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} | 9 | 2016-01-09T02:03:07Z | 2019-09-18T15:10:07Z | 2019-09-18T15:10:07Z | NONE | null | Sometimes you just want to have a quick look at your data in IPython and having `to_json` to support `indent` (as both `json.dumps` and `simplejson.dumps` do) would be very handy.
A workaround, which however doesn't always work, is to do `json.dumps(series.to_dict())`, which unfortunately is annoying when you don't have a JSON-serializable type (e.g., `numpy.bool_`).
I'm leaving this here as a food for discussion.
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| null | 4 | 2016-01-09T08:58:23Z | 2018-08-15T16:42:28Z | 2016-01-09T20:16:47Z | NONE | null | It used the gunziped filenames as the first column name.
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| null | 2 | 2016-01-09T12:59:01Z | 2016-01-09T14:07:08Z | 2016-01-09T14:00:08Z | NONE | null | I ran into an issue with `plumbum.Path` objects that I want to put inside a Series. They have an `__iter__` that raises an error when the path is not a directory. Is this a design error of `plumbum`, i.e. `__iter__` should never throw, or is this something that `pandas` should work around, e.g. wrap in a try-except?
``` python
>>> pd.Series(pb.local.path('/bin/ls'))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/base.py", line 63, in __repr__
return str(self)
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/base.py", line 42, in __str__
return self.__unicode__()
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/series.py", line 956, in __unicode__
max_rows=max_rows)
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/series.py", line 994, in to_string
name=name, max_rows=max_rows)
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/series.py", line 1024, in _get_repr
result = formatter.to_string()
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/format.py", line 225, in to_string
fmt_values = self._get_formatted_values()
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/format.py", line 215, in _get_formatted_values
na_rep=self.na_rep)
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/format.py", line 1976, in format_array
return fmt_obj.get_result()
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/format.py", line 1992, in get_result
fmt_values = self._format_strings()
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/format.py", line 2034, in _format_strings
fmt_values.append(' %s' % _format(v))
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/format.py", line 2018, in _format
return '%s' % formatter(x)
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/format.py", line 2005, in <lambda>
(lambda x: com.pprint_thing(x, escape_chars=('\t', '\r', '\n')))
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/common.py", line 2822, in pprint_thing
elif is_sequence(thing) and _nest_lvl < \
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/pandas/core/common.py", line 2378, in is_sequence
iter(x)
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/plumbum/path/base.py", line 42, in __iter__
return iter(self.list())
File "/mnt/data/doc/work/deep_blue_genome/python3/venv/lib/python3.4/site-packages/plumbum/path/local.py", line 112, in list
return [self / fn for fn in os.listdir(str(self))]
NotADirectoryError: [Errno 20] Not a directory: '/bin/ls'
>>> sys.version
'3.4.3 (default, Oct 13 2015, 23:27:37) \n[GCC 5.2.0]'
>>> pandas.__version__
'0.17.1'
```
Thanks!
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} | 2 | 2016-01-09T14:51:54Z | 2019-11-12T23:07:55Z | 2019-11-12T23:07:55Z | CONTRIBUTOR | null | ``` python
import pandas as pd
x = pd.DataFrame(pd.Series(["a", "b", "c"], dtype = "category"))
print x.dtypes
df = pd.concat(
[x, pd.Series([0, 1, 2], index = [0, 1, 3])],
axis = 1)
print df.dtypes
```
This prints:
```
0 category
dtype: object
0 object
0 float64
dtype: object
```
Notice that `category` has unexpectedly changed to `object`, even though Categoricals can hold missing values (as code -1).
`pd.show_versions()` yields:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-994-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 1.5.6
setuptools: 18.4
Cython: None
numpy: 1.10.2
scipy: 0.16.1
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 7 | 2016-01-09T19:01:04Z | 2016-04-13T01:16:36Z | 2016-04-13T01:15:51Z | NONE | null | So the `query` function calls `eval` under the hood, which ends up meaning that the function call ends failing with the following when numexpr is not present. This means that the default engine (numexpr) will lead to error on systems without having extra dependencies present.
```
ImportError: 'numexpr' not found. Cannot use engine='numexpr'
```
I see four solutions, which I list in increasing order of difficulty:
1. Make the `numexpr` dependency much clearer in the docstrings. Explicitly say that an ImportError will occur if you use the default arguments without having numexpr installed.
2. Make the python engine default
3. Let the numexpr engine fallback on the python engine in the case of import error
4. Make numexpr a required dependency
_edit_ It appears that option (3) is preferred here.
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} | 1 | 2016-01-10T13:20:44Z | 2016-01-10T13:56:04Z | 2016-01-10T13:46:30Z | NONE | null | Trying to share some tables produced in pandas with a coauthor over email. Would be great if I could export to markdown from a pandas dataframe so that I could just copy the text into the email and use toggle. Probably there's other use cases for markdown style table output.
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| null | 2 | 2016-01-10T14:10:49Z | 2016-01-15T03:07:59Z | 2016-01-15T03:07:59Z | CONTRIBUTOR | null | I'm working on a `from_dummies` right now, and I'm trying to achieve (roughly)
``` python
df = pd.from_dummies(pd.get_dummies(df))
```
One problem is that `get_dummies` will append all the dummy columns to the end of the DataFrame, after the numeric columns.
``` python
In [15]: df = pd.DataFrame({"A": [1, 2], "B": ['a', 'b'], 'C': [1, 2]})
In [18]: pd.get_dummies(df)
Out[18]:
A C B_a B_b
0 1 1 1 0
1 2 2 0 1
```
So when we go to invert with `from_dummies` we don't know when column goes where. If instead we had
``` python
A B_a B_b C
0 1 1 0 1
1 2 0 1 2
```
we'd know what the original ordering was by position.
Looking through the docs, I don't see anywhere where we say that the ordering must be this way, but it'll still be an API change and I'm not sure whether it's worth it.
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} | 1 | 2016-01-10T17:25:19Z | 2016-01-14T12:42:24Z | 2016-01-11T13:28:21Z | CONTRIBUTOR | null | Adding a test for issue #5613, which seems to have been solved
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} | 2 | 2016-01-10T18:28:57Z | 2016-01-14T12:42:14Z | 2016-01-14T12:36:22Z | CONTRIBUTOR | null | Fix issue #9244
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} | 1 | 2016-01-11T00:58:10Z | 2016-01-13T13:42:08Z | 2016-01-13T13:42:08Z | CONTRIBUTOR | null | When reading a Stata file incrementally, the value labels are read even when specifying convert_categoricals=False (this does not happen when reading the entire file at once).
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} | 33 | 2016-01-11T01:22:19Z | 2016-04-21T07:13:19Z | 2016-02-20T18:40:31Z | CONTRIBUTOR | null | This needs more testing, but basically seems to work for reading uncompressed SAS7BDAT files. I will add support for compression in a few days.
closes #4052
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| null | 1 | 2016-01-11T05:44:40Z | 2016-01-11T13:32:09Z | 2016-01-11T13:31:50Z | NONE | null | Here is a short example. The only difference between `df1` and `df2` is that the latter has unicode column names:
``` python
import pandas as pd
data = [
(11, 1.2),
(21, 2.2),
(31, 3.2),
]
df1 = pd.DataFrame(data, columns=['col1', 'col2'])
df2 = pd.DataFrame(data, columns=[u'col1', u'col2'])
df1.to_hdf('test1.h5', 'data')
df2.to_hdf('test2.h5', 'data')
```
The first write works as expected. But the second, `df2.to_hdf`, emits this warning:
```
/Users/z001j0s/anaconda/envs/boa2/lib/python2.7/site-packages/pandas/io/pytables.py:257: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->unicode,key->axis0] [items->None]
f(store)
/Users/z001j0s/anaconda/envs/boa2/lib/python2.7/site-packages/pandas/io/pytables.py:257: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->unicode,key->block0_items] [items->None]
f(store)
/Users/z001j0s/anaconda/envs/boa2/lib/python2.7/site-packages/pandas/io/pytables.py:257: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->unicode,key->block1_items] [items->None]
f(store)
```
I'm testing with pandas 0.17.1 and python 2.7.11. I do _not_ see this warning with python 3.5.1.
Is this warning intentional? In other words, should python 2 users know to avoid unicode column names? Or, should pandas somehow give the same experience between python 2 and python 3, making this a bug?
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} | 7 | 2016-01-11T07:25:49Z | 2016-02-03T05:17:34Z | 2016-02-01T23:13:25Z | CONTRIBUTOR | null | Previously the values check for pivot tables was only looking for lists or tuples. This patch allows for any generators or other iterables (such as dict.keys() in python 3) to be passed as values. They will be converted to lists when they are identified as iterables, excluding strings. The performance hit from the extra list type assignment to iterables that are already lists or tuples should be minor.
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| null | 5 | 2016-01-11T22:35:40Z | 2018-02-01T08:15:06Z | 2016-01-11T22:57:32Z | NONE | null | The following code throws that error:
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame(np.random.randn(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
In [4]: import tempfile
In [5]: tf = tempfile.NamedTemporaryFile()
In [6]: df.to_csv(tf)
```
Am I missing something?
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} | 3 | 2016-01-11T22:41:13Z | 2016-01-12T14:42:11Z | 2016-01-12T14:42:07Z | CONTRIBUTOR | null | This would appear in the `dir` and cause tab completion to a field that would raise a `ValueError`. When I brought this up in gitter it seemed like it was safe to remove it.
I couldn't find any tests that hit this descriptor.
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| null | 13 | 2016-01-12T00:56:25Z | 2016-01-13T23:58:28Z | 2016-01-12T14:46:05Z | NONE | null | md5sum:
bb39ad5a080b647ecf245ae126a8eb93 remote_drive/mir-count.csv
bb39ad5a080b647ecf245ae126a8eb93 local_drive/mir-count.csv
in python
import pandas as pd
print pd.**version**
-# Shows 0.17.1
LOCAL_FILE="local_drive/mir-count.csv"
REMOTE_FILE="remote_drive/mir-count.csv"
pd.read_csv(LOCAL_FILE)
-# File reads without errors.
for l in open(mir_expression_file, 'r'):
print l
-#Prints lines of file, no errors.
pd.read_csv(REMOTE_FILE)
-# Exception stack trace:
Traceback (most recent call last):
File "/home/kpillman/localwork/bioinformatics/scripts/project_specific/conn_circrna/circrna_count_mir_targets.py", line 22, in <module>
mir_expression_df = pd.read_csv(mir_expression_file, index_col=0)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 498, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 275, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 590, in __init__
self._make_engine(self.engine)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 731, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 1103, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "pandas/parser.pyx", line 518, in pandas.parser.TextReader.__cinit__ (pandas/parser.c:5030)
ValueError: No columns to parse from file
Notes:
Not every file on the mounted drive fails to be read. May do fail though and it is reproducible which files will fail and which will not.
I am using cifs:
/etc/fstab line:
//sacgf.ersa.edu.au/sacgf /data/sacgf cifs uid=kpillman,gid=kpillman,credentials=/home/kpillman/.smbcredentials,_netdev 0 0
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} | 36 | 2016-01-12T04:57:15Z | 2016-04-05T16:18:26Z | 2016-04-05T16:17:48Z | CONTRIBUTOR | null | In recent efforts using Pandas on multiple machines I've found that some of the functions are tricky to serialize. Apparently this might be due to runtime generation. Here are a few examples of serialization breaking, occasionally in unpleasant ways:
``` python
In [1]: import pandas as pd
In [2]: import pickle
In [3]: pd.read_csv
Out[3]: <function pandas.io.parsers._make_parser_function.<locals>.parser_f>
In [4]: pickle.loads(pickle.dumps(pd.read_csv))
AttributeError: Can't pickle local object '_make_parser_function.<locals>.parser_f'
```
Lest you think that this is just a problem with pickle (which has many flaws), `dill`, a much more robust function serialization library, also fails (the failure here is py35 only.) (cc @mmckerns)
``` python
In [5]: import dill
In [6]: dill.loads(dill.dumps(pd.read_csv))
PicklingError: Can't pickle <function _make_parser_function.<locals>.parser_f at 0x7f71f5ec1158>: it's not found as pandas.io.parsers._make_parser_function.<locals>.parser_f
```
In this particular case though `cloudpickle` will work.
Other functions have this problem as well. Consider the series methods:
``` python
In [7]: pickle.loads(pickle.dumps(pd.Series.sum))
AttributeError: Can't pickle local object '_make_stat_function.<locals>.stat_func'
```
In this case, concerningly `cloudpickle` completes, but returns a wrong result:
``` python
In [9]: import cloudpickle
In [11]: pd.Series.sum
Out[11]: <function pandas.core.generic._make_stat_function.<locals>.stat_func>
In [12]: cloudpickle.loads(cloudpickle.dumps(pd.Series.sum))
Out[12]: <function stat_func>
```
I've been able to fix some of these in https://github.com/cloudpipe/cloudpickle/pull/46 but generally speaking I'm running into a number of problems here. It would be useful if, during the generation of these functions we could at least pay attention to assigning metadata like `__name__` correctly. This one in particular confused me for a while:
``` python
In [15]: pd.Series.cumsum.__name__
Out[15]: 'sum'
```
### What would help?
- Testing that most of the API is serializable
- Looking at what metadata the serialization libraries use, and making sure that this metadata is enough to properly identify the function. Some relevant snippets from cloudpickle follow:
``` python
def save_instancemethod(self, obj):
# Memoization rarely is ever useful due to python bounding
if obj.__self__ is None:
self.save_reduce(getattr, (obj.im_class, obj.__name__))
else:
if PY3:
self.save_reduce(types.MethodType, (obj.__func__, obj.__self__), obj=obj)
else:
self.save_reduce(types.MethodType, (obj.__func__, obj.__self__, obj.__self__.__class__),
obj=obj)
def _reduce_method_descriptor(obj):
return (getattr, (obj.__objclass__, obj.__name__))
```
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| null | 2 | 2016-01-12T06:48:48Z | 2016-01-12T09:35:28Z | 2016-01-12T09:11:15Z | NONE | null | Rather than presume that it is an sqlite database and provide a confusing error.
Running (without sqlalchemy installed):
``` python
import pandas
db_uri = "postgresql://user:pass@localhost/db"
sql_query = "SELECT * FROM test"
pandas.read_sql(sql_query, db_uri)
```
Produces:
``` python
pandas/io/sql.pyc in execute(self, *args, **kwargs)
1532 cur = self.con
1533 else:
-> 1534 cur = self.con.cursor()
1535 try:
1536 if kwargs:
AttributeError: 'str' object has no attribute 'cursor'
```
Which links to:
https://github.com/pydata/pandas/blob/master/pandas/io/sql.py#L1534
Zooming into the problem:
1. https://github.com/pydata/pandas/blob/master/pandas/io/sql.py#L631-L637
2. https://github.com/pydata/pandas/blob/master/pandas/io/sql.py#L612-L619
3. https://github.com/pydata/pandas/blob/master/pandas/io/sql.py#L64-L68
So it looks like the URI is passed through and simply assumed to be a sqlite/mysql database object (using the old method), even if it isn't.
Raising an import error when a `URI string` is passed back by `_engine_builder` should solve the issue.
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| null | 7 | 2016-01-12T11:41:52Z | 2016-01-13T18:55:02Z | 2016-01-12T12:10:05Z | NONE | null | We are observing some inconsistent behaviour of the dataframe `query` method under windows.
I enclose a smallish (3600 rows) dataframe where we find the issue.
The following code fails randomly under Windows, but seems to work smoothly on Mac:
``` python
import pandas as pd
df = pd.read_csv("pd_bug.txt")
for x in df.A.unique():
try:
assert len(df.query('(A == @x) | (B == @x)')) == len(df[(df.A == x) | (df.B == x)])
except AssertionError:
print x
```
We are observing a failure every other run (often with two or three values failing).
Here is the output of `pd.show_versions()` on the failing machine:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 19.1.1
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: None
```
[pd_bug.txt](https://github.com/pydata/pandas/files/87187/pd_bug.txt)
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| null | 1 | 2016-01-12T13:53:27Z | 2016-01-12T14:39:24Z | 2016-01-12T14:39:24Z | NONE | null | python 3.0+
window microsoft visual studio 2008 and 2010 both install
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| null | 2 | 2016-01-12T14:10:28Z | 2016-01-12T18:15:59Z | 2016-01-12T18:15:59Z | CONTRIBUTOR | null | closes #11977
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| null | 9 | 2016-01-12T18:17:28Z | 2016-01-30T21:48:17Z | 2016-01-30T15:33:41Z | CONTRIBUTOR | null | closes #11977
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} | 0 | 2016-01-12T21:55:00Z | 2016-01-12T23:19:37Z | 2016-01-12T23:19:37Z | CONTRIBUTOR | null | already long deprecated, changing to `FutureWarning`.
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| null | 1 | 2016-01-13T08:14:08Z | 2016-01-13T19:24:06Z | 2016-01-13T19:23:54Z | NONE | null | Tried to plot a specific DataFrame: but the vertical gridlines are not showing up:
My setup:
``` python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: import matplotlib
In [4]: import sys
In [5]: %matplotlib inline
In [6]: sys.version
Out [6]: '3.5.1 |Anaconda 2.4.1 (64-bit)| (default, Dec 7 2015, 15:00:12) [MSC v.1900 64 bit (AMD64)]'
In [7]: np.__version__
Out[7]: '1.10.1'
In [8]: matplotlib.__version__
Out [8]: '1.5.1'
In [9]: pd.__version__
Out [9]: '0.17.1'
```
If i try to plot a random dataFrame it works as expected:
``` python
In [10]: N = 2000
In [11]: df = pd.DataFrame(np.random.randn(N, 5), index=pd.date_range('1/1/1975', periods=N))
In [12]: df.plot(grid=True)
```
But if i try to plot the dataframe contained in the zip
[plot_xgrid_error.zip](https://github.com/pydata/pandas/files/88600/plot_xgrid_error.zip)
i get the following without vertical grid lines:
``` python
In [13]: data = pd.read_hdf(r'plot_xgrid_error.h5', 'data')
In [14]: data.plot(grid=True)
```
May somebody can help me?
Why the vertical grid lines not showing up?
Is it supposed to do so?
Found something similar here #11076
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} | 5 | 2016-01-13T10:53:27Z | 2016-01-15T14:07:41Z | 2016-01-15T14:07:40Z | NONE | null | I set the matplotlib style to "fivethirtyeight", which changes many settings in the axes, colors, etc.
This is what I get when doing a simple line plot:
```
pd.DataFrame(dict(a=[1, 2, 3], b=[5, 10, 2])).plot.line()
```
The style matches what I was expecting.
(hist, area and other type of plots I tried work as well)
But when I want to get a bar plot, I get this:
```
pd.DataFrame(dict(a=[1, 2, 3], b=[5, 10, 2])).plot.bar()
```
Most elements of the style seem to be here, but the colors are the default matplotlib ones, not the ones in "fivethirtyeight" (and the same goes with a boxplot)
Any clue about what might be causing this?
These are the lines I used to style my plots:
```
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('fivethirtyeight')
%matplotlib inline
COLORS = [(0.3333333333333333, 0.6588235294117647, 0.40784313725490196),
(0.2980392156862745, 0.4470588235294118, 0.6901960784313725),
(0.7686274509803922, 0.3058823529411765, 0.3215686274509804),
(0.5058823529411764, 0.4470588235294118, 0.6980392156862745),
(0.8, 0.7254901960784313, 0.4549019607843137),
(0.39215686274509803, 0.7098039215686275, 0.803921568627451),
(0.33999999999999997, 0.86, 0.57920000000000016)]
plt.rcParams['figure.figsize'] = (14.0, 7.0)
plt.rcParams['axes.prop_cycle'] = plt.cycler('color', COLORS)
```
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| null | 0 | 2016-01-13T13:52:16Z | 2016-01-13T13:59:45Z | 2016-01-13T13:59:45Z | CONTRIBUTOR | null | {
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} | 1 | 2016-01-13T13:57:55Z | 2016-02-23T18:50:26Z | 2016-02-23T18:50:26Z | CONTRIBUTOR | null | `.to_csv` has a `decimal` parameter to specify the decimal mark for floats since version 0.16
It would be nice to have the same thing in `.to_latex` and `.to_html`.
I'll work on a PR.
This issue is a spin-off of #7061, in which @TomAugspurger said:
> There’s a _possibility_ that we’ll be able to replace some of the `to_latex` code with a Jinja template, similar to the Style stuff. So don’t spend too much time on it :)
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} | 14 | 2016-01-13T15:35:36Z | 2016-01-16T04:14:26Z | 2016-01-14T14:11:00Z | MEMBER | null | @jreback I re-enabled flake8 checking for unused imports. We should be explicit about what imports are part of a module API (and those warnings explicitly suppressed with `# noqa`) and delete all unused imports.
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} | 3 | 2016-01-13T15:40:31Z | 2016-01-14T12:42:31Z | 2016-01-14T12:42:31Z | CONTRIBUTOR | null | This is a port of @ogrisel's fix in #10070 to deal with 1d arrays. There's absolutely nothing new here beyond what's in the original patch.
closes #11502
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} | 0 | 2016-01-13T18:13:36Z | 2020-10-07T03:24:42Z | 2020-10-07T03:24:42Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/pull/11892
possible to return `RangeIndex` more often for:
- [x] `union` #12109
- [ ] `difference` & `sym_diff`
- [x] revisit `__floordiv__` #12070
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} | 2 | 2016-01-13T23:35:30Z | 2016-01-14T01:20:02Z | 2016-01-14T01:20:02Z | CONTRIBUTOR | null | see output at the end: https://travis-ci.org/jreback/pandas/jobs/102213682
this only shows the summary stats. Once these are much lower we can remove the `-q` and
show the actual errors.
As more files are fixed can move the checks to `lint.sh`
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| null | 10 | 2016-01-14T04:50:39Z | 2020-09-06T18:48:21Z | 2016-01-20T03:13:49Z | CONTRIBUTOR | null | Continuation of #11500
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} | 5 | 2016-01-14T10:40:40Z | 2016-02-10T17:37:09Z | 2016-02-10T17:37:09Z | NONE | null | I have a timeseries in dataframe named dfi with non-eqispaced times as index
```
print dfi.value
print
print "len "+ str(len(dfi))
```
Output:
```
datetime
2015-10-01 13:58:10.427 -10.072100
2015-10-01 13:58:11.419 -10.072100
2015-10-01 13:58:12.417 -10.072100
2015-10-01 13:58:13.420 -10.072100
2015-10-01 13:58:14.426 -10.072100
2015-10-01 13:58:15.427 -10.072100
2015-10-01 13:58:16.418 -10.072100
2015-10-01 13:58:17.418 -9.753230
2015-10-01 13:58:18.416 -9.753230
2015-10-01 13:58:19.428 -9.753230
2015-10-01 13:58:20.427 -9.753230
2015-10-01 13:58:21.419 -9.753230
2015-10-01 13:58:22.416 -9.753230
2015-10-01 13:58:23.429 -9.753230
2015-10-01 13:58:24.416 -9.753230
2015-10-01 13:58:25.428 -9.753230
2015-10-01 13:58:26.418 -9.753230
2015-10-01 13:58:27.416 -9.396140
2015-10-01 13:58:28.416 -9.396140
2015-10-01 13:58:29.429 -9.396140
2015-10-01 13:58:32.416 -9.396140
2015-10-01 13:58:33.427 -9.396140
2015-10-01 13:58:34.428 -9.396140
2015-10-01 13:58:35.462 -9.396140
2015-10-01 13:58:36.416 -9.396140
2015-10-01 13:58:37.427 -9.010000
2015-10-01 13:58:38.428 -9.010000
2015-10-01 13:58:39.435 -9.010000
2015-10-01 13:58:40.437 -9.010000
2015-10-01 13:58:41.416 -9.010000
...
2015-10-03 23:59:28.052 -0.759718
2015-10-03 23:59:29.040 -0.759718
2015-10-03 23:59:30.048 -0.759718
2015-10-03 23:59:31.048 -0.759718
2015-10-03 23:59:32.060 -0.759718
2015-10-03 23:59:33.049 -0.759718
2015-10-03 23:59:34.051 -0.759718
2015-10-03 23:59:35.041 -0.759718
2015-10-03 23:59:36.061 -0.759718
2015-10-03 23:59:37.059 -1.010490
2015-10-03 23:59:38.040 -1.010490
2015-10-03 23:59:39.051 -1.010490
2015-10-03 23:59:40.040 -1.010490
2015-10-03 23:59:41.072 -1.010490
2015-10-03 23:59:42.049 -1.010490
2015-10-03 23:59:43.038 -1.010490
2015-10-03 23:59:44.040 -1.010490
2015-10-03 23:59:45.040 -1.010490
2015-10-03 23:59:48.049 -1.133730
2015-10-03 23:59:49.049 -1.133730
2015-10-03 23:59:50.048 -1.133730
2015-10-03 23:59:52.050 -1.133730
2015-10-03 23:59:53.050 -1.133730
2015-10-03 23:59:54.059 -1.133730
2015-10-03 23:59:55.049 -1.133730
2015-10-03 23:59:56.041 -1.133730
2015-10-03 23:59:59.039 -1.296430
2015-10-04 00:00:00.050 -1.296430
2015-10-04 00:00:01.060 -1.296430
2015-10-04 00:00:02.040 -1.296430
Name: value, dtype: float64
```
I get an error when running this code:
```
print period_seconds
period_nanos=int(period_seconds*(10**9))
print period_nanos
res= dfi.value.resample(pd.tseries.offsets.Nano(period_nanos), how=[np.min, np.max,'mean'])
```
Output + error:
```
4.035752
4035751999
ValueError Traceback (most recent call last)
<ipython-input-14-92e377227823> in <module>()
5 period_nanos=int(period_seconds*(10**9))
6 print period_nanos
----> 7 res= dfi.value.resample(pd.tseries.offsets.Nano(period_nanos), how=[np.min, np.max,'mean'])
8
9 nullrows=pd.isnull(res).any(1).nonzero()[0]
C:\Users\USER1\Anaconda2\lib\site-packages\pandas\core\generic.pyc in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base)
3641 fill_method=fill_method, convention=convention,
3642 limit=limit, base=base)
-> 3643 return sampler.resample(self).__finalize__(self)
3644
3645 def first(self, offset):
C:\Users\USER1\Anaconda2\lib\site-packages\pandas\tseries\resample.pyc in resample(self, obj)
80
81 if isinstance(ax, DatetimeIndex):
---> 82 rs = self._resample_timestamps()
83 elif isinstance(ax, PeriodIndex):
84 offset = to_offset(self.freq)
C:\Users\USER1\Anaconda2\lib\site-packages\pandas\tseries\resample.pyc in _resample_timestamps(self, kind)
274 axlabels = self.ax
275
--> 276 self._get_binner_for_resample(kind=kind)
277 grouper = self.grouper
278 binner = self.binner
C:\Users\USER1\Anaconda2\lib\site-packages\pandas\tseries\resample.pyc in _get_binner_for_resample(self, kind)
118 kind = self.kind
119 if kind is None or kind == 'timestamp':
--> 120 self.binner, bins, binlabels = self._get_time_bins(ax)
121 elif kind == 'timedelta':
122 self.binner, bins, binlabels = self._get_time_delta_bins(ax)
C:\Users\USER1\Anaconda2\lib\site-packages\pandas\tseries\resample.pyc in _get_time_bins(self, ax)
179
180 # general version, knowing nothing about relative frequencies
--> 181 bins = lib.generate_bins_dt64(ax_values, bin_edges, self.closed, hasnans=ax.hasnans)
182
183 if self.closed == 'right':
pandas\lib.pyx in pandas.lib.generate_bins_dt64 (pandas\lib.c:20875)()
ValueError: Values falls after last bin
```
packages versions:
```
import pip
installed_packages = pip.get_installed_distributions()
installed_packages_list = sorted(["%s==%s" % (i.key, i.version)
for i in installed_packages])
for i in installed_packages_list:
print i
```
Output:
```
alabaster==0.7.6
anaconda-client==1.2.1
argcomplete==1.0.0
astropy==1.1.1
babel==2.1.1
backports-abc==0.4
backports.ssl-match-hostname==3.4.0.2
beautifulsoup4==4.4.1
bitarray==0.8.1
blaze==0.9.0
bokeh==0.11.0
boto==2.38.0
bottleneck==1.0.0
cdecimal==2.3
cffi==1.2.1
clyent==1.2.0
colorama==0.3.3
comtypes==1.1.2
conda-build==1.18.2
conda-env==2.4.5
conda==3.19.0
configobj==5.0.6
cryptography==0.9.1
cycler==0.9.0
cython==0.23.4
cytoolz==0.7.4
datashape==0.5.0
decorator==4.0.6
docutils==0.12
enum34==1.1.2
et-xmlfile==1.0.1
fastcache==1.0.2
flask==0.10.1
funcsigs==0.4
futures==3.0.3
gevent-websocket==0.9.3
gevent==1.0.1
greenlet==0.4.9
grin==1.2.1
h5py==2.5.0
idna==2.0
ipaddress==1.0.14
ipykernel==4.1.1
ipython-genutils==0.1.0
ipython==4.0.1
ipywidgets==4.1.0
itsdangerous==0.24
jdcal==1.2
jedi==0.9.0
jinja2==2.8
jsonschema==2.4.0
jupyter-client==4.1.1
jupyter-console==4.0.3
jupyter-core==4.0.6
jupyter==1.0.0
llvmlite==0.8.0
lxml==3.5.0
markupsafe==0.23
matplotlib==1.5.1
menuinst==1.3.2
mistune==0.7.1
multipledispatch==0.4.8
nbconvert==4.1.0
nbformat==4.0.1
networkx==1.10
nltk==3.1
nose==1.3.7
notebook==4.1.0
numba==0.22.1
numexpr==2.4.6
numpy==1.10.1
odo==0.4.0
openpyxl==2.3.2
pandas==0.17.1
path.py==0.0.0
patsy==0.4.0
pep8==1.6.2
pickleshare==0.5
pillow==3.0.0
pip==7.1.2
ply==3.8
psutil==3.2.2
py==1.4.30
pyasn1==0.1.9
pycosat==0.6.1
pycparser==2.14
pycrypto==2.6.1
pycurl==7.19.5.3
pyflakes==1.0.0
pygments==2.0.2
pyopenssl==0.15.1
pyparsing==2.0.3
pyreadline==2.1
pytest==2.8.1
python-dateutil==2.4.2
pytz==2015.7
pywin32==219
pyyaml==3.11
pyzmq==15.2.0
qtconsole==4.1.1
requests==2.9.0
rope==0.9.4
scikit-image==0.11.3
scikit-learn==0.17
scipy==0.16.0
setuptools==19.1.1
simplegeneric==0.8.1
singledispatch==3.4.0.3
six==1.10.0
snowballstemmer==1.2.0
sockjs-tornado==1.0.1
sphinx-rtd-theme==0.1.7
sphinx==1.3.1
spyder==2.3.8
sqlalchemy==1.0.11
statsmodels==0.6.1
sympy==0.7.6.1
tables==3.2.2
toolz==0.7.4
tornado==4.3
traitlets==4.0.0
ujson==1.33
unicodecsv==0.14.1
werkzeug==0.11.3
wheel==0.26.0
xlrd==0.9.4
xlsxwriter==0.7.7
xlwings==0.6.1
xlwt==1.0.0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12038 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12038/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12038/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12038/events | https://github.com/pandas-dev/pandas/issues/12038 | 126,697,234 | MDU6SXNzdWUxMjY2OTcyMzQ= | 12,038 | Error in doc of DatetimeIndex.indexer_between_time | {
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} | 5 | 2016-01-14T16:53:55Z | 2016-01-15T09:48:17Z | 2016-01-15T09:48:17Z | NONE | null | Hello everyone,
This is not a major issue: I was trying to use the `tz` parameter as indicated on the [documentation](http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.DatetimeIndex.indexer_between_time.html?highlight=indexer_between_time#pandas.DatetimeIndex.indexer_between_time) of `DatetimeIndex.indexer_between_time` and it turns out that `tz` is not implemented.
Here is an example:
``` python
import pandas as pd
pd.DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 01:00:00', '2016-01-01 02:00:00']).indexer_between_time('01:00', '02:00', tz='Europe/Paris')
```
```
Traceback (most recent call last):
File "~/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 3066, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-98-258497b42264>", line 1, in <module>
pd.DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 01:00:00', '2016-01-01 02:00:00']).indexer_between_time('01:00', '02:00', tz='Europe/Paris')
TypeError: indexer_between_time() got an unexpected keyword argument 'tz'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12039 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12039/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12039/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12039/events | https://github.com/pandas-dev/pandas/issues/12039 | 126,749,214 | MDU6SXNzdWUxMjY3NDkyMTQ= | 12,039 | Plotting methods modify color arrays in place | {
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} | 0 | 2016-01-14T21:12:49Z | 2016-01-16T12:49:06Z | 2016-01-16T12:49:06Z | CONTRIBUTOR | null | Ran into a weird issue where pandas plotting was changing values in color arrays I was passing to it.
Here is a simple example (Pandas 0.17.1; Python 2.7; OSX):
``` python
colors = sns.color_palette("Blues", 3)
print len(colors)
pd.DataFrame(np.random.rand(10, 2)).plot(color=colors)
print len(colors)
```
outputs:
``` python
3
5
```
I've seen various other kinds of changes, including sometimes changing the values but not the length of the color array. A particularly nasty one was where I had taken some actual data and passed it through a matplotlib norm and colormap, and the modifications made their way all the way back up to my data.
I assume this can easily be handled by copying the color array, but I'm not sure.
Sorry if this has been reported...I checked recent issues and didn't see anything, but there are a lot :)
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https://api.github.com/repos/pandas-dev/pandas/issues/12040 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12040/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12040/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12040/events | https://github.com/pandas-dev/pandas/pull/12040 | 126,797,009 | MDExOlB1bGxSZXF1ZXN0NTYxMjUxNzA= | 12,040 | BUG: .plot modifing `colors` input | {
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} | 1 | 2016-01-15T02:57:40Z | 2016-01-16T12:49:09Z | 2016-01-16T12:49:06Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/12039
Just added a defensive copy in `_get_standard_colors` on list-likes.
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} | 1 | 2016-01-15T04:23:46Z | 2018-07-06T20:21:09Z | 2017-07-11T09:55:50Z | NONE | null | Trying to add conditional formatting to an excel file written from pandas using pd.ExcelWriter and...
This doesn't work:
``` python
# results is a dictionary of DataFrames
data = pd.Panel(results)
writer = pd.ExcelWriter(file_name+".xlsx", engine='xlsxwriter')
data.to_excel(writer)
for worksheet in writer.sheets.values():
worksheet.conditional_format('E2:E38', {'type': '2_color_scale'})
writer.save()
writer.close()
```
And this does:
``` python
# results is a dictionary of DataFrames
writer = pd.ExcelWriter(file_name+".xlsx", engine='xlsxwriter')
for k,v in results.iteritems():
v.to_excel(writer, sheet_name=k)
for worksheet in writer.sheets.values():
worksheet.conditional_format('E2:E38', {'type': '2_color_scale'})
writer.save()
writer.close()
```
Is something not implemented in Panel structure? or is this a design decision along the lines of what causes issue #8553?
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.23.4
numpy: 1.9.3
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: 1.2.8
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0rc3
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.7
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
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} | 3 | 2016-01-15T06:11:22Z | 2017-11-09T21:18:41Z | 2016-02-08T15:28:58Z | NONE | null | When doing linear regression and encoding categorical variables, perfect collinearity can be a problem. To get around this, the suggested approach is to use n-1 columns. It would be useful if `pd.get_dummies()` had a boolean parameter that returns n-1 for each categorical column that gets encoded.
Example:
```
>>> df
Account Network Device
0 Account1 Search Smartphone
1 Account1 Display Tablet
2 Account2 Search Smartphone
3 Account3 Display Smartphone
4 Account2 Search Tablet
5 Account3 Search Smartphone
```
```
>>> pd.get_dummies(df)
Account_Account1 Account_Account2 Account_Account3 Network_Display \
0 1 0 0 0
1 1 0 0 1
2 0 1 0 0
3 0 0 1 1
4 0 1 0 0
5 0 0 1 0
Network_Search Device_Smartphone Device_Tablet
0 1 1 0
1 0 0 1
2 1 1 0
3 0 1 0
4 1 0 1
5 1 1 0
```
Instead, I'd like to have some parameter such as `drop_first=True` in `get_dummies()` and it does something like this:
```
>>> new_df = pd.DataFrame(index=df.index)
>>> for i in df:
new_df = new_df.join(pd.get_dummies(df[i]).iloc[:, 1:])
>>> new_df
Account2 Account3 Search Tablet
0 0 0 1 0
1 0 0 0 1
2 1 0 1 0
3 0 1 0 0
4 1 0 1 1
5 0 1 1 0
```
**Sources**
http://fastml.com/converting-categorical-data-into-numbers-with-pandas-and-scikit-learn/
http://stackoverflow.com/questions/31498390/how-to-get-pandas-get-dummies-to-emit-n-1-variables-to-avoid-co-lineraity
http://dss.princeton.edu/online_help/analysis/dummy_variables.htm
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} | 1 | 2016-01-15T08:44:51Z | 2016-01-15T12:19:09Z | 2016-01-15T09:47:45Z | CONTRIBUTOR | null | A minor documentation update to the [DatetimeIndex.indexer_between_time](http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.DatetimeIndex.indexer_between_time.html) function.
Closes #12038
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} | 6 | 2016-01-15T09:03:23Z | 2016-07-19T01:52:00Z | 2016-07-19T01:52:00Z | CONTRIBUTOR | null | I need to append several big Series to a big categorical Series.
Trying to update categories FAST i've found out that `Index.difference` uses Python's `set`, which is slow on creating LARGE set (i have up to 500k categories and 1.3M values).
numpy's `setdiff1` is more than an order of magnitude faster (as of datetime64 Categorical):
```
tmp_unique = tmp.unique()
new_cats = pd.Index(pd.np.setdiff1d(tmp_unique[~pd.isnull(tmp_unique)], to.cat.categories))
```
Not so fast:
```
new_cats = pd.Index(tmp_unique[~pd.isnull(tmp_unique)]).difference(to.cat.categories)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12045 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12045/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12045/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12045/events | https://github.com/pandas-dev/pandas/issues/12045 | 126,862,093 | MDU6SXNzdWUxMjY4NjIwOTM= | 12,045 | Printing None and NaN values in Pandas dataframe produces confusing results | {
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} | 3 | 2016-01-15T12:17:04Z | 2016-03-23T17:56:45Z | 2016-03-23T17:56:45Z | NONE | null | Printing a dataframe where a variable contains None values produces confusing results. Large dataframes are automatically split to print to screen. If all the values on one side of the splits are None, they are actually displayed as NaN. This can be demonstrated with the following code.
```
import pandas as pd
tempDF = pd.DataFrame({'id': np.arange(1,11),
'text': ['some words'] + [None]*9})
print('Full database\n-------------\n',tempDF,'\n')
with pd.option_context('display.max_rows', 8, 'display.max_columns', 3):
print('Split database\n--------------\n',tempDF,'\n')
```
This produces the following output:
```
Full database
-------------
id text
0 1 some words
1 2 None
2 3 None
3 4 None
4 5 None
5 6 None
6 7 None
7 8 None
8 9 None
9 10 None
Split database
--------------
id text
0 1 some words
1 2 None
2 3 None
3 4 None
.. .. ...
6 7 NaN
7 8 NaN
8 9 NaN
9 10 NaN
[10 rows x 2 columns]
```
Above the split, the variable 'text' has one cell which has a genuine string ('some words'). All the None values on that side of the split are correctly displayed as 'None'. However, on the bottom part of the split, all the cells contain None values but are confusingly displayed as Nan.
Expected behaviour: All None values should be displayed as 'None' rather than 'NaN'.
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.17.1
nose: None
pip: 1.5.6
setuptools: 3.6
Cython: None
numpy: 1.10.2
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.2
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
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12046 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12046/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12046/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12046/events | https://github.com/pandas-dev/pandas/pull/12046 | 126,877,887 | MDExOlB1bGxSZXF1ZXN0NTYxNzIwMjI= | 12,046 | Fix asymmetric error bars for series (closes #9536) | {
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| null | 9 | 2016-01-15T14:01:16Z | 2016-05-07T19:06:57Z | 2016-05-07T19:06:57Z | NONE | null | closes #9536
This fix is for handling asymmetric error bars for series.
It adapts to the syntax for
http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.errorbar
where a sequence of shape 2xN is expected in case of asymmetric error bars.
If a single series is to be plotted and the error sequence is of shape 2xN
it will be used as asymmetric error bars.
Previously a 2xN error sequence was assumed to be 2 symmetric error sequences
for 2 series. Thus in the end only the first error sequence was used.
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https://api.github.com/repos/pandas-dev/pandas/issues/12047 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12047/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12047/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12047/events | https://github.com/pandas-dev/pandas/issues/12047 | 126,886,684 | MDU6SXNzdWUxMjY4ODY2ODQ= | 12,047 | plot() with two localized time series with no common values gives matplotlib error | {
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| null | 3 | 2016-01-15T14:48:57Z | 2021-04-21T05:42:17Z | null | NONE | null | To reproduce:
```
import numpy as np
import pandas as pd
import pytz
import matplotlib.pyplot as plt
pd.show_versions()
values = np.arange(10)
start1 = '2015-02-21 00:00:00'
start2 = '2015-02-21 00:30:00'
index1 = pd.date_range(start1, periods=len(values), freq='H')
index2 = pd.date_range(start2, periods=len(values), freq='H')
df1 = pd.DataFrame(values, index1)
df2 = pd.DataFrame(values, index2)
df1 = df1.tz_localize(pytz.FixedOffset(0))
df2 = df2.tz_localize(pytz.FixedOffset(0))
ax2 = df1.plot()
df2.plot(ax=ax2)
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Darwin
OS-release: 15.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 19.2
Cython: None
numpy: 1.10.2
scipy: 0.16.0
statsmodels: None
IPython: 3.1.0
sphinx: 1.2.3
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: 2.7.3
Traceback (most recent call last):
File "pandasPlotBug.py", line 31, in <module>
df2.plot(ax=ax2)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/pandas/tools/plotting.py", line 3671, in __call__
sort_columns=sort_columns, **kwds)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/pandas/tools/plotting.py", line 2556, in plot_frame
**kwds)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/pandas/tools/plotting.py", line 2384, in _plot
plot_obj.generate()
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/pandas/tools/plotting.py", line 987, in generate
self._make_plot()
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/pandas/tools/plotting.py", line 1664, in _make_plot
**kwds)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/pandas/tools/plotting.py", line 1678, in _plot
lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/pandas/tools/plotting.py", line 1300, in _plot
return ax.plot(*args, **kwds)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/matplotlib/__init__.py", line 1812, in inner
return func(ax, *args, **kwargs)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/matplotlib/axes/_axes.py", line 1424, in plot
for line in self._get_lines(*args, **kwargs):
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/matplotlib/axes/_base.py", line 386, in _grab_next_args
for seg in self._plot_args(remaining, kwargs):
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/matplotlib/axes/_base.py", line 364, in _plot_args
x, y = self._xy_from_xy(x, y)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/matplotlib/axes/_base.py", line 197, in _xy_from_xy
bx = self.axes.xaxis.update_units(x)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/matplotlib/axis.py", line 1391, in update_units
self.set_units(default)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/matplotlib/axis.py", line 1461, in set_units
self.callbacks.process('units')
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/matplotlib/cbook.py", line 563, in process
proxy(*args, **kwargs)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/matplotlib/cbook.py", line 430, in __call__
return mtd(*args, **kwargs)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/matplotlib/lines.py", line 604, in recache_always
self.recache(always=True)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/matplotlib/lines.py", line 612, in recache
x = np.asarray(xconv, np.float_)
File "/Users/dmann/miniconda3/lib/python3.4/site-packages/numpy/core/numeric.py", line 474, in asarray
return array(a, dtype, copy=False, order=order)
TypeError: float() argument must be a string or a number, not 'pandas._period.Period'
```
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} | 3 | 2016-01-15T15:10:20Z | 2016-01-24T22:40:08Z | 2016-01-24T22:40:08Z | NONE | null | File has this content:
```
A,A
3,3
```
When I do `pd.read_csv(filepath)` - all looks fine, however if I specify _skiprows_ and _dtype_ parameters it fails with the following error:
```
pd.read_csv(filepath, skiprows=1, dtype=str)
```
> AttributeError: type object 'str' has no attribute 'items'
---
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-238.9.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US
**pandas: 0.17.1**
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} | 9 | 2016-01-15T15:25:59Z | 2016-01-16T16:30:52Z | 2016-01-16T16:30:52Z | CONTRIBUTOR | null | (24 hrs ago) good build: https://travis-ci.org/pydata/pandas/jobs/102356098: 1.11.0.dev0+51d2ecd
(1 hr ago) breaking lots of things: https://travis-ci.org/pydata/pandas/jobs/102596904: 1.11.0.dev0+aa6335c
@shoyer IIRC a couple of your PR's were merged in the last day.
here's an example:
```
======================================================================
FAIL: test_coercion_with_setitem_and_series (pandas.tests.test_indexing.TestSeriesNoneCoercion)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/pydata/pandas/pandas/tests/test_indexing.py", line 5247, in test_coercion_with_setitem_and_series
expected_series.values, strict_nan=True)
File "/home/travis/build/pydata/pandas/pandas/util/testing.py", line 866, in assert_numpy_array_equal
raise_assert_detail(obj, msg, left, right)
File "/home/travis/build/pydata/pandas/pandas/util/testing.py", line 825, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
numpy array values are different (33.33333 %)
[left]: [NaT, 2000-01-02T00:00:00.000000000+0000, 2000-01-03T00:00:00.000000000+0000]
[right]: [NaT, 2000-01-02T00:00:00.000000000+0000, 2000-01-03T00:00:00.000000000+0000]
```
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} | 2 | 2016-01-15T15:44:14Z | 2016-01-19T12:35:13Z | 2016-01-19T12:35:13Z | CONTRIBUTOR | null | ```
In [1]: import pandas as pd
In [2]: index = pd.date_range('2011-01-01', '2011-01-02', tz='utc') # without tz='utc' it works
In [3]: index
Out[3]: DatetimeIndex(['2011-01-01', '2011-01-02'], dtype='datetime64[ns, UTC]', freq='D')
In [4]: s = pd.Series(range(2), index=index)
In [5]: s.loc[index] = 5
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-5-992315c98f0d> in <module>()
----> 1 s.loc[index] = 5
/usr/lib/python2.7/dist-packages/pandas/core/indexing.pyc in __setitem__(self, key, value)
114
115 def __setitem__(self, key, value):
--> 116 indexer = self._get_setitem_indexer(key)
117 self._setitem_with_indexer(indexer, value)
118
/usr/lib/python2.7/dist-packages/pandas/core/indexing.pyc in _get_setitem_indexer(self, key)
109
110 try:
--> 111 return self._convert_to_indexer(key, is_setter=True)
112 except TypeError:
113 raise IndexingError(key)
/usr/lib/python2.7/dist-packages/pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis, is_setter)
1148 mask = check == -1
1149 if mask.any():
-> 1150 raise KeyError('%s not in index' % objarr[mask])
1151
1152 return _values_from_object(indexer)
KeyError: "['2011-01-01T01:00:00.000000000+0100' '2011-01-02T01:00:00.000000000+0100'] not in index"
```
On python 2.7.11, pandas 0.17.1, linux.
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} | 14 | 2016-01-15T16:11:43Z | 2016-09-30T09:23:14Z | 2016-09-30T09:22:54Z | CONTRIBUTOR | null | Followed up in #14326
---
Towards fixing bug #11544, with respect to comments on merged PR #11870.
I've removed the `NotImplemented` error for `parse_dates` and `date_parser` keywords in `read_excel`, seeing that they are implemented (see discussion in #11870). I've added an example of the intended functionality in the documentation
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} | 1 | 2016-01-15T16:41:24Z | 2016-02-06T21:50:04Z | 2016-02-06T21:50:04Z | CONTRIBUTOR | null | http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.DataFrame.convert_objects.html#pandas.DataFrame.convert_objects
Probably other docstrings missing deprecations if anyone wants to track those down.
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} | 12 | 2016-01-15T17:10:44Z | 2016-06-25T12:38:20Z | 2016-06-25T12:38:17Z | CONTRIBUTOR | null | Continued in #13252
---
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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} | 6 | 2016-01-15T18:28:11Z | 2016-01-19T12:35:00Z | 2016-01-19T12:35:00Z | CONTRIBUTOR | null | Fixes https://github.com/pydata/pandas/issues/12050 .
Taking just the values of an index object looses the timezone information.
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} | 2 | 2016-01-15T18:28:32Z | 2016-01-15T19:28:52Z | 2016-01-15T18:39:15Z | NONE | null | I have had issues with DataFrame.query() producing erroneous results on machines with Window 8.1, Windows Server 2012 R2 and Windows 10.
The issue can be re-produced with the code below which is creating a simple data frame and then querying it successively and checking the results. The issue appears to be intermittent: running the script multiple times will show the query failing on different tests. The issue also seems to depend on the data frame size: the the issue does not seem to existing for data frames below ~1,000 rows, data frames with ~1000 to 100,000 rows return the wrong results, and data frames with more than 100k rows commonly return no results.
``` Python
import numpy as np
import pandas as pd
# create a data frame to test with
num_groups = 100
rows_per_group = 1000
# set the evaluation engine
use_python_engine = True
df = pd.DataFrame({
'grp_col': np.arange(num_groups).repeat(rows_per_group),
'val_col': np.random.rand(num_groups * rows_per_group)
})
# check counts via group by
print df.groupby('grp_col').size()
# look for problems in DataFrame.query
tests_per_group = 1000
print '-------------------------'
print ' expected rows per group: ' + str(rows_per_group)
print '-------------------------'
bad_result = []
for i in range(num_groups):
print 'group: ' + str(i)
query_str = 'grp_col == ' + str(i)
for j in range(0, tests_per_group):
if use_python_engine:
result = df.query(query_str, engine='python')
else:
result = df.query(query_str)
min_grp = result.grp_col.min()
max_grp = result.grp_col.max()
if len(result) != rows_per_group or i != min_grp or i != max_grp:
bad_result.append(result)
print ' bad query result on test #{}, num rows: {}'.format(str(j), str(len(result)))
print ' min group: ' + str(min_grp)
print ' max group: ' + str(max_grp)
```
I believe the perhaps lies with the numexpr evaluation. In the above script, changing the evaluation engine to python, does not return any errors. Also, the script below demonstrates the issue occur when doing numexpr by itself:
``` Python
import numpy as np
import numexpr as ne
# create an array to test with
num_items = 10000
arr = np.array(np.random.randint(0, 100, num_items))
# get a value to query from the 1st entry in the array
query_value = arr[0]
query_value
print 'query value: ' + str(query_value)
# get the corresponding query result
count = len(arr[arr == query_value])
print 'query result count: ' + str(count)
print ''
num_tests = 100
for i in range(num_tests):
curr_test = len(arr[ne.evaluate("arr == " + str(query_value))])
if curr_test != count:
print "***Query problem:"
print " on test: " + str(i)
print " returned count: " + str(curr_test)
```
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} | 12 | 2016-01-15T18:52:01Z | 2020-01-01T16:02:31Z | 2020-01-01T16:01:14Z | CONTRIBUTOR | null | What do people think about inheriting pandas classes from `abc.collections` classes?
I think the main ones are `DataFrame` & `Series` inheriting from `Mapping` and `Index` from `Sequence`.
This would enable other libraries to infer classes' behavior in a tighter way, for example to work out whether an object is list-like but not dict-like, they can check the types, rather than `hasattr(obj, '__iter__') and not hasattr(obj, 'values')`. And it doesn't cost anything.
But as far as I'm aware, other libraries in the pydata ecosystem don't do this, so this would be out of the norm.
https://docs.python.org/3/library/collections.abc.html
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} | 2 | 2016-01-15T20:58:19Z | 2016-01-17T16:21:55Z | 2016-01-17T16:21:48Z | CONTRIBUTOR | null | we build numpy master from source, so pulling wheels would be great.
xref https://github.com/pydata/pandas/issues/12049
- [x] 2.7 [can enable when this is merged](https://github.com/numpy/numpy/pull/7033)
- [x] 3.5 #12065
- [x] remove need for pydata builds entirely.
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} | 5 | 2016-01-15T20:59:07Z | 2016-01-16T16:30:52Z | 2016-01-16T16:30:52Z | CONTRIBUTOR | null | closes #12049
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} | 1 | 2016-01-16T00:25:30Z | 2016-01-16T17:33:36Z | 2016-01-16T17:33:32Z | NONE | null | made doc changes regarding #11989
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} | 17 | 2016-01-16T02:24:42Z | 2016-01-26T14:33:47Z | 2016-01-26T14:32:58Z | CONTRIBUTOR | null | closes #9714
closes #11899
closes #11871
makes ISO parser in C handle the following. I think 2 & 3 aren't actually iso8601 anymore, but close and unambiguous.
1. dates without '-' separator
2. dates without space before tz
3. dates without leading 0s in month/day
(this ONLY works if the date has separators, eg. `"2015-1-1"` parses, but `"201511"` doesn't because it's ambiguous)
asv results - adds a small amount of overhead to the standard case (`'2014-01-01'`)
```
before after ratio
[1ae6384 ] [e922b05 ]
5.17ms 5.34ms 1.03 timeseries.timeseries_to_datetime_iso8601.time_timeseries_to_datetime_iso8601
5.15ms 5.50ms 1.07 timeseries.timeseries_to_datetime_iso8601.time_timeseries_to_datetime_iso8601_format
- 111.89ms 5.27ms 0.05 timeseries.timeseries_to_datetime_iso8601.time_timeseries_to_datetime_iso8601_format_no_sep
- 2.02s 5.33ms 0.00 timeseries.timeseries_to_datetime_iso8601.time_timeseries_to_datetime_iso8601_nosep
- 2.97s 218.25ms 0.07 timeseries.timeseries_to_datetime_iso8601.time_timeseries_to_datetime_iso8601_tz_spaceformat
```
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} | 15 | 2016-01-16T19:16:32Z | 2016-01-20T03:18:31Z | 2016-01-20T03:18:13Z | CONTRIBUTOR | null | yapf with manual modifications for error strings and bad yapf formatting.
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} | 1 | 2016-01-16T19:18:40Z | 2016-01-17T09:04:52Z | 2016-01-17T03:43:47Z | CONTRIBUTOR | null | No KeyError was raised when grouping by a non-existant column
Fixes #11741
Xref issue #11640, PR #11717
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} | 5 | 2016-01-16T19:27:18Z | 2018-06-28T10:23:02Z | 2018-06-28T10:23:02Z | CONTRIBUTOR | null | xref #12060
@jreback - my thought was to just convert the `dateutil` timezone offset (`tzoffset`) to a pytz offset?
``` python
# dateutil
In [1]: pd.Timestamp('2013/01/01 00:00:00+09:00')
Out[1]: Timestamp('2013-01-01 00:00:00+0900', tz='tzoffset(None, 32400)')
# C parser
In [2]: pd.Timestamp('2013-01-01 00:00:00+09:00')
Out[2]: Timestamp('2013-01-01 00:00:00+0900', tz='pytz.FixedOffset(540)')
In [3]: pd.date_range('2013-01-01 00:00:00+09:00', '2014/01/01 00:00:00+09:00')
TypeError Traceback (most recent call last)
<ipython-input-3-d34c322923bc> in <module>()
----> 1 pd.date_range('2013-01-01 00:00:00+09:00', '2014/01/01 00:00:00+09:00')
...
TypeError: Start and end cannot both be tz-aware with different timezones
```
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} | 1 | 2016-01-16T19:37:45Z | 2016-01-17T03:20:24Z | 2016-01-17T03:20:24Z | CONTRIBUTOR | null | - adds back some 3.5 deps previously missing in conda
- closes #12057 (adds 3.5 wheel from nump)
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} | 4 | 2016-01-17T00:05:43Z | 2021-04-21T05:43:30Z | null | MEMBER | null | I see in the tests things like `test_categorical_repr_datetime` which exceeds the 80-character display width. This doesn't seem right to me, but want to check whether I'm missing something.
xref https://github.com/pandas-dev/pandas/issues/15380 | {
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} | 6 | 2016-01-17T01:05:59Z | 2021-03-09T17:05:04Z | 2021-03-09T17:05:04Z | MEMBER | null | xref #11892 (see changes and discussion)
RangeIndex, a new Int64Index subclass that will be used as the new default index (currently) has the following signature: `RangeIndex(start=None, stop=None, step=None, name=None...)`
For converting an `xrange`/`range` object into a RangeIndex, we have the constructor `RangeIndex.from_range`.
Although it isn't documented, the current constructor supports accepting a `RangeIndex` instances as the `start` argument, in which case a copy of the original index is produced, e.g., `RangeIndex(RangeIndex(0, 10, 1)) -> RangeIndex(0, 10, 1)`.
I think this is a mistake: it is reusing the `start` parameter for a different type of argument, at odds with the documented behavior and predictable function signatures. If a user wants to create another RangeIndex from a RangeIndex, they can simply use the Index construtor, e.g., `Index(RangeIndex(...))`.
@jreback argues that not accepting a `RangeIndex` as the first argument to `RangeIndex` would be an API break, because it would create an Index constructor that is not idempotent. It would be "a major break with the current Index design".
Thoughts from anyone else?
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} | 1 | 2016-01-17T03:55:02Z | 2016-01-17T16:21:48Z | 2016-01-17T16:21:48Z | CONTRIBUTOR | null | - remove pydata 2.7 test build
- change 2.7 numpy_dev build to use wheels
- remove need for BUILD_TYPE, all builds are now conda
closes #12057
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} | 6 | 2016-01-17T06:54:09Z | 2016-01-18T00:32:51Z | 2016-01-17T16:46:00Z | MEMBER | null | Ouch my wrists. I will rebase and fix the post-rebase entropy tomorrow.
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} | 6 | 2016-01-17T16:18:25Z | 2016-01-20T14:04:48Z | 2016-01-20T14:04:48Z | CONTRIBUTOR | null | xref #12034
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} | 2 | 2016-01-17T17:15:05Z | 2016-01-20T14:10:11Z | 2016-01-20T14:10:11Z | CONTRIBUTOR | null | ```
In [10]: df = DataFrame({'A' : range(5)})
In [11]: df
Out[11]:
A
0 0
1 1
2 2
3 3
4 4
In [12]: df.index
Out[12]: RangeIndex(start=0, stop=5, step=1)
In [13]: df.reset_index()
Out[13]:
index A
0 0 0
1 1 1
2 2 2
3 3 3
4 4 4
In [14]: df.reset_index().index
Out[14]: Int64Index([0, 1, 2, 3, 4], dtype='int64')
```
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} | 1 | 2016-01-17T17:45:03Z | 2016-02-02T15:16:18Z | 2016-02-02T15:16:18Z | NONE | null | I've got a DataFrame with a TimedeltaIndex that fails when I try to resample it to the same frequency.
Minimal working example
``` python
import pandas as pd
index = pd.timedelta_range('0', periods=9, freq='10L')
series = pd.Series(range(9), index=index)
series.resample('10L') # throws ValueError
```
pandas versions
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.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: 0.17.1
nose: None
pip: 1.4.1
setuptools: 1.1.6
Cython: None
numpy: 1.8.0rc1
scipy: 0.13.0b1
statsmodels: None
IPython: 2.0.0
sphinx: 1.2.2
patsy: None
dateutil: 1.5
pytz: 2013.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.6
matplotlib: 1.3.1
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: None
```
The error thrown is:
```
pandas/tseries/resample.pyc in _resample_timestamps(self, kind)
306 axlabels.inferred_freq) == self.freq:
307 result = obj.copy()
--> 308 result.index = res_index
309 else:
310 result = obj.reindex(res_index, method=self.fill_method,
... (more stacks skipped) ...
ValueError: Length mismatch: Expected axis has 9 elements, new values have 8 elements
```
This appears to be a problem with the logic in `TimeGrouper._resample_timestamps()` lines 298 to 308, the error is being thrown on line 308. It looks like res_index is being set to the wrong thing but I'm not familiar enough with the code to know how to fix properly.
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https://api.github.com/repos/pandas-dev/pandas/issues/12073 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12073/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12073/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12073/events | https://github.com/pandas-dev/pandas/issues/12073 | 127,114,856 | MDU6SXNzdWUxMjcxMTQ4NTY= | 12,073 | Intermittent issue with rolling_min function - Calculation "blows up" | {
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| null | 6 | 2016-01-17T18:45:12Z | 2016-01-17T20:04:40Z | 2016-01-17T19:59:48Z | NONE | null | I noticed a strange behavior that happens intermittently and only happens on my laptop. Not reproducible on my Windows 8 Desktop which concerns me.
Run the following:
```
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
#inputdata
windows=np.array([5,10,15,60,120,300,0,0,0,0] ) #parameters
dates=pd.date_range('1/1/2000 15:00:00', periods =100000, freq='1H')
hitemps=-50+np.round(np.random.rand(100000),3)*100 # simulated hourly high temperature readings
lowtemps=hitemps-2*np.round(np.random.rand(100000),3) # low temps
d = {'HiTemp': hitemps, 'LoTemp' : lowtemps}
```
When running the following about 10 to 20 times, about 1 out of ~10 times the columns get populated with weird data. With my actual data set (too long to attach) it happens more often, like 1 in 4 times. Run the below code repeatedly. Sample problem and correct output attached in xls (further below):
```
df=DataFrame(data=d, index=dates)
# create new df columns of forward looking moves down, with varying time frames (window sizes). use window size in column name
for i, windowsize in enumerate(windows):
if windowsize==0:
continue
else:
colname_df=str('Fw'+str(windowsize)+'Dwn')
df[colname_df]=df.HiTemp-(pd.rolling_min(df.LoTemp, window=windowsize).shift(-windowsize))
df
```
[rolling_min bug report outputs.xlsx](https://github.com/pydata/pandas/files/93555/rolling_min.bug.report.outputs.xlsx)
This was captured on a 2014 MacBook Air running Windows 10 via Bootcamp (see above comment, problem not happening on HP Windows Desktop).
Installed versions result:
pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 19.1.1
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: None
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| null | 3 | 2016-01-18T08:07:55Z | 2016-01-20T18:57:52Z | 2016-01-19T12:29:34Z | NONE | null | pandas version 0.17.0
I'm using a Class Name to set names.
```
from pandas import DataFrame
class Name(object):
def __init__(self, name):
self.name = name
def __repr__(self):
return self.name
```
I want to create a simple DataFrame
```
df = DataFrame([{"Hi":Name("hello"), "My":"Friend"}])
# df = DataFrame([{Name("Hi"):"hello", "My":"Friend"}])
```
I try to export it in Excel with:
```
from pandas import ExcelWriter
xlw = ExcelWriter("welcome.xlsx")
df.to_excel(xlw, "test")
xlw.save()
```
but I have the following error:
[](http://i.stack.imgur.com/v2hqX.png)
in the pandas/io/excel.py
```
def _conv_value(val):
# Convert numpy types to Python types for the Excel writers.
if com.is_integer(val):
val = int(val)
elif com.is_float(val):
val = float(val)
elif com.is_bool(val):
val = bool(val)
elif isinstance(val, Period):
val = "%s" % val
elif com.is_list_like(val):
val = str(val)
# <---- a test is missing here.
return val
```
adding else:
val = str(val) solve the pb.
the export is correct with
- df = DataFrame([{"Hi":"hello", "My":"Friend"}])
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https://api.github.com/repos/pandas-dev/pandas/issues/12077 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12077/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12077/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12077/events | https://github.com/pandas-dev/pandas/issues/12077 | 127,208,008 | MDU6SXNzdWUxMjcyMDgwMDg= | 12,077 | Categorical(vals, cats) bad performance with NaNs | {
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| null | 4 | 2016-01-18T11:31:50Z | 2016-01-25T15:29:27Z | 2016-01-25T15:29:27Z | CONTRIBUTOR | null | NaNs in `datetime64` data values GREATLY reduce performance of `Categorical(values, cats)`:
```
import pandas as pd
tmp=pd.Series(pd.DatetimeIndex(pd.np.datetime64('1995-01-01 00:00')+i for i in range(1000000)))
to = tmp.astype('category')
cats = to.cat.categorical._categories.values
%timeit pd.Categorical(tmp.values, cats)
1 loops, best of 3: 250 ms per loop
tmp[500000] = pd.NaT
%timeit pd.Categorical(tmp.values, cats)
1 loops, best of 3: 10.1 s per loop
tmp[tmp.isnull()] = pd.np.datetime64('0')
%timeit pd.Categorical(tmp.values, cats)
1 loops, best of 3: 251 ms per loop
```
Small issue with printing Categorical datetime64:
```
ds = pd.Categorical([pd.np.datetime64("2014-01-01"), pd.NaT])
>>> ds
[2014-01-01, 2014-01-01] <--- NO, -1 is NOT a category:-)
Categories (1, datetime64[ns]): [2014-01-01]
>>> ds.astype('datetime64')
array(['2014-01-01T03:00:00.000000000+0300', 'NaT'], dtype='datetime64[ns]')
```
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: en
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.7.1
Cython: 0.23.4
numpy: 1.9.3
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.3
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: 1.2.8
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.7
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: 2.8
```
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} | 9 | 2016-01-18T12:10:28Z | 2016-01-28T21:27:38Z | 2016-01-28T21:27:38Z | CONTRIBUTOR | null | ```
In [2]: df = pd.DataFrame(columns=['a','b','c','d'], data=[[1,'b1','c1',3], [1,'b2','c2',4]])
In [3]: df = df.pivot_table(index='a', columns=['b','c'], values='d').reset_index()
In [4]: df
Out[4]:
b a b1 b2
c c1 c2
0 1 3 4
In [5]: df.drop('a', axis=1)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-b59fbf92d28f> in <module>()
----> 1 df.drop('a', axis=1)
/home/nicolas/Git/pandas/pandas/core/generic.pyc in drop(self, labels, axis, level, inplace, errors)
1617 new_axis = axis.drop(labels, level=level, errors=errors)
1618 else:
-> 1619 new_axis = axis.drop(labels, errors=errors)
1620 dropped = self.reindex(**{axis_name: new_axis})
1621 try:
/home/nicolas/Git/pandas/pandas/core/index.pyc in drop(self, labels, level, errors)
5729 inds.append(loc)
5730 else:
-> 5731 inds.extend(lrange(loc.start, loc.stop))
5732 except KeyError:
5733 if errors != 'ignore':
AttributeError: 'numpy.ndarray' object has no attribute 'start'
```
This is related to issue #11640. I have been working on a solution that I submitted in the pull request #11717, but the said solution was controversial, so I'm creating this issue to separate the problems.
I'll make a PR soon enough.
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} | 6 | 2016-01-18T14:48:39Z | 2016-01-20T14:24:23Z | 2016-01-20T14:10:00Z | CONTRIBUTOR | null | closes #12071
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} | 6 | 2016-01-18T16:15:09Z | 2016-01-26T15:55:44Z | 2016-01-26T15:55:44Z | NONE | null | Right now trying this results in "IndexError: list index out of range". It should say can't merge DataFrame with a Series...
I know this for quite a while now, but still get trapped on it every once in a while. This would be very helpful for beginners.
Other people also get confused: http://stackoverflow.com/questions/27281734/pandas-merge-on-index-not-working
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} | 4 | 2016-01-18T16:32:44Z | 2016-01-19T12:08:21Z | 2016-01-19T12:07:40Z | MEMBER | null | The commented line shown produces an error on fresh clones of pandas, and is not needed AFAICT.
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| null | 6 | 2016-01-18T17:25:30Z | 2021-02-16T07:21:05Z | 2016-01-19T15:56:53Z | NONE | null | I'm running pandas 0.17.1 on Python 2.7.10 on RHEL 7.12, and I am experiencing the following behavior:
```
In [1]: import pandas
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-1-d6ac987968b6> in <module>()
----> 1 import pandas
/var/lib/jenkins/jobs/Test-Client/workspace/venv/lib/python2.7/site-packages/pandas/__init__.py in <module>()
60 # use the closest tagged version if possible
61 import pdb; pdb.set_trace()
---> 62 from ._version import get_versions
63 v = get_versions()
64 __version__ = v.get('closest-tag',v['version'])
KeyError: 'version'
```
This appears with both Python and IPython. I've inserted a debugger into the `__init__` script and examined what get_versions returns at that time. It looks like this:
```
(Pdb) v
{u'e\x00r\x00r': None, u'f\x00u\x00l\x00l\x00-\x00r\x00e\x00v': u'6\x00c\x003\x000\x00c\x00b\x00e\x00c\x00f\x008\x00e\x005\x00a\x00e\x006\x001\x000\x00f\x002\x00a\x00', u'd\x00i\x00r': False, u'v\x00e\x00r\x00s': u'0\x00.\x001\x00'}
```
`get_versions()` just calls json.loads on a `version_json` dictionary in the `_version` file (generated by the versioneer). The `version_json` dictionary appears fine at time of import, however:
```
(Pdb) version_json
'\n{\n "dirty": false,\n "error": null,\n "full-revisionid": "6c30cbecf8e5ae610f2a37ba821116bd9f77044d",\n "version": "0.17.1"\n}\n'
```
It looks like the ultrajson library included with pandas works differently for me on the server than my local development machine. Both have ultrajson version 1.33.
I should also note that when I try to rerun `import pandas` in the same REPL, I get a different error:
```
In [2]: import pandas
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-2-d6ac987968b6> in <module>()
----> 1 import pandas
/var/lib/jenkins/jobs/Test-Client/workspace/venv/lib/python2.7/site-packages/pandas/__init__.py in <module>()
11 "pandas from the source directory, you may need to run "
12 "'python setup.py build_ext --inplace' to build the C "
---> 13 "extensions first.".format(module))
14
15 from datetime import datetime
ImportError: C extension: hashtable not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.
```
Thanks,
Sam
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} | 1 | 2016-01-18T19:58:26Z | 2016-01-18T20:29:59Z | 2016-01-18T20:29:50Z | NONE | null | I am running into an issue which appears only in pandas 0.17 or higher where tz aware datetime columns get converted into their integer values by a groupby operation
``` python
## This is an issue when timestamps are TZ aware
dts = [pd.Timestamp('20160101', tz='UTC'), pd.Timestamp('20160102', tz='UTC')]
values = [1,2]
df = pd.DataFrame({'DATE':dts, 'VALUE':values})
df.groupby('VALUE').DATE.max()
Out[1]:
VALUE
1 1451606400000000000
2 1451692800000000000
Name: DATE, dtype: int64
## This is not an issue when tmestamps are not TZ aware
dts = [pd.Timestamp('20160101'), pd.Timestamp('20160102')]
values = [1,2]
df = pd.DataFrame({'DATE':dts, 'VALUE':values})
df.groupby('VALUE').DATE.max()
Out[2]:
VALUE
1 2016-01-01
2 2016-01-02
Name: DATE, dtype: datetime64[ns]
## Version information
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.11.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 19.1.1
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.10
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 12 | 2016-01-18T22:33:29Z | 2016-09-23T07:12:08Z | 2016-06-05T23:41:37Z | CONTRIBUTOR | null | Continued in https://github.com/pydata/pandas/pull/13375
---
closes #7271
By passing a dict of {column name/column index: dtype}, multiple columns can be cast to different data types in a single command. Now users can do:
`df = df.astype({'my_bool', 'bool', 'my_int': 'int'})`
or:
`df = df.astype({0, 'bool', 1: 'int'})`
instead of:
```
df['my_bool'] = df.my_bool.astype('bool')
df['my_int'] = df.my_int.astype('int')
```
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| null | 5 | 2016-01-18T23:53:42Z | 2016-03-21T01:18:56Z | 2016-03-21T01:18:56Z | NONE | null | I did not see this behavior listed in the current issue list for version 0.17.1.
When using an observance in a rule for adding an election day (every even year), using this function to determine the observance:
```
def election_observance(dt):
if dt.year % 2 == 1:
return None
else:
return dt + pd.DateOffset(weekday=TU(1))
```
the Calendar date filter throws an error when the observance is type None.
```
37 start_date = pd.to_datetime('20160108')
38 end_date = pd.to_datetime('20171231')
---> 39 my_cal = cal.holidays(start_date, end_date, return_name=True)
40 my_cal
/Users/pete/IPython Notebook Example/lib/python2.7/site-packages/pandas/tseries/holiday.pyc in holidays(self, start, end, return_name)
375 if self._cache is None or start < self._cache[0] or end > self._cache[1]:
376 for rule in self.rules:
--> 377 rule_holidays = rule.dates(start, end, return_name=True)
378
379 if holidays is None:
/Users/pete/IPython Notebook Example/lib/python2.7/site-packages/pandas/tseries/holiday.pyc in dates(self, start_date, end_date, return_name)
216 if self.end_date is not None:
217 filter_end_date = min(self.end_date.tz_localize(filter_end_date.tz), filter_end_date)
--> 218 holiday_dates = holiday_dates[(holiday_dates >= filter_start_date) &
219 (holiday_dates <= filter_end_date)]
220 if return_name:
pandas/tslib.pyx in pandas.tslib._Timestamp.__richcmp__ (pandas/tslib.c:17992)()
TypeError: Cannot compare type 'Timestamp' with type 'NoneType'
```
Using a bogus return value like `dt = pd.to_datetime('1/1/2000')` returns a Timestamp for the filter to compare eliminated the error, but doesn't feel like this should be the correct behavior.
Perhaps I should use a better return value ?
Certainly there is no 'empty' Timestamp, as the filter errors on what appear to be pre-Gregorian calendar dates.
## Complete code
```
def election_observance(dt):
if dt.year % 2 == 1:
return None
else:
return dt + pd.DateOffset(weekday=TU(1))
from pandas.tseries.holiday import AbstractHolidayCalendar
from pandas.tseries.holiday import Holiday
from pandas.tseries.holiday import MO, TU, TH, FR, nearest_workday
from pandas.tseries.holiday import USMemorialDay, USLaborDay, USColumbusDay, USThanksgivingDay, USMartinLutherKingJr, USPresidentsDay, GoodFriday, EasterMonday
from pandas.tseries.offsets import *
class ExampleCalendar(AbstractHolidayCalendar):
rules = [
Holiday('New Years Day', month=1, day=1, observance=nearest_workday),
USMartinLutherKingJr,
USPresidentsDay,
USMemorialDay,
Holiday('July 4th', month=7, day=4, observance=nearest_workday),
USLaborDay,
USColumbusDay,
Holiday('Veterans Day', month=11, day=11, observance=nearest_workday),
USThanksgivingDay,
Holiday('Christmas', month=12, day=25, observance=nearest_workday),
GoodFriday,
EasterMonday,
Holiday('Day After Thanksgiving Day', month=11, day=1, offset=DateOffset(weekday=FR(4))),
Holiday('New Years Day', month=1, day=1),
Holiday('Veterans Day', month=11, day=11, observance=nearest_workday),
Holiday('West Virginia Day', month=6, day=20),
Holiday('US Election Day', month=11, day=2, observance=election_observance),
]
cal = ExampleCalendar()
start_date = pd.to_datetime('20160108')
end_date = pd.to_datetime('20181231')
my_cal = cal.holidays(start_date, end_date, return_name=True)
```
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} | 5 | 2016-01-19T07:53:17Z | 2016-01-19T13:34:15Z | 2016-01-19T13:18:34Z | NONE | null | Setting a new column with the sum of two existing columns does not work correctly for large dataframes: X['C']=X['A']+X['B'] may or may not work correctly for some runs and frame of size > 10000 X['A'] and X['B'] may contain any float number. This issue is absent in pandas 0.16.2.
I am running 64 bit python on Windows 10. See attached notebook for details:
[ReadDebug1.zip](https://github.com/pydata/pandas/files/95513/ReadDebug1.zip)
To reproduce:
### +++++++++++++++++++++++++++++++++++++
import pandas as pd
DataFrameSize=10001 ## will work with 10000 or less
XR = pd.DataFrame({'A' : pd.Series(1,index=list(range(DataFrameSize)),dtype='float32'),
'B' : pd.Series(2,index=list(range(DataFrameSize)),dtype='float32')})
def CleanDebug(X):
X.loc[:,'C']=X.loc[:,'A']+X.loc[:,'B']
#X['C']=X['A']+X['B']
return X
for i in xrange(1000):
print 'iteration ',i
ry = CleanDebug(XR)
assert abs(ry.C.sum()-30003)<1
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} | 8 | 2016-01-19T10:03:21Z | 2016-01-27T13:03:32Z | 2016-01-27T12:59:13Z | CONTRIBUTOR | null | I'm a huge fan of Pandas. Thanks for all the hard work!
I believe I have stumbled across a small bug in Pandas 0.17.1 which was not present in 0.16.2. Indexing into Series of _timezone-aware_ `datetime64`s fails using `__getitem__` but indexing succeeds if the `datetime64`s are timezone-_naive_. Here is a minimal code example and the exception produced by Pandas 0.17.1:
``` python
In [37]: dates_with_tz = pd.date_range("2011-01-01", periods=3, tz="US/Eastern")
In [46]: dates_with_tz
Out[46]:
DatetimeIndex(['2011-01-01 00:00:00-05:00', '2011-01-02 00:00:00-05:00',
'2011-01-03 00:00:00-05:00'],
dtype='datetime64[ns, US/Eastern]', freq='D')
In [38]: s_with_tz = pd.Series(dates_with_tz, index=['a', 'b', 'c'])
In [39]: s_with_tz
Out[39]:
a 2011-01-01 00:00:00-05:00
b 2011-01-02 00:00:00-05:00
c 2011-01-03 00:00:00-05:00
dtype: datetime64[ns, US/Eastern]
In [40]: s_with_tz['a']
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-40-81d0bf655282> in <module>()
----> 1 s_with_tz['a']
/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc in __getitem__(self, key)
555 def __getitem__(self, key):
556 try:
--> 557 result = self.index.get_value(self, key)
558
559 if not np.isscalar(result):
/usr/local/lib/python2.7/dist-packages/pandas/core/index.pyc in get_value(self, series, key)
1778 s = getattr(series,'_values',None)
1779 if isinstance(s, Index) and lib.isscalar(key):
-> 1780 return s[key]
1781
1782 s = _values_from_object(series)
/usr/local/lib/python2.7/dist-packages/pandas/tseries/base.pyc in __getitem__(self, key)
98 getitem = self._data.__getitem__
99 if np.isscalar(key):
--> 100 val = getitem(key)
101 return self._box_func(val)
102 else:
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
```
If the dates are timezone-aware then we can access them using `loc` but, as far as I'm aware, we should be able to use `__getitem__` in this situation too:
``` python
In [41]: s_with_tz.loc['a']
Out[41]: Timestamp('2011-01-01 00:00:00-0500', tz='US/Eastern')
```
However, if the dates are timezone-_naive_ then indexing using `__getitem__` works as expected:
``` python
In [32]: dates_naive = pd.date_range("2011-01-01", periods=3)
In [33]: dates_naive
Out[33]: DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], dtype='datetime64[ns]', freq='D')
In [34]: s = pd.Series(dates_naive, index=['a', 'b', 'c'])
In [35]: s
Out[35]:
a 2011-01-01
b 2011-01-02
c 2011-01-03
dtype: datetime64[ns]
In [36]: s['a']
Out[36]: Timestamp('2011-01-01 00:00:00')
```
So indexing into a `Series` using `__getitem__` works if the data is a list of timezone-_naive_ `datetime64`s but indexing fails if the `datetime64`s are timezone-_aware_.
``` python
In [47]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-23-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 1.5.6
setuptools: 15.2
Cython: 0.23.1
numpy: 1.10.1
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.2
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
Jinja2: None
```
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} | 19 | 2016-01-19T15:57:13Z | 2021-03-31T20:21:14Z | 2016-01-20T23:55:54Z | CONTRIBUTOR | null | Partial solution for https://github.com/pydata/pandas/issues/11655 (**Conditional HTML styling hides MultiIndex structure**) and https://github.com/pydata/pandas/issues/11610 (**Followup to Conditional HTML Styling**).
The Style API is inconsistent with `DataFrame.to_html()` when printing index names. Currently the style API doesn't print any index names but `to_html` and the standard notebook repr functions do.
This PR adds a row for index headings in the `Styler._translate` method. This PR does not cause multi-index rownames to span multiple rows, but I can work on adding that if people want.
## Reproducing Output:
``` python
import pandas as pd
import numpy as np
np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4),
columns=list('BCDE'))],
axis=1)
```
``` python
df.set_index(['A', 'B']).style
```
##### Current Output
<img width="506" alt="screen shot 2016-01-19 at 10 19 08 am" src="https://cloud.githubusercontent.com/assets/3064019/12422922/32254fb2-be97-11e5-8096-fcadbd902daa.png">
##### Patched Output:
<img width="441" alt="screen shot 2016-01-19 at 10 19 19 am" src="https://cloud.githubusercontent.com/assets/3064019/12422947/46affb6c-be97-11e5-83c7-acd276bdb88f.png">
## TODO:
- [X] Add Tests
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} | 2 | 2016-01-19T16:27:03Z | 2016-01-30T15:22:58Z | 2016-01-30T15:22:58Z | NONE | null | using filename url statement
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} | 12 | 2016-01-19T18:47:48Z | 2016-02-08T15:28:59Z | 2016-02-08T15:28:58Z | CONTRIBUTOR | null | closes #12042
Some times it's useful to only accept n-1 variables out of n categorical levels.
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} | 8 | 2016-01-19T22:11:01Z | 2020-04-24T17:43:51Z | null | CONTRIBUTOR | null | Currently, `rolling_quantile()` accepts only a single float for the `quantile` argument. I find myself wanting to compute multiple quantiles over the same data. Instead of doing three calls to `rolling_quantile()`, I'd like to be able to call `rolling_quantile()` once with a sequence of floats as the `quantile` argument and get back a list of results. This has benefits both in terms of code conciseness and efficiency.
This suggested behavior would be analogous to how np.percentile works. http://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.percentile.html
Currently:
```
>>> import pandas as pd
>>> ser = pd.Series(np.array([1,2,3,4,5,6,7,8,9]))
>>> ser.rolling(window=3).quantile(quantile=0.5)
0 NaN
1 NaN
2 2
3 3
4 4
5 5
6 6
7 7
8 8
dtype: float64
```
Desired enhancement:
```
>>> ser.rolling(window=3).quantile(quantile=[0.25,0.5,1])
[0 NaN
1 NaN
2 1
3 2
4 3
5 4
6 5
7 6
8 7
dtype: float64, 0 NaN
1 NaN
2 2
3 3
4 4
5 5
6 6
7 7
8 8
dtype: float64, 0 NaN
1 NaN
2 3
3 4
4 5
5 6
6 7
7 8
8 9
dtype: float64]
```
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| null | 0 | 2016-01-19T23:09:38Z | 2016-01-21T15:01:37Z | 2016-01-21T13:08:12Z | CONTRIBUTOR | null | Though the color can be changed by a param, I still think it's more intuitive and sensible to be set to defferent colors by default.
Anyone agrees?
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} | 8 | 2016-01-20T06:20:53Z | 2016-01-21T08:02:27Z | 2016-01-21T08:02:17Z | MEMBER | null | I suppressed warnings in test_parsers.py because of the amount of literal strings in the file.
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| null | 2 | 2016-01-20T06:59:55Z | 2018-07-17T22:27:45Z | 2016-01-20T14:01:42Z | NONE | null | When reading in a CSV file, you can easily convert a column into the `datetime` format using:
```
pd.read_csv(filepath, parse_dates=['TIMESTAMP'])
```
If the column holding the original time information uses an unsupported format, you can currently pass a parser function to it too, for example if the timestamp is "unixtime in milliseconds":
```
convert = lambda x: datetime.datetime.fromtimestamp(float(x) / 1e3)
df = pd.read_csv(filepath, parse_dates=['TIMESTAMP'], date_parser=convert)
```
However, if the data is already read in, you can also do such conversions easily with `.to_datetime()` which provides a `unit` parameter for the most common cases:
```
pd.to_datetime(df['UNIXTIME'], unit='ms')
```
I think it would be quite useful to have this `unit` parameter in `.read_csv()` too, calling it `date_unit` to avoid confusion. The example with the extra conversion function would then look like:
```
df = pd.read_csv(filepath, parse_dates=['TIMESTAMP'], date_unit='ms')
```
Or using a list of units to allow multiple columns with different units:
```
df = pd.read_csv(filepath, parse_dates=['TIMESTAMP', 'ANOTHER_TIMESTAMP], date_unit=['ms', 'us'])
```
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} | 18 | 2016-01-20T12:23:14Z | 2016-01-27T15:12:25Z | 2016-01-27T15:12:25Z | NONE | null | I am using read_csv to read some gzip compressed log files. Some of these files are corrupted and they cannot be uncompressed.
At different iterations in the loop that reads these files my script crashes with a core dumped message:
**\* Error in `/usr/bin/python': corrupted double-linked list: 0x0000000003836790 ***
or just:
Segmentation fault (core dumped)
This is a stripped-down version (just looping over one of the corrupted files) of the code where this error occurs:
```
import pandas as pd
for i in xrange(n):
try:
pd.read_csv(fPath,delim_whitespace=True,header=None, compression='gzip')
except Exception,e:
continue
```
The traceback of the catched exception is:
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 498, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 285, in _read
return parser.read()
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 747, in read
ret = self._engine.read(nrows)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 1197, in read
data = self._reader.read(nrows)
File "pandas/parser.pyx", line 766, in pandas.parser.TextReader.read (pandas/parser.c:7988)
File "pandas/parser.pyx", line 788, in pandas.parser.TextReader._read_low_memory (pandas/parser.c:8244)
File "pandas/parser.pyx", line 842, in pandas.parser.TextReader._read_rows (pandas/parser.c:8970)
File "pandas/parser.pyx", line 829, in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:8838)
File "pandas/parser.pyx", line 1833, in pandas.parser.raise_parser_error (pandas/parser.c:22649)
CParserError: Error tokenizing data. C error: Calling read(nbytes) on source failed. Try engine='python'.
If I remove the delim_whitespace argument the loop completes without segmentation fault. I tried adding low_memory=False but the program still crashes.
I am using pandas version 0.17.1 on Ubuntu 14.04 OS.
It looks like a similar issue to #5664 but the problems should have been resolved in v0.16.1
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} | 2 | 2016-01-20T12:59:05Z | 2016-01-21T06:46:19Z | 2016-01-21T06:46:19Z | CONTRIBUTOR | null | And residual categorical.
What should we do with api/sparse/matrix as those are all unused imports--noqa for each line?
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} | 2 | 2016-01-20T14:41:50Z | 2016-01-25T15:31:23Z | 2016-01-25T15:31:23Z | CONTRIBUTOR | null | ```
/home/travis/build/pydata/pandas/pandas/tseries/tests/test_period.py:420: DeprecationWarning: parsing timezone aware datetimes is deprecated; this will raise an error in the future
```
full build [here](https://travis-ci.org/pydata/pandas/jobs/103526993)
note this is only on the numpy-dev builds (last 2)
@shoyer recent change to show a deprecation warning on passing tz-aware strings to numpy [here](https://github.com/numpy/numpy/pull/6453) is now causing loads of tests to show these.
so in order to keep backward compat I suppose we need a wrapper function
```
In [1]: np.__version__
Out[1]: '1.12.0.dev0+a38942a'
In [2]: np.datetime64('2007-01-01 09:00:00.001Z')
/Users/jreback/miniconda/envs/numpy_dev/bin/ipython:1: DeprecationWarning: parsing timezone aware datetimes is deprecated; this will raise an error in the future
#!/bin/bash /Users/jreback/miniconda/envs/numpy_dev/bin/python.app
Out[2]: numpy.datetime64('2007-01-01T09:00:00.001')
In [3]: np.datetime64('2007-01-01 09:00:00.001')
Out[3]: numpy.datetime64('2007-01-01T09:00:00.001')
In [4]: np.datetime64('2007-01-01 09:00:00.001').item()
Out[4]: datetime.datetime(2007, 1, 1, 9, 0, 0, 1000)
```
current numpy
```
In [2]: np.__version__
Out[2]: '1.10.2'
In [3]: np.datetime64('2007-01-01 09:00:00.001Z')
Out[3]: numpy.datetime64('2007-01-01T04:00:00.001-0500')
In [5]: np.datetime64('2007-01-01 09:00:00.001Z').item()
Out[5]: datetime.datetime(2007, 1, 1, 9, 0, 0, 1000)
```
so maybe
`np_datetime64_compat(....)` that can then introspect our defined `_np_version_under1p11` (define in `pandas/__init__.py`) and add a 'Z' if needed (pre-suppose that we change the input formats to
`2007-01-01 09:00:00.001'`) for clarity.
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} | 1 | 2016-01-20T18:54:49Z | 2016-01-23T16:05:32Z | 2016-01-23T16:05:32Z | CONTRIBUTOR | null | hmm, this looks pretty new. should fix this code.
```
...../home/travis/build/pydata/pandas/pandas/tests/test_panel.py:201: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
```
build is [here](https://travis-ci.org/pydata/pandas/jobs/103526993). This is a numpy-dev build, need to check out the other builds.
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} | 2 | 2016-01-20T19:55:18Z | 2016-01-23T16:05:21Z | 2016-01-23T16:05:21Z | CONTRIBUTOR | null | closes #12101
changed deprecated getargspec to signature for python 3 while keeping the getargspec for python 2
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} | 14 | 2016-01-20T21:01:30Z | 2016-05-05T22:39:21Z | 2016-01-29T15:14:46Z | NONE | null | closes #11413
This PR leverages Python's ZipFile functionality to automatically unzip files read into DataFrames using read_csv().
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