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} | 2 | 2016-04-26T23:38:06Z | 2016-04-27T12:29:55Z | 2016-04-27T12:29:55Z | CONTRIBUTOR | null | ```
In [4]: DataFrame(np.random.randn(1000000,1)).to_csv('test.csv',index=False)
```
branch
```
In [1]: %memit pd.read_csv('test.csv',skiprows=999999)
peak memory: 65.74 MiB, increment: 1.59 MiB
In [2]: %memit pd.read_csv('test.csv',skiprows=999999)
peak memory: 65.89 MiB, increment: 0.22 MiB
In [3]: %memit pd.read_csv('test.csv',skiprows=999999)
peak memory: 65.98 MiB, increment: 0.28 MiB
```
master
```
In [1]: %memit pd.read_csv('test.csv',skiprows=999999)
peak memory: 169.84 MiB, increment: 105.79 MiB
In [2]: %memit pd.read_csv('test.csv',skiprows=999999)
peak memory: 171.27 MiB, increment: 24.11 MiB
In [3]: %memit pd.read_csv('test.csv',skiprows=999999)
peak memory: 173.39 MiB, increment: 24.63 MiB
```
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} | 3 | 2016-04-27T00:15:47Z | 2016-06-03T15:04:18Z | 2016-06-03T15:04:18Z | NONE | null | Hi,
I'm getting strange results when comparing a numpy float to a pandas series. It seems to think that the numpy float is some sort of array...
``` python
import numpy as np
import pandas as pd
np.float64(0) < pd.Series([1,2,3],dtype=np.float64)
```
#### Expected Output
```
0 True
1 True
2 True
dtype: bool
```
#### Actual Output
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-0803a1472416> in <module>()
1 import numpy as np
2 import pandas as pd
----> 3 np.float64(0) < pd.Series([1,2,3],dtype=np.float64)
global np.float64 = <type 'numpy.float64'>
global pd.Series = <class 'pandas.core.series.Series'>
global dtype = undefined
/usr/local/lib/python2.7/dist-packages/pandas/core/ops.pyc in wrapper(self=0 1.0
1 2.0
2 3.0
dtype: float64, other=array(0.0), axis=None)
737 return NotImplemented
738 elif isinstance(other, (np.ndarray, pd.Index)):
--> 739 if len(self) != len(other):
global len = undefined
self = 0 1.0
1 2.0
2 3.0
dtype: float64
other = array(0.0)
740 raise ValueError('Lengths must match to compare')
741 return self._constructor(na_op(self.values, np.asarray(other)),
TypeError: len() of unsized object
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.3.0-040300-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.3.1
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
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} | 27 | 2016-04-27T04:40:51Z | 2016-05-03T01:17:01Z | 2016-05-03T01:08:47Z | NONE | null | - [x] closes #12887
- [X] tests passed (Tests specific to this issue now pass with scipy dev and numpy dev versions that I was using when I opened the issue)
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
@jreback @TomAugspurger
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} | 6 | 2016-04-27T11:53:48Z | 2016-04-28T13:40:00Z | 2016-04-28T13:39:57Z | CONTRIBUTOR | null | I need such a patch to read huge `sas` tables encoded in `cp1251`. I'm not experienced enough to determine if such a patch is really needed here, but well.. it gives some speed in certain situations.
Optimize string encoding-decoding, leave default implementation for CPython optimized encodings,
(see https://docs.python.org/3.4/library/codecs.html#standard-encodings)
string
```
import pandas as pd
s1 = pd.Series(pd.util.testing.makeStringIndex(k=100000)).astype('category')
encs = 'utf-8', 'utf-16', 'utf-32', 'latin1', 'iso-8859-1', 'mbcs', 'ascii', 'cp1251', 'cp1252'
for enc in encs:
s2 = s1.str.encode(enc).astype('category')
print(enc)
%timeit s1.str.encode(enc)
%timeit s2.str.decode(enc)
```
unicode
```
import pandas as pd
s1 = pd.Series(pd.util.testing.makeUnicodeIndex(k=100000)).astype('category')
encs = 'utf-8', 'utf-16', 'utf-32'
for enc in encs:
s2 = s1.str.encode(enc).astype('category')
print(enc)
%timeit s1.str.encode(enc)
%timeit s2.str.decode(enc)
```
("10 loops, best of 3: xxx ms per loop")
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} | 2 | 2016-04-27T12:28:23Z | 2016-04-27T13:33:34Z | 2016-04-27T13:33:18Z | MEMBER | null | Fixes few doc build warnings + small restructure of headings more consistent with previous releases)
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} | 3 | 2016-04-27T15:29:39Z | 2016-04-28T14:04:38Z | 2016-04-28T14:04:31Z | MEMBER | null | Title is self-explanatory. Note that this behaviour was already implicitly enforced, but this makes the enforcement explicit. Closes #12794.
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} | 30 | 2016-04-27T15:38:35Z | 2016-07-14T10:21:38Z | 2016-07-14T10:21:25Z | MEMBER | null | `reshape` largely exists for compat reasons with `numpy` but in the interests of moving away from that, this PR will cause objects like `Series`, `Index`, and `Categorical` to deprecate OR raise when such a method is called.
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https://api.github.com/repos/pandas-dev/pandas/issues/13013 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13013/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13013/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13013/events | https://github.com/pandas-dev/pandas/issues/13013 | 151,501,494 | MDU6SXNzdWUxNTE1MDE0OTQ= | 13,013 | cannot truncate a timeseries without sorting it first | {
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| null | 2 | 2016-04-27T22:00:26Z | 2016-04-27T22:55:02Z | 2016-04-27T22:54:52Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
pd.Series(index=pd.to_datetime(['2016-04-15 21:18:04.683115', '2016-04-15 21:18:05.384231',
'2016-04-15 21:18:05.504638', '2016-04-15 21:18:05.510117',
'2016-04-15 21:18:05.370865', '2016-04-15 21:18:05.577177',
'2016-04-15 21:18:06.088652', '2016-04-15 21:18:05.865269',
'2016-04-15 21:18:05.867652', '2016-04-15 21:18:07.207526'])).truncate(before = '2016-04-15 21:18:03.577077', after ='2016-04-15 22:18:03.577077')
```
I get the error:
> ValueError: index must be monotonic increasing or decreasing \pandas\indexes\base.py", line 2791, in _searchsorted_monotonic
#### Expected Output
```
2016-04-15 21:18:04.683115 NaN
2016-04-15 21:18:05.384231 NaN
2016-04-15 21:18:05.504638 NaN
2016-04-15 21:18:05.510117 NaN
2016-04-15 21:18:05.370865 NaN
2016-04-15 21:18:05.577177 NaN
2016-04-15 21:18:06.088652 NaN
2016-04-15 21:18:05.865269 NaN
2016-04-15 21:18:05.867652 NaN
2016-04-15 21:18:07.207526 NaN
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
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
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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} | 8 | 2016-04-28T05:54:12Z | 2016-05-01T22:25:47Z | 2016-05-01T15:29:52Z | NONE | null | - [ ] closes #12988
- [ ] tests added, passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] `comp.isin(values)` now coerces `values` from a set into a list. This was failing when `comp` is a datetime dtype and `values` is a set, where `.isin` will try to run `to_datetime(values)` on the unordered set and fail.
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https://api.github.com/repos/pandas-dev/pandas/issues/13015 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13015/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13015/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13015/events | https://github.com/pandas-dev/pandas/issues/13015 | 151,564,999 | MDU6SXNzdWUxNTE1NjQ5OTk= | 13,015 | TST: Can't Seem to Run Individual Test Suites in pandas/io | {
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| null | 5 | 2016-04-28T07:08:34Z | 2016-04-29T19:00:45Z | 2016-04-29T18:55:02Z | MEMBER | null | ```
$ python /home/user/Repositories/pandas/pandas/io/tests/test_data.py
Traceback (most recent call last):
File "/home/user/Repositories/pandas/pandas/io/tests/test_data.py", line 4, in <module>
from pandas import compat
File "/home/user/miniconda2/envs/pandas-dev/lib/python2.7/site-packages/pandas/__init__.py", line 39, in <module>
from pandas.core.api import *
File "/home/user/miniconda2/envs/pandas-dev/lib/python2.7/site-packages/pandas/core/api.py", line 10, in <module>
from pandas.core.groupby import Grouper
File "/home/user/miniconda2/envs/pandas-dev/lib/python2.7/site-packages/pandas/core/groupby.py", line 18, in <module>
from pandas.core.frame import DataFrame
File "/home/user/miniconda2/envs/pandas-dev/lib/python2.7/site-packages/pandas/core/frame.py", line 39, in <module>
from pandas.core.series import Series
File "/home/user/miniconda2/envs/pandas-dev/lib/python2.7/site-packages/pandas/core/series.py", line 2929, in <module>
import pandas.tools.plotting as _gfx # noqa
File "/home/user/miniconda2/envs/pandas-dev/lib/python2.7/site-packages/pandas/tools/plotting.py", line 25, in <module>
import pandas.tseries.converter as conv
File "/home/user/miniconda2/envs/pandas-dev/lib/python2.7/site-packages/pandas/tseries/converter.py", line 7, in <module>
import matplotlib.units as units
File "/home/user/miniconda2/envs/pandas-dev/lib/python2.7/site-packages/matplotlib/__init__.py", line 135, in <module>
__version__ = str(get_versions()['version'])
File "/home/user/miniconda2/envs/pandas-dev/lib/python2.7/site-packages/matplotlib/_version.py", line 21, in get_versions
return json.loads(version_json)
AttributeError: 'module' object has no attribute 'loads'
```
## INSTALLED VERSIONS
pandas: 0.18.0+174.g65ed3af
nose: 1.3.7
pip: 8.1.1
setuptools: 20.0
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.2
IPython: None
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
Somewhere in the last 32 commits or so to the `master` branch this broke because my branch for refactoring `test_parsers` was that many commits behind when it last worked. I also confirmed the breakage on `master` too.
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https://api.github.com/repos/pandas-dev/pandas/issues/13016 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13016/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13016/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13016/events | https://github.com/pandas-dev/pandas/issues/13016 | 151,646,081 | MDU6SXNzdWUxNTE2NDYwODE= | 13,016 | Pandas pytables interface doesn't create empty table datasets | {
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} | 27 | 2016-04-28T13:49:01Z | 2021-04-23T05:11:04Z | null | NONE | null | Pandas used to allow the writing of empty HDF5 datasets through its pytables interface code. However, after upgrading to 0.17 (from 0.11), we've discovered that this behaviour is intentionally
short circuited. The library behaves as though the dataset is being written, but simply ignores the request and the resulting HDF5 file doesn't contain the requested table.
The offending code is in pandas/io/pytables.py:_write_to_group()
```
# we don't want to store a table node at all if are object is 0-len
# as there are not dtypes
if getattr(value, 'empty', None) and (format == 'table' or append):
return
```
We've worked around it by patching our installed copy of pandas, but we'd like to know the provocation behind this code before submitting a pull request. The comment implies that the lack of dtypes in the dataset is the cause, however each pandas column has type information even if empty.
Any clarification would be appreciated
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} | 10 | 2016-04-28T14:03:33Z | 2021-04-24T03:50:22Z | 2021-04-24T03:50:21Z | NONE | null | Writing a dataframe to csv using `df.to_csv("/path/to/file.csv")` causes the creation of an "unnamed" field containing the indexes of the rows. Continually writing/reading to/from this file will result in many "unnamed" fields. Once there are three unnamed fields you can no longer use loc to replace values, what's more, it fails silently.
```
In [2]: df = DataFrame({'A': [1,2,3],'B': [4,5,501]})
In [3]: df
Out[3]:
A B
0 1 4
1 2 5
2 3 501
In [4]: df.B.loc[df.B > 500]
Out[4]:
2 501
Name: B, dtype: int64
In [5]: df.B.loc[df.B > 500] = None
In [6]: df
Out[6]:
A B
0 1 4.0
1 2 5.0
2 3 NaN
```
So far so good, I was able to replace all values in `df.B` with `NaN`. I then write this out and read it back in
```
In [7]: df.to_csv("./test.csv")
In [8]: df = read_csv("./test.csv")
In [9]: df.columns
Out[9]: Index([u'Unnamed: 0', u'A', u'B'], dtype='object')
In [10]: df
Out[10]:
Unnamed: 0 A B
0 0 1 4.0
1 1 2 5.0
2 2 3 NaN
```
As you can see, this has create an unnamed field, but let's continue
```
In [14]: df.B.fillna(501, inplace=True)
```
This is jsut to get 501 in place of the `NaN` I created earlier which I forgot to do before writing out
```
In [15]: df.B
Out[15]:
0 4.0
1 5.0
2 501.0
Name: B, dtype: float64
In [16]: df.B.loc[df.B > 500]
Out[16]:
2 501.0
Name: B, dtype: float64
In [17]: df.B.loc[df.B > 500] = None
...SettingWithCopyWarning...
In [18]: df.B
Out[18]:
0 4.0
1 5.0
2 NaN
Name: B, dtype: float64
```
Everything working fine
```
In [19]: df.fillna(501, inplace=True)
In [20]: df.to_csv("./test.csv")
In [21]: df = read_csv("./test.csv")
In [22]: df.columns
Out[22]: Index([u'Unnamed: 0', u'Unnamed: 0.1', u'A', u'B'], dtype='object')
In [23]: df
Out[23]:
Unnamed: 0 Unnamed: 0.1 A B
0 0 0 1 4.0
1 1 1 2 5.0
2 2 2 3 501.0
```
Writing and reading again creates a 2nd unnamed field
```
In [24]: df.B.loc[df.B > 500]
Out[24]:
2 501.0
Name: B, dtype: float64
In [25]: df.B.loc[df.B > 500] = None
In [26]: df.B
Out[26]:
0 4.0
1 5.0
2 NaN
Name: B, dtype: float64
```
Which is no problem, everything still works so far...however
```
In [27]: df.fillna(501, inplace=True)
In [28]: df
Out[28]:
Unnamed: 0 Unnamed: 0.1 A B
0 0 0 1 4.0
1 1 1 2 5.0
2 2 2 3 501.0
In [29]: df.to_csv("./test.csv")
In [30]: df = read_csv("./test.csv")
In [31]: df.columns
Out[31]: Index([u'Unnamed: 0', u'Unnamed: 0.1', u'Unnamed: 0.1', u'A', u'B'], dtype='object')
In [32]: df
Out[32]:
Unnamed: 0 Unnamed: 0.1 Unnamed: 0.1 A B
0 0 0 0 1 4.0
1 1 1 1 2 5.0
2 2 2 2 3 501.0
```
We now have 3 unnamed fields
```
In [33]: df.B.loc[df.B > 500]
Out[33]:
2 501.0
Name: B, dtype: float64
In [34]: df.B.loc[df.B > 500] = None
In [35]: df.B
Out[35]:
0 4.0
1 5.0
2 501.0
Name: B, dtype: float64
```
The method of replacing all values over 500 with `Nan` no longer works but also throws no errors or warnings.
You CAN get around this using `df.loc[df.B > 500, 'B'] = None` but obviously you shouldn't have to.
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} | 10 | 2016-04-28T15:36:02Z | 2017-01-16T16:22:36Z | 2016-04-28T15:46:22Z | CONTRIBUTOR | null | Current conversion of R objects into Python usng `pandas2ri.ri2py` does not work for the `<class 'rpy2.robjects.vectors.ListVector'>` object. As noted [here](http://pandas.pydata.org/pandas-docs/stable/r_interface.html), `pandas2ri.py2ri()` and `pandas2ri.ri2py()` are current methods to handle conversion. However, previous deprecated `com.convert_robj()` correctly converts the R object to Python dictionary:
``` python
import rpy2
from rpy2.robjects.numpy2ri import numpy2ri
from rpy2.robjects.packages import importr
import numpy as np
# FISHER EXACT TEST EXAMPLE
cont = np.reshape(np.arange(0,4), (2,2))
statspackage = importr('stats', robject_translations={'format_perc': '_format_perc'})
result = statspackage.fisher_test(numpy2ri(cont), simulate_p_value = True, B = 100)
# DEPRECATED CONVERSION
import pandas.rpy.common as com
pyresultdict = com.convert_robj(result)
print(type(pyresultdict)) # PYTHON DICTIONARY TYPE
# CURRENT CONVERSION
from rpy2.robjects import pandas2ri
pandas2ri.activate()
pyresultdict = pandas2ri.ri2py(result)
print(type(pyresultdict)) # REMAINS R LISTVECTOR TYPE
```
``` python
# Expected Output
<class 'dict'>
<class 'dict'>
```
```
# output of rpy2.__version__
2.5.4
# output of pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 37 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: None
pip: 8.1.0
setuptools: 2.1
Cython: None
numpy: 1.10.4
scipy: 0.17.0
...
```
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} | 4 | 2016-04-28T16:49:51Z | 2016-04-30T14:25:43Z | 2016-04-30T14:25:43Z | NONE | null | #### Code Setup w/ Fake Dataset
``` python
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
# running this code in terminal/ipython
%matplotlib qt
import matplotlib.pylab
import matplotlib.pyplot as plt
geographies = ['north america', 'central/south america', 'europe', 'africa', 'asia', 'oceania']
geo_proportions = [0.35, 0.10, 0.30, 0.05, 0.15, 0.05]
task_proportions = [[0.3,0.7],[0.4,0.6],[0.3,0.7],[0.5,0.5],[0.7,0.3],[0.4,0.6]]
cats = ['unsuccessful','completed']
pieces = []
for geo, geo_prop, task_prop in zip(geographies,geo_proportions,task_proportions):
shape = int(geo_prop * 100000)
data = {'intl':np.repeat(geo,shape), 'task_completion':np.random.choice(cats, shape, p=task_prop)}
frame = DataFrame(data)
pieces.append(frame)
df = pd.concat(pieces, ignore_index=True)
```
Here's data frame at this point:
``` python
In [3]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 100000 entries, 0 to 99999
Data columns (total 2 columns):
intl 100000 non-null object
task_completion 100000 non-null object
dtypes: object(2)
memory usage: 2.3+ MB
```
#### Expected Output
``` python
# when running this code I expect the following chart
df.groupby(['task_completion','intl']).size().unstack(0).plot(kind='bar',stacked=True)
```
#### Error
However, my real dataset uses Categorical types:
``` python
df['intl'] = df['intl'].astype('category')
df['task_completion'] = df['task_completion'].astype('category')
df['task_completion'] = df['task_completion'].cat.set_categories(['unsuccessful','completed'], ordered=True)
```
Now frame is:
``` python
In [5]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 100000 entries, 0 to 99999
Data columns (total 2 columns):
intl 100000 non-null category
task_completion 100000 non-null category
dtypes: category(2)
memory usage: 976.6 KB
```
And I get the following error when trying to plot:
``` python
In [6]: df.groupby(['task_completion','intl']).size().unstack(0).plot(kind='bar',stacked=True)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-8d6e104892c4> in <module>()
----> 1 df.groupby(['task_completion','intl']).size().unstack(0).plot(kind='bar',stacked=True)
/Users/adrianpalacios/anaconda/lib/python3.4/site-packages/pandas/tools/plotting.py in __call__(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
3735 fontsize=fontsize, colormap=colormap, table=table,
3736 yerr=yerr, xerr=xerr, secondary_y=secondary_y,
-> 3737 sort_columns=sort_columns, **kwds)
3738 __call__.__doc__ = plot_frame.__doc__
3739
/Users/adrianpalacios/anaconda/lib/python3.4/site-packages/pandas/tools/plotting.py in plot_frame(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
2609 yerr=yerr, xerr=xerr,
2610 secondary_y=secondary_y, sort_columns=sort_columns,
-> 2611 **kwds)
2612
2613
/Users/adrianpalacios/anaconda/lib/python3.4/site-packages/pandas/tools/plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds)
2436 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
2437
-> 2438 plot_obj.generate()
2439 plot_obj.draw()
2440 return plot_obj.result
/Users/adrianpalacios/anaconda/lib/python3.4/site-packages/pandas/tools/plotting.py in generate(self)
1023 self._compute_plot_data()
1024 self._setup_subplots()
-> 1025 self._make_plot()
1026 self._add_table()
1027 self._make_legend()
/Users/adrianpalacios/anaconda/lib/python3.4/site-packages/pandas/tools/plotting.py in _make_plot(self)
1959 rect = self._plot(ax, self.ax_pos + w, y, self.bar_width,
1960 start=start, label=label,
-> 1961 log=self.log, **kwds)
1962 pos_prior = pos_prior + np.where(mask, y, 0)
1963 neg_prior = neg_prior + np.where(mask, 0, y)
/Users/adrianpalacios/anaconda/lib/python3.4/site-packages/pandas/tools/plotting.py in _plot(cls, ax, x, y, w, start, log, **kwds)
1913 @classmethod
1914 def _plot(cls, ax, x, y, w, start=0, log=False, **kwds):
-> 1915 return ax.bar(x, y, w, bottom=start, log=log, **kwds)
1916
1917 @property
/Users/adrianpalacios/anaconda/lib/python3.4/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
1810 warnings.warn(msg % (label_namer, func.__name__),
1811 RuntimeWarning, stacklevel=2)
-> 1812 return func(ax, *args, **kwargs)
1813 pre_doc = inner.__doc__
1814 if pre_doc is None:
/Users/adrianpalacios/anaconda/lib/python3.4/site-packages/matplotlib/axes/_axes.py in bar(self, left, height, width, bottom, **kwargs)
2127 edgecolor=e,
2128 linewidth=lw,
-> 2129 label='_nolegend_'
2130 )
2131 r.update(kwargs)
/Users/adrianpalacios/anaconda/lib/python3.4/site-packages/matplotlib/patches.py in __init__(self, xy, width, height, angle, **kwargs)
640
641 self._x = float(xy[0])
--> 642 self._y = float(xy[1])
643 self._width = float(width)
644 self._height = float(height)
TypeError: only length-1 arrays can be converted to Python scalars
```
Interestingly, taking out the stacked kwarg works:
``` python
df.groupby(['task_completion','intl']).size().unstack(0).plot(kind='bar')
```
#### Output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Darwin
OS-release: 15.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.22.1
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
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} | 2 | 2016-04-28T18:58:36Z | 2016-05-18T22:10:30Z | 2016-05-18T22:10:30Z | CONTRIBUTOR | null | Similar to #4746, trying to `resample` with an index containing `NaT` produces rather ambiguous error messages of the form:
> ValueError: Shape of passed values is (2, 5), indices imply (2, 4)
Something like `cannot resample from index containing Not-a-Time (NaT)` would be more helpful to users.
#### MWE
``` python
import pandas as pd
import numpy as np
times = pd.DatetimeIndex(['20160428T110000',
'20160428T110001',
'20160428T110002',
'20160428T110003',
'20160428T110004',
pd.NaT,
'20160428T110001',
'20160428T110002',
'20160428T110003',
'20160428T110004'])
df = pd.DataFrame(np.random.randn(10,2),
index=times,
columns=['A', 'B'])
df.resample('3s')
```
_This example index contains duplicate values, but results are the same w/ or w/o them._
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 23 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21.1
numpy: 1.8.2
scipy: 0.14.0
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.3
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.6.4
lxml: 3.4.1
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.5.2
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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} | 8 | 2016-04-28T20:40:37Z | 2021-04-24T03:52:09Z | null | NONE | null | #### When column x has float64 type, the custom formatter is applied to every value
```
t = pd.DataFrame([[0], [10.4], [3]], columns=['x'])
display(HTML(t.to_html(formatters=dict(x='xxx'.format))))
x
------
0 xxx
1 xxx
2 xxx
```
#### But when column x is mixed type, the formatter isn't called for (only) floating point values
```
t = pd.DataFrame([['a'], [0], [10.4], [3]], columns=['x'])
display(HTML(t.to_html(formatters=dict(x='xxx'.format))))
x
------
0 xxx
1 xxx
2 10.4
3 xxx
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 1.5.4
setuptools: 20.7.0
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: None
boto: None
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} | 3 | 2016-04-28T20:40:50Z | 2018-09-05T11:31:04Z | 2018-09-05T11:31:04Z | CONTRIBUTOR | null | brought up in #12925
some examples in #15167
Resampling fence posts are off when upsampling < 1 Day.
```
In [5]: df = DataFrame(data=[[1, 3], [-5, 10], [0, 0]],index=pd.timedelta_range('00:00:00','00:10:00',freq='5T'))
In [6]: df.resample('1T').asfreq()
Out[6]:
0 1
00:00:00 1.0 3.0
00:01:00 NaN NaN
00:02:00 NaN NaN
00:03:00 NaN NaN
00:04:00 NaN NaN
00:05:00 -5.0 10.0
00:06:00 NaN NaN
00:07:00 NaN NaN
00:08:00 NaN NaN
00:09:00 NaN NaN
In [8]: df.resample('1T').mean()
Out[8]:
0 1
00:00:00 1.0 3.0
00:01:00 NaN NaN
00:02:00 NaN NaN
00:03:00 NaN NaN
00:04:00 NaN NaN
00:05:00 -5.0 10.0
00:06:00 NaN NaN
00:07:00 NaN NaN
00:08:00 NaN NaN
00:09:00 NaN NaN
00:10:00 0.0 0.0
```
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| null | 2 | 2016-04-29T00:04:47Z | 2016-04-29T13:57:01Z | 2016-04-29T13:56:53Z | NONE | null | I have created two dataframes with earthquake data that I am trying to compare. The elements in the dataframes should be equal. The files are attached below.
[broken_df.zip](https://github.com/pydata/pandas/files/241604/broken_df.zip)
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
df1 = pd.read_pickle('d1.pkl') # these files are attached in the .zip
df2 = pd.read_pickle('d2.pkl')
df1.equals(df2) # returns False
(df1 == df2).all().all() # returns True
```
However, If I save the dataframes as csv files (also attached), load them and rerun the same code it returns True for both checks as expected.
#### Expected Output
``` Python
True
True
```
#### output of ``pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-85-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.0
setuptools: 20.7.0
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: None
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} | 12 | 2016-04-29T00:25:06Z | 2016-05-07T22:41:26Z | 2016-05-07T16:53:42Z | CONTRIBUTOR | null | - [x] closes #11382
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2016-04-29T01:42:49Z | 2016-04-29T16:09:20Z | 2016-04-29T16:09:14Z | MEMBER | null | - [x] closes #12979
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Enabled true division. It also prevents differences between py2 / py3.
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} | 2 | 2016-04-29T02:48:16Z | 2016-04-29T15:59:42Z | 2016-04-29T15:59:08Z | MEMBER | null | - [x] closes #9903
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- ~~whatsnew entry~~
No what's new as the fixed timing is unclear.
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} | 2 | 2016-04-29T03:17:39Z | 2021-04-24T03:52:44Z | null | NONE | null | deprecate `between` in favor of other indexing options (e.g. `loc`)
xref: https://github.com/pydata/pandas/issues/12398, #12402 (not merged)
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} | 0 | 2016-04-29T03:35:37Z | 2016-04-29T13:40:30Z | 2016-04-29T13:40:30Z | MEMBER | null | - [x] closes #8542
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- ~~whatsnew entry~~
No what's new as the fixed timing is unclear.
**NOTE** Different from `TimeGrouper` with single key, it doesn't interpolate missing datetimes between existing keys (same as current behavior of key length >= 3).
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} | 1 | 2016-04-29T11:50:55Z | 2016-05-13T23:14:52Z | 2016-05-13T23:14:43Z | NONE | null | ---
Tutorial at [pandas.pydata.org/pandas-docs/stable/10min.html](http://pandas.pydata.org/pandas-docs/stable/10min.html) has the following code, where the obvious mistake is having only 1 key "foo" instead of 2 keys: "foo" and "bar" throughout the whole example
---
# Join
## SQL style merges. See the Database style joining
```
In [77]: left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
In [78]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
In [79]: left
Out[79]:
key lval
0 foo 1
1 foo 2
In [80]: right
Out[80]:
key rval
0 foo 4
1 foo 5
In [81]: pd.merge(left, right, on='key')
Out[81]:
key lval rval
0 foo 1 4
1 foo 1 5
2 foo 2 4
3 foo 2 5
```
Here is how it **should** be (replaced second 'foo' with 'bar':
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} | 3 | 2016-04-29T19:06:07Z | 2016-04-29T19:15:09Z | 2016-04-29T19:15:00Z | NONE | null | This works:
```
>>> pd.to_datetime(1, unit='D')
Timestamp('1970-01-02 00:00:00')
```
However, the following gives incorrect output:
```
>>> pd.to_datetime(1, unit='D', errors='coerce')
NaT
```
#### Details of Configuration:
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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| null | 1 | 2016-04-29T20:28:29Z | 2016-04-29T20:40:35Z | 2016-04-29T20:40:31Z | NONE | null | Hello,
I find very usefull `pandas.read_sas` but, as I'm collaborating a lot with SAS users, I'm wondering if there are some plans to implement `pandas.DataFrame.to_sas`?
Up to now, the only possibility I see is to pass through common file formats like `pandas.DataFrame.to_csv`.
Thanks a lot for your help.
Best regards.
P.S.: Sorry to push that as an issue, but I don't find how to suggest new features.
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} | 7 | 2016-04-29T21:33:36Z | 2018-09-25T12:55:05Z | 2018-09-25T12:55:05Z | CONTRIBUTOR | null | With 0.17.1, justification is handled correctly:
```
In [3]: df = pd.DataFrame(np.random.randn(3, 3), columns=['one', 'two', 'three'])
In [4]: print df.to_string(index=False)
one two three
0.096065 -0.529437 -2.058535
0.588013 0.221911 -2.400129
0.848234 -0.093931 1.221708
```
But in 0.18.0, it's broken:
```
In [4]: print df.to_string(index=False)
one two three
1.164170 -0.027236 -0.114962
-1.405931 0.019395 0.743320
0.598321 0.655802 -1.061834
```
This is caused by PR #11942. The issue it was meant to fix (#11833) is actually caused by using the format string "% d" for formatting integers, which produces a leading space if the integer is positive (where the minus sign would go for a negative integer). My preferred solution would be to change the formatter to "%d" (and to revert #11942), which would have the added bonus of eliminating double spaces between some columns (see the first `to_string` output in #11833, for example). A bunch of formatting tests would have to be adjusted, though. Is there anyone who prefers the extra space?
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} | 1 | 2016-04-29T23:01:27Z | 2016-04-30T18:47:41Z | 2016-04-30T18:47:41Z | CONTRIBUTOR | null | closes #11758
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} | 2 | 2016-04-29T23:49:03Z | 2021-05-12T13:27:56Z | 2021-05-12T13:27:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
In [2]: import pandas as pd
In [3]: a = pd.Series([True,False,True])
In [4]: a
Out[4]:
0 True
1 False
2 True
dtype: bool
In [6]: a[0] = pd.np.nan
In [7]: a
Out[7]:
0 NaN
1 0.0
2 1.0
dtype: float64
```
#### Expected Output
based on type promotions mentioned [here](http://pandas.pydata.org/pandas-docs/stable/gotchas.html#nan-integer-na-values-and-na-type-promotions) I would expect the series to get promoted to `object` to allow for `NaN`s.
> When introducing NAs into an existing Series or DataFrame via reindex or some other means, boolean and integer types will be promoted to a different dtype in order to store the NAs. These are summarized by this table:
```
In [7]: a
Out[7]:
0 NaN
1 False
2 True
dtype: object
```
#### output of `pd.show_versions()`
``````
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.6.7
Cython: None
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0```
``````
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https://api.github.com/repos/pandas-dev/pandas/issues/13035 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13035/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13035/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13035/events | https://github.com/pandas-dev/pandas/issues/13035 | 151,991,584 | MDU6SXNzdWUxNTE5OTE1ODQ= | 13,035 | index.set_levels produces weird results when index is repetative | {
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} | 2 | 2016-04-30T00:07:56Z | 2019-09-05T16:13:39Z | 2019-09-05T16:13:38Z | NONE | null | My understanding of how multiindexes work is fuzzy, but as far as I can tell set_levels isn't updating the labels properly. The example probably explains better than I can:
#### Code
``` python
df = pd.DataFrame(data = ["foo","bar"])
df2 = pd.concat([df,df,df],keys = ["a","b","c"])
print df2
newIndex = df2.index.get_level_values(0).map(lambda x:x + "!")
df2.index.set_levels(newIndex,level=0,inplace=True)
print df2
```
#### Expected Output
```
0
a 0 foo
1 bar
b 0 foo
1 bar
c 0 foo
1 bar
0
a! 0 foo
1 bar
b! 0 foo
1 bar
c! 0 foo
1 bar
```
#### Actual Output
```
0
a 0 foo
1 bar
b 0 foo
1 bar
c 0 foo
1 bar
0
a! 0 foo
1 bar
0 foo
1 bar
b! 0 foo
1 bar
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.3.0-040300-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.3.1
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
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} | 6 | 2016-04-30T01:21:19Z | 2016-04-30T23:23:12Z | 2016-04-30T23:22:19Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Replace repeated `list.append` with `np.union1d` in `IntIndex.make_union`. `make_union` is used in numeric ops.
**NOTE:** It is also possible to fix `IntIndex.intersect` to use `np.intersect1d`, but it doesn't increase the performance (because the length of the result is smaller).
The below microbench assumes array's 90% is sparse.
```
import numpy as np
import pandas as pd
np.random.seed(1)
N = 1000000
a = np.array([np.nan] * N)
b = np.array([np.nan] * N)
indexer_a = np.unique(np.random.randint(0, N, N / 10))
indexer_b = np.unique(np.random.randint(0, N, N / 10))
a[indexer_a] = np.random.randint(0, 100, len(indexer_a))
b[indexer_b] = np.random.randint(0, 100, len(indexer_b))
sa = pd.SparseArray(a)
sb = pd.SparseArray(b)
```
#### on current master
```
%timeit sa.sp_index.make_union(sb.sp_index)
#10 loops, best of 3: 52.7 ms per loop
%timeit sa + sb
10 loops, best of 3: 47.8 ms per loop
```
#### After this PR
```
%timeit sa.sp_index.make_union(sb.sp_index)
100 loops, best of 3: 11.6 ms per loop
%timeit sa + sb
100 loops, best of 3: 15.3 ms per loop
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13037 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13037/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13037/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13037/events | https://github.com/pandas-dev/pandas/pull/13037 | 152,009,942 | MDExOlB1bGxSZXF1ZXN0Njg0NTg0Mzk= | 13,037 | BUG: subclassed .align returns normal DataFrame | {
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} | 2 | 2016-04-30T04:59:59Z | 2016-04-30T14:56:46Z | 2016-04-30T14:56:43Z | MEMBER | null | - [x] closes #12983
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Also fixed a bug which `Series.align(DataFrame)` raises `AttributeError`.
```
pd.Series([1, 2], index=['a', 'b']).align(pd.DataFrame([1, 2], index=['a', 'c']))
# AttributeError: 'Series' object has no attribute 'columns'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13038 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13038/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13038/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13038/events | https://github.com/pandas-dev/pandas/pull/13038 | 152,011,232 | MDExOlB1bGxSZXF1ZXN0Njg0NTg5Mjc= | 13,038 | TST/BUG: Categorical dtypes cause error when attempting stacked bar plot | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
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- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
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| null | 20 | 2016-04-30T06:20:39Z | 2016-05-11T02:30:23Z | 2016-05-10T16:43:06Z | MEMBER | null | With [a little bit of magic](https://github.com/shoyer/pandas-magic/blob/master/pandas_magic/__init__.py#L13-L33), we could make the following syntax work:
``` python
(df[lambda: sepal_length > 3]
.groupby(lambda: pd.cut(sepal_width, 5))
.apply(lambda: petal_width.mean()))
```
Syntax like `df[lambda: sepal_length > 3]` is an alternative to more verbose alternatives like the recently added `df[lambda x: x.sepal_length > 3]` (https://github.com/pydata/pandas/issues/11485). Here we use `lambda` essentially in place of a macro that would allow for delayed evaluation (which of course are not supported by Python syntax).
My proposal is to add support for such "thunks" to every pandas method that accepts a callable function that currently must take a single argument work on DataFrames.
Under the covers, this works by (1) copying the `globals()` dictionary at evaluation time and (2) injecting the current DataFrame into it. We would further ensure that this only works on lambda functions, by checking `f.func_name == '<lambda>'`.
The main gotcha is that [it isn't possible to dynamically override ~~local~~ non-global variables](http://stackoverflow.com/questions/8028708/dynamically-set-local-variable-in-python) without [some true dark magic](https://gist.github.com/njsmith/2347382#file-inject-py-L40). This means that code like the following is going to behave contrary to expectations:
``` python
def x_plus_one(df):
x = 0
# uses x = 0 instead of df.x
return df.pipe(lambda: x + 1)
df = pd.DataFrame({'x': np.arange(100)})
result = x_plus_one(df) # all 1s, not range(1, 101)
```
Is this so bad? Shadowing variables in an outer scope is already poor design, but this is a pretty serious departure from expectations.
The other danger is that this could mask bugs, e.g., if a user mistakenly types `df.pipe(lambda: x)` instead of `df.pipe(lambda x: x)`. This is an unavoidable danger of spelling two APIs with similar syntax.
On the plus side, this proposal is safer than @njsmith's "true dark magic" context manager (see above) for injecting DataFrame columns, because there's no possibility of variable assignment inside a `lambda`.
Would this be a good idea for pandas?
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| null | 4 | 2016-04-30T12:42:50Z | 2020-04-30T05:52:56Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas
df = pandas.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9], 'd': [10, 11, 12]})
df.plot(y=['a', 'b'], secondary_y=['c', 'd'])
```
I only get the primary axis results:
Others seem to be using this - am I missing something?
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.5
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.39.0
```
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} | 11 | 2016-04-30T13:22:19Z | 2016-05-06T12:11:02Z | 2016-05-06T12:10:40Z | CONTRIBUTOR | null | - [x ] closes #10001
- [x ] tests added / passed
- [x ] passes `git diff upstream/master | flake8 --diff`
- [x ] whatsnew entry
closes GH10001
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} | 1 | 2016-04-30T14:02:26Z | 2016-05-11T22:51:09Z | 2016-05-11T22:51:03Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
pd.Series([pd.Timestamp('2015-01-01', tz='US/Eastern'),
pd.Timestamp('2015-01-01', tz='Asia/Tokyo')]) + pd.Timedelta('1 days')
# TypeError: unsupported operand type(s) for +: 'numpy.ndarray' and 'Timedelta'
pd.Series([pd.Period('2015-01-01', freq='D')]) + pd.Timedelta('1 days')
# TypeError: unsupported operand type(s) for +: 'numpy.ndarray' and 'Timedelta'
```
#### Expected Output
ops must be applied to each elem
#### output of `pd.show_versions()`
current master.
**NOTE:** This works:
```
pd.Series([pd.Timestamp('2015-01-01', tz='US/Eastern'),
pd.Timestamp('2015-01-01', tz='Asia/Tokyo')]) + pd.tseries.offsets.Day()
#0 2015-01-02 00:00:00-05:00
#1 2015-01-02 00:00:00+09:00
# dtype: object
pd.Series([pd.Period('2015-01-01', freq='D')]) + pd.tseries.offsets.Day()
#0 2015-01-02
# dtype: object
```
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} | 3 | 2016-04-30T17:29:37Z | 2020-11-19T18:58:15Z | 2020-11-19T18:58:15Z | CONTRIBUTOR | null | after #13033
I left this alone, but should an empty string just be a `NaT` (like we do elsewhere), even when a `format`is present?
```
In [1]: td = pd.Series(['May 04', 'Jun 02', ''], index=[1, 2, 3])
In [2]: td
Out[2]:
1 May 04
2 Jun 02
3
dtype: object
In [3]: pd.to_datetime(td, format='%b %y', errors='coerce')
Out[3]:
1 2004-05-01
2 2002-06-01
3 NaT
dtype: datetime64[ns]
In [4]: pd.to_datetime(td, format='%b %y', errors='raise')
ValueError: time data '' does not match format '%b %y' (match)
```
related is that we don't coerce empty strings here either (and we have an odd error message).
```
In [1]: pd.to_datetime([1, ''], unit='s', errors='coerce')
Out[1]: DatetimeIndex(['1970-01-01 00:00:01', 'NaT'], dtype='datetime64[ns]', freq=None)
In [2]: pd.to_datetime([1, ''], unit='s')
ValueError: invalid literal for long() with base 10: ''
```
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} | 1 | 2016-04-30T22:07:32Z | 2017-02-27T21:39:11Z | 2016-04-30T23:10:59Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
original: http://stackoverflow.com/questions/36960086/groupby-how-to-extract-seconds-from-datetime-with-diff
```
data = """
idx A ID3 DATETIME
0 B-028 b76cd912ff "2014-10-08 13:43:27"
1 B-054 4a57ed0b02 "2014-10-08 14:26:19"
2 B-076 1a682034f8 "2014-10-08 14:29:01"
3 B-023 b76cd912ff "2014-10-08 18:39:34"
4 B-023 f88g8d7sds "2014-10-08 18:40:18"
5 B-033 b76cd912ff "2014-10-08 18:44:30"
6 B-032 b76cd912ff "2014-10-08 18:46:00"
7 B-037 b76cd912ff "2014-10-08 18:52:15"
8 B-046 db959faf02 "2014-10-08 18:59:59"
9 B-053 b76cd912ff "2014-10-08 19:17:48"
10 B-065 b76cd912ff "2014-10-08 19:21:38"
"""
df = pd.read_csv(io.StringIO(data), delim_whitespace=True, index_col=[0], parse_dates=['DATETIME'])
In [237]: df.groupby('ID3')['DATETIME'].transform(lambda x: x.diff()).dtypes
Out[237]: dtype('<M8[ns]')
In [238]: df[['ID3','DATETIME']].groupby('ID3').transform(lambda x: x.diff()).dtypes
Out[238]:
DATETIME timedelta64[ns]
dtype: object
#### Expected Output
I would expect the same DTYPEs for both: `NDFrameGroupBy.transform()` and `SeriesGroupBy.transform()`
#### output of ``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 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.4
patsy: None
dateutil: 2.5.1
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.5
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
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} | 0 | 2016-04-30T22:53:14Z | 2016-04-30T23:15:55Z | 2016-04-30T23:15:55Z | CONTRIBUTOR | null | closes #12927
TST: use 32bit_repr when printing formatting of sparse
COMPAT: ensure consensus name when Indexes are union/intersected if possible
COMPAT: skip some coercion tests on 32-bit
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} | 7 | 2016-05-01T14:01:00Z | 2016-05-07T14:53:12Z | 2016-05-07T14:53:12Z | CONTRIBUTOR | null | xref #12810
```
In [1]: sp = pd.SparseDataFrame([1, 2, 3])
In [2]: np.cumsum(sp)
ValueError: No axis named None for object type <class 'pandas.sparse.frame.SparseDataFrame'>
In [3]: np.cumsum(sp, axis=0)
Out[3]:
0
0 1.0
1 3.0
2 6.0
In [4]: np.cumsum(sp, axis=1)
Out[4]:
0
0 1.0
1 2.0
2 3.0
```
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} | 14 | 2016-05-01T18:30:07Z | 2021-04-24T03:55:35Z | null | NONE | null | ### Problem
In _pd.read_sql_ and _pd.read_sql_table_ when the _chunksize_ parameter is set, Pandas builds a DataFrame with dtypes inferred from the data **in the chunk**. This can be a problem if an INTEGER colum contains null values in some chunks but not in others, leading the same column to be int64 in some cases and in others float64. A similar problem happens with strings.
In ETL processes or simply when dumping large queries to disk in HDF5 format, the user currently has the burden of explicitly having to handle the type conversions of potentially many columns.
### Solution?
Instead of guessing the type from a subset of the data, it should be possible to obtain the type information from the database and map it to the appropriate dtypes.
It is possible to obtain column information from Sqlalchemy when querying a full table by inspecting its metadata, but I was unsuccessfull in findind a way to do it for a general query.
Although I am unaware of all the possible type problems that can arise [DBAPI](https://www.python.org/dev/peps/pep-0249/#type-objects-and-constructors) does actually enforce the cursor.description to specify whether each result column is nullable.
Pandas could use this information (optionally) to always interpret nullable numeric columns as floats and strings as object columns.
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} | 7 | 2016-05-01T20:50:07Z | 2016-05-07T14:43:02Z | 2016-05-07T14:42:56Z | CONTRIBUTOR | null | - [x] closes #10904
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 4 | 2016-05-01T21:40:16Z | 2018-06-28T10:23:02Z | 2018-06-28T10:23:02Z | CONTRIBUTOR | null | I ran into an issue where the behavior of `.apply()` changed from 0.16 to 0.17, causing different results on tz aware data. Extracting the hour of day from datetimes is different for `Series(x).apply(func)` vs `func(x)`. Below is a minimal example of the issue in 0.17, it seems the behavior is the same in 0.18 but different (though still not equal) on master, also shown below.
#### On 0.17.1 and 0.18.0:
``` python
>>> import pandas as pd
>>> def hour_of_day(dt):
... return dt.hour
...
>>> dt = pd.to_datetime(1462068217, unit='s')
>>> dt_localized = dt.tz_localize('UTC').tz_convert('US/Pacific')
>>> dt_list = [dt, dt_localized]
>>> apply_series = pd.Series(dt_list).apply(hour_of_day)
>>> map_series = pd.Series(map(hour_of_day, dt_list))
>>> print dt_list
[Timestamp('2016-05-01 02:03:37'), Timestamp('2016-04-30 19:03:37-0700', tz='US/Pacific')]
>>> print apply_series
0 2
1 2
dtype: int64
>>> print map_series
0 2
1 19
dtype: int64
>>> print apply_series - map_series
0 0
1 -17
dtype: int64
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.5
lxml: 3.6.0
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.6.7.None
psycopg2: None
Jinja2: None
```
#### On 0.16.2 (what I expected):
``` python
# Same setup as above
>>> print apply_series
0 2
1 19
dtype: int64
>>> print map_series
0 2
1 19
dtype: int64
>>> print apply_series - map_series
0 0
1 0
dtype: int64
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: None
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
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
```
#### On master:
``` python
# Same setup as above
>>> print apply_series
0 19
1 19
dtype: int32
>>> print map_series
0 2
1 19
dtype: int64
>>> print apply_series - map_series
0 17
1 0
dtype: int64
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: 05e734ab171be0fda838c6b12839c38fa588da2c
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0+203.g05e734a
nose: None
pip: 8.1.1
setuptools: 20.7.0
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
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
```
#### Expected Output
I would expect the output to be [2, 19], as in 0.16, and matching map(f, data).
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} | 2 | 2016-05-01T21:44:32Z | 2016-05-03T00:38:26Z | 2016-05-01T22:59:20Z | CONTRIBUTOR | null | xref #11758, fix for bug in #13033
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| null | 1 | 2016-05-02T06:07:15Z | 2016-05-02T06:13:46Z | 2016-05-02T06:13:46Z | NONE | null | If using MultiIndex as columns, saving to CSV adds an extra line with no data between the column headers and the data. I couldn't find information that this is something that should happen so it was unexpected to me.
#### Code Sample, a copy-pastable example if possible
In IPython:
``` python
import pandas as pd
pd.DataFrame({('a','b'): [1, 2], ('c','d'): [3, 4]}).to_csv('temp.csv', index=False)
cat temp.csv
```
Outputs:
```
a,c
b,d
,
1,3
2,4
```
#### Expected Output
I expected not to have an empty row between column names and the data:
```
a,c
b,d
1,3
2,4
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.2-gnu-1
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_DK.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.10.1
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.4
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
```
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} | 13 | 2016-05-02T06:33:38Z | 2021-10-07T11:04:13Z | null | NONE | null | If I use MultiIndex columns and if a level happens to have empty values for all columns, the saved CSV file cannot be read. I expected to recover the dataframe from the saved CSV perfectly.
I believe #6618 might be related, because this is somehow related to how Pandas uses an empty data row to separate column names and actual data when using MultiIndex columns.
#### Code Sample, a copy-pastable example if possible
This works as expected:
``` ipython
In [1]: pd.DataFrame({('a','b'): [1, 2], ('c','d'): [3, 4]}).to_csv('temp.csv', index=False)
In [2]: pd.read_csv('temp.csv', header=[0,1])
Out[2]:
a c
b d
0 1 3
1 2 4
```
However, if a level is empty (i.e., all columns are `''` on that level), it doesn't work:
``` ipython
In [3]: pd.DataFrame({('a',''): [1, 2], ('c',''): [3, 4]}).to_csv('temp.csv', index=False)
In [4]: pd.read_csv('temp.csv', header=[0,1])
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-73-9f097e07e5a9> in <module>()
----> 1 pd.read_csv('temp.csv', header=[0,1])
/usr/lib/python3.5/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
527 skip_blank_lines=skip_blank_lines)
528
--> 529 return _read(filepath_or_buffer, kwds)
530
531 parser_f.__name__ = name
/usr/lib/python3.5/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
293
294 # Create the parser.
--> 295 parser = TextFileReader(filepath_or_buffer, **kwds)
296
297 if (nrows is not None) and (chunksize is not None):
/usr/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
610 self.options['has_index_names'] = kwds['has_index_names']
611
--> 612 self._make_engine(self.engine)
613
614 def _get_options_with_defaults(self, engine):
/usr/lib/python3.5/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
745 def _make_engine(self, engine='c'):
746 if engine == 'c':
--> 747 self._engine = CParserWrapper(self.f, **self.options)
748 else:
749 if engine == 'python':
/usr/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
1132 self._extract_multi_indexer_columns(
1133 self._reader.header, self.index_names, self.col_names,
-> 1134 passed_names
1135 )
1136 )
/usr/lib/python3.5/site-packages/pandas/io/parsers.py in _extract_multi_indexer_columns(self, header, index_names, col_names, passed_names)
906 "Passed header=[%s] are too many rows for this "
907 "multi_index of columns"
--> 908 % ','.join([str(x) for x in self.header])
909 )
910
CParserError: Passed header=[0,1] are too many rows for this multi_index of columns
```
#### Expected Output
Expected that the empty columns are read correctly because I had explicitly specified the rows to use as column index:
```
a c
0 1 3
1 2 4
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.2-gnu-1
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_DK.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.10.1
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.4
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
```
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} | 0 | 2016-05-02T13:20:12Z | 2016-05-02T14:07:32Z | 2016-05-02T14:07:32Z | CONTRIBUTOR | null | {
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| null | 14 | 2016-05-02T14:42:09Z | 2021-06-11T13:08:21Z | null | NONE | null | related issues:
- [x] #13121
- [x] #5839
- [x] #9867
- [x] #13255
- [x] #20066
- [ ] #14927 (about detecting mutating functions)
_Thank you very much for provide us pandas which is really a good packages I have used!_
### There were several times when I use pandas but get inconsistent return format which finally leads to a break!
- for example:
``` python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(10,1), columns = ['x'])
df['y'] = 1
df.groupby('y').apply(lambda x: x.x)
#x 0 1 2 3 4 5 6 \
#y
#1 -1.12114 0.679616 -1.392863 0.032637 -0.051134 0.594201 -0.238833
#x 7 8 9
#y
#1 0.95173 1.07469 -0.062198
df2 = df.copy()
df2['y'] = np.random.randint(1,3,10)
# do something similar like above
df2.groupby('y').apply(lambda x: x.x)
#y
#1 3 0.032637
# 8 1.074690
#2 0 -1.121140
# 1 0.679616
# 2 -1.392863
# 4 -0.051134
# 5 0.594201
# 6 -0.238833
# 7 0.951730
# 9 -0.062198
#Name: x, dtype: float64
```
even though, I knew the way to avoid this issue, I still believe it is better to return the same format. It's really annoying when some inconsistent return format happened which force me to look backwards to use some awkward methods to avoid it.
So, if possible, I do suggest the above function will only return one kind of format.
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} | 7 | 2016-05-02T16:00:24Z | 2016-05-07T14:59:43Z | 2016-05-07T14:59:43Z | CONTRIBUTOR | null | It would be nice if the `tz_localize` function of a `DatetimeIndex` had an optional flag for silently returning `NaT` instead of throwing a `NonExistentTimeError`, if the timestamp is not valid in the given timezone (for example due to DST changes).
I ran into this problem while trying to `tz_localize` a large index, and it seems to me that this would be a much better solution than manually handling the exception with a lambda expression in a (slow) python loop.
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} | 13 | 2016-05-02T20:42:59Z | 2016-05-07T15:00:19Z | 2016-05-07T14:59:43Z | CONTRIBUTOR | null | closes #13057
Author: dancsi [email protected]
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| null | 3 | 2016-05-03T02:37:11Z | 2016-05-03T17:50:13Z | 2016-05-03T17:50:13Z | CONTRIBUTOR | null | Add warning section in indexing docs. Close #12947.
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} | 1 | 2016-05-03T04:31:17Z | 2016-08-19T10:49:54Z | 2016-08-19T10:49:54Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
To be able to do on-disk filtering per column, one needs to store not only in `table` format, but also create an index in the HDF file for the columns required to be indexed, or setting it to `True`, for all columns to be indexed.
This needs to be done by using the method parameter `data_columns`, which is completely missing from the docstring of `to_hdf`:
``` python
df.to_hdf('teststore.hdf', 'df',format='t', data_columns=True)
```
Docstring is [here](https://github.com/pydata/pandas/blob/e462ece64b7cb12fe7cad6e085f62f3bfe9785c6/pandas/core/generic.py#L1053-L1093)
#### output of `pd.show_versions()`
## 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
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: 0.7.1
IPython: 4.2.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
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https://api.github.com/repos/pandas-dev/pandas/issues/13062 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13062/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13062/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13062/events | https://github.com/pandas-dev/pandas/issues/13062 | 152,699,471 | MDU6SXNzdWUxNTI2OTk0NzE= | 13,062 | HDF query with long literal string fails | {
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| null | 6 | 2016-05-03T04:55:27Z | 2016-05-05T05:51:06Z | 2016-05-03T13:02:39Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
c_ids = ['50ef419195e6e40eac000001', '50ef419195e6e40eac000002', '50ef419195e6e40eac000003']
df = pd.DataFrame({'class_id':c_ids, 'a':range(3)})
df.to_hdf('teststore.hdf', 'df',format='t', data_columns=True)
print('with variable')
c_id = '50ef419195e6e40eac000001'
print(pd.read_hdf('teststore.hdf', 'df', where='class_id=c_id'), '\n')
print('with literal string')
print(pd.read_hdf('teststore.hdf', 'df', where='class_id=50ef419195e6e40eac000001'))
```
#### Actual output
``` bash
with variable
a class_id
0 0 50ef419195e6e40eac000001
with literal string
File "<unknown>", line 1
class_id ==50 ef419195e6e40eac000001
^
SyntaxError: invalid syntax
```
#### Expected Output
``` bash
with variable
a class_id
0 0 50ef419195e6e40eac000001
with literal string
a class_id
0 0 50ef419195e6e40eac000001
```
#### my own analysis
I figured it must have something to do with the length of the query, so I went down in class_id size to 14 characters, which is when it starts to work again:
``` python
c_ids = ['e6e40eac000001', 'e6e40eac000002', 'e6e40eac000003']
df = pd.DataFrame({'class_id':c_ids, 'a':range(3)})
df.to_hdf('teststore.hdf', 'df',format='t', data_columns=True)
print('with variable')
c_id = 'e6e40eac000001'
print(pd.read_hdf('teststore.hdf', 'df', where='class_id=c_id'), '\n')
print('with literal string')
print(pd.read_hdf('teststore.hdf', 'df', where='class_id=e6e40eac000001'))
```
#### output of `pd.show_versions()`
## 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
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: 0.7.1
IPython: 4.2.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
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} | 1 | 2016-05-03T07:22:43Z | 2016-05-03T09:03:49Z | 2016-05-03T09:03:03Z | NONE | null | I ran in to this bug while trying to parse the few dates through `parse_dates` of `pandas.read_csv()`. In the following code snippet, I'm trying to parse dates that have format `dd/mm/yy` which is resulting me an improper conversion. For some cases, the date field is considered as month and vice versa.
To keep it simple, for some cases `dd/mm/yy` get converted to `yyyy-dd-mm` instead of `yyyy-mm-dd`.
### Case 1:
```
04/10/96 is parsed as 1996-04-10, which is wrong.
```
### Case 2:
```
15/07/97 is parsed as 1997-07-15, which is correct.
```
### Case 3:
```
10/12/97 is parsed as 1997-10-12, which is wrong.
```
#### Code Sample
```
import pandas as pd
df = pd.read_csv('date_time.csv')
print 'Data in csv:'
print df
print df['start_date'].dtypes
print '----------------------------------------------'
df = pd.read_csv('date_time.csv', parse_dates = ['start_date'])
print 'Data after parsing:'
print df
print df['start_date'].dtypes
```
#### Current Output
<pre><code>----------------------
Data in csv:
----------------------
start_date
0 04/10/96
1 15/07/97
2 10/12/97
3 06/03/99
4 //1994
5 /02/1967
object
----------------------
Data after parsing:
----------------------
start_date
0 1996-04-10
1 1997-07-15
2 1997-10-12
3 1999-06-03
4 1994-01-01
5 1967-02-01
datetime64[ns]
</code></pre>
#### Expected Output
```
----------------------
Data in csv:
----------------------
start_date
0 04/10/96
1 15/07/97
2 10/12/97
3 06/03/99
4 //1994
5 /02/1967
object
----------------------
Data after parsing:
----------------------
start_date
0 1996-10-04
1 1997-07-15
2 1997-12-10
3 1999-03-06
4 1994-01-01
5 1967-02-01
datetime64[ns]
```
#### Output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 8.1
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: None
### More Comments:
I could use `date_parser` or `pandas.to_datetime()` to specify the proper format for date. But in my case, I have few date fields like ['//1997', '/02/1967'] for which I need to convert ['01/01/1997','01/02/1967']. The `parse_dates` helps me converting those type of date fields to the expected format without making me to write extra line of code.
[parse_date_bug.zip](https://github.com/pydata/pandas/files/246420/parse_date_bug.zip)
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} | 2 | 2016-05-03T10:31:13Z | 2016-05-03T13:01:29Z | 2016-05-03T13:01:29Z | NONE | null | Hi, i am use 0.16.2. I got the "IndexError: index out of bounds", the script is:
``` python
import pandas as pd
def test():
names = ['openid', 'clientid', 'roleid', 'rolename', 'school', 'IP', 'snid', 'rolelogin_time', 'amount', 'val', 'consume_sum', 'own_after', 'first_payment_time', 'last_payment_time', 'last_login_time', 'first_pay_level', 'level', 'vip_level']
df = pd.read_csv("2016-04-29.csv", names=names)
df_role = df.set_index(["openid", "clientid"], drop=False)
clientid_role = df_role.groupby("clientid").openid.nunique()
test()
```
here is the output:
```
Traceback (most recent call last):
File "test.py", line 11, in <module>
test()
File "test.py", line 7, in test
clientid_role = df_role.groupby("clientid").openid.nunique()
File "<string>", line 18, in nunique
File "/root/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 514, in __getattr__
if attr in self.obj:
File "/root/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 704, in __contains__
return key in self._info_axis
File "/root/anaconda/lib/python2.7/site-packages/pandas/core/index.py", line 4521, in __contains__
self.get_loc(key)
File "/root/anaconda/lib/python2.7/site-packages/pandas/core/index.py", line 5055, in get_loc
loc = self._get_level_indexer(key, level=0)
File "/root/anaconda/lib/python2.7/site-packages/pandas/core/index.py", line 5291, in _get_level_indexer
loc = level_index.get_loc(key)
File "/root/anaconda/lib/python2.7/site-packages/pandas/core/index.py", line 1572, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "pandas/index.pyx", line 134, in pandas.index.IndexEngine.get_loc (pandas/index.c:3824)
File "pandas/index.pyx", line 143, in pandas.index.IndexEngine.get_loc (pandas/index.c:3578)
File "pandas/src/util.pxd", line 41, in util.get_value_at (pandas/index.c:15287)
IndexError: index out of bounds
```
2016-04-29.csv is almost 760M, the code and csv can clone from [https://github.com/Skycrab/pandas_test](https://github.com/Skycrab/pandas_test)
see also [https://github.com/pydata/pandas/issues/5727](https://github.com/pydata/pandas/issues/5727)
#### output of `pd.show_versions()`
`In [4]: pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None`
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} | 17 | 2016-05-03T11:07:21Z | 2016-08-19T11:53:36Z | 2016-08-19T11:53:17Z | CONTRIBUTOR | null | - [x] closes #13061
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
How about this for #13061 ?
I don't think pandas has tests for each documented field, correct?
Do you want a whatsnew entry for this? If yes, how does that work? The guidelines don't talk about that.
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} | 9 | 2016-05-03T15:46:29Z | 2016-05-07T14:53:12Z | 2016-05-07T14:53:12Z | MEMBER | null | Title is self-explanatory. Closes #13048.
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} | 9 | 2016-05-03T17:11:46Z | 2016-05-05T16:14:29Z | 2016-05-05T16:14:28Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
given freq is not applied if data is empty.
```
s = pd.Series(index=pd.PeriodIndex([], freq='D'))
s.resample('M').mean()
# Series([], Freq: D, dtype: float64)
```
#### Expected
```
s.resample('M').mean()
# Series([], Freq: M, dtype: float64)
```
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} | 32 | 2016-05-03T17:13:57Z | 2020-08-07T11:53:10Z | 2020-08-07T11:53:10Z | CONTRIBUTOR | null | ``` python
# is missing the UTF8 BOM (encoded with default encoding UTF8)
with open('path_to_f', 'w') as f:
df.to_csv(f, encoding='utf-8-sig')
# is not missing the UTF8 BOM (encoded with passed encoding utf-8-sig)
df.to_csv('path_to_f', encoding='utf-8-sig')
```
I expect:
``` python
with open('path_to_f', 'w') as f:
df.to_csv(f, encoding='utf-8-sig')
```
To crash with `TypeError: write() argument must be str, not bytes`
and I expect:
``` python
with open('path_to_f', 'wb') as f:
df.to_csv(f, encoding='utf-8-sig')
```
To write the file correctly.
### Copy pasta
``` python
#!/usr/bin/env python3
import pandas as pd
df = pd.DataFrame()
with open('file_one', 'w') as f:
df.to_csv(f, encoding='utf-8-sig')
assert open('file_one', 'rb').read() == b'""\n'
# is not missing the UTF8 BOM (encoded with passed encoding utf-8-sig)
df.to_csv('file_two', encoding='utf-8-sig')
assert open('file_two', 'rb').read() == b'\xef\xbb\xbf""\n'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13069 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13069/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13069/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13069/events | https://github.com/pandas-dev/pandas/pull/13069 | 152,828,048 | MDExOlB1bGxSZXF1ZXN0Njg3NDE3NjQ= | 13,069 | BUG: Addition raises TypeError if Period is on rhs | {
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} | 4 | 2016-05-03T17:17:24Z | 2016-05-07T18:54:14Z | 2016-05-07T18:53:51Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
`Period` addition raises `TypeError` if `Period` is on right hand side.
```
1 + pd.Period('2011-01', freq='M')
# TypeError: unsupported operand type(s) for +: 'int' and 'pandas._period.Period'
```
#### Expected
```
1 + pd.Period('2011-01', freq='M')
# Period('2011-02', 'M')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13070 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13070/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13070/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13070/events | https://github.com/pandas-dev/pandas/pull/13070 | 152,834,554 | MDExOlB1bGxSZXF1ZXN0Njg3NDYzMjI= | 13,070 | DOC: add warning section in indexing docs | {
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} | 1 | 2016-05-03T17:48:35Z | 2016-05-07T14:51:43Z | 2016-05-07T14:51:35Z | CONTRIBUTOR | null | Replace PR #13060 and close #12947.
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https://api.github.com/repos/pandas-dev/pandas/issues/13071 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13071/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13071/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13071/events | https://github.com/pandas-dev/pandas/pull/13071 | 152,835,127 | MDExOlB1bGxSZXF1ZXN0Njg3NDY3Mzg= | 13,071 | BUG: PeriodIndex and Period subtraction error | {
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} | 15 | 2016-05-03T17:51:08Z | 2016-05-06T11:50:06Z | 2016-05-06T11:50:06Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Similar to #5202. `PeriodIndex` subtraction raises `AttributeError` either side is `Period` (scalar).
```
pd.PeriodIndex(['2011-01', '2011-02'], freq='M') - pd.Period('2011-01', freq='M')
# AttributeError: 'PeriodIndex' object has no attribute 'ordinal'
```
#### Expected
If `Period(NaT)` is included in either side, result is `Float64Index` to hold `nan`.
```
pd.PeriodIndex(['2011-01', '2011-02'], freq='M') - pd.Period('2011-01', freq='M')
# Int64Index([0, 1], dtype='int64')
pd.PeriodIndex(['2011-01', 'NaT'], freq='M') - pd.Period('2011-01', freq='M')
Float64Index([0.0, nan], dtype='float64')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13072 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13072/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13072/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13072/events | https://github.com/pandas-dev/pandas/pull/13072 | 152,838,584 | MDExOlB1bGxSZXF1ZXN0Njg3NDkyNzg= | 13,072 | BUG: Series ops with object dtype may incorrectly fail | {
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} | 1 | 2016-05-03T18:08:54Z | 2016-05-11T22:51:03Z | 2016-05-11T22:51:03Z | MEMBER | null | - [x] closes #13043
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This must be after #13069.
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} | 15 | 2016-05-03T19:44:07Z | 2016-05-13T00:43:14Z | 2016-05-13T00:20:25Z | MEMBER | null | Closes #12298 by stripping the `Categorical` elements of their original categories so that only their included values are considered during the counting / aggregation.
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| null | 8 | 2016-05-03T21:40:49Z | 2016-05-03T21:57:04Z | 2016-05-03T21:54:31Z | NONE | null | It would be nice to have this implemented so that a parallel apply using `dask` could be used on dataframes with `Categorical` type columns.
Right now if I try to use `Dask` for a parallelized apply like so:
``` python
import dask.dataframe as dd
df_test = pd.DataFrame({'id': ['a', 'a', 'a', 'b', 'b'],
'cat': ['t','h','t','h','t']})
df_test['cat'] = df_test['cat'].astype('category')
df_test = dd.from_pandas(df_test, npartitions=20)
dd_result = df_test.groupby('id').apply(lambda x: (x['cat'] == 't').sum())
pd_result = dd_result.compute()
```
I get the following error:
``` python
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-19-3dadd485a741> in <module>()
3 dfd = dd.from_pandas(df, npartitions=20)
4 dd_result = dfd.groupby('item').apply(gen_will_nsf) # or whatever
----> 5 pd_result = dd_result.compute()
/usr/local/lib/python2.7/dist-packages/dask/base.pyc in compute(self, **kwargs)
35
36 def compute(self, **kwargs):
---> 37 return compute(self, **kwargs)[0]
38
39 @classmethod
/usr/local/lib/python2.7/dist-packages/dask/base.pyc in compute(*args, **kwargs)
108 for opt, val in groups.items()])
109 keys = [var._keys() for var in variables]
--> 110 results = get(dsk, keys, **kwargs)
111
112 results_iter = iter(results)
/usr/local/lib/python2.7/dist-packages/dask/threaded.pyc in get(dsk, result, cache, num_workers, **kwargs)
55 results = get_async(pool.apply_async, len(pool._pool), dsk, result,
56 cache=cache, queue=queue, get_id=_thread_get_id,
---> 57 **kwargs)
58
59 return results
/usr/local/lib/python2.7/dist-packages/dask/async.pyc in get_async(apply_async, num_workers, dsk, result, cache, queue, get_id, raise_on_exception, rerun_exceptions_locally, callbacks, **kwargs)
479 _execute_task(task, data) # Re-execute locally
480 else:
--> 481 raise(remote_exception(res, tb))
482 state['cache'][key] = res
483 finish_task(dsk, key, state, results, keyorder.get)
AttributeError: 'Categorical' object has no attribute 'tobytes'
Traceback
---------
File "/usr/local/lib/python2.7/dist-packages/dask/async.py", line 264, in execute_task
result = _execute_task(task, data)
File "/usr/local/lib/python2.7/dist-packages/dask/async.py", line 246, in _execute_task
return func(*args2)
File "/usr/local/lib/python2.7/dist-packages/dask/dataframe/shuffle.py", line 256, in partition
p.append(d)
File "/usr/local/lib/python2.7/dist-packages/partd/encode.py", line 23, in append
data = valmap(self.encode, data)
File "/usr/local/lib/python2.7/dist-packages/toolz/dicttoolz.py", line 84, in valmap
rv.update(zip(iterkeys(d), map(func, itervalues(d))))
File "/usr/local/lib/python2.7/dist-packages/partd/pandas.py", line 108, in serialize
for block in blocks] # this can be slightly faster if we merge both operations
File "/usr/local/lib/python2.7/dist-packages/partd/numpy.py", line 101, in serialize
return x.tobytes()
```
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-76-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.1
pip: 8.1.1
setuptools: 19.1.1
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.2
sphinx: 1.2.2
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
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} | 2 | 2016-05-03T22:26:58Z | 2021-04-24T03:59:34Z | null | NONE | null | @jreback Just opening this up as the next step after pydata/pandas#12887.
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} | 5 | 2016-05-03T23:27:27Z | 2016-05-21T23:58:25Z | 2016-05-21T23:58:25Z | CONTRIBUTOR | null | xref https://github.com/dask/partd/pull/7
```
In [1]: df1 = DataFrame({'A' : Series(list('aba')).astype('category', categories=list('ab'), ordered=True)})
In [2]: df2 = DataFrame({'A' : Series(list('aba')).astype('category', categories=list('ba'), ordered=True)})
In [3]: tm.assert_frame_equal(df1, df2)
In [11]: df2 = DataFrame({'A' : Series(list('aba')).astype('category', categories=list('ab'), ordered=False)})
In [12]: tm.assert_frame_equal(df1, df2)
In [13]: df2 = DataFrame({'A' : Series(list('aba')).astype('category', categories=list('ba'), ordered=False)})
In [14]: tm.assert_frame_equal(df1, df2)
```
all of these should fail as the ordered categoricals don't compare equal in the categories, and ordered is not being compared (nor categories)
Furthermore the `assert_series_equal` fail for the same.
`assert_categorical_equal` DOES the right thing, but not being called here. (not that this _should_ say `categories` are not equal?)
```
In [17]: tm.assert_categorical_equal(df1.A.values, df2.A.values)
AssertionError: Index are different
Index values are different (100.0 %)
[left]: Index([u'a', u'b'], dtype='object')
[right]: Index([u'b', u'a'], dtype='object')
```
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} | 6 | 2016-05-04T01:49:30Z | 2018-06-29T00:45:49Z | 2018-06-29T00:45:49Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
Split from #13071. Currently it returns int and loses freq info.
```
pd.Period('2011-03', freq='M') - pd.Period('2011-01', freq='M')
#2
```
#### Expected Output
Current ideas are:
1. Return `Timedelta` adding support of `Monthly` freq.
2. Return `Perioddelta` newly created.
3. Return `DateOffset`.
#### output of `pd.show_versions()`
on current master.
CC: @jreback @MaximilianR
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} | 3 | 2016-05-04T01:53:08Z | 2016-07-10T21:02:56Z | 2016-07-10T21:02:56Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
Split from #13071. `DatetimeIndex` - `Period` is not supported ATM, but errors are not understandable.
```
pd.DatetimeIndex(['2011-01-01', '2011-02-01']) - pd.Period('2011-01-01', freq='D')
# ValueError: Cannot do arithmetic with non-conforming periods
pd.DatetimeIndex(['2011-01-01', '2011-01-02'], freq='D') - pd.Period('2011-01-01', freq='D')
# AttributeError: 'DatetimeIndex' object has no attribute 'ordinal'
```
#### Expected Output
show understandable error message.
Or it should be supported converting `Period` to `Timestamp`.
```
pd.DatetimeIndex(['2011-01-01', '2011-02-01']) - pd.Period('2011-01-01', freq='D').to_timestamp()
# TimedeltaIndex(['0 days', '31 days'], dtype='timedelta64[ns]', freq=None)
```
#### output of `pd.show_versions()`
on current master
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} | 17 | 2016-05-04T02:52:47Z | 2016-05-21T14:53:09Z | 2016-05-21T14:12:53Z | CONTRIBUTOR | null | - [x] closes #13067 (existing PR)
- [x] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
closes #13067
closes #13212
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https://api.github.com/repos/pandas-dev/pandas/issues/13080 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13080/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13080/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13080/events | https://github.com/pandas-dev/pandas/pull/13080 | 152,928,065 | MDExOlB1bGxSZXF1ZXN0Njg4MTAzMTM= | 13,080 | TST: fix assert_categorical_equal message | {
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} | 1 | 2016-05-04T04:40:04Z | 2016-05-11T22:41:43Z | 2016-05-11T22:41:36Z | MEMBER | null | - [x] stage 1 of #13076
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] no whatsnew as it is internal test method
```
a = pd.Categorical([1, 2, 3, 4])
b = pd.Categorical([1, 2, 3, 5])
tm.assert_categorical_equal(a, b)
# Categorical.categories are different
# ...
a = pd.Categorical([1, 2, 4, 3], categories=[1, 2, 3, 4])
b = pd.Categorical([1, 2, 3, 4], categories=[1, 2, 3, 4])
tm.assert_categorical_equal(a, b)
# Categorical.codes values are different (50.0 %)
# ...
```
This must be after #13088.
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} | 2 | 2016-05-04T12:25:01Z | 2016-05-04T13:57:14Z | 2016-05-04T13:57:14Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
Merging comparison dispatch logic.
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} | 3 | 2016-05-04T12:32:14Z | 2016-05-05T12:03:46Z | 2016-05-05T12:03:38Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Follow-up for #13036. Perf improvements are not significant because the number of previous `list.append` call is not so large.
```
import numpy as np
import pandas as pd
np.random.seed(1)
N = 1000000
a = np.array([np.nan] * N)
b = np.array([np.nan] * N)
indexer_a = np.unique(np.random.randint(0, N, N / 10))
indexer_b = np.unique(np.random.randint(0, N, N / 10))
a[indexer_a] = np.random.randint(0, 100, len(indexer_a))
b[indexer_b] = np.random.randint(0, 100, len(indexer_b))
sa = pd.SparseArray(a)
sb = pd.SparseArray(b)
%timeit a.sp_index.intersect(sb.sp_index0)
# before
#100 loops, best of 3: 3.04 ms per loop
# after
#100 loops, best of 3: 2.11 ms per loop
```
```
def make_sparse_array(length, num_blocks, block_size, fill_value):
a = np.array([fill_value] * length)
for block in range(num_blocks):
i = np.random.randint(0, length)
a[i:i + block_size] = np.random.randint(0, 100, len(a[i:i + block_size]))
return pd.SparseArray(a, fill_value=fill_value)
N = 1000000
B = 10000
BS = 10
a = make_sparse_array(length=N, num_blocks=B, block_size=BS, fill_value=np.nan)
b = make_sparse_array(length=N, num_blocks=B, block_size=BS, fill_value=np.nan)
%timeit a + b
# before
#10 loops, best of 3: 70.8 ms per loop
# after
#10 loops, best of 3: 66 ms per loop
```
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} | 1 | 2016-05-04T13:01:38Z | 2017-10-01T14:55:33Z | 2017-10-01T14:55:33Z | CONTRIBUTOR | null | Upsampling returns a Series, Downsampling returns a DataFrame. Is this valid?
``` python
In [80]: series=pd.Series(range(10), index=pd.PeriodIndex(start='2000', freq='D', periods=10))
In [81]: series
Out[81]:
2000-01-01 0
2000-01-02 1
2000-01-03 2
2000-01-04 3
2000-01-05 4
2000-01-06 5
2000-01-07 6
2000-01-08 7
2000-01-09 8
2000-01-10 9
Freq: D, dtype: int64
In [83]: series.resample('M').ohlc()
Out[83]:
open high low close
2000-01 0 9 0 9
# returns a DataFrame
In [84]: series.resample('H').ohlc()
Out[84]:
2000-01-01 00:00 0.0
...
2000-01-10 23:00 NaN
Freq: H, dtype: float64
# returns a series
```
on master
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} | 6 | 2016-05-04T14:52:24Z | 2020-01-07T11:49:58Z | 2020-01-07T11:49:58Z | NONE | null | You get a confusing error message when trying to concat on non-unique (but also non-exactly-equal) indices. Small example:
```
In [57]: df1 = pd.DataFrame({'col1': [1, 2, 3]}, index=[0, 0, 1])
...: df2 = pd.DataFrame({'col2': [1, 2, 3]}, index=[0, 1, 2])
In [59]: pd.concat([df1, df2], axis=1)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-59-756087e4d415> in <module>()
----> 1 pd.concat([df1, df2], axis=1)
/home/joris/scipy/pandas/pandas/tools/concat.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
205 verify_integrity=verify_integrity,
206 copy=copy)
--> 207 return op.get_result()
208
209
/home/joris/scipy/pandas/pandas/tools/concat.py in get_result(self)
405 new_data = concatenate_block_managers(
406 mgrs_indexers, self.new_axes, concat_axis=self.axis,
--> 407 copy=self.copy)
408 if not self.copy:
409 new_data._consolidate_inplace()
/home/joris/scipy/pandas/pandas/core/internals.py in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
4849 placement=placement) for placement, join_units in concat_plan]
4850
-> 4851 return BlockManager(blocks, axes)
4852
4853
/home/joris/scipy/pandas/pandas/core/internals.py in __init__(self, blocks, axes, do_integrity_check, fastpath)
2784
2785 if do_integrity_check:
-> 2786 self._verify_integrity()
2787
2788 self._consolidate_check()
/home/joris/scipy/pandas/pandas/core/internals.py in _verify_integrity(self)
2994 for block in self.blocks:
2995 if block._verify_integrity and block.shape[1:] != mgr_shape[1:]:
-> 2996 construction_error(tot_items, block.shape[1:], self.axes)
2997 if len(self.items) != tot_items:
2998 raise AssertionError('Number of manager items must equal union of '
/home/joris/scipy/pandas/pandas/core/internals.py in construction_error(tot_items, block_shape, axes, e)
4258 raise ValueError("Empty data passed with indices specified.")
4259 raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
-> 4260 passed, implied))
4261
4262
ValueError: Shape of passed values is (2, 6), indices imply (2, 4)
```
---
Original reported issue by @gregsifr :
<details>
I am working with a large dataframe of customers which I was unable to concat. After spending some time I narrowed the problem area down to the below (pickled) dataframes and code.
When trying to concat the dataframes using the following code the error shown below is returned:
```
import pandas as pd
import pickle
df1 = pickle.loads('ccopy_reg\n_reconstructor\np1\n(cpandas.core.frame\nDataFrame\np2\nc__builtin__\nobject\np3\nNtRp4\n(dp5\nS\'_metadata\'\np6\n(lp7\nsS\'_typ\'\np8\nS\'dataframe\'\np9\nsS\'_data\'\np10\ng1\n(cpandas.core.internals\nBlockManager\np11\ng3\nNtRp12\n((lp13\ncpandas.core.index\n_new_Index\np14\n(cpandas.core.index\nMultiIndex\np15\n(dp16\nS\'labels\'\np17\n(lp18\ncnumpy.core.multiarray\n_reconstruct\np19\n(cpandas.core.base\nFrozenNDArray\np20\n(I0\ntS\'b\'\ntRp21\n(I1\n(L2L\ntcnumpy\ndtype\np22\n(S\'i1\'\nI0\nI1\ntRp23\n(I3\nS\'|\'\nNNNI-1\nI-1\nI0\ntbI00\nS\'\\x00\\x00\'\ntbag19\n(g20\n(I0\ntS\'b\'\ntRp24\n(I1\n(L2L\ntg23\nI00\nS\'\\x00\\x01\'\ntbasS\'names\'\np25\n(lp26\nNaNasS\'levels\'\np27\n(lp28\ng14\n(cpandas.core.index\nIndex\np29\n(dp30\nS\'data\'\np31\ng19\n(cnumpy\nndarray\np32\n(I0\ntS\'b\'\ntRp33\n(I1\n(L1L\ntg22\n(S\'O8\'\nI0\nI1\ntRp34\n(I3\nS\'|\'\nNNNI-1\nI-1\nI63\ntbI00\n(lp35\nVCUSTOMER_A\np36\natbsS\'name\'\np37\nNstRp38\nag14\n(g29\n(dp39\ng31\ng19\n(g32\n(I0\ntS\'b\'\ntRp40\n(I1\n(L2L\ntg34\nI00\n(lp41\nVVISIT_DT\np42\naVPURCHASE\np43\natbsg37\nNstRp44\nasS\'sortorder\'\np45\nNstRp46\nacpandas.tseries.index\n_new_DatetimeIndex\np47\n(cpandas.tseries.index\nDatetimeIndex\np48\n(dp49\nS\'tz\'\np50\nNsS\'freq\'\np51\nNsg31\ng19\n(g32\n(I0\ntS\'b\'\ntRp52\n(I1\n(L22L\ntg22\n(S\'M8\'\nI0\nI1\ntRp53\n(I4\nS\'<\'\nNNNI-1\nI-1\nI0\n((d(S\'ns\'\nI1\nI1\nI1\ntttbI00\nS\'\\x00\\x00\\x1c\\xca\\xf9\\xceO\\x10\\x00\\x00k[\\x8e\\x1dP\\x10\\x00\\x00\\xba\\xec"lP\\x10\\x00\\x00\\t~\\xb7\\xbaP\\x10\\x00\\x00X\\x0fL\\tQ\\x10\\x00\\x00\\xa7\\xa0\\xe0WQ\\x10\\x00\\x00\\x94T\\x9eCR\\x10\\x00\\x00\\xe3\\xe52\\x92R\\x10\\x00\\x002w\\xc7\\xe0R\\x10\\x00\\x00\\x81\\x08\\\\/S\\x10\\x00\\x00\\xd0\\x99\\xf0}S\\x10\\x00\\x00\\xbdM\\xaeiT\\x10\\x00\\x00\\x0c\\xdfB\\xb8T\\x10\\x00\\x00[p\\xd7\\x06U\\x10\\x00\\x00\\xaa\\x01lUU\\x10\\x00\\x00\\xf9\\x92\\x00\\xa4U\\x10\\x00\\x00\\xe6F\\xbe\\x8fV\\x10\\x00\\x005\\xd8R\\xdeV\\x10\\x00\\x00\\x84i\\xe7,W\\x10\\x00\\x00\\xd3\\xfa{{W\\x10\\x00\\x00"\\x8c\\x10\\xcaW\\x10\\x00\\x00\\x0f@\\xce\\xb5X\\x10\'\ntbsg37\nS\'date\'\np54\nstRp55\na(lp56\ng19\n(g32\n(I0\ntS\'b\'\ntRp57\n(I1\n(L2L\nL22L\ntg22\n(S\'f8\'\nI0\nI1\ntRp58\n(I3\nS\'<\'\nNNNI-1\nI-1\nI0\ntbI00\nS\'\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\'\ntba(lp59\ng14\n(g15\n(dp60\ng17\n(lp61\ng19\n(g20\n(I0\ntS\'b\'\ntRp62\n(I1\n(L2L\ntg23\nI00\nS\'\\x00\\x00\'\ntbag19\n(g20\n(I0\ntS\'b\'\ntRp63\n(I1\n(L2L\ntg23\nI00\nS\'\\x00\\x01\'\ntbasg25\n(lp64\nNaNasg27\n(lp65\ng14\n(g29\n(dp66\ng31\ng19\n(g32\n(I0\ntS\'b\'\ntRp67\n(I1\n(L1L\ntg34\nI00\n(lp68\ng36\natbsg37\nNstRp69\nag14\n(g29\n(dp70\ng31\ng19\n(g32\n(I0\ntS\'b\'\ntRp71\n(I1\n(L2L\ntg34\nI00\n(lp72\ng42\nag43\natbsg37\nNstRp73\nasg45\nNstRp74\na(dp75\nS\'0.14.1\'\np76\n(dp77\nS\'axes\'\np78\ng13\nsS\'blocks\'\np79\n(lp80\n(dp81\nS\'mgr_locs\'\np82\nc__builtin__\nslice\np83\n(I0\nI2\nL1L\ntRp84\nsS\'values\'\np85\ng57\nsasstbsb.')
df2 = pickle.loads('ccopy_reg\n_reconstructor\np1\n(cpandas.core.frame\nDataFrame\np2\nc__builtin__\nobject\np3\nNtRp4\n(dp5\nS\'_metadata\'\np6\n(lp7\nsS\'_typ\'\np8\nS\'dataframe\'\np9\nsS\'_data\'\np10\ng1\n(cpandas.core.internals\nBlockManager\np11\ng3\nNtRp12\n((lp13\ncpandas.core.index\n_new_Index\np14\n(cpandas.core.index\nMultiIndex\np15\n(dp16\nS\'labels\'\np17\n(lp18\ncnumpy.core.multiarray\n_reconstruct\np19\n(cpandas.core.base\nFrozenNDArray\np20\n(I0\ntS\'b\'\ntRp21\n(I1\n(L2L\ntcnumpy\ndtype\np22\n(S\'i1\'\nI0\nI1\ntRp23\n(I3\nS\'|\'\nNNNI-1\nI-1\nI0\ntbI00\nS\'\\x00\\x00\'\ntbag19\n(g20\n(I0\ntS\'b\'\ntRp24\n(I1\n(L2L\ntg23\nI00\nS\'\\x00\\x01\'\ntbasS\'names\'\np25\n(lp26\nNaNasS\'levels\'\np27\n(lp28\ng14\n(cpandas.core.index\nIndex\np29\n(dp30\nS\'data\'\np31\ng19\n(cnumpy\nndarray\np32\n(I0\ntS\'b\'\ntRp33\n(I1\n(L1L\ntg22\n(S\'O8\'\nI0\nI1\ntRp34\n(I3\nS\'|\'\nNNNI-1\nI-1\nI63\ntbI00\n(lp35\nVCUSTOMER_B\np36\natbsS\'name\'\np37\nNstRp38\nag14\n(g29\n(dp39\ng31\ng19\n(g32\n(I0\ntS\'b\'\ntRp40\n(I1\n(L2L\ntg34\nI00\n(lp41\nVVISIT_DT\np42\naVPURCHASE\np43\natbsg37\nNstRp44\nasS\'sortorder\'\np45\nNstRp46\nacpandas.tseries.index\n_new_DatetimeIndex\np47\n(cpandas.tseries.index\nDatetimeIndex\np48\n(dp49\nS\'tz\'\np50\nNsS\'freq\'\np51\nNsg31\ng19\n(g32\n(I0\ntS\'b\'\ntRp52\n(I1\n(L24L\ntg22\n(S\'M8\'\nI0\nI1\ntRp53\n(I4\nS\'<\'\nNNNI-1\nI-1\nI0\n((d(S\'ns\'\nI1\nI1\nI1\ntttbI00\nS\'\\x00\\x00k[\\x8e\\x1dP\\x10\\x00\\x00\\xba\\xec"lP\\x10\\x00\\x00\\t~\\xb7\\xbaP\\x10\\x00\\x00X\\x0fL\\tQ\\x10\\x00\\x00\\xa7\\xa0\\xe0WQ\\x10\\x00\\x00\\x94T\\x9eCR\\x10\\x00\\x00\\xe3\\xe52\\x92R\\x10\\x00\\x002w\\xc7\\xe0R\\x10\\x00\\x00\\x81\\x08\\\\/S\\x10\\x00\\x00\\xd0\\x99\\xf0}S\\x10\\x00\\x00\\xbdM\\xaeiT\\x10\\x00\\x00\\x0c\\xdfB\\xb8T\\x10\\x00\\x00[p\\xd7\\x06U\\x10\\x00\\x00\\xaa\\x01lUU\\x10\\x00\\x00\\xf9\\x92\\x00\\xa4U\\x10\\x00\\x00\\xe6F\\xbe\\x8fV\\x10\\x00\\x005\\xd8R\\xdeV\\x10\\x00\\x00\\x84i\\xe7,W\\x10\\x00\\x00\\xd3\\xfa{{W\\x10\\x00\\x00"\\x8c\\x10\\xcaW\\x10\\x00\\x00\\xc0\\xae9gX\\x10\\x00\\x00\\xc0\\xae9gX\\x10\\x00\\x00\\xc0\\xae9gX\\x10\\x00\\x00\\x0f@\\xce\\xb5X\\x10\'\ntbsg37\nS\'date\'\np54\nstRp55\na(lp56\ng19\n(g32\n(I0\ntS\'b\'\ntRp57\n(I1\n(L2L\nL24L\ntg22\n(S\'f8\'\nI0\nI1\ntRp58\n(I3\nS\'<\'\nNNNI-1\nI-1\nI0\ntbI00\nS\'\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\xf0\\x0c$sA\\x00\\x00\\x00\\xf0\\x0c$sA\\x00\\x00\\x00\\xf0\\x0c$sA\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\\x00\\x00\\x00\\x00\\x00\\x00\\xf8\\x7f\'\ntba(lp59\ng14\n(g15\n(dp60\ng17\n(lp61\ng19\n(g20\n(I0\ntS\'b\'\ntRp62\n(I1\n(L2L\ntg23\nI00\nS\'\\x00\\x00\'\ntbag19\n(g20\n(I0\ntS\'b\'\ntRp63\n(I1\n(L2L\ntg23\nI00\nS\'\\x00\\x01\'\ntbasg25\n(lp64\nNaNasg27\n(lp65\ng14\n(g29\n(dp66\ng31\ng19\n(g32\n(I0\ntS\'b\'\ntRp67\n(I1\n(L1L\ntg34\nI00\n(lp68\ng36\natbsg37\nNstRp69\nag14\n(g29\n(dp70\ng31\ng19\n(g32\n(I0\ntS\'b\'\ntRp71\n(I1\n(L2L\ntg34\nI00\n(lp72\ng42\nag43\natbsg37\nNstRp73\nasg45\nNstRp74\na(dp75\nS\'0.14.1\'\np76\n(dp77\nS\'axes\'\np78\ng13\nsS\'blocks\'\np79\n(lp80\n(dp81\nS\'mgr_locs\'\np82\nc__builtin__\nslice\np83\n(I0\nI2\nL1L\ntRp84\nsS\'values\'\np85\ng57\nsasstbsb.')
customers, tables = ['CUSTOMER_A', 'CUSTOMER_B'], [df1.iloc[:], df2.iloc[:]]
tables = pd.concat(tables, keys=customers, axis=1)
```
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-22-8096f8962dec> in <module>()
6
7 customers, tables = ['CUSTOMER_A', 'CUSTOMER_B'], [df1.iloc[:], df2.iloc[:]]
----> 8 tables = pd.concat(tables, keys=customers, axis=1)
/home/code/anaconda2/lib/python2.7/site-packages/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
833 verify_integrity=verify_integrity,
834 copy=copy)
--> 835 return op.get_result()
836
837
/home/code/anaconda2/lib/python2.7/site-packages/pandas/tools/merge.pyc in get_result(self)
1023 new_data = concatenate_block_managers(
1024 mgrs_indexers, self.new_axes,
-> 1025 concat_axis=self.axis, copy=self.copy)
1026 if not self.copy:
1027 new_data._consolidate_inplace()
/home/code/anaconda2/lib/python2.7/site-packages/pandas/core/internals.pyc in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
4474 for placement, join_units in concat_plan]
4475
-> 4476 return BlockManager(blocks, axes)
4477
4478
/home/code/anaconda2/lib/python2.7/site-packages/pandas/core/internals.pyc in __init__(self, blocks, axes, do_integrity_check, fastpath)
2535
2536 if do_integrity_check:
-> 2537 self._verify_integrity()
2538
2539 self._consolidate_check()
/home/code/anaconda2/lib/python2.7/site-packages/pandas/core/internals.pyc in _verify_integrity(self)
2745 for block in self.blocks:
2746 if block._verify_integrity and block.shape[1:] != mgr_shape[1:]:
-> 2747 construction_error(tot_items, block.shape[1:], self.axes)
2748 if len(self.items) != tot_items:
2749 raise AssertionError('Number of manager items must equal union of '
/home/code/anaconda2/lib/python2.7/site-packages/pandas/core/internals.pyc in construction_error(tot_items, block_shape, axes, e)
3897 raise ValueError("Empty data passed with indices specified.")
3898 raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
-> 3899 passed, implied))
3900
3901
ValueError: Shape of passed values is (4, 31), indices imply (4, 25)
```
However if the dataframes are sliced e.g. [:10] OR [10:], the concat works:
```
customers, tables = ['CUSTOMER_A', 'CUSTOMER_B'], [df1.iloc[:20], df2.iloc[:20]]
tables = pd.concat(tables, keys=customers, axis=1)
tables
```
Output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-58-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.6.7
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.39.0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13085 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13085/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13085/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13085/events | https://github.com/pandas-dev/pandas/issues/13085 | 153,138,599 | MDU6SXNzdWUxNTMxMzg1OTk= | 13,085 | pip/PyPI not updated with 0.18.1 | {
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} | 2 | 2016-05-04T23:27:19Z | 2016-05-05T00:35:19Z | 2016-05-04T23:28:30Z | NONE | null | I was on PyPI just now, and the [only versions](https://pypi.python.org/pypi/pandas) of pandas available are 0.17.1 and 0.18.0. Trying https://pypi.python.org/pypi/pandas/0.18.1 leads to a 404 page. I tried installing via `pip`, and it can't find 0.18.1 either. Is there an issue communicating with PyPI, or is something else going on?
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} | 6 | 2016-05-05T01:19:37Z | 2017-02-26T20:25:55Z | 2017-02-26T20:12:44Z | NONE | null | #### Code Sample
```
def get_user_account_credentials(): # https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L160
from oauth2client.client import OAuth2WebServerFlow
from oauth2client.file import Storage
from oauth2client.tools import run_flow, argparser
reauth = False
flow = OAuth2WebServerFlow(
client_id=('495642085510-k0tmvj2m941jhre2nbqka17vqpjfddtd'
'.apps.googleusercontent.com'),
client_secret='kOc9wMptUtxkcIFbtZCcrEAc',
scope=['https://www.googleapis.com/auth/bigquery'],
redirect_uri='urn:ietf:wg:oauth:2.0:oob')
storage = Storage('bigquery_credentials.dat')
credentials = storage.get()
if credentials is None or credentials.invalid or reauth:
credentials = run_flow(flow, storage, argparser.parse_args([]))
return credentials
def _generate_bq_schema(df, default_type='STRING'): # https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L735
""" Given a passed df, generate the associated Google BigQuery schema.
Parameters
----------
df : DataFrame
default_type : string
The default big query type in case the type of the column
does not exist in the schema.
"""
type_mapping = {
'i': 'INTEGER',
'b': 'BOOLEAN',
'f': 'FLOAT',
'O': 'STRING',
'S': 'STRING',
'U': 'STRING',
'M': 'TIMESTAMP'
}
fields = []
for column_name, dtype in df.dtypes.iteritems():
fields.append({'name': column_name,
'type': type_mapping.get(dtype.kind, default_type)})
return {'fields': fields}
def get_service(): # https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L267
from apiclient.discovery import build
import httplib2
http = httplib2.Http()
credentials = get_user_account_credentials()
http = credentials.authorize(http)
return build('bigquery', 'v2', http=http)
def verify_schema(dataset_id, table_id, schema): # https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L476
from apiclient.errors import HttpError
bigquery_service = get_service()
try:
return (bigquery_service.tables().get(
projectId='publicdata',
datasetId=dataset_id,
tableId=table_id
).execute()['schema']) == schema
except HttpError as ex:
pass
# self.process_http_error(ex)
def main():
shakespeare_df = pd.DataFrame({'word': 'foo', 'word_count': [1], 'corpus': 'bar', 'corpus_date': [2016]},
columns=['word', 'word_count', 'corpus', 'corpus_date'])
table_schema = _generate_bq_schema(shakespeare_df)
print 'Is the schema verified?'
print verify_schema('samples', 'shakespeare', table_schema)
if __name__ == '__main__':
main()
```
#### Expected Output
We want `verify_schema` to return True since the dataframe's columns are in the correct order, are named correctly, and have the correct types. The BigQuery table schema, however, has `description` and `mode` in addition to `name` and `type`.
Therefore `verify_schema` should remove `description` and `mode` from the BigQuery table schema when comparing it to the dataframe's schema.
#### Solution:
```
def get_user_account_credentials(): # https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L160
from oauth2client.client import OAuth2WebServerFlow
from oauth2client.file import Storage
from oauth2client.tools import run_flow, argparser
reauth = False
flow = OAuth2WebServerFlow(
client_id=('495642085510-k0tmvj2m941jhre2nbqka17vqpjfddtd'
'.apps.googleusercontent.com'),
client_secret='kOc9wMptUtxkcIFbtZCcrEAc',
scope=['https://www.googleapis.com/auth/bigquery'],
redirect_uri='urn:ietf:wg:oauth:2.0:oob')
storage = Storage('bigquery_credentials.dat')
credentials = storage.get()
if credentials is None or credentials.invalid or reauth:
credentials = run_flow(flow, storage, argparser.parse_args([]))
return credentials
def _generate_bq_schema(df, default_type='STRING'): # https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L735
""" Given a passed df, generate the associated Google BigQuery schema.
Parameters
----------
df : DataFrame
default_type : string
The default big query type in case the type of the column
does not exist in the schema.
"""
type_mapping = {
'i': 'INTEGER',
'b': 'BOOLEAN',
'f': 'FLOAT',
'O': 'STRING',
'S': 'STRING',
'U': 'STRING',
'M': 'TIMESTAMP'
}
fields = []
for column_name, dtype in df.dtypes.iteritems():
fields.append({'name': column_name,
'type': type_mapping.get(dtype.kind, default_type)})
return {'fields': fields}
def get_service(): # https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L267
from apiclient.discovery import build
import httplib2
http = httplib2.Http()
credentials = get_user_account_credentials()
http = credentials.authorize(http)
return build('bigquery', 'v2', http=http)
def verify_schema(dataset_id, table_id, schema): # https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L476
from apiclient.errors import HttpError
bigquery_service = get_service()
try:
return (bigquery_service.tables().get(
projectId='publicdata',
datasetId=dataset_id,
tableId=table_id
).execute()['schema']) == schema
except HttpError as ex:
pass
# self.process_http_error(ex)
def verify_schema_modified(dataset_id, table_id, schema):
from apiclient.errors import HttpError
bigquery_service = get_service()
try:
original_schema = bigquery_service.tables().get(
projectId='publicdata',
datasetId=dataset_id,
tableId=table_id
).execute()['schema']
modified_schema_list = [{key:field[key] for key in field if key != 'mode' and key != 'description'}
for field in original_schema['fields']]
return {'fields': modified_schema_list} == schema
except HttpError as ex:
pass
#self.process_http_error(ex)
def main():
shakespeare_df = pd.DataFrame({'word': 'foo', 'word_count': [1], 'corpus': 'bar', 'corpus_date': [2016]},
columns=['word', 'word_count', 'corpus', 'corpus_date'])
table_schema = _generate_bq_schema(shakespeare_df)
print 'Is the schema verified?'
print verify_schema('samples', 'shakespeare', table_schema)
print verify_schema_modified('samples', 'shakespeare', table_schema)
if __name__ == '__main__':
main()
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: 5fd6ed036324f5de28816e2b66348e8a56b96f04
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1+8.g5fd6ed0
nose: 1.3.6
pip: 8.1.1
setuptools: 20.8.1
Cython: 0.22
numpy: 1.11.0
scipy: 0.13.0b1
statsmodels: None
xarray: None
IPython: 4.0.0
sphinx: 1.3.1
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
```
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| null | 28 | 2016-05-05T01:37:38Z | 2017-01-21T23:14:36Z | 2017-01-21T23:14:36Z | NONE | null | - [x] closes #13086
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2016-05-05T05:10:49Z | 2016-05-07T17:08:16Z | 2016-05-07T17:08:11Z | MEMBER | null | - [x] split from #13080
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
Also did little cleanup of `testing.pyx` and `testing.py`.
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| null | 5 | 2016-05-05T06:47:00Z | 2016-08-10T16:29:17Z | 2016-08-09T23:25:11Z | CONTRIBUTOR | null | Support more date and datetime formats
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} | 9 | 2016-05-05T11:17:10Z | 2021-04-24T04:02:47Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Unfortunately can't reproduce it with other examples. Only this which I load from csv.
`txn.loc[pd.Timestamp('2014-06-04 00:00:00'):]`
``` ---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-35-ce3d2db63a82> in <module>()
----> 1 txn.loc[pd.Timestamp('2014-06-04 00:00:00'):]
/opt/miniconda/lib/python2.7/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1284 return self._getitem_tuple(key)
1285 else:
-> 1286 return self._getitem_axis(key, axis=0)
1287
1288 def _getitem_axis(self, key, axis=0):
/opt/miniconda/lib/python2.7/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1398 if isinstance(key, slice):
1399 self._has_valid_type(key, axis)
-> 1400 return self._get_slice_axis(key, axis=axis)
1401 elif is_bool_indexer(key):
1402 return self._getbool_axis(key, axis=axis)
/opt/miniconda/lib/python2.7/site-packages/pandas/core/indexing.py in _get_slice_axis(self, slice_obj, axis)
1306 labels = obj._get_axis(axis)
1307 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop,
-> 1308 slice_obj.step, kind=self.name)
1309
1310 if isinstance(indexer, slice):
/opt/miniconda/lib/python2.7/site-packages/pandas/tseries/index.py in slice_indexer(self, start, end, step, kind)
1503
1504 try:
-> 1505 return Index.slice_indexer(self, start, end, step, kind=kind)
1506 except KeyError:
1507 # For historical reasons DatetimeIndex by default supports
/opt/miniconda/lib/python2.7/site-packages/pandas/indexes/base.py in slice_indexer(self, start, end, step, kind)
2698 """
2699 start_slice, end_slice = self.slice_locs(start, end, step=step,
-> 2700 kind=kind)
2701
2702 # return a slice
/opt/miniconda/lib/python2.7/site-packages/pandas/indexes/base.py in slice_locs(self, start, end, step, kind)
2877 start_slice = None
2878 if start is not None:
-> 2879 start_slice = self.get_slice_bound(start, 'left', kind)
2880 if start_slice is None:
2881 start_slice = 0
/opt/miniconda/lib/python2.7/site-packages/pandas/indexes/base.py in get_slice_bound(self, label, side, kind)
2826 except ValueError:
2827 # raise the original KeyError
-> 2828 raise err
2829
2830 if isinstance(slc, np.ndarray):
KeyError: 1401840000000000000
```
#### Expected Output
Slice of everything after '2014-06-04 00:00:00'.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-77-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.30.0
```
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} | 3 | 2016-05-05T12:31:50Z | 2016-05-06T11:42:22Z | 2016-05-06T11:42:22Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
The current error message below is confusing.
```
tm.assertNotIsInstance(pd.Series([1]), pd.Series)
# AssertionError: Expected object to be of type <class 'pandas.core.series.Series'>, found <class 'pandas.core.series.Series'> instead
```
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} | 3 | 2016-05-05T13:04:21Z | 2016-12-26T22:49:17Z | 2016-12-26T22:49:17Z | CONTRIBUTOR | null | conda packages are now available [here](https://github.com/wesm/feather/issues/134)
so this should be straightforward. a couple of things to note.
- should support the `LocalPath/PathLib` objects (easy as we do this in other `read_*`)
- non-supported on windows ATM. need to skip for testing. `feather-format` will just raise an `ImportError` for now.
- catch and re-raise for non-supported things, mainly object dtypes that are not all strings. Alternatively / maybe better to introspect these columns (via `lib.infer_dtype`) to prevent in the first place
- test unicode support / encoding (not sure supported as of yet)
- biggest issue is how / if to support `Series` and a `DataFrame` with a non-default index. As we need a facility to write/read meta-data within feather [see here](https://github.com/wesm/feather/issues/74). Both these can simply `.reset_index()`, but the problem is they can be stored but we don't know on read-back what to `.set_index()`. Can just raise for now.
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} | 1 | 2016-05-05T15:58:48Z | 2016-05-05T17:20:38Z | 2016-05-05T17:20:38Z | CONTRIBUTOR | null | A more explicit (and helpful) error message would have helped me discover sooner that format="table" supported categorical data.
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} | 6 | 2016-05-05T16:20:59Z | 2021-04-24T04:04:25Z | null | CONTRIBUTOR | null | I found what seems to me a behavior that might be wrong when joining a simple index df with a multiindex df.
I'm admittedly not an SQL expert and I'm not sure this is really a bug.
Consider the case:
``` python
X = pd.DataFrame([[2, 3], [5, 7]], columns=['a','p']).set_index('a')
Y = pd.DataFrame([[1, 2, 3], [3, 4, 8], [5, 6, 9]],
columns=['a','b','c']).set_index(['a','b'])
X
# p
# a
#2 3
#5 7
Y
# c
# a b
#1 2 3
#3 4 8
#5 6 9
X.join(Y, how='left')
# p c
# a b
#5 6 7 9
```
I see the rational for not returning the line indexed by `a=2` in `X`: since there is no value for the level `b` to associate to this line it is discarded.
But I'm wondering if this output would not be also reasonable:
```
p c
a b
2 Nan 3 Nan
5 6 7 9
```
In SQL the result of (where I replace `X` by `X.reset_index()` and similarly for `Y`):
``` sql
select *
from X, Y
where Y.a = X.a (+)
```
would contain the line
```
a b p c
2 Nan 3 Nan
```
Any thought?
For completeness, the same issue is present for `how='outer'`.
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} | 12 | 2016-05-05T17:20:47Z | 2016-05-11T07:34:15Z | 2016-05-05T17:36:14Z | NONE | null | Hey everyone,
I installed pandas via pip git
`pip install git+git://github.com/pydata/pandas.git`
and during the installation it says:
`Running setup.py install for pandas ... done
Successfully installed pandas-0.18.0+207.gc6110e2`
But when I want to import it, it gives an error
`C extension: hashtable not built.
If you want to import pandas from the source directory, you may need to run
'python setup.py build_ext --inplace' to build the C extensions first.`
I already tried to run this but the response is always:
`python: can't open file 'setup.py': [Errno 2] No such file or directory`
Maybe I am in the wrong directory. So in which I am suppose to run it?
Also I looked in site-packages but can't find pandas or something similar.
I am confused since it said it runs the setup.py during the installation...
Can anyone help? What I am doing wrong?
Thanks in advance
Leni
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} | 2 | 2016-05-05T17:31:59Z | 2016-05-05T17:36:14Z | 2016-05-05T17:36:14Z | CONTRIBUTOR | null | - [x] closes #13095
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2016-05-05T18:39:59Z | 2017-02-10T15:52:09Z | 2017-02-10T15:52:09Z | CONTRIBUTOR | null | from https://github.com/pydata/pandas/pull/13091
Currently _most_ of the tests pass with pytest, but there are 69 that don't. Most of these look like they're pickle or SQL / external resources issues.
pytest output here: https://gist.github.com/MaximilianR/225a071337e335ebdcc1d17637126126
So this does need a champion who could work through those issues. That's probably not me at the moment, but you have my moral support!
mpl is converting!
https://github.com/matplotlib/matplotlib/pull/5405
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} | 2 | 2016-05-05T18:56:45Z | 2016-05-12T13:12:58Z | 2016-05-12T13:12:58Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy
s = pd.Series([1, 2, 3, 4, numpy.nan])
s.quantile(0.5)
nan
```
#### Expected Output
I would expect 2.5 as output (as with version 0.17.1).
#### output of `pd.show_versions()`
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-85-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
pandas: 0.18.1
nose: None
pip: 1.5.4
setuptools: 2.2
Cython: None
numpy: 1.11.0
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
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} | 0 | 2016-05-05T20:58:03Z | 2016-05-06T11:39:51Z | 2016-05-06T11:39:51Z | CONTRIBUTOR | null | for completeness
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| null | 3 | 2016-05-05T22:27:30Z | 2016-05-05T23:44:29Z | 2016-05-05T22:37:44Z | NONE | null | #### Summary
When freezing an application that uses `pandas.io.sql.read_sql()` (alias: `pd.read_sql()`) via cx_freeze, the build succeeds but the execution fails with `AttributeError: 'str' object has no attribute 'cursor'`.
#### Code
Below are the minimum requirements to recreate the issue. Put both files in an empty directory and run `python build_exe.py build`. Then execute `.\build\exe.win-amd64-3.4\test.exe`. An error will be displayed (text below).
```
# test.py
import pandas as pd
URL = <redacted> # mysql+pyodbc://...
QUERY = <redacted>
print(pd.read_sql(QUERY, URL).tail()) # comment this line for a successful build+execute
print("got to the end")
```
```
# build_exe.py $ python build_exe.py build
from cx_Freeze import setup, Executable
setup(
name="a",
version="0.0.1",
description="a",
options={
"build_exe": {
"packages": ["sqlalchemy.dialects.mysql", ],
"silent": True,
}
},
executables=[
Executable(
"test.py",
base=None,
),
],
)
```
#### Error output:
```
Traceback (most recent call last):
File "C:\WinPython34_x64\python-3.4.3.amd64\lib\site-packages\cx_Freeze\initscripts\Console.py", line 27, in <module>
exec(code, m.__dict__)
File "test.py", line 16, in <module>
File "C:\WinPython34_x64\python-3.4.3.amd64\lib\site-packages\pandas\io\sql.py", line 499, in read_sql
chunksize=chunksize)
File "C:\WinPython34_x64\python-3.4.3.amd64\lib\site-packages\pandas\io\sql.py", line 1599, in read_query
cursor = self.execute(*args)
File "C:\WinPython34_x64\python-3.4.3.amd64\lib\site-packages\pandas\io\sql.py", line 1559, in execute
cur = self.con.cursor()
AttributeError: 'str' object has no attribute 'cursor'
```
#### Expected Output
N/A
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 21.0.0
Cython: 0.22
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.8.6
lxml: None
bs4: 4.4.1
html5lib: 0.9999999
httplib2: 0.9.1
apiclient: 1.4.1
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
```
#### Other Versions:
```
cx_freeze: 4.3.4
pyodbc: 3.0.10
```
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} | 2 | 2016-05-06T07:41:01Z | 2017-11-22T02:03:52Z | 2017-11-22T02:03:52Z | NONE | null | Edit #10506 breaks if the DataFrame contains unicode column names with non-ASCII characters.
```
import pandas as pd
df = pd.DataFrame({u'a': [1, 2, 3], u'ä': [4, 5, 6]})
df.filter(regex=u'a')
```
throws me a
```
---------------------------------------------------------------------------
UnicodeEncodeError Traceback (most recent call last)
<ipython-input-10-9de5a19c260e> in <module>()
----> 1 df.filter(regex=u'a')
C:\Users\...\AppData\Local\Continuum\32bit\Anaconda\envs\test\lib\site-packages\pandas\core\generic.pyc in filter(self, items, like, regex, axis)
2013 matcher = re.compile(regex)
2014 return self.select(lambda x: matcher.search(str(x)) is not None,
-> 2015 axis=axis_name)
2016 else:
2017 raise TypeError('Must pass either `items`, `like`, or `regex`')
C:\Users\...\AppData\Local\Continuum\32bit\Anaconda\envs\test\lib\site-packages\pandas\core\generic.pyc in select(self, crit, axis)
1545 if len(axis_values) > 0:
1546 new_axis = axis_values[
-> 1547 np.asarray([bool(crit(label)) for label in axis_values])]
1548 else:
1549 new_axis = axis_values
C:\Users\...\AppData\Local\Continuum\32bit\Anaconda\envs\test\lib\site-packages\pandas\core\generic.pyc in <lambda>(x)
2012 elif regex:
2013 matcher = re.compile(regex)
-> 2014 return self.select(lambda x: matcher.search(str(x)) is not None,
2015 axis=axis_name)
2016 else:
UnicodeEncodeError: 'ascii' codec can't encode character u'\xe4' in position 0: ordinal not in range(128)
```
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| null | 4 | 2016-05-06T07:58:45Z | 2016-05-06T14:34:38Z | 2016-05-06T14:32:54Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy as np
df=pd.DataFrame({'a':np.random.normal(5,1,1000)})
df.a.plot(kind='kde')
```
#### Expected Output
In previous versions, a kde plot was shown. Now I get a "Kernel died unexpectedly" error.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.8-040408-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_ZA.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.5
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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} | 1 | 2016-05-06T15:00:40Z | 2016-05-06T15:07:33Z | 2016-05-06T15:07:09Z | CONTRIBUTOR | null | `.transform` does not appear to be respecting the axis argument of the groupby in the combine stage. The code below yields:
`ValueError: Length mismatch: Expected axis has 4 elements, new values have 5 elements`
```
#### Code Sample, a copy-pastable example if possible
import pandas as pd
df = pd.DataFrame(pd.np.arange(20).reshape(4,5))
df.groupby(lambda x: x <= 3, axis=1).transform(lambda x: x)
#### Expected Output
0 1 2 3 4
0 0 1 2 3 4
1 5 6 7 8 9
2 10 11 12 13 14
3 15 16 17 18 19
#### output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-74-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 1.5.4
setuptools: 20.1.1
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.1.1
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.5.0
bs4: None
html5lib: 0.999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
Jinja2: None
```
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