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} | 4 | 2016-05-17T15:07:21Z | 2016-07-03T23:28:31Z | 2016-07-03T23:28:31Z | NONE | null | Please see stackoverflow for example of issue
http://stackoverflow.com/questions/37279260/why-doesnt-pandas-allow-a-categorical-column-to-be-used-in-groupby?noredirect=1#comment62084780_37279260
```
>>> pd.__version__
'0.18.1'
>>>
# import the pandas module
import pandas as pd
# Create an example dataframe
raw_data = {'Date': ['2016-05-13', '2016-05-13', '2016-05-13', '2016-05-13', '2016-05-13','2016-05-13', '2016-05-13', '2016-05-13', '2016-05-13', '2016-05-13', '2016-05-13', '2016-05-13', '2016-05-13', '2016-05-13', '2016-05-13', '2016-05-13', '2016-05-13'],
'Portfolio': ['A', 'A', 'A', 'A', 'A', 'A', 'B', 'B','B', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'C'],
'Duration': [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3],
'Yield': [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1],}
df = pd.DataFrame(raw_data, columns = ['Date', 'Portfolio', 'Duration', 'Yield'])
df['Portfolio'] = pd.Categorical(df['Portfolio'],['C', 'B', 'A'])
df=df.sort_values('Portfolio')
dfs = df.groupby(['Date','Portfolio'], as_index =False).sum()
print(dfs)
Date Portfolio Duration Yield
Date Portfolio
13/05/2016 C NaN NaN NaN NaN
B NaN NaN NaN NaN
A NaN NaN NaN NaN
```
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} | 22 | 2016-05-17T15:46:18Z | 2016-06-14T14:08:55Z | 2016-06-14T13:53:54Z | MEMBER | null | - [x] closes #12309
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entries: name passing, unsupported `dtype`s, `copy=true` for `DateTimeIndex`
This is a rebased & updated version of #12331
I would say the [last commit](https://github.com/pydata/pandas/pull/13205/commits/cd89efc88afd6fc5b0e973da4338cefdbea52d51) is the most critical one: it exposes the consequences of making numeric indices only support their default `dtype`. By the way: I had interpreted [your comment](https://github.com/pydata/pandas/pull/12331#discussion_r54882912) as "raise if `dtype` is set", but I don't think this is what we want, since it breaks the obj -> str(obj) -> obj roundtrip. So I raise only if `dtype` is not the expected dtype.
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} | 10 | 2016-05-17T16:15:24Z | 2019-05-01T20:07:48Z | 2017-06-07T20:53:48Z | NONE | null | ``` python
xl.to_sql(con=conn, name='d1187b08-4943-4c8d-a7f6-6c06b7cb9509', flavor='mysql', if_exists='replace')
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "C:\Dev\Project\www\venv\project\lib\site-packages\pandas\core\generic.py", line 1165, in to_sql
chunksize=chunksize, dtype=dtype)
File "C:\Dev\Project\www\venv\project\lib\site-packages\pandas\io\sql.py", line 571, in to_sql
chunksize=chunksize, dtype=dtype)
File "C:\Dev\Project\www\venv\project\lib\site-packages\pandas\io\sql.py", line 1660, in to_sql
dtype=dtype)
File "C:\Dev\Project\www\venv\project\lib\site-packages\pandas\io\sql.py", line 1417, in __init__
super(SQLiteTable, self).__init__(*args, **kwargs)
File "C:\Dev\Project\www\venv\project\lib\site-packages\pandas\io\sql.py", line 669, in __init__
self.table = self._create_table_setup()
File "C:\Dev\Project\www\venv\project\lib\site-packages\pandas\io\sql.py", line 1478, in _create_table_setup
',\n '.join(create_tbl_stmts) + "\n)"]
File "C:\Dev\Project\www\venv\project\lib\site-packages\pandas\io\sql.py", line 1363, in _get_valid_mysql_name
raise ValueError("Invalid MySQL identifier '%s'" % uname)
ValueError: Invalid MySQL identifier '`d1187b08-4943-4c8d-a7f6-6c06b7cb9509`'
```
#### Expected Output
(no output, just successful write to db table)
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 18.0.1
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: 0.9.3
xlwt: 0.8.0
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999999
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: 2.38.0
pandas_datareader: None
#
Proposed fix:
Line 1478 of pandas\io\sql.py, wrapping the escape(self.name) with ``:
create_stmts = ["CREATE TABLE `" + escape(self.name) + "` (\n" +
',\n '.join(create_tbl_stmts) + "\n)"]
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} | 4 | 2016-05-17T19:14:01Z | 2017-03-28T19:10:59Z | 2017-03-28T19:10:59Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
In [108]: i = pd.Index([])
In [109]: bool(i)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-109-43b3c3723ad4> in <module>()
----> 1 bool(i)
C:\Users\Chris\Anaconda\envs\py35\lib\site-packages\pandas\indexes\base.py in __nonzero__(self)
1227 raise ValueError("The truth value of a {0} is ambiguous. "
1228 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
-> 1229 .format(self.__class__.__name__))
1230
1231 __bool__ = __nonzero__
ValueError: The truth value of a Index is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
In [110]: i.empty
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-110-1524e0ba750e> in <module>()
----> 1 i.empty
AttributeError: 'Index' object has no attribute 'empty'
```
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} | 12 | 2016-05-17T21:17:29Z | 2020-12-07T08:17:00Z | 2020-01-07T23:45:41Z | NONE | null | ```
#!/bin/env python
"""
Example bug in derived Pandas Series.
__finalized__ is not called in arithmetic binary operators, but it is in in some booleans cases.
>>> m = MySeries([1, 2, 3], name='test')
>>> m.x = 42
>>> n=m[:2]
>>> n
0 1
1 2
dtype: int64
>>> n.x
42
>>> o=n+1
>>> o
0 2
1 3
dtype: int64
>>> o.x
Traceback (most recent call last):
...
AttributeError: 'MySeries' object has no attribute 'x'
>>> m = MySeries([True, False, True], name='test2')
>>> m.x = 42
>>> n=m[:2]
>>> n
0 True
1 False
dtype: bool
>>> n.x
42
>>> o=n ^ True
>>> o
0 False
1 True
dtype: bool
>>> o.x
42
>>> p = n ^ o
>>> p
0 True
1 True
dtype: bool
>>> p.x
42
"""
import pandas as pd
class MySeries(pd.Series):
_metadata = ['x']
@property
def _constructor(self):
return MySeries
if __name__ == "__main__":
import doctest
doctest.testmod()
```
#### Expected Output
In all cases, the metadata 'x' should be transferred from the passed values when applying binary operators.
When the right-hand value is a constant, the left-hand value metadata should be used in **finalize** for arithmetic operators, just like it is for Boolean binary operators.
When two series are used in binary operators, some resolution should be possible in **finalize**.
I would pass the second (right-hand) value by calling **finalize**(self, other=other), leaving the resolution to the derived class implementer, but there might be a smarter approach.
#### output of `pd.show_versions()`
pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: None
setuptools: 20.2.2
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 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: 2.5.2
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: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
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} | 12 | 2016-05-17T21:36:03Z | 2016-05-24T00:32:35Z | 2016-05-23T20:45:45Z | CONTRIBUTOR | null | - [x] closes #13149
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
**Update**
1. Float64Index.astype(int) raises ValueError if a NaN is present.
Previously, it converted NaN's to the smallest negative integer.
2. TimedeltaIndex.astype(int) and DatetimeIndex.astype(int) return.
Int64Index, which is consistent with behavior of other Indexes.
Previously, they returned a numpy.array of ints.
3. Added:
- bool parameter 'copy' to Index.astype()
- shared doc string to .astype()
- tests on .astype() (consolidated and added new)
- bool parameter 'copy' to Categorical.astype()
4. Fixed core.common.is_timedelta64_ns_dtype().
5. Set a default NaT representation to a string type in a parameter of DatetimeIndex._format_native_types().
Previously, it produced a unicode u'NaT' in Python2.
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} | 12 | 2016-05-17T21:55:33Z | 2017-09-19T13:46:29Z | 2016-05-18T09:36:39Z | NONE | null | I am running the following code (**pandas 0.18.0, sqlalchemy 1.0.12**):
`skus = pd.read_sql_table('ads_skus', SQLALCHEMY_CONN, schema='a')`
I receive the error:
`ValueError: Table ads_skus not found`
However, if I run:
```
i = inspect(SQLALCHEMY_CONN)
for table_name in i.get_table_names(schema='a'):
print(table_name)
```
where `SQLALCHEMY` is an `Engine` object. I see `ads_skus` in the list that is output. This looks like a bug if pandas is using the SQLAlchemy engine.
_Note: I used the same function for a bunch of similar tables (which are also in the schema `'a'`). They all worked - it only fails on this table._
_Note 2: I noticed that "ads_skus" is the only table which has foreign keys to another schema. That seems to be the differentiating factor._
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| null | 9 | 2016-05-18T02:24:34Z | 2016-05-19T00:39:03Z | 2016-05-18T12:38:20Z | NONE | null | Hi, I am running my own dataset with scikit-learn. My dataset contains '2016/02/28', '20:12:22', '100' and 'string', Date, time, integer and string.
I have successfully parsed the data with Pandas and have reached CLF using SVM to test out.
However, upon clf.fit(X, y), an error was returned ValueError: invalid literal for float(): 25:13:15, apparently in my dataset, there is a time of 25:13:15 which reflect 02:13:15am.
Am I able to convert datetime into float or numeric array?
Please advise how should I go about fixing this issue.
Thank you.
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} | 0 | 2016-05-18T05:13:41Z | 2016-05-21T14:12:53Z | 2016-05-21T14:12:53Z | CONTRIBUTOR | null | ref: https://github.com/pydata/pandas/pull/13079#discussion_r63645752
``` python
In [5]: pd.DataFrame(index=pd.PeriodIndex([],freq='D')).resample('M').mean().index
Out[5]: Index([], dtype='object')
```
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} | 0 | 2016-05-18T12:43:27Z | 2016-05-19T13:14:51Z | 2016-05-19T13:14:46Z | CONTRIBUTOR | null | Example code:
``` python
import pandas
import json
testjson = u'''
[{"Ünicøde":0,"sub":{"A":1, "B":2}},
{"Ünicøde":1,"sub":{"A":3, "B":4}}]
'''.encode('utf8')
pd.io.json.json_normalize(json.loads(testjson))
```
Output:
```
Traceback (most recent call last):
File "...lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2885, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-12-f866f9c7ec7c>", line 5, in <module>
pd.io.json.json_normalize(json.loads(testjson))
File ".../lib/python2.7/site-packages/pandas/io/json.py", line 715, in json_normalize
data = nested_to_record(data)
File ".../lib/python2.7/site-packages/pandas/io/json.py", line 617, in nested_to_record
newkey = str(k)
UnicodeEncodeError: 'ascii' codec can't encode character u'\xdc' in position 0: ordinal not in range(128)
```
Expected output
```
sub.A sub.B Ünicøde
0 1 2 0
1 3 4 1
```
The cause are probably
https://github.com/pydata/pandas/blob/master/pandas/io/json.py#L618
and https://github.com/pydata/pandas/blob/master/pandas/io/json.py#L620
Those lines seemingly were introduced to deal with numeric types, but fail when `k` is a Unicode object containing non-ascii characters.
It seems to be the same bug in principle as https://github.com/pydata/pandas/issues/13101
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| null | 3 | 2016-05-18T12:53:45Z | 2016-05-18T13:28:23Z | 2016-05-18T13:28:23Z | CONTRIBUTOR | null | - [x] closes #13207
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https://api.github.com/repos/pandas-dev/pandas/issues/13216 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13216/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13216/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13216/events | https://github.com/pandas-dev/pandas/pull/13216 | 155,504,005 | MDExOlB1bGxSZXF1ZXN0NzA1Mjk0Mjg= | 13,216 | BUG: DataFrame.align bug with join keyword (GH13914) | {
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| null | 24 | 2016-05-18T13:40:42Z | 2017-02-01T20:48:01Z | 2017-02-01T20:48:01Z | CONTRIBUTOR | null | - [x] closes #13194
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 9 | 2016-05-18T15:25:39Z | 2021-05-12T13:27:56Z | 2021-05-12T13:27:56Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
a = pd.DataFrame({'a':[1,1,2,2], 'b':[1,1,2,2], 'c':[1,1,1,1]})
a.groupby(['a','b'], as_index=False).apply(lambda x: 1)
Out[4]:
a b
1 1 1
2 2 1
dtype: int64
```
#### Expected Output
```
Out[4]:
0 1
1 1
dtype: int64
```
(this is what you get with a unique `by` column --
```
a.groupby(['a'], as_index=False).apply(lambda x: 1)
Out[8]:
0 1
1 1
dtype: int64
```
#### output of `pd.show_versions()`
pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 3.5.1.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: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
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: 2.5.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 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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https://api.github.com/repos/pandas-dev/pandas/issues/13218 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13218/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13218/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13218/events | https://github.com/pandas-dev/pandas/issues/13218 | 155,542,027 | MDU6SXNzdWUxNTU1NDIwMjc= | 13,218 | loffset doesn't work in resample if used with agg() | {
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} | 5 | 2016-05-18T16:10:15Z | 2016-12-31T16:56:53Z | 2016-12-31T16:56:53Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
# Create DF
df = pd.DataFrame(np.random.rand(5,2), columns=list('AB'), index=pd.date_range('2010-01-01 09:00:00', periods=5, freq='s'))
print(df)
A B
2010-01-01 09:00:00 0.283113 0.559642
2010-01-01 09:00:01 0.754942 0.621557
2010-01-01 09:00:02 0.102002 0.892100
2010-01-01 09:00:03 0.885400 0.524359
2010-01-01 09:00:04 0.324761 0.706758
#Resample, loffset works fine using mean(), is offset by 2 hours.
print(df.resample('2s', loffset='2h').mean())
A B
2010-01-01 11:00:00 0.519028 0.590599
2010-01-01 11:00:02 0.493701 0.708229
2010-01-01 11:00:04 0.324761 0.706758
# Resample with agg(), loffset doesn't work anymore.
print(df.resample('2s', loffset='2h').agg(dict(A='sum', B='mean')))
A B
2010-01-01 09:00:00 1.038055 0.590599
2010-01-01 09:00:02 0.987402 0.708229
2010-01-01 09:00:04 0.324761 0.706758
```
#### Expected Output
I'd expect the same as with mean(), this is how resample worked in the past with resample(how=...).
But maybe I also misunderstood something about the change. If so please enlighten me.
Since I need this to keep things going, I use the following workaround for now, please let me know if this is the way to go or if there's a more efficient way:
```
print(df.resample('2s', loffset='2h').agg(dict(A='sum', B='mean')).tshift(2, 'h'))
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
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: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
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} | 4 | 2016-05-18T16:35:22Z | 2016-10-14T17:29:00Z | 2016-06-01T11:13:41Z | NONE | null | I caught this bug while updating from version 0.18.0 to 0.18.1. The kwarg `usecols` no longer accepts unicode column labels.
Example below:
``` python
from io import StringIO
import pandas as pd
s = u'''AAA,BBB,CCC,DDD
0.056674973,8,True,a
2.613230982,2,False,b
3.568935038,7,False,a
'''
buff = StringIO(s)
print pd.read_csv(buff, usecols=[u'AAA', u'BBB'])
>> ValueError: The elements of 'usecols' must either be all strings or all integers
```
I note that 0.18.1 introduced the requirement that `usecols` be all string or all ints. This makes sense but it looks like the implementation also throws away unicode strings.
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} | 12 | 2016-05-18T17:22:25Z | 2019-10-18T01:07:39Z | 2019-10-18T01:07:38Z | NONE | null | ```
import pandas as pd
N = 6
df = pd.DataFrame(data={"size":[0.1]*N,
"group":[None]*N},
index=range(N))
gb = df.groupby(by=['group'])
d2 = gb.transform('sum')
print d2
```
#### Expected Output
```
# not actually sure what should be returned, perhaps just the original values
Here is what I got
size
0 2.605041e-316
1 6.899144e-310
2 2.584561e+161
3 1.485397e-313
4 1.916117e+214
5 3.075369e+203
#### output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-54-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.0
setuptools: 20.7.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.2.0
sphinx: 1.3.6
patsy: 0.4.1
dateutil: 2.2
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.2.0-b1
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.5
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
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} | 5 | 2016-05-18T17:24:41Z | 2018-10-26T08:41:52Z | 2018-10-25T13:31:37Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/blob/master/pandas/tseries/period.py#L920
`return Index(com._concat_compat(to_concat), name=name)`
now should reference `pandas.types.concat._concat_compat`
I _think_ this should probably fail - concat-ing a `PeriodIndex` with other index types?
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} | 11 | 2016-05-18T17:42:27Z | 2016-06-18T21:26:02Z | 2016-06-18T21:26:02Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame([["A", 1],["B", 2]], columns=["Letter", "Number"])
def highlight(s):
return ['background-color: yellow']
df.style.apply(highlight, axis=0, subset=[0])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/Users/paulperry/anaconda/lib/python3.5/site-packages/IPython/core/formatters.py in __call__(self, obj)
341 method = _safe_get_formatter_method(obj, self.print_method)
342 if method is not None:
--> 343 return method()
344 return None
345 else:
/Users/paulperry/anaconda/lib/python3.5/site-packages/pandas/core/style.py in _repr_html_(self)
163 def _repr_html_(self):
164 """Hooks into Jupyter notebook rich display system."""
--> 165 return self.render()
166
167 def _translate(self):
/Users/paulperry/anaconda/lib/python3.5/site-packages/pandas/core/style.py in render(self)
356 the rendered HTML in the notebook.
357 """
--> 358 self._compute()
359 d = self._translate()
360 # filter out empty styles, every cell will have a class
/Users/paulperry/anaconda/lib/python3.5/site-packages/pandas/core/style.py in _compute(self)
422 r = self
423 for func, args, kwargs in self._todo:
--> 424 r = func(self)(*args, **kwargs)
425 return r
426
/Users/paulperry/anaconda/lib/python3.5/site-packages/pandas/core/style.py in _apply(self, func, axis, subset, **kwargs)
429 subset = _non_reducing_slice(subset)
430 if axis is not None:
--> 431 result = self.data.loc[subset].apply(func, axis=axis, **kwargs)
432 else:
433 # like tee
/Users/paulperry/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1282 def __getitem__(self, key):
1283 if type(key) is tuple:
-> 1284 return self._getitem_tuple(key)
1285 else:
1286 return self._getitem_axis(key, axis=0)
/Users/paulperry/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py in _getitem_tuple(self, tup)
783
784 # no multi-index, so validate all of the indexers
--> 785 self._has_valid_tuple(tup)
786
787 # ugly hack for GH #836
/Users/paulperry/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py in _has_valid_tuple(self, key)
136 if i >= self.obj.ndim:
137 raise IndexingError('Too many indexers')
--> 138 if not self._has_valid_type(k, i):
139 raise ValueError("Location based indexing can only have [%s] "
140 "types" % self._valid_types)
/Users/paulperry/anaconda/lib/python3.5/site-packages/pandas/core/indexing.py in _has_valid_type(self, key, axis)
1365
1366 raise KeyError("None of [%s] are in the [%s]" %
-> 1367 (key, self.obj._get_axis_name(axis)))
1368
1369 return True
KeyError: 'None of [[0]] are in the [columns]'
Out[16]:
<pandas.core.style.Styler at 0x11ae32cf8>
```
The code appears to be applying it to the columns, not the rows, but passing a single value as the subset renders the table with the correct row highlighted.
```
df.style.apply(highlight, axis=0, subset=0)
Letter Number
0 A 1 <- in html this row is highlighted yellow
1 B 2
```
Passing a bad row index (e.g. 3) also correctly fails with 'None of [[3]] are in the [index]'
`df.style.apply(highlight, axis=1, subset=['Letter'])` works, but
`df.style.apply(highlight, axis=1, subset=['Letter','Number'])` Fails
#### Expected Output
```
df.style.apply(highlight, axis=0, subset=[0])
Letter Number
0 A 1 <- in html this row should be highlighted yellow
1 B 2
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.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: 8.1.2
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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https://api.github.com/repos/pandas-dev/pandas/issues/13223 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13223/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13223/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13223/events | https://github.com/pandas-dev/pandas/issues/13223 | 155,610,891 | MDU6SXNzdWUxNTU2MTA4OTE= | 13,223 | Resampling with NaT in TimedeltaIndex raises MemoryError | {
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} | 1 | 2016-05-18T21:56:12Z | 2018-08-09T10:33:35Z | 2018-08-09T10:33:34Z | CONTRIBUTOR | null | `DatetimeIndex` can handle `NaT`:
```
In [2]: dti = pd.DatetimeIndex(['1970-01-01 00:00:00', pd.NaT, '1970-01-01 00:00:02'])
In [3]: pd.DataFrame([2, 3, 5], dti).resample('1s').mean()
Out[3]:
0
1970-01-01 00:00:00 2.0
1970-01-01 00:00:01 NaN
1970-01-01 00:00:02 5.0
```
But `TimedeltaIndex` can't:
```
In [4]: tdi = pd.to_timedelta(['0s', pd.NaT, '2s'])
In [5]: pd.DataFrame([2, 3, 5], tdi).resample('1s').mean()
...
MemoryError:
```
Versions:
```
python: 2.7.11.final.0
OS: Linux
machine: x86_64
pandas: 0.18.1+38.gc7e6f5f.dirty
numpy: 1.10.2
```
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} | 1 | 2016-05-18T21:58:15Z | 2017-10-01T14:55:33Z | 2017-10-01T14:55:33Z | CONTRIBUTOR | null | `PeriodIndex` can't handle `NaT`:
```
In [2]: pi = pd.PeriodIndex(['1970-01-01 00:00:00', pd.NaT, '1970-01-01 00:00:02'], freq='S')
In [3]: pd.DataFrame([2, 3, 5], pi).resample('1s').mean()
...
ValueError: Start and end must not be NaT
```
But `DatetimeIndex` obtained from `PeriodIndex` can:
```
In [4]: pd.DataFrame([2, 3, 5], pi.to_datetime()).resample('1s').mean()
Out[4]:
0
1970-01-01 00:00:00 2.0
1970-01-01 00:00:01 NaN
1970-01-01 00:00:02 5.0
```
Shouldn't these behave the same?
Versions:
```
python: 2.7.11.final.0
OS: Linux
machine: x86_64
pandas: 0.18.1+38.gc7e6f5f.dirty
numpy: 1.10.2
```
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} | 12 | 2016-05-19T01:48:21Z | 2017-04-05T02:06:51Z | 2016-06-18T21:26:02Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/13222, #13225
- Update docstring and notebook on output shapes matching
- Change build process to use nbconvert to execute the notebook and then convert using NBConvert.
Second commit puts some more restrictions on the output of a user's function passed to `.apply`.
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} | 6 | 2016-05-19T09:21:19Z | 2021-04-30T05:30:53Z | 2021-04-30T05:30:53Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame(np.random.randn(6,4), columns=list('ABCD'))
# In ipython-notebook, I'm now just looking at the DF by calling it. This seems to create a view of the DF? If df is not called, the warning doesn't show. It will also show when doing x = df for example.
df
df = df.loc[df['A'] > 0]
# Now calling this, SettingWithCopyWarning shows suggesting to use .loc which is what I do?
df.loc[:, 'A'] = 1
```
#### Expected Output
What works without a warning is to use df.assign. But what about single row changes a la df.loc[1,'A'] = 1
Looking at the docs, df.loc seems to be the suggested way that should work fine.
Maybe I'm just missing something and the mistake is on my side. If so I'd be happy to know.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
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: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
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| null | 3 | 2016-05-19T10:05:00Z | 2021-04-30T05:36:23Z | 2021-04-30T05:36:23Z | NONE | null | When the data indexed with datetime are plotted via DataFrame.plot(), the default date ticks behave inconsistently depending on the frequency of the data index.
Here's a [short notebook](https://gist.github.com/marcinbielecki/9a8c1e3f7900a4761202769aecf746e0) that reproduces the issue. The data at daily, weekly and annual frequencies are plotted with ticks aligning to "round" years, in this example multiples of 5 (5n). However, data at monthly and quarterly frequencies are plotted with ticks aligning to 5n-1.
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https://api.github.com/repos/pandas-dev/pandas/issues/13228 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13228/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13228/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13228/events | https://github.com/pandas-dev/pandas/issues/13228 | 155,753,871 | MDU6SXNzdWUxNTU3NTM4NzE= | 13,228 | Using str() in .map() on floats gives string with higher precision than before | {
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} | 15 | 2016-05-19T14:41:19Z | 2020-01-07T23:45:41Z | 2020-01-07T23:45:41Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
# In 0.17.1:
x = pd.Series(1/3)
x.map(lambda x: str(x)).to_dict()
{0: '0.333333333333'}
x.astype(str).map(lambda x: x).to_dict()
{0: '0.333333333333'}
# In 0.18.1:
x = pd.Series(1/3)
x.map(lambda x: str(x)).to_dict()
{0: '0.3333333333333333'}
x.astype(str).map(lambda x: x).to_dict()
{0: '0.333333333333'}
```
#### Expected Output
I'd expect the same output as in 0.17.x and before.
I do this a lot to convert floats to decimal.decimal with .map(lambda x: D(str(x))) which is slightly faster than using .astype(str).map(D).
This also messed up many of my unit-tests where I convert DFs to string dicts. Thanks to those I found this at all.
I checked the change docs but couldn't find something that points to why this should have changed.
#### output of `pd.show_versions()`
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} | 4 | 2016-05-19T15:49:59Z | 2021-04-15T06:49:37Z | 2021-04-15T06:49:28Z | CONTRIBUTOR | null | http://blog.dominodatalab.com/using-r-and-python-for-common-sas-functions/?utm_source=&utm_medium=&utm_campaign=
might be interesting to update `comparison_with_SAS/R` with this.
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} | 6 | 2016-05-19T18:09:42Z | 2020-01-07T23:45:40Z | 2020-01-07T23:45:40Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
#0.17.1:
df = pd.DataFrame([1,2,3,4,5], columns=list('A'), index=pd.date_range('2010-01-01 09:00:00', periods=5, freq='s')).reset_index()
df['date'] = df['index']
del df['index']
df.loc[4,'A'] = 4
df.loc[4,'date'] = pd.NaT
print(df.sort_values(['A','date']))
A date
0 1 2010-01-01 09:00:00
1 2 2010-01-01 09:00:01
2 3 2010-01-01 09:00:02
4 4 NaT
3 4 2010-01-01 09:00:03
#0.18.1:
df = pd.DataFrame([1,2,3,4,5], columns=list('A'), index=pd.date_range('2010-01-01 09:00:00', periods=5, freq='s')).reset_index()
df['date'] = df['index']
del df['index']
df.loc[4,'A'] = 4
df.loc[4,'date'] = pd.NaT
print(df.sort_values(['A','date']))
A date
0 1 2010-01-01 09:00:00
1 2 2010-01-01 09:00:01
2 3 2010-01-01 09:00:02
3 4 2010-01-01 09:00:03
4 4 NaT
```
#### Expected Output
This one was hard to find. The order stays the same as in 0.17.1 when using only sort_values('date'), but using multiple cols, it changes sorting datetimes with NaT. Couldn't find anything in the Changelogs that points to a reason for this.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
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: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
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} | 14 | 2016-05-19T18:31:16Z | 2016-06-05T14:08:12Z | 2016-06-05T14:08:12Z | CONTRIBUTOR | null | We use HDF5 to store our pandas dataframes on disk. We only store one dataframe per HDF5, so the feature of pandas.read_hdf() that allows omitting the key when a HDF file contains a single Pandas object is very nice for our workflow.
However, said feature doesn't work when the dataframe saved contains one or more categorical columns:
```
import pandas as pd
df = pd.DataFrame({'col1': [11, 21, 31], 'col2': ['a', 'b', 'a']})
# This works fine.
df.to_hdf('no_cat.hdf5', 'data', format='table')
df2 = pd.read_hdf('no_cat.hdf5')
print((df == df2).all().all())
# But this produces an exception.
df.assign(col2=pd.Categorical(df.col2)).to_hdf('cat.hdf5', 'data', format='table')
df3 = pd.read_hdf('cat.hdf5')
# ValueError: key must be provided when HDF file contains multiple datasets.
```
It looks like this is because pandas.read_hdf() doesn't ignore the metadata used to store the categorical codes:
```
print(pd.HDFStore('cat.hdf5'))
<class 'pandas.io.pytables.HDFStore'>
File path: cat.hdf5
/data frame_table (typ->appendable,nrows->3,ncols->2,indexers->[index])
/data/meta/values_block_1/meta series_table (typ->appendable,nrows->2,ncols->1,indexers->[index],dc->[values])
```
it'd be nice if this feature worked even when some of the columns are categoricals. It should be possible to ignore that metadata that pandas creates when looking if there is only one dataset stored, no?
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} | 12 | 2016-05-19T20:54:42Z | 2017-03-04T21:15:37Z | 2017-03-04T21:15:37Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
pd.PeriodIndex([1.1], freq='M')
# ValueError: Given date string not likely a datetime.
pd.PeriodIndex(np.array([1.1]), freq='M')
# PeriodIndex(['1970-02'], dtype='int64', freq='M'
```
#### Expected Output
Consistently raise or coerce to `Period`
#### output of `pd.show_versions()`
0.18.1
CC: @MaximilianR
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} | 12 | 2016-05-19T21:27:26Z | 2016-06-01T11:13:44Z | 2016-06-01T11:13:41Z | CONTRIBUTOR | null | - [x] closes #13219
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This is my first time contributing to this project. Let me know what needs to be improved, especially with regards to the test and the bugfix. It was simpler than I imagined which makes me want to say I did it wrong.
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} | 6 | 2016-05-19T23:21:12Z | 2016-05-23T13:05:48Z | 2016-05-23T13:05:43Z | CONTRIBUTOR | null | - [x] closes #12599
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 2 | 2016-05-20T07:41:17Z | 2016-05-25T12:14:52Z | 2016-05-25T12:14:52Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` Python
import numpy as np
import pandas as pd
index = pd.date_range('2000-01-01', freq='2D', periods=5)
df = pd.DataFrame(index=index, data={'col1':[0,0,1,1,2], 'col2':[1,1,1,1,1]})
df.groupby('col1').resample('1W', label='left').sum()
```
#### The dataframe
```
col1 col2
2000-01-01 0 1
2000-01-03 0 1
2000-01-05 1 1
2000-01-07 1 1
2000-01-09 2 1
```
#### Expected Output
```
col1 col2
col1
0 1999-12-26 0 1
2000-01-02 0 1
1 2000-01-02 2 2
2 2000-01-02 2 1
```
#### Actual ouput
```
TypeError Traceback (most recent call last)
<ipython-input-53-6e7ac0fde8b3> in <module>()
----> 1 df.groupby('col1').resample('1W', label='left').sum()
/Users/roycoding/venv-lib-upgrade/lib/python2.7/site-packages/pandas/core/groupby.pyc in resample(self, rule, *args, **kwargs)
1080 """
1081 from pandas.tseries.resample import get_resampler_for_grouping
-> 1082 return get_resampler_for_grouping(self, rule, *args, **kwargs)
1083
1084 @Substitution(name='groupby')
/Users/roycoding/venv-lib-upgrade/lib/python2.7/site-packages/pandas/tseries/resample.pyc in get_resampler_for_grouping(groupby, rule, how, fill_method, limit, kind, **kwargs)
910 fill_method=fill_method,
911 limit=limit,
--> 912 **kwargs)
913
914
TypeError: _maybe_process_deprecations() got an unexpected keyword argument 'label'
```
Without the `label` keyword, I get this (with expected right labeled dates):
```
col1 col2
col1
0 2000-01-02 0 1
2000-01-09 0 1
1 2000-01-09 2 2
2 2000-01-09 2 1
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 1.5.6
setuptools: 3.6
Cython: None
numpy: 1.11.0
scipy: 0.15.1
statsmodels: 0.6.1
xarray: None
IPython: 3.2.1
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.5
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13236 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13236/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13236/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13236/events | https://github.com/pandas-dev/pandas/issues/13236 | 155,922,105 | MDU6SXNzdWUxNTU5MjIxMDU= | 13,236 | COMPAT: .map iterates over python types rather than storage type | {
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} | 17 | 2016-05-20T09:27:17Z | 2017-09-12T12:54:54Z | 2017-09-12T12:54:54Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as P
S=P.Series([0.6,0.2,15])
```
**pandas 0.18+numpy 0.10**:
```
In [1]: print S.dtype
float64
In [2]: print S.values.dtype
float64
In [3]: print S.map(type)
0 <type 'numpy.float64'>
1 <type 'numpy.float64'>
2 <type 'numpy.float64'>
dtype: object
```
**pandas 0.18.1+numpy 0.11.0**:
```
In [5]: print S.dtype
float64
In [6]: print S.values.dtype
float64
In [7]: print S.map(type)
0 <type 'float'>
1 <type 'float'>
2 <type 'float'>
dtype: object
```
I expect to get the same dtype for the 3 print, why this is changed in last version?
#### output of `pd.show_versions()`
```
pandas: 0.18.1
nose: 1.3.7
pip: 1.5.4
setuptools: 21.0.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2016.4
blosc: 1.2.7
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.5
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.4.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
Thank you
Gla
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| null | 13 | 2016-05-20T12:37:21Z | 2019-03-18T06:19:53Z | 2016-05-20T12:49:25Z | NONE | null | #### Code Sample, a copy-pastable example if possible
under 64bit windows problem
it's ok on win32 platform ,but it does not work under 64bit,unless I remove the dtype and read the DataFrame and then transform the format asfloat ...
I cant debug into the file
market_head_list = ['TradingDay','ClosePrice']
dt['TradingDay'] = np.str
dt['ClosePrice'] = float # I used np.float64 too ,but it still cant work
csv_str_now ='1.7976931348623157e+308, 1.7976931348623157e+308 '
```
csv_str = StringIO.StringIO((csv_str_now))
df = pd.read_csv(csv_str, engine='c', names=market_head_list,dtype=dt)
```
#### output of `pd.show_versions()`
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
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: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
File "pandas\parser.pyx", line 805, in pandas.parser.TextReader.read (pandas\parser.c:8620)
File "pandas\parser.pyx", line 827, in pandas.parser.TextReader._read_low_memory (pandas\parser.c:8876)
File "pandas\parser.pyx", line 904, in pandas.parser.TextReader._read_rows (pandas\parser.c:9893)
File "pandas\parser.pyx", line 1011, in pandas.parser.TextReader._convert_column_data (pandas\parser.c:11286)
File "pandas\parser.pyx", line 1092, in pandas.parser.TextReader._convert_tokens (pandas\parser.c:12659)
ValueError: cannot safely convert passed user dtype of <f8 for object dtyped data in column 1
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} | 11 | 2016-05-20T14:11:10Z | 2018-04-11T02:29:55Z | 2018-04-11T02:29:55Z | NONE | null | It appears that resample is now dropping timezone information on the index. Is this expected?
```
a = pp.Series(index=pp.DatetimeIndex(['2013-01-01 06:00', '2013-01-01 07:00', '2013-01-02 06:00'],
tz=pytz.timezone('America/Los_Angeles')),
data=[1, 2, 3])
b = a.resample('D', how='max')
print repr(a.index.tz)
print repr(b.index.tz)
print a.index.tz == b.index.tz
```
produces the following in 0.16.1. Notably the timezones for the original series and the resampled series are the same.
```
<DstTzInfo 'America/Los_Angeles' LMT-1 day, 16:07:00 STD>
<DstTzInfo 'America/Los_Angeles' LMT-1 day, 16:07:00 STD>
True
```
However that's not the case for 0.18.1. Caveat is that we're using the new resample syntax: `b = a.resample('D').max()`
```
<DstTzInfo 'America/Los_Angeles' LMT-1 day, 16:07:00 STD>
<DstTzInfo 'America/Los_Angeles' PST-1 day, 16:00:00 STD>
False
```
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| null | 1 | 2016-05-20T16:14:33Z | 2016-05-20T16:32:58Z | 2016-05-20T16:32:37Z | NONE | null | Initialize a dataframe: (I am using pandas version 0.17.1)
```
import pandas as pd
a=pd.DataFrame({'f':[1,2,3,4]})
```
First if I call nunique from an empty dataframe, I get what I expect:
```
a[a['f']>10]['f'].nunique()
0
```
But If I call nunique from groupby, I get an error:
```
a[a['f']>10].groupby('f')['f'].nunique()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/ci1/plus/tc10b/sles11sp2_gcc-4.8.3_x86-64/PythonModules-2.7.8/20160310/lib/python/pandas/core/groupby.py", line 2695, in nunique
return Series(out if ids[0] != -1 else out[1:],
IndexError: index 0 is out of bounds for axis 0 with size 0
```
On the other hand, the 'unique' function doesn't have this issue:
```
a[a['f']>10]['f'].unique()
array([], dtype=int64)
a[a['f']>10].groupby('f')['f'].unique()
Series([], Name: f, dtype: float64)
```
I would like the 'nunique' function to behave like 'unique' when handling an empty dataframe + groupby. (Or please let me know if this issue is already fixed in later versions.) Thanks!
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| null | 2 | 2016-05-20T17:45:35Z | 2016-05-20T17:50:30Z | 2016-05-20T17:49:55Z | NONE | null | #### Code Sample, a copy-pastable example if possible
a=pd.DataFrame({'f':[1,2,3,4]})
b=pd.DataFrame({'f':[2,3,4,5]})
## pd.merge(a['f'],b,on='f')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/ci1/plus/tc10b/sles11sp2_gcc-4.8.3_x86-64/PythonModules-2.7.8/20160310/lib/python/pandas/tools/merge.py", line 34, in merge
copy=copy, indicator=indicator)
File "/opt/ci1/plus/tc10b/sles11sp2_gcc-4.8.3_x86-64/PythonModules-2.7.8/20160310/lib/python/pandas/tools/merge.py", line 190, in **init**
self.join_names) = self._get_merge_keys()
File "/opt/ci1/plus/tc10b/sles11sp2_gcc-4.8.3_x86-64/PythonModules-2.7.8/20160310/lib/python/pandas/tools/merge.py", line 412, in _get_merge_keys
left_keys.append(left[lk]._values)
File "/opt/ci1/plus/tc10b/sles11sp2_gcc-4.8.3_x86-64/PythonModules-2.7.8/20160310/lib/python/pandas/core/series.py", line 557, in __getitem__
result = self.index.get_value(self, key)
File "/opt/ci1/plus/tc10b/sles11sp2_gcc-4.8.3_x86-64/PythonModules-2.7.8/20160310/lib/python/pandas/core/index.py", line 1790, in get_value
return self._engine.get_value(s, k)
File "pandas/index.pyx", line 103, in pandas.index.IndexEngine.get_value (pandas/index.c:3204)
File "pandas/index.pyx", line 111, in pandas.index.IndexEngine.get_value (pandas/index.c:2903)
File "pandas/index.pyx", line 159, in pandas.index.IndexEngine.get_loc (pandas/index.c:3908)
KeyError: 'f'
#### Expected Output
```
f
```
0 2
1 3
2 4
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.0.38-0.5-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
pandas: 0.17.1
nose: 1.3.6
pip: 1.5.6
setuptools: 0.6
Cython: 0.21.1
numpy: 1.9.3
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.2.2
patsy: 0.3.0
dateutil: 2.3
pytz: 2015.4
blosc: None
bottleneck: 0.8.0
tables: 3.2.2
numexpr: 2.4
matplotlib: 1.4.3
openpyxl: 2.0.4
xlrd: 0.9.0
xlwt: 0.7.4
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: 0.6.2.None
psycopg2: None
Jinja2: None
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} | 3 | 2016-05-20T18:25:40Z | 2016-05-25T12:15:02Z | 2016-05-25T12:14:52Z | CONTRIBUTOR | null | - [x] closes #13235
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 7 | 2016-05-20T21:52:40Z | 2020-05-22T03:13:30Z | 2020-05-22T03:13:29Z | MEMBER | null | - [x] `PeriodIndex.freq` (mandatory), #13250
- [x] `DatetimeIndex.tz` (mandatory)
- [x] `DatetimeIndex.freq` (add `check_freq` option with default False, because lots of tests compare op result (lost freq) and expected result created by `date_range` (has freq))
- [x] `TimedeltaIndex.freq` (same as above), #13250
- [x] `CategoricalIndex` internals (`.codes` and `.categories`) (ref #13076, add `check_categorical` with default True), PR ##13249
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| null | 2 | 2016-05-20T21:53:55Z | 2016-05-24T20:06:27Z | 2016-05-24T20:06:27Z | NONE | null | In writing some math code in pandas, I find it necessary to do things like
```
df2 = df.sub(ser, axis='columns')
```
instead of the shorter and more intuitive
```
df2 = df - ser
```
in order to control the axis along which the series is broadcast.
I think it would be a big improvement syntactically if pandas would automatically broadcast down the axis that didn't have a matching name.
Example:
```
df = pd.DataFrame(np.random.rand(3, 2), columns=['a', 'b'])
df.index.name = 'dim0'
df.columns.name = 'dim1'
df
dim1 a b
dim0
0 0.755744 0.321682
1 0.915464 0.413154
2 0.647672 0.457927
```
subtract:
```
df - df['a'] # does not give the desired result
a b 0 1 2
dim0
0 NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN
```
subtract, specifying which axis to match on (broadcasting happens on the other axis):
```
df.sub(df['a'], axis='index') # gives the desired result
dim1 a b
dim0
0 0.0 -0.434062
1 0.0 -0.502310
2 0.0 -0.189745
```
I am suggesting that the "-" operator would look at the names of the indices in the operands and match on the axis that has the same name in the two operands.
By way of motivation, I'm doing mass spectral matching of compounds, so I could name my indices 'chemical' and 'mass'.
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} | 8 | 2016-05-20T22:13:53Z | 2016-08-15T20:19:42Z | 2016-08-15T20:19:33Z | CONTRIBUTOR | null | Currently the `to_gbq` method fails when `verbose=False`, as the printing of progress is done regardless of the value of the `verbose` flag, and therefore `total_rows` gets called without being set. This change suppresses printing of progress when `verbose=False`.
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| null | 1 | 2016-05-20T22:53:37Z | 2016-05-20T23:33:52Z | 2016-05-20T23:33:45Z | NONE | null | Pandas's option.get_all_data incorrectly parses the maturity dates for all options, so 2017 options from Yahoo will be parsed to 2020 option. This is because a problem logic to use to_datetime() to convert the date string. A simple and practical fix is to append "20" in front of the string before calling to_datetime(). Line to fix is in data.py's _process_data function. - Yi Gu 2016
# frame["Expiry"] = to_datetime("20"+frame.Rootexp.str[-6:])
# sample code to produce the error
from pandas.io.data import Options
myopt = Options("GS", "yahoo")
data = myopt.get_all_data()
print data.ix[0].name
#### Expected Output
(310.0, Timestamp('2020-01-17 00:00:00'), 'put', u'GS170120P00310000')
After the code correction, the correct output should be
(310.0, Timestamp('2017-01-20 00:00:00'), 'put', u'GS170120P00310000')
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 22 Model 48 Stepping 1, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
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: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.2
pytz: 2016.3
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: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
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} | 1 | 2016-05-21T01:19:16Z | 2016-05-21T14:05:21Z | 2016-05-21T14:05:17Z | MEMBER | null | Title is self-explanatory. xref #13148.
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} | 6 | 2016-05-21T05:34:17Z | 2017-03-14T13:33:58Z | 2017-03-14T13:33:58Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
pn1 = pd.Panel(np.array([1.0], dtype=np.float32, ndmin=3)) # panel of 1.0
pn2 = pd.Panel(np.array([np.nan], dtype=np.float32, ndmin=3)) # panel of nan
print(pd.concat([pn1, pn1]).values.dtype)
print(pd.concat([pn2, pn2]).values.dtype)
```
#### Current Output
```
float32
float64
```
#### Expected Output
```
float32
float32
```
#### 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: 0.18.1
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.1.0dev
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.6.7.None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
```
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} | 1 | 2016-05-21T06:05:08Z | 2016-05-21T18:46:03Z | 2016-05-21T18:45:57Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
`Timestamp` subtraction resuts in `Timedelta`
```
pd.Timestamp('2011-01-01') - pd.Timestamp('2010-01-01')
# Timedelta('365 days 00:00:00')
```
But it doesn't when broadcasted to `DataFrame`. It's because `DataFrame` op calls
`_try_corce_results` which coerces the result to `datetime64`.
```
df = pd.DataFrame({'a': [pd.Timestamp('2011-01-01')]})
df - pd.Timestamp('2010-01-01')
# a
#0 1971-01-01
```
#### Expected Output
```
# a
#0 365 days
```
#### output of `pd.show_versions()`
current master
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} | 1 | 2016-05-21T13:43:29Z | 2016-05-21T23:58:48Z | 2016-05-21T23:58:25Z | MEMBER | null | - [x] closes #13076
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
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} | 2 | 2016-05-21T13:48:54Z | 2016-05-22T00:05:48Z | 2016-05-22T00:05:22Z | MEMBER | null | - [x] related to #13242
closes #13251
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
This must be after #13079.
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} | 3 | 2016-05-21T14:16:25Z | 2016-05-22T00:05:22Z | 2016-05-22T00:05:22Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/pull/13079#issuecomment-220780044
just need to make sure that we are raising IncompatibleFrequency as appropriate (and testing explicity for this rather than ValueError) when constructing Periods.
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} | 10 | 2016-05-21T16:27:20Z | 2016-07-11T10:18:53Z | 2016-07-11T10:18:22Z | CONTRIBUTOR | null | Follow-up in https://github.com/pydata/pandas/pull/13614
---
This solves #11886
It checks whether the passed dtype variable is a dictionary.
If not, it creates a new dictionary with keys as the columns of the dataframe.
It then passes this dictionary to the pandasSQL_builder.
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} | 3 | 2016-05-21T22:18:50Z | 2019-01-21T14:21:32Z | 2019-01-21T14:21:32Z | CONTRIBUTOR | null | See PR: #13233 and pandas/io/tests/parser/usecols.py `test_usecols_with_multibyte_unicode_characters`
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
from pandas.compat import StringIO
s = '''あああ,いい,ううう,ええええ
0.056674973,8,True,a
2.613230982,2,False,b
3.568935038,7,False,a
'''
data = {
'あああ': {
0: 0.056674972999999997,
1: 2.6132309819999997,
2: 3.5689350380000002
},
'いい': {0: 8, 1: 2, 2: 7}
}
expected = pd.DataFrame(data)
df = pd.read_csv(StringIO(s), usecols=[u'あああ', u'いい'])
```
```
>>> df = pd.read_csv(StringIO(s), usecols=[u'あああ', u'いい'])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/io/parsers.py", line 562, in parser_f
return _read(filepath_or_buffer, kwds)
File "pandas/io/parsers.py", line 315, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "pandas/io/parsers.py", line 645, in __init__
self._make_engine(self.engine)
File "pandas/io/parsers.py", line 799, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "pandas/io/parsers.py", line 1263, in __init__
raise ValueError("Usecols do not match names.")
ValueError: Usecols do not match names.
>>> df = pd.read_csv(StringIO(s), usecols=[u'あああ', u'いい'], engine='python')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/io/parsers.py", line 562, in parser_f
return _read(filepath_or_buffer, kwds)
File "pandas/io/parsers.py", line 315, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "pandas/io/parsers.py", line 645, in __init__
self._make_engine(self.engine)
File "pandas/io/parsers.py", line 805, in _make_engine
self._engine = klass(self.f, **self.options)
File "pandas/io/parsers.py", line 1609, in __init__
self.columns, self.num_original_columns = self._infer_columns()
File "pandas/io/parsers.py", line 1905, in _infer_columns
columns = self._handle_usecols(columns, columns[0])
File "pandas/io/parsers.py", line 1958, in _handle_usecols
col_indices.append(usecols_key.index(u))
ValueError: u'\u3042\u3042\u3042' is not in list
```
#### Expected Output
```
あああ いい
0 0.056675 8
1 2.613231 2
2 3.568935 7
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: 71f5139f14b38a1a3c1d9fd7d9219947399faec0
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1+54.g71f5139.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 20.7.0
Cython: 0.24
numpy: 1.11.0
scipy: None
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: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
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} | 1 | 2016-05-22T14:15:41Z | 2020-09-15T22:20:49Z | 2020-09-15T22:20:49Z | CONTRIBUTOR | null | from [mailing list](https://groups.google.com/forum/#!topic/pydata/Bl7QLr-Y5Z0)
to avoid imprecision errors as the rolling computations are evaluated marginally (sliding the window and adding new / subtracting old). We tend to accumulate floating point errors.
I looks like just an extra sum and subtract so I think the perf impact would be minimal.
This might be able to be applied to `.rolling(..).mean()` as well. Note that `.rolling(..).std/var` already use Welford's algo for better precision.
https://en.wikipedia.org/wiki/Kahan_summation_algorithm
implementation from `numpy`
```
cdef double kahan_sum(double *darr, npy_intp n):
cdef double c, y, t, sum
cdef npy_intp i
sum = darr[0]
c = 0.0
for i from 1 <= i < n:
y = darr[i] - c
t = sum + y
c = (t-sum) - y
sum = t
return sum
```
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} | 3 | 2016-05-23T00:13:47Z | 2016-07-08T13:32:53Z | 2016-05-23T12:20:46Z | NONE | null | ``` python
import pandas as pd
from itertools import cycle, islice
N = 4
df = pd.DataFrame(index=pd.date_range('2000', periods=N))
df['col1'] = list(islice(cycle(['A', 'B']), N))
df['col2'] = list(islice(cycle(['a', 'b', 'c']), N))
print(df)
```
returns
```
col1 col2
2000-01-01 A a
2000-01-02 B b
2000-01-03 A c
2000-01-04 B a
```
If I then do the following groupbys, lines 2 and 4 return the same result (a multiindex), but line 3 returns the result of running `.unstack` on line 1.
``` python
print(df.groupby(['col1', pd.TimeGrouper('W')]).size()) # 1. unstacked
print(df.groupby(['col2', pd.TimeGrouper('W')]).size()) # 2. unstacked
print(df.groupby('col1').resample('W').size()) # 3. multiindex
print(df.groupby('col2').resample('W').size()) # 4. unstacked
```
```
col1
A 2000-01-02 1
2000-01-09 1
B 2000-01-02 1
2000-01-09 1
dtype: int64
col2
a 2000-01-02 1
2000-01-09 1
b 2000-01-02 1
c 2000-01-09 1
dtype: int64
2000-01-02 2000-01-09
col1
A 1 1
B 1 1
col2
a 2000-01-02 1
2000-01-09 1
b 2000-01-02 1
c 2000-01-09 1
dtype: int64
```
This seems like an inconsistency to me, in the sense that, if 1 and 3 do not return the same result, neither should 2 and 4. Is this a bug, or is `df.groupby(col).resample('x')` supposed to behave like this, and is it supposed to behave differently to `df.groubpy[(col, pd.TimeGrouper(x)])`?
Note that in `df` col1 has two groups of identical size, while col2 has unequal sized groups. I'm not sure if that's the problem, but this inconsistency goes away for some choices of `N` (e.g. all four lines above return multiindex for `N=3` and `N=24`?!)
``` python
print(df.groupby('col1').size())
print(df.groupby('col2').size())
```
```
col1
A 2
B 2
dtype: int64
col2
a 2
b 1
c 1
dtype: int64
```
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| null | 1 | 2016-05-23T07:52:07Z | 2016-05-23T12:17:44Z | 2016-05-23T12:17:12Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
test_str = u'1.7976931348623157e+308,1.7976931348623157e+308'
ts_dbl = u'1.7976931348623157e+308'
print np.float64(ts_dbl)
dt={}
tmpio = StringIO(test_str)
market_head_list = [ "ClosePrice", "SettlementPrice"]
dt['ClosePrice']=np.float64
dt['SettlementPrice']=np.float64
df =pd.read_csv(tmpio,engine = 'c',names=market_head_list,dtype=dt)
```
#### Expected Output
```
data = self._reader.read(nrows)
```
File "pandas\parser.pyx", line 805, in pandas.parser.TextReader.read (pandas\parser.c:8620)
File "pandas\parser.pyx", line 827, in pandas.parser.TextReader._read_low_memory (pandas\parser.c:8876)
File "pandas\parser.pyx", line 904, in pandas.parser.TextReader._read_rows (pandas\parser.c:9893)
File "pandas\parser.pyx", line 1011, in pandas.parser.TextReader._convert_column_data (pandas\parser.c:11286)
File "pandas\parser.pyx", line 1092, in pandas.parser.TextReader._convert_tokens (pandas\parser.c:12659)
ValueError: cannot safely convert passed user dtype of <f8 for object dtyped data in column 0
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.16.0
statsmodels: None
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
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: 2.8
boto: 2.38.0
pandas_datareader: None
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} | 2 | 2016-05-23T10:22:41Z | 2019-10-22T01:53:38Z | 2019-10-22T01:53:38Z | NONE | null | I expected the following code to give me a representation of my data that is rounded or truncated to 2 decimals.
```
import pandas as pd
import numpy as np
df = pd.DataFrame(data = np.random.randn(4,4), columns=['A','B','C','D'])
df.style.set_precision(2)
```
However, it will always return 2 digits instead, so for example, I want 2.37419 to be displayed as 2.37, but instead this code will give me 2.4. Am I overseeing something or is this a bug?
Note that the following code works as I expected, but then I change a general setting whereas I would like to apply it only to the current output:
```
pd.set_option('display.precision',2)
df
```
I'm using pandas version 0.18.1 in Jupyter notebook on Firefox, with python 2.7.11.final.0 and IPython 4.1.2 on 64-bits linux.
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} | 12 | 2016-05-23T12:50:11Z | 2017-11-14T18:36:41Z | 2017-09-12T12:54:54Z | CONTRIBUTOR | null | xref #13236 as `.map` was recently clarified to box ints/floats (and all other dtypes) to python/pandas types (rather than numpy scalars). `__iter__` should do the same (it already is done for datetimelikes), need to add for int/floats.
Furthermore `Series.tolist()` also returns python types (#10904)
This will make it more consistent with pandas iteration strategy.
We also already do this for `object` types
```
In [12]: type(list(Series([Timestamp('20130101'),Timestamp('20130101',tz='US/Eastern'),1,1.0,'foo']))[2])
Out[12]: int
```
.tolist()
```
In [3]: type(Series([1,2,3]).tolist()[2])
Out[3]: int
```
let's fix for pure types
```
In [14]: type(list(Series([1,2,3]))[2])
Out[14]: numpy.int64
```
When solving this issue, also have to look at:
- [ ] Inconsistent conversion in `to_dict` to numpy/python scalar (#14216)
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| null | 1 | 2016-05-23T15:03:09Z | 2016-05-23T15:31:48Z | 2016-05-23T15:31:20Z | NONE | null | Somewhere between 0.16.1 and 0.18.1, QUOTE_NONNUMERIC started also quoting numeric values unexpectedly.
```
import csv
df = pandas.DataFrame({
'c_string': ['a', 'b,c'],
'c_int': [42, numpy.nan],
'c_float': [1.0, 3.2],
'c_bool': [True, False],
})
print df.to_csv(quoting=csv.QUOTE_NONNUMERIC)
```
In 0.16.1, the output was:
```
"","c_bool","c_float","c_int","c_string"
0,True,1.0,42.0,"a"
1,False,3.2,"","b,c"
```
In 0.18.1, the output is:
```
"","c_bool","c_float","c_int","c_string"
0,True,"1.0","42.0","a"
1,False,"3.2","","b,c"
```
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} | 4 | 2016-05-23T18:37:22Z | 2016-05-26T18:15:44Z | 2016-05-26T18:15:44Z | NONE | null | I am trying to use `astype` together with `loc` to convert just a subset of columns to a specified dtype. However, I can't understand what is actually happening. Is this the expected behavior when combining
`astype` and `loc`?
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
df = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6], 'c': [7, 8, 9]})
print df.loc[:, ['a', 'b']].astype(np.uint8).dtypes
df.loc[:, ['a', 'b']] = df.loc[:, ['a', 'b']].astype(np.uint8)
print df.dtypes
```
Output:
```
>>> df.loc[:, ['a', 'b']].astype(np.uint8).dtypes
a uint8
b uint8
dtype: object
>>> df.loc[:, ['a', 'b']] = df.loc[:, ['a', 'b']].astype(np.uint8)
>>> df.dtypes
a int64
b int64
c int64
dtype: object
```
#### Expected Output
```
>>> df.loc[:, ['a', 'b']].astype(np.uint8).dtypes
a uint8
b uint8
dtype: object
>>> df.loc[:, ['a', 'b']] = df.loc[:, ['a', 'b']].astype(np.uint8)
>>> df.dtypes
a uint8
b uint8
c int64
dtype: object
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-36-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: 20.6.7
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
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: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
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} | 1 | 2016-05-23T21:54:00Z | 2016-05-24T13:23:41Z | 2016-05-24T13:23:37Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
```
c = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
c.codes
# array([0, 1], dtype=int8)
# NG, must be int8 dtype
c = c.remove_unused_categories()
c.codes
# array([0, 1])
```
It is because `np.unique` uses platform int for `unique_inverse`
```
np.unique(np.array([0, 3, 2, 3], dtype=np.int8), return_inverse=True)
(array([0, 2, 3], dtype=int8), array([0, 2, 1, 2]))
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13262/events | https://github.com/pandas-dev/pandas/issues/13262 | 156,376,368 | MDU6SXNzdWUxNTYzNzYzNjg= | 13,262 | ENH: Support mangle_dupe_cols=False in pd.read_csv() | {
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} | 9 | 2016-05-23T21:59:20Z | 2021-08-20T18:42:44Z | null | MEMBER | null | #12935 added full support for duplicate column names (in header or in `names`) by mangling them. While this has been considered _acceptable_ by users, ideally, we would like to not have to mangle them.
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} | 3 | 2016-05-23T21:59:27Z | 2016-05-26T16:11:08Z | 2016-05-26T16:10:52Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
Not to use `np.testing.assert_equal`, as `assert_numpy_array_equal` is more strict to check dtypes.
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https://api.github.com/repos/pandas-dev/pandas/issues/13264 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13264/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13264/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13264/events | https://github.com/pandas-dev/pandas/pull/13264 | 156,378,982 | MDExOlB1bGxSZXF1ZXN0NzExMjk1ODI= | 13,264 | TST: assert_dict_equal to check input type | {
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} | 2 | 2016-05-23T22:16:04Z | 2016-05-24T01:11:42Z | 2016-05-24T01:11:06Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
`assert_dict_equal` now checks input is `dict` instance. Fixed some tests which passes `Series` to it.
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https://api.github.com/repos/pandas-dev/pandas/issues/13265 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13265/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13265/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13265/events | https://github.com/pandas-dev/pandas/issues/13265 | 156,415,229 | MDU6SXNzdWUxNTY0MTUyMjk= | 13,265 | pd.read_html, AttributeError: AttributeError: 'NoneType' object has no attribute 'next_element' | {
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| null | 2 | 2016-05-24T04:00:04Z | 2016-05-26T08:02:58Z | 2016-05-24T13:10:22Z | NONE | null | When I try to use `pa.read_html()` to extract data from a html page, I got some error infos.
How to solve this?
Thanks.
#### Code
```
import pandas as pd
url = 'http://data.earthquake.cn/datashare/globeEarthquake_csn.html'
dfs = pd.read_html(html)
dfs
```
#### Some Infos
- `pd.__version__` : `u'0.18.1'`
- `pd.get_option('display.encoding')`: `'UTF-8'`
- webpage: `charset=gb2312`
- Platform: Jupyter/notebook
- Python : 2.7.6
#### Error:
```
AttributeError Traceback (most recent call last)
<ipython-input-53-f541be1b6614> in <module>()
1 import pandas as pd
2 url = 'http://data.earthquake.cn/datashare/globeEarthquake_csn.html'
----> 3 dfs = pd.read_html(html)
4 dfs
/usr/local/lib/python2.7/dist-packages/pandas/io/html.pyc in read_html(io, match, flavor, header, index_col, skiprows, attrs, parse_dates, tupleize_cols, thousands, encoding)
872 _validate_header_arg(header)
873 return _parse(flavor, io, match, header, index_col, skiprows,
--> 874 parse_dates, tupleize_cols, thousands, attrs, encoding)
/usr/local/lib/python2.7/dist-packages/pandas/io/html.pyc in _parse(flavor, io, match, header, index_col, skiprows, parse_dates, tupleize_cols, thousands, attrs, encoding)
737
738 ret = []
--> 739 for table in tables:
740 try:
741 ret.append(_data_to_frame(data=table,
/usr/local/lib/python2.7/dist-packages/pandas/io/html.pyc in <genexpr>((table,))
195 def parse_tables(self):
196 tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
--> 197 return (self._build_table(table) for table in tables)
198
199 def _parse_raw_data(self, rows):
/usr/local/lib/python2.7/dist-packages/pandas/io/html.pyc in _build_table(self, table)
347
348 def _build_table(self, table):
--> 349 header = self._parse_raw_thead(table)
350 body = self._parse_raw_tbody(table)
351 footer = self._parse_raw_tfoot(table)
/usr/local/lib/python2.7/dist-packages/pandas/io/html.pyc in _parse_raw_thead(self, table)
353
354 def _parse_raw_thead(self, table):
--> 355 thead = self._parse_thead(table)
356 res = []
357 if thead:
/usr/local/lib/python2.7/dist-packages/pandas/io/html.pyc in _parse_thead(self, table)
411
412 def _parse_thead(self, table):
--> 413 return table.find_all('thead')
414
415 def _parse_tbody(self, table):
/usr/local/lib/python2.7/dist-packages/bs4/element.pyc in find_all(self, name, attrs, recursive, text, limit, **kwargs)
1257 if not recursive:
1258 generator = self.children
-> 1259 return self._find_all(name, attrs, text, limit, generator, **kwargs)
1260 findAll = find_all # BS3
1261 findChildren = find_all # BS2
/usr/local/lib/python2.7/dist-packages/bs4/element.pyc in _find_all(self, name, attrs, text, limit, generator, **kwargs)
527 if isinstance(element, Tag)
528 and element.name == name)
--> 529 return ResultSet(strainer, result)
530 results = ResultSet(strainer)
531 while True:
/usr/local/lib/python2.7/dist-packages/bs4/element.pyc in __init__(self, source, result)
1722 that created it."""
1723 def __init__(self, source, result=()):
-> 1724 super(ResultSet, self).__init__(result)
1725 self.source = source
/usr/local/lib/python2.7/dist-packages/bs4/element.pyc in <genexpr>((element,))
524 elif isinstance(name, basestring):
525 # Optimization to find all tags with a given name.
--> 526 result = (element for element in generator
527 if isinstance(element, Tag)
528 and element.name == name)
/usr/local/lib/python2.7/dist-packages/bs4/element.pyc in descendants(self)
1275 while current is not stopNode:
1276 yield current
-> 1277 current = current.next_element
1278
1279 # CSS selector code
AttributeError: 'NoneType' object has no attribute 'next_element'
```
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| null | 13 | 2016-05-24T09:37:29Z | 2021-05-05T19:21:07Z | 2021-05-05T19:21:07Z | NONE | null | See example below. There is no "sex" in the styled table.
```
from IPython.display import display as d
import pandas as pd
df=pd.DataFrame.from_dict(dict(survived=[1,0,0,0,1,1,1,1], sex=["Male", "Female", "Male", "Female",
"Male", "Female","Female", "Female" ]))
table=pd.crosstab(df.survived, df.sex)
table.loc["rate"] = table.iloc[-1] / table.sum()
d(table)
ftable = table.style.format("{:.0%}", subset=pd.IndexSlice["rate",:])
d(ftable)
```
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} | 0 | 2016-05-24T12:58:38Z | 2016-05-24T15:28:17Z | 2016-05-24T15:28:17Z | CONTRIBUTOR | null | closes #8287
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| null | 6 | 2016-05-24T13:25:38Z | 2016-05-24T19:00:49Z | 2016-05-24T13:32:24Z | NONE | null | Hi there!
Just installed Anaconda for Windows (pandas 0.18.1, numpy 1.11.0) and cannot fix the following problem, when I am trying to import pandas:
import pandas as pd
Traceback (most recent call last):
File "C:\Users\Username\AppData\Roaming\Python\Python35\site-packages\IPython\core\interactiveshell.py", line 2885, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "", line 1, in
import pandas as pd
File "C:\Program Files (x86)\JetBrains\PyCharm 2016.1.3\helpers\pydev_pydev_bundle\pydev_import_hook.py", line 21, in do_import
module = self._system_import(name, args, *kwargs)
File "C:\Users\Username\AppData\Roaming\Python\Python35\site-packages\pandas__init__.py", line 22, in
from pandas.compat.numpy import *
File "C:\Program Files (x86)\JetBrains\PyCharm 2016.1.3\helpers\pydev_pydev_bundle\pydev_import_hook.py", line 21, in do_import
module = self._system_import(name, args, *kwargs)
ImportError: No module named 'pandas.compat.numpy'
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https://api.github.com/repos/pandas-dev/pandas/issues/13269 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13269/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13269/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13269/events | https://github.com/pandas-dev/pandas/issues/13269 | 156,538,001 | MDU6SXNzdWUxNTY1MzgwMDE= | 13,269 | Dividing Time-series | {
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| null | 1 | 2016-05-24T15:28:55Z | 2016-05-24T15:31:46Z | 2016-05-24T15:31:34Z | NONE | null | Dividing time-series does not work as expected;
#### Code Sample, a copy-pastable example if possible
``` python
ts1 = pd.TimeSeries(range(10))
ts2 = pd.TimeSeries(range(10))
ts1[1:]/ts2[:-1]
```
the output of this operation is:
```
0 NaN
1 1.0
2 1.0
3 1.0
4 1.0
5 1.0
6 1.0
7 1.0
8 1.0
9 NaN
```
#### Expected Output
What I expected is the same as the division of two `np.ndarray`s
``` python
ts1.values[1:]/ts2.values[:-1]
```
array([ inf, 2. , 1.5 , 1.33333333, 1.25 ,
1.2 , 1.16666667, 1.14285714, 1.125 ])
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-12-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: pt_BR.UTF-8
pandas: 0.18.1
nose: 1.2.1
pip: 8.1.2
setuptools: 20.1.1
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.7.2.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/13270 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13270/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13270/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13270/events | https://github.com/pandas-dev/pandas/pull/13270 | 156,538,610 | MDExOlB1bGxSZXF1ZXN0NzEyNDE5ODg= | 13,270 | DOC: fixed typos in GroupBy document | {
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} | 1 | 2016-05-24T15:31:39Z | 2016-05-24T15:35:02Z | 2016-05-24T15:34:58Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 7 | 2016-05-24T15:48:30Z | 2016-06-28T22:23:50Z | 2016-06-28T22:18:02Z | NONE | null | Related to [this question](http://stackoverflow.com/questions/37396264/pandas-equivalent-of-resample-for-integer-index).
I would like to propose either to expand `.resample` to work on indexes that are integers or to create another method that does the same thing (maybe more general `.upsample` and `.downsample` methods).
If it was implemented as an expansion of resample I imagine something like:
```
df = pd.DataFrame(np.random.randn(10,2))
df.resample(5, np.std)
```
Which would produce:
``` python
0 1
0 1.184582 0.492113
5 0.533134 0.982562
```
which is a pretty forward downsample case. For upsampling maybe some discussion is needed on how to return the indexes.
This should be simple to program and I think it would be a very useful shortcut.
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} | 4 | 2016-05-24T18:21:48Z | 2016-05-27T00:15:42Z | 2016-05-27T00:15:29Z | CONTRIBUTOR | null | - [x] closes #12907
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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| null | 15 | 2016-05-24T20:14:54Z | 2017-03-28T00:00:26Z | 2017-03-28T00:00:26Z | NONE | null | - [ ] closes #xxxx
- [ ] tests added / passed
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… the IndexSlice same as it does for axis=1
```
df = pd.DataFrame(index = ['x', 'y', 'z'])
df['a'] = [1,2,3]
df['b'] = [4,5,6]
df['c'] = [7,8,9]
df
df.style.background_gradient(subset='x', axis=1)
```
# otherwise this throws some vague exception. seems like if you pass in
axis this should just work
`df.style.background_gradient(subset=pd.IndexSlice['x',], axis=1)` #same
as this
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} | 11 | 2016-05-24T23:24:22Z | 2016-05-25T19:26:55Z | 2016-05-25T17:32:03Z | MEMBER | null | 1) Allow mixed-case infinity strings for the Python engine
Bug was traced back via `lib.maybe_convert_numeric` to the `floatify` function in `pandas/src/parse_helper.h`. In addition to correcting the bug and adding tests for it, this PR also moves the `test_inf_parsing` test from `c_parser_only.py` to `common.py` in the `pandas/io/tests/parser` dir.
2) Interpret `+inf` as positive infinity for both engines
`float('+inf')` in Python is interpreted as positive infinity, so we should allow it too in parsing.
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} | 2 | 2016-05-25T02:20:41Z | 2016-05-25T12:09:28Z | 2016-05-25T12:09:25Z | MEMBER | null | 1) Allows `float` values for `nrows` for the Python engine
2) Prevents abuse of the `nrows` argument for the CParser (e.g. you can passing `nrows=1.2`)
Closes #10476.
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
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} | 33 | 2016-05-25T04:08:22Z | 2017-03-04T21:16:03Z | 2017-03-04T21:15:37Z | CONTRIBUTOR | null | - [x] closes #13232
- [x] tests added / passed
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Material clean up of PeriodIndex constructor, which was doing a few weird things (https://github.com/pydata/pandas/issues/13232#issuecomment-220788816), and generally getting messy.
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} | 10 | 2016-05-25T08:47:58Z | 2017-07-30T22:54:02Z | 2017-07-30T22:54:02Z | NONE | null | the following works fine
> > > c = pd.Series(rand(3))
> > > pd.crosstab(c,c)
however, when giving the series a name, it fails:
> > > c.name='test'
> > > pd.crosstab(c,c)
Traceback (most recent call last):
File "C:\Python35\Lib\site-packages\pandas\indexes\multi.py", line 515, in _get_level_number
'level number' % level)
ValueError: The name test occurs multiple times, use a level number
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<pyshell#56>", line 1, in <module>
pd.crosstab(c,c)
File "C:\Python35\Lib\site-packages\pandas\tools\pivot.py", line 462, in crosstab
aggfunc=len, margins=margins, dropna=dropna)
File "C:\Python35\Lib\site-packages\pandas\tools\pivot.py", line 127, in pivot_table
table = agged.unstack(to_unstack)
File "C:\Python35\Lib\site-packages\pandas\core\frame.py", line 3946, in unstack
return unstack(self, level, fill_value)
File "C:\Python35\Lib\site-packages\pandas\core\reshape.py", line 398, in unstack
return _unstack_multiple(obj, level)
File "C:\Python35\Lib\site-packages\pandas\core\reshape.py", line 252, in _unstack_multiple
clocs = [index._get_level_number(i) for i in clocs]
File "C:\Python35\Lib\site-packages\pandas\core\reshape.py", line 252, in <listcomp>
clocs = [index._get_level_number(i) for i in clocs]
File "C:\Python35\Lib\site-packages\pandas\indexes\multi.py", line 519, in _get_level_number
raise KeyError('Level %s not found' % str(level))
KeyError: 'Level test not found'
The use case for having series with the same name is e.g. where you want to crosstab data selected from a dataframe with a multi index. For example: c1 = mydf.xs(val1).mycolumn and c2 = mydf.xs(val2).mycolumn and then you want to do pd.crosstab(c1,c2). This will produce the error as in the simple example above.
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} | 1 | 2016-05-25T09:27:40Z | 2016-05-25T11:50:25Z | 2016-05-25T11:50:10Z | NONE | null | I've boiled my problem down to this example. I am running Python 2.7 on Windows 7 and using pandas 0.17.1.
```
>>> import pandas as pd
>>> print pd.__version__
0.17.1
>>> x = pd.DataFrame({"a":["james"], "b":[True], "c":["john"]})
>>> x
a b c
0 james True john
>>> x.loc[1] = None
>>> x
a b c
0 james 1 john ##<< THE TYPE HAS CHANGED
1 NaN NaN NaN
>>> x.loc[1,"a"] = "james1"
>>> x.loc[1,"c"] = "john1"
>>> x.loc[1,"b"] = True
>>> x
a b c
0 james 1 john
1 james1 True john1 ## << BUT THE NEXT INSERT WAS OK
>>> x.loc[2] = None ## << AND THIS DOESN'T REPLICATE THE ISSUE
>>> x
a b c
0 james 1 john
1 james1 True john1
2 NaN NaN NaN
```
In the above I try to add an extra row to the DF. I fill the row "by hand", so to speak, because in my actual situation I don't know all the column values yet and I might need to add an extra column so I fill in the known data first.
The first enlargement however changes the `True` to a `1`. Subsequent enlargements don't have this issue. But this does mean eventually I have a DF with a column of 0's, 1's and bools, which is annoying.
I've posted this on SO too:
http://stackoverflow.com/questions/37431232/python-pandas-changes-column-value-when-setting-with-enlargement?noredirect=1#comment62368221_37431232
Some suggestions indicate this may well be a bug, so I'm posting it as an issue
Interestingly, if I add the complete row at a time in the above example, the problem doesn't occur...
```
>>> x = pd.DataFrame({"a":["james"], "b":[True], "c":["john"]})
>>> x
a b c
0 james True john
>>> x.loc[1] = {"a": "james", "b" : False, "c" : "henry"}
>>> x
a b c
0 james True john
1 james False henry
```
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} | 5 | 2016-05-25T09:49:49Z | 2016-05-25T17:15:54Z | 2016-05-25T12:00:32Z | NONE | null | While writing a piece of code similar to the example below, I stumbled on a problematic interaction between groupby, diff and merge.
My debugging efforts showed that this problem is likely related to the "fast_apply" optimisation Pandas uses when using apply().
As shown below, when using string values as grouping keys, the first value encountered overrides all the others. Removing either the diff call, the merge, or replacing "a" and "b" by numerical values all solve the issue, and give a correct (albeit obviously incomplete for my purpose) result.
#### Code
```
import pandas as pd
import numpy as np
bar = np.random.randint(0, 10, 4)
foo = ['a'] * 2 + ['b'] * 2
values = zip(foo, bar)
df = pd.DataFrame(values, columns=['foo', 'bar'])
print df
def try_diff_merge(df):
df['inf_bar'] = df.bar < 6
df['inf_bar_diff'] = df['inf_bar'].diff()
df_merge = df.groupby(['inf_bar']).count()
df_merge.drop(['inf_bar_diff'], axis=1, inplace=True)
df_merge = pd.merge(df, df_merge, how='left', right_index=True,
left_on=['inf_bar'])
return df_merge
df_group = df.groupby(['foo']).apply(try_diff_merge)
print df_group
```
#### Output
```
foo bar
0 a 2
1 a 0
2 b 6
3 b 8
foo_x bar_x inf_bar inf_bar_diff foo_y bar_y
0 a 2 True NaN 2 2
1 a 0 True False 2 2
2 a 6 False NaN 2 2
3 a 8 False False 2 2
```
#### Expected Output
```
foo bar
0 a 2
1 a 0
2 b 6
3 b 8
foo_x bar_x inf_bar inf_bar_diff foo_y bar_y
0 a 2 True NaN 2 2
1 a 0 True False 2 2
2 b 6 False NaN 2 2
3 b 8 False False 2 2
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 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: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.39.0
None
```
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} | 1 | 2016-05-25T10:03:45Z | 2016-05-25T17:35:10Z | 2016-05-25T17:35:10Z | CONTRIBUTOR | null | If blosc is installed show_versions() will caused python to produce a malloc_error_break (double free) error on exiting. This can be seen in the travis runs under `source activate pandas && ci/print_versions.py` section but can also be easily replicated on Linux and Mac. The fix for this is to replace imp (which has been deprecated) with importlib. I will create and link a PR.
`````` python
>>> import pandas
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: e0a2e3bc51f9e178f72c44e6de06700ee0bf31c6
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1+66.ge0a2e3b
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: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: 1.3.2
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
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: 2.8
boto: None
pandas_datareader: None```
>>>
python(1726,0x7fff77074000) malloc: *** error for object 0x102573a00: pointer being freed was not allocated
*** set a breakpoint in malloc_error_break to debug
Abort trap: 6
``````
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| null | 2 | 2016-05-25T11:04:47Z | 2016-05-31T15:30:06Z | 2016-05-31T15:30:06Z | NONE | null | - closes #8725
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} | 5 | 2016-05-25T13:46:46Z | 2016-05-25T17:35:50Z | 2016-05-25T17:35:10Z | CONTRIBUTOR | null | - [ ] closes #13282
Remove imp from pandas.show_versions() to fix memory problem.
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} | 1 | 2016-05-25T17:11:32Z | 2016-05-26T16:13:01Z | 2016-05-26T16:13:01Z | CONTRIBUTOR | null | allows for proper is / == comparisons
Had this odd semantic difference as these were really different objects (though they DID hash the same)
This doesn't actually affect any user code.
```
In [1]: from pandas.core import common as com
In [2]: t1 = com.DatetimeTZDtype('datetime64[ns, US/Eastern]')
In [3]: t2 = com.DatetimeTZDtype('datetime64[ns, US/Eastern]')
In [4]: t1 == t2
Out[4]: True
In [5]: t1 is t2
Out[5]: False
In [6]: hash(t1)
Out[6]: 5756291921003024619
In [7]: hash(t2)
Out[7]: 5756291921003024619
```
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} | 2 | 2016-05-25T17:55:40Z | 2020-09-21T00:04:38Z | null | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/37444307/equality-of-pandas-index-objects-with-different-dtypes-and-elements/37444423#37444423)
```
In [1]: i1 = pd.DatetimeIndex(['1971-10-01'], dtype='datetime64[ns]', freq=None)
In [2]: i2 = pd.Index([u'100171'], dtype='object')
In [3]: i1
Out[3]: DatetimeIndex(['1971-10-01'], dtype='datetime64[ns]', freq=None)
In [4]: pd.DatetimeIndex(i2)
Out[4]: DatetimeIndex(['1971-10-01'], dtype='datetime64[ns]', freq=None)
```
We _might_ be able to elminate the coercion in `.equals()` in some cases.
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} | 3 | 2016-05-25T20:38:41Z | 2019-06-27T20:39:25Z | 2019-06-27T20:39:25Z | NONE | null | I have the following array of Timestamps:
``` python
ts_array = np.array([[Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 15:50:00+0000', tz='UTC', offset='5T')],
[Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 17:10:00+0000', tz='UTC', offset='5T')],
[Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T'),
Timestamp('2016-05-02 20:25:00+0000', tz='UTC', offset='5T')]], dtype=object)
```
I can't create a DataFrame from this array using the DataFrame constructor:
``` python
pd.DataFrame(ts_array)
```
```
Traceback (most recent call last):
File "/Users/jkelleher/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2885, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-46-ae20c6b6248f>", line 1, in <module>
pd.DataFrame(ts_array)
File "/Users/jkelleher/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 255, in __init__
copy=copy)
File "/Users/jkelleher/anaconda/lib/python2.7/site-packages/pandas/core/frame.py", line 432, in _init_ndarray
return create_block_manager_from_blocks([values], [columns, index])
File "/Users/jkelleher/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 3986, in create_block_manager_from_blocks
mgr = BlockManager(blocks, axes)
File "/Users/jkelleher/anaconda/lib/python2.7/site-packages/pandas/core/internals.py", line 2591, in __init__
(block.ndim, self.ndim))
AssertionError: Number of Block dimensions (1) must equal number of axes (2)
```
I can create the DataFrame from the array using `from_records`:
``` python
ts_df = pd.DataFrame.from_records(ts_array)
```
However, when I attempt to transpose this DataFrame, I wind up with the same `AssertionError` as before.
```
AssertionError: Number of Block dimensions (1) must equal number of axes (2)
```
If I convert the Timestamps to Datetimes, the error persists. I can, however, convert the Timestamps to Datetime64 objects, and this fixes the problem.
``` python
dt64_array = np.array([[ts.to_datetime64() for ts in sublist] for sublist in ts_array])
pd.DataFrame(dt64_array)
```
```
Out[56]:
0 1 2
0 2016-05-02 15:50:00 2016-05-02 15:50:00 2016-05-02 15:50:00
1 2016-05-02 17:10:00 2016-05-02 17:10:00 2016-05-02 17:10:00
2 2016-05-02 20:25:00 2016-05-02 20:25:00 2016-05-02 20:25:00
```
``` python
pd.DataFrame(dt64_array).transpose()
```
```
Out[57]:
0 1 2
0 2016-05-02 15:50:00 2016-05-02 17:10:00 2016-05-02 20:25:00
1 2016-05-02 15:50:00 2016-05-02 17:10:00 2016-05-02 20:25:00
2 2016-05-02 15:50:00 2016-05-02 17:10:00 2016-05-02 20:25:00
```
Though I found a suitable workaround, I feel like pandas should be able to construct and operate on DataFrames of Timestamps as easily as other other objects.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.8.0.dev0+970e99e
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
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: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
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} | 7 | 2016-05-25T22:17:16Z | 2016-05-31T14:12:51Z | 2016-05-31T14:12:51Z | CONTRIBUTOR | null | Preparing a commit for another issue in `.describe()`, I encountered this puzzling bug, surprisingly easy to trigger.
#### Symptoms
``` python
df = pd.DataFrame({'A': list("BCDE"), 0: [1,2,3,4]})
df.describe()
# Long traceback listing formatting and internal functions...
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long'
```
However:
``` python
df.describe(include='all')
0 A
count 4.000000 4
unique NaN 4
top NaN D
freq NaN 1
mean 2.500000 NaN
std 1.290994 NaN
min 1.000000 NaN
25% 1.750000 NaN
50% 2.500000 NaN
75% 3.250000 NaN
max 4.000000 NaN
# It's OK if we don't print on screen:
x = df.describe()
x.columns
Out[8]: Index([0], dtype='int64')
# Fixing this suspicious index (int works too):
x.columns = x.columns.astype(object)
x
Out[10]:
0
count 4.000000
mean 2.500000
std 1.290994
min 1.000000
25% 1.750000
50% 2.500000
75% 3.250000
max 4.000000
```
Same issue happens with a simpler data frame:
``` python
df0 = pd.DataFrame([1,2,3,4])
# It's OK now
df0.describe()
Out[28]:
0
count 4.000000
mean 2.500000
std 1.290994
min 1.000000
25% 1.750000
50% 2.500000
75% 3.250000
max 4.000000
# Modify column index:
df0.columns = pd.Index([0], dtype=object)
df0.describe()
# ...
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long'
```
Current version (but the bug is also present in pandas release 0.18.1):
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.20-1
machine: x86_64
processor: Intel(R)_Core(TM)_i5-2520M_CPU_@_2.50GHz
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1+64.g7ed22fe.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 21.0.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0.dev0+3f3c371
IPython: 4.0.1
...
```
#### Reason
Some internal function gets confused by dtypes of a column index, I guess. But the faulty index is created in `.describe()`.
``` python
# Output from %debug df.describe()
# NDFrame.describe() in pandas/core/generic.py:
#
4943 data = self
4944 else:
4945 data = self.select_dtypes(include=include, exclude=exclude)
4946
4947 ldesc = [describe_1d(s, percentiles) for _, s in data.iteritems()]
4948 # set a convenient order for rows
4949 names = []
4950 ldesc_indexes = sorted([x.index for x in ldesc], key=len)
4951 for idxnames in ldesc_indexes:
4952 for name in idxnames:
4953 if name not in names:
4954 names.append(name)
4955
4956 d = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1)
1> 4957 d.columns = self.columns._shallow_copy(values=d.columns.values)
4958 d.columns.names = data.columns.names
4959 return d
```
`_shallow_copy()` in the marked line changes `d.columns`:
``` python
ipdb> p d.columns
Int64Index([0], dtype='int64')
ipdb> n
> /home/users/piotr/workspace/pandas-pijucha/pandas/core/generic.py(4958)describe()
1 4957 d.columns = self.columns._shallow_copy(values=d.columns.values)
-> 4958 d.columns.names = data.columns.names
4959 return d
ipdb> p d.columns
Index([0], dtype='int64')
```
#### Possible solutions
Lines 4957-4958 are actually used to fix issues that `pd.concat` brings about. They try to pass the column structure from `self` to `d`.
I think a simpler solution is replacing these lines with:
``` python
d = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1)
d.columns = data.columns
return d
```
or
``` python
d = pd.DataFrame(pd.concat(ldesc, axis=1), index = pd.Index(names), columns = data.columns)
return d
```
`data` is a subframe of `self` and retains the same column structure.
`pd.concat` has some parameters that help pass a hierarchical index but can't do anything on its own with a categorical one.
I'm going to submit a pull request with this fix together with some others related with `describe()`. I hope I haven't overlooked anything obvious. But if so, any comments are very welcome.
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| null | 5 | 2016-05-26T07:05:58Z | 2016-05-27T00:50:06Z | 2016-05-26T10:58:22Z | NONE | null | I am trying to write results from a dataframe to hdf using the append (or .to_hdf) function. A few of my columns are large string whose length will vary (around 1k characters). Because its length will vary, I have to set, min_itemsize, for each of the variable columns. I do not want these fields to be treated as data_columns, but each time I define the column in the field min_itemsize, they become data_columns when looking at the result in hdf.
```
>>> df = pd.DataFrame(columns=['long string', 'a', 'b', 'longstring2'])
>>>df.to_hdf('test', 'key', data_columns=['a']) # => only a is a data_column
>>>df.to_hdf('test', 'key', data_columns=['a'], min_itemsize={'long string': 1000, 'lonstring2': 1000}) # => now a, longstring2, and long string are datacolumns.
```
Is there any way to define min_itemsizes but not make those fields data_columns? I ask because when I make them data_columns I get warnings that my rowsize is too large.
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} | 3 | 2016-05-26T07:54:13Z | 2016-05-27T12:39:37Z | 2016-05-27T12:39:27Z | NONE | null | I am not getting the expected output of the command `a.combine_first(b)`. All values are _NaN_. As you will see in my code, I want my index and columns to be the union of the indices and columns and the values to be of _a_ and only of _b_ if it does not exist in _a_ or is _NaN_ in _a_. Hence, I do not believe _DataFrame.merge_ is appropriate for this task, but I may be wrong.
Here is the code to reproduce:
```
from __future__ import print_function
import pandas as pd
import dateutil.parser
import numpy as np
date = dateutil.parser.parse('2016-05-03').date()
a_values = [[100.0, 0.0, 0, 0, 100], [97.29, 2.70, np.nan, 0, 100], [97.33, 2.66, 0, 0, 100]]
a_index = pd.MultiIndex.from_tuples([('an', date), ('rc', date), ('avg/total', '')], names=['ls', 'date'])
a_columns = ['complete', 'no', 'over', 'partial', 'total']
a = pd.DataFrame(a_values, index=a_index, columns=a_columns)
b_values = np.arange(5 * 4).reshape(5, 4)
b_index = pd.MultiIndex.from_product([['ad', 'aj', 'an', 'rc', 'ra'], pd.Index([date])], names=['ls', 'date'])
b_columns = ['no', 'partial', 'complete', 'over']
b = pd.DataFrame(b_values, index=b_index, columns=b_columns)
c_values = [[2, 0, 3, 1, np.nan], [6, 4, 7, 5, np.nan], [100.0, 0.0, 0, 0, 100], [97.33, 2.66, 0, 0, 100], [14, 12, 15, 13, np.nan], [97.29, 2.70,\
14, 0, 100]]
c_index = pd.MultiIndex.from_tuples([('ad', date), ('aj', date), ('an', date), ('avg/total', ''), ('ra', date), ('rc', date)], names=['ls', 'date'\
])
c_columns = ['complete', 'no', 'over', 'partial', 'total']
c = pd.DataFrame(c_values, index=c_index, columns=c_columns)
print('=========================')
print('DataFrame a:')
print(a)
print()
print('DataFrame b:')
print(b)
print()
print('=========================')
print()
print("a.index[0] == b.index[2]? {0}".format(a.index[0] == b.index[2]))
print("a.index[1] == b.index[3]? {0}".format(a.index[1] == b.index[3]))
print()
print('=========================')
print()
print("a.combine_first(b):")
print(a.combine_first(b))
print()
print("expected:")
print(c)
```
And here is the expected output, along with some intermediate steps:
```
=========================
DataFrame a:
complete no over partial total
ls date
an 2016-05-03 100.00 0.00 0 0 100
rc 2016-05-03 97.29 2.70 NaN 0 100
avg/total 97.33 2.66 0 0 100
DataFrame b:
no partial complete over
ls date
ad 2016-05-03 0 1 2 3
aj 2016-05-03 4 5 6 7
an 2016-05-03 8 9 10 11
rc 2016-05-03 12 13 14 15
ra 2016-05-03 16 17 18 19
=========================
a.index[0] == b.index[2]? True
a.index[1] == b.index[3]? True
=========================
a.combine_first(b):
complete no over partial total
ls date
ad 2016-05-03 NaN NaN NaN NaN NaN
aj 2016-05-03 NaN NaN NaN NaN NaN
an 2016-05-03 NaN NaN NaN NaN NaN
avg/total NaT NaN NaN NaN NaN NaN
ra 2016-05-03 NaN NaN NaN NaN NaN
rc 2016-05-03 NaN NaN NaN NaN NaN
expected:
complete no over partial total
ls date
ad 2016-05-03 2.00 0.00 3 1 NaN
aj 2016-05-03 6.00 4.00 7 5 NaN
an 2016-05-03 100.00 0.00 0 0 100
avg/total NaT 97.33 2.66 0 0 100
ra 2016-05-03 14.00 12.00 15 13 NaN
rc 2016-05-03 97.29 2.70 14 0 100
```
#### output of `pd.show_versions()`
```
In [270]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.11.0-26-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.3
Cython: 0.19.2
numpy: 1.8.0
scipy: 0.13.0
statsmodels: 0.5.0
IPython: 0.12.1
sphinx: None
patsy: 0.2.1
dateutil: 1.5
pytz: 2013.7
bottleneck: None
tables: 3.0.0
numexpr: 2.2.2
matplotlib: 1.4.2
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.7.2
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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} | 1 | 2016-05-26T09:47:41Z | 2018-12-09T16:10:58Z | 2018-12-09T16:10:58Z | NONE | null | ```
new_store = pd.HDFStore('data/test.h5', format='table')
new_store['df'] = pd.DataFrame(np.random.rand(5,1))
new_store.put('df2', pd.DataFrame(np.random.rand(5,1)), format='table')
print('df stored as table: %s' % new_store.get_storer('df').is_table)
print('df2 stored as table: %s' % new_store.get_storer('df2').is_table)
```
df stored as table: False
df2 stored as table: True
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-30-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
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} | 3 | 2016-05-26T10:34:36Z | 2017-09-12T10:31:32Z | 2017-09-12T10:31:32Z | NONE | null | #### Code Sample, a copy-pastable example if possible
This throws UnicodeDecodeError in python2
`pd.pivot_table(pd.DataFrame({'foo': [1,2,3]}), index=['foo'], aggfunc=len, margins=True, margins_name=u'Δοκιμή')`
#### Expected Output
Not throwing exception!
#### output of `pd.show_versions()`
<pre>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 32
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 20.10.1
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: None
boto: None
pandas_datareader: None
</pre>
The above is a well-known problem when mixing unicode and non-unicode strings in python2: The fix (for python 2) is very simple, just add a `u` in front of the `exception_msg` string literal in https://github.com/pydata/pandas/blob/master/pandas/tools/pivot.py#L178 i.e line 178 should be `exception_msg = u'Conflicting name "{0}" in margins'.format(margins_name)`
I am not sure what's the correct way to fix that in python 3 and a python2-3 compatible way so I won't provide a PR for that.
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} | 3 | 2016-05-26T11:06:58Z | 2016-05-26T23:56:27Z | 2016-05-26T23:56:09Z | CONTRIBUTOR | null | - [x] closes #5888, xref #12686
- [x] passes `git diff upstream/master | flake8 --diff`
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} | 7 | 2016-05-26T12:29:22Z | 2020-04-10T03:40:25Z | 2020-04-10T03:40:24Z | CONTRIBUTOR | null | xref #13288
```
In [2]: i = Index([0,'A'])
In [3]: i._shallow_copy([0])
Out[3]: Index([0], dtype='int64')
```
We should assert that we have a valid dtype in the constructed object (so should do this in `_simple_new` to avoid these guarantee violations)
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} | 2 | 2016-05-26T12:47:55Z | 2016-05-26T15:47:57Z | 2016-05-26T15:47:57Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/132906479#L1631
seems specifying the path wrong?
@TomAugspurger
@jorisvandenbossche
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} | 14 | 2016-05-26T17:59:00Z | 2019-12-26T21:19:17Z | 2019-12-26T21:19:17Z | CONTRIBUTOR | null | When constructing a `Series` object using a numpy structured data array, if you try and cast it to a `str` (or print it), it throws:
```
TypeError(ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'')
```
You can print a single value from the series, but not the whole series.
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy as np
c_dtype = np.dtype([('a', 'i8'), ('b', 'f4')])
cdt_arr = np.array([(1, 0.4), (256, -13)], dtype=c_dtype)
pds = pd.Series(cdt_arr, index=['A', 'B'])
print('pds.iloc[0]: {}'.format(str(pds.iloc[0]))) # (1, 0.4000000059604645)
print('pds.iloc[1]: {}'.format(str(pds.iloc[1]))) # (256, -13.0)
print('pds.loc["A"]: {}'.format(str(pds.loc['A']))) # Works
print('pds.loc["B"]: {}'.format(str(pds.loc['B']))) # Works
def print_error(x):
try:
o = str(x) # repr(x) also causes the same errors
print(o)
except TypeError as e:
print('TypeError({})'.format(e.args[0]))
a = pds.iloc[0:1]
b = pds.loc[['A', 'B']]
print('pds.iloc[0:1]:')
print_error(a)
print('pds.loc["A", "B"]:')
print_error(b)
print('pds:')
print_error(pds)
print('pd.DataFrame([pds]).T:')
print_error(pd.DataFrame([pds]).T)
print('pds2:')
cdt_arr_2 = np.array([(1, 0.4)], dtype=c_dtype)
pds2 = pd.Series(cdt_arr_2, index=['A'])
print_error(pds2)
```
#### Output (actual):
```
$ python demo_index_bug.py
pds.iloc[0]: (1, 0.4000000059604645)
pds.iloc[1]: (256, -13.0)
pds.loc["A"]: (1, 0.4000000059604645)
pds.loc["B"]: (256, -13.0)
pds.iloc[0:1]:
TypeError(ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'')
pds.loc["A", "B"]:
TypeError(ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'')
pds:
TypeError(ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'')
pd.DataFrame([pds]).T:
0
A (1, 0.4000000059604645)
B (256, -13.0)
pds2:
TypeError(ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'')
```
#### output of `pd.show_versions()`:
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.2-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 21.0.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
#### Stack Trace
I swallowed the stack traces to show where this was failing, so here's the traceback for that last error:
```
Traceback (most recent call last):
File "demo_dtype_bug.py", line 37, in <module>
print(pds2)
File "~/.local/lib/python3.5/site-packages/pandas/core/base.py", line 46, in __str__
return self.__unicode__()
File "~/.local/lib/python3.5/site-packages/pandas/core/series.py", line 984, in __unicode__
max_rows=max_rows)
File "~/.local/lib/python3.5/site-packages/pandas/core/series.py", line 1025, in to_string
dtype=dtype, name=name, max_rows=max_rows)
File "~/.local/lib/python3.5/site-packages/pandas/core/series.py", line 1053, in _get_repr
result = formatter.to_string()
File "~/.local/lib/python3.5/site-packages/pandas/formats/format.py", line 225, in to_string
fmt_values = self._get_formatted_values()
File "~/.local/lib/python3.5/site-packages/pandas/formats/format.py", line 215, in _get_formatted_values
float_format=self.float_format, na_rep=self.na_rep)
File "~/.local/lib/python3.5/site-packages/pandas/formats/format.py", line 2007, in format_array
return fmt_obj.get_result()
File "~/.local/lib/python3.5/site-packages/pandas/formats/format.py", line 2026, in get_result
fmt_values = self._format_strings()
File "~/.local/lib/python3.5/site-packages/pandas/formats/format.py", line 2059, in _format_strings
is_float = lib.map_infer(vals, com.is_float) & notnull(vals)
File "~/.local/lib/python3.5/site-packages/pandas/core/common.py", line 250, in notnull
res = isnull(obj)
File "~/.local/lib/python3.5/site-packages/pandas/core/common.py", line 91, in isnull
return _isnull(obj)
File "~/.local/lib/python3.5/site-packages/pandas/core/common.py", line 101, in _isnull_new
return _isnull_ndarraylike(obj)
File "~/.local/lib/python3.5/site-packages/pandas/core/common.py", line 192, in _isnull_ndarraylike
result = np.isnan(values)
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
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} | 2 | 2016-05-26T20:25:57Z | 2021-10-06T12:57:43Z | 2021-10-06T12:57:43Z | CONTRIBUTOR | null | I _think_ this would be just: `.ewm(span).mean() * span`
Now:
``` python
In [4]: pd.Series(range(10)).ewm(span=2).sum()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-8e0e72f35eb3> in <module>()
----> 1 pd.Series(range(10)).ewm(span=2).sum()
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/window.py in __getattr__(self, attr)
122
123 raise AttributeError("%r object has no attribute %r" %
--> 124 (type(self).__name__, attr))
125
126 def _dir_additions(self):
AttributeError: 'EWM' object has no attribute 'sum'
```
CC @erbian
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} | 6 | 2016-05-26T20:39:43Z | 2016-05-31T15:48:24Z | 2016-05-31T14:12:51Z | CONTRIBUTOR | null | - [x] closes #13104, #13288
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
BUG #13104:
- Percentiles are now rounded to the least precision that keeps
them unique.
- Supplying duplicates in percentiles will raise ValueError.
BUG #13288
- Fixed a column index of the output data frame.
Previously, if a data frame had a column index of object type and
the index contained numeric values, the output column index could
be corrupt. It led to ValueError if the output was displayed.
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} | 8 | 2016-05-26T22:18:51Z | 2016-07-01T14:18:37Z | 2016-07-01T14:18:37Z | CONTRIBUTOR | null | Example of something that previously worked (before 7bbd031104ee161b2fb79ba6f5732910661f94f8):
``` .python
>>> s = pd.Series([lambda x: x] * 10)
>>> s[s.index > 5] = lambda x: x + 1
```
But now the second line tries to apply the function in the rhs to the elements of the Series, rather than assigning them (and throws an exception). This is very counter-intuitive when using `__setitem__` rather than calling `Series.where` directly.
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} | 0 | 2016-05-26T22:21:08Z | 2016-05-26T22:28:35Z | 2016-05-26T22:28:35Z | CONTRIBUTOR | null | very small bug fix w.r.t. tz concatting
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} | 3 | 2016-05-26T23:58:42Z | 2019-10-21T19:34:18Z | null | MEMBER | null | On `master` (commit <a href="https://github.com/pydata/pandas/commit/40b4bb4bb2a7018ed08025c5c93cd1080a0b5f7f">40b4bb4</a>):
``` python
>>> data = """A
1
CAT
3"""
>>> f = lambda x: x
>>> read_csv(StringIO(data), na_values='CAT', converters={'A': f}, engine='c')
A
0 1
1 CAT
2 3
>>> read_csv(StringIO(data), na_values='CAT', converters={'A': f}, engine='python')
A
0 1
1 NaN
2 3
```
I expect both to give the same output, though I believe the Python output is more correct because it respects `na_values` unlike the C engine. I thought the simple fix would be to remove the `continue` statement <a href="https://github.com/pydata/pandas/blob/master/pandas/parser.pyx#L1008">here</a>, but that causes test failures, so probably a more involved refactoring might be needed to align the order of converter application, `NaN` value conversion, and `dtype` conversion.
IMO this should be added to #12686, as this is a difference in behaviour between the two engines.
xref #5232
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} | 1 | 2016-05-27T03:40:45Z | 2017-03-20T17:51:28Z | 2017-03-20T17:51:28Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as PD
time_str = '2015-10-01 23:59:00-04:00'
pd_dto = PD.to_datetime(time_str, utc=True).tz_convert('US/Eastern')
actual_yday = pd_dto.date().timetuple().tm_yday
print 'pandas thinks', pd_dto, 'is the', pd_dto.dayofyear, 'yday'
print 'when really its the', actual_yday
```
#### Expected Output
I expect the dayofyear to agree with tm_yday, but they do not.
#### output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
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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