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https://api.github.com/repos/pandas-dev/pandas/issues/12104 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12104/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12104/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12104/events | https://github.com/pandas-dev/pandas/issues/12104 | 127,802,321 | MDU6SXNzdWUxMjc4MDIzMjE= | 12,104 | Setting values on a DataFrame with tz aware DatetimeIndex fails | {
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| null | 1 | 2016-01-20T22:47:28Z | 2016-01-20T23:05:03Z | 2016-01-20T23:04:48Z | NONE | null | ``` python
tdf = pd.DataFrame({'a': np.arange(10), 'b': np.arange(10) + 5},
index=pd.date_range('20160119 09:00', freq='5T',
periods=10, tz='US/Eastern'))
new_values = tdf.sample(2)['b'] + 100
tdf.loc[new_values.index, 'b'] = new_values.values
```
This fails with `KeyError: "['2016-01-19T14:00:00.000000000+0000' '2016-01-19T14:05:00.000000000+0000'] not in index`
This used to work. Not sure which version broke it.
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| null | 1 | 2016-01-20T23:15:15Z | 2016-01-20T23:39:14Z | 2016-01-20T23:39:14Z | CONTRIBUTOR | null | x=pd.merge(df1,df2,left_on..,right_on.. indicator=True, suffixes=['leftDataFrameSuffix','rightdfSuffix]
The output will show an indicator column which will show 'left', 'right' or 'both' instead of 'leftDataFrameSuffix', 'rightdfSuffix' or 'both'.
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| null | 2 | 2016-01-20T23:55:42Z | 2016-01-21T00:51:38Z | 2016-01-21T00:01:32Z | NONE | null | ERROR: type should be string, got "https://github.com/pydata/pandas/blob/master/pandas/io/pytables.py#L539\n\n```\n try:\n self._handle = tables.open_file(self._path, self._mode, **kwargs)\n except (IOError) as e: # pragma: no cover\n if 'can not be written' in str(e):\n print('Opening %s in read-only mode' % self._path)\n self._handle = tables.open_file(self._path, 'r', **kwargs)\n else:\n raise\n```\n\nThe above code prints to stdout when attempting to read from a readonly file. This is a problem for programs which use stdout for their own output. It should either print to sys.stderr or whatever log the rest of the library uses.\n" | {
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| null | 2 | 2016-01-21T01:18:50Z | 2016-01-21T06:47:55Z | 2016-01-21T06:47:54Z | CONTRIBUTOR | null | … generic)
Besides groupby we're clean.
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| null | 8 | 2016-01-21T03:21:30Z | 2018-07-11T10:29:24Z | 2016-01-21T03:53:13Z | NONE | null | ```
assert_frame_equal(desired_df, df)
File "C:\Anaconda\lib\site-packages\pandas\util\testing.py", line 1049, in assert_frame_equal
obj='DataFrame.iloc[:, {0}]'.format(i))
File "C:\Anaconda\lib\site-packages\pandas\util\testing.py", line 927, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "C:\Anaconda\lib\site-packages\pandas\util\testing.py", line 721, in assert_attr_equal
result = left_attr == right_attr
TypeError: data type not understood
```
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} | 1 | 2016-01-21T12:02:01Z | 2016-01-21T12:26:59Z | 2016-01-21T12:26:50Z | CONTRIBUTOR | null | xref #12034
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| null | 1 | 2016-01-22T06:35:51Z | 2016-01-22T15:47:53Z | 2016-01-22T15:47:53Z | CONTRIBUTOR | null | getargspec is dprecated in python 3; signature is used instead
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| null | 1 | 2016-01-22T12:33:40Z | 2016-01-22T19:23:08Z | 2016-01-22T15:47:37Z | CONTRIBUTOR | null | {
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| null | 4 | 2016-01-22T15:32:56Z | 2016-01-27T16:55:31Z | 2016-01-27T16:00:17Z | NONE | null | Since many methods of constructing a list of tuples in Python3 return iterators, this flattens them. Although using from_arrays often works better.
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| null | 2 | 2016-01-22T17:52:36Z | 2016-01-23T04:42:46Z | 2016-01-23T04:42:46Z | NONE | null | > > > test = numpy.DataFrame(numpy.random.normal(size=(80000,2)))
> > > k = test[0] - test[1]
Works most of the time, but sometimes gives a column of zero (or near zeros) as output.
Does not seem to affect small dataframes.
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} | 6 | 2016-01-23T20:27:09Z | 2016-01-25T18:01:49Z | 2016-01-25T15:40:17Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/11974
This replaces the implementation of `nanskew` and `nankurt` by an algorithm that does not produce round-off error due to subtracting very similar large values. The sample skew and kurtosis are computed directly from the moments, rather than expanding the moments, which leads to cancellation errors. The algorithm implemented here is about as efficient as the original implementation (in terms of time and memory); a more efficient one-pass algorithm is certainly possible but would require some Cython code.
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} | 6 | 2016-01-24T03:42:02Z | 2016-01-24T18:30:01Z | 2016-01-24T18:29:54Z | CONTRIBUTOR | null | It is currently impossible to build a debug version of Pandas on Mac OS, since some of the C function declarations are declared as `inline`, and compiling in debug mode turns off optimizations and hence inlining. While this could be solved by adding a compile argument to `setup.py` to use the C89 behavior for inline as in #10510, I believe an ultimately cleaner solution may consist of declaring inlined functions as `static`, so that the code is properly C99-compliant.
For the implementation, I added the `static` keyword to the definitions of `PANDAS_INLINE` and `P_INLINE` (and removed duplicate occurrences of `static` from some function declarations), so that future implementations that use those defines are automatically compliant. This is slightly different from e.g. Cython, which defines `CYTHON_INLINE` as `inline` and declares everything as `static CYTHON_INLINE`.
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} | 8 | 2016-01-24T05:40:27Z | 2016-01-24T22:54:23Z | 2016-01-24T22:53:31Z | MEMBER | null | Split apart these two very large modules and created a new `pandas.indexes` subpackage. It would be nice to move all the index class code from `pandas.tseries` there in a followup patch.
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} | 0 | 2016-01-24T05:52:59Z | 2016-02-12T20:42:51Z | 2016-02-12T20:42:51Z | CONTRIBUTOR | null | The `core.style.Styler._translate` method uses `__getitem__` indexing when working with numerical cell locations (https://github.com/pydata/pandas/blob/master/pandas/core/style.py#L240). When using numeric dataframe columns the location based index causes confusion with the column name index, causing an error.
``` python
df = pd.DataFrame({i: range(10) for i in range(1,10)})
df.style._translate()
```
The expected behaviour should be to always use location based indexing. Changing [line 240](https://github.com/pydata/pandas/blob/master/pandas/core/style.py#L240) to:
``` python
"value": self.data.iloc[r].iloc[c],
```
Fixes the error.
I do currently have a PR to fix this but wanted to file the bug report first.
##### Error code
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/usr/local/lib/python3.5/site-packages/IPython/core/formatters.py in __call__(self, obj)
339 method = _safe_get_formatter_method(obj, self.print_method)
340 if method is not None:
--> 341 return method()
342 return None
343 else:
/usr/local/lib/python3.5/site-packages/pandas/core/style.py in _repr_html_(self)
158 Hooks into Jupyter notebook rich display system.
159 '''
--> 160 return self.render()
161
162 def _translate(self):
/usr/local/lib/python3.5/site-packages/pandas/core/style.py in render(self)
259 """
260 self._compute()
--> 261 d = self._translate()
262 # filter out empty styles, every cell will have a class
263 # but the list of props may just be [['', '']].
/usr/local/lib/python3.5/site-packages/pandas/core/style.py in _translate(self)
220 cs = [DATA_CLASS, "row%s" % r, "col%s" % c]
221 cs.extend(cell_context.get("data", {}).get(r, {}).get(c, []))
--> 222 row_es.append({"type": "td", "value": self.data.iloc[r][c],
223 "class": " ".join(cs), "id": "_".join(cs[1:])})
224 props = []
/usr/local/lib/python3.5/site-packages/pandas/core/series.py 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/python3.5/site-packages/pandas/core/index.py in get_value(self, series, key)
1788
1789 try:
-> 1790 return self._engine.get_value(s, k)
1791 except KeyError as e1:
1792 if len(self) > 0 and self.inferred_type in ['integer','boolean']:
pandas/index.pyx in pandas.index.IndexEngine.get_value (pandas/index.c:3204)()
pandas/index.pyx in pandas.index.IndexEngine.get_value (pandas/index.c:2903)()
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3843)()
pandas/hashtable.pyx in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6525)()
pandas/hashtable.pyx in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6463)()
KeyError: 0
```
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 19.4
Cython: None
numpy: 1.10.4
scipy: 0.16.0
statsmodels: None
IPython: 4.0.3
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: 2.8
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| null | 8 | 2016-01-24T06:05:36Z | 2016-01-24T19:49:26Z | 2016-01-24T19:49:26Z | CONTRIBUTOR | null | Addresses https://github.com/pydata/pandas/issues/12125.
The previous behaviour was to use a `__getitem__` lookup which caused incorrect behaviour with numeric columns. The adjusted code uses `iloc` numerical indexing to resolve the issue.
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} | 1 | 2016-01-24T19:00:21Z | 2016-01-25T15:31:23Z | 2016-01-25T15:31:23Z | CONTRIBUTOR | null | closes #12100
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```
before after ratio
[1330b9fe] [404911c6]
17.25ms 1.21ms 0.07 categoricals.categorical_constructor_with_datetimes.time_datetimes_with_nat
```
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} | 3 | 2016-01-25T06:17:06Z | 2016-01-26T17:55:45Z | 2016-01-26T14:00:02Z | MEMBER | null | Similar to test_frame.py, but only 8K lines to deal with this time.
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| null | 2 | 2016-01-25T06:56:29Z | 2016-01-30T15:20:09Z | 2016-01-30T15:20:09Z | CONTRIBUTOR | null | closes #11720
Adds line_terminator optional argument to to_clipboard
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| null | 2 | 2016-01-25T07:32:27Z | 2016-01-30T15:19:56Z | 2016-01-30T15:19:56Z | CONTRIBUTOR | null | closes #11812
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} | 2 | 2016-01-25T08:52:00Z | 2016-04-17T14:31:40Z | 2016-04-17T14:31:40Z | CONTRIBUTOR | null | Specifying `dropna=False` drops column level names:
```
import pandas as pd
df = pd.DataFrame({"Col1": [23], "Col2": [45]})
names1 = pd.crosstab(df["Col1"], df["Col2"]).columns.names
names2 = pd.crosstab(df["Col1"], df["Col2"], dropna=False).columns.names
print(names1, names2)
>>>['Col2'] [None]
```
P.S. At first i thought `dropna=False` will help to count `NaN` values, though it seems that `fillna` is needed. ~~So i don't understand, why this parameter is needed at all.~~ If entire column is `NaN` then we'll get `KeyError: '__dummy__'` anyway.
pandas: 0.17.1
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} | 16 | 2016-01-25T17:13:39Z | 2019-09-23T11:23:41Z | 2016-01-27T15:12:25Z | CONTRIBUTOR | null | Issue #12098
Add src->buffer = NULL; after garbage collecting src->buffer in the buffer_rd_bytes routine in io.c to fix the segfault
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} | 26 | 2016-01-25T19:11:18Z | 2020-06-08T22:10:04Z | 2020-06-08T22:10:04Z | NONE | null | So, I don't know if this is by design, but it sure confused me. `to_timedelta()` operates differently on the number `1000` compared to the string `'1000'`:
``` python
In[3]: pd.to_timedelta(1000, unit='ms')
Out[3]: Timedelta('0 days 00:00:01')
In[4]: pd.to_timedelta(1000.0, unit='ms')
Out[4]: Timedelta('0 days 00:00:01')
```
and
``` python
In[6]: pd.to_timedelta('1000ms')
Out[6]: Timedelta('0 days 00:00:01')
```
while
``` python
In[5]: pd.to_timedelta('1000', unit='ms')
Out[5]: Timedelta('0 days 00:00:00.000001')
```
I've tried my best to come up with a reason for why this behaviour would be desirable, but I can't think of any.
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} | 2 | 2016-01-25T19:43:27Z | 2016-02-13T10:33:27Z | 2016-02-13T10:24:44Z | CONTRIBUTOR | null | Fixes #12134
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} | 6 | 2016-01-25T20:12:21Z | 2016-01-27T23:38:41Z | 2016-01-26T16:26:34Z | MEMBER | null | See https://github.com/pydata/pandas/pull/12002#issuecomment-174550631
Doc builds are failing, testing if using the latest `ipython` version solves it.
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} | 0 | 2016-01-25T20:17:18Z | 2016-01-27T22:39:25Z | 2016-01-27T22:39:25Z | CONTRIBUTOR | null | This has been working for quite some time and I don't think the code changes broke this: https://ci.appveyor.com/project/jreback/pandas-465/build/1.0.145
so something changed in what conda is pull in I think (as I cannot repro this, though I have a slightly older build env)
if anyone wants to take a look
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} | 1 | 2016-01-25T21:52:05Z | 2016-02-02T15:16:18Z | 2016-02-02T15:16:18Z | CONTRIBUTOR | null | @jorisvandenbossche comments for followup on #11841
- [x] the repr: can we follow here PEP8 as well? :-) (I mean spaces after the comma's, I think this would make it a bit more readable, maybe also quotes around the strings)
- [x] `upsample` has been removed? (but is still in the documentation) Or is this now `asfreq`?
> doc are updated, it IS now `.asfreq`
- [x] `apply gives Exception: Must produce aggregated value` (for a series) if you pass it a function that does not return a aggregated value. For groupby this works, would be nice to have this consistent.
When applying it on a resampled dataframe, you get the cryptic `ValueError: cannot copy sequence with size 3 to array axis with dimension 180` message. Typical example is just a `.apply(lambda x: x)`
- [x] `Resampler.fillna` has no explanation
- [x] `DatetimeIndexResampler` has no docstring yet
- [x] `apply, agg, aggregate` have no docstring, transform a very brief
In theory it would be best if this were addressed in this PR, but given the PEP8 changes waiting on this PR, it's OK for me to leave this for a follow-up PR
- [x] Further, an inconsistency between `r.agg()` and `r[].agg()` in:
```
In [81]: df = pd.DataFrame(np.random.randn(1000, 3),
....: index=pd.date_range('1/1/2012', freq='S', periods=1000
),
....: columns=['A', 'B', 'C'])
In [82]: r = df.resample('3T')
In [83]: r.agg({'r1':'mean', 'r2':'sum'})
SpecificationError: nested dictionary is ambiguous in aggregation
In [84]: r[['A', 'B']].agg({'r1':'mean', 'r2':'sum'})
Out[84]:
r1 r2
A B A B
2012-01-01 00:00:00 -0.059093 0.040993 -10.636800 7.378766
2012-01-01 00:03:00 -0.037877 0.055133 -6.817820 9.923978
2012-01-01 00:06:00 -0.101921 0.061913 -18.345769 11.144373
2012-01-01 00:09:00 0.097927 -0.074492 17.626838 -13.408612
2012-01-01 00:12:00 -0.027901 0.035319 -5.022220 6.357459
2012-01-01 00:15:00 -0.037696 0.022259 -3.769580 2.225884
```
- [x] validate that we have tests for all combinations of aggregation
```
grouped.agg({'C': 'mean', 'D': 'sum'})
grouped[['C', 'D']].agg({'C': 'mean', 'D': 'sum'})
grouped.agg({'C': ['mean', 'sum'], 'D': ['mean', 'sum']})
grouped.agg({'C': {'r': 'mean', 'r2': 'sum'}, 'D': {'r': 'mean', 'r2': 'sum'}})
```
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| null | 14 | 2016-01-26T00:36:40Z | 2016-07-18T07:56:00Z | null | CONTRIBUTOR | null | Right now, when we calculate something like a rolling max, if a float32 dataset is being passed in, it upcasts it to a float64. This increases the memory requirements of a big dataset considerably.
This is due to the fact that the skiplist implementation only works with doubles. Would you be opposed if I added instantiations for int32, int64, float32, uint64, uint32?
This way, we can avoid copies for big datasets (which take up a lot of memory, and time).
cc @kawochen
xref #3007
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} | 4 | 2016-01-26T06:01:42Z | 2016-02-17T01:13:25Z | 2016-02-17T01:13:25Z | CONTRIBUTOR | null | In #10686, I should have made all the strings in `encode` Unicode strings. Now `'abc'` packed in P2 becomes (or rather remains as) `b'abc'` when unpacked in P3. This I think is the desired behavior (bytes remain as bytes and text remains as text), but it causes errors in `decode`, because, for example, `'typ'` (`==u'type'` in P2) is expected while `b'typ'` (`=='typ'` in P2) is the key.
Reading in the other direction is fine because P2 is more tolerant of these things.
To reproduce this,
```
(P2) python generate_legacy_storage_files.py your_dir msgpack
(P3) pandas.read_msgpack(the_file_just_created)
```
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| null | 6 | 2016-01-26T11:40:51Z | 2016-01-26T14:31:38Z | 2016-01-26T13:50:02Z | NONE | null | map seems to remove the default behaviour - workaround is to use a lambda function
``` python
import collections
import pandas as pd
a=pd.Series([1,2,3])
print 'a = '
print a
c=collections.defaultdict(int)
c[1]=1
c[2]=4
b=a.map(c)
print 'b = '
print b
# b.iloc[2] set to Nan (ignoring defaultdict)
d=a.map(lambda x : c[x])
print 'd = '
print d
# d.iloc[2] set to 0 (desired default value)
pd.show_versions()
a =
0 1
1 2
2 3
dtype: int64
b =
0 1
1 4
2 NaN
dtype: float64
d =
0 1
1 4
2 0
dtype: int64
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: 2.5.1 (dt dec pq3 ext)
Jinja2: None
```
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} | 5 | 2016-01-26T12:52:12Z | 2021-09-28T18:13:10Z | null | CONTRIBUTOR | null | Is there a way to right-align numbers in the HTML repr of tables? Right now they are left aligned, and it is a bit of a pain.
I see there is a lot of work on conditional HTML formatting recently (e.g, #10250). Is it already possible to achieve this?
/cc @TomAugspurger
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} | 6 | 2016-01-26T15:08:40Z | 2019-11-03T17:24:25Z | 2019-11-03T12:59:56Z | NONE | null | Working with the new style API, I would like to turn several columns of a dataframe into something like a heatmap. I would like to use the same range for all columns in the subset, i.e., the colour map should take a minimum and maximum value I specify, regardless of what actual range each column has. Currently using the "min" and "max" this is not intuitive. I would much prefer to be able to specify explicit vmin and vmax values, analogous to the seaborn.heatmap function.
Thanks for the style API, extremely useful.
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| null | 2 | 2016-01-26T15:46:11Z | 2016-01-26T18:04:22Z | 2016-01-26T18:04:22Z | NONE | null | I came across an intermittent issue that took some experimentation to track down. I was using a oneliner function for taring dataframes (or series, I've noticed the issue there as well) based on the last 50 rows:
``` python
def tare(df):
return df - df[-50:].mean()
```
On rare occasions, this gave me an obviously wrong result (whole columns close to or equal to zero). negating the mean series and then adding
``` python
def tare(df):
return df + (-df[-50:].mean())
# OR
def tare(df):
return df + (-1.0 * df[-50:].mean())
```
will not solve the issue either.
Is there any other way I could/should write the function that will not cause the same issue?
Below is a full reproduction that works on my system.
``` python
import pandas as pd
def tare(df):
return df - df[-50:].mean()
df = pd.read_csv('bug.txt')
for i in range(20000):
try:
assert tare(df).max().max() > 1.0
except:
print 'Failed on iteration {}.'.format(i)
raise
```
Requires the file [bug.txt](https://github.com/pydata/pandas/files/105078/bug.txt) containing a subset of my data.
I haven't tried reproducing it using random data, but I guess that should be possible.
```
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 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 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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} | 1 | 2016-01-26T16:00:03Z | 2021-03-21T21:08:13Z | 2021-03-21T21:08:13Z | CONTRIBUTOR | null | Right now, if you copy the HTML table representation of a `DataFrame` from Jupyter to Google Sheets or Open office Calc, you lose the top left empty cell (and the top left cell gets the first column name, which is incorrect).
Inserting an ` ` into that cell prevents this from happening. Would a PR be accepted which did this?
One way of achieving this is to set [`BLANK_VALUE`](https://github.com/pydata/pandas/blob/564166529071d33844712ab6038f6a237ddcb8f1/pandas/core/style.py#L174) to ` `. In Google documents then, the pasted cell is present but empty. In open office, the resulting cell has a single space in it (which seems OK).
I don't know what the wider implications of setting `BLANK_VALUE` are - I just want to set the upper left portion of the table so that the heading is correctly aligned when I copy the table. So maybe there is a better way?
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} | 5 | 2016-01-26T16:16:00Z | 2021-02-10T19:43:50Z | 2021-02-10T19:43:50Z | CONTRIBUTOR | null | [Right now it says](https://github.com/pydata/pandas/blob/564166529071d33844712ab6038f6a237ddcb8f1/pandas/core/style.py#L357):
``` python
def apply(self, func, axis=0, subset=None, **kwargs):
"""
Apply a function column-wise, row-wise, or table-wase,
updating the HTML representation with the result.
.. versionadded:: 0.17.1
Parameters
----------
func: function
axis: int, str or None
apply to each column (``axis=0`` or ``'index'``)
or to each row (``axis=1`` or ``'columns'``) or
to the entire DataFrame at once with ``axis=None``.
subset: IndexSlice
a valid indexer to limit ``data`` to *before* applying the
function. Consider using a pandas.IndexSlice
kwargs: dict
pass along to ``func``
Returns
-------
self
Notes
-----
This is similar to ``DataFrame.apply``, except that ``axis=None``
applies the function to the entire DataFrame at once,
rather than column-wise or row-wise.
"""
```
I spent ages trying to figure out how to feed any kind of function into this, only to eventually find the [Styler's docstring](https://github.com/pydata/pandas/blob/564166529071d33844712ab6038f6a237ddcb8f1/pandas/core/style.py#L68-L77):
``` python
"""
Notes
-----
Most styling will be done by passing style functions into
``Styler.apply`` or ``Styler.applymap``. Style functions should
return values with strings containing CSS ``'attr: value'`` that will
be applied to the indicated cells.
If using in the Jupyter notebook, Styler has defined a ``_repr_html_``
to automatically render itself. Otherwise call Styler.render to get
the genterated HTML.
"""
```
Really, the docstring of apply should state that the passed function should return CSS to be applied to the element. Otherwise, it just sounds like something which modifies the data before it is presented.
My use case is that I have a column full of [`uncertainties.ufloat`](https://pythonhosted.org/uncertainties/user_guide.html), and I want to convert them to a particular convenient string representation before they are formatted in a HTML table. I still can't figure out how to cleanly do this...
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} | 3 | 2016-01-26T17:12:42Z | 2019-11-02T01:13:53Z | 2019-11-02T01:13:52Z | CONTRIBUTOR | null | xref #12141
currently we have backup routines for everywhere we use bottleneck.
If we made it a requirement (rather than a highly recommended dep) we could forgo this.
its easily installable via `conda` and `pip` (wheels); this used to be an issue.
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} | 1 | 2016-01-26T18:01:22Z | 2020-05-22T03:25:11Z | null | CONTRIBUTOR | null | xref #12130
we should clear() the _cache variable (set by `cache_readonly`) in between unit tests. same with the DataFrame tests.
this will prevent any tests from mutating these 'fixtures' and causing issues in later tests
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} | 0 | 2016-01-27T00:41:15Z | 2016-01-27T12:59:13Z | 2016-01-27T12:59:13Z | CONTRIBUTOR | null | closes #12089
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} | 1 | 2016-01-27T04:36:59Z | 2016-03-14T13:02:22Z | 2016-03-14T13:02:22Z | CONTRIBUTOR | null | This gives pretty decent explanation of the infer_datetime_format parameter:
http://pandas.pydata.org/pandas-docs/version/0.17.1/whatsnew.html#id55
The api docs are fairly opaque.
Improve api docs to make it clearer:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
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} | 1 | 2016-01-27T10:55:54Z | 2016-02-06T20:04:13Z | 2016-02-06T20:04:13Z | MEMBER | null | ```
csv = pd.read_csv(file_path, chunksize=10)
chunk = next(csv)
```
yields
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-21-ec4c9826eb96> in <module>()
1 csv = pd.read_csv(file_path, chunksize=10)
----> 2 chunk = next(csv)
TypeError: TextFileReader object is not an iterator
```
which I didn't expect. Sure, I can do `csv = iter(pd.read_csv(file_path, chunksize=10))`, but since I don't see any advantage (i.e. indexing) in having a non-iterator iterable, maybe read_csv could directly return an iterator? In other words,
```
In [3]: csv = pd.read_csv(file_path, chunksize=100)
In [4]: print(len(list(iter(csv))), len(list(iter(csv))))
(23, 0)
```
is maybe not what people expect from a non-iterator iterable.
(Even) I should be able to submit a PR. The only question is whether 1) this is worth fixing 2) this is worth fixing in pandas rather than in pytables.
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} | 8 | 2016-01-27T11:52:29Z | 2021-04-23T00:39:57Z | null | MEMBER | null | ```
df = pd.DataFrame([[1,2,3],[4,5,6]],
columns=pd.MultiIndex.from_tuples([(1,'a'), (1,'b'), (2,'c')])).astype(str)
store = pd.HDFStore('/tmp/store.hdf')
store.append('test', df, min_itemsize={1 : 20})
```
yields
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-cea3042a011b> in <module>()
----> 1 store.append('test', df, min_itemsize={1 : 20})
/home/pietro/nobackup/repo/pandas/pandas/io/pytables.py in append(self, key, value, format, append, columns, dropna, **kwargs)
915 kwargs = self._validate_format(format, kwargs)
916 self._write_to_group(key, value, append=append, dropna=dropna,
--> 917 **kwargs)
918
919 def append_to_multiple(self, d, value, selector, data_columns=None,
/home/pietro/nobackup/repo/pandas/pandas/io/pytables.py in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1260
1261 # write the object
-> 1262 s.write(obj=value, append=append, complib=complib, **kwargs)
1263
1264 if s.is_table and index:
/home/pietro/nobackup/repo/pandas/pandas/io/pytables.py in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwargs)
3783 self.create_axes(axes=axes, obj=obj, validate=append,
3784 min_itemsize=min_itemsize,
-> 3785 **kwargs)
3786
3787 for a in self.axes:
/home/pietro/nobackup/repo/pandas/pandas/io/pytables.py in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs)
3466
3467 # validate our min_itemsize
-> 3468 self.validate_min_itemsize(min_itemsize)
3469
3470 # validate our metadata
/home/pietro/nobackup/repo/pandas/pandas/io/pytables.py in validate_min_itemsize(self, min_itemsize)
3105 raise ValueError(
3106 "min_itemsize has the key [%s] which is not an axis or "
-> 3107 "data_column" % k)
3108
3109 @property
ValueError: min_itemsize has the key [1] which is not an axis or data_column
```
... which is actually true (if "data_column" is interpreted as "queryable column" rather than just "column of data"), but should not be a blocker (at least, judging from the documentation).
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} | 4 | 2016-01-27T14:57:47Z | 2020-06-06T13:54:33Z | 2016-01-27T15:14:08Z | NONE | null | When using the `GroupBy.apply()` method with a custom function handle, the first group is iterated over twice. Explicitly looping over each group and applying the function directly does not yield the error.
Error reproduction
``` python
>>> import pandas as pd
>>> df = pd.DataFrame({'one':[1,2,3,1,2,3],'two':[4,5,6,7,8,9]})
>>> df
one two
0 1 4
1 2 5
2 3 6
3 1 7
4 2 8
5 3 9
>>> gp = df.groupby('one')
>>> def foo(group):
... print(group.iloc[[0]].index[0])
...
>>> gp.apply(foo)
0
0
1
2
Empty DataFrame
Columns: []
Index: []
>>> len(gp)
3
```
The example prints the row index of the first group member. The '0' is printed twice.
Versions:
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: None
pip: 8.0.1
setuptools: 19.4
Cython: None
numpy: 1.10.1
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 12 | 2016-01-27T15:27:03Z | 2021-01-25T18:54:07Z | 2021-01-25T18:54:06Z | NONE | null | ```
pd.date_range(pd.Timestamp('2015-1-1', tz='US/Eastern'), pd.Timestamp('2016-1-1', tz='US/Eastern'), freq='H') + pd.DateOffset(days=1)
Traceback (most recent call last):
File "/home/jeff/.virtualenvs/omnipotent/local/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-21-9574a4185a77>", line 1, in <module>
pd.date_range(pd.Timestamp('2015-1-1', tz='US/Eastern'), pd.Timestamp('2016-1-1', tz='US/Eastern'), freq='H') + pd.DateOffset(days=1)
File "/home/jeff/.virtualenvs/omnipotent/local/lib/python2.7/site-packages/pandas/tseries/base.py", line 412, in __add__
return self._add_delta(other)
File "/home/jeff/.virtualenvs/omnipotent/local/lib/python2.7/site-packages/pandas/tseries/index.py", line 731, in _add_delta
new_values = self._add_offset(delta).asi8
File "/home/jeff/.virtualenvs/omnipotent/local/lib/python2.7/site-packages/pandas/tseries/index.py", line 750, in _add_offset
result = result.tz_localize(self.tz)
File "/home/jeff/.virtualenvs/omnipotent/local/lib/python2.7/site-packages/pandas/util/decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "/home/jeff/.virtualenvs/omnipotent/local/lib/python2.7/site-packages/pandas/tseries/index.py", line 1724, in tz_localize
ambiguous=ambiguous)
File "pandas/tslib.pyx", line 3781, in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:64980)
NonExistentTimeError: 2015-03-08 02:00:00
```
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-61-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 7.1.0
setuptools: 18.0.1
Cython: None
numpy: 1.10.4
scipy: 0.16.1
statsmodels: None
IPython: 4.0.3
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: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
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} | 1 | 2016-01-27T15:33:44Z | 2016-01-30T19:22:40Z | 2016-01-30T19:22:40Z | NONE | null | `pd.read_excel` with keyword argument `squeeze=True` raises an AttributeError. This is unexpected, in that:
- I would expect it to work analogously to `pd.read_csv`, which does not exhibit the same problem
- it worked fine in a previous version (0.15 I think?)
Steps to reproduce:
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.17.1'
In [3]: df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
In [4]: df.to_csv('tmp.csv', index=False)
In [5]: df.to_excel('tmp.xlsx', index=False)
In [6]: pd.read_csv('tmp.csv', index_col=0, squeeze=True)
Out[6]:
a
1 3
2 4
Name: b, dtype: int64
In [7]: pd.read_excel('tmp.xlsx', index_col=0, squeeze=True)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-ed256b323894> in <module>()
----> 1 pd.read_excel('tmp.xlsx', index_col=0, squeeze=True)
C:\Anaconda3\lib\site-packages\pandas\io\excel.py in read_excel(io, sheetname, header, skiprows, skip_footer, index_col, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, has_index_names, converters, engine, **kwds)
168 date_parser=date_parser, na_values=na_values, thousands=thousands,
169 convert_float=convert_float, has_index_names=has_index_names,
--> 170 skip_footer=skip_footer, converters=converters, **kwds)
171
172 class ExcelFile(object):
C:\Anaconda3\lib\site-packages\pandas\io\excel.py in _parse_excel(self, sheetname, header, skiprows, skip_footer, index_col, has_index_names, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, verbose, **kwds)
437
438 output[asheetname] = parser.read()
--> 439 output[asheetname].columns = output[asheetname].columns.set_names(header_names)
440
441 if ret_dict:
C:\Anaconda3\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
2353 or name in self._metadata
2354 or name in self._accessors):
-> 2355 return object.__getattribute__(self, name)
2356 else:
2357 if name in self._info_axis:
AttributeError: 'Series' object has no attribute 'columns'
```
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} | 0 | 2016-01-27T20:44:14Z | 2016-01-27T22:39:25Z | 2016-01-27T22:39:25Z | CONTRIBUTOR | null | closes #12139
closes #12159
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} | 0 | 2016-01-27T22:28:31Z | 2016-07-10T21:39:51Z | 2016-07-10T21:39:51Z | CONTRIBUTOR | null | xref #6815/#6813 .freq in Timestamp rather than .offset (internally used) (0.14.0 ???)
This is a bit non-trivial so pulling out to a separate issue from #6581
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} | 0 | 2016-01-27T23:41:25Z | 2016-07-20T20:21:46Z | 2016-06-23T15:37:45Z | MEMBER | null | Our custom `AccessorDocumenter`s (defined in docs/source.conf.py) are not working anymore with sphinx 1.3.x
See eg the doc build on travis for the errors, and the api overview page on the dev docs.
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} | 18 | 2016-01-28T03:29:49Z | 2016-02-12T20:51:26Z | 2016-02-12T20:42:51Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/11692
Closes https://github.com/pydata/pandas/issues/12134
Closes https://github.com/pydata/pandas/issues/12125
This adds a `.format` method to Styler for formatting the display value
(the actual text) of each scalar value.
In the processes of cleaning up the template, I close #12134 (spurious 0)
and #12125 (KeyError from using `iloc` improperly)
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} | 4 | 2016-01-28T09:05:35Z | 2020-01-21T01:06:10Z | 2020-01-21T01:06:10Z | CONTRIBUTOR | null | There is a problem when writing pickles with Pandas 0.14.1 and reading them with Pandas 0.17.1. The problem occurs if the pickles contain `isodate.UTC` or other timezone objects.
The problem can be reduced to this test case:
**my_tz.py**:
```
from datetime import tzinfo
class MyTz(tzinfo):
def __init__(self):
pass
```
**write_pickle_test.py** (run with Pandas 0.14.1):
```
import pandas as pd
import cPickle
from my_tz import MyTz
data = pd.Series(), MyTz()
with open('test.pickle', 'wb') as f:
cPickle.dump(data, f)
```
**read_pickle_test.py** (run with Pandas 0.17.1):
```
import pandas as pd
pd.read_pickle('test.pickle')
```
Reading the pickle with `pickle.load()` would fail when trying to load the `Series` object:
```
TypeError: _reconstruct: First argument must be a sub-type of ndarray
```
which is why `pd.read_pickle()` attempts to use `pandas.compat.pickle_compat.load()`. But then we get this instead for the `MyTz` object:
```
$ python read_pickle.py
Traceback (most recent call last):
File "read_pickle_test.py", line 2, in <module>
pd.read_pickle('test.pickle')
File "pandas/io/pickle.py", line 60, in read_pickle
return try_read(path)
File "pandas/io/pickle.py", line 57, in try_read
return pc.load(fh, encoding=encoding, compat=True)
File "pandas/compat/pickle_compat.py", line 116, in load
return up.load()
File "/usr/lib64/python2.7/pickle.py", line 858, in load
dispatch[key](self)
File "pandas/compat/pickle_compat.py", line 16, in load_reduce
if type(args[0]) is type:
IndexError: tuple index out of range
```
I see the following problems in [pandas.compat.pickle_compat.load_reduce()](/pydata/pandas/blob/9bc82438268742575e501d099c6f22ce0a2dcac1/pandas/compat/pickle_compat.py#L13):
(1) It attemps to access `args[0]` while `args` can be empty.
```
if type(args[0]) is type:
n = args[0].__name__
```
(2) The above code is dead code anyway, since `n` isn't referenced to anywhere else in the function. Removing those two lines fixes the unpickling problem completely.
Note that the `MyTz` class in the test above does implement a proper `__init__()` method as required in [Python documentation for tzinfo](https://docs.python.org/2.7/library/datetime.html#tzinfo-objects): _"Special requirement for pickling: A tzinfo subclass must have an `__init__` method that can be called with no arguments, else it can be pickled but possibly not unpickled again. This is a technical requirement that may be relaxed in the future."_ While `isodate.UTC` violates this requirement, that doesn't seem to be the cause of this problem.
(3) Also, at the very end of `load_reduce()`:
```
stack[-1] = value
```
is code that is never reached, since all previous code branches either return or raise. Also, this line is invalid, since `value` isn't initialized anywhere in the function.
---
I originally sent this as [a comment](/pydata/pandas/issues/6871#issuecomment-176043180) to issue #6871, but since it's not exactly the same thing (only a similar traceback), I think it deserves a separate issue.
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} | 3 | 2016-01-28T12:55:13Z | 2016-02-13T15:18:24Z | 2016-02-13T15:18:24Z | CONTRIBUTOR | null | While working on issue #12031 (adding a `decimal` parameter to `.to_latex()` and `.to_html()`), I realized a solution would be pretty straight-forward if only `FloatArrayFormatter` was a bit cleaned.
Currently, there are two methods that do a similar work:
- `.get_formatted_data()`, which returns the array with its float values converted to strings using the parameters given at initialisation.
- `.get_result()`, which does something similar, but with a fixed-width output suitable for screen printing.
The two methods don't share any piece of code.
(The first method can already deal with a `decimal` parameter. The second cannot, and it is the one used by `.to_latex`.)
This is in part my fault, since my pull request #11681 could have been more factorized.
Anyway, I'm volunteering to do a PR.
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} | 1 | 2016-01-28T13:32:32Z | 2016-01-29T16:20:32Z | 2016-01-28T21:23:45Z | CONTRIBUTOR | null | DEPR: Removal of cols keyword in favor of subset in DataFrame.duplicated() and DataFrame.drop_duplicates(), xref #6680
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| null | 6 | 2016-01-28T13:37:22Z | 2018-07-30T17:38:29Z | 2018-07-30T15:48:48Z | NONE | null | I am appending an HDF5 store with a pandas dataframe and Python crashes without any traceback.
``` python
chunk.to_hdf('all_data.h5', 'df',mode='a',format='table',append=True)
```
Discussion on stackoverflow:
http://stackoverflow.com/questions/31419768/pandas-appending-suing-to-hdf-now-crashes-after-updating-to-pytables-3-2
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| null | 2 | 2016-01-28T13:46:58Z | 2016-01-28T14:03:46Z | 2016-01-28T14:03:46Z | NONE | null | I am summing a column of a pandas dataframe with a scalar and the result is a column full of zeros. The weird thing is that sometimes it will return zeros and sometimes it won't.
With this code I get a similar error:
``` python
import pandas as pd
import numpy as np
from pdb import set_trace as pdb
for _ in xrange(100):
data = np.random.randint(1,100000,1000000)
df = pd.DataFrame(data,columns=['column'])
scalar = np.random.randint(1,100000)
df.column += scalar
if df.column.max()==0:
pdb()
```
My `df.column` gets full of zeros every now and then.
Discussion on stackoverflow:
http://stackoverflow.com/questions/35057969/pandas-sum-column-with-scalar-results-in-zeros
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| null | 9 | 2016-01-28T16:44:17Z | 2016-07-01T16:55:07Z | 2016-03-23T12:56:52Z | NONE | null | Passing the `y` argument of a plot to `DataFrame.eval` allows plotting a numerical expression.
Makes numerical data exploration much easier. E.g. graphically finding the norm of two curves.
See example here:
[example notebook](https://gist.github.com/TsvikiHirsh/46fd47bc35dc1874234d)
No support for multicolumn plotting, e.g. for `["a","4*b"]`
Should be very easy to apply the same on the `index`.
Could be very useful for data exploration, without modifying the original `DataFrame`
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} | 3 | 2016-01-28T17:58:27Z | 2016-02-10T17:29:34Z | 2016-02-10T17:29:34Z | NONE | null | Hi,
I get a very strange behavior when i try to resample categorical data with and timedelta index, as compared to a datetime index.
``` pandas
>> d1 = pd.DataFrame({'Group_obj': 'A'}, index=pd.date_range('2000-1-1', periods=20, freq='s'))
>> d1['Group'] = d1['Group_obj'].astype('category')
>> d1
Group_obj Group
2000-01-01 00:00:00 A A
2000-01-01 00:00:01 A A
2000-01-01 00:00:02 A A
2000-01-01 00:00:03 A A
2000-01-01 00:00:04 A A
2000-01-01 00:00:05 A A
2000-01-01 00:00:06 A A
2000-01-01 00:00:07 A A
2000-01-01 00:00:08 A A
2000-01-01 00:00:09 A A
2000-01-01 00:00:10 A A
2000-01-01 00:00:11 A A
2000-01-01 00:00:12 A A
2000-01-01 00:00:13 A A
2000-01-01 00:00:14 A A
2000-01-01 00:00:15 A A
2000-01-01 00:00:16 A A
2000-01-01 00:00:17 A A
2000-01-01 00:00:18 A A
2000-01-01 00:00:19 A A
>> corr = d1.resample('10s', how=lambda x: (x.value_counts().index[0]))
>> corr
Group_obj Group
2000-01-01 00:00:00 A A
2000-01-01 00:00:10 A A
>> corr.dtypes
Group_obj object
Group object
dtype: object
>> d2 = d1.set_index(pd.to_timedelta(list(range(20)), unit='s'))
>> fxx = d2.resample('10s', how=lambda x: (x.value_counts().index[0]))
>> fxx
Group_obj Group
00:00:00 A NaN
00:00:10 A NaN
>> fxx.dtypes
Group_obj object
Group float64
dtype: object
```
It seems to me the aggregated result in case of using timedelta as an index for the category is always NaN.
Should this be?
Thx
PS: is there a way to specify the dtype for the aggregated columns?
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} | 4 | 2016-01-28T19:15:12Z | 2016-02-10T16:20:49Z | 2016-02-10T16:20:49Z | NONE | null | I am using Python 3.
I tried saving a DataFrame with Unicode labels using the `.to_msgpack` method. I didn't specify an encoding, because I assumed it would use UTF-8, which is the default encoding for Python in my locale (en_US.UTF-8) as well as just a sensible encoding to use in general.
Instead, it tried to encode labels in Latin-1, which failed. Latin-1 seems like a strangely antiquated default to use in modern code.
I can work around it by passing the `encoding='utf-8'` option, but it would be helpful if UTF-8 were the default, as it is in other Python I/O.
Here's my version information:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-51-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 1.5.4
setuptools: 2.2
Cython: 0.23.3
numpy: 1.10.4
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: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
```
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} | 0 | 2016-01-28T22:02:05Z | 2016-02-17T13:20:15Z | 2016-02-17T13:20:15Z | CONTRIBUTOR | null | this eliminates some deprecation warnings
```
pandas/compat/__init__.py:
113 : return inspect.getargspec(f).args
# change here
pandas/tests/series/test_analytics.py:
4 : from inspect import getargspec
468 : if 'numeric_only' in getargspec(f).args:
pandas/util/decorators.py:
278 : from inspect import getargspec
279 : spec = getargspec(func)
```
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} | 2 | 2016-01-29T01:55:18Z | 2016-07-11T08:22:19Z | 2016-07-11T08:22:19Z | NONE | null | I run into use-cases for this feature often. It seems so simple that it's hard to believe this is purely an oversight.
So, why does `Series.plot()` not have a `kind='scatter'` option?
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} | 5 | 2016-01-29T10:52:53Z | 2016-02-06T21:02:48Z | 2016-02-06T20:04:13Z | MEMBER | null | If the approach is not overkill, it can be used also in `UTF8Recoder` and `UnicodeReader` from `pandas/common/io.py`. Closes #12153.
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| null | 10 | 2016-01-29T13:13:11Z | 2017-03-19T04:43:40Z | 2016-01-29T13:46:24Z | NONE | null | I open a stackoverflow question here :
http://stackoverflow.com/questions/35083277/pandas-concat-on-sparse-dataframes-a-mystery
And someone ask me to open an issue on Github.
To summarise, I don't really understand what's going on after a concat of 2 sparse data frame...
After such concat, df.density or df.memory_usage, for example, will throw an error.
Moreover, the basic structure of the sparse result seems strange...
I'm sorry that the bug is not defined better.
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https://api.github.com/repos/pandas-dev/pandas/issues/12176 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12176/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12176/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12176/events | https://github.com/pandas-dev/pandas/issues/12176 | 129,792,935 | MDU6SXNzdWUxMjk3OTI5MzU= | 12,176 | confusing ImportError message in `__init__.py` | {
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} | 1 | 2016-01-29T15:15:58Z | 2016-02-27T14:28:05Z | 2016-02-27T14:28:05Z | NONE | null | In some cases the error message could be more clear, as discussed here:
https://github.com/pydata/pandas/commit/69cab57c9889391a587656ff8c6a7f9b38187bb1#commitcomment-15762471
my example:
```
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/marcin/install/lib/python2.7/site-packages/pandas/__init__.py", line 13, in <module>
"extensions first.".format(module))
ImportError: C extension: No module named pytz.tzinfo 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.
```
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} | 0 | 2016-01-29T15:37:02Z | 2017-03-23T13:56:43Z | 2017-03-23T13:56:43Z | CONTRIBUTOR | null | [for example on the 3.5 build](https://travis-ci.org/pydata/pandas/jobs/105685173)
like to `assert_produces_warning` on these to remove from the test output; these are legit.
```
./home/travis/build/pydata/pandas/pandas/indexes/base.py:2385: RuntimeWarning: unorderable types: str() < int(), sort order is undefined for incomparable objects
return this.join(other, how=how, return_indexers=return_indexers)
```
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} | 1 | 2016-01-29T15:42:09Z | 2016-04-18T17:46:27Z | 2016-04-18T17:46:27Z | CONTRIBUTOR | null | this is pretty old code, I think you you simply eliminate the try:excepts: here entirey: https://github.com/pydata/pandas/blob/master/pandas/computation/tests/test_eval.py#L52
```
In [4]: pd.test(raise_warnings='release')
Running unit tests for pandas
pandas version 0.17.1+289.g0977cb6
numpy version 1.10.2
pandas is installed in pandas
Python version 2.7.11 |Continuum Analytics, Inc.| (default, Dec 6 2015, 18:57:58) [GCC 4.2.1 (Apple Inc. build 5577)]
nose version 1.3.7
...................................................pandas/computation/tests/test_eval.py:53: DeprecationWarning: BaseException.message has been deprecated as of Python 2.6
msg = e.message
pandas/computation/tests/test_eval.py:53: DeprecationWarning: BaseException.message has been deprecated as of Python 2.6
msg = e.message
...............pandas/computation/tests/test_eval.py:310: DeprecationWarning: BaseException.message has been deprecated as of Python 2.6
emsg = e.message
S..............pandas/computation/tests/test_eval.py:310: DeprecationWarning: BaseException.message has been deprecated as of Python 2.6
emsg = e.message
S............................................................................................................................................................................................................
```
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} | 1 | 2016-01-29T16:20:38Z | 2016-01-29T16:39:09Z | 2016-01-29T16:39:09Z | CONTRIBUTOR | null | TST: `run.test()` now defaults warnings based on release version
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} | 3 | 2016-01-29T18:16:15Z | 2016-02-01T20:09:16Z | 2016-02-01T20:09:16Z | CONTRIBUTOR | null | This works as expected:
``` python
>>> s = pd.Series(['Name', 'email|Name|address', 'address|email'])
>>> s.str.get_dummies(sep='|')
Name address email
0 1 0 0
1 1 1 1
2 0 1 1
```
However, if any of the categorical variables is named exactly `'name'`, then there's a problem.
``` python
>>> s = pd.Series(['name', 'email|Name|address', 'address|email'])
>>> s.str.get_dummies(sep='|')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-1a39a6dcd56b> in <module>()
----> 1 s.str.get_dummies(sep='|')
/Users/dgrady/anaconda/envs/python3/lib/python3.5/site-packages/pandas/core/strings.py in get_dummies(self, sep)
1377 data = self._orig.astype(str) if self._is_categorical else self._data
1378 result = str_get_dummies(data, sep)
-> 1379 return self._wrap_result(result, use_codes=(not self._is_categorical))
1380
1381 @copy(str_translate)
/Users/dgrady/anaconda/envs/python3/lib/python3.5/site-packages/pandas/core/strings.py in _wrap_result(self, result, use_codes, name)
1100 if not hasattr(result, 'ndim'):
1101 return result
-> 1102 name = name or getattr(result, 'name', None) or self._orig.name
1103
1104 if result.ndim == 1:
/Users/dgrady/anaconda/envs/python3/lib/python3.5/site-packages/pandas/core/generic.py in __nonzero__(self)
729 raise ValueError("The truth value of a {0} is ambiguous. "
730 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
--> 731 .format(self.__class__.__name__))
732
733 __bool__ = __nonzero__
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
```
This is presumably related to the `pandas/core/strings.py` code in the stacktrace.
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 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: 2.8
```
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| null | 2 | 2016-01-29T19:56:31Z | 2016-01-30T14:58:28Z | 2016-01-30T14:58:10Z | NONE | null | Using `Series.reindex()` does not work when the Series uses a MultiIndex and the desired re-index also uses a MultiIndex. An error message reports that the join is ambiguous, even when it is not.
In the following example, I'm setting up some schedule data for individuals who belong to households. Three tables will be created: `households`, `persons`, and `activities`. There is an implicit hierarchy: activities belong to persons which belong to households. Their indexes reflect this relationship.
``` python
import pandas as pd
import numpy as np
households = pd.DataFrame({'dwelling_type': np.random.choice(['house', 'apartment'], size=5, replace=True)})
households.index.name = 'hh_id'
person_ids = [0,1,0,1,2,3,0,0,0,1]
person_hhlds = np.repeat(households.index.values, [2,4,1,1,2])
persons_index = pd.MultiIndex.from_arrays([person_hhlds, person_ids], names=['hh_id', 'person_id'])
persons = pd.DataFrame(index=persons_index)
persons['sex'] = np.random.choice(list('FM'), size=10, replace=True)
persons['age'] = np.random.randint(18,60,10)
activity_ids = [0,0,0,1,0,0,1,2,0,0,0,1,0,0,1,2,3]
activity_persons = np.repeat(persons_index.get_level_values('person_id').values, [1,1,2,1,3,1,1,2,1,4])
activity_hhlds = np.repeat(persons_index.get_level_values('hh_id').values, [1,1,2,1,3,1,1,2,1,4])
activity_index = pd.MultiIndex.from_arrays([activity_hhlds, activity_persons, activity_ids],
names=['hh_id', 'person_id','activity_id'])
activities = pd.DataFrame(index=activity_index)
activities['type'] = np.random.choice(['work', 'school', 'shop'], size=len(activities), replace=True)
activities['zone'] = np.random.randint(1,10, len(activities))
```
Going from a regular Index to a MultiIndex works as expected. In this case, I'm trying to join `dwelling_type` data from the households table to the persons table:
``` python
person_dwelling_type = households.dwelling_type.reindex(persons.index, level='hh_id')
print pd.DataFrame({'person_dwelling_type': person_dwelling_type})
```
```
>>
hh_id person_id
0 0 house
1 house
1 0 apartment
1 apartment
2 apartment
3 apartment
2 0 apartment
3 0 house
4 0 apartment
1 apartment
Name: dwelling_type, dtype: object
```
But when I try to do a similar operation, joining `age` from persons to activities, I get an error:
``` python
activity_age = persons.age.reindex(activities.index, level=['hh_id', 'person_id'])
print activity_age
```
```
>> ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-15-e2b526341a93> in <module>()
----> 1 activity_age = persons.age.reindex(activities.index, level=['hh_id', 'person_id'])
2 pd.DataFrame({'activity_age': activity_age})
C:\Anaconda\lib\site-packages\pandas\core\series.pyc in reindex(self, index, **kwargs)
2266 @Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs)
2267 def reindex(self, index=None, **kwargs):
-> 2268 return super(Series, self).reindex(index=index, **kwargs)
2269
2270 @Appender(generic._shared_docs['fillna'] % _shared_doc_kwargs)
C:\Anaconda\lib\site-packages\pandas\core\generic.pyc in reindex(self, *args, **kwargs)
1960 # perform the reindex on the axes
1961 return self._reindex_axes(axes, level, limit, tolerance,
-> 1962 method, fill_value, copy).__finalize__(self)
1963
1964 def _reindex_axes(self, axes, level, limit, tolerance, method,
C:\Anaconda\lib\site-packages\pandas\core\generic.pyc in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
1974 new_index, indexer = ax.reindex(
1975 labels, level=level, limit=limit, tolerance=tolerance,
-> 1976 method=method)
1977
1978 axis = self._get_axis_number(a)
C:\Anaconda\lib\site-packages\pandas\core\index.pyc in reindex(self, target, method, level, limit, tolerance)
5269 target, indexer, _ = self._join_level(target, level, how='right',
5270 return_indexers=True,
-> 5271 keep_order=False)
5272 else:
5273 if self.equals(target):
C:\Anaconda\lib\site-packages\pandas\core\index.pyc in _join_level(self, other, level, how, return_indexers, keep_order)
2407
2408 if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
-> 2409 raise TypeError('Join on level between two MultiIndex objects '
2410 'is ambiguous')
2411
TypeError: Join on level between two MultiIndex objects is ambiguous
```
This seems to be a completely unintuitive result, given that the relationship is unambiguously one-to-many, even though MultiIndex's are used.
I'm able to work around this by dropping levels and replacing Indexs, but to me it seems that this should just be an easy thing to accomplish.
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} | 16 | 2016-01-30T02:17:56Z | 2016-02-10T18:51:40Z | 2016-02-10T18:51:40Z | CONTRIBUTOR | null | xref #11778
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} | 6 | 2016-01-30T02:23:37Z | 2016-01-30T14:06:04Z | 2016-01-30T02:28:43Z | NONE | null | `import pandas as pd
import datetime
from pandas_datareader import data, wb
import csv
import json
# declaring libraries for the analysis
out= open("testfile.csv", "rt") # read the .csv file
data = csv.reader(out)
data = [[row[0],row[1] + "_" + row[2],row[3] +"_" + row[4], row[5],row[6]] for row in data] #this part is mainly to concatenate the City and country
out.close()
out=open("data.csv", "wt")
output = csv.writer(out)
for row in data:
output.writerow(row)
out.close()
# <------------ End of concatenation ------------->
# The result is written out and saved in data.csv (I don't want to override the original source)
df = pd.read_csv('data.csv') #using pandas and dataframe, I can read in data and loop it through between the Arrival Date and Departure Date
df.DateDpt = pd.to_datetime(df.DateDpt)
df.DateAr = pd.to_datetime(df.DateAr)
df = df.set_index('DateAr')
new_df = pd.DataFrame()
for i, data in df.iterrows():
data = data.to_frame().transpose()
data = data.reindex(pd.date_range(start=data.index[0], end=data.DateDpt[0])).fillna(method='ffill').reset_index().rename(columns={'index': 'DateAr'})
new_df = pd.concat([new_df, data])
new_df = new_df[['AuthorID', 'ArCity_ArCountry', 'DptCity_DptCountry', 'DateAr', 'DateDpt']]
print (new_df)
# <---- End of iteration ---->
# Json file : <------ JSON ----------------->
json_dict = {}
for arrival_date, data in new_df.groupby('DateAr'):
matching_dates = data[data.DateDpt==arrival_date]
not_matching_dates = data[data.DateDpt!=arrival_date]
json_dict[arrival_date.strftime('%Y-%m-%d')] = {}
if not matching_dates.empty:
for city, flights in matching_dates.groupby('ArCity_ArCountry'):
json_dict[arrival_date.strftime('%Y-%m-%d')][city] = [str(v) for v in flights.AuthorID.to_dict().values()]
if not not_matching_dates.empty:
for city, flights in not_matching_dates.groupby('DptCity_DptCountry'):
json_dict[arrival_date.strftime('%Y-%m-%d')][city] = [str(v) for v in flights.AuthorID.to_dict().values()]
with open('json_dict.json', 'w') as f:
json.dump(json_dict, f, indent=4, sort_keys=True)
# <------- End of JSON ---------------->`
CSV File (testfile.csv)
AuthorID ArCity ArCountry DptCity DptCountry DateDpt DateAr
AAA Paris France NewYork UnitedState 2008-03-10 2001-02-02
BBB Paris France NewYork UnitedState 2008-03-10 2001-02-02
CCC Paris France Beijing Japan 2008-03-10 2001-02-02
DDD Paris France NewYork UnitedState 2008-03-10 2001-02-02
EEE Paris France London UK 2008-03-10 2001-02-02
FFF Paris France NewYork UnitedState 2008-03-10 2001-02-02
GGG Paris France Beijing Japan 2008-03-10 2001-02-02
Expected output:
```
{
"2001-02-02": {
"Beijing _Japan": [
"CCC",
"GGG"
],
"London_UK": [
"EEE"
],
"NewYork_UnitedState": [
"AAA"
"BBB"
"DDD"
"FFF"
],
"Paris_France": [
"AAA"
"BBB"
"CCC"
"DDD"
"EEE"
"FFF"
]
},
.
.
.
}
But the current output is
`{
"2001-02-02": {
"Beijing _Japan": [
"GGG"
],
"London_UK": [
"EEE"
],
"NewYork_UnitedState": [
"FFF"
]
},
"2001-02-03": {
"Beijing _Japan": [
"GGG"
],
"London_UK": [
"EEE"
],
"NewYork_UnitedState": [
"FFF"
]
},
.
.
.
}`
``
```
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| null | 3 | 2016-01-30T15:47:27Z | 2016-02-01T00:25:12Z | 2016-01-30T19:22:40Z | CONTRIBUTOR | null | closes #12157
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} | 0 | 2016-01-30T16:45:53Z | 2016-07-29T00:15:15Z | 2016-07-29T00:15:15Z | MEMBER | null | See [this comment](https://github.com/pydata/pandas/pull/12173#discussion_r51347252). Basically we now have
```
In [8]: read_csv(StringIO(u'A,B\n1,2\n3,4'))
Out[8]:
A B
0 1 2
1 3 4
```
but
```
In [9]: concat(read_csv(StringIO(u'A,B\n1,2\n3,4'), chunksize=1))
Out[9]:
A B
0 1 2
0 3 4
```
We want the latter to also have index (0,1).
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| null | 1 | 2016-01-30T19:00:24Z | 2016-01-30T19:19:08Z | 2016-01-30T19:19:08Z | CONTRIBUTOR | null | When loading an excel file with pandas that contains a row with integers that have preceding zeros such as 01, the number is automatically parsed as integer and results as 1. This even happens when the row is formatted as 'text' or the number is entered as '02 (second row). I believe it would be correct if the row was parsed as string in that case.
df=pd.read_excel(r'c:\temp\string.xlsx','Sheet1')
df['row1'][0]
df['row1'][1]
1
2
instead of
01
02
example excel file:
https://www.dropbox.com/s/k7coyy8gnntl1hv/string.xlsx?dl=0
The issue was discussed here:
http://stackoverflow.com/questions/35084022/load-xlsx-into-pandas-as-string?noredirect=1#comment57890144_35084022
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} | 10 | 2016-01-31T02:01:50Z | 2021-07-28T11:07:56Z | null | NONE | null | Currently, Pandas interpolation interpolates all gaps, regardless of there size and the limit parameter is used to limit the number of replacement : if there is a gap of 3 values and limit=2, pandas replaces the first 2 values.
I have difficulty understanding why someone would want to do interpolation on only a few missing items in a consecutive series of missing.
Personally, depending on the length of the gap, I would like to decide to interpolate the whole gap or none of it. For example, in an hourly time-series, interpolation of missing hours up to a maximum of 3 consecutive hours:
gaps <= 3 would be interpolated
gaps > 3 remain untouched.
I would appreciated a option for interpolation such as R "na.approx" in which a "maxgap" parameters is available. maxgap: maximum number of consecutive NAs to fill. Any longer gaps will be left unchanged.
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} | 4 | 2016-01-31T02:25:51Z | 2016-05-26T12:54:22Z | 2016-05-26T12:54:22Z | CONTRIBUTOR | null | From the docs I see that the difference between .merge and .join is that .join operates using indexes by default, but it also lets you use columns, so I tried to use it for that since it sounds natural when coming from the SQL world.
From the [docs](http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.DataFrame.join.html):
> on : column name, tuple/list of column names, or array-like
> Column(s) to use for joining, otherwise join on index. If multiples columns given, the passed DataFrame must have a MultiIndex. Can pass an array as the join key if not already contained in the calling DataFrame. Like an Excel VLOOKUP operation
From my understanding, if on is absent a join operation is performed on the index, if on is present, it would be reasonable to think that the same operation would be performed.
Having said that:
``` python
a = pd.DataFrame({'address' : ["1820 SOME STREET", "32 ANOTHER STREET",
"1140 BIG STREET", "20 SMALL STREET",
"1090 AVENUE NAME"],
'id' : [1,2,3,4,5]
})
b = pd.DataFrame({'address' : ["1090 AVENUE NAME",
"1140 BIG STREET", "20 SMALL STREET",
"1820 SOME STREET", "32 ANOTHER STREET"],
'id' : [5,3,4,1,2]})
a.join(b, on='id', lsuffix='_x', rsuffix='_y', how='left')
```
Output:
```
address_x id_x address_y id_y
0 1820 SOME STREET 1 1140 BIG STREET 3
1 32 ANOTHER STREET 2 20 SMALL STREET 4
2 1140 BIG STREET 3 1820 SOME STREET 1
3 20 SMALL STREET 4 32 ANOTHER STREET 2
4 1090 AVENUE NAME 5 NaN NaN
```
And if I add id as index:
``` python
a.set_index('id', inplace=True)
b.set_index('id', inplace=True)
a.join(b, lsuffix='_x', rsuffix='_y', how='left')
```
Output:
```
address_x address_y
id
1 1820 SOME STREET 1820 SOME STREET
2 32 ANOTHER STREET 32 ANOTHER STREET
3 1140 BIG STREET 1140 BIG STREET
4 20 SMALL STREET 20 SMALL STREET
5 1090 AVENUE NAME 1090 AVENUE NAME
```
Is that the correct behavior? If yes, I think the documentation is misleading. It took me a lot to find the bug in my code and I ended up using merge since .join works in an unexpected way.
I don't think I'm the [only one](http://stackoverflow.com/questions/10114399/pandas-simple-join-not-working) with this issue, so maybe a change in the documentation would help to clarify how .join works.
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} | 2 | 2016-01-31T04:39:13Z | 2020-03-31T05:35:28Z | null | CONTRIBUTOR | null | Converting to object before then unstack works, whereas automatic upcast does not happen. Missing values are replaced with NaT instead of automatically upcasting to object and filling with the requested value.
```
In [44]: td = [Timedelta(days=i) for i in range(4)]
In [45]: data = Series(td)
In [46]: data.index = MultiIndex.from_tuples(
....: [('x', 'a'), ('x', 'b'), ('y', 'b'), ('z', 'a')])
In [47]: data.unstack(fill_value='foo')
Out[47]:
a b
x 0 days 1 days
y NaT 2 days
z 3 days NaT
In [48]: data.astype(np.object_).unstack(fill_value='foo')
Out[48]:
a b
x 0 days 00:00:00 1 days 00:00:00
y foo 2 days 00:00:00
z 3 days 00:00:00 foo
```
Culprit is some outdated code in _maybe_promote.
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} | 14 | 2016-01-31T18:59:52Z | 2016-02-11T13:04:13Z | 2016-02-11T13:04:13Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/11783
Deprecates pd.options.display.mpl_style in favor of matplotlib's style sheets.
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} | 7 | 2016-02-01T01:52:37Z | 2016-03-09T17:25:23Z | 2016-03-09T17:25:23Z | CONTRIBUTOR | null | closes part of #7621
WIP, but wanted to put it up as is, since it probably interacts with #12129
cc: @kawochen
this is how I modified the schema - open to suggestions
``` python
# normal block
{'dtype': dtype,
'data': ExtType(0, <bytes>)}
# Categorical block
{'dtype': 'categorical',
'data': {
'codes': {'dtype': 'int8', 'data': ExtType(0, <bytes>),
'categories': {'dtype': dtype, 'data': ExtType(0, <bytes>)}
}
```
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} | 1 | 2016-02-01T04:46:39Z | 2016-02-01T20:09:21Z | 2016-02-01T20:09:16Z | CONTRIBUTOR | null | closes #12180
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} | 9 | 2016-02-01T16:11:20Z | 2016-05-26T12:54:50Z | 2016-05-26T12:54:22Z | CONTRIBUTOR | null | closes #12188
I modified the description in DataFrame.join to make clear the difference with DataFrame.merge, also added examples.
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} | 7 | 2016-02-01T16:14:44Z | 2016-02-22T11:07:11Z | 2016-02-13T15:18:24Z | CONTRIBUTOR | null | I'm working on #12164
This is a work in progress.
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} | 0 | 2016-02-01T18:58:36Z | 2016-02-01T21:38:25Z | 2016-02-01T21:38:25Z | CONTRIBUTOR | null | closes #11693
xref #11736
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} | 0 | 2016-02-01T19:56:57Z | 2016-02-01T21:20:39Z | 2016-02-01T21:20:39Z | CONTRIBUTOR | null | https://github.com/numpy/numpy/issues/7163
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} | 0 | 2016-02-01T20:49:10Z | 2016-02-01T21:20:40Z | 2016-02-01T21:20:40Z | CONTRIBUTOR | null | closes #12196
xref https://github.com/numpy/numpy/issues/7163
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} | 3 | 2016-02-01T20:49:46Z | 2016-02-02T15:53:18Z | 2016-02-02T15:52:52Z | CONTRIBUTOR | null | The AWS s3 host used by read_csv currently defaults to s3.amazonaws.com and there is currently no way to change this. This pull requests adds functionality where the code checks the AWS_S3_HOST environment variable for a user-specified host and defaults back to the current s3.amazonaws.com host.
This is a simple code change, but it will allow users from around the globe to use read_csv to directly read from files in s3 when the s3 bucket is not located in the US East region.
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} | 1 | 2016-02-01T21:39:18Z | 2016-02-01T23:58:45Z | 2016-02-01T23:58:45Z | NONE | null | Reproduce:
``` python
import pandas as pd
import numpy as np
from scipy import fftpack
x = np.array([1.,2.,3.,4.,5.])
xs = pd.Series(x)
print x
tmp = fftpack.rfft(x)
print x
print x
tmp = fftpack.rfft(xs)
print x
"""
output:
[ 1. 2. 3. 4. 5.]
[ 1. 2. 3. 4. 5.]
[ 1. 2. 3. 4. 5.]
[ 15. -2.5 3.4409548 -2.5 0.81229924]
"""
```
posted also in scipy.github:
https://github.com/scipy/scipy/issues/5790
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} | 3 | 2016-02-02T01:09:41Z | 2016-06-24T14:32:55Z | 2016-06-24T14:31:23Z | NONE | null | Is it possible to ignore or prevent automatic typing when executing a sql command and populating a data frame? I have NA's in certain columns and those columns are converted to float64 (when they should be int64 with some NAs) and when written out (using to_csv) leave decimal points which does not make sense for that column. I have no way of knowing if a certain sql query will return the same column as int64 or float64. I propose setting dtype=object or dtype=False so I can do an easy data dump.
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| null | 1 | 2016-02-02T06:19:57Z | 2016-02-02T17:18:14Z | 2016-02-02T17:18:04Z | NONE | null | I thought that `reindex_axis` with argument `copy=False` should change it inplace. From [docs](http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.reindex_axis.html):
> **copy** : _boolean_, _default True_
> Return a new object, even if the passed indexes are the same
However:
```
df = pd.DataFrame({'A': [1], 'B': [2]})
In [105]: df.reindex_axis(['B', 'A'], axis=1, copy=False)
Out[105]:
B A
0 2 1
In [106]: df
Out[106]:
A B
0 1 2
```
Am I misunderstood that argument?
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} | 1 | 2016-02-02T10:02:05Z | 2016-02-12T20:40:12Z | 2016-02-12T20:40:12Z | NONE | null | ``` python
import pandas as pd
import numpy as np
df = pd.DataFrame({'date': pd.date_range(start='2016-01-01', periods=4, freq='W'),
'group': [1, 1, 2, 2],
'val': [5, 6, 7, 8]})
df['val'] = df['val'].astype(np.int32)
df.set_index('date', inplace=True)
df['val'].dtype
#[out] dtype('int32')
```
Calling resample() on the dataframe does not change the type.
``` python
df1 = df.resample('1D', fill_method='ffill')
df1['val'].dtype
#[out] dtype('int32')
```
However, when calling resample() in a group by statement, float type is returned!
``` python
df2 = df.groupby('group').resample('1D', fill_method='ffill')
df2['val'].dtype
#[out] dtype('float64')
```
Why is val converted to float in the group by statement?
I originally posted this on [stack overflow](http://stackoverflow.com/questions/35085130/resampling-converts-int-to-float-but-only-in-group-by).
```
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: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 7.1.2
setuptools: 18.2
Cython: 0.23.4
numpy: 1.10.1
scipy: None
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 6 | 2016-02-02T13:24:59Z | 2016-03-20T15:10:03Z | 2016-03-20T15:10:03Z | CONTRIBUTOR | null | I try to read a csv file were some lines are longer than the rest.
Pandas `C engine` throws an error with these lines.
But I do not was to skip these as discussed in [ a comment on a similar issue](https://github.com/pydata/pandas/issues/2886#issuecomment-30425534).
I prefer to "cut" the "bad columns" off using usecols. But I get the following errors:
```
df = pd.read_csv(file_path, sep=',', skiprows=1, header=None,
usecols=range(0,23), nrows=None,
engine='python')
```
Throws:
`ValueError: Expected 10 fields in line 100, saw 20`
Why is the `ValueError` raised although I explicitly defined the columns to use?
References:
- #2886
- [Dealing with bad lines](http://github.com/pydata/pandas/issues/2886) -- does not apply
- [Dealing with bad lines II](http://nipunbatra.github.io/2013/06/reading-unclean-data-csv-using-pandas/)
- [Handling Variable Number of Columns with Pandas - Python](http://stackoverflow.com/questions/15242746/handling-variable-number-of-columns-with-pandas-python)
- [pandas read_csv and filter columns with usecols](http://stackoverflow.com/questions/15017072/pandas-read-csv-and-filter-columns-with-usecols), especially: http://stackoverflow.com/a/27791362
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