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BUG: Remove locale conversion from Stata file date
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| 6 | 2017-01-24T14:56:32Z | 2017-02-17T18:58:22Z | 2017-01-25T12:01:11Z |
CONTRIBUTOR
| null |
Prevent locale from affecting Stata file date creation, which must
be en_US.
xref #13856
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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TST: fix excel tests when openpyxl is not installed
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| 6 | 2017-01-24T15:08:22Z | 2017-01-24T21:24:04Z | 2017-01-24T21:24:04Z |
MEMBER
| null |
Some tests fail when you don't have openpyxl installed, so explicitly skipping those (they need it for certain exts because they write a frame in the test)
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DOC: clean-up docstring str.replace (GH15056)
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| 0 | 2017-01-24T15:35:54Z | 2017-01-24T18:49:00Z | 2017-01-24T18:49:00Z |
MEMBER
| null |
I was too late to give feedback, so a few changes to the docstring additions of #15056
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Duplicate columns in DataFrame do not show up in DataFrame.itertuples
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| 1 | 2017-01-24T19:04:17Z | 2018-07-06T20:21:15Z | 2017-01-24T19:10:23Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas
>>> df = pandas.read_csv('test.csv')
>>> df
A A.1 B
0 1 2 3
>>> df.columns
Index(['A', 'A.1', 'B'], dtype='object')
>>> for row in df.itertuples(index=False):
... print(row)
...
Pandas(A=1, _1=2, B=3)
```
#### Problem description
The row should show up as `Pandas(A=1, A.1=2, B=3)` OR start supporting `mangle_dupe_cols=False` to overwrite duplicate columns.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 9.0.1
setuptools: 30.3.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: None
boto: 2.45.0
pandas_datareader: None
</details>
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MDU6SXNzdWUyMDI5MTcyNzU=
| 15,213 |
DEPS: drop numexpr < 2.4.6
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| 2 | 2017-01-24T19:32:21Z | 2017-03-02T10:37:00Z | 2017-02-15T15:21:24Z |
CONTRIBUTOR
| null |
since 2.4.4 is specifically exluded because of some multi-threading issues, this eliminates the pandas checks for this (as well as some compat code for earlier versions of numexpr).
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| 202,917,498 |
MDU6SXNzdWUyMDI5MTc0OTg=
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DEPS: drop bottleneck < 1.0
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| 1 | 2017-01-24T19:33:19Z | 2017-08-01T20:09:23Z | 2017-08-01T20:09:23Z |
CONTRIBUTOR
| null |
should prob address #9422 simultaneously.
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COMPAT: datetime parsing with locale
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| 0 | 2017-01-24T19:46:18Z | 2017-01-26T02:31:04Z | 2017-01-26T02:31:04Z |
CONTRIBUTOR
| null |
we recently adding testing under a non-US locale for all tests with a larger than 1 unicode character (chinese), [here](https://github.com/pandas-dev/pandas/pull/15211), not just slow (which we have had for a while), and IT (which doesn't show these issues).
https://github.com/pandas-dev/pandas/pull/15208 fixes almost everything, but this remains.
You can easily repro this by adding to ``pandas/__init__.py`` at the top.
```
import locale
locale.setlocale(locale.LC_ALL, 'zh_CN.UTF-8')
```
```
======================================================================
ERROR: test_to_datetime_format_microsecond (pandas.tseries.tests.test_timeseries.TimeConversionFormats)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tseries/tools.py", line 407, in _convert_listlike
values, tz = tslib.datetime_to_datetime64(arg)
File "pandas/tslib.pyx", line 1756, in pandas.tslib.datetime_to_datetime64 (pandas/tslib.c:30671)
raise TypeError('Unrecognized value type: %s' % type(val))
TypeError: Unrecognized value type: <class 'str'>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tseries/tests/test_timeseries.py", line 5374, in test_to_datetime_format_microsecond
result = to_datetime(val, format=format)
File "/home/travis/build/jreback/pandas/pandas/tseries/tools.py", line 427, in to_datetime
return _convert_listlike(np.array([arg]), box, format)[0]
File "/home/travis/build/jreback/pandas/pandas/tseries/tools.py", line 410, in _convert_listlike
raise e
File "/home/travis/build/jreback/pandas/pandas/tseries/tools.py", line 377, in _convert_listlike
errors=errors)
File "pandas/tslib.pyx", line 3600, in pandas.tslib.array_strptime (pandas/tslib.c:58664)
raise ValueError("time data %r does not match "
ValueError: time data '01-Apr-2011 00:00:01.978' does not match format '%d-%b-%Y %H:%M:%S.%f' (match)
```
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MDExOlB1bGxSZXF1ZXN0MTAzMDIzMjcw
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ENH: add MultiIndex.to_dataframe
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| 8 | 2017-01-24T23:06:04Z | 2017-01-25T21:42:51Z | 2017-01-25T21:20:37Z |
CONTRIBUTOR
| null |
ENH: allow hashing of MultiIndex
closes #12397
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Categorical Column GroupBy agg with as_index=False produces NaN rows 7.5X Slower with unexpected extra Cardinality
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| 6 | 2017-01-25T01:27:38Z | 2018-05-01T15:09:11Z | 2018-05-01T15:09:11Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randint(10,100, size=(200,6)), columns=['C'+str(i) for i in range(6)])
df['C0'] = ['A','B','C','D']*50
df['C1'] = ['E','F']*100
df['C2'] = ['H','I','J','K', 'L']*40
for col in df.columns[:3]:
df[col] = df[col].astype('category')
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 6 columns):
C0 200 non-null category
C1 200 non-null category
C2 200 non-null category
C3 200 non-null int32
C4 200 non-null int32
C5 200 non-null int32
dtypes: category(3), int32(3)
memory usage: 3.1 KB
%time ix_true = df.groupby(df.columns.tolist()[:3], as_index=True)['C5'].max()
Wall time: 2 ms
ix_true.shape
(20,)
%time ix_false = df.groupby(df.columns.tolist()[:3], as_index=False)['C5'].max()
Wall time: 15 ms
ix_false.shape
(40, 4)
ix_true
C0 C1 C2
A E H 93
I 99
J 88
K 91
L 94
B F H 98
I 89
J 94
K 92
L 96
C E H 96
I 96
J 85
K 88
L 98
D F H 96
I 84
J 71
K 96
L 94
Name: C5, dtype: int32
ix_false
C0 C1 C2 C5
0 A E H 93.0
1 A E I 99.0
2 A E J 88.0
3 A E K 91.0
4 A E L 94.0
5 A F H NaN
6 A F I NaN
7 A F J NaN
8 A F K NaN
9 A F L NaN
10 B E H NaN
11 B E I NaN
12 B E J NaN
13 B E K NaN
14 B E L NaN
15 B F H 98.0
16 B F I 89.0
17 B F J 94.0
18 B F K 92.0
19 B F L 96.0
20 C E H 96.0
21 C E I 96.0
22 C E J 85.0
23 C E K 88.0
24 C E L 98.0
25 C F H NaN
26 C F I NaN
27 C F J NaN
28 C F K NaN
29 C F L NaN
30 D E H NaN
31 D E I NaN
32 D E J NaN
33 D E K NaN
34 D E L NaN
35 D F H 96.0
36 D F I 84.0
37 D F J 71.0
38 D F K 96.0
39 D F L 94.0
```
#### Problem description
Using ```as_index=False``` in ```df.groupby(df.columns.tolist()[:3], as_index=False)['C5'].max()``` with categorical columns produces NaN output rows.
#### Expected Output
I expect that the output should not contain any extra Cardinality explosion and have the same number of rows. as the ```as_index=True``` case.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 8.1.1
setuptools: 20.10.1
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.7.1
IPython: 4.2.0
sphinx: 1.3.6
patsy: 0.4.0
dateutil: 2.5.0
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.2.5
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.6.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTAzMDQxNTc3
| 15,218 |
BLD: hashtable.pxd is a pxd depends for _join, hashtable, and index
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| 1 | 2017-01-25T01:51:36Z | 2017-01-25T12:01:34Z | 2017-01-25T12:01:34Z |
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| 203,008,497 |
MDExOlB1bGxSZXF1ZXN0MTAzMDUyNTc0
| 15,219 |
ENH union_categoricals supports ignore_order GH13410
|
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| 8 | 2017-01-25T04:15:02Z | 2017-02-22T16:32:47Z | 2017-02-22T16:31:40Z |
NONE
| null |
xref #13410 (ignore_order portion)
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.loc indexing of heterogeneous dataframe returns different dtype
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| 2 | 2017-01-25T05:14:06Z | 2017-01-27T00:47:54Z | 2017-01-25T08:22:53Z |
NONE
| null |
#### Code Sample
```python
import pandas as pd
import numpy as np
#working data
names = ['d1', 'd2', 'd3', 'd4', 'd5']
formats = ['u1', '<f8', 'u1', 'u1', 'u1']
dtype = dict(names=names, formats=formats)
data = {'d1':[1,11], 'd2':[2,12], 'd3':[3,13], 'd4':[4,14], 'd5':[5,15]}
#create a pandas dataframe with uint8 variables except for a double in d2 slot.
df_create = np.rec.fromarrays(data.values(), dtype=dtype, names=data.keys())
df_create = pd.DataFrame(df_create)
df_create.loc[:,'d2'] *= 0.12345
#create a pandas dataframe with all variables uint8
df_mod = pd.DataFrame.from_dict(data, dtype=np.dtype('u1'))
#convert d2 to double and modify
df_mod.loc[:,'d2'] = df_mod.loc[:,'d2'].astype(np.dtype('float64'))
df_mod.loc[:,'d2'] *= 0.12345
print('type of df_create.loc[0,\'d1\']: {}'.format(type(df_create.loc[0,'d1'])))
print('type of df_create.loc[0,\'d2\']: {}'.format(type(df_create.loc[0,'d2'])))
print('type of df_create.iloc[0,0]: {}'.format(type(df_create.iloc[0,2])))
print('type of df_create.iloc[0,1]: {}'.format(type(df_create.iloc[0,1])))
print('type of df_create.ix[0,0]: {}'.format(type(df_create.iloc[0,2])))
print('type of df_create.ix[0,1]: {}'.format(type(df_create.iloc[0,1])))
print('')
print('type of df_mod.loc[0,\'d1\']: {}'.format(type(df_mod.loc[0,'d1'])))
print('type of df_mod.loc[0,\'d2\']: {}'.format(type(df_mod.loc[0,'d2'])))
print('type of df_mod.iloc[0,0]: {}'.format(type(df_mod.iloc[0,2])))
print('type of df_mod.iloc[0,1]: {}'.format(type(df_mod.iloc[0,1])))
print('type of df_mod.ix[0,0]: {}'.format(type(df_mod.iloc[0,2])))
print('type of df_mod.ix[0,1]: {}'.format(type(df_mod.iloc[0,1])))
print('')
print('All dtypes for dataframe df_mod:')
print(df_mod.dtypes)
print('')
pd.show_versions()
```
produces the following output:
type of df_create.loc[0,'d1']: <class 'numpy.float64'>
type of df_create.loc[0,'d2']: <class 'numpy.float64'>
type of df_create.iloc[0,0]: <class 'numpy.float64'>
type of df_create.iloc[0,1]: <class 'numpy.float64'>
type of df_create.ix[0,0]: <class 'numpy.float64'>
type of df_create.ix[0,1]: <class 'numpy.float64'>
type of df_mod.loc[0,'d1']: <class 'numpy.float64'>
type of df_mod.loc[0,'d2']: <class 'numpy.float64'>
type of df_mod.iloc[0,0]: <class 'numpy.float64'>
type of df_mod.iloc[0,1]: <class 'numpy.float64'>
type of df_mod.ix[0,0]: <class 'numpy.float64'>
type of df_mod.ix[0,1]: <class 'numpy.float64'>
All dtypes for dataframe df_mod:
d1 uint8
d2 float64
d3 uint8
d4 uint8
d5 uint8
dtype: object
#### Problem description
As far as I can see, the indexing mechanism is converting non-float64s to float64s. According to the dtypes, the internal representation of the data remains uint8, but when exposed by some indexing mechanism, the uint8 data is converted to float64.
#### Expected Output
type of df_create.loc[0,'d1']: <class 'numpy.uint8'>
type of df_create.loc[0,'d2']: <class 'numpy.float64'>
type of df_create.iloc[0,0]: <class 'numpy.uint8'>
type of df_create.iloc[0,1]: <class 'numpy.float64'>
type of df_create.ix[0,0]: <class 'numpy.uint8'>
type of df_create.ix[0,1]: <class 'numpy.float64'>
type of df_mod.loc[0,'d1']: <class 'numpy.uint8'>
type of df_mod.loc[0,'d2']: <class 'numpy.float64'>
type of df_mod.iloc[0,0]: <class 'numpy.uint8'>
type of df_mod.iloc[0,1]: <class 'numpy.float64'>
type of df_mod.ix[0,0]: <class 'numpy.uint8'>
type of df_mod.ix[0,1]: <class 'numpy.float64'>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.18.0
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.4.0
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTAzMDYxOTgw
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BUG: TimedeltaIndex raising ValueError when boolean indexing (#14946)
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| 3 | 2017-01-25T06:21:01Z | 2017-12-20T02:04:06Z | 2017-01-26T15:01:14Z |
MEMBER
| null |
- [x] closes #14946
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Implementing patch suggested by @jreback [here](https://github.com/pandas-dev/pandas/issues/14946#issuecomment-268666453) that prevents raising a `ValueError` when boolean indexing with a `TimedeltaIndex`
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DOC: Merge FAQ and gotcha (rebase of GH13768)
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| 3 | 2017-01-25T10:36:01Z | 2017-01-25T23:48:06Z | 2017-01-25T23:48:06Z |
MEMBER
| null |
Rebase and clean-up of https://github.com/pandas-dev/pandas/pull/13768
closes #9809
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| 1 | 2017-01-25T11:09:43Z | 2017-01-25T15:43:14Z | 2017-01-25T15:43:14Z |
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| 10 | 2017-01-25T12:40:40Z | 2017-01-27T14:20:49Z | 2017-01-27T14:20:49Z |
CONTRIBUTOR
| null |
on top of #15216
closes #15227
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Problem in Pandas 0.18.1 with Python 3.5
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| 2 | 2017-01-25T16:26:08Z | 2017-01-25T18:54:47Z | 2017-01-25T18:54:33Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
import numpy as np
u=pd.DataFrame([np.nan]*5)
u.sum()
```
#### Problem description
It returns 0.0 but it is written in http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sum.html.
That it should (and it seems normal) return nan.
"If an entire row/column is NA, the result will be NA"
#### Expected Output
It should return nan.
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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read_sql index_col does not accept tuple
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| 1 | 2017-01-25T16:43:54Z | 2020-05-16T03:50:56Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
data = pandas.read_sql_query( sql, dbConn, index_col=('StartTime',equipCol) )
```
#### Problem description
The index_col argument does not accept a Python tuple, only a list. It should handle both.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 9.0.1
setuptools: 32.1.0
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2014.10
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: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTAzMTY3NzU1
| 15,227 |
Steal the algorithm used to combine hashes from tupleobject.c
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| 4 | 2017-01-25T17:39:24Z | 2017-01-25T23:53:11Z | 2017-01-25T23:53:11Z |
CONTRIBUTOR
| null |
closes #15224
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| 1 | 2017-01-25T20:17:27Z | 2017-01-25T20:22:41Z | 2017-01-25T20:22:26Z |
CONTRIBUTOR
| null |
```python
panel = pd.util.testing.makePanel(nper=2).to_frame()
panel.loc['2000-01-03']
panel.iloc[0]
```
#### Problem description
iloc returns only first row of the DataFrame, so it does not allow taking advantage of the multiple index levels.
#### Expected Output
I would expect iloc[0] to return all the elements that share the first value of the 0-level of the MultiIndex
|
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| 15,229 |
ENH: string sql query passed to read_sql_query should be wrapped in sqlalchemy.text when using an sqlalchemy engine
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[] | null | 6 | 2017-01-25T21:24:56Z | 2020-05-16T03:51:08Z | null |
CONTRIBUTOR
| null |
#### Code Sample, copy-pastable if proper database credentials are used
```python
import pandas as pd
from sqlalchemy import create_engine, text
# change the connection string as required
engine = create_engine('postgresql+psycopg2://hal@localhost/hal')
engine.execute('CREATE TABLE test_orders (id SERIAL PRIMARY KEY, customer_type VARCHAR)')
engine.execute("INSERT INTO test_orders (customer_type) VALUES ('urban'), ('urban'), ('rural')")
statement = "SELECT COUNT(*) FROM test_orders WHERE customer_type LIKE 'urban%'"
statement2 = "SELECT COUNT(*) FROM test_orders WHERE customer_type LIKE :c"
# does not work, exception shown in the expected output section
try:
df = pd.read_sql_query(statement, engine)
except Exception as e:
print(str(type(e) + ': ' + str(e))
# does not work, exception shown in the expected output section
try:
df = pd.read_sql_query(statement, engine, params={'c': 'urban%'})
except Exception as e:
print(str(e))
# works
try:
df = pd.read_sql_query(text(statement), engine)
df2 = pd.read_sql_query(text(statement), engine, params={'c': 'urban%'})
except Exception as e:
print(str(e))
```
#### Problem description
Using plain-text sql with sqlalchemy engines don't work
* if the `%` character is present in the query
* with the argument `params`.
The queries work as expected if the sql is wrapped with `sqlalchemy.text`. Also, wrapping a plain-text query with `sqlalchemy.text` should not have any effect on the output or performance of the query.
However that is not an intuitive behavior for many analysts, who don't know what sqlalchemy is or what ORMs are in general, or that different database drivers may use `%` to format parameters.
Here's a stackoverflow Q-A on the same sqlalchemy usage issue:
http://stackoverflow.com/questions/8657508/strange-sqlalchemy-error-message-typeerror-dict-object-does-not-support-inde
We can prevent this error by wrapping the plain-text sql by `text` if an sqlalchemy engine is passed as the `con` argument to the function.
Something like this:
```python
def read_sql_query(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, chunksize=None):
if isinstance(sql, string) and isinstance(con, Engine):
sql = text(sql)
...
```
I'd be happy to submit a PR for this code change + tests & documentation updates if the maintainers agree this to be an acceptable resolution.
#### Expected Output
```
# output from the first try-except block
<class 'TypeError'>: 'dict' object does not support indexing
# output from the second try-except block
(psycopg2.ProgrammingError) syntax error at or near ":"
LINE 1: ...ELECT COUNT(*) FROM test_orders WHERE customer_type LIKE :c1
^
[SQL: 'SELECT COUNT(*) FROM test_orders WHERE customer_type LIKE :c1'] [parameters: {'c1': 'urban%'}]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 28.8.0
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: 0.7.9.None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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| 15,230 |
API: to_series/to_frame Index methods
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| 3 | 2017-01-25T21:44:15Z | 2017-10-09T12:17:05Z | 2017-10-09T12:17:05Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/15216#issuecomment-275240736
``Index.to_series()``
``MultiIndex.to_frame()``
should we harmonize? (e.g. make both available to both? for compat), leave it like this?
should also prob add the ``index=True`` kw to ``.to_series()`` for compat.
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MDU6SXNzdWUyMDMyNDcxMDE=
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Spurious casting to float when assigning both integers and floats
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| 7 | 2017-01-25T22:54:26Z | 2021-05-08T00:40:24Z | null |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [2]: df = pd.DataFrame([[1,2],[3,4]], columns=['a', 'b'])
In [3]: df.loc[2] = pd.Series({'a' : 5, 'b' : 6})
In [4]: df.dtypes # right!
Out[4]:
a int64
b int64
dtype: object
In [5]: df.loc[3] = pd.Series({'a' : 7})
In [6]: df.dtypes
Out[6]:
a float64
b float64
dtype: object
```
#### Problem description
Column __a__ should not be casted to float, since at no time it is assumed to contain null values.
#### Expected Output
```
In [6]: df.dtypes
Out[6]:
a int64
b float64
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.19.0+376.gdc40058.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 28.0.0
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.8.0.dev0+f80669e
xarray: None
IPython: 5.1.0.dev
sphinx: 1.4.8
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.2.0.dev
tables: 3.2.2
numexpr: 2.6.0
feather: None
matplotlib: 2.0.0rc2
openpyxl: None
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: None
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: 0.2.1
</details>
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BUG/API: Return a Series from DataFrame.asof with no match
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| 3 | 2017-01-26T00:12:31Z | 2017-01-26T12:37:55Z | 2017-01-26T12:37:53Z |
CONTRIBUTOR
| null |
- [x] closes #15118
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Technically this is an API change, but more of a bug so didn't think there was a need to warn / deprecate?
Also there seems to be room to clean up the whole `asof` implementation, xref #10343, but wanted to keep that separate
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TST: incorrect locale specification for datetime format
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| 0 | 2017-01-26T00:21:58Z | 2017-01-26T02:31:04Z | 2017-01-26T02:31:04Z |
CONTRIBUTOR
| null |
closes #15215
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MDU6SXNzdWUyMDMyOTMwODE=
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BUG: groupby.count fails on windows with large categorical index
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| 6 | 2017-01-26T05:01:56Z | 2017-02-01T20:45:37Z | 2017-02-01T20:45:37Z |
CONTRIBUTOR
| null |
#### Problem description
On windows machines the native `int` is `int32` which causes an overflow error in `cartesian_product` in `tools.util`. The problem line is `lenX = np.fromiter((len(x) for x in X), dtype=int)`
```python
def cartesian_product(X):
"""
Numpy version of itertools.product or pandas.compat.product.
Sometimes faster (for large inputs)...
Parameters
----------
X : list-like of list-likes
Returns
-------
product : list of ndarrays
Examples
--------
>>> cartesian_product([list('ABC'), [1, 2]])
[array(['A', 'A', 'B', 'B', 'C', 'C'], dtype='|S1'),
array([1, 2, 1, 2, 1, 2])]
See also
--------
itertools.product : Cartesian product of input iterables. Equivalent to
nested for-loops.
pandas.compat.product : An alias for itertools.product.
"""
msg = "Input must be a list-like of list-likes"
if not is_list_like(X):
raise TypeError(msg)
for x in X:
if not is_list_like(x):
raise TypeError(msg)
if len(X) == 0:
return []
lenX = np.fromiter((len(x) for x in X), dtype=int) <----- HERE
cumprodX = np.cumproduct(lenX)
a = np.roll(cumprodX, 1)
a[0] = 1
if cumprodX[-1] != 0:
b = cumprodX[-1] / cumprodX
else:
# if any factor is empty, the cartesian product is empty
b = np.zeros_like(cumprodX)
return [np.tile(np.repeat(np.asarray(com._values_from_object(x)), b[i]),
np.product(a[i]))
for i, x in enumerate(X)]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTAzMjY3MzQ4
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Make Series.map() documentation a bit more verbose
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| 3 | 2017-01-26T08:23:51Z | 2017-03-02T23:27:32Z | 2017-03-02T23:27:31Z |
CONTRIBUTOR
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pandas.io.gbq.to_gbq creates duplicate rows, pandas==0.19.2
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| 1 | 2017-01-26T12:10:12Z | 2017-01-26T12:27:39Z | 2017-01-26T12:27:05Z |
NONE
| null |
```python
df.to_gbq(data, dataset, chunksize=10000, verbose=False, reauth=False, if_exists='append', private_key=private_key)
```
#### Problem description
We only insert DataFrame one time
It seems is in insert because we also see duplicates in Bigquery WebUIConsole
can be related with [https://github.com/pandas-dev/pandas/issues/5840](https://github.com/pandas-dev/pandas/issues/5840)?
<details>
pandas==0.19.2
</details>
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PERF: correctly report memory used by Index's
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| 3 | 2017-01-26T13:56:24Z | 2017-01-27T14:43:12Z | 2017-01-27T14:43:11Z |
CONTRIBUTOR
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| 203,407,963 |
MDU6SXNzdWUyMDM0MDc5NjM=
| 15,238 |
How to filter excel data by row?
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| 3 | 2017-01-26T15:56:04Z | 2017-01-27T09:18:12Z | 2017-01-26T15:59:34Z |
NONE
| null |
How to filter excel data by row? Say my data is
```
for f in glob.glob('/Users/seb/PycharmProjects/data-transform/data/[I..O]*.xlsx'):
df = pd.read_excel(f)
all_data = all_data.append(df, ignore_index=True)
print(all_data.describe())
print(all_data.head())
```
But not sure how to filter for rows with certain string, so that input:
```
Date Time Variable value
08/01/2017 06:00:05 Occupancy_T4CHK_zoneA_1_Live 83
08/01/2017 06:00:05 Occupancy_T4CHK_totalConcourse_1_Live 510
08/01/2017 06:00:05 ProjQueueTime_T4CHK_zoneA_1_Live 919.048
08/01/2017 06:00:05 Flow_T4CHK_zoneA_1_Live 0
08/01/2017 06:00:05 ExpQueueTime_T4CHK_zoneA_1_Live 357.688
08/01/2017 06:00:05 Flow_T4CHK_zoneB_1_Live 0
08/01/2017 06:00:05 ProjQueueTime_T4CHK_zoneC_1_Live 114.911
08/01/2017 06:00:05 Flow_T4CHK_zoneC_1_Live 0
08/01/2017 06:00:05 ExpQueueTime_T4CHK_zoneC_1_Live 355.909
08/01/2017 06:00:05 Flow_T4CHK_zoneD_1_Live 0
08/01/2017 06:00:05 Flow_T4CHK_zoneE_1_Live 0
08/01/2017 06:00:05 Flow_T4CHK_zoneF_1_Live 0
08/01/2017 06:00:05 Flow_T4CHK_zoneG_1_Live 0
08/01/2017 06:00:05 Flow_T4CHK_zoneH_1_Live 0
```
produces output is
```
08/01/2017 06:00:05 Flow_T4CHK_zoneD_1_Live 0
08/01/2017 06:00:05 Flow_T4CHK_zoneE_1_Live 0
08/01/2017 06:00:05 Flow_T4CHK_zoneF_1_Live 0
08/01/2017 06:00:05 Flow_T4CHK_zoneG_1_Live 0
08/01/2017 06:00:05 Flow_T4CHK_zoneH_1_Live 0
08/01/2017 06:00:10 Flow_T4CHK_zoneD_1_Live 0
08/01/2017 06:00:10 Flow_T4CHK_zoneE_1_Live 0
08/01/2017 06:00:10 Flow_T4CHK_zoneF_1_Live 0
08/01/2017 06:00:10 Flow_T4CHK_zoneG_1_Live 0
08/01/2017 06:00:10 Flow_T4CHK_zoneH_1_Live 0
```
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Inconsistent treatment of categories in multi-level unstack
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| 8 | 2017-01-27T05:06:29Z | 2021-05-19T12:53:59Z | 2021-05-19T12:53:58Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'Date': pd.date_range(start='2016/1/1', periods=3, freq='W'),
'Station':['Kings Cross', 'Sydney', 'Newtown'],
'Hours': ["1PM", "2PM", "3PM"],
'Exit':[10, 30, 50], 'Entry':[0, 60, 20]})
df.Station = df.Station.astype('category')
df.Hours = df.Hours.astype('category')
df1= (df.set_index(['Date','Hours', 'Station'])[['Entry', 'Exit']].
unstack(['Hours', 'Station']).
stack(['Hours', 'Station'], dropna=False).
reset_index())
print(df1.info())
assert df.Hours.dtype == df1.Hours.dtype, "Hours dtype has changed"
assert df.Station.dtype == df1.Station.dtype, "Station dtype has changed"
```
#### Problem description
Hours and Station are categories. After the stack/unstack Hours remains a category but Station becomes an object.
If I switch the order of the Station and Hours in unstack the Station remains categorical and Hours becomes an object.
#### Expected Output
Hours and Station both remain categorical (prefered) or both become the underlying type of.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.41-36.55.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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Timestamp.replace raises ValueError instead of TypeError when given wrong argument
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| 7 | 2017-01-27T05:15:45Z | 2017-01-28T18:29:12Z | 2017-01-28T18:29:11Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
from pandas import Timestamp
Timestamp("2012").replace(apple=3)
```
#### Problem description
The reimplementation of Timestamp.replace (https://github.com/pandas-dev/pandas/commit/f8bd08e9c2fc6365980f41b846bbae4b40f08b83#diff-05de2950b2c86c9828531957e02d1b87L662) raises a ValueError when a keyword argument does not exist.
Previous implementation used datetime.replace that raises a TypeError when an argument is not valid.
This is a regression and it is probably better to keep API compatibility with the datetime.replace function (modulo extra functionalities).
For autodocumentation/autocomplete purposes, it could also be useful to have explicitly in the function signature the keywords, so instead of::
` def replace(self, **kwds):
`
have::
` def replace(self, year=None, month=None, day=None, hour=None, minute=None, second=None, microsecond=None, nanosecond=None, tzinfo=None)
`
#### Actual Output
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas\tslib.pyx", line 715, in pandas.tslib.Timestamp.replace (pandas\tslib.c:14832)
ValueError: invalid name apple passed
#### Expected Output
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas\tslib.pyx", line 715, in pandas.tslib.Timestamp.replace (pandas\tslib.c:14832)
TypeError: 'apple' is an invalid keyword argument for this function
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.3
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5.2
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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API cleanup: Remove global duplicates of DataFrame methods
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[] | null | 4 | 2017-01-27T15:48:10Z | 2017-01-28T23:17:21Z | 2017-01-27T15:53:00Z |
NONE
| null |
In the currently online pandas 0.19.2 documentation, I see the following inconsistency:
* There is [pandas.DataFrame.pivot_table](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot_table.html), but also [pandas.pivot_table](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html).
* In the same way, there is [pandas.DataFrame.pivot](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot.html) and also [pandas.pivot](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot.html). (BTW., the latter has the dataframe argument missing.)
* There is [pandas.melt](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html), but no pandas.DataFrame.melt.
The global versions of these methods (and probably others) are identical to the local ones except for an additional DataFrame argument, I think that all these should only be DataFrame methods. This would e.g. consistently enable chaining.
So let me suggest that the global versions should be removed, of course after a deprecation period.
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ENH:read_html() handles tables with multiple header rows #13434
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| 29 | 2017-01-27T18:08:49Z | 2017-03-29T23:35:08Z | 2017-03-29T23:28:23Z |
CONTRIBUTOR
| null |
- [x] closes #13434
- [x] 2 tests added / passed
- [x] Multiple tr rows within a thead are now parsed, creating a multiindex header.
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PERF: benchmark seems to be hanging
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| 1 | 2017-01-27T18:48:23Z | 2017-02-04T23:30:09Z | 2017-02-04T23:30:09Z |
CONTRIBUTOR
| null |
this seems to hang on 0.19.2 / master (didn't test anything else)
```
((pandas) bash-3.2$ asv continuous v0.19.2 master --bench FrameConstructorDT
· 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 2 total benchmarks (2 commits * 1 environments * 1 benchmarks)
[ 0.00%] · For pandas commit hash 3853fe6d:
[ 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
[ 50.00%] ··· Running frame_ctor.FrameConstructorDTIndexFromOffsets.time_frame_ctor
```
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DOC: groupby aligns Series when passed as groupands
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| 9 | 2017-01-27T19:01:08Z | 2017-03-23T19:10:26Z | 2017-03-23T19:10:26Z |
CONTRIBUTOR
| null |
xref #15338
Calling groupby with an unaligned index on the same frame in multiple threads can result in incorrect results. The following example demonstrates:
```python
import numpy as np
import pandas as pd
from multiprocessing.pool import ThreadPool
def build_frame(N, seed):
rs = np.random.RandomState(seed)
amounts = rs.exponential(0.1, N) * 100
ids = np.random.randint(0, 500, N)
df = pd.DataFrame({'amount': amounts, 'id': ids})
# XXX: call groupby once before makes everything pass. Note that the
# pre-filter by amount is necessary, without it things still fail.
#df[df.amount < 0].groupby(df.id)
return df
def f(x):
return x[x.amount < 20].groupby(x.id).amount.count()
N = 100000
NTHREADS = 8
SEED = 100
df = build_frame(N, SEED)
pool = ThreadPool(NTHREADS)
args = [df] * NTHREADS * 2
r1 = pool.map(f, args)
r2 = pool.map(f, args)
# Print out lengths, which don't always match
print([len(i) for i in r1])
print([len(i) for i in r2])
# Check that results are equivalent
matches = [a.equals(b) for (a, b) in zip(r1, r2)]
assert all(matches)
```
On my machine, running `python test.py` results in:
```
[45, 471, 500, 429, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500]
[500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500]
Traceback (most recent call last):
File "test.py", line 38, in <module>
assert all(matches)
AssertionError
```
A few notes:
- Calling groupby once beforehand fixes everything. (see comment in `build_frame`). To me this indicates that the groupby call sets up some state (that's cached) that may not be threadsafe. Also, the second call to `pool.map` always returns correct results, only the first calls fail.
- Doing this without threading results in the same answer every time.
- Without filtering by amount, the result is also always correct.
Tested with pandas master, as well as pandas 0.19.0 and up.
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PERF: high memory in MI
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| 14 | 2017-01-27T19:29:36Z | 2017-03-02T17:18:51Z | 2017-02-15T15:25:57Z |
CONTRIBUTOR
| null |
closes #13904
Creates an efficient MultiIndexHashTable in cython. This allows us to efficiently store a multi-index for fast indexing (``.get_loc()`` and ``.get_indexer()`), with the current tuple-based (and gil holding) use of the PyObject Hash Table.
This uses the ``pandas.tools.hashing`` routines to hash each of the 'values' of a MI to a single uint64.
So this makes MI more memory friendly and much more efficient. You get these speedups, because the creation of the hashtable is now much more efficient.
```
before after ratio
[48fc9d61] [f47131ce]
- 871.05ms 423.93ms 0.49 reindex.Reindexing.time_reindex_multiindex
- 230.31ms 72.51ms 0.31 indexing.MultiIndexing.time_is_monotonic
- 747.11ms 199.49ms 0.27 indexing.MultiIndexing.time_multiindex_get_indexer
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
This doesn't quite close the above issue yet. Still working on getting rid of all/most of the actual instantiation of array-of-tuples. but that's the next stage.
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MDU6SXNzdWUyMDM3MzgyNzk=
| 15,246 |
Length/size of datetime groups cast as datetimes
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| 1 | 2017-01-27T20:56:52Z | 2017-01-27T20:59:38Z | 2017-01-27T20:59:22Z |
CONTRIBUTOR
| null |
```
In [7]: pandas.Series([datetime.datetime.utcnow()]*4).groupby([1, 1, 2, 2]).agg('size')
Out[7]:
1 2
2 2
dtype: int64
In [8]: pandas.Series([datetime.datetime.utcnow()]*4).groupby([1, 1, 2, 2]).agg(len)
Out[8]:
1 1970-01-01 00:00:00.000000002
2 1970-01-01 00:00:00.000000002
dtype: datetime64[ns]
In [9]: pandas.Series([datetime.datetime.utcnow()]*4).groupby([1, 1, 2, 2]).agg(numpy.size)
Out[9]:
1 1970-01-01 00:00:00.000000002
2 1970-01-01 00:00:00.000000002
dtype: datetime64[ns]
```
#### Output of ``pd.show_versions()``
<details>
commit: 3853fe6d2b884f186b93933ef53ff5e475c2d80c
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0+395.g3853fe6
nose: 1.3.7
pip: 8.1.2
setuptools: 22.0.0
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
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
pandas_datareader: None
</details>
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DataFrame slice of dtype np.float64 returns all zeros when using .loc for row/column selection
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| 2 | 2017-01-27T21:09:08Z | 2017-01-29T09:52:54Z | 2017-01-28T22:16:19Z |
NONE
| null |
Hi all,
I noticed this bug today when I was trying to cast a large table (~56,000 rows by 8500 columns) from `np.float64` to `np.float32`. Since not **every** column in the table is made of `float.64`s I first selected columns that were.
#### Code Sample, a copy-pastable example if possible
```python
float_cols = curtable.dtypes[curtable.dtypes=="float64"]
```
The output then looked something like this:
```python
float_cols
['GTEX-1117F-0426-SM-5EGHI',
'GTEX-1117F-0626-SM-5N9CS',
'GTEX-1117F-0726-SM-5GIEN',
'GTEX-1117F-1326-SM-5EGHH',
'GTEX-1117F-2426-SM-5EGGH',
'GTEX-1117F-2526-SM-5GZY6',
'GTEX-1117F-2826-SM-5GZXL',
'GTEX-1117F-2926-SM-5GZYI',
'GTEX-1117F-3026-SM-5GZYU',
'GTEX-1117F-3226-SM-5N9CT',
'GTEX-111CU-0126-SM-5GZWZ',
'GTEX-111CU-0226-SM-5GZXC',
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'GTEX-111FC-3326-SM-5GZYV',
'GTEX-111VG-0008-SM-5Q5BG',
'GTEX-111VG-0326-SM-5GZX7',
'GTEX-111VG-0526-SM-5N9BW',
'GTEX-111VG-0726-SM-5GIDC',
'GTEX-111VG-1626-SM-5EGIO',
'GTEX-111VG-1926-SM-5GIDO',
'GTEX-111VG-2226-SM-5N9DU',
'GTEX-111VG-2326-SM-5N9BK',
'GTEX-111VG-2426-SM-5GZXD',
'GTEX-111VG-2526-SM-5GZXP',
'GTEX-111VG-2626-SM-5GZY2',
'GTEX-111YS-0006-SM-5NQBE',
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'GTEX-111YS-0326-SM-5GZZ3',
'GTEX-111YS-0426-SM-5987O',
'GTEX-111YS-0526-SM-5GZXJ',
'GTEX-111YS-0626-SM-5GZXV',
'GTEX-111YS-0726-SM-5GZY8',
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'GTEX-1128S-0926-SM-5GZZU',
'GTEX-1128S-1626-SM-5H12O',
'GTEX-1128S-2126-SM-5H12U',
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'GTEX-1128S-2426-SM-5H11B',
'GTEX-1128S-2526-SM-5H11N',
'GTEX-1128S-2726-SM-5H12C',
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'GTEX-113IC-0006-SM-5NQ9C',
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'GTEX-113JC-0008-SM-5QGR6',
'GTEX-113JC-0126-SM-5EGJW',
'GTEX-113JC-0726-SM-5GZZR',
'GTEX-113JC-0926-SM-5H114',
'GTEX-113JC-1026-SM-5H117',
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'GTEX-117YX-1826-SM-5H12K',
'GTEX-117YX-1926-SM-5GIEC',
'GTEX-117YX-2126-SM-5GIEL',
'GTEX-117YX-2226-SM-5EGJJ',
'GTEX-117YX-2326-SM-5H12W',
'GTEX-117YX-2426-SM-5GZZJ',
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'GTEX-117YX-2626-SM-5EQ53',
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'GTEX-1192X-1126-SM-5EGGU',
'GTEX-1192X-1526-SM-5H11I',
'GTEX-1192X-1826-SM-5GIE2',
...]
```
However, when I use this column list to select these rows, the returned DataFrame is all zeros:
```python
np.all(curtable.loc[:, float_cols] == 0)
True
```
But when I just use one column, seems like there is no issue.
```python
np.all(curtable.loc[:, float_cols[0]] == 0)
False
```
#### Problem description
My understanding is that the `.loc` indexer can be supplied with a list of values from the index/columns to select those cells within a table. Perhaps this is an issue when working with particularly large tables (in my case, 56000 by 8500)
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: None
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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| 203,742,437 |
MDExOlB1bGxSZXF1ZXN0MTAzNTY4Mjk4
| 15,248 |
Timestamp replace compatibility
|
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| 7 | 2017-01-27T21:17:36Z | 2017-01-28T18:57:14Z | 2017-01-28T18:29:12Z |
CONTRIBUTOR
| null |
- [x ] closes #15240
- [] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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Timestamp subtraction should work for differing timezones
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| 13 | 2017-01-27T23:03:56Z | 2019-11-30T21:44:34Z | 2017-12-15T01:18:01Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```ipython
In [1]: import pandas as pd
In [2]: pd.Timestamp("2016-01-01", tz="Europe/Berlin") - pd.Timestamp("now", tz="UTC")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-691a8df26ecd> in <module>()
----> 1 pd.Timestamp("2016-01-01", tz="Europe/Berlin") - pd.Timestamp("now", tz="UTC")
pandas\tslib.pyx in pandas.tslib._Timestamp.__sub__ (pandas\tslib.c:23697)()
TypeError: Timestamp subtraction must have the same timezones or no timezones
```
#### Problem description
If *both* timestamps have a timezone specified, the result of this operation is perfectly well-defined. It's quite surprising that I have to riddle my code with `lhs.tz_convert("UTC") - rhs.tz_convert("UTC")` lines to get the difference of timestamps.
#### Expected Output
Timedelta('-393 days +06:29:07.057926')
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: 2.4.0
xlrd: None
xlwt: None
xlsxwriter: 0.9.6
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8.1
boto: None
pandas_datareader: None
</details>
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| 15,250 |
Pandas not working in Anaconda3 4.2. Not able to update also
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[] | null | 7 | 2017-01-27T23:32:10Z | 2017-01-28T00:18:10Z | 2017-01-27T23:43:51Z |
NONE
| null |
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-2-d6ac987968b6> in <module>()
----> 1 import pandas
D:\Anaconda3-4.2\lib\site-packages\pandas\__init__.py in <module>()
20
21 # numpy compat
---> 22 from pandas.compat.numpy import *
23
24 try:
D:\Anaconda3-4.2\lib\site-packages\pandas\compat\numpy\__init__.py in <module>()
4 import numpy as np
5 from distutils.version import LooseVersion
----> 6 from pandas.compat import string_types, string_and_binary_types
7
8 # turn off all numpy warnings
ImportError: cannot import name 'string_types'
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MDU6SXNzdWUyMDM3NjY0MDg=
| 15,251 |
DEPS: drop python 3.4
|
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| 19 | 2017-01-27T23:33:49Z | 2017-09-08T14:44:33Z | 2017-05-16T00:02:13Z |
CONTRIBUTOR
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.loc with date string and timestamp behave differently
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| 2 | 2017-01-28T00:40:30Z | 2020-03-31T06:03:37Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> t0 = pd.Timestamp('2010-01-01')
>>> t1 = pd.Timestamp('2012-02-02')
>>> df = pd.DataFrame([1, 2, 3], index=pd.MultiIndex.from_tuples([(t0, 'A'), (t0, 'B'), (t1, 'A')]), columns=['C'])
>>> # Compare the following results:
>>> print(df.ix['2010-01-01'])
# C
# A 1
# B 2
>>> print(df.loc['2010-01-01'])
# C
# 2010-01-01 A 1
# B 2
>>> print(df.ix[t0])
# C
# A 1
# B 2
>>> print(df.loc[t0])
# C
# A 1
# B 2
```
#### Problem description
The result of `df.loc['2010-01-01']` is different from that of `df.ix['2010-01-01']` or `df.loc[pd.Timestamp('2010-01-01')]`; it contains additional index level for date. (`df.ix[]` returns the same data frame for date string and timestamp slicer.)
#### Expected Output
```
----
C
A 1
B 2
----
C
A 1
B 2
----
C
A 1
B 2
----
C
A 1
B 2
----
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2+0.g825876c.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTAzNTk4OTE3
| 15,253 |
BUG GH15240 Timestamp.replace raised ValueError instead of TypeError.
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[] | null | 2 | 2017-01-28T03:24:25Z | 2017-01-28T03:29:09Z | 2017-01-28T03:26:16Z |
NONE
| null |
- [ ] closes #15240
- [1] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry 514
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MDU6SXNzdWUyMDM4MjIyNzM=
| 15,254 |
Assigning new dataframe to cell in dataframe results in nan
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| 1 | 2017-01-28T18:19:34Z | 2018-07-06T20:21:15Z | 2017-01-28T18:30:39Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
# Create a simple nested DataFrame
d = {"id": pd.Series([0,1,2]),
"inner_df": pd.Series([pd.DataFrame(np.random.rand(4,4)) for _ in range(3)])}
outer_df = pd.DataFrame(d)
print("Old inner df in first row:")
print(outer_df.loc[0, "inner_df"])
# Now update the inner_df in the first row
new_inner_df = pd.DataFrame(np.random.rand(4, 4))
print("\n*** Results using loc ***")
outer_df.loc[0, "inner_df"] = new_inner_df
print("Expected new inner df in first row:")
print(new_inner_df)
print("Real new inner df in first row:")
print(outer_df.loc[0, "inner_df"])
print("\n*** Results using set_value ***")
outer_df.set_value(0, 'inner_df', new_inner_df)
print("Expected new inner df in first row:")
print(new_inner_df)
print("Real new inner df in first row:")
print(outer_df.loc[0, "inner_df"])
print("\n*** Results using concatenated indexing***")
outer_df["inner_df"][0] = new_inner_df
print("Expected new inner df in first row:")
print(new_inner_df)
print("Real new inner df in first row:")
print(outer_df.loc[0, "inner_df"])
```
#### Problem description
The most intuitive method using loc doesn't work if the new value is a dataframe. This leads users to use the third method using concatenated indexing, which should be avoided.
The second method using set_value is also not mentioned in the tutorials/docs on indexing. Only by luck, I found a (semi-)related stack overflow question, which hinted to try set_value.
Thus I recommend:
1. Allow assigning a DataFrame to a single cell in a DataFrame or show an error hinting on set_value
2. Mention set_value here: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
#### Expected Output
(included in script)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
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TST: MultiIndex Slicing maintains first level index (#12697)
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| 4 | 2017-01-29T09:58:31Z | 2017-12-20T02:04:06Z | 2017-01-29T21:52:56Z |
MEMBER
| null |
- [x] closes #12697
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
The original issue appears to work correctly on master and doesn't seem to have been fixed by a PR from 0.20.0
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Do not cast .transform() output back to input dtype (closes #10972)
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| 12 | 2017-01-29T13:45:47Z | 2017-03-03T07:56:38Z | 2017-03-02T23:19:30Z |
CONTRIBUTOR
| null |
- [x] closes #10972
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Maybe need more tests, and check performance?
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API: change default behaviour of str.match from deprecated extract to match (GH5224)
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| 3 | 2017-01-29T21:48:52Z | 2017-03-22T18:51:12Z | 2017-03-22T18:51:11Z |
MEMBER
| null |
I just stumbled on this, and seems we didn't have this in our deprecations to do list (https://github.com/pandas-dev/pandas/issues/6581).
This PR changes the default behaviour of `str.match` from extracting groups to just a match (True/False). The previous default behaviour was deprecated since 0.13.0 (https://github.com/pandas-dev/pandas/pull/5224)
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Subclassing pandas.Index
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[] | null | 3 | 2017-01-30T01:48:38Z | 2021-05-08T00:44:05Z | null |
MEMBER
| null |
@spencerkclark is working on a custom `pandas.Index` subclass for xarray (see https://github.com/pydata/xarray/issues/1084) like `pandas.DatetimeIndex` to handle arrays of `netcdftime.datetime` objects. This index is primarily intended for use with xarray, but ideally we'd like it to work in pandas `Series` and `DataFrame` objects, too.
The subclass will include implementations of at least `get_loc`, `get_slice_bound` and `get_value` (this one should probably be unnecessary, but it's needed for `pandas.Series`). To minimize fragility, it will not subclass `DatetimeIndex` but will instead copy some of the relevant code (thank you open source!).
Two questions for other pandas devs:
- Is there any fundamental reason why a custom `pandas.Index` subclass won't work on a `Series` or `DataFrame`?
- Does this seem like a reasonable thing to do, or we are setting ourselves up for suffering in the future? I'll update this issue when we have a concrete PR to look at.
At a bare minimum, we should probably add some tests to pandas to ensure that a basic subclass works.
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| 15,259 |
CLN: remove build some warnings
|
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| 1 | 2017-01-30T02:29:10Z | 2017-01-30T14:15:10Z | 2017-01-30T14:15:10Z |
CONTRIBUTOR
| null |
CLN: from algos_rank_helper.pxi.in
CLN: and algos_groupby_helper.pxi.in
CLN: hashtable warns
CLN: parser warnings
closes #15190
|
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MDExOlB1bGxSZXF1ZXN0MTAzNjg3ODIx
| 15,260 |
API: Reformat output of groupby.describe (#4792)
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| 13 | 2017-01-30T07:12:15Z | 2017-12-20T02:04:08Z | 2017-02-10T00:19:24Z |
MEMBER
| null |
- [x] closes #4792
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
Doesn't look like this was address in a PR in 0.20, but the original issue works on master.
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MDU6SXNzdWUyMDM5Nzc2Mzc=
| 15,261 |
Styler.render generates unquoted attribute values, which are illegal in XHTML
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[] | null | 1 | 2017-01-30T10:51:56Z | 2017-01-30T11:40:55Z | 2017-01-30T11:40:55Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> df = pd.DataFrame([[4,5]], columns=['A', 'B'])
>>> df.style.render()[355:405]
u'<th class="col_heading level0 col0" colspan=1>\n '
```
#### Problem description
Including the generated html where valid XHTML is expected (for example, [in a Jekyll site using kramdown, the default markdown parser](http://stackoverflow.com/questions/41933713/pandas-dataframe-rendered-as-html-shows-up-weirdly-with-literal-tags-when-incl)) leads to weird behaviour.
#### Expected Output
The same thing, but with all attributes quoted, e.g. `colspan=1` => `colspan="1"`. As far as I can tell, there's no downside to always quoting attribute values.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Linux
OS-release: 3.13.0-107-generic
machine: i686
processor: i686
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 25.1.0
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 1.5.7
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.1.3
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 0.8.0
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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| 204,041,654 |
MDU6SXNzdWUyMDQwNDE2NTQ=
| 15,262 |
BUG in MultiIndex truncated repr with integer level names
|
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| 11 | 2017-01-30T15:29:29Z | 2017-02-24T20:18:20Z | 2017-02-24T20:18:20Z |
NONE
| null |
Reproducible example:
```
In [10]: df = pd.DataFrame({'col': range(9)}, index=pd.MultiIndex.from_product([['A0', 'A1', 'A2'], ['B0', 'B1', 'B2']], names=[1,2]))
In [11]: df
Out[11]:
col
1 2
A0 B0 0
B1 1
B2 2
A1 B0 3
B1 4
B2 5
A2 B0 6
B1 7
B2 8
In [12]: pd.options.display.max_rows = 4
In [13]: df
Out[13]:
col
1 2
A0 A0 0
A0 1
... ...
A2 A2 7
A2 8
[9 rows x 1 columns]
```
So the truncated repr shows incorrectly the first index level (with integer level name `1`) again for the second level.
---
Original post:
#### Code Sample, a copy-pastable example if possible
```python
import wget
import pandas
import pickle
url = 'https://www.dropbox.com/s/aldllo0bi3m3wkl/stock?dl=1'
filename = wget.download(url)
df = pickle.load(open(filename))
df
#bad display, index duplicated?
df.head()
#expected display
```
#### Problem description
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th></th> <th>merged</th> </tr> <tr> <th>1</th> <th>2</th> <th></th> </tr> </thead> <tbody> <tr> <th>a.</th> <th>a.</th> <td>2</td> </tr> <tr> <th>abel</th> <th>abel</th> <td>1</td> </tr> <tr> <th>agnes</th> <th>agnes</th> <td>2</td> </tr> <tr> <th rowspan="2" valign="top">alain</th> <th>alain</th> <td>8</td> </tr> <tr> <th>alain</th> <td>2</td> </tr> </tbody> </table>
I have created a multi-index based on 2 columns .
Those two columns wont appear properly, index_column "2" being duplicated from "1"
When displaying up to the 60th first rows of dataframe, it's fine, then it duplicates again the column 1 in the column 2.
#### Expected Output
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th></th> <th>merged</th> </tr> <tr> <th>1</th> <th>2</th> <th></th> </tr> </thead> <tbody> <tr> <th>a.</th> <th>masson-dubois</th> <td>2</td> </tr> <tr> <th>abel</th> <th>pinchard</th> <td>1</td> </tr> <tr> <th>agnes</th> <th>paquet</th> <td>2</td> </tr> <tr> <th rowspan="2" valign="top">alain</th> <th>corcia</th> <td>8</td> </tr> <tr> <th>hudelot-noellat</th> <td>2</td> </tr> </tbody></table>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.4-moby
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.11.2
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 204,046,297 |
MDU6SXNzdWUyMDQwNDYyOTc=
| 15,263 |
pandas.DataFrame.to_dict compatibility with sklearn.feature_extraction.DictVectorizer
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[] | null | 4 | 2017-01-30T15:45:21Z | 2017-02-24T17:02:29Z | 2017-01-30T15:48:37Z |
NONE
| null |
#### Abstract
Currently pandas to_dict function and scikit-learn's sklearn.feature_extraction.DictVectorizer don't get along. I provide a simple script to make these compatible.
#### Description
Panda's current to_dict function returns a dictionary of the format:
`dict[column_name] <- (dict[index] <- val)`
Scikit-learn's DictVectorizer requires a list of dicts of the format:
`list[index] <- (dict[column_name] <- val)`
If pandas could provide this style of formatting as an optional parameter in the to_dict function, this issue could be easily resolved for future uses, as this is clearly an understandable indexing of a dataframe.
#### Gist Code
https://gist.github.com/ryanpeach/af3e7b217aa2fd5c98a262acce828da6
```
def pandas_to_dict(df):
""" A to_dict method for pandas DataFrames that converts frames to a format
compatible with sklearn.feature_extraction.DictVectorizer
REF: http://scikit-learn.org/stable/modules/feature_extraction.html#dict-feature-extraction
Parameters:
df: DataFrame
Returns:
out_list: list indexed by df index, containing dicts indexed by column name referencing row value.
""""
# Get Index
out_list = []
for i in range(len(df)):
index = df.iloc[i]
# Get Column
out_dict = {}
for c in df.columns:
out_dict[c] = index[c]
# Add Column to the List
out_list.append(out_dict)
return out_list
```
Use it if you'd like, I wrote it for my own use and just wanted to make the community aware.
I posted this on scikit-learn as well.
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MDExOlB1bGxSZXF1ZXN0MTAzNzY2NzEy
| 15,264 |
CLN: remove build warnings from tslib.pyx, saslib.pyx, some from lib.pyx
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| 1 | 2017-01-30T16:11:53Z | 2017-01-30T23:27:01Z | 2017-01-30T17:39:51Z |
CONTRIBUTOR
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| 204,092,249 |
MDExOlB1bGxSZXF1ZXN0MTAzNzkzMjQ5
| 15,265 |
Fix overflow error in cartesian_product
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| 4 | 2017-01-30T18:32:15Z | 2017-02-01T20:46:03Z | 2017-02-01T20:45:37Z |
CONTRIBUTOR
| null |
When the numbers in `X` are large it can cause an overflow error on windows machine where the native `int` is 32 bit. Switching to np.intp alleviates this problem.
Other fixes would include switching to np.uint32 or np.uint64.
#15234
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#14854: DatetimeIndex compiled together in test_datetime.py
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| 19 | 2017-01-30T19:08:37Z | 2017-02-08T19:43:55Z | 2017-02-03T01:15:55Z |
CONTRIBUTOR
| null |
### Partially completes #14854
Grouped all the ```DatetimeIndex``` related tests under one file - ```pandas/tests/indexes/test_datetime.py```.
### Tests reorganized
There were some tests in other classes which could be shifted to the as well. From **```pandas/pandas/tseries/tests/test_timeseries.py```**
- [test_pass_datetimeindex_to_index](https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/tests/test_timeseries.py#L326) from class ```TestTimeSeries```
- [test_datetimeindex_accessors](https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/tests/test_timeseries.py#L3972) from class ```TestDatetime64```
- [test_datetimeindex_diff](https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/tests/test_timeseries.py#L4111) from class ```TestDatetime64```
- [test_nanosecond_field](https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/tests/test_timeseries.py#L4105) from class ```TestDatetime64```
- [test_datetimeindex_constructor](https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/tests/test_timeseries.py#L4146) from class ```TestDatetime64```
1. Should these also be included in the ```test_datetime.py``` in their respective classes or let them be?
2. In **```pandas/tests/indexes/test_datetimelike.py```** there were some tests which were using the ```DatetimeLike``` class defined there. I could import that class in the ```test_datetime.py``` and transfer the whole ```TestDatetimeIndex``` but should it be done?
### Mostly passes ``git diff upstream/master | flake8 --diff``
```
./pandas/tests/indexes/test_datetime.py:1121:5: F811 redefinition of unused 'test_intersection' from line 572
./pandas/tests/indexes/test_datetime.py:1751:5: F811 redefinition of unused 'test_union' from line 589
```
3. Should I merge the two ```test_intersection``` and ```test_union``` functions?
## Further TODOs from the following discussion and the #14854 under datetime
- [x] Split ```datetimes/test_datetime.py``` into smaller chunks
- [x] In ```datetimes/test_misc.py``` lines from 167-202 should go under timestamp related tests (?)
- [x] Transfer of function (```test_datetimeindex_accessors```) from class ```TestDatetime64``` in ```datetimes/test_misc.py``` to class ```TestDatetimeIndexOps``` of ```datetime/test_ops.py``` (?)
- [x] Function ```test_round``` from ```datetimes/test_datetime.py``` to ```pandas/tests/scalars/test_timestamp.py```. (and test_timedelta.py). So anything specifically Timestamp related will go there.
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MDU6SXNzdWUyMDQxMDkxMjM=
| 15,267 |
read_stata does not convert dates
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[] | null | 6 | 2017-01-30T19:40:44Z | 2017-01-30T20:44:17Z | 2017-01-30T20:42:08Z |
NONE
| null |
Running Pandas 0.19.2 on Python 3.5.2 (macOS), the docstring for the `read_stata` function indicates that its function is to "Convert date variables to DataFrame time values"
<img width="754" alt="screenshot 2017-01-30 13 36 30" src="https://cloud.githubusercontent.com/assets/81476/22438376/58738822-e6f1-11e6-8e01-1cc3aca0cb99.png">
Yet, setting this to true does not perform this, nor does passing a list of date variables as an argument (as works with other `read_*` functions. The data type remains as an `object`:
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MDU6SXNzdWUyMDQxMjExMzI=
| 15,268 |
DataFrame with PeriodIndex causes KeyError on get_value
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| 5 | 2017-01-30T20:30:47Z | 2018-07-06T20:21:15Z | 2017-01-30T20:36:20Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
from pandas import to_datetime, period_range, DataFrame
import pandas as pd
print(pd.__version__)
start_of_time = to_datetime('2016-10-17 01:16:39.133000')
end_of_time = to_datetime('2017-01-04 23:58:37.905000')
avs_date_range = period_range(start_of_time, end_of_time, freq='D')
bins = DataFrame(dict(foo=[0] * len(avs_date_range), bar=[0] * len(avs_date_range)),
index=avs_date_range)
current = range(10)
for idx, bin in bins.iterrows():
for i in range(6):
bins.set_value(idx, 'foo', bin['foo'] + 1)
f_count = bins.get_value(idx, 'foo')
bins.set_value(idx, 'bar', len(current) - f_count)
print(bins)
```
#### Problem description
If I comment out the setting of index this works as expected with PeriodIndex defined this creates KeyError.
#### Output of ``pd.show_versions()``
<details>
0.19.1
Traceback (most recent call last):
File "pandas\index.pyx", line 161, in pandas.index.IndexEngine.get_loc (pandas\index.c:4289)
File "pandas\src\hashtable_class_helper.pxi", line 404, in pandas.hashtable.Int64HashTable.get_item (pandas\hashtable.c:8534)
TypeError: an integer is required
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Program Files (x86)\JetBrains\PyCharm 5.0.1\helpers\pydev\pydevd.py", line 2403, in <module>
globals = debugger.run(setup['file'], None, None, is_module)
File "C:\Program Files (x86)\JetBrains\PyCharm 5.0.1\helpers\pydev\pydevd.py", line 1794, in run
launch(file, globals, locals) # execute the script
File "C:\Program Files (x86)\JetBrains\PyCharm 5.0.1\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/dlippman/src/ETDataView/test.py", line 19, in <module>
f_count = bins.get_value(idx, 'foo')
File "C:\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\pandas\core\frame.py", line 1900, in get_value
return engine.get_value(series.get_values(), index)
File "pandas\index.pyx", line 105, in pandas.index.IndexEngine.get_value (pandas\index.c:3567)
File "pandas\index.pyx", line 113, in pandas.index.IndexEngine.get_value (pandas\index.c:3250)
File "pandas\index.pyx", line 163, in pandas.index.IndexEngine.get_loc (pandas\index.c:4373)
KeyError: Period('2016-10-17', 'D')
</details>
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MDU6SXNzdWUyMDQxNjY0MTQ=
| 15,269 |
DEPR: set_value / get_value
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| 17 | 2017-01-30T23:56:41Z | 2018-03-21T13:26:17Z | 2017-10-05T12:18:20Z |
CONTRIBUTOR
| null |
we already have too many public indexers.....
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MDExOlB1bGxSZXF1ZXN0MTAzODYxNDI3
| 15,270 |
ENH: Add empty property to Index.
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| 12 | 2017-01-31T02:22:52Z | 2017-03-28T19:11:01Z | 2017-03-28T19:10:59Z |
CONTRIBUTOR
| null |
Previously, attempting to evaluate an Index in a boolean context prints
an error message listing various alternatives, one of which is `.empty`,
which was not actually implemented on `Index`.
- [x] closes #13207
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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[] | null | 5 | 2017-01-31T03:23:59Z | 2017-04-03T15:18:44Z | 2017-04-03T15:18:44Z |
CONTRIBUTOR
| null |
Alters the ```read_gbq``` function to leave ```dialect``` unset by default. This allows the user to specify the dialect using a [query prefix](https://cloud.google.com/bigquery/docs/reference/standard-sql/enabling-standard-sql#sql-prefix), while leaving the default behavior unchanged.
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| 204,219,185 |
MDU6SXNzdWUyMDQyMTkxODU=
| 15,272 |
Inconsistent handling of index after groupby operation
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| 2 | 2017-01-31T07:01:52Z | 2021-08-05T23:33:54Z | 2021-08-05T23:33:54Z |
NONE
| null |
[from this SO question](http://stackoverflow.com/q/41949697/2336654)
#### snippet 1
```import pandas as pd
df = pd.DataFrame(dict(A=[0, 1, 2, 3]))
# returns results identical to df.A
print(df.groupby(df.A // 2).A.nsmallest(2))
# returns results out of order
print(df.groupby(df.A // 2).A.nlargest(2))
0 0
1 1
2 2
3 3
Name: A, dtype: int64
A
0 1 1
0 0
1 3 3
2 2
Name: A, dtype: int64
```
#### snippet 2
```import pandas as pd
df = pd.DataFrame(dict(A=[0, 1, 2, 3]))
print(df.groupby(df.A // 2).A.apply(pd.Series.sample, n=2))
```
#### Problem description
When the results of a `groupby` operation return the same results as what was in a the group in the first place, the index is left identical to the object being grouped. This doesn't sound so horrible until you realize that it is inconsistent with very comparable operations. This is observed in snippet 1. However, snippet 2 puts a finer point on it. The same code sample produces randomly different results.
#### Expected Output
```
A
0 1 0
0 1
1 3 2
2 3
Name: A, dtype: int64
A
0 1 1
0 0
1 3 3
2 2
Name: A, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: 0.2.1
</details>
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MDU6SXNzdWUyMDQzMTM0NzY=
| 15,273 |
to_json on multiindex does not escape strings properly
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| 3 | 2017-01-31T14:27:59Z | 2021-05-19T12:53:59Z | 2021-05-19T12:53:59Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import datetime
pd.read_json(pd.DataFrame(True, index=pd.date_range(datetime.date(2017, 1, 20), datetime.date(2017, 1, 23)), columns=['foo', 'bar']).stack().to_json())
```
#### Problem description
The result of `pd.DataFrame(True, index=pd.date_range(datetime.date(2017, 1, 20), datetime.date(2017, 1, 23)), columns=['foo', 'bar']).stack().to_json()` contains
`r'{"[1484870400000,"foo"]":true,"[1484870400000,"bar"]"'`,
#### Expected Output
Whereas it should be:
`r'{"[1484870400000,\"foo\"]":true,"[1484870400000,\"bar\"]"'`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.5.final.0
python-bits: 32
OS: Windows
machine: AMD64
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
Cython: 0.25.1
numpy: 1.11.2
pandas_datareader: None
</details>
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| 15,274 |
read_hdf is not read-threadsafe on MAC and Windows
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| 3 | 2017-01-31T16:26:11Z | 2017-01-31T17:12:15Z | 2017-01-31T16:35:05Z |
NONE
| null |
#### Problem description
pd.read_hdf() segfaults when called from multiple threads in read only mode.
From the documentation I expected that reading a HDF5 file in another thread would work. In the example each file is mapped out to its own thread and the thread is responsible for opening and closing the file. Note that when I used HDF5 files with compression the segfault happened everytime. When I was making the minimal example, I used no compression and fround it does not segfault about 80% of the time. The test code shows that when complib is set, but complevel is 0 (i.e no compression) the failure rate is 100%.
This problem is also reported in #12236
#### Test Code
Note this fails reliably when complib is not None. When complib=None the failure rate is ~ 20% for me in limited testing.
This is another example of # but shows effect of compression settings on the issue
```python
import numpy as np
import pandas as pd
import concurrent.futures as cf
# Failure rate
# - Fails with Segmentation fault
COMPLIB, COMPLEVEL = None, 0 # 2 out of 10 tests
COMPLIB, COMPLEVEL = 'zlib', 9 # 3 out of 3 tests
COMPLIB, COMPLEVEL = 'zlib', 0 # 3 out of 3 tests
COMPLIB, COMPLEVEL = 'blosc', 0 # 3 out of 3 tests
COMPLIB, COMPLEVEL = 'blosc', 1 # 3 out of 3 tests
COMPLIB, COMPLEVEL = 'lzo', 0 # 3 out of 3 tests
COMPLIB, COMPLEVEL = 'bzip2', 0 # 3 out of 3 tests
# create test data
a = np.random.rand(1000000)
b = np.random.rand(1000000)
d1 = pd.DataFrame(dict(a=a*1, b=b*1))
d2 = pd.DataFrame(dict(a=a*2, b=b*2))
d3 = pd.DataFrame(dict(a=a*3, b=b*3))
d4 = pd.DataFrame(dict(a=a*4, b=b*4))
d1.to_hdf('d1.h5', '/data', complib=COMPLIB, complevel=COMPLEVEL)
d2.to_hdf('d2.h5', '/data', complib=COMPLIB, complevel=COMPLEVEL)
d3.to_hdf('d3.h5', '/data', complib=COMPLIB, complevel=COMPLEVEL)
d4.to_hdf('d4.h5', '/data', complib=COMPLIB, complevel=COMPLEVEL)
files = ['d{}.h5'.format(i) for i in range(1, 5)]
# map reads out to threads
e = cf.ThreadPoolExecutor()
futures = [e.submit(pd.read_hdf, f, '/data/', mode='r') for f in files]
```
#### Expected Output
a list of 4 futures that can be
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
Crash Report
<details>
python(7164,0x700000c10000) malloc: *** error for object 0x7f8ba3655e60: pointer being freed was not allocated
*** set a breakpoint in malloc_error_break to debug
Abort trap: 6
</details>
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| 204,456,410 |
MDU6SXNzdWUyMDQ0NTY0MTA=
| 15,275 |
DataFrameGroupBy.idxmin() returns DataFrame, documentation says Series
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| 6 | 2017-01-31T23:36:06Z | 2019-10-21T19:34:19Z | null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame([[0, 0],
[3, 0],
[1, 1]], index=list('ABC'), columns=list('ab'))
gby = df.groupby(by='b')
print(type(gby)) # <class 'pandas.core.groupby.DataFrameGroupBy'>
print(type(gby.idxmin())) # <class 'pandas.core.frame.DataFrame'>
```
#### Problem description
According to [the documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.DataFrameGroupBy.idxmin.html), this is supposed to output a `pandas.core.Series`. To me, that seems to be what makes sense, but I'm not sure how or why this ended up returning a `DataFrame`. Is this just an issue with the documentation, or is it an issue with the code?
**Edit**: Ah, I understand why this returns a `DataFrame` now - if you have multiple columns the `idxmin()` might be different for each column. Seems that the documentation needs to be updated. I can make a PR if that's appropriate.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.6-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 34.0.3
Cython: 0.25.2
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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to_sql is too slow
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| 25 | 2017-02-01T03:48:24Z | 2021-09-23T03:46:37Z | 2017-02-01T03:59:51Z |
NONE
| null |
#### Code Sample,
```python
df_name.to_sql('table_name',
schema = 'public',
con = engine,
index = False,
if_exists = 'replace')
```
#### Problem description
Im writing a 500,000 row dataframe to a postgres AWS database and it takes a very, very long time to push the data through.
It is a fairly large SQL server and my internet connection is excellent so I've ruled those out as contributing to the problem.
In comparison, csv2sql or using cat and piping into psql on the command line is much quicker.
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| 204,504,903 |
MDU6SXNzdWUyMDQ1MDQ5MDM=
| 15,277 |
DataFrame.fillna() downcast argument does not work when the value for fillna is a dict instead of a scalar
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| 0 | 2017-02-01T06:08:04Z | 2017-02-06T15:26:42Z | 2017-02-06T15:26:42Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'a': [1, None]})
df1 = df.fillna(0, downcast='infer')
df2 = df.fillna({'a': 0}, downcast='infer')
print(df1)
print(df2)
```
#### Problem description
When using the method `DataFrame.fillna()`, the `downcast` option is not working when the `value` for `fillna` is a dict instead of a scalar.
#### Expected Output
In both previous DataFrame examples, the column should be downcasted to integer dtype instead of float.
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-107-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: ca_ES.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0.post20161106
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: 0.2.1
</details>
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| 204,506,646 |
MDExOlB1bGxSZXF1ZXN0MTA0MDgxNDg3
| 15,278 |
BUG: Fix downcast argument for DataFrame.fillna()
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| 5 | 2017-02-01T06:24:11Z | 2019-02-09T21:40:20Z | 2017-02-06T15:26:42Z |
CONTRIBUTOR
| null |
- [X] closes #15277
- [X] tests added / passed
- [x] passes git diff upstream/master | flake8 --diff
- [X] whatsnew entry
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str.find returns dtype: float64
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| 2 | 2017-02-01T10:42:26Z | 2017-02-01T13:51:55Z | 2017-02-01T13:51:37Z |
NONE
| null |
str.find fails to return integers
As per the docs it should return Series/Index of integer values
#### Code Sample, a copy-pastable example if possible
```
df[:5]
0 1
0 ZAR12015 FastEthernet2/0/38
1 AFA12286 FastEthernet4/0/22
2 CAA12029 FastEthernet2/0/35
3 IFAA2302 FastEthernet4/0/14
4 HFQ12009 FastEthernet5/38
df.loc[:,"1"].str.find("Ether")
6982 4.0
6983 4.0
6984 4.0
Name: 1, dtype: float64
df.loc[:,"1"].apply( lambda x : x[x.find("Ether"):])
AttributeError Traceback (most recent call last)
<ipython-input-39-73570bc63928> in <module>()
----> 1 df.loc[:,"1"].apply( lambda x : x[x.find("Ether"):])
C:\Users\X91437\Documents\Program Files\Anaconda\lib\site-packages\pandas\core\series.py in apply(self, func, convert_dtype, args, **kwds)
2290 else:
2291 values = self.asobject
-> 2292 mapped = lib.map_infer(values, f, convert=convert_dtype)
2293
2294 if len(mapped) and isinstance(mapped[0], Series):
pandas\src\inference.pyx in pandas.lib.map_infer (pandas\lib.c:66116)()
<ipython-input-39-73570bc63928> in <lambda>(x)
----> 1 df.loc[:,"1"].apply( lambda x : x[x.find("Ether"):])
AttributeError: 'float' object has no attribute 'find'
```
# Your code here
```
#### Expected Output
6982 4
6983 4
6984 4
Name: 1, dtype: int
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
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: 5.2.1
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
</details>
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| 15,280 |
read_csv does not correclty parse in header with BOM utf-8
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[] | null | 2 | 2017-02-01T12:01:24Z | 2017-02-01T13:08:07Z | 2017-02-01T12:22:01Z |
NONE
| null |
This issues was #4793 ... but it has been closed although the issue still exists ... please see my last post there
|
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| 204,611,486 |
MDU6SXNzdWUyMDQ2MTE0ODY=
| 15,281 |
Subtracting empty Series with dtype datetime64[ns]
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| 1 | 2017-02-01T15:16:52Z | 2017-02-01T15:25:21Z | 2017-02-01T15:24:53Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({
'dates_1' : pd.date_range(start='2015-01-01', periods=5),
'dates_2' : pd.date_range(start='2016-01-01', periods=5)
})
df.dates_2 - df.dates_1
df_2 = df.drop(df.index)
df_2.dates_2 - df_2.dates_1
```
#### Problem description
Subtracting two empty Series with dtypes of datetime64[ns] causes a TypeError to be thrown.
#### Expected Output
Would expect the subtraction to return an empty series of timedelta64[ns].
i.e,
Series([], dtype: timedelta64[ns])
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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COMPAT: xarray 0.9.0
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| 1 | 2017-02-01T17:12:52Z | 2017-02-01T22:55:41Z | 2017-02-01T22:55:41Z |
CONTRIBUTOR
| null |
https://github.com/pandas-dev/pandas/blob/master/pandas/tests/test_generic.py#L905
I believe should be changed to be version dependent, IOW, 0.9.0 https://github.com/pydata/xarray/issues/700 fixes this.
we should fix one of the builds at say 0.8.2, and let the others float (they all float now)
@shoyer ?
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ENH: Store comments from read_csv in an property (`df.comment`) and allow saving
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| 3 | 2017-02-01T19:05:32Z | 2021-05-08T00:49:02Z | null |
NONE
| null |
#### Problem description
This is an enhancement request to allow handling and saving comment strings with DataFrame text file IO. Two related stackoverflow questions about such feature:
http://stackoverflow.com/questions/39724298/pandas-extract-comment-lines
http://stackoverflow.com/questions/29233496/write-comments-in-csv-file-with-pandas
#### Code Sample, a copy-pastable example if possible
```python
df = pd.read_csv("mydata.csv", comment="#")
df.comment
"this is a comment from mydata.csv file"
df.to_csv("output.csv", comment="#") # saves the `comment` string by pasting "#" before its each line and putting it before the table.
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: 1.3.7
pip: 9.0.1
setuptools: 29.0.1
Cython: 0.24
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.4.5
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: 1.0.14
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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| 15,284 |
BUG: pd.read_json with numpy=True coerces columns to int if the first column is an int
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[] | null | 2 | 2017-02-01T19:20:03Z | 2020-05-08T20:43:50Z | 2020-05-08T20:43:49Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
print("pandas version", pd.__version__)
df = pd.DataFrame({'a': [0,0,0], 'b': [1.9995, 2.0005, 3.9995]})
print(df.dtypes)
df_json = df.to_json()
print("note that the `b` column is now a `int`")
df_from_json = pd.read_json(df_json, numpy=True)
print(df_from_json)
print(df_from_json.dtypes)
# and using dtype='float64' still rounds
df_from_json = pd.read_json(df_json, numpy=True, dtype='float64')
print(df_from_json)
print(df_from_json.dtypes)
# however, if the first column is a float, then the value is maintained
df2_json = df[['b', 'a']].to_json()
df2_from_json = pd.read_json(df2_json, numpy=True)
print(df2_from_json)
print(df2_from_json.dtypes)
```
#### Problem description
Setting `numpy=True` coerces column dtypes to `int64` if the "left / first" column is an int. This causes unexpected rounding errors and the behavior is not consistent: if the first column is a float then subsequent columns are not coerced to float.
#### Expected Output
```
a b
0 0 1.9995
1 0 2.0005
2 0 3.9995
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTA0MjA1NzI4
| 15,285 |
COMPAT: xarray 0.8.2 test compat w.r.t. CategoricalIndex idempotency
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| 2 | 2017-02-01T19:37:19Z | 2017-02-01T22:55:41Z | 2017-02-01T22:55:41Z |
CONTRIBUTOR
| null |
closes #15282
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MultiIndex.from_product throws on read-only array
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| 5 | 2017-02-01T20:10:44Z | 2019-12-12T21:05:09Z | 2019-12-12T21:05:09Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
a = np.array(range(3))
b = ['a', 'b']
# Succeeds, as expected
pd.MultiIndex.from_product([a, b])
a.setflags(write=False)
# Raises a ValueError with "buffer source array is read-only"
pd.MultiIndex.from_product([a, b])
```
#### Problem description
Nothing in the MultiIndex.from_product documentation suggests the inputs need to be mutable. Moreover, it is surprising that the arguments are being mutated.
#### Expected Output
Not raising.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.17-pv-ts1
machine: x86_64
processor:
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2-ts2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.3
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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| 204,708,663 |
MDU6SXNzdWUyMDQ3MDg2NjM=
| 15,287 |
Assign dataframe from another and add a column original also adds it to new df
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| 1 | 2017-02-01T21:20:47Z | 2017-02-01T22:03:19Z | 2017-02-01T22:03:09Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
x = pd.DataFrame([1,2,3])
y = x
y["test"] = "test"
print y
y
0 test
0 1 test
1 2 test
2 3 test
print x
x
0 test
0 1 test
1 2 test
2 3 test
```
#### Problem description
When creating a copy of a dataframe and then adding a new column of type string to the new dataframe, the original dataframe also has the new column added to it.
#### Expected Output
The original dataframe remains unaltered.
#### Output of ``pd.show_versions()``
<details>
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 9.0.1
setuptools: 25.1.3
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.3
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.3
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: 1.1.2
pymysql: 0.7.4.None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
</details>
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| 204,756,069 |
MDU6SXNzdWUyMDQ3NTYwNjk=
| 15,288 |
Unescaped quote marks in to_json output when run on multi indexed object
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| 1 | 2017-02-02T01:17:34Z | 2017-02-02T01:19:58Z | 2017-02-02T01:19:58Z |
NONE
| null |
#### Code Sample
```python
import pandas as pd
import numpy as np
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ['one',
'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
series = pd.Series(np.random.randn(8), index=index)
print(series.to_json())
```
Output:
```json
{"["bar","one"]":-0.0591166327,"["bar","two"]":0.871145093,"["baz","one"]":0.61280938,"["baz","two"]":0.3848564991,"["foo","one"]":-0.8986592304,"["foo","two"]":-0.4631529084,"["qux","one"]":-1.3482521044,"["qux","two"]":0.5209236198}
```
#### Problem description
The output contains unescaped quotation marks, which makes the output invalid json.
#### Expected Output
Quote marks should be escaped, for example:
```json
{"[\"bar\",\"one\"]":-0.0591166327,"[\"bar\",\"two\"]":0.871145093,"[\"baz\",\"one\"]":0.61280938,"[\"baz\",\"two\"]":0.3848564991,"[\"foo\",\"one\"]":-0.8986592304,"[\"foo\",\"two\"]":-0.4631529084,"[\"qux\",\"one\"]":-1.3482521044,"[\"qux\",\"two\"]":0.5209236198}
```
#### Output of ``pd.show_versions()``
I reproduced it in pandas install from master:
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.8-200.fc24.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8
pandas: 0.19.0+401.g8452080
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
pandas_datareader: None
</details>
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MDU6SXNzdWUyMDQ3ODEyMDc=
| 15,289 |
series.replace with empty dictlike raises ValueError: not enough values to unpack
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| 2 | 2017-02-02T04:41:49Z | 2017-02-02T20:26:55Z | 2017-02-02T20:26:55Z |
CONTRIBUTOR
| null |
#### Problem description
`DataFrame.replace` and `Series.replace` fail unceremoniously on an empty Series or dict argument; I would expect it to simply do nothing.
While there is a fair bit of type-based wizardry going on in this function, I don't suppose there is any actual ambiguity in how this edge case should behave?
#### Code Sample, a copy-pastable example if possible
```python
from pandas import Series
s = Series([0,0,1,1,2,2])
print(s.replace(Series([7,7])).tolist())
print(s.replace(Series([7])).tolist())
print(s.replace(Series([])).tolist())
```
#### Output
```text
[7, 7, 7, 7, 2, 2]
[7, 7, 1, 1, 2, 2]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-82-d889d2100216> in <module>()
3 print(s.replace(Series([7,7])).tolist())
4 print(s.replace(Series([7])).tolist())
----> 5 print(s.replace(Series([])).tolist())
/usr/local/lib/python3.5/dist-packages/pandas-0.19.2-py3.5-linux-x86_64.egg/pandas/core/generic.py in replace(self, to_replace, value, inplace, limit, regex, method, axis)
3438
3439 items = list(compat.iteritems(to_replace))
-> 3440 keys, values = zip(*items)
3441
3442 are_mappings = [is_dict_like(v) for v in values]
ValueError: not enough values to unpack (expected 2, got 0)
```
#### Expected Output
```
[7, 7, 7, 7, 2, 2]
[7, 7, 1, 1, 2, 2]
[0, 0, 1, 1, 2, 2]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.2.0
tables: None
numexpr: 2.6.2
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUyMDQ3ODQ3OTY=
| 15,290 |
as_index =True for groupby transform inconsistent with agg
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| 13 | 2017-02-02T05:17:46Z | 2021-05-08T00:51:10Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
index = pd.date_range('10/1/1999', periods=1100, name='Date')
ts = pd.DataFrame({'Entry': np.random.normal(300, 50, 1100),
'Exit': np.random.normal(300, 50, 1100)}, index)
ts['Year'] = ts.index.year
ts['Day'] = ts.index.weekday_name
zscore = lambda x: (x - x.mean()) / x.std()
ts.groupby(['Year', 'Day'], as_index=True).transform(lambda g:g).head(5)
```
#### Problem description
Unlike agg, when using transform
1. as_index =True does not set the index to the groupby by (i.e ['Year', 'Day']).
2. as_index = False does not prepend the groupby by column to the output of the transform as is done with agg.
I found this inconsistency very confusing and hard to work with.
This is also noted in ##5755
#### Expected Output
If as_index =True, the index of the result should the groupby by (i.e ['Year', 'Day']).
if as_index =False, the output should have the the groupby by (i.e ['Year', 'Day']) prepended to the as is done with agg.
I've really struggled using transform, partly as the semantics of transform were unclear to me. Specifically :
1. Which columns is the transform applied to. Is it all columns of the df or only those not in the groupby by? It looks like its the latter. This seems to be true with agg too. It would have been helpful to me if this was clearer in the documentation
2. Is the transform passed a df of columns or is it applied column by column? My guess is it's the former if the transform takes a df otherwise it's the latter. This seems to be hinted at in some parts of the documentation but it could be made more explicit.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.41-36.55.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTA0Mjc3MTQy
| 15,291 |
ENH: Added ability to freeze panes from DataFrame.to_excel() (#15160)
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| 15 | 2017-02-02T05:28:00Z | 2017-03-04T23:50:18Z | 2017-02-16T17:47:46Z |
CONTRIBUTOR
| null |
- [X] closes #15160
- [x] tests passed- Existing tests passed. New test added and passed.
- [X] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry- Added to 0.20.
The ability to freeze panes has been added to the to_excel() method on data frames. The parameter is a tuple of 2 integers, representing the bottom-most row and right-most column to freeze, respectively. (This is the same format xlsxwriter uses). The default value is None, resulting in no freezing.
Example usage:
``df.to_excel(writer, sheet_name='Sheet1', freeze_panes=(1,1))``
The following engines are supported:
xlsxwriter
xlwt
openpyxl22
The parameter exists for openpyxl20 and openpyxl1 but it has no effect, in anticipation of support for these versions being dropped (#15184).
Can one of the maintainers confirm this approach for engine support is adequate, or suggest an alternative? Once that is settled I can write a test to create a workbook using each engine and then try reading it back in using openpyxl.
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Github repository and topics
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[] | null | 2 | 2017-02-02T09:33:31Z | 2017-02-10T10:39:56Z | 2017-02-02T13:38:37Z |
NONE
| null |
Hello,
Github recently add topics
see https://github.com/blog/2309-introducing-topics
https://help.github.com/articles/about-topics/
Maybe this repository should be tagged.
See also https://github.com/pydata/pandas-datareader/issues/278
Kind regards
|
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| 204,899,408 |
MDU6SXNzdWUyMDQ4OTk0MDg=
| 15,293 |
sample can only pick items of length 1. should be able to choose arbitrary length.
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[] | null | 2 | 2017-02-02T15:05:36Z | 2019-02-20T04:38:44Z | 2019-02-20T04:38:44Z |
NONE
| null |
#### sample can only pick items of length 1. should be able to choose arbitrary length.
sample is very useful for statistical bootstrapping, however, on time series data, it is frequently necessary to sample 'sections' from a Series/DataFrame. Each sample might be '50 days', hence 50 rows. This is a very common use case, which I've hacked into my projects several times already.
I am happy to try and edit this into pandas.generic.sample, but would appreciate some guidance as to how best to implement it, as unlike the current implementation, it will return a list of DataFrames (rather than the same type that was provided). Suggestions welcome.
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BUG: Support empty dict-likes in replace()
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| 2 | 2017-02-02T17:02:03Z | 2017-02-02T20:27:06Z | 2017-02-02T20:26:55Z |
CONTRIBUTOR
| null |
- [x] closes #15289
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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- [ ] closes #xxxx
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read_msgpack returns garbage for non-existing files in python2
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| 4 | 2017-02-03T10:17:51Z | 2017-10-30T12:53:40Z | 2017-10-30T12:53:40Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: u'0.19.2'
In [3]: pd.read_msgpack('/tmp/bla.txt')
Out[3]: [47, 116, 109, 112, 47, 98, 108, 97, 46, 116, 120, 116]
```
That file does not exist.
The same code with the same version on python 3 correctly raises an exception.
#### Problem description
Reading a non existing file returns a list of integers instead of failing if pandas is used in python 2. This makes detecting error quite hard.
#### Expected Output
An exception.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.6-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.utf8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 34.1.0
Cython: None
numpy: 1.11.3
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
```
</details>
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Weird behavior using nlargest/nsmallest when there are the n smallest/largest values are identical
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| 4 | 2017-02-03T11:22:54Z | 2017-04-06T13:41:25Z | 2017-04-06T13:41:25Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
python -c "import pandas as pd; df = pd.DataFrame(dict(a=[1, 1, 2, 3], b=[1, 2, 3, 4])); print(df.nsmallest(2, 'a'))"
```
#### Problem description
When using nlargest/nsmallest and the n largest / smallest values are identical, the method seems to return the dataframe concatenated with the filtered version of itself.
Furthermore if all values are identical, you get the full dataframe concatenated with itself, regardless of the choice of `n`
#### Expected Output
Not really sure, I guess in the example above you should simply get a dataframe that looks like this
```pd.DataFrame(dict(a=[1, 1], b=[1, 2]))```
however if you were to have
``` df = pd.DataFrame(dict(a=[1, 1, 1, 1], b=[1, 2, 3, 4]))```
and asked for
```df.nlargest(2, 'a')``` you should again get
```pd.DataFrame(dict(a=[1, 1], b=[1, 2]))```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IE.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 28.3.0
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: None
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.2.0
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
</details>
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| 205,171,997 |
MDU6SXNzdWUyMDUxNzE5OTc=
| 15,298 |
No module named 'request' (from panda import PandaRequest)
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[] | null | 5 | 2017-02-03T14:29:51Z | 2019-12-22T20:14:34Z | 2017-02-03T14:31:35Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import panda as pd
Traceback:
File "/usr/local/lib/python3.4/dist-package/panda/__init__.py", line 1, in <module>
from request import PandaRequest
```
#### Problem description
I'm working with python 3.4.2, gcc 4.9.1
I have installed everything with pip, all the requirement is good. All the version is good and up to date (my pip3.4 and setuptools, is up to date too).
I've installed Panda and Pandas. (the same)
I've tried to install request, requests (already had)
Don't really know, what to do now :)
#### Expected Output
I can't enter inside the module itself. I can't event know the version.
#### Output of ``pd.show_versions()``
Thanks to help me
NLT
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MDExOlB1bGxSZXF1ZXN0MTA0NTY4MzIy
| 15,299 |
Fix nsmallest/nlargest With Identical Values
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| 20 | 2017-02-03T16:38:25Z | 2017-04-06T13:44:30Z | 2017-04-06T13:41:25Z |
NONE
| null |
- [x] closes #15297
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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ENH: .isnull and .notnull have been added as methods to Index to make this more consistent with the Series API
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TST: Timestamp and Timeseries tests reorg (gh14854)
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| 18 | 2017-02-03T20:46:57Z | 2017-02-04T18:30:05Z | 2017-02-04T18:27:59Z |
CONTRIBUTOR
| null |
- [x] Partially completes #14854
- [x] Tests reorganized (```test_timeseries.py``` + ```test_tslib.py``` -> ```test_timestamp.py```)
- [x] Passes ``git diff upstream/master | flake8 --diff``
|
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MDU6SXNzdWUyMDUyNzczMzc=
| 15,302 |
BUG: read_csv with date_parser lock file open on failure
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| 13 | 2017-02-03T21:44:28Z | 2021-09-12T18:06:50Z | null |
NONE
| null |
#### Problem description
When using the date_parser functionality of read_csv() if the file read fails then the file is left locked open. My use case is that I am trying to enforce a strict datetime format that must include the time zone offset. Another thread stated that the way to accomplish this is with date_parser. My issue is that I would like to move a file that fails being loaded to another directory, but I cannot because the failure in the date_parser keeps the file open until the python session is terminated.
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import datetime
def strict_parser(dates):
datetimes = [pd.Timestamp(datetime.datetime.strptime(date, '%Y-%m-%d %H:%M:%S%z'), tz='UTC') for date in dates]
return pd.DatetimeIndex(datetimes)
filename = 'c:/temp/data.csv'
data = pd.read_csv(filename, parse_dates=['datetime'], index_col=['datetime'], date_parser=strict_parser)
```
The file data.csv is:
datetime,data
2010-05-05 09:30:00-0500,10
2010-05-05 09:35:00-0500,20
2010-05-05 09:40:00,30
#### Output of ``pd.show_versions()``
pandas: 0.19.1
</details>
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| 205,301,437 |
MDU6SXNzdWUyMDUzMDE0Mzc=
| 15,303 |
Can't use dt.ceil, dt.floor, dt.round with coarser target frequencies
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| 11 | 2017-02-03T23:52:51Z | 2021-08-22T11:28:43Z | null |
CONTRIBUTOR
| null |
#### Code Sample
This works fine:
```
In [16]: hours = pd.Series(pd.date_range('2017-01-01', '2017-01-31', freq='H'))
In [17]: hours.dt.ceil('D')
Out[17]:
0 2017-01-01
1 2017-01-02
2 2017-01-02
3 2017-01-02
4 2017-01-02
...
```
But this doesn't:
```
In [18]: days = pd.Series(pd.date_range('2017-01-01', '2017-01-31', freq='D'))
In [19]: days.dt.ceil('M')
...
/home/andrew/git/pandas-rosnfeld-py3/pandas/tseries/offsets.py in nanos(self)
510 @property
511 def nanos(self):
--> 512 raise ValueError("{0} is a non-fixed frequency".format(self))
513
514
ValueError: <MonthEnd> is a non-fixed frequency
```
#### Problem description
I haven't done an exhaustive test, but it seems that one can't use coarser frequencies like 'M', 'Q', 'A' in the dt.ceil/floor/round set of methods.
#### Expected Output
It would seem reasonable to be able to take a series of scattered dates and be able to "coarsen" them into month-ends, quarter-ends, year-ends, etc.
#### Output of ``pd.show_versions()``
<details>
(admittedly some pretty old stuff in this environment but I also got the same behavior on 0.19.2 with latest of everything else)
INSTALLED VERSIONS
------------------
commit: 74e20a0c4146919a82312c1027dcb57b48b613e3
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-62-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.19.0+300.g74e20a0.dirty
nose: 1.3.7
pip: 7.1.2
setuptools: 19.1.1
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
feather: None
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.2
lxml: 3.5.0
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: 0.6.7.None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: None
</details>
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| 15,304 |
passing a function to DataFrameGroupBy.agg - confusing documentation/behaviour
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[] | null | 7 | 2017-02-04T01:45:49Z | 2017-02-05T20:29:10Z | null |
NONE
| null |
#### Problem description
For clarity, I'll split this into 3 sub-issues related to DataFrameGroupBy.agg and its documentation.
##### 1. Not clear what kind of custom function should be provided
The docstring on [DataFrameGroupBy.agg](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.DataFrameGroupBy.agg.html#pandas.core.groupby.DataFrameGroupBy.agg) says that you can pass in a function, but it's unclear what that function should expect to receive as input, and how what it returns relates to the return value of agg.
After some poking around, it seems to me that the typical pattern is to pass a function that takes a series and returns a scalar, with the dataframe returned by agg having the property that `aggregated.loc[group_foo, col_bar]` is the result of calling the function on the series that is the column `col_bar` for rows belonging to `group_foo`.
If that is the expected behaviour, it should be explained in the docs.
##### 2. Passing an arbitrary kwarg changes the function behaviour
I stumbled on this weirdness while trying to figure out how agg worked:
```python
>>> df = pd.DataFrame(np.random.rand(4,2), columns=['b', 'c'])
>>> df['a'] = [1, 0, 0, 0]
>>> g = df.groupby('a')
>>> g.agg(lambda x: np.product(x.shape))
b c
a
0 3.0 3.0
1 1.0 1.0
>>> g.agg(lambda x, foo=0: np.product(x.shape), foo=0)
b c
a
0 9 9
1 3 3
```
In other words, by passing in any meaningless kwarg, my function is now called `ngroups` times with a dataframe for each group, rather than being called with a series `ngroups * ncolumns` times.
Also, when called in this way, it seems my function can return a list or series of length `ncolumns` which gets expanded (this doesn't work in the non-kwarg version):
```python
>>> g.agg(lambda x, foo=0: [20, 17], foo=0)
b c
a
0 20 17
1 20 17
```
##### 3. Note on numpy special casing is confusing
The docstring has this note:
> Numpy functions mean/median/prod/sum/std/var are special cased so the default behavior is applying the function along axis=0 (e.g., np.mean(arr_2d, axis=0)) as opposed to mimicking the default Numpy behavior (e.g., np.mean(arr_2d)).
Which is confusing because passing in `lambda x: np.mean(x)` does seem to give the 'right' answer (the same one as passing `lambda x: np.mean(x, axis=0)`, or `np.mean`, or `'mean'`). This is true whether or not I throw in a kwarg.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Linux
OS-release: 3.13.0-107-generic
machine: i686
processor: i686
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 25.1.0
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 1.5.7
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.1.3
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 0.8.0
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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Rolling apply on DataFrame with Datetime index returns NaN
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| 2 | 2017-02-04T12:37:19Z | 2017-08-10T12:32:47Z | 2017-08-10T12:32:47Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'A': [0, 1, 2, 3, 4]},
index=[pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130101 09:00:01'),
pd.Timestamp('20130101 09:00:02'),
pd.Timestamp('20130101 09:00:03'),
pd.Timestamp('20130101 09:00:04')])
rolled = df.rolling('3s', 3)
print rolled.sum()
print rolled.apply(sum)
```
outputs
```
A
2013-01-01 09:00:00 NaN
2013-01-01 09:00:01 NaN
2013-01-01 09:00:02 3.0
2013-01-01 09:00:03 6.0
2013-01-01 09:00:04 9.0
A
2013-01-01 09:00:00 NaN
2013-01-01 09:00:01 NaN
2013-01-01 09:00:02 NaN
2013-01-01 09:00:03 NaN
2013-01-01 09:00:04 NaN
```
#### Problem description
rolled.apply.sum() should give the same output as rolled.apply(sum) but instead only returns nan.
#### Expected Output
same as rolled.sum()
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 20.7.0
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: 2.38.0
pandas_datareader: None
</details>
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| 205,379,701 |
MDU6SXNzdWUyMDUzNzk3MDE=
| 15,306 |
Groupby transform idxmax return floats
|
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| 3 | 2017-02-04T21:42:03Z | 2019-03-30T19:17:29Z | 2019-03-30T19:17:29Z |
NONE
| null |
`Transform` with `idxmax` return wrong output - floats instead `datetimes`
Sample:
rng = pd.date_range('1/1/2011', periods=15, freq='D')
np.random.seed(4)
stocks = pd.DataFrame({
'price':(np.random.randn(15).cumsum() + 10) },index = rng)
stocks['week_id'] = pd.to_datetime(stocks.index).week #used for the groupby
print (stocks)
price week_id
2011-01-01 10.050562 52
2011-01-02 10.550513 52
2011-01-03 9.554604 1
2011-01-04 10.248203 1
2011-01-05 9.829901 1
2011-01-06 8.245324 1
2011-01-07 7.597617 1
2011-01-08 8.196192 1
2011-01-09 8.528442 1
2011-01-10 7.380966 2
2011-01-11 7.999635 2
2011-01-12 7.911648 2
2011-01-13 8.336721 2
2011-01-14 8.668974 2
2011-01-15 7.512158 2
<!-- -->
print (stocks.groupby(stocks['week_id'])['price'].transform('idxmax'))
2011-01-01 1.293926e+18
2011-01-02 1.293926e+18
2011-01-03 1.294099e+18
2011-01-04 1.294099e+18
2011-01-05 1.294099e+18
2011-01-06 1.294099e+18
2011-01-07 1.294099e+18
2011-01-08 1.294099e+18
2011-01-09 1.294099e+18
2011-01-10 1.294963e+18
2011-01-11 1.294963e+18
2011-01-12 1.294963e+18
2011-01-13 1.294963e+18
2011-01-14 1.294963e+18
2011-01-15 1.294963e+18
Freq: D, Name: price, dtype: float64
print (stocks.groupby(stocks['week_id'])['price'].idxmax())
week_id
1 2011-01-04
2 2011-01-14
52 2011-01-02
Name: price, dtype: datetime64[ns]
[SO question](http://stackoverflow.com/questions/42045549/using-groupby-and-transform-to-get-idxmax-per-week).
print (pd.show_versions())
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: sk_SK
LOCALE: None.None
pandas: 0.19.2+0.g825876c.dirty
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
None
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MDU6SXNzdWUyMDUzODg0MzI=
| 15,307 |
ENH: Add AutoFilter option to to_excel
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| 0 | 2017-02-05T00:09:59Z | 2018-11-14T22:46:48Z | null |
CONTRIBUTOR
| null |
As originally mentioned in #15160 , `DataFrame.to_excel()` should support an optional parameter that would turn Excel's AutoFilter on.
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