 2 | 2016-07-31T16:47:22Z | 2016-08-01T20:58:45Z | 2016-08-01T20:58:38Z |
MEMBER
| null |
Title is self-explanatory. Seems like it was already done in `DataFrame.to_string()` some time ago.
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BUG: disallow 'w' mode in pd.read_hdf (#13623)
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| 6 | 2016-07-31T18:38:17Z | 2016-08-01T19:22:46Z | 2016-08-01T19:22:43Z |
CONTRIBUTOR
| null |
- [x] closes #13623
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Passing mode='w' to read_hdf raises ValueError.
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MAINT: Nicer error msg for NULL byte in read_csv
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| 2 | 2016-07-31T20:54:47Z | 2016-08-01T21:17:16Z | 2016-08-01T21:17:12Z |
MEMBER
| null |
Provides a nicer error message for the Python engine in `read_csv` when the data contains a `NULL` byte.
Closes #2741.
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MDU6SXNzdWUxNjg1NDI2NDk=
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`read_csv` with character `"`
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| 1 | 2016-07-31T22:49:18Z | 2016-07-31T23:34:27Z | 2016-07-31T23:33:47Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Input file: (content of `example` file)
```
a1 b1 c1 d1
" c y z
a3 b3 c3 d3
" d4 s4 d4
```
Code:
``` python
extb = pd.read_csv('example', encoding='utf8', sep='\t', header=None); extb[:]
```
Output:
```
0 1 2 3
0 a1 b1 c1 d1
1 \tc\ty\tz\na3\tb3\tc3\td3\n d4 s4 d4
```
#### Expected Output
```
a1 b1 c1 d1
" c y z
a3 b3 c3 d3
" d4 s4 d4
```
#### Question: Can i control input file that have character `"` ?
|
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BUG: loffset not applied when using resample with agg() (GH13218)
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| 12 | 2016-08-01T02:13:15Z | 2016-08-22T22:39:12Z | 2016-08-13T22:46:07Z |
CONTRIBUTOR
| null |
- [x] closes #13218
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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Add setting for switching between "production" and "interactive" mode
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[] | null | 10 | 2016-08-01T10:27:46Z | 2021-05-01T22:52:38Z | 2021-05-01T22:52:38Z |
NONE
| null |
It seems that future development in Pandas creates a distinction between "production" and "interactive" use. I frequently use Pandas to do interactive data analyses. As such, things like indexing shortcuts are very handy. However, I also can see that some of this magic can create very subtle bugs in production systems.
One potential solution is to have a global configuration switch. When an analysis project starts, many of these "shortcut" functions could be made available. Once the code is ready, the user can change this setting to "production" which would issue warnings for functions deemed unsafe.
Any thoughts on this?
This issue came up while talking to @jreback on #13548 .
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COMPAT: windows builds breaking after #13788
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| 6 | 2016-08-01T10:52:02Z | 2016-08-01T20:00:45Z | 2016-08-01T20:00:45Z |
CONTRIBUTOR
| null |
see [here](https://github.com/pydata/pandas/pull/13788#issuecomment-236409748)
looks like a pointer specification, see #13788
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CLN: Removed / filled stub read_csv tests
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| 2 | 2016-08-01T11:00:41Z | 2016-08-01T19:21:44Z | 2016-08-01T19:21:39Z |
MEMBER
| null |
Title is self-explanatory.
xref #4782
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BLD: Fixed pointer ambiguity in 'tokenizer.c'
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| 6 | 2016-08-01T13:07:23Z | 2016-08-01T20:01:29Z | 2016-08-01T20:00:45Z |
CONTRIBUTOR
| null |
- [x] closes build compatibility issue #13863
- [x] tests added / passed (see notes)
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry (No whatsnew entry was necessary)
## notes
The branch was tested on win7 64bit virtual machine with anaconda 4.1.1 both with python 2.7 (VS2008) and with python 3.5 (VS2015). Running **test.bat** returned this on each platform:
### python 2.7
```
======================================================================
FAIL: test_constructor_bad_file (pandas.io.tests.test_common.TestMMapWrapper)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\vmuser\pandas\pandas\io\tests\test_common.py", line 110, in tes
t_constructor_bad_file
tm.assertRaisesRegexp(err, msg, common.MMapWrapper, non_file)
File "C:\Users\vmuser\pandas\pandas\util\testing.py", line 2373, in assertRais
esRegexp
_callable(*args, **kwargs)
File "C:\Users\vmuser\pandas\pandas\util\testing.py", line 2398, in __exit__
return self.handle_success(exc_type, exc_value, traceback)
File "C:\Users\vmuser\pandas\pandas\util\testing.py", line 2411, in handle_suc
cess
raise_with_traceback(e, traceback)
File "C:\Users\vmuser\pandas\pandas\util\testing.py", line 2373, in assertRais
esRegexp
_callable(*args, **kwargs)
File "C:\Users\vmuser\pandas\pandas\io\common.py", line 355, in __init__
self.mmap = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
AssertionError: "The parameter is incorrect" does not match "[Error 87] "
----------------------------------------------------------------------
Ran 10634 tests in 494.796s
FAILED (SKIP=148, failures=1)
```
**pd.show_versions()**
```
INSTALLED VERSIONS
------------------
commit: 3b0e28e255c0319bc19397e07493bb92b4190d53
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.18.1+291.g3b0e28e
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
pandas_datareader: None
```
### python 3.5
```
======================================================================
ERROR: test_to_latex_filename (pandas.tests.formats.test_format.TestDataFrameFor
matting)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\vmuser\pandas\pandas\tests\formats\test_format.py", line 2800,
in test_to_latex_filename
df.to_latex(path)
File "C:\Users\vmuser\pandas\pandas\core\frame.py", line 1666, in to_latex
encoding=encoding)
File "C:\Users\vmuser\pandas\pandas\formats\format.py", line 658, in to_latex
latex_renderer.write_result(f)
File "C:\Users\vmuser\pandas\pandas\formats\format.py", line 894, in write_res
ult
buf.write(' & '.join(crow))
File "C:\Anaconda2\envs\py35\lib\encodings\cp1251.py", line 19, in encode
return codecs.charmap_encode(input,self.errors,encoding_table)[0]
UnicodeEncodeError: 'charmap' codec can't encode character '\xdf' in position 7:
character maps to <undefined>
======================================================================
FAIL: test_constructor_bad_file (pandas.io.tests.test_common.TestMMapWrapper)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Users\vmuser\pandas\pandas\util\testing.py", line 2373, in assertRais
esRegexp
_callable(*args, **kwargs)
File "C:\Users\vmuser\pandas\pandas\io\common.py", line 355, in __init__
self.mmap = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
OSError: [WinError 87] Параметр задан неверно
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\vmuser\pandas\pandas\io\tests\test_common.py", line 110, in tes
t_constructor_bad_file
tm.assertRaisesRegexp(err, msg, common.MMapWrapper, non_file)
File "C:\Users\vmuser\pandas\pandas\util\testing.py", line 2373, in assertRais
esRegexp
_callable(*args, **kwargs)
File "C:\Users\vmuser\pandas\pandas\util\testing.py", line 2398, in __exit__
return self.handle_success(exc_type, exc_value, traceback)
File "C:\Users\vmuser\pandas\pandas\util\testing.py", line 2411, in handle_suc
cess
raise_with_traceback(e, traceback)
File "C:\Users\vmuser\pandas\pandas\compat\__init__.py", line 333, in raise_wi
th_traceback
raise exc.with_traceback(traceback)
File "C:\Users\vmuser\pandas\pandas\util\testing.py", line 2373, in assertRais
esRegexp
_callable(*args, **kwargs)
File "C:\Users\vmuser\pandas\pandas\io\common.py", line 355, in __init__
self.mmap = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
AssertionError: "The parameter is incorrect" does not match "[WinError 87] Параметр задан неверно"
----------------------------------------------------------------------
Ran 10634 tests in 437.328s
FAILED (SKIP=146, errors=1, failures=1)
```
**pd.show_versions()**
```
INSTALLED VERSIONS
------------------
commit: 3b0e28e255c0319bc19397e07493bb92b4190d53
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.18.1+291.g3b0e28e
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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MDExOlB1bGxSZXF1ZXN0Nzk1Njc5MTY=
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BUG: SparseDataFrame may not preserve passed dtype
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| 2 | 2016-08-01T13:27:30Z | 2016-08-03T10:18:07Z | 2016-08-03T10:18:04Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
`SparseDataFrame` may reset passed sparse data's `dtype` and `fill_value` as below.
**on current master:**
```
arr = pd.SparseArray([1, 0, 3, 0], dtype=np.int64, fill_value=0)
# OK, dtype and fill_value are kept
s = pd.SparseSeries(arr, name='x')
s.dtype, s.fill_value
(dtype('int64'), 0)
# NG, dtype is reset
df = pd.SparseDataFrame(s)
df['x'].dtype, df['x'].fill_value
(dtype('float64'), 0)
# NG, dtype is reset
df = pd.SparseDataFrame({'x': s})
df['x'].dtype, df['x'].fill_value
(dtype('float64'), 0)
# NG, dtype and fill_value are reset
df = pd.SparseDataFrame({'x': arr})
df['x'].dtype, df['x'].fill_value
(dtype('float64'), nan)
```
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MDExOlB1bGxSZXF1ZXN0Nzk1NzI3NDI=
| 13,867 |
TST: use types.api in plotting
|
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| 2 | 2016-08-01T13:58:59Z | 2016-08-01T19:20:34Z | 2016-08-01T19:20:30Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
remove warning related to #13147 and #13621
|
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| 13,868 |
DEPR: Remove legacy offsets
|
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| 9 | 2016-08-01T14:15:12Z | 2016-08-02T10:21:23Z | 2016-08-02T10:21:21Z |
CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Follow-up to #13590. Remove legacy offset aliases that remained in `pandas/tseries/frequencies.py`: `_period_alias_dictionary()` and `_period_alias_dict`.
|
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| 13,869 |
BUG: DataFrame.loc[i:j] works differently for DatetimeIndex
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| 7 | 2016-08-01T15:36:54Z | 2019-05-13T00:28:41Z | 2019-05-13T00:28:41Z |
CONTRIBUTOR
| null |
This code all "works," but in a surprising way:
```
import pandas as pd
t0 = 1234567890123400000
df1 = pd.DataFrame(index=pd.DatetimeIndex([t0, t0 + 1000, t0 + 2000, t0 + 3000]))
df2 = pd.DataFrame(index=range(4))
df1.loc[:2, 'a'] = np.arange(2)
df2.loc[:2, 'a'] = np.arange(3)
```
We create `df1` with a DatetimeIndex and `df2` with an integer index. We then create a new column `a` in each, using `.loc[]` with an integer slice. With `df1` we get the intuitive, normal Python slice behavior where `[:2]` means "the first 2 elements", whereas with `df2` we get the bizarre—but [documented](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.loc.html)—`DataFrame.loc` slice behavior where `[:2]` means "elements up to index 2, inclusive."
I don't see why the type of index the DataFrame has should affect the semantics of slicing with `.loc[]`. I happen to think the exclusive-end behavior is correct in all cases, though apparently Pandas has decided (or at least documented) that `.loc[]` slicing is inclusive (in which case the DatetimeIndex case looks like a bug).
Also note that trying to "read" `df1.loc[:2, 'a']` (e.g. to print it) fails, saying:
```
TypeError: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'>
with these indexers [2] of <class 'int'>
```
It's sort of strange that you can assign to this slice but not read from it.
I'm using Pandas 0.18.1.
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MDU6SXNzdWUxNjg2ODY2ODU=
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DOC: link to install instructions in README.md
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| 6 | 2016-08-01T16:25:53Z | 2017-03-06T11:31:27Z | 2017-03-06T11:31:16Z |
NONE
| null |
_edit: the install instructions in the github README.md are out of date. They should link to the install instructions on the website_
---
I'm not sure what the expectation is here, but it seems to me that something's not right... The [installation instructions](https://github.com/pydata/pandas/#where-to-get-it) say that `easy_install` has access to binary packages. However, `easy_install` fails if the appropriate compiler is not available. Would it be possible to either clarify the instructions to explain that binary packages are not available via `easy_install`, or fix the package to work without a compiler?
#### Code Sample, a copy-pastable example if possible
```
C:\> easy_install.exe pandas
```
#### Expected Output
Pandas should be successfully installed
#### Actual Output
`error: Setup script exited with error: Microsoft Visual C++ 9.0 is required (Unable to require vcvarsall.bat). Get it from http://aka.ms/vcpython27`
#### output of `pd.show_versions()`
`easy_install` is trying to install `pandas-0.18.1`.
However, it is not succeeding. As a result, `import pandas as pd` throws `ImportError` and I cannot run this command.
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MDU6SXNzdWUxNjg2OTcwMzY=
| 13,871 |
ERR: more informative error messages on invalid freq for Period when they are point-in-time
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| 4 | 2016-08-01T17:19:59Z | 2021-05-07T20:41:10Z | null |
CONTRIBUTOR
| null |
`Period` and `PeriodIndex` raise `ValueError` when `freq` is any of the following strings:
```
'BM', 'CBM', 'MS', 'BMS', 'CBMS', 'BQ', 'QS', 'BQS', 'BA', 'AS', 'BAS', 'BH'
```
`date_range` is fine in all cases. Also confirmed on `master`.
Possible related to #4878.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
FREQS = ['B', 'C', 'D', 'W', 'M', 'BM', 'CBM', 'MS', 'BMS', 'CBMS', 'Q',
'BQ', 'QS', 'BQS', 'A', 'BA', 'AS', 'BAS', 'BH', 'H', 'T', 'min',
'S', 'L', 'U', 'N']
for freq in FREQS:
try:
pd.Period('2016-01-01', freq=freq)
except ValueError:
print('Cannot create Period with freq={}'.format(freq))
p = pd.Period('2016-01-01', freq='N')
try:
p.asfreq(freq)
except ValueError:
print('Cannot modify Period to freq={}'.format(freq))
try:
pd.PeriodIndex(start='2016-01-01', periods=10, freq=freq)
except ValueError:
print('Cannot create PeriodIndex with freq={}'.format(freq))
pi = pd.PeriodIndex(start='2016-01-01', periods=10, freq='N')
try:
pi.asfreq(freq)
except ValueError:
print('Cannot modify PeriodIndex to freq={}'.format(freq))
try:
pd.date_range(start='2016-01-01', periods=10, freq=freq)
except ValueError:
print('Cannot create date_range with freq={}'.format(freq))
```
#### Output
```
Cannot create Period with freq=BM
Cannot modify Period to freq=BM
Cannot create PeriodIndex with freq=BM
Cannot modify PeriodIndex to freq=BM
Cannot create Period with freq=CBM
Cannot modify Period to freq=CBM
Cannot create PeriodIndex with freq=CBM
Cannot modify PeriodIndex to freq=CBM
Cannot create Period with freq=MS
Cannot modify Period to freq=MS
Cannot create PeriodIndex with freq=MS
Cannot modify PeriodIndex to freq=MS
Cannot create Period with freq=BMS
Cannot modify Period to freq=BMS
Cannot create PeriodIndex with freq=BMS
Cannot modify PeriodIndex to freq=BMS
Cannot create Period with freq=CBMS
Cannot modify Period to freq=CBMS
Cannot create PeriodIndex with freq=CBMS
Cannot modify PeriodIndex to freq=CBMS
Cannot create Period with freq=BQ
Cannot modify Period to freq=BQ
Cannot create PeriodIndex with freq=BQ
Cannot modify PeriodIndex to freq=BQ
Cannot create Period with freq=QS
Cannot modify Period to freq=QS
Cannot create PeriodIndex with freq=QS
Cannot modify PeriodIndex to freq=QS
Cannot create Period with freq=BQS
Cannot modify Period to freq=BQS
Cannot create PeriodIndex with freq=BQS
Cannot modify PeriodIndex to freq=BQS
Cannot create Period with freq=BA
Cannot modify Period to freq=BA
Cannot create PeriodIndex with freq=BA
Cannot modify PeriodIndex to freq=BA
Cannot create Period with freq=AS
Cannot modify Period to freq=AS
Cannot create PeriodIndex with freq=AS
Cannot modify PeriodIndex to freq=AS
Cannot create Period with freq=BAS
Cannot modify Period to freq=BAS
Cannot create PeriodIndex with freq=BAS
Cannot modify PeriodIndex to freq=BAS
Cannot create Period with freq=BH
Cannot modify Period to freq=BH
Cannot create PeriodIndex with freq=BH
Cannot modify PeriodIndex to freq=BH
```
#### 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: 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: None
pandas_datareader: None
None
```
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| 168,731,211 |
MDU6SXNzdWUxNjg3MzEyMTE=
| 13,872 |
ERR: set errno in read_csv
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| 6 | 2016-08-01T20:12:12Z | 2021-05-17T15:17:30Z | 2021-05-17T15:17:29Z |
NONE
| null |
The same is true for Pandas on Python 2.7 with except IOError and on Python 3.5 with OSError. The following is how to reproduce the bug on Python 3.5.
Pandas is not reporting any errno within the exceptions. As a result, there is no way to differentiate between File Not Found Error (ENOENT) and other types of OSErrors.
#### Code Sample
```
try:
pd.read_csv("dummy_file")
except OSError as e:
print(e.errno, errno.ENOENT)
```
#### Current Output
(None, 2)
#### Expected Output
(2, 2)
#### 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: 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
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
pandas_datareader: None
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.iloc[:] and .loc[:] return the original object
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| 8 | 2016-08-01T20:36:03Z | 2017-06-14T23:03:28Z | 2017-06-14T23:03:27Z |
MEMBER
| null |
Each of these asserts should fail:
``` python
# on pandas 0.18.1
import pandas as pd
df = pd.DataFrame({'x': range(3)})
assert df.iloc[:] is df
assert df.loc[:] is df
series = df.x
assert series.iloc[:] is series
assert series.loc[:] is series
```
Instead, for the sake of predictability, these should always return a new DataFrame/Series, even if they reuse the same data. See [here](https://github.com/pydata/pandas/issues/13842#issuecomment-236685599) for an example of how this resulted in a user facing bug within pandas.
Thanks @mborysow for pointing this out.
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BUG: Fix Period and PeriodIndex support of combined offsets aliases
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| 21 | 2016-08-01T21:16:57Z | 2016-08-08T14:12:32Z | 2016-08-08T14:11:31Z |
CONTRIBUTOR
| null |
- [x] closes #13730
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Essentially, makes sure any `freq` string passed to a `Period` or `PeriodIndex` goes through [`_Period._maybe_convert_freq()`](https://github.com/pydata/pandas/blob/master/pandas/src/period.pyx#L682-L697), which calls [`to_offset()`](https://github.com/pydata/pandas/blob/master/pandas/tseries/frequencies.py#L389-L451), which is where the logic for combining aliases is.
All the examples in #13730 result in the correct output, and all existing tests pass. I have not written any new ones yet — I first wanted to get the opinion of the maintainers.
This PR builds on #13868 (without it, some existing tests fail).
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| 12 | 2016-08-01T22:02:20Z | 2020-09-22T22:33:48Z | 2020-09-22T22:33:47Z |
CONTRIBUTOR
| null |
It's not an issue in py3.5, but in older versions this is annoying - because a decorator is used in the definition, the function signature on `to_datetime` is lost. This is the big one I've run into, but there may be some other places in the API it comes up.
I saw this blog today - could use one of the strategies there. It may make sense to vendor rather than adding another dependency?
https://hynek.me/articles/decorators/
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BUG: Series creation with datetime64 with non-ns unit as object dtype
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| 2 | 2016-08-01T23:50:25Z | 2016-08-02T10:14:12Z | 2016-08-02T10:14:09Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
found a bug related to `datetime64` with non-ns unit.
```
# OK
pd.Series(np.array([np.datetime64('2011-01-01')]))
#0 2011-01-01
# dtype: datetime64[ns]
# OK
pd.Series([np.datetime64('2011-01-01')], dtype=object)
#0 2011-01-01
# dtype: object
# NG
pd.Series(np.array([np.datetime64('2011-01-01')]), dtype=object)
#0 1970-01-01 00:00:00.000014
# dtype: object
# OK
pd.Series(np.array([np.datetime64('2011-01-01', 'ns')]), dtype=object)
#0 2011-01-01 00:00:00
# dtype: object
```
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| 8 | 2016-08-02T01:37:11Z | 2019-06-01T15:03:08Z | 2019-06-01T15:03:08Z |
CONTRIBUTOR
| null |
xref #5602, #9343, #13806 (comments)
#### Code Sample, a copy-pastable example if possible
```
In [2]: s = pd.Series(['a', 123, 'b'])
In [3]: s.str.startswith('a')
Out[3]:
0 True
1 NaN
2 False
dtype: object
```
#### Expected Output
raise an exception
Reasoning: If I provide NaN, it's okay to return NaN, but silently convert is hard to find. The user cannot easily drop hidden integers in an 'O' series.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: 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: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: 0.7.2
IPython: 5.0.0
sphinx: 1.4.1
patsy: None
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: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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| 13,878 |
solve list cannot add to tuple error in make_signature function
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[] | null | 5 | 2016-08-02T07:07:05Z | 2016-10-28T14:58:29Z | 2016-10-28T14:58:29Z |
NONE
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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skipfooter doesn't really "skip" in read_csv
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| 2 | 2016-08-02T08:45:43Z | 2016-11-29T21:19:56Z | 2016-11-29T21:18:28Z |
MEMBER
| null |
On `master`:
``` python
from pandas import read_csv
from pandas.compat import StringIO
data = 'a,b,c\ncat,foo,bar\ndog,foo,"baz' # Note the stray quotation mark
read_csv(StringIO(data), engine='python', skipfooter=1)
...
_csv.Error: unexpected end of data
```
If we were truly "skipping" the last row, no error should have been raised. However, this occurs because the data is all parsed <em>in memory</em> first with Python's `csv` library.
Whether this is intended behaviour or not has implications for the C engine in terms of implementing analogous `skipfooter` behaviour. Or perhaps it has something to do with the fact that `error_bad_lines` and `error_warn_lines` parameters not with the Python engine?
xref #5232
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Series.idxmax() and Series.max() show inconsistent behaviour with pd.np.Inf
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| 1 | 2016-08-02T08:56:16Z | 2016-08-02T09:54:52Z | 2016-08-02T09:54:31Z |
NONE
| null |
`idxmax` is supposed to return the index of the maximum element. However, it seems to ignore special values like infinity (`pd.np.Inf`), leading to the following inconsistency:
``` python
>>> from pandas import DataFrame
>>> s = pd.Series([0,5,pd.np.Inf])
>>> s.max() == s.ix[s.idxmax()]
```
This snippet will return `False`, since `s.idxmax()` will return `1`, the index of value `5`, while `s.max()` returns `Inf` as expected.
#### Expected Output
``` python
True
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.6.4-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
pandas: 0.18.1
nose: 1.3.6
pip: 8.1.2
setuptools: 25.1.0
Cython: None
numpy: 1.11.1
scipy: 0.14.0
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: None
patsy: 0.4.1
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pytz: 2015.2
blosc: None
bottleneck: None
tables: None
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html5lib: None
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apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
```
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| 13,881 |
TST: Add first line comment tests in read_csv
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| 2 | 2016-08-02T10:38:42Z | 2016-08-03T10:15:51Z | 2016-08-03T10:15:47Z |
MEMBER
| null |
Title is self-explanatory. Closes #4623.
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DOC: Update README to link to install instructions.
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| 5 | 2016-08-02T12:30:44Z | 2017-04-05T02:07:02Z | 2016-08-28T18:26:12Z |
CONTRIBUTOR
| null |
Want any updates to the actual install instructions? Should we remove the section on wakari? Do we have a preference for conda-forge over the default channel?
|
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Pandas threading crash
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[] | null | 4 | 2016-08-02T17:59:55Z | 2016-08-02T19:16:37Z | 2016-08-02T19:13:26Z |
NONE
| null |
The following code crashes Pandas 0.18.1 with SIGSEGV:
```
#!/usr/bin/env python3
import threading
import pandas as pd
import numpy as np
def lets_crash():
t = 0.02 * np.arange(100000)
data = pd.DataFrame({'t': t})
data['t'] * 1.5
threads = []
for i in range(100):
t = threading.Thread(target=let_crash)
t.daemon = True
t.start()
threads.append(t)
for t in threads:
t.join()
```
(from [http://stackoverflow.com/questions/25782912/pandas-and-numpy-thread-safety](http://stackoverflow.com/questions/25782912/pandas-and-numpy-thread-safety))
Note that the threads run independently from each other, so I don't know how to avoid this short of serialising all access to Pandas.
I tried to get a stack trace, but it seems to crash in several different places.
#### Expected Output
None, but gives SIGSEGV instead
#### 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: de_DE.UTF-8
pandas: 0.18.1
nose: None
pip: None
setuptools: None
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 2.4.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2014.10
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: None
boto: None
pandas_datareader: None
I can also reproduce the bug with python 2.7.12, and also with pandas 0.17.
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| 13,884 |
Round trip through HDF5 with format=table and localized DatetimeIndex discards index name
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| 2 | 2016-08-02T19:08:37Z | 2016-08-04T21:04:30Z | 2016-08-04T21:04:30Z |
CONTRIBUTOR
| null |
This should work, but the assert fails:
```
import pandas as pd
df = pd.DataFrame({'a': [1,2,3]})
df.index = pd.DatetimeIndex([1234567890123456787, 1234567890123456788, 1234567890123456789])
df.index = df.index.tz_localize('UTC') # this line makes the index name go away
df.index.name = 'expected'
df.to_hdf('hello.h5', 'world', format='table')
df2 = pd.read_hdf('hello.h5', 'world')
assert df2.index.name == df.index.name, "HDF5 stored name not expected: {}".format(df2.index.name)
```
It works fine if you don't localize the DatetimeIndex, or if you don't use format='table'.
The index name "expected" is actually stored in the "info" attribute inside the HDF5 file whether it's localized or not. But the format is slightly different. If not localized:
```
(dp7
Vindex_name
p8
Vexpected
```
If localized:
```
(tRp10
sVindex_name
p11
Vexpected
```
I don't know enough to say whether the bug is in read_hdf(), to_hdf(), or PyTables.
I'm using Pandas 0.18.1.
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BUG: Ignore the BOM in UTF8 BOM CSV files
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| 5 | 2016-08-03T07:35:54Z | 2016-08-05T10:37:31Z | 2016-08-05T10:37:31Z |
MEMBER
| null |
Title is self-explanatory. Closes #4793.
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DOC: small releasenote fixes
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| 1 | 2016-08-03T13:39:26Z | 2016-08-03T14:51:58Z | 2016-08-03T14:51:54Z |
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pandas.DataFrame.to_html() does not provide an option to remove all styling attributes
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| 2 | 2016-08-03T16:04:47Z | 2016-08-03T19:47:55Z | 2016-08-03T19:47:33Z |
NONE
| null |
DataFrame.to_html() renders by default a table element with a border='1' attribute. The text-align style attribute on the <thead> <tr> is also set to "right".
Even if it is easy to override by code or CSS, it should be good to have an option that removes or clear all styling attributes on the generated table. For example df.to_html(no_style=True)
Cheers,
Mikaël
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BUG: preserve DatetimeIndex.name in HDFStore/read_hdf() with tz (#13884)
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| 7 | 2016-08-03T17:19:40Z | 2016-08-04T21:04:31Z | 2016-08-04T21:04:30Z |
CONTRIBUTOR
| null |
- [x] closes #13884
- [x] tests added / passed
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BUG: Empty lists shouldn't be counted as DateOffsets. See #13844
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| 9 | 2016-08-03T17:56:03Z | 2016-08-26T20:43:21Z | 2016-08-26T20:43:20Z |
CONTRIBUTOR
| null |
- [x] closes #13844
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| 13,890 |
BUG: avoid "b" prefix for bytes in to_csv() on Python 3 (#9712)
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[] | null | 12 | 2016-08-03T19:34:05Z | 2016-09-14T13:23:22Z | 2016-09-13T10:05:42Z |
CONTRIBUTOR
| null |
- [x] closes #9712
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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BUG: allow describe() for on boolean-only columns
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| 2 | 2016-08-03T20:00:38Z | 2016-08-08T14:06:28Z | 2016-08-08T14:06:28Z |
NONE
| null |
I know I can obtain the expected output by using `include=['bool']`, but that feels bad to me as a user. I want `describe()` to know that I'm only asking for boolean columns and not freak out.
Thank you!
#### Code Sample, a copy-pastable example if possible
``` python
>>> test_df = pd.DataFrame({'test_ind_1': [False, False, True, True, False, True , True, False], 'test_ind_2': [False, True, True, False, False, True, True,True]})
>>> test_df.describe()
```
#### Expected Output
``` python
│ test_ind_1 test_ind_2
│count 8 8
│unique 2 2
│top True True
│freq 4 5
```
#### Actual output
``` python
ValueError: No objects to concatenate
```
#### output of `pd.show_versions()`
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.18.2.el7.x86_64
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: 23.0.0
Cython: 0.22.1
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.5.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
```
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| 13,892 |
HDF read query issue
|
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[] | null | 8 | 2016-08-03T20:24:48Z | 2016-08-05T14:42:23Z | 2016-08-05T14:42:22Z |
NONE
| null |
I created an HDF file using:
```
X.to_hdf('test.hdf', 'X', format='t', data_columns=['date', 'evaluation'])
```
I am interested in reading rows matching a query:
```
where = "((date >= '2010-01-01' and date < '2012-01-01') & (evaluation > 0)) | (date == '2012-01-01')"
```
However running
```
pd.read_hdf('test.hdf', 'X', where=where)
```
Does not return all matching rows. If I alternatively run
```
pd.read_hdf('test.hdf', 'X').query(where)
```
I get the correct rows.
#### Code Sample, a copy-pastable example if possible
I uploaded my example to this [repository](https://github.com/potash/pandas-hdf-select-bug). After cloning the repository unzip the buggy HDF files:
```
unzip test.zip
```
The `test.hdf` file is the one producing the bug, but the `test2.hdf`-- which only differs by removing all columns except the ones used in the query-- but does not produce the bug!
You can test the two hdf files using `python test.py test.hdf` and `python test.py test2.hdf`.
#### Expected Output
Both runs should print
```
29540
29540
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-91-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.1
pip: 8.1.2
setuptools: 24.0.2
Cython: None
numpy: 1.11.1
scipy: 0.13.3
statsmodels: 0.5.0
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.2.1
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.6.0
matplotlib: 1.3.1
openpyxl: 2.2.0-b1
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.2.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.4.5 (dt dec mx pq3 ext)
jinja2: 2.8
boto: 2.20.1
pandas_datareader: None
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| 13,893 |
Failure to load Pandas in Docker
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| 4 | 2016-08-03T22:12:53Z | 2016-08-03T22:58:34Z | 2016-08-03T22:17:54Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
More difficult than that. Trying to import pandas when Running Python 3.5 in a Docker container, passing the --user option to 'docker run'. in this case, running Python 3.5 in a docker container using HTCondor
#### Expected Output
Should quietly load
#### Actual Output
Traceback (most recent call last):
File "foo.py", line 1, in <module>
import pandas
File "/opt/conda/lib/python3.5/site-packages/pandas/**init**.py", line 39, in <module>
from pandas.core.api import *
File "/opt/conda/lib/python3.5/site-packages/pandas/core/api.py", line 10, in <module>
from pandas.core.groupby import Grouper
File "/opt/conda/lib/python3.5/site-packages/pandas/core/groupby.py", line 18, in <module>
from pandas.core.frame import DataFrame
File "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py", line 39, in <module>
from pandas.core.series import Series
File "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py", line 2944, in <module>
import pandas.tools.plotting as _gfx # noqa
File "/opt/conda/lib/python3.5/site-packages/pandas/tools/plotting.py", line 27, in <module>
import pandas.tseries.converter as conv
File "/opt/conda/lib/python3.5/site-packages/pandas/tseries/converter.py", line 7, in <module>
import matplotlib.units as units
File "/opt/conda/lib/python3.5/site-packages/matplotlib/__init__.py", line 1131, in <module>
rcParams = rc_params()
File "/opt/conda/lib/python3.5/site-packages/matplotlib/**init**.py", line 965, in rc_params
fname = matplotlib_fname()
File "/opt/conda/lib/python3.5/site-packages/matplotlib/**init**.py", line 794, in matplotlib_fname
configdir = _get_configdir()
File "/opt/conda/lib/python3.5/site-packages/matplotlib/__init__.py", line 649, in _get_configdir
return _get_config_or_cache_dir(_get_xdg_config_dir())
File "/opt/conda/lib/python3.5/site-packages/matplotlib/__init__.py", line 626, in _get_config_or_cache_dir
return _create_tmp_config_dir()
File "/opt/conda/lib/python3.5/site-packages/matplotlib/__init__.py", line 555, in _create_tmp_config_dir
tempdir = os.path.join(tempdir, 'matplotlib-%s' % getpass.getuser())
File "/opt/conda/lib/python3.5/getpass.py", line 170, in getuser
return pwd.getpwuid(os.getuid())[0]
KeyError: 'getpwuid(): uid not found: 99'
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.22.2.el7.x86_64
machine: x86_64
processor:
byteorder: little
LC_ALL: C.UTF-8
LANG: C.UTF-8
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
pandas_datareader: None
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MDExOlB1bGxSZXF1ZXN0Nzk5OTMyNDU=
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API/BUG: Fix Series ops inconsistencies
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| 8 | 2016-08-03T22:55:56Z | 2016-08-25T10:21:00Z | 2016-08-25T10:20:52Z |
MEMBER
| null |
- [x] closes #1134, closes #4581, closes #13538
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This includes the fix only for #1134 and not for other inconsistencies (like #13637), as it needs further discussions.
Changes:
- series comparison operator to check whether labels are identical (currently: ignores labels)
- series boolean operator to align with labels (currently: only keeps left index)
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| 2 | 2016-08-03T23:19:42Z | 2016-08-07T00:55:47Z | 2016-08-07T00:55:47Z |
CONTRIBUTOR
| null |
https://ci.appveyor.com/project/jreback/pandas-465/build/1.0.847/job/fb6cmln4nrxw52ef
the commit was just some tests
any ideas?
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API: Series/DataFrame logical op NaN propagation
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| 1 | 2016-08-03T23:20:38Z | 2019-11-12T23:32:46Z | 2019-11-12T23:32:46Z |
MEMBER
| null |
The same as #13806, but for discuss differences with `DataFrame`.
```
pd.Series([True, False, np.nan]) | pd.Series([True, False, True])
#0 True
#1 False
#2 False
# dtype: bool
pd.Series([True, False, True]) | pd.Series([True, False, np.nan])
#0 True
#1 False
#2 True
# dtype: bool
pd.DataFrame([True, False, np.nan]) | pd.DataFrame([True, False, True])
# 0
#0 True
#1 False
#2 NaN
pd.DataFrame([True, False, True]) | pd.DataFrame([True, False, np.nan])
# 0
#0 True
#1 False
#2 NaN
```
There looks to be 2 points:
## 1. `NaN` comparison result
As @TomAugspurger described in #13806, the basic rule is below. Based on this, `DataFrame` result is incorrect.
```
In [40]: float('nan') or 1
Out[40]: nan
In [41]: 1 or float('nan')
Out[41]: 1
```
## 2. filling `NaN` result
`Series` fills `NaN` included in the result with `False`. `DataFrame` should also?
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[] | null | 8 | 2016-08-04T00:42:30Z | 2016-12-26T21:42:27Z | 2016-12-26T21:42:27Z |
CONTRIBUTOR
| null |
- [x] closes #13257
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Use 'f' as a string conversion type to specify the number of decimals to
represent. The 'g' format will instead fall back to the 'e' format
causing the unexpected behavior seen in GHI13257.
See: [old-string-formatting](https://docs.python.org/3.5/library/stdtypes.html#old-string-formatting)
Replicated code from the issue, with output:
<img width="702" alt="example output with precision" src="https://cloud.githubusercontent.com/assets/7526790/17386623/77eb83f4-59ba-11e6-8193-fde0e839a184.png">
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| 169,269,387 |
MDExOlB1bGxSZXF1ZXN0ODAwMDQ0MjA=
| 13,898 |
BUG: allow describe() for DataFrames with only boolean columns
|
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| 8 | 2016-08-04T01:10:51Z | 2016-08-08T14:06:28Z | 2016-08-08T14:06:28Z |
CONTRIBUTOR
| null |
- [x] closes #13891
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Closes #13891. Existing tests pass. ~~No new tests added yet.~~
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| 13,899 |
BUG: union_categorical fastpath sort
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| 2 | 2016-08-04T01:19:35Z | 2016-08-04T10:38:38Z | 2016-08-04T10:38:38Z |
CONTRIBUTOR
| null |
Follow up to a problem with #13846. This path was tested, but the test just happened to be constructed in such a way it worked with the buggy implementation.
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ENH: sparse astype now supports int64 and bool
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| 4 | 2016-08-04T10:26:13Z | 2016-08-04T21:12:21Z | 2016-08-04T21:09:18Z |
MEMBER
| null |
- [x] split from #13849
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
`SparseXXX` now support `astype` other than `float64` dtype. It raises `ValueError` when its `sp_values` or `fill_value` cannot be converted to specified dtype.
```
s = pd.SparseSeries([1., 0., 2., 0.], fill_value=0)
s.astype(np.int64)
#0 1
#1 0
#2 2
#3 0
# dtype: int64
# BlockIndex
# Block locations: array([0, 2], dtype=int32)
# Block lengths: array([1, 1], dtype=int32)
pd.SparseSeries([1, np.nan, 2, np.nan]).astype(np.int64)
# ValueError: unable to coerce current fill_value nan to int64 dtype
```
Also, `fill_value` setter now checks input value dtype and raises `ValueError` if value cannot be stored to current dtype.
```
s = pd.SparseSeries([1, 0, 2, 0], fill_value=0, dtype=np.int64)
s.fill_value = 2
s
#0 1
#1 2
#2 2
#3 2
# dtype: int64
# BlockIndex
# Block locations: array([0, 2], dtype=int32)
# Block lengths: array([1, 1], dtype=int32)
s.fill_value = np.nan
# ValueError: unable to set fill_value nan to int64 dtype
```
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MDU6SXNzdWUxNjkzNDk5MjM=
| 13,901 |
Groupby doesn't accept level=[0] for Index.
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| 7 | 2016-08-04T11:11:55Z | 2016-09-22T10:34:46Z | 2016-09-22T10:34:46Z |
NONE
| null |
Today I've found weird behaviour of level parameter of groupby. I need to groupby two dataframes by index and this code is working as long as I'm using Multiindex.
``` python
index1 = pandas.Index([1,2,3,4,5])
index2 = pandas.Index([1,2,3,4,5])
first = pandas.DataFrame({'a':[1,2,3,4,5]}, index=index1)
second = df2 = pandas.DataFrame({'a':[2,3,4,5,6]}, index=index2)
pandas.concat([first, second]).groupby(level=range(first.index.nlevels)).min()
```
Unfortunately when I'm using simple Index I get:
```
ValueError: level > 0 only valid with MultiIndex
```
In other way if I passed 0 instead of [0], everything is working properly. It's weird and cause that it's hard to write reusable code.
#### INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-42-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.4.1
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: 1.1.2
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
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| 169,393,144 |
MDU6SXNzdWUxNjkzOTMxNDQ=
| 13,902 |
ENH: Faster merge_asof() through a single-pass algo
|
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[] | null | 4 | 2016-08-04T14:48:45Z | 2016-08-08T13:42:05Z | 2016-08-08T13:42:05Z |
CONTRIBUTOR
| null |
Out of curiosity, I took a crack at a single-pass `merge_asof()`. My sample passes the existing regression tests but is "wrong" in that it works only for a single object-type "by" parameter. I use `PyObjectHashTable` while scanning through the right DataFrame to cache the most recently found row for each "by" object.
I could add a little type differentiation if there is interest. I see that Tempita is getting some use in pandas. The main question is whether I can use multiple columns in the "by" parameter, which would be useful for matching things like `['ticker', 'exchange']`. Still investigating.
```
$ asv continuous master -b "join_merge.merge_asof_*"
· Creating environments
· Discovering benchmarks
·· Uninstalling from conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
·· Installing into conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..
· Running 4 total benchmarks (2 commits * 1 environments * 2 benchmarks)
[ 0.00%] · For pandas commit hash c4302949:
[ 0.00%] ·· Building for conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..
[ 0.00%] ·· Benchmarking conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 25.00%] ··· Running join_merge.merge_asof_by.time_merge_asof_by 41.07ms
[ 50.00%] ··· Running join_merge.merge_asof_noby.time_merge_asof_noby 12.90ms
[ 50.00%] · For pandas commit hash 97de42ab:
[ 50.00%] ·· Building for conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..
[ 50.00%] ·· Benchmarking conda-py2.7-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 75.00%] ··· Running join_merge.merge_asof_by.time_merge_asof_by 608.08ms
[100.00%] ··· Running join_merge.merge_asof_noby.time_merge_asof_noby 81.03ms
before after ratio
[97de42ab] [c4302949]
- 81.03ms 12.90ms 0.16 join_merge.merge_asof_noby.time_merge_asof_noby
- 608.08ms 41.07ms 0.07 join_merge.merge_asof_by.time_merge_asof_by
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
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ENH: Faster merge_asof() performs a single pass when joining tables (#13902)
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| 9 | 2016-08-04T14:52:49Z | 2016-08-08T12:47:12Z | 2016-08-08T12:46:12Z |
CONTRIBUTOR
| null |
This version passes existing regression tests but is ultimately wrong
because it requires the "by" column to be a single object. A proper version
would handle int (and possily float) columns through type differentiation.
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Example of High Memory Usage of MultiIndex
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| 26 | 2016-08-04T15:39:32Z | 2020-09-02T17:02:43Z | 2017-02-15T15:25:57Z |
NONE
| null |
This is a dupe of #1752, in which @jreback recommended a new issue be opened. I have a (hopefully) reproduce-able example which shows how using MultiIndex can explode RAM usage.
At the top I define a function to get the memory of the current Python process. This will be called as I create arrays to get the "true" space of each object.
``` python
import numpy as np
import pandas as pd
import os
import psutil
import numba as nb
def print_mem():
proc = psutil.Process(os.getpid())
print proc.memory_info()[0] / float(2**20), "MB"
print_mem()
>> 74.20703125 MB
```
I need some functions to construct a plausible MultiIndex which matches roughly the actual data I'm using.
``` python
@nb.jit(nopython=True, nogil=True)
def nbf_number_new_level(repetitions):
assert len(repetitions.shape) == 1
n = repetitions.sum()
ret = np.zeros(shape=n, dtype=np.uint64)
i = 0
for j in range(len(repetitions)):
n_repetitions = repetitions[j]
c = 0
for k in range(n_repetitions):
ret[i] = c
c += 1; i += 1
return ret
def expand_index(idx, repetitions, new_name=None):
assert len(idx) == len(repetitions), "Index and repetitions must be the same length"
repetitions = np.array(repetitions)
assert np.all(repetitions > 0), "All repetitions must be > 0"
names = list(idx.names) if isinstance(idx, pd.MultiIndex) else [idx.name]
names.append(new_name)
new_level_value = nbf_number_new_level(repetitions)
value_arrays = [idx.get_level_values(i).repeat(repetitions) for i in range(idx.nlevels)] \
if isinstance(idx, pd.MultiIndex) else \
[idx.values.repeat(repetitions)]
value_arrays.append(new_level_value)
ret = pd.MultiIndex.from_arrays(value_arrays)
ret.names = names
return ret
```
Constructing the actual MultiIndex:
``` python
randomizer = np.random.RandomState(12345)
nhousehholds = 200000
hhids = pd.Index(np.arange(nhousehholds), name='hhid')
print nhousehholds, "households"
print_mem()
>>200000 households
>>83.95703125 MB
persons_per_hh = randomizer.randint(1, 9, size=nhousehholds)
person_ids = expand_index(hhids, persons_per_hh, new_name='pid')
print len(person_ids), "persons"
print_mem()
>>898254 persons
>>105.6953125 MB
tours_per_person = randomizer.choice([1, 2, 3], size=len(person_ids), replace=True, p=[0.85, 0.1, 0.05])
tour_ids = expand_index(person_ids, tours_per_person, new_name='tour_id')
print len(tour_ids), "tours"
print_mem()
>>1078155 tours
>>121.734375 MB
trips_per_tour = randomizer.choice([2,3,4,5], size=len(tour_ids), replace=True, p=[0.6, 0.25, 0.1, 0.05])
trip_ids = expand_index(tour_ids, trips_per_tour, new_name='trip_id')
print len(trip_ids), "trips"
print_mem()
trips = pd.DataFrame(index=trip_ids)
trips['origin'] = randomizer.randint(101, 200, size=len(trips))
trips['destination'] = randomizer.randint(101, 200, size=len(trips))
print_mem()
>>2803731 trips
>>150.17578125 MB
>>171.70703125 MB
```
Based on this, my fake trip table is taking up about 171.7 - 121.7 = 50MB of RAM. I haven't done any fancy indexing, just initialized the table. But, when I call `trips.info()` (which appears to instantiate the array of PyTuples) this happens:
``` python
print trips.info()
print_mem()
>><class 'pandas.core.frame.DataFrame'>
MultiIndex: 2803731 entries, (0, 0, 0, 0) to (199999, 0, 0, 1)
Data columns (total 2 columns):
origin int32
destination int32
dtypes: int32(2)
memory usage: 42.8+ MB
None
723.64453125 MB
```
My process's memory usage balloons to 723MB!. Doing the math, the cached indexer takes up 723.6 - 171.7 = 551 MB, a **tenfold** increase over the actual DataFrame!
For this fake dataset, this is not so much of a problem, but my production code is 20x the size and I soak up 27 GB of RAM when I as much as look at my trips table.
Any performance tips would be appreciated; the only thing I can think of right now is "don't use a MultiIndex". It sounds like a 'proper' fix for this lies deep in the indexing internals which is far above my pay grade. But I wanted to at least log this issue with the community.
Output of `pd.show_versions()`:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.4
pip: 8.1.1
setuptools: 0.6c11
Cython: 0.22
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.1.1
numexpr: 2.5.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: 2.36.0
pandas_datareader: None
```
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MDU6SXNzdWUxNjk0MTMyNDc=
| 13,905 |
DatetimeIndex + TimeDelta gives wrong results when timezone is set
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| 1 | 2016-08-04T16:11:28Z | 2018-01-02T11:23:50Z | 2018-01-02T11:23:50Z |
CONTRIBUTOR
| null |
When I add a time delta to a DatetimeIndex which is tz-aware, the results appear as UTC, but sometimes claim to be in the original non-UTC timezone.
#### Code
```
import pandas as pd
index = pd.DatetimeIndex(['2016-06-28 05:30', '2016-06-28 05:31'], dtype='datetime64[ns, America/Chicago]')
offset = pd.Series(['00:00:05', '00:00:05'], dtype='timedelta64[ns]', index=index)
print(index + offset)
print(index + offset.values)
```
#### Actual Output
```
2016-06-28 05:30:00-05:00 2016-06-28 10:30:05
2016-06-28 05:31:00-05:00 2016-06-28 10:31:05
dtype: datetime64[ns]
DatetimeIndex(['2016-06-28 10:30:05-05:00', '2016-06-28 10:31:05-05:00'], dtype='datetime64[ns, America/Chicago]', freq=None)
```
#### Expected Output
```
2016-06-28 05:30:00-05:00 2016-06-28 05:30:05-05:00
2016-06-28 05:31:00-05:00 2016-06-28 05:31:05-05:00
dtype: datetime64[ns]
DatetimeIndex(['2016-06-28 05:30:05-05:00', '2016-06-28 05:31:05-05:00'], dtype='datetime64[ns, America/Chicago]', freq=None)
```
Note however that this does print the expected result:
```
print(index + pd.to_timedelta('00:00:05'))
```
I'm using Pandas 0.18.1.
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| 169,420,431 |
MDU6SXNzdWUxNjk0MjA0MzE=
| 13,906 |
Chained groupby-resample object raises error when used twice with different column selector
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[] | null | 1 | 2016-08-04T16:44:23Z | 2016-08-04T16:49:45Z | 2016-08-04T16:49:41Z |
NONE
| null |
Going from 0.18.0 to 0.18.1, a chained groupby - resample object can now only be used once. A second similar operation raises a KeyError when a column that is not used in the first operation is selected.
#### Code Sample, a copy-pastable example if possible
```
N = 5
df = pd.DataFrame({'a': np.ones(N), 'b': np.ones(N), 'c': np.ones(N)},
index=pd.date_range('2016-01-01', periods=N))
group = df.groupby('a').resample('1D')
# OK:
print group[['b']].count()
# Error:
print group[['c']].count()
```
#### Actual Output
```
b
a
1.0 2016-01-01 1
2016-01-02 1
2016-01-03 1
2016-01-04 1
2016-01-05 1
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-101-875a81207428> in <module>()
----> 1 group[['c']].count()
C:\Miniconda2\lib\site-packages\pandas\tseries\resample.pyc in __getitem__(self, key)
174 def __getitem__(self, key):
175 try:
--> 176 return super(Resampler, self).__getitem__(key)
177 except (KeyError, com.AbstractMethodError):
178
C:\Miniconda2\lib\site-packages\pandas\core\base.pyc in __getitem__(self, key)
331 bad_keys = list(set(key).difference(self.obj.columns))
332 raise KeyError("Columns not found: %s"
--> 333 % str(bad_keys)[1:-1])
334 return self._gotitem(list(key), ndim=2)
335
KeyError: "Columns not found: 'c'"
```
#### Expected Output
```
b
a
1.0 2016-01-01 1
2016-01-02 1
2016-01-03 1
2016-01-04 1
2016-01-05 1
c
a
1.0 2016-01-01 1
2016-01-02 1
2016-01-03 1
2016-01-04 1
2016-01-05 1
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
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: 1.5.1
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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MDExOlB1bGxSZXF1ZXN0ODAxMTk5Nzk=
| 13,907 |
ENH: Accept list as level for groupby in non-MultiIndexed objects
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| 9 | 2016-08-04T17:44:14Z | 2016-09-22T10:34:50Z | 2016-09-22T10:34:46Z |
CONTRIBUTOR
| null |
- [x] closes #13901
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
|
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