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} | 6 | 2014-11-01T18:38:12Z | 2014-11-02T15:37:28Z | 2014-11-02T14:09:38Z | CONTRIBUTOR | null | Trying to set a value on a mixed-type Panel4D currently fails:
```
In [1]: from pandas.util import testing as tmd
In [2]: p4d = tmd.makePanel4D()
In [3]: p4d['foo'] = 'bar'
In [4]: p4d.iloc[0,0,0,0] = 0
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-bb74b95f7244> in <module>()
----> 1 p4d.iloc[0,0,0,0] = 0
/home/stahlous/pd_dev/pandas/pandas/core/indexing.py in __setitem__(self, key, value)
119 indexer = self._convert_to_indexer(key, is_setter=True)
120
--> 121 self._setitem_with_indexer(indexer, value)
122
123 def _has_valid_type(self, k, axis):
/home/stahlous/pd_dev/pandas/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
356 # indexer here
357 if (len(labels) == 1 and
--> 358 isinstance(self.obj[labels[0]].index, MultiIndex)):
359 item = labels[0]
360 obj = self.obj[item]
/home/stahlous/pd_dev/pandas/pandas/core/generic.py in __getattr__(self, name)
1927 """
1928 if name in self._internal_names_set:
-> 1929 return object.__getattribute__(self, name)
1930 elif name in self._metadata:
1931 return object.__getattribute__(self, name)
AttributeError: 'Panel' object has no attribute 'index'
```
Fortunately, it looks like a simple fix. If this looks OK. I'll add a note to `v0.15.1.txt` (doesn't have a GH issue number yet).
This fix is needed for my latest attempt at the fillna bug #8395.
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} | 2 | 2014-11-01T19:20:29Z | 2014-11-05T21:18:58Z | 2014-11-05T21:18:58Z | CONTRIBUTOR | null | According to the doc of read_sql, dialect can be string or a csv.Dialect instance.
However this is not the case. If you look at the code, it can only really take a csv.Dialect instance. i.e. this doesn't work:
``` python
fh = io.StringIO(u"pomme\tpoire\n1\t\2")
pd.read_csv(fh, dialect='excel-tab')
```
but, this works:
``` python
fh = io.StringIO(u"pomme\tpoire\n1\t\2")
pd.read_csv(fh, dialect=csv.get_dialect('excel-tab'))
```
I would expect it to be able to take any string returned by the csv.list_dialects() function, similar to the way csv.reader works. So we can either remove the mention of string from the doc, or make it work as intended by checking if the dialect string is part of csv.list_dialects(). Let me know if this makes sense and I'll submit a PR.
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} | 7 | 2014-11-01T21:01:31Z | 2014-11-02T21:51:43Z | 2014-11-02T21:51:43Z | NONE | null | I have a hierarchical `pandas.DataFrame` that looks as follows...
```
In [12]: data.tail()
Out[12]:
country currency_unit rgdpe \
countrycode year
ZWE 2007-01-01 Zimbabwe Zimbabwe Dollar 45666.082031
2008-01-01 Zimbabwe Zimbabwe Dollar 35789.878906
2009-01-01 Zimbabwe Zimbabwe Dollar 49294.878906
2010-01-01 Zimbabwe Zimbabwe Dollar 51259.152344
2011-01-01 Zimbabwe Zimbabwe Dollar 55453.312500
```
I would like to turn `data` into a `pandas.Panel` object. Previously I would do this using the `to_panel` method without issue. However, after upgrading to Pandas 0.15, this approach no longer works...
```
In [13]: data.to_panel()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-13-dfa9fb7a4e83> in <module>()
----> 1 data.to_panel()
/Users/drpugh/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in to_panel(self)
1061 placement=block.mgr_locs, shape=shape,
1062 labels=[major_labels, minor_labels],
-> 1063 ref_items=selfsorted.columns)
1064 new_blocks.append(newb)
1065
/Users/drpugh/anaconda/lib/python2.7/site-packages/pandas/core/reshape.pyc in block2d_to_blocknd(values, placement, shape, labels, ref_items)
1206
1207 if mask.all():
-> 1208 pvalues = np.empty(panel_shape, dtype=values.dtype)
1209 else:
1210 dtype, fill_value = _maybe_promote(values.dtype)
TypeError: data type not understood
```
Has there been an API change? I could not find anything in the release notes to suggest that the above would no longer work.
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TST/BUG: support Categorical reshaping via .unstack
closes #8704
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} | 9 | 2014-11-02T07:59:54Z | 2014-11-02T21:37:50Z | 2014-11-02T19:15:27Z | CONTRIBUTOR | null | This PR fixes #8658 and also adds `pd.lib.unbox_if_zerodim` that extracts values from zerodim arrays and adds detection of `pd.Period`, `datetime.date` and `datetime.time` in pd.lib.isscalar.
I tried making `unbox_if_zerodim` usage more implicit but there's just too much to test when this is introduced (think every method that accepts a scalar must have another test to check it also accepts zerodim array). Definitely too much to add for a single PR, but overall zerodim support could be added later on class-by-class basis.
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| null | 79 | 2014-11-02T09:08:13Z | 2017-02-05T00:50:03Z | 2016-09-09T22:48:05Z | MEMBER | null | closes #7640
closes #8625
This is a work in progress, but it's far enough along that I'd love to get
some feedback.
TODOs (more called out in the code):
- [ ] documentation
- [x] docstrings
- [x] implement an IntervalTree under the hood to handle index methods
- [x] index methods handle point queries properly:
- [x] `get_loc`
- [x] `get_indexer`
- [x] `slice_locs`
- [x] index methods handle interval queries properly
- [x] `get_loc`
- [x] `get_indexer` (for non-overlapping monotonic indexes)
- [x] comparison operations (should be lexicographic)
- [x] ensure `is_monotonic` always works (also lexicographic)
- [x] ensure `order` works
- [x] cast arrays of intervals when input to `pd.Index`
- [x] cythonize the bottlenecks:
- [x] `values`
- [x] `from_intervals`?
- [x] `Interval`?
- [ ] `from_tuples`?
- [ ] `Categorical`/`cut`
- [ ] nice to haves (not essential for MVP)
- [ ] `get_indexer` in full generality (for randomly ordered indexes)
- [ ] support for arithmetic operations
- [ ] `MultiIndex` -- should at least give a sane error messages
- [ ] serialization (HDF5) -- should at least raise `NotImplementedError`
- [ ] consider creating `interval_range` -- like `period_range`
- [ ] add `to_interval` for casting strings (e.g., `to_interval('(0, 1]')`)
- [ ] release the GIL in IntervalTree queries
- [ ] loads more tests:
- [x] `Series.loc.__getitem__`
- [ ] `Series.loc.__setitem__`
- [ ] indexing a dataframe
- [ ] groupby
- [x] reset_index
- [x] add tests in test_index for the subcommon API
CC @jreback @cpcloud @immerrr
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} | 2 | 2014-11-02T09:42:50Z | 2016-02-27T14:41:27Z | 2016-02-27T14:41:27Z | CONTRIBUTOR | null | `np.isscalar` doesn't detect a lot of types that are scalar from pandas point of view, such as dates, times, `None`, `pd.Period`, so `pd.lib.isscalar` should be preferred instead.
Adding the issue as requested in #8706.
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} | 2 | 2014-11-02T13:19:53Z | 2018-07-08T14:33:47Z | 2018-07-06T22:02:35Z | CONTRIBUTOR | null | mentioned in #8705
need to be able to combine like categorical (e.g. ordered,categories are ==)
into a 2-d structure so can store in a single CategoricalBlock
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} | 3 | 2014-11-02T13:27:10Z | 2014-11-02T21:28:33Z | 2014-11-02T21:28:33Z | NONE | null | Hello,
when I try to get a DataFrame (or sub Panel) from a Panel using index as list it raises the following error:
```
TypeError: unhashable type: 'list'
```
See the code
```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import pandas.io.data as web
start = "2010-01-01"
end = "2010-01-30"
df = web.DataReader("AAPL", 'yahoo', start, end)
print("DF 1")
data = df[['Open', 'Close']] # returns pandas.core.frame.DataFrame
print(data)
print(type(data))
print("DF 2")
data = df['Open'] # returns pandas.core.series.Series
print(data)
print(type(data))
print("DF 3")
data = df[['Open']] # returns pandas.core.frame.DataFrame
print(data)
print(type(data))
panel = web.DataReader(["AAPL", "GOOGL"], 'yahoo', start, end)
#Items axis: Open to Adj Close
#Major_axis axis: 2010-01-04 00:00:00 to 2010-01-29 00:00:00
#Minor_axis axis: AAPL to GOOGL
panel = panel.transpose(2, 1, 0) # symbol is panel index
#Items axis: AAPL to GOOGL
#Major_axis axis: 2010-01-04 00:00:00 to 2010-01-29 00:00:00
#Minor_axis axis: Open to Adj Close
print(panel)
print("P 1")
data = panel["AAPL"] # returns pandas.core.frame.DataFrame
print(data)
print(type(data))
print("P 2")
#data = panel[["AAPL"]] # Error: Should return a DataFrame but raises TypeError: unhashable type: 'list'
#print(data)
#print(type(data))
print("P 3")
#data = panel[["AAPL", "GOOGL"]] # Error: Should return a "sub" Panel but raises TypeError: unhashable type: 'list'
#print(data)
#print(type(data))
```
But that can be a misunderstanding on my side.
Thanks
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} | 31 | 2014-11-02T15:03:00Z | 2017-02-22T18:38:09Z | 2017-02-22T16:06:33Z | CONTRIBUTOR | null | This raises
```
s = Series(range(5),pd.timedelta_range('1day',periods=5))
s.plot()
```
This will show the timedeltas with a formatted (albeit string index)
```
s.index = s.index.format()
s.plot()
```
wonder if we can just register a converter somehow? like #8614
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} | 3 | 2014-11-02T15:06:20Z | 2021-04-11T05:23:25Z | null | CONTRIBUTOR | null | ```
s = Series(list('abbc')).astype('category')
```
This raises
```
s.hist()
```
This works
```
s.value_counts().plot()
```
I think this should work, beyond this, anyone have ideas on what/how to plot categorical data?
cc @JanSchulz
cc @TomAugspurger
cc @jorisvandenbossche
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} | 8 | 2014-11-02T17:29:02Z | 2016-07-21T15:41:53Z | 2015-11-28T21:12:40Z | NONE | null | Hello,
each request using `pandas.io.data.DataReader` ends by a server request.
That's not reasonable.
Maybe a cache for DataReader should be considered.
Kind regards
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} | 0 | 2014-11-02T19:30:43Z | 2014-11-02T21:28:33Z | 2014-11-02T21:28:33Z | CONTRIBUTOR | null | closes #8710
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} | 0 | 2014-11-02T21:43:14Z | 2014-11-19T22:05:10Z | 2014-11-19T22:05:10Z | CONTRIBUTOR | null | Something feels wrong about this:
``` python
In [35]: s = pd.Series(np.arange(6), index=list('abcdef'))
In [36]: s
Out[36]:
a 0
b 1
c 2
d 3
e 4
f 5
dtype: int64
In [37]: s.loc['e'::-1]
Out[37]: Series([], dtype: int64)
In [38]: s.iloc[4::-1]
Out[38]:
e 4
d 3
c 2
b 1
a 0
dtype: int64
In [39]: s['e'::-1]
Out[39]: Series([], dtype: int64)
```
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} | 4 | 2014-11-02T21:48:09Z | 2021-04-11T05:23:45Z | null | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26701849/pandas-groupby-and-finding-maximum-in-groups-returning-value-and-count)
Thinking that this is a nice general idiom that might deserve a function:
`df.loc[df.groupby(level='host').idxmax()['no']]`
maybe:
a) `df.groupby(level='host').idxmax(loc='no')` ? (e.g. have the idxmin/idxmin) functions take a 'loc' argument. or is this bending the api a big much?
b) `df.groupby(level='host').loc('no').idxmax()`
c) `df.groupby(level='hist').loc[lambda x: x.idxmax()['no']]`
d) `df.groupby(level='hist').loc('no','idxmax')`
```
df_logfile = pd.DataFrame({
'host' : ['this.com', 'this.com', 'this.com', 'that.com', 'other.net',
'other.net', 'other.net'],
'service' : ['mail', 'mail', 'web', 'mail', 'mail', 'web', 'web' ] })
df = df_logfile.groupby(['host','service'])['service'].agg({'no':'count'})
mask = df.groupby(level=0).agg('idxmax')
df_count = df.loc[mask['no']]
df_count = df_count.reset_index()
# yields
host service no
0 other.net web 2
1 that.com mail 1
2 this.com mail 2
```
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| null | 2 | 2014-11-03T00:11:19Z | 2014-11-03T08:21:13Z | 2014-11-03T00:14:55Z | CONTRIBUTOR | null | closes #8703
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} | 3 | 2014-11-03T03:26:28Z | 2014-11-05T21:18:37Z | 2014-11-05T21:18:37Z | CONTRIBUTOR | null | Closes #8703
I wasn't sure where to put the test, hopefully this is right.
Follow-up of #8718
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} | 3 | 2014-11-03T12:36:48Z | 2019-12-31T15:04:45Z | 2019-12-31T15:04:45Z | MEMBER | null | When adding a column with a float label to a DataFame with integer column index, you get a `ValueError: cannot insert 0.5, already exists` error with an incorrect message:
```
In [15]: df = pd.DataFrame(np.random.randn(8,4))
In [16]: df
Out[16]:
0 1 2 3
0 -0.482266 -1.239583 -1.177658 1.676881
1 0.314620 -0.373801 -1.535093 0.829534
2 0.887573 -0.054425 0.313028 0.590081
3 -0.665164 -0.887124 0.720014 -0.361315
4 -0.082880 -0.435376 -0.124390 -0.883354
5 2.122313 0.155019 0.762171 -0.401316
6 1.368269 0.469080 -0.274609 -1.799257
7 -0.631479 0.302715 0.008982 -0.356644
In [17]: df[0.5] = np.NaN
...
ValueError: cannot insert 0.5, already exists
In [18]: df.loc[:,0.5] = np.NaN
...
TypeError: the label [0.5] is not a proper indexer for this index type (Int64Index)
```
Of course, when the column labels are converted to floats (`df.columns = pd.Float64Index(df.columns)`), this does work.
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} | 1 | 2014-11-03T15:31:11Z | 2014-11-04T01:04:30Z | 2014-11-04T01:04:30Z | CONTRIBUTOR | null | I see `Exception TypeError: 'data type not understood' in 'util._checknull_old' ignored` in the ipython notebook warning color after turning on `use_inf_as_null`.
The code to reproduce it is
```
import pandas as pd
pd.options.mode.use_inf_as_null = True
pd.DataFrame([[1]]).dtypes
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8723 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8723/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8723/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8723/events | https://github.com/pandas-dev/pandas/issues/8723 | 47,616,484 | MDU6SXNzdWU0NzYxNjQ4NA== | 8,723 | Formatting dates & timestamps | {
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| null | 11 | 2014-11-03T16:54:06Z | 2021-04-11T05:24:21Z | null | CONTRIBUTOR | null | The following:
``` python
import pandas.io.data as web
df = web.DataReader(["AAPL", "MSFT", "EBAY", "AMZN", "CSCO", "GOOG", "YHOO", "AOL", "IBM"], "yahoo", start='2014-10-1').to_frame()['Close']
df.index = df.index.droplevel('minor')
df = df.reset_index()
# > df.head()
# Date Close
#0 2014-10-01 99.18
#1 2014-10-01 317.46
#2 2014-10-01 43.47
#3 2014-10-01 25.03
#4 2014-10-01 55.46
f, ax = plt.subplots(figsize=(10,4))
df.boxplot(by='Date', ax=ax)
ax.xaxis.set_major_locator(matplotlib.dates.WeekdayLocator(byweekday=(0,1,2,3,4,5,6),interval=7))
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%b\n%d'))
```
results in:
As you can see, the `set_major_locator` and `set_major_formatter` commands failed. This is with 0.15 and the latest `Python 3.4.1 :: Anaconda 2.1.0 (x86_64)`.
Is this a bug? How can I specify a **tick frequency** and **formatting** for timestamps for boxplots **created by Pandas**? Does Pandas have any equivalent commands? Is there any way to massage the indices so that I can still apply `set_major_locator` and `set_major_formatter`?
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https://api.github.com/repos/pandas-dev/pandas/issues/8724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8724/events | https://github.com/pandas-dev/pandas/pull/8724 | 47,632,146 | MDExOlB1bGxSZXF1ZXN0MjM3ODMyNjk= | 8,724 | DOC: Select row with maximum value from each group | {
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} | 2 | 2014-11-03T19:16:17Z | 2014-11-04T01:15:48Z | 2014-11-03T23:20:50Z | CONTRIBUTOR | null | Re: http://stackoverflow.com/q/26701849/190597
Included is a cookbook recipe for finding maximal rows per group.
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} | 2 | 2014-11-03T23:20:15Z | 2016-09-02T11:31:08Z | 2016-09-02T11:30:54Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26724872/pandas-map-values-of-categorical-variable-to-a-predefined-list-of-dummy-columns)
I think should not coerce to floats but be an int (or a categorical)
```
In [14]: df = pd.DataFrame({'hour': [0, 1, 3, 8, 13, 14], 'val': np.random.randn(6)})
In [15]: df['hour_cat'] = pd.Categorical(df['hour'], categories=range(24))
In [16]: df
Out[16]:
hour val hour_cat
0 0 0.326395 0
1 1 1.179421 1
2 3 -1.043560 3
3 8 0.430482 8
4 13 0.558709 13
5 14 -0.385931 14
In [17]: df.dtypes
Out[17]:
hour int64
val float64
hour_cat category
dtype: object
In [6]: pd.get_dummies(df['hour_cat'])
Out[6]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
In [7]: pd.get_dummies(df['hour_cat']).dtypes
Out[7]:
0 float64
1 float64
2 float64
3 float64
4 float64
5 float64
6 float64
7 float64
8 float64
9 float64
10 float64
11 float64
12 float64
13 float64
14 float64
15 float64
16 float64
17 float64
18 float64
19 float64
20 float64
21 float64
22 float64
23 float64
dtype: object
```
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} | 0 | 2014-11-03T23:21:37Z | 2014-11-04T01:04:30Z | 2014-11-04T01:04:30Z | CONTRIBUTOR | null | closes #8722
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} | 3 | 2014-11-04T16:19:30Z | 2018-07-06T21:53:45Z | 2018-07-06T21:53:45Z | NONE | null | I would be very pleased if Pandas supported multiple comment characters when reading data from files. According to:
```
import pandas as pd
df = pd.read_table("data.dat", comment=("#","@"), delim_whitespace=True)
```
I don't know if this is requires a minor or major implementation effort?
Best,
Erik
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} | 21 | 2014-11-04T16:35:56Z | 2015-03-06T22:59:19Z | 2015-03-06T22:59:19Z | CONTRIBUTOR | null | xref #9141
Fixes an issue when looking for 'totalRows' on a large dataset that is not finished in between two subsequent checks.
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| null | 23 | 2014-11-04T20:12:39Z | 2015-12-18T21:35:46Z | 2014-11-28T13:17:06Z | NONE | null | I recently updated the panda version to 0.15 from 0.14 . I use the below code, but the chunksize parameter is not working,It doesn't write the records in batches to the Database. I totally have 50,000 records in my DataFrame and it takes long time to execute. Thanks in advance
from sqlalchemy import create_engine
import iopro.pyodbc
engine = create_engine('mssql+pyodbc://147.117.20.144/HRMS_Test')
df.to_sql('test',engine, if_exists='append',index= False, index_label=None, chunksize=100)
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} | 14 | 2014-11-04T20:55:20Z | 2015-03-31T22:39:59Z | 2015-03-15T19:52:37Z | CONTRIBUTOR | null | I [Edit: thought I got] a `SettingWarningCopyWarning` when running the following code:
``` python
frame[columnone][frame[columntwo]>x]=y
```
The docs seem to imply that I can safely ignore this error.
``` python
# passed via reference (will stay)
In [273]: dfb['c'][dfb.a.str.startswith('o')] = 42
```
Is it therefore generally true that I can always ignore errors when the command I'm running is of the form:
``` python
frame[columnames][booleanconditiononframe]=x
```
I use this all the time, and I _think_ it always works. If that's true, it would be great if it was specifically called out in a 'you can ignore this warning when...' section. If that's not true (e.g. it works for scalar x / numeric x but not for series x), it would be great if that were called out too.
Sorry if this seems trivial, but I'm trying to explain this to a colleague who's new to pandas and he's confused, and it looks like even old hands can be confused when confronted with this warning (see #6757).
[Edit: looking at his code again, I suspect the SettingWithCopy warning was from a different line of code - (I wish warnings made it clear where they were from). All the same, it would still be great if the docs could be clear if there are circumstances where you always get a reference rather than a copy]
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} | 0 | 2014-11-04T21:54:40Z | 2016-12-06T21:34:25Z | 2016-12-06T19:10:40Z | CONTRIBUTOR | null | xref #8860, soln might be the same
from [SO](http://stackoverflow.com/questions/26741204/pandas-pivot-table-alphabetically-sorts-categorical-data-incorrectly-when-addi)
```
In [32]: df = DataFrame({'Sales' : [100,120,220], 'Month' : ['January','January','January'], 'Year' : [2013,2014,2013]})
In [33]: df
Out[33]:
Month Sales Year
0 January 100 2013
1 January 120 2014
2 January 220 2013
In [34]: df['Month'] = df['Month'].astype('category').cat.set_categories(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'])
In [35]: df.dtypes
Out[35]:
Month category
Sales int64
Year int64
dtype: object
In [36]: df['Month']
Out[36]:
0 January
1 January
2 January
Name: Month, dtype: category
Categories (12, object): [January < February < March < April ... September < October < November < December]
In [37]: df.pivot_table(values="Sales", index="Month")
Out[37]:
Month
January 146.666667
February NaN
March NaN
April NaN
May NaN
June NaN
July NaN
August NaN
September NaN
October NaN
November NaN
December NaN
Name: Sales, dtype: float64
In [38]: df.pivot_table(values="Sales", index="Month").index
Out[38]: Index([u'January', u'February', u'March', u'April', u'May', u'June', u'July', u'August', u'September', u'October', u'November', u'December'], dtype='object')
In [39]: result = df.pivot_table(values='Sales', index="Month", columns="Year", aggfunc="sum")
In [40]: result
Out[40]:
Year 2013 2014
Month
April NaN NaN
August NaN NaN
December NaN NaN
February NaN NaN
January 320 120
July NaN NaN
June NaN NaN
March NaN NaN
May NaN NaN
November NaN NaN
October NaN NaN
September NaN NaN
```
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} | 1 | 2014-11-04T22:07:56Z | 2020-04-25T14:07:14Z | null | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26744370/most-efficient-way-to-convert-pandas-series-of-integers-to-strings)
so the null check is pretty cheap. if no nulls, then can just bypass nansafe an use the underlying numpy routine. should be a nice speedup.
``
In [13]: arr = np.random.randint(1,10,size=1000000)
In [14]: s = Series(arr)
In [15]: s.notnull().all()
Out[15]: True
In [16]: %timeit s.notnull().all()
1000 loops, best of 3: 1.35 ms per loop
In [17]: %timeit s.astype(str)
1 loops, best of 3: 2.52 s per loop
In [18]: %timeit s.values.astype(str)
10 loops, best of 3: 37.7 ms per loop
```
```
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} | 21 | 2014-11-05T00:07:56Z | 2014-11-08T23:18:50Z | 2014-11-08T23:18:50Z | NONE | null | I'd like to pass certain kwargs to the hist function of a SeriesGroupBy object (alpha, legend, etc). The plot function provides more kwargs (e.g. legend, but not alpha) but no **kwargs to forward to matplotlib. The documentation (viewed with ? in ipython) suggests that there should be a kwds argument, but in reality this is not the case.
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| null | 1 | 2014-11-05T04:22:40Z | 2014-11-05T06:33:22Z | 2014-11-05T06:32:55Z | NONE | null | It appears that the mean aggregate function is inconsistent when operating on boolean values.
```
from pandas import DataFrame
df1 = DataFrame({'a': [1, 1], 'bools': [True, True]})
df2 = DataFrame({'a': [1, 1], 'bools': [True, False]})
print df1.mean().bools # 1.0
print df2.mean().bools # 0.5
print df1.groupby('a').mean() # DataFrame with 1: True
print df2.groupby('a').mean() # DataFrame with 1: 0.5
```
For some reason the mean of a set of True values is True instead of 1.0, but only for GroupBy objects and not for DataFrame objects.
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} | 4 | 2014-11-05T04:24:31Z | 2015-12-13T18:33:06Z | 2015-08-28T18:16:56Z | CONTRIBUTOR | null | Just had something odd come up while trying to come up with something for [this SO question](http://stackoverflow.com/questions/26747689/mapping-pandas-dataframe-rows-to-a-pandas-series).
If we use `DataFrame.apply()` to try and create dictionaries from the rows of a dataframe, it seems to return the `dict.values()` method rather than returning the dict itself.
``` python
df = pd.DataFrame({'k': ['a', 'b', 'c'], 'v': [1, 2, 3]})
df.apply(lambda row: {row['k']: row['v']}, axis=1)
Out[52]:
0 <built-in method values of dict object at 0x07...
1 <built-in method values of dict object at 0x03...
2 <built-in method values of dict object at 0x07...
dtype: object
```
Looks like it's probably something to do with trying to grab the `values` attribute when the output of the applied function is a Series or something similar.
Library versions:
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.0
nose: 1.3.4
Cython: 0.21
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.3.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 1.5
pytz: 2014.7
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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} | 1 | 2014-11-05T13:08:33Z | 2018-07-06T21:51:24Z | 2018-07-06T21:51:23Z | CONTRIBUTOR | null | discussion #8714
consolidate array dtype upcasting selection in `common/_concat_compat` and
`internals/get_empty_dtype_and_na`
cc @immerrr
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} | 8 | 2014-11-05T16:34:18Z | 2014-11-05T21:21:26Z | 2014-11-05T21:21:26Z | CONTRIBUTOR | null | It is currently possible to pass a list of labels for a given dataframe to `DataFrame.loc` in the case of a multi index, but the list cannot be empty unlike in the case of a non-hierarchical index.
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} | 4 | 2014-11-05T19:20:27Z | 2016-08-01T20:38:04Z | 2016-08-01T20:38:04Z | CONTRIBUTOR | null | For example:
``` python
In [1]: import pandas as pd
In [2]: pd.read_csv('/root')
---------------------------------------------------------------------------
IOError Traceback (most recent call last)
...
IOError: Initializing from file failed
```
The ones I've noticed so far can be fixed by calling the appropriate `PyErr_*` function at the site of the error in `io.c` and fixing the `cdef extern from "parsers/io.h"` section to `except NULL` from the modified functions.
There's even one instance of `raise IOError` that only happens on ENOMEM. :/
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} | 1 | 2014-11-05T20:39:06Z | 2014-11-05T21:21:15Z | 2014-11-05T21:21:15Z | CONTRIBUTOR | null | closes #8737
```
In [5]: multi_index = pd.MultiIndex.from_product((['foo', 'bar', 'baz'], ['alpha', 'beta']))
In [6]: df = DataFrame(np.random.randn(5, 6), index=range(5), columns=multi_index).sortlevel(0,axis=1)
In [7]: df
Out[7]:
bar baz foo
alpha beta alpha beta alpha beta
0 -0.725086 -0.715498 1.141087 -0.586018 0.906581 -0.260218
1 -1.111794 -0.393149 2.101567 0.648850 0.366892 0.554206
2 1.258239 1.472659 -0.174970 -0.355639 -0.843127 1.101716
3 -0.775790 0.419361 -0.521444 1.245519 0.759791 0.183877
4 -0.001001 -1.526741 -1.721501 0.527531 0.407099 -0.415245
In [8]: df.loc[:, ([], slice(None))]
Out[8]:
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4]
In [9]: df.loc[:, ([], slice(None))].columns
Out[9]:
MultiIndex(levels=[[u'bar', u'baz', u'foo'], [u'alpha', u'beta']],
labels=[[], []])
```
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| null | 7 | 2014-11-06T00:30:13Z | 2014-11-06T01:57:12Z | 2014-11-06T01:57:12Z | MEMBER | null | pandas 0.14 [changed `.iloc`](http://pandas.pydata.org/pandas-docs/version/0.15.0/whatsnew.html#whatsnew-0140-api) to accept out-of-bounds indexers for slices. I think we should do the same for `.loc`, for similar reasons of consistency and efficiency.
Note that this is already currently inconsistent for int and float indexes (not entirely sure why):
```
>>> pd.Series(range(5), np.arange(5)).loc[-1:]
KeyError
>>> pd.Series(range(5), np.arange(5.0)).loc[-1:]
0 0
1 1
2 2
3 3
4 4
dtype: int64
```
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Changed failure to get quote time from raising RemoteDataError to setting to np.nan
@jorisvandenbossche This fixes the doc error you noted on #8631
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} | 5 | 2014-11-06T07:58:37Z | 2015-03-10T01:43:38Z | 2014-11-06T12:42:30Z | NONE | null | ``` python
index = pd.to_datetime(pd.Series([
'2014-10-26 07:35:49',
'2014-10-26 07:45:08',
'2014-10-26 08:04:58'
]))
df = pd.DataFrame(np.arange(len(index)), index=index)
df = df.tz_localize('Asia/Krasnoyarsk', ambiguous='NaT')
df.resample('D')
AmbiguousTimeError: Cannot infer dst time from Timestamp('2014-10-26 01:00:00'), try using the 'ambiguous' argument
```
UTC offset changed from +8 to +7 at 2:00 local time.
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} | 32 | 2014-11-06T11:08:01Z | 2021-04-11T05:28:41Z | null | CONTRIBUTOR | null | Motivating from [SO](http://stackoverflow.com/questions/26762100/reconstruct-a-categorical-variable-from-dummies-in-pandas)
This is the inverse of `pd.get_dummies`. So maybe `invert_dummies` is better?
I think this name makes more sense though.
This seems a reasonable way to do it. Am I missing anything?
```
In [46]: s = Series(list('aaabbbccddefgh')).astype('category')
In [47]: s
Out[47]:
0 a
1 a
2 a
3 b
4 b
5 b
6 c
7 c
8 d
9 d
10 e
11 f
12 g
13 h
dtype: category
Categories (8, object): [a < b < c < d < e < f < g < h]
In [48]: df = pd.get_dummies(s)
In [49]: df
Out[49]:
a b c d e f g h
0 1 0 0 0 0 0 0 0
1 1 0 0 0 0 0 0 0
2 1 0 0 0 0 0 0 0
3 0 1 0 0 0 0 0 0
4 0 1 0 0 0 0 0 0
5 0 1 0 0 0 0 0 0
6 0 0 1 0 0 0 0 0
7 0 0 1 0 0 0 0 0
8 0 0 0 1 0 0 0 0
9 0 0 0 1 0 0 0 0
10 0 0 0 0 1 0 0 0
11 0 0 0 0 0 1 0 0
12 0 0 0 0 0 0 1 0
13 0 0 0 0 0 0 0 1
In [50]: x = df.stack()
# I don't think you actually need to specify ALL of the categories here, as by definition
# they are in the dummy matrix to start (and hence the column index)
In [51]: Series(pd.Categorical(x[x!=0].index.get_level_values(1)))
Out[51]:
0 a
1 a
2 a
3 b
4 b
5 b
6 c
7 c
8 d
9 d
10 e
11 f
12 g
13 h
Name: level_1, dtype: category
Categories (8, object): [a < b < c < d < e < f < g < h]
```
NB. this is buggy ATM.
```
In [51]: Series(pd.Categorical(x[x!=0].index.get_level_values(1)),categories=df.categories)
```
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| null | 1 | 2014-11-06T11:54:04Z | 2014-11-06T12:09:38Z | 2014-11-06T12:09:38Z | CONTRIBUTOR | null | Shouldn't this be `pd.show_versions()`? Maybe it's a typo.
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} | 5 | 2014-11-06T17:05:22Z | 2021-04-11T05:29:09Z | null | NONE | null | What about including a method to get the start and stop after a regex search of items in a DataFrame . Perhaps using .str.extract?
Returning the start as a new column would perhaps be as follows:
```
df['start'] = df['string'].str.extract(pattern, output = 'start')
```
an alternative suggestion from jkitchen on StackOverflow was to use start_index = True, or end_index = True
```
df['start'] = df['string'].str.extract(pattern, start_index = True)
```
For multiple parameters (e.g. start and end) as outputs, there needs to be a way to avoid running the search twice. One solution would be to give the output as a tuple:
```
df['regex_output_tuple'] = df['string'].str.extract(pattern, output = ('start','end'))
```
I don't use regex very often, so I don't know if there are other parameters that people want after a regex search. If there really is just the text in the groups, the start and the end, perhaps there's a way to put the output directly into new columns?
```
df['groups'], df['start'], df['end'] = df['string'].str.extract(pattern, output = ('groups','start','end'))
```
I think it makes sense that non-matches return a NaN, just as in the regular extract function. This would mix integer and float datatypes in the df['start'] column, but I guess we all know about that situation :)
I'm not an experienced programmer, so sorry if I misunderstood some basic concepts.
Please see the question in StackOverflow for example code and comments:
http://stackoverflow.com/questions/26658213/how-can-i-find-the-start-and-end-of-a-regex-match-using-a-python-pandas-datafram
A block of example data and code is below, as requested by jreback.
```
import pandas as pd
import re
#some example query sequences, markup strings, hit sequences.
q1,q2,q3 = 'MPIMGSSVYITVELAIAVLAILG','MPIMGSSVYITVELAIAVLAILG','MPI-MGSSVYITVELAIAVLAIL'
m1,m2,m3 = '|| || ||||||||||||||||','|| | ||| :|| || |:: |','||: ::|: :||||| |:: '
h1,h2,h3 = 'MPTMGFWVYITVELAIAVLAILG','MP-NSSLVYIGLELVIACLSVAG','MPLETQDALYVALELAIAALSVA'
#create a pandas dataframe to hold the aligned sequences
df = pd.DataFrame({'query':[q1,q2,q3],'markup':[m1,m2,m3],'hit':[h1,h2,h3]})
#create a regex search string to find the appropriate subset in the query sequence,
desired_region_from_query = 'PIMGSS'
regex_desired_region_from_query = '(P-*I-*M-*G-*S-*S-*)'
#Pandas has a nice extract function to slice out the matched sequence from the query:
df['extracted'] = df['query'].str.extract(regex_desired_region_from_query)
#However I need the start and end of the match in order to extract the equivalent regions
#from the markup and hit columns. For a single string, this is done as follows:
match = re.search(regex_desired_region_from_query, df.loc[2,'query'])
sliced_hit = df.loc[2,'hit'][match.start():match.end()]
print('sliced_hit, non-vectorized example: ', sliced_hit)
#HERE the new syntax is necessary
#e.g. df['start'], df['end'] = df['string'].str.extract(pattern, output = ('start','end'))
#My current workaround in pandas is as follows.
#define function to obtain regex output (start, stop, etc) as a tuple
def get_regex_output(x):
m = re.search(regex_desired_region_from_query, x)
return (m.start(), m.end())
#apply function
df['regex_output_tuple'] = df['query'].apply(get_regex_output)
#convert the tuple into two separate columns
columns_from_regex_output = ['start','end']
for n, col in enumerate(columns_from_regex_output):
df[col] = df['regex_output_tuple'].apply(lambda x: x[n])
#delete the unnecessary column
df = df.drop('regex_output_tuple', axis=1)
```
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} | 12 | 2014-11-06T17:34:58Z | 2020-01-30T20:23:56Z | 2018-07-06T21:49:52Z | NONE | null | What about implementing the following slice function? The df.start and df.end columns contain the start and end index required to slice the df.string.
```
df.sliced = df.string[df.start:df.end]
```
Currently we can slice columns with a fixed start and end index
```
df.sliced = df.string.str.slice(1, -1)
```
However it would be great if we could do this using variable start and stop indices from the dataframe itself, without the need for lambda functions.
Possible complications:
I can imagine that this would be complicated by the presence of NaN values in the column.
You could either force users to clean up their data first, so they can only apply the function if the column dtypes of the start and stop are integers (basically: take your dirty boots off before stepping into the house!).
Or you could be nice, and apply the slice function to anything in the target column that looks like a string, using anything in the start and end columns that looks like an integer. (not that I would have a clue how to do that!) Using this strategy, return NaN only when invalid strings, NaN or floats, or index-out-of-range are encountered?
This problem was raised along with #8747 in a StackOverflow question. Some code and examples are given.
http://stackoverflow.com/questions/26658213/how-can-i-find-the-start-and-end-of-a-regex-match-using-a-python-pandas-datafram
edit: here is some example code, including a current workaround. Sorry, I'll make sure the code is included immediately next time.
```
import pandas as pd
#some example strings for slicing, start indices, stop indices
h1,h2,h3 = 'MPTMGFWVYITVE','MP-NSSLVYIGLE','MPLETQDALYVAL'
s1, s2, s3 = 1,1,2
e1, e2, e3 = 7,7,8
#create a pandas dataframe to hold the aligned sequences
df = pd.DataFrame({'hit':[h1,h2,h3],'start': [s1, s2, s3],'end': [e1, e2, e3]})
#reorder columns
df = df[['hit', 'start', 'end']]
#HERE is where the new syntax would be useful to slice the strings, example
#df.sliced = df.hit.str[df.start:df.end]
#or the equivalent df['sliced'] = df['hit'].str[df['start']:df['end']]
#Current workaround using a lambda function that specifies the start & end columns.
fn_slice_hit = lambda x : x['hit'][x['start']:x['end']]
#apply the slice function to the dataframe
df['sliced'] = df.apply(fn_slice_hit, axis = 1)
In [2]: df
Out[2]:
hit start end sliced
0 MPTMGFWVYITVE 1 7 PTMGFW
1 MP-NSSLVYIGLE 1 7 P-NSSL
2 MPLETQDALYVAL 2 8 LETQDA
[3 rows x 4 columns]
```
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} | 7 | 2014-11-07T11:46:37Z | 2019-04-08T18:35:42Z | 2018-07-06T21:34:08Z | NONE | null | Would be nice to have the query method support partial string matching, so you could do the equivalence of df[df['A'].str.contains("abc")] using query: df.query("A contains 'abc'").
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} | 4 | 2014-11-07T15:22:20Z | 2020-04-26T17:33:14Z | null | CONTRIBUTOR | null | Unlike `pandas.DataFrame`, `pandas.Series` does not have the methods to be converted to an html table.
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| null | 2 | 2014-11-07T15:37:31Z | 2014-11-07T17:10:45Z | 2014-11-07T17:10:33Z | CONTRIBUTOR | null | While they have two members `labels` and `names` , single indices lack the member `levels` of multi-indices.
It can be useful to set it to `[df.values]` to treat the single indices in the same way as hierarchical indices thanks to duck typing.
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} | 33 | 2014-11-07T22:53:11Z | 2015-02-01T03:19:50Z | 2014-11-27T02:52:38Z | CONTRIBUTOR | null | Update tokenizer's handling of skipped lines.
Fixes a problem with read_csv(delim_whitespace=True) and
read_table(engine='c') when lines being skipped have trailing
whitespace.
closes #8679
closes #8681
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} | 13 | 2014-11-08T07:14:47Z | 2014-11-19T22:15:58Z | 2014-11-19T22:05:10Z | CONTRIBUTOR | null | This should fix #8716.
Some of this refactoring may be useful for #8613, so I'd like someone to look through this.
cc'ing @shoyer, @jreback and @jorisvandenbossche .
TODO:
- add tests for label slicing with negative step (ix, loc)
- [x] int64index
- [x] float64index
- [x] objectindex
- [x] fix datetime/period indices
- add tests for label slicing with negative step (datetime, string, native pandas scalar) x (ix, loc)
- [x] datetimeindex
- [x] periodindex
- [x] timedeltaindex
- [x] clean up old partial string code?
- [x] benchmark
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} | 0 | 2014-11-08T16:26:11Z | 2014-11-18T10:14:22Z | 2014-11-18T10:14:22Z | MEMBER | null | The `step` argument in the `slice` StringMethods has no effect:
```
In [18]: s = pd.Series(['abcde', 'fghij'])
In [19]: s.str.slice?
Definition: s.str.slice(self, start=None, stop=None, step=1)
Slice substrings from each element in array
Parameters
----------
start : int or None
stop : int or None
Returns
-------
sliced : array
In [22]: s.str.slice(step=1)
Out[22]:
0 abcde
1 fghij
dtype: object
In [23]: s.str.slice(step=2)
Out[23]:
0 abcde
1 fghij
dtype: object
In [24]: s[0][::2]
Out[24]: 'ace'
```
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} | 1 | 2014-11-08T17:29:35Z | 2014-11-09T12:13:49Z | 2014-11-09T12:13:49Z | MEMBER | null | @jreback fixed the link to the bug fixes and reorderd it a bit (this is already included in the online docs)
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} | 0 | 2014-11-08T17:31:27Z | 2014-11-13T09:19:40Z | 2014-11-13T09:19:40Z | MEMBER | null | I don't think we use the matplotlib directives anywhere (and the todo was twice in the list), so removed them from conf.py
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} | 8 | 2014-11-08T18:57:44Z | 2014-11-11T23:37:43Z | 2014-11-11T23:37:43Z | CONTRIBUTOR | null | pd.Timedelta is a great addition, thanks!
Is it the intended behavior of pd.Timedelta to only accept np.int64, python ints, and python floats? I ran into this when I tried to construct a list of pd.Timedeltas from the np.int32 array returned by pd.DatetimeIndex.dayofyear.
``` python
>>> import pandas as pd
>>> import numpy as np
>>> pd.Timedelta(days=np.int64(1))
Timedelta('1 days 00:00:00')
>>> pd.Timedelta(days=np.int32(1))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/tslib.pyx", line 1638, in pandas.tslib.Timedelta.__new__ (pandas/tslib.c:27693)
ValueError: cannot construct a TimeDelta from the passed arguments, allowed keywords are [days, seconds, microseconds, milliseconds, minutes, hours, weeks]
>>> pd.Timedelta(days=np.int16(1))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/tslib.pyx", line 1638, in pandas.tslib.Timedelta.__new__ (pandas/tslib.c:27693)
ValueError: cannot construct a TimeDelta from the passed arguments, allowed keywords are [days, seconds, microseconds, milliseconds, minutes, hours, weeks]
>>> pd.Timedelta(days=np.float64(1))
Timedelta('1 days 00:00:00')
>>> pd.Timedelta(days=np.float32(1))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/tslib.pyx", line 1638, in pandas.tslib.Timedelta.__new__ (pandas/tslib.c:27693)
ValueError: cannot construct a TimeDelta from the passed arguments, allowed keywords are [days, seconds, microseconds, milliseconds, minutes, hours, weeks]
>>> pd.Timedelta(days=1)
Timedelta('1 days 00:00:00')
>>> pd.Timedelta(days=1.0)
Timedelta('1 days 00:00:00')
```
Here's my version info:
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.17.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21
numpy: 1.9.1
scipy: 0.14.0
statsmodels: None
IPython: 2.3.0
sphinx: 1.2.3
patsy: None
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: 0.6.2.None
psycopg2: None
```
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} | 3 | 2014-11-08T20:14:45Z | 2014-11-10T01:18:32Z | 2014-11-08T23:18:50Z | CONTRIBUTOR | null | Fixes #8733 (and any other bug that would result from trying to send an arbitrary keyword to a groupby method).
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} | 5 | 2014-11-08T22:57:28Z | 2014-11-09T21:35:09Z | 2014-11-09T14:01:12Z | CONTRIBUTOR | null | Add a pandas tutorial to the tutorial links.
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} | 6 | 2014-11-09T03:52:24Z | 2016-01-08T14:07:14Z | 2016-01-08T14:07:14Z | MEMBER | null | There are some nice improvements available in the unittest module on Python 2.7 or later that aren't available on Python 2.6 without using the unittest2 library. In particular, I miss using `assertRaises` as a context manager and `skiptest`.
How about adding the module as a testing requirement on Python 2.6?
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} | 2 | 2014-11-09T13:08:09Z | 2014-11-11T05:06:50Z | 2014-11-09T22:04:09Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/40415856
https://travis-ci.org/pydata/pandas/jobs/40425986
cc @dstephens99
These are hitting a `ValueError` on multiple-pr's I have seen. Maybe they are getting partial data?
So need to skip on that particular error (not exactly sure where that is coming from).
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} | 11 | 2014-11-09T17:22:49Z | 2020-05-16T03:36:07Z | null | NONE | null | In [3]: pd.show_versions()
INSTALLED VERSIONS
commit: 9a66dbb
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-32-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.0-62-g9a66dbb
nose: None
Cython: 0.20.1post0
numpy: 1.8.1
scipy: 0.13.3
statsmodels: None
IPython: 1.2.1
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2014.7
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
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} | 1 | 2014-11-09T23:20:25Z | 2014-12-01T00:09:33Z | 2014-12-01T00:09:33Z | CONTRIBUTOR | null | ``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 9), index=range(10))
df.plot()
```
Results in this plot (all the same font size):
``` python
df.plot(fontsize=4)
```
Results in this plot (only the x-axis font size is changed):
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} | 10 | 2014-11-09T23:54:51Z | 2014-12-10T09:07:53Z | 2014-12-10T09:07:40Z | CONTRIBUTOR | null | fixes #8765
``` python
import pandas as pd
df = pd.DataFrame(np.random.randn(10, 9), index=range(10))
df.plot(fontsize=7)
```
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} | 11 | 2014-11-10T00:13:09Z | 2014-11-13T16:51:41Z | 2014-11-13T11:15:40Z | CONTRIBUTOR | null | Add support for exporting DataFrames containing categorical data.
closes #8633
xref #7621
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} | 6 | 2014-11-10T00:18:12Z | 2014-11-10T12:43:32Z | 2014-11-10T12:43:32Z | CONTRIBUTOR | null | xref #8551
cc @jnmclarty
```
.SS.E
======================================================================
ERROR: test_wdi_search (pandas.io.tests.test_wb.TestWB)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/pandas/pandas/util/testing.py", line 1417, in wrapper
return t(*args, **kwargs)
File "/Users/jreback/pandas/pandas/io/tests/test_wb.py", line 25, in test_wdi_search
result = search('gdp.*capita.*constant').loc[6716:,['id','name']]
File "/Users/jreback/pandas/pandas/core/indexing.py", line 1200, in __getitem__
return self._getitem_tuple(key)
File "/Users/jreback/pandas/pandas/core/indexing.py", line 704, in _getitem_tuple
self._has_valid_tuple(tup)
File "/Users/jreback/pandas/pandas/core/indexing.py", line 131, in _has_valid_tuple
if not self._has_valid_type(k, i):
File "/Users/jreback/pandas/pandas/core/indexing.py", line 1260, in _has_valid_type
(key.start, self.obj._get_axis_name(axis))
KeyError: 'start bound [6716] is not the [index]'
----------------------------------------------------------------------
Ran 5 tests in 14.969s
```
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} | 1 | 2014-11-10T03:32:07Z | 2015-02-09T00:44:01Z | 2014-11-10T12:43:19Z | CONTRIBUTOR | null | Fixes a previously poorly designed test in io.wb.
Closes #8768
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} | 7 | 2014-11-10T09:22:12Z | 2018-07-06T21:30:07Z | 2018-07-06T21:30:00Z | NONE | null | Hello everyone, updating to Pandas 0.15.1 breaks my code because of the following behaviour. When performing an aggregation operation on a groupby object containing a categorical, with as_index=False, this column is now dropped from the aggregation result. This may be an unexpected consequence of #8585 , and certainly looks as a bug to me.
``` python
import pandas as pd
import numpy as np
d = {'Foo': [1, 2, 3, 4, 5, 6, 11],
'Bar': ['AA', 'AA', 'AA', 'BB', 'BB', 'BB',
'CC'],
'Baz': [10, 20, 30, 40, 50, 60, 70]}
df = pd.DataFrame(d)
ls = np.linspace(0, 30, 7)
groups = df.groupby([pd.cut(df['Foo'], ls), 'Bar'], as_index=False)
df2 = groups['Baz'].agg({'Mean Baz': 'mean'})
```
produces:
``` python
>>> pd.__version__
'0.15.1'
>>> df2
```
| | Bar | Mean Baz |
| --- | --- | --- |
| 0 | AA | 20 |
| 1 | BB | 45 |
| 2 | BB | 60 |
| 3 | CC | 70 |
, whereas I expect the following:
``` python
>>> pd.__version__
'0.15.0'
```
| | Foo | Bar | Mean Baz |
| --- | --- | --- | --- |
| 0 | (0, 5] | AA | 20 |
| 1 | (0, 5] | BB | 45 |
| 2 | (5, 10] | BB | 60 |
| 3 | (10, 15] | CC | 70 |
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} | 5 | 2014-11-10T12:14:42Z | 2014-11-27T17:48:12Z | 2014-11-27T17:48:12Z | NONE | null | Pandas 0.14.1 does parse UTC 'Z' designator:
``` python
In [1]: import pandas as pd;pd.__version__
Out[1]: '0.14.1'
In [2]: pd.Timestamp("2014-11-10 11:00Z")
Out[2]: Timestamp('2014-11-10 11:00:00+0000', tz='UTC')
```
Beginning with pandas 0.15.0, it seems to be broken:
``` python
In [1]: import pandas as pd;pd.__version__
Out[1]: '0.15.0'
In [2]: pd.Timestamp("2014-11-10 11:00Z")
Out[2]: Timestamp('2014-11-10 11:00:00')
```
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} | 5 | 2014-11-10T12:35:54Z | 2019-08-22T09:10:57Z | 2014-11-14T19:31:48Z | NONE | null | Consider the following index crossing a DST transition:
``` python
In [1]: import pandas as pd;pd.__version__
Out[1]: '0.15.0'
In [2]: index = pd.date_range(pd.Timestamp("2014-10-25 03:00", tz="Europe/Paris"), periods=10, freq="3H")
In [3]: index
Out[3]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-10-25 03:00:00+02:00, ..., 2014-10-26 05:00:00+01:00]
Length: 10, Freq: 3H, Timezone: Europe/Paris
```
Pandas cannot infer the index frequency:
``` python
In [4]: pd.infer_freq(index) is None
Out[4]: True
```
If we convert the index to UTC, the frequency can be inferred:
``` python
In [5]: pd.infer_freq(index.tz_convert("UTC"))
Out[5]: '3H'
```
If the index does not cross a DST boundary, the frequency can be inferred as well:
``` python
In [6]: index = pd.date_range(pd.Timestamp("2014-10-25 03:00", tz="Europe/Paris"), periods=6, freq="3H")
In [7]: index
Out[7]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-10-25 03:00:00+02:00, ..., 2014-10-25 18:00:00+02:00]
Length: 6, Freq: 3H, Timezone: Europe/Paris
In [8]: pd.infer_freq(index)
Out[8]: '3H'
```
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| null | 5 | 2014-11-10T12:43:23Z | 2014-11-11T14:41:39Z | 2014-11-11T14:08:22Z | CONTRIBUTOR | null | Cannot get io.ga to work since update to 14.1 (using 0.15.0 right now):
TypeError: list indices must be integers, not Index
``` python
import pandas.io.ga as ga
df = ga.read_ga(
property_id = "UA-XXX",
profile_id = "XXX",
metrics = ['users'],
dimensions = ['dayOfWeek','hour'],
start_date = "2014-02-03",
end_date = "2014-08-03"
)
```
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} | 4 | 2014-11-10T13:01:45Z | 2018-04-09T15:13:16Z | 2018-04-09T15:13:16Z | NONE | null | The semantic of the frequencies "D" and "24H" in 'date_range' has changed in pandas 0.15.0: they are now both equivalent to a "calendar daily" frequency, ie local time is kept the same across DST transition:
``` python
In [1]: import pandas as pd;pd.__version__
Out[1]: '0.15.0'
In [2]: list(pd.date_range(pd.Timestamp("2014-10-24 06:00", tz="Europe/Paris"), periods=4, freq="D"))
Out[2]:
[Timestamp('2014-10-24 06:00:00+0200', tz='Europe/Paris', offset='D'),
Timestamp('2014-10-25 06:00:00+0200', tz='Europe/Paris', offset='D'),
Timestamp('2014-10-26 06:00:00+0100', tz='Europe/Paris', offset='D'),
Timestamp('2014-10-27 06:00:00+0100', tz='Europe/Paris', offset='D')]
In [3]: list(pd.date_range(pd.Timestamp("2014-10-24 06:00", tz="Europe/Paris"), periods=4, freq="24H"))
Out[3]:
[Timestamp('2014-10-24 06:00:00+0200', tz='Europe/Paris', offset='24H'),
Timestamp('2014-10-25 06:00:00+0200', tz='Europe/Paris', offset='24H'),
Timestamp('2014-10-26 06:00:00+0100', tz='Europe/Paris', offset='24H'),
Timestamp('2014-10-27 06:00:00+0100', tz='Europe/Paris', offset='24H')]
```
But what if we do want a true "24H" frequency ? I think "D" and "24H" should not share the same semantic, Out[2] is OK but Out[3] should be:
``` python
[Timestamp('2014-10-24 06:00:00+0200', tz='Europe/Paris'),
Timestamp('2014-10-25 06:00:00+0200', tz='Europe/Paris'),
Timestamp('2014-10-26 05:00:00+0100', tz='Europe/Paris'),
Timestamp('2014-10-27 05:00:00+0100', tz='Europe/Paris')]
```
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} | 4 | 2014-11-10T13:22:33Z | 2017-04-25T21:51:22Z | 2017-04-25T21:51:22Z | NONE | null | another problem with memory report (#7619) in df.info() like #8578 (locked) and fix to multindex hidden in #8456 (see [whatsnew](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#enhancements)). it count the space used by index hashtable. for each entry the klib hashtable keeps a copy of the index data as well as a key. for strings that is a copy of the string + pointer. in worst case there is `2*next_power_of_2(len(index))` entries and problem exists whatever dtype the index use.
```
x.index._engine.mapping
Out[13]: <pandas.hashtable.PyObjectHashTable at 0x7fb92f32bef0>
```
cc @shoyer, @njsmith
cc @asobrien
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} | 17 | 2014-11-10T14:45:09Z | 2017-02-17T15:26:53Z | null | MEMBER | null | From [SO](http://stackoverflow.com/questions/26845727/pandas-plot-multiple-columns-of-a-timeseries-with-labels-example-from-pandas-d/26845829#26845829), I noticed that `Series.plot()` plots on the current axis if no ax is specified, and `DataFrame.plot` not (so creates a new figure when no ax is specified).
Some things about this:
- Is this deliberate? I think this is possible:
- [doc](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html) of `Series.plot()` mention this on `ax`: "If not passed, uses gca()", while the docs for `DataFrame.plot()` don't say this
- The idea is maybe that a series you often want to add to a figure, and a DataFrame (with already multiple columns) you just want to plot?
- But still, this is somewhat inconsistent?
So, if this is deliberate, we should:
- clarify the docstring of `DataFrame.plot()` (now nothing is said about this)
- remove the `plt.figure()` calls from the documentation, as these have no effect (eg second plot here: http://pandas.pydata.org/pandas-docs/stable/visualization.html#basic-plotting-plot). This is what caused the confusion on SO.
or if not:
- implement that `DataFrame.plot()` also first tries to get the current axis with `gca`
@TomAugspurger @sinhrks
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| null | 1 | 2014-11-10T15:05:55Z | 2014-11-10T23:48:07Z | 2014-11-10T23:48:07Z | NONE | null | Hello, I'm trying to apply this
```
from scipy.spatial import distance
def myrolling_apply(df):
print type(df)
print df
print df.dtypes
a = df['predicted'].values
b = df['actual'].values
#dst = distance.euclidean(a,b)
return(df)
rrbug = pd.read_csv('bug.csv')
rrbug = pd.rolling_apply(rrbug, 24, myrolling_apply)
```
To this dataframe https://dl.dropboxusercontent.com/u/1118905/bug.csv. I would aspect to have a dataframe BUT df is a ndarray, as you see from the results:
```
<type 'numpy.ndarray'>
[ 18.038866 18.81628 15.652594 19.418134 23.685213 21.636703
32.264257 44.563431 63.160193 46.759563 44.06377 45.75921
57.929419 58.121308 61.65729 59.300808 64.033246 57.590802
56.642796 53.321256 68.65092 57.555255 39.222144 34.622948]
```
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} | 12 | 2014-11-10T16:00:36Z | 2014-12-15T10:21:59Z | 2014-12-08T07:42:33Z | NONE | null | i am experiencing issues with writing NA values for a column of boolean with missing values.
``` python
data = [True, None, False]
df = pd.DataFrame(a, columns=['test'])
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 3 entries, 0 to 2
Data columns (total 1 columns):
test 2 non-null object
dtypes: object(1)
memory usage: 48.0 bytes
```
I am reading data from a mssql server and am trying to write it unchanged in another table.
As the data contains NULL values, pandas changes the dtype to object. when calling to_sql, object fields seem to be mapped to a "text" column type, and the export then fails:
``` python
df.to_sql('test_table', engine_local, if_exists='append', index=True)
DataError: (DataError) ('22018', '[22018] [Microsoft][ODBC SQL Server Driver][SQL Server]Operand type clash: bit is incompatible with text (206) (SQLExecDirectW); [42000] [Microsoft][ODBC SQL Server Driver][SQL Server]Statement(s) could not be prepared. (8180)') u'INSERT INTO test_table ([index], test) VALUES (?, ?)' ((0L, True), (1L, None), (2L, False))
```
'object' dtypes are tricky to handle systematically I guess, but maybe we could add the ability to tell pandas how to handle it on a column by column basis?.
something like:
``` python
df.to_sql('test_table', engine_local, if_exists='append', index=True, col_types={'test': bool})
```
could do the trick?
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| null | 3 | 2014-11-10T16:32:05Z | 2014-11-11T14:08:34Z | 2014-11-10T23:56:22Z | NONE | null | I recently upgraded from pandas 14.1 to 15.0 and now I am having trouble with the Google Analytics IO tool. Even when running simple queries like:
``` python
ga_data = ga.read_ga(metrics='sessions',
dimensions='date',
start_date='2014-06-06',
end_date='2014-10-25',
filters='sourceMedium==google / organic',
account_id='123456789') #obscured account id of course
```
I get the error "TypeError: list indices must be integers, not Index", whereas I use to get back ga_data as a (iirc) Series object with the dates as the index.
Some context: I use Anaconda from Continuum Analytics. My upgrade process was:
1. conda update conda
2. conda update anaconda
3. conda update pandas
The latter also upgraded numpy and one other package (I don't recall the other packages).
Sorry, but I do not have any pull requests to fix (still really new to this). Please let me know if there's anything else I can provide (e.g. the stack trace).
Thanks!
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} | 9 | 2014-11-10T17:06:44Z | 2016-03-10T21:16:57Z | 2016-03-10T21:16:49Z | NONE | null | OS: Solaris 11u2, x86 in a VirtualBox VM.
Python 2.7
Compiler: gcc 4.8.2
There is a Solaris bug (CR 16837431) where /usr/lib/python2.7/conf/Makefile contains paths to build compilers and not installed compilers. I adjusted the shell environment:
```
export CC=gcc
export CXX=g++
export LDSHARED="gcc -shared -Wall -lm"
export BLDSHARED="gcc -shared -Wall -lm"
export LDCXXSHARED="g++ -shared -Wall -lm"
```
I then installed pandas via pip. All of the prerequisites for pandas 0.15 were downloaded and installed without incident. Pandas downloaded and built with many errors when compiling the the various C and C++ source extensions, but ultimately reported that it installed successfully.
Starting a python interpreter and importing pandas resulted in:
```
root@victims11:~# python
Python 2.7.3 (default, Jun 17 2014, 12:54:42) [C] on sunos5
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
ld.so.1: isapython2.7: fatal: relocation error: file /usr/lib/python2.7/site-packages/pandas/algos.so: symbol signbit: referenced symbol not found
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/site-packages/pandas/__init__.py", line 7, in <module>
from . import hashtable, tslib, lib
File "pandas/tslib.pyx", line 2842, in init pandas.tslib (pandas/tslib.c:80195)
ImportError: ld.so.1: isapython2.7: fatal: relocation error: file /usr/lib/python2.7/site-packages/pandas/algos.so: symbol signbit: referenced symbol not found
>>>
```
I also cloned and built the current pandas git repository with pretty much the same results.
There are a number of functions reported with implicit declarations by gcc at compile time: signbit, cmp_pandas_datetimestruct, isinf and endcode. Looking at the shared object built, those symbols are unresolved by the link editor, so my linker flags are not doing the job. signbit just happened to be the first symbol it tried to resolve and choked on.
Any help with this would be greatly appreciated.
The following is the pip pandas install output:
```
root@victims11:~# pip install -U pandas --proxy=$http_proxy --download-cache=cache
Downloading/unpacking pandas
Downloading pandas-0.15.1.tar.gz (4.5MB): 4.5MB downloaded
Storing download in cache at ./cache/https%3A%2F%2Fpypi.python.org%2Fpackages%2Fsource%2Fp%2Fpandas%2Fpandas-0.15.1.tar.gz
Running setup.py (path:/tmp/pip_build_root/pandas/setup.py) egg_info for package pandas
warning: no files found matching 'README.rst'
no previously-included directories found matching 'doc/build'
warning: no previously-included files matching '*.so' found anywhere in distribution
warning: no previously-included files matching '*.pyd' found anywhere in distribution
warning: no previously-included files matching '*.pyc' found anywhere in distribution
warning: no previously-included files matching '*~' found anywhere in distribution
warning: no previously-included files matching '#*' found anywhere in distribution
warning: no previously-included files matching '.git*' found anywhere in distribution
warning: no previously-included files matching '.DS_Store' found anywhere in distribution
warning: no previously-included files matching '*.png' found anywhere in distribution
Requirement already up-to-date: python-dateutil in /usr/lib/python2.7/site-packages (from pandas)
Requirement already up-to-date: pytz>=2011k in /usr/lib/python2.7/site-packages (from pandas)
Requirement already up-to-date: numpy>=1.7.0 in /usr/lib/python2.7/site-packages (from pandas)
Requirement already up-to-date: six in /usr/lib/python2.7/site-packages (from python-dateutil->pandas)
Installing collected packages: pandas
Running setup.py install for pandas
skipping 'pandas/index.c' Cython extension (up-to-date)
building 'pandas.index' extension
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/index.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/index.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/index.c:232:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/klib/khash_python.h:3:0,
from pandas/index.c:236:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:11:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, kh_float64_hash_equal)
^
pandas/src/klib/khash_python.h:13:1: note: in expansion of macro ‘KHASH_MAP_INIT_FLOAT64’
KHASH_MAP_INIT_FLOAT64(float64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:35:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, khval_t, 1, \
^
pandas/src/klib/khash_python.h:38:1: note: in expansion of macro ‘KHASH_MAP_INIT_PYOBJECT’
KHASH_MAP_INIT_PYOBJECT(pymap, Py_ssize_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:41:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, char, 0, \
^
pandas/src/klib/khash_python.h:44:1: note: in expansion of macro ‘KHASH_SET_INIT_PYOBJECT’
KHASH_SET_INIT_PYOBJECT(pyset)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash_python.h:49:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(strbox, kh_pyobject_t)
^
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/datetime/np_datetime.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/datetime/np_datetime.c:18:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/datetime/np_datetime.c:10:0:
/usr/include/python2.7/datetime.h:188:25: warning: ‘PyDateTimeAPI’ defined but not used [-Wunused-variable]
static PyDateTime_CAPI *PyDateTimeAPI = NULL;
^
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/datetime/np_datetime_strings.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime_strings.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/datetime/np_datetime_strings.c:17:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
pandas/src/datetime/np_datetime_strings.c:127:1: warning: ‘get_gmtime’ defined but not used [-Wunused-function]
get_gmtime(NPY_TIME_T *ts, struct tm *tms)
^
gcc -shared -Wall -lm -Wall build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/index.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime_strings.o -L/usr/lib -lpython2.7 -o build/lib.solaris-2.11-i86pc.32bit-2.7/pandas/index.so
skipping 'pandas/algos.c' Cython extension (up-to-date)
building 'pandas.algos' extension
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/algos.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/algos.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/algos.c:232:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/klib/khash_python.h:3:0,
from pandas/algos.c:246:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:11:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, kh_float64_hash_equal)
^
pandas/src/klib/khash_python.h:13:1: note: in expansion of macro ‘KHASH_MAP_INIT_FLOAT64’
KHASH_MAP_INIT_FLOAT64(float64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:35:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, khval_t, 1, \
^
pandas/src/klib/khash_python.h:38:1: note: in expansion of macro ‘KHASH_MAP_INIT_PYOBJECT’
KHASH_MAP_INIT_PYOBJECT(pymap, Py_ssize_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:41:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, char, 0, \
^
pandas/src/klib/khash_python.h:44:1: note: in expansion of macro ‘KHASH_SET_INIT_PYOBJECT’
KHASH_SET_INIT_PYOBJECT(pyset)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash_python.h:49:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(strbox, kh_pyobject_t)
^
pandas/algos.c: In function ‘__pyx_pf_6pandas_5algos_24roll_mean’:
pandas/algos.c:26752:7: warning: implicit declaration of function ‘signbit’ [-Wimplicit-function-declaration]
__pyx_t_12 = (signbit(__pyx_v_val) != 0);
^
gcc -shared -Wall -lm -Wall build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/algos.o -L/usr/lib -lpython2.7 -o build/lib.solaris-2.11-i86pc.32bit-2.7/pandas/algos.so
skipping 'pandas/lib.c' Cython extension (up-to-date)
building 'pandas.lib' extension
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/lib.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/lib.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/lib.c:232:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
gcc -shared -Wall -lm -Wall build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/lib.o -L/usr/lib -lpython2.7 -o build/lib.solaris-2.11-i86pc.32bit-2.7/pandas/lib.so
skipping 'pandas/tslib.c' Cython extension (up-to-date)
building 'pandas.tslib' extension
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/tslib.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/tslib.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/tslib.c:232:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/klib/khash_python.h:3:0,
from pandas/tslib.c:243:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:11:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, kh_float64_hash_equal)
^
pandas/src/klib/khash_python.h:13:1: note: in expansion of macro ‘KHASH_MAP_INIT_FLOAT64’
KHASH_MAP_INIT_FLOAT64(float64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:35:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, khval_t, 1, \
^
pandas/src/klib/khash_python.h:38:1: note: in expansion of macro ‘KHASH_MAP_INIT_PYOBJECT’
KHASH_MAP_INIT_PYOBJECT(pymap, Py_ssize_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:41:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, char, 0, \
^
pandas/src/klib/khash_python.h:44:1: note: in expansion of macro ‘KHASH_SET_INIT_PYOBJECT’
KHASH_SET_INIT_PYOBJECT(pyset)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash_python.h:49:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(strbox, kh_pyobject_t)
^
pandas/tslib.c: In function ‘__pyx_f_6pandas_5tslib__check_dts_bounds’:
pandas/tslib.c:21503:3: warning: implicit declaration of function ‘cmp_pandas_datetimestruct’ [-Wimplicit-function-declaration]
__pyx_t_2 = ((cmp_pandas_datetimestruct(__pyx_v_dts, (&__pyx_v_6pandas_5tslib__NS_MIN_DTS)) == -1) != 0);
^
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ufuncobject.h:317:0,
from pandas/tslib.c:233:
pandas/tslib.c: At top level:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/__ufunc_api.h:241:1: warning: ‘_import_umath’ defined but not used [-Wunused-function]
_import_umath(void)
^
pandas/tslib.c:12509:18: warning: ‘__pyx_f_6pandas_5tslib__tz_format’ defined but not used [-Wunused-function]
static PyObject *__pyx_f_6pandas_5tslib__tz_format(PyObject *__pyx_v_obj, PyObject *__pyx_v_zone) {
^
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/datetime/np_datetime.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/datetime/np_datetime.c:18:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/datetime/np_datetime.c:10:0:
/usr/include/python2.7/datetime.h:188:25: warning: ‘PyDateTimeAPI’ defined but not used [-Wunused-variable]
static PyDateTime_CAPI *PyDateTimeAPI = NULL;
^
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/datetime/np_datetime_strings.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime_strings.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/datetime/np_datetime_strings.c:17:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
pandas/src/datetime/np_datetime_strings.c:127:1: warning: ‘get_gmtime’ defined but not used [-Wunused-function]
get_gmtime(NPY_TIME_T *ts, struct tm *tms)
^
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/period.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/period.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from pandas/src/period.h:12,
from pandas/src/period.c:1:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
pandas/src/period.c:33:20: warning: ‘NULL_AF_INFO’ defined but not used [-Wunused-variable]
static asfreq_info NULL_AF_INFO;
^
gcc -shared -Wall -lm -Wall build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/tslib.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime_strings.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/period.o -L/usr/lib -lpython2.7 -o build/lib.solaris-2.11-i86pc.32bit-2.7/pandas/tslib.so
skipping 'pandas/parser.c' Cython extension (up-to-date)
building 'pandas.parser' extension
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/parser.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/parser.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/parser.c:233:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/klib/khash_python.h:3:0,
from pandas/parser.c:236:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:11:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, kh_float64_hash_equal)
^
pandas/src/klib/khash_python.h:13:1: note: in expansion of macro ‘KHASH_MAP_INIT_FLOAT64’
KHASH_MAP_INIT_FLOAT64(float64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:35:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, khval_t, 1, \
^
pandas/src/klib/khash_python.h:38:1: note: in expansion of macro ‘KHASH_MAP_INIT_PYOBJECT’
KHASH_MAP_INIT_PYOBJECT(pymap, Py_ssize_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:41:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, char, 0, \
^
pandas/src/klib/khash_python.h:44:1: note: in expansion of macro ‘KHASH_SET_INIT_PYOBJECT’
KHASH_SET_INIT_PYOBJECT(pyset)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash_python.h:49:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(strbox, kh_pyobject_t)
^
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ufuncobject.h:317:0,
from pandas/parser.c:234:
pandas/parser.c: At top level:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/__ufunc_api.h:241:1: warning: ‘_import_umath’ defined but not used [-Wunused-function]
_import_umath(void)
^
pandas/parser.c:19875:18: warning: ‘__pyx_f_6pandas_6parser__get_na_mask’ defined but not used [-Wunused-function]
static PyObject *__pyx_f_6pandas_6parser__get_na_mask(parser_t *__pyx_v_parser, int __pyx_v_col, int __pyx_v_line_start, int __pyx_v_line_end, kh_str_t *__pyx_v_na_hashset) {
^
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/parser/tokenizer.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/parser/tokenizer.o
In file included from pandas/src/parser/tokenizer.h:36:0,
from pandas/src/parser/tokenizer.c:20:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/parser/tokenizer.c: In function ‘parser_buffer_bytes’:
pandas/src/parser/tokenizer.c:562:11: warning: unused variable ‘src’ [-Wunused-variable]
void *src = self->source;
^
pandas/src/parser/tokenizer.c: In function ‘to_longlong_thousands’:
pandas/src/parser/tokenizer.c:1864:11: warning: unused variable ‘p_end’ [-Wunused-variable]
char *p_end;
^
pandas/src/parser/tokenizer.c: In function ‘xstrtod’:
pandas/src/parser/tokenizer.c:2039:16: warning: suggest parentheses around comparison in operand of ‘&’ [-Wparentheses]
p += (tsep != '\0' & *p == tsep);
^
pandas/src/parser/tokenizer.c: In function ‘precise_xstrtod’:
pandas/src/parser/tokenizer.c:2206:20: warning: suggest parentheses around comparison in operand of ‘&’ [-Wparentheses]
p += (tsep != '\0' & *p == tsep);
^
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/parser/io.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/parser/io.o
In file included from pandas/src/parser/tokenizer.h:36:0,
from pandas/src/parser/io.h:2,
from pandas/src/parser/io.c:1:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
gcc -shared -Wall -lm -Wall build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/parser.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/parser/tokenizer.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/parser/io.o -L/usr/lib -lpython2.7 -o build/lib.solaris-2.11-i86pc.32bit-2.7/pandas/parser.so
skipping 'pandas/hashtable.c' Cython extension (up-to-date)
building 'pandas.hashtable' extension
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/hashtable.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/hashtable.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/hashtable.c:233:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/klib/khash_python.h:3:0,
from pandas/hashtable.c:235:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:11:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, kh_float64_hash_equal)
^
pandas/src/klib/khash_python.h:13:1: note: in expansion of macro ‘KHASH_MAP_INIT_FLOAT64’
KHASH_MAP_INIT_FLOAT64(float64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:35:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, khval_t, 1, \
^
pandas/src/klib/khash_python.h:38:1: note: in expansion of macro ‘KHASH_MAP_INIT_PYOBJECT’
KHASH_MAP_INIT_PYOBJECT(pymap, Py_ssize_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:41:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, char, 0, \
^
pandas/src/klib/khash_python.h:44:1: note: in expansion of macro ‘KHASH_SET_INIT_PYOBJECT’
KHASH_SET_INIT_PYOBJECT(pyset)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash_python.h:49:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(strbox, kh_pyobject_t)
^
gcc -shared -Wall -lm -Wall build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/hashtable.o -L/usr/lib -lpython2.7 -o build/lib.solaris-2.11-i86pc.32bit-2.7/pandas/hashtable.so
skipping 'pandas/src/sparse.c' Cython extension (up-to-date)
building 'pandas._sparse' extension
gcc -DNDEBUG -Wall -fPIC -DPIC -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/sparse.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/sparse.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/sparse.c:232:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
gcc -shared -Wall -lm -Wall build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/sparse.o -L/usr/lib -lm -lpython2.7 -o build/lib.solaris-2.11-i86pc.32bit-2.7/pandas/_sparse.so
skipping 'pandas/src/testing.c' Cython extension (up-to-date)
building 'pandas._testing' extension
gcc -DNDEBUG -Wall -fPIC -DPIC -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/testing.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/testing.o
gcc -shared -Wall -lm -Wall build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/testing.o -L/usr/lib -lm -lpython2.7 -o build/lib.solaris-2.11-i86pc.32bit-2.7/pandas/_testing.so
skipping 'pandas/msgpack.cpp' Cython extension (up-to-date)
building 'pandas.msgpack' extension
gcc -DNDEBUG -Wall -fPIC -DPIC -D__LITTLE_ENDIAN__=1 -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/msgpack.cpp -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/msgpack.o
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/msgpack.cpp:234:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
pandas/msgpack.cpp: In function ‘PyObject* __pyx_pf_6pandas_7msgpack_4unpackb(PyObject*, PyObject*, PyObject*, PyObject*, int, PyObject*, PyObject*, PyObject*)’:
pandas/msgpack.cpp:5581:33: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
__pyx_t_2 = ((__pyx_v_off < __pyx_v_buf_len) != 0);
^
pandas/msgpack.cpp: In function ‘PyObject* __pyx_f_6pandas_7msgpack_8Unpacker_read_from_file(__pyx_obj_6pandas_7msgpack_Unpacker*)’:
pandas/msgpack.cpp:7164:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
if (((__pyx_t_2 < __pyx_t_3) != 0)) {
^
pandas/msgpack.cpp: In function ‘PyObject* __pyx_pf_6pandas_7msgpack_8Unpacker_8read_bytes(__pyx_obj_6pandas_7msgpack_Unpacker*, Py_ssize_t)’:
pandas/msgpack.cpp:7644:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
if (((__pyx_t_1 < __pyx_t_2) != 0)) {
^
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:26:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/msgpack.cpp:234:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/__multiarray_api.h: At global scope:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:1629:1: warning: ‘int _import_array()’ defined but not used [-Wunused-function]
_import_array(void)
^
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ufuncobject.h:317:0,
from pandas/msgpack.cpp:235:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/__ufunc_api.h:241:1: warning: ‘int _import_umath()’ defined but not used [-Wunused-function]
_import_umath(void)
^
g++ -shared -Wall -lm -Wall build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/msgpack.o -L/usr/lib -lpython2.7 -o build/lib.solaris-2.11-i86pc.32bit-2.7/pandas/msgpack.so
building 'pandas.json' extension
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/ujson/python -Ipandas/src/ujson/lib -Ipandas/src/datetime -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/ujson/python/ujson.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/ujson/python/ujson.o -D_GNU_SOURCE
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/ujson/python -Ipandas/src/ujson/lib -Ipandas/src/datetime -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/ujson/python/objToJSON.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/ujson/python/objToJSON.o -D_GNU_SOURCE
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/ujson/python/objToJSON.c:40:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
pandas/src/ujson/python/objToJSON.c: In function ‘NpyArr_encodeLabels’:
pandas/src/ujson/python/objToJSON.c:1178:20: warning: unused variable ‘labelsTmp’ [-Wunused-variable]
PyArrayObject* labelsTmp = NULL;
^
pandas/src/ujson/python/objToJSON.c: In function ‘Object_beginTypeContext’:
pandas/src/ujson/python/objToJSON.c:1367:5: warning: implicit declaration of function ‘isinf’ [-Wimplicit-function-declaration]
if (npy_isnan(val) || npy_isinf(val))
^
pandas/src/ujson/python/objToJSON.c:1765:5: warning: implicit declaration of function ‘encode’ [-Wimplicit-function-declaration]
encode (defaultObj, enc, NULL, 0);
^
pandas/src/ujson/python/objToJSON.c: At top level:
pandas/src/ujson/python/objToJSON.c:227:14: warning: ‘PyIntToINT64’ defined but not used [-Wunused-function]
static void *PyIntToINT64(JSOBJ _obj, JSONTypeContext *tc, void *outValue, size_t *_outLen)
^
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/ujson/python -Ipandas/src/ujson/lib -Ipandas/src/datetime -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/ujson/python/JSONtoObj.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/ujson/python/JSONtoObj.o -D_GNU_SOURCE
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/ujson/python/JSONtoObj.c:41:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/ujson/python -Ipandas/src/ujson/lib -Ipandas/src/datetime -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/ujson/lib/ultrajsonenc.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/ujson/lib/ultrajsonenc.o -D_GNU_SOURCE
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/ujson/python -Ipandas/src/ujson/lib -Ipandas/src/datetime -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/ujson/lib/ultrajsondec.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/ujson/lib/ultrajsondec.o -D_GNU_SOURCE
pandas/src/ujson/lib/ultrajsondec.c:79:13: warning: ‘ClearError’ defined but not used [-Wunused-function]
static void ClearError( struct DecoderState *ds)
^
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/ujson/python -Ipandas/src/ujson/lib -Ipandas/src/datetime -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/datetime/np_datetime.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime.o -D_GNU_SOURCE
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/datetime/np_datetime.c:18:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/datetime/np_datetime.c:10:0:
/usr/include/python2.7/datetime.h:188:25: warning: ‘PyDateTimeAPI’ defined but not used [-Wunused-variable]
static PyDateTime_CAPI *PyDateTimeAPI = NULL;
^
gcc -DNDEBUG -Wall -fPIC -DPIC -Ipandas/src/ujson/python -Ipandas/src/ujson/lib -Ipandas/src/datetime -Ipandas/src/klib -Ipandas/src -I/usr/lib/python2.7/site-packages/numpy/core/include -I/usr/include/python2.7 -c pandas/src/datetime/np_datetime_strings.c -o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime_strings.o -D_GNU_SOURCE
In file included from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1804:0,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:17,
from /usr/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/datetime/np_datetime_strings.c:17:
/usr/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
pandas/src/datetime/np_datetime_strings.c:127:1: warning: ‘get_gmtime’ defined but not used [-Wunused-function]
get_gmtime(NPY_TIME_T *ts, struct tm *tms)
^
gcc -shared -Wall -lm -Wall build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/ujson/python/ujson.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/ujson/python/objToJSON.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/ujson/python/JSONtoObj.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/ujson/lib/ultrajsonenc.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/ujson/lib/ultrajsondec.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime.o build/temp.solaris-2.11-i86pc.32bit-2.7/pandas/src/datetime/np_datetime_strings.o -L/usr/lib -lpython2.7 -o build/lib.solaris-2.11-i86pc.32bit-2.7/pandas/json.so
warning: no files found matching 'README.rst'
no previously-included directories found matching 'doc/build'
warning: no previously-included files matching '*.so' found anywhere in distribution
warning: no previously-included files matching '*.pyd' found anywhere in distribution
warning: no previously-included files matching '*.pyc' found anywhere in distribution
warning: no previously-included files matching '*~' found anywhere in distribution
warning: no previously-included files matching '#*' found anywhere in distribution
warning: no previously-included files matching '.git*' found anywhere in distribution
warning: no previously-included files matching '.DS_Store' found anywhere in distribution
warning: no previously-included files matching '*.png' found anywhere in distribution
Successfully installed pandas
Cleaning up...
```
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https://api.github.com/repos/pandas-dev/pandas/issues/8781 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8781/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8781/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8781/events | https://github.com/pandas-dev/pandas/pull/8781 | 48,314,827 | MDExOlB1bGxSZXF1ZXN0MjQxNzgwMTY= | 8,781 | Allowing empty slice in multi-indices | {
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} | 8 | 2014-11-10T21:14:40Z | 2014-11-11T00:12:45Z | 2014-11-11T00:11:42Z | CONTRIBUTOR | null | This solves #8737 in the case where there is at least one boolean indexer. The patch of #8739 does not allow the following:
``` python
import pandas as pd
import numpy as np
multi_index = pd.MultiIndex.from_product((['foo', 'bar', 'baz'], ['alpha', 'beta']))
df = pd.DataFrame(np.random.randn(5, 6), index=range(5), columns=multi_index)
df = df.sortlevel(0, axis=1)
df.loc[:, (['foo'], [])]
```
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} | 7 | 2014-11-11T02:06:09Z | 2014-11-14T19:31:56Z | 2014-11-14T19:31:48Z | CONTRIBUTOR | null | Fixes #8772 by not converting values from UTC to local timezone when trying to infer sub-daily frequencies. Across a DST transition this will result in a non-unique deltas and an inferred frequency of None.
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} | 4 | 2014-11-11T05:07:23Z | 2014-11-12T12:59:26Z | 2014-11-11T14:09:56Z | CONTRIBUTOR | null | Per comment in #8761
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} | 10 | 2014-11-11T13:52:55Z | 2015-07-29T08:52:30Z | 2015-07-28T21:50:15Z | NONE | null | closes #8336
This is a potential resolution to GH8336 It borrows the same code flow from _get_formatted_index in
pandas.core.format
``` python
import pandas as pd
pd.show_versions()
df = pd.DataFrame({
"A": [191, 191, 193, 193, 193],
"B": [1, 1, 2, 2, 3],
"C": ["foo", "foo", "foo", "foo", "foo"],
"D": ["bar", "bar", "bar", "bar", "bar"]})
print "\nOutput:"
print df.set_index(["A", "B"]).to_latex()
```
``` latex
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.17.2-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: C
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.6.0
IPython: 2.3.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.0
openpyxl: 2.1.1
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
Output:
\begin{tabular}{llll}
\toprule
& & C & D \\
\midrule
191 & 1 & & \\
193 & 2 & foo & bar \\
\bottomrule
\end{tabular}
```
After PR
``` latex
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.17.2-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: C
pandas: 0.15.1-18-g81c8a5f
nose: None
Cython: None
numpy: 1.9.1
scipy: None
statsmodels: None
IPython: 2.3.0
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2014.9
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
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
Output:
\begin{tabular}{llll}
\toprule
& & C & D \\
\midrule
191 & 1 & & \\
& 1 & foo & bar \\
193 & 2 & foo & bar \\
& 2 & foo & bar \\
& 3 & foo & bar \\
\bottomrule
\end{tabular}
```
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} | 14 | 2014-11-11T17:18:26Z | 2014-11-16T20:34:40Z | 2014-11-15T02:33:45Z | NONE | null | closes [#8771](https://github.com/pydata/pandas/issues/8771)
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} | 2 | 2014-11-11T17:46:19Z | 2014-11-11T23:37:48Z | 2014-11-11T23:37:43Z | CONTRIBUTOR | null | closes #8757
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} | 9 | 2014-11-11T18:14:18Z | 2015-03-08T16:26:35Z | 2015-03-08T16:26:09Z | NONE | null | I've rewritten the current qtpandas model and uploaded it to my pandas fork:
https://github.com/datalyze-solutions/pandas/tree/qt4/pandas/sandbox
Its still in sandbox. An example can be found in tests named TestApp.py. I just testet Qt4 and python2.7, Qt5 might be interesting.
Any reviews are welcome! :smiley:
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| null | 4 | 2014-11-11T20:42:15Z | 2014-11-11T22:58:46Z | 2014-11-11T22:31:14Z | NONE | null | I'm having problems with `groupby` with `TimeGrouper` followed by agg when `DataFrame` has two lines with unique values.
``` python
from datetime import datetime
import pandas as pd
import numpy as np
result = np.empty(3, dtype=[('name', 'object'),
('value', 'int64'),
('date', 'datetime64[us]')])
# doesn't work
result[0] = ('a', 1, datetime.now())
result[1] = ('b', 1, datetime.now())
# this works
#result[0] = ('a', 1, datetime.now())
#result[1] = ('a', 1, datetime.now())
# this also works
#result[0] = ('a', 1, datetime.now())
#result[1] = ('b', 1, datetime.now())
#result[2] = ('c', 1, datetime.now())
df = pd.DataFrame.from_records(result, index='date')
df = df[df['name'].map(lambda x: not x is None)] # filter blank series
group_list = ['name', pd.TimeGrouper(freq='%sS' % 60)]
grouped = df.groupby(group_list)
result = grouped.agg(['sum', 'count'])
print result.to_records()
```
The main idea is to group by name and date by resample it into 60 seconds intervals. On this example the desired output would be something like this:
``` python
[('a', Timestamp('2014-11-11 20:22:00'), 1, 1)
('b', Timestamp('2014-11-11 20:22:00'), 1, 1)]
```
But it returns this:
``` python
[(<pandas.tseries.resample.TimeGrouper object at 0x2fbea10>, 'b', 1, 1, 1)
('name', 'a', 1, 1, 1)]
```
Sometimes `agg` ignores data if there is nothing to aggregate on, this doesn't seems right to me.
> > > show_versions()
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.29.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.1
nose: None
Cython: 0.21
numpy: 1.9.1
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: None
numexpr: 2.4
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 5 | 2014-11-11T22:15:07Z | 2015-09-04T00:51:47Z | 2015-09-04T00:51:47Z | CONTRIBUTOR | null | Hello All!
Moving into Pandas from R/Matlab/Stata. One feature I'm finding I really miss: a field generated during a merge that reports, for each row, whether that row came from the left dataset, the right dataset, or was successfully merged.
I do a lot of work in social science where our data is VERY dirty. For example, I'm often merging transliterated names from Pakistan, and so I just want to see after a merge how many records successfully merged, and then easily pull out the records of each time to compare. I'm including a kludge I'm using below, but an in-line option would be so nice, and I think others in my area would also appreciate it.
Thanks!
``` python
df1['left'] = 1
df2['right'] = 1
mergedDF = pd.merge(df1,df2, how='outer', left_on='key1', right_on='key2')
def mergeVar(x):
if x['left'] == 1 and x['right'] == 1 :
return 'both'
elif x['left'] == 1 and x['right'] != 1:
return 'leftOnly'
else: return 'rightOnly'
mergedDF['mergeReport'] = mergedDF.apply(mergeVar, axis=1)
mergedDF.drop(['left', 'right'], axis = 1)
```
(I'd also be happy to help make it, but I'm relatively new to python, so would need some hand-holding to learn how to integrate a new option into an existing function...)
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} | 1 | 2014-11-11T23:16:38Z | 2014-12-04T11:30:36Z | 2014-12-04T11:30:36Z | NONE | null | This should work:
``` python
with pandas.HDFStore('file.h5') as f:
print(f)
```
as a more convenient form of
``` python
with contextlib.closing(pandas.HDFStore('file.h5')) as f:
print(f)
```
AFAIK this is a easy as adding the following to `HDFStore`
``` python
def _enter_(self): pass
def _exit_(self): self.close()
```
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} | 4 | 2014-11-12T00:12:57Z | 2014-11-14T12:49:41Z | 2014-11-14T12:49:34Z | CONTRIBUTOR | null | closes #3995
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} | 15 | 2014-11-12T01:55:30Z | 2014-11-17T00:34:50Z | 2014-11-17T00:34:49Z | CONTRIBUTOR | null | This is implemented by storing the codes directly in the table. And a metadata VLArray of the categories.
Query and appending work as expected. The only quirk is that I don't allow you to append to a table unless the new data has exactly the same categories. Otherwise the codes become meaningless.
This has the nice property of drastically shrinking the storage cost compared to regular strings (which are stored as fixed width of the maximum for that particular column).
I bumped the actual HDF5 storage version to current (was 0.10.1). Its not strictly necessary as this is a completely optional feature, but I am adding the sub-group space 'meta' (which FYI we can use for other things, e.g. to store the column labels and avoid the 64KB limit in attrs, their is an issue about this somewhere)
```
In [14]: df = DataFrame({'a' : Series(list('abccdef')).astype('category'), 'b' : np.random.randn(7)})
In [15]: df
Out[15]:
a b
0 a -0.094609
1 b -1.814638
2 c 0.214974
3 c -0.195395
4 d 0.206022
5 e 1.130589
6 f -0.832810
In [19]: store = pd.HDFStore('test.h5',mode='w')
In [20]: store.append('df',df,data_columns=['a'])
In [21]: store.select('df',where=["a in ['b','d']"])
Out[21]:
a b
1 b -1.814638
4 d 0.206022
In [22]: store.select('df',where=["a in ['b','d']"]).dtypes
Out[22]:
a category
b float64
dtype: object
In [25]: store.get_storer('df').group
Out[25]:
/df (Group) u''
children := ['table' (Table), 'meta' (Group)]
In [26]: store.get_storer('df').group.table
Out[26]:
/df/table (Table(7,)) ''
description := {
"index": Int64Col(shape=(), dflt=0, pos=0),
"values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
"a": Int8Col(shape=(), dflt=0, pos=2)}
byteorder := 'little'
chunkshape := (3855,)
autoindex := True
colindexes := {
"a": Index(6, medium, shuffle, zlib(1)).is_csi=False,
"index": Index(6, medium, shuffle, zlib(1)).is_csi=False}
In [27]: store.get_storer('df').group.meta
Out[27]:
/df/meta (Group) u''
children := ['a' (VLArray)]
```
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} | 19 | 2014-11-12T09:45:34Z | 2015-03-11T12:11:02Z | 2015-03-11T12:11:02Z | MEMBER | null | From http://stackoverflow.com/questions/26882499/reset-time-part-of-a-pandas-timestamp#26882499
There is now a `DatetimeIndex.normalize()` method to set times to midnight.
Would it be useful to also have this method available on the individual `Timestamp` object?
Further, this method could also be added to the `.dt` accessor (udpate: for this, there already is a similar issue: #5502).
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} | 2 | 2014-11-12T14:04:22Z | 2014-12-05T02:34:01Z | 2014-12-05T02:34:01Z | MEMBER | null | I was playing a bit with `pd.Grouper`. Shouldn't the following work:
```
In [109]: df = pd.DataFrame({'A':[0,0,1,1,2,2], 'B':[1,2,3,4,5,6]})
In [110]: df
Out[110]:
A B
0 0 1
1 0 2
2 1 3
3 1 4
4 2 5
5 2 6
In [111]: df.groupby(pd.Grouper(key='A')).sum()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-111-b9e051aabea3> in <module>()
----> 1 df.groupby(pd.Grouper(key='A')).sum()
C:\Anaconda\lib\site-packages\pandas\core\groupby.pyc in f(self)
108 raise SpecificationError(str(e))
109 except Exception:
--> 110 result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
111 if _convert:
112 result = result.convert_objects()
C:\Anaconda\lib\site-packages\pandas\core\groupby.pyc in aggregate(self, arg, *a
rgs, **kwargs)
2634 return getattr(self, cyfunc)()
2635
-> 2636 if self.grouper.nkeys > 1:
2637 return self._python_agg_general(arg, *args, **kwargs)
2638 else:
AttributeError: 'Int64Index' object has no attribute 'nkeys'
```
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| null | 24 | 2014-11-12T14:53:51Z | 2021-04-11T05:31:32Z | null | NONE | null | When pandas is used to interpolate data, the results are not the same as what you get from scipy.interpolate.interp1d.
When using with simple data, the differences are small (see images). However, when used with real-world data, the differences can be large enough to throw off some algorithms that depend on the values of the interpolated data.
In the images, notice two things. First that the results are not the same between the two methods and second, that pandas omits the last point. Manually adding the last point fixes the simple sin(x) case, but not the lat/lon case.
I've also tried with method/kind='linear' with much the same results.
Tested on pandas 0.14.1
To replicate, just run the code below:
```
import numpy
import pandas
from matplotlib import pyplot
import scipy.interpolate
def pandas_interpolate(df, interp_column, method='cubic'):
df = df.set_index(interp_column)
df = df.reindex(numpy.arange(df.index.min(), df.index.max(), 0.0005))
df = df.interpolate(method=method)
df = df.reset_index()
df = df.rename(columns={'index': interp_column})
return df
def scipy_interpolate(df, interp_column, method='cubic'):
series = {}
new_x = numpy.arange(df[interp_column].min(), df[interp_column].max(), 0.0005)
for column in df:
if column == interp_column:
series[column] = new_x
else:
interp_f = scipy.interpolate.interp1d(df[interp_column], df[column], kind=method)
series[column] = interp_f(new_x)
return pandas.DataFrame(series)
if __name__ == '__main__':
df = pandas.read_csv('interp_test.csv')
pd_interp = pandas_interpolate(df, 'distance_km', 'cubic')
scipy_interp = scipy_interpolate(df, 'distance_km', 'cubic')
#pyplot.plot(df['lon'], df['lat'], label='raw data')
pyplot.plot(pd_interp['lon'], pd_interp['lat'], label='pandas')
pyplot.plot(scipy_interp['lon'], scipy_interp['lat'], label='scipy interp1d')
pyplot.legend(loc='best')
pyplot.figure()
df2 = pandas.DataFrame({'x': numpy.arange(10), 'sin(x)': numpy.sin(numpy.arange(10))})
pd_interp2 = pandas_interpolate(df2, 'x', 'cubic')
scipy_interp2 = scipy_interpolate(df2, 'x', 'cubic')
pyplot.plot(pd_interp2['x'], pd_interp2['sin(x)'], label='pandas')
pyplot.plot(scipy_interp2['x'], scipy_interp2['sin(x)'], label='scipy interp1d')
pyplot.legend(loc='best')
pyplot.show()
```
The data file interp_test.csv can be found at https://github.com/cgkanchi/pandas_interpolate_bug
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| null | 3 | 2014-11-12T16:11:42Z | 2014-11-12T16:30:23Z | 2014-11-12T16:29:54Z | CONTRIBUTOR | null | This is a datatype thing I believe. When I construct a DataFrame and then compare rows of that, NaNs are not ignored contrary to the documentation.
```
import pandas as pd
import numpy as np
a = pd.DataFrame([{'A': 1, 'B': 'b', 'C': np.NAN}, {'A': 1, 'B': 'b', 'C': 1}])
b = pd.DataFrame([{'A': 1, 'B': 'b', 'C': np.NAN}, {'A': 1, 'B': 'b', 'C': 1}])
a.loc[0]
A 1
B b
C NaN
Name: 0, dtype: object
aa = a.loc[0]
bb = b.loc[0]
aa.equals(bb)
False
# which essentially does this in Block
aa.values == bb.values
Out[35]: array([ True, True, False], dtype=bool)
```
Constructing the series outright works as advertised.
```
a = pd.Series({'A': 1, 'B': 'b', 'C': np.NAN})
b = pd.Series({'A': 1, 'B': 'b', 'C': np.NAN})
a.equals(b)
True
a.values == b.values
Out[30]: array([ True, True, True], dtype=bool)
```
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} | 5 | 2014-11-12T23:20:56Z | 2016-07-04T10:58:42Z | 2016-07-04T10:58:42Z | CONTRIBUTOR | null | ```
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as m
print 'Matplotlib version:', m.__version__
Matplotlib version: 1.4.2
print 'Pandas version:', pd.__version__
Pandas version: 0.15.1
plt.style.use('ggplot')
m.rc("text.latex",preamble=r"\usepackage{cmbright}")
m.rcParams['font.size'] = 20 ###<--- This line causes trouble.
```
## df is a pandas dataframe
1) it messes up the following:
```
df.boxplot("column name",by="splitcol")
```
The heading and subheading overlap.
regards
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} | 2 | 2014-11-12T23:41:02Z | 2018-07-06T21:26:34Z | 2018-07-06T21:26:34Z | CONTRIBUTOR | null | Hi,
the heading and subheading in the DataFrame.boxplot and DataFrame.hist are generated automatically. I have several issues here.
1) Would like to have the option to turn it on/off/modify content.
2) Default distance between subheading and heading should be larger
3) Default generation of heading/subheading should be consistent
At the moment the functions generate different headings/subheadings.
regards
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| null | 1 | 2014-11-13T05:46:38Z | 2014-11-13T08:48:24Z | 2014-11-13T08:48:24Z | CONTRIBUTOR | null | Should be 'Removing', I think, if I'm not misunderstanding.
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} | 1 | 2014-11-13T09:38:39Z | 2015-04-04T18:28:30Z | 2015-04-04T18:28:30Z | CONTRIBUTOR | null | `Series.mask` just performs a mask operation. `Series.where` accepts substitute values. (In the meantime, workaround is simple - just invert the where selection).
Version 0.15.1 docs:
```
Series.mask(cond)
Returns copy whose values are replaced with nan if the inverted condition is True
Series.where(cond, other=nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True)
Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.
```
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