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} | 16 | 2015-11-25T23:45:11Z | 2020-06-09T19:09:07Z | 2020-05-17T22:17:51Z | CONTRIBUTOR | null | xref #11603
might be slightly non-trivial as the current impl does NOT use an explict iterator, rather does a sliding window in cython and marginal calcs.
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} | 5 | 2015-11-26T02:30:13Z | 2016-02-01T18:59:22Z | 2016-02-01T18:59:22Z | CONTRIBUTOR | null | Please review .
Closes #11693
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} | 3 | 2015-11-26T04:20:15Z | 2015-12-01T12:11:08Z | 2015-12-01T12:10:28Z | CONTRIBUTOR | null | closes #11697
thanks @ajspeck
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| null | 16 | 2015-11-26T12:12:59Z | 2017-11-05T20:46:24Z | 2015-11-29T17:30:39Z | NONE | null | Hi,
I think there is a bug in `to_pickle`
```
>>> data = pd.read_hdf(DG.load_path + 'time_series/PSI_TS_after_alignment_and_reshaping.h5', 'table')
>>> data.shape
(1006, 288095)
>>> data.to_pickle(DG.load_path + 'time_series/PSI_TS_after_alignment_and_reshaping2.pkl')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/donbeo/MyApps/phd_python/lib/python3.4/site-packages/pandas/core/generic.py", line 994, in to_pickle
return to_pickle(self, path)
File "/Users/donbeo/MyApps/phd_python/lib/python3.4/site-packages/pandas/io/pickle.py", line 14, in to_pickle
pkl.dump(obj, f, protocol=pkl.HIGHEST_PROTOCOL)
OSError: [Errno 22] Invalid argument
>>> pd.version.version
'0.16.2'
>>>
```
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} | 8 | 2015-11-26T17:02:50Z | 2015-12-07T00:33:58Z | 2015-12-07T00:33:58Z | NONE | null | running "pd.to_datetime("2015-11-18 15:30:00+05:30").tz_localize('UTC').tz_convert('Asia/Kolkata')" returns following o/p
Timestamp('2015-11-18 16:30:00+0530', tz='Asia/Kolkata')
It should have returned as Timestamp('2015-11-18 15:30:00+0530', tz='Asia/Kolkata') as no daylight saving is applicable in India.
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} | 2 | 2015-11-26T18:28:51Z | 2015-11-29T19:51:39Z | 2015-11-29T17:32:18Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/11699
xref https://github.com/pydata/pandas/issues/11189#issuecomment-152613636
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| null | 1 | 2015-11-27T00:47:46Z | 2015-11-27T01:02:45Z | 2015-11-27T01:01:22Z | NONE | null | Saving DataFrame's to HDF format is broken for the new timezone aware datetime64[ns] datatype.
See the following example:
``` python
import pandas as pd
import numpy as np
from datetime import datetime
print "pandas version", pd.__version__
df1 = pd.DataFrame(data=np.random.randn(5,3), index=pd.DatetimeIndex(start=datetime.now(),freq='S',periods=5))
df1 = df1.reset_index() #Create a column of type datetime64[ns] (timezone naiive)
print df1.info()
df1.to_hdf('df.hdf', 'data_frame') #WORKS!!!
df2 = pd.DataFrame(data=np.random.randn(5,3), index=pd.DatetimeIndex(start=datetime.now(),freq='S',periods=5, tz='Australia/Sydney'))
df2 = df2.reset_index() #Create a column of type datetime64[ns, Australia/Sydney] (timezone AWARE)
print df2.info()
df2.to_hdf('df.hdf', 'data_frame') #BREAKS IN pandas 17.0!!!
```
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} | 7 | 2015-11-27T10:34:00Z | 2016-01-30T15:38:17Z | 2016-01-30T15:38:17Z | NONE | null | Pandas fails to load an excel file as a dict fo dataframe when the last sheet is empty when sheetname=None.
Deleting the last sheet manually fix the problem.
Maybe Pandas should be improved to be robust to this common case.
Also I post this to raise awareness of this issue in case someone is scratching his head wondering why pandas is not loading his excel file
In [151]:
df_dict=pd.read_excel('D:\trash_test.xlsx',sheetname=None)
print df_dict.keys() # pandas fails to load the file
df=pd.read_excel('D:\trash_test.xlsx',sheetname='a_test')
print df # pandas loads first sheet normally
df=pd.read_excel('D:\trash_test.xlsx',sheetname='a_test')
print df # pandas loads second sheet normally
df=pd.read_excel('D:\trash_test.xlsx',sheetname='not_default')
print df # pandas fails to load 'not_default' the last sheet which is empty (to be expected?)
Index([], dtype='object')
Empty DataFrame
Columns: [some_stuff, 1]
Index: []
Empty DataFrame
Columns: [some_stuff, 1]
Index: []
---
XLRDError Traceback (most recent call last)
<ipython-input-151-f10441544ad3> in <module>()
6 df=pd.read_excel('D:\trash_test.xlsx',sheetname='a_test')
7 print df # pandas loads second sheet normally
----> 8 df=pd.read_excel('D:\trash_test.xlsx',sheetname='not_default')
9 print df # pandas fails to load 'not_default' the last sheet which is empty (to be expected?)
C:\Anaconda\lib\site-packages\pandas\io\excel.pyc in read_excel(io, sheetname, header, skiprows, skip_footer, index_col, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, has_index_names, converters, engine, *_kwds)
168 date_parser=date_parser, na_values=na_values, thousands=thousands,
169 convert_float=convert_float, has_index_names=has_index_names,
--> 170 skip_footer=skip_footer, converters=converters, *_kwds)
171
172 class ExcelFile(object):
C:\Anaconda\lib\site-packages\pandas\io\excel.pyc in _parse_excel(self, sheetname, header, skiprows, skip_footer, index_col, has_index_names, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, verbose, **kwds)
370
371 if isinstance(asheetname, compat.string_types):
--> 372 sheet = self.book.sheet_by_name(asheetname)
373 else: # assume an integer if not a string
374 sheet = self.book.sheet_by_index(asheetname)
C:\Anaconda\lib\site-packages\xlrd\book.pyc in sheet_by_name(self, sheet_name)
439 sheetx = self._sheet_names.index(sheet_name)
440 except ValueError:
--> 441 raise XLRDError('No sheet named <%r>' % sheet_name)
442 return self.sheet_by_index(sheetx)
443
XLRDError: No sheet named <'not_default'>
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| null | 5 | 2015-11-27T13:42:00Z | 2015-11-30T11:36:16Z | 2015-11-30T11:36:16Z | CONTRIBUTOR | null | to_clipboard uses ascii encoding. And it cannot be changed.
```
>>> import pandas as pd
>>> pd.__version__
'0.17.0'
>>> pd.DataFrame(["Hello world"]).to_clipboard()
>>> pd.DataFrame(["Здравствуй, мир"]).to_clipboard()
...
UnicodeEncodeError: 'ascii' codec can't encode characters in position 22-31: ordinal not in range(128)
>>> pd.DataFrame(["Здравствуй, мир"]).to_clipboard(encoding="utf-8")
...
TypeError: to_string() got an unexpected keyword argument 'encoding'
```
Had to use part of to_clipboard source, where it exports dataframe to StringIO buffer, then put it to clipboard with Qt.
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} | 2 | 2015-11-27T15:54:03Z | 2015-12-01T15:15:06Z | 2015-12-01T15:15:06Z | CONTRIBUTOR | null | SAS xport files allow floats to be stored with fewer than 8 bytes, with implicit zeros in the high order bytes. Currently we only handle full width (8 byte) floats. This PR adds support for truncated floats.
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} | 16 | 2015-11-27T20:09:28Z | 2015-12-01T20:38:28Z | 2015-12-01T20:38:19Z | CONTRIBUTOR | null | closes #11447
Updated excel.py to use pandas.io.common.get_filepath_or_buffer (handles s3 urls) instead of urlopen, only when _is_s3_url returns true. This was the minimally invasive change.
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} | 7 | 2015-11-27T21:14:17Z | 2015-12-03T04:47:39Z | 2015-12-02T15:05:09Z | CONTRIBUTOR | null | closes #11698
Please review.
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| null | 9 | 2015-11-28T13:08:39Z | 2017-02-07T23:58:04Z | 2015-11-29T17:28:21Z | NONE | null | I am reading a very large csv file (the NYC taxi dataset at https://storage.googleapis.com/tlc-trip-data/2015/), only two columns:
index_col=False,skipinitialspace=True,usecols=['pickup_longitude', 'pickup_latitude'], chunksize=...
I load it progressively by varying-size chunks, and use 2 threads to do the progressive loading.
After reading about 10M lines (the number varies from one run to the other), it dumps a core.
Here is what GDB finds-out:
Fatal Python error: GC object already tracked
Fatal Python error: GC object already tracked
Program received signal SIGABRT, Aborted.
[Switching to Thread 0x7fffddd98700 (LWP 10284)]
0x00007ffff782dcc9 in __GI_raise (sig=sig@entry=6)
at ../nptl/sysdeps/unix/sysv/linux/raise.c:56
56 ../nptl/sysdeps/unix/sysv/linux/raise.c: No such file or directory.
(gdb) where
#0 0x00007ffff782dcc9 in __GI_raise (sig=sig@entry=6)
```
at ../nptl/sysdeps/unix/sysv/linux/raise.c:56
```
#1 0x00007ffff78310d8 in __GI_abort () at abort.c:89
#2 0x000000000045a4f2 in Py_FatalError ()
#3 0x000000000052b5ec in PyTuple_New ()
#4 0x000000000050c73d in ?? ()
#5 0x000000000050d3f6 in Py_BuildValue ()
#6 0x00007fffec3d01d8 in buffer_rd_bytes (source=0x7fffd8006650,
```
nbytes=<optimized out>, bytes_read=0x7fffddd96d08, status=0x7fffddd96d04)
at pandas/src/parser/io.c:123
```
#7 0x00007fffec3cf065 in parser_buffer_bytes (nbytes=<optimized out>,
```
self=0x7fffd8003480) at pandas/src/parser/tokenizer.c:610
```
#8 _tokenize_helper (self=0x7fffd8003480, nrows=nrows@entry=3186,
```
all=all@entry=0) at pandas/src/parser/tokenizer.c:1872
```
#9 0x00007fffec3cf3e7 in tokenize_nrows (self=<optimized out>,
```
nrows=nrows@entry=3186) at pandas/src/parser/tokenizer.c:1905
```
#10 0x00007fffec39a3c4 in __pyx_f_6pandas_6parser_10TextReader__tokenize_rows (
```
__pyx_v_self=0x7fffdddd5050, __pyx_v_nrows=3186) at pandas/parser.c:8745
```
#11 0x00007fffec3a21a2 in __pyx_f_6pandas_6parser_10TextReader__read_rows (
```
__pyx_v_self=0x7fffdddd5050, __pyx_v_rows=0x7fffd8249a88, __pyx_v_trim=0)
at pandas/parser.c:8970
```
#12 0x00007fffec393f0c in __pyx_f_6pandas_6parser_10TextReader__read_low_memory
```
(__pyx_v_self=0x7fffdddd5050, __pyx_v_rows=0x7fffcb815948)
```
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} | 21 | 2015-11-28T17:46:30Z | 2016-01-29T14:49:25Z | 2016-01-29T14:40:50Z | CONTRIBUTOR | null | ## Bug 1 [edit: issues #11640 and #12078, fixed by PR #12158 but not entirely tested]
There was a bug deeply hidden in `pandas/core/index.py`, where it was assumed that the `.get_loc` method of an index would only return an integer or a slice, while it can also return a mask.
Simply correcting that bug has two consequences:
- now for a dataframe `df` with multi-indexed columns, `df.drop('a', axis=1)` works if `df['a']` returns something, e.g. if the column `('a', '')` exists
- likewise, `df.groupby('a')` also works in that case
Example:
```
In [2]: df = pd.DataFrame(columns=['a','b','c','d'], data=[[1,'b1','c1',3], [1,'b2','c2',4]])
In [3]: dg = df.pivot_table(index='a', columns=['b','c'], values='d').reset_index()
In [4]: dg
Out[4]:
b a b1 b2
c c1 c2
0 1 3 4
In [5]: dg['a']
Out[5]:
0 1
Name: a, dtype: int64
In [8]: dg.drop('a', axis=1)
Out[8]:
b b1 b2
c c1 c2
0 3 4
In [9]: dg.groupby('a').mean()
Out[9]:
b b1 b2
c c1 c2
a
1 3 4
```
## Bug 2 [edit: #11741, solved by PR #12063]
I also correct a little bug I mentioned in the talk on issue #11640: `.groupby` failed to raise a `KeyError` for a non-existing column when there was only one row:
```
In [6]: dg.groupby('z').mean()
Out[6]:
b a b1 b2
c c1 c2
z 1 3 4
```
Now we do have:
```
In [4]: dg.groupby('z').mean()
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
...
KeyError: 'z'
```
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} | 5 | 2015-11-28T18:03:48Z | 2016-02-11T23:35:54Z | 2016-02-11T23:35:54Z | CONTRIBUTOR | null | In pandas 0.17.1, a TypeError is returned when trying to subtract a timezone-aware timestamp from a NaT timestamp:
``` python
In [1]: import pandas as pd
In [2]: pd.Timestamp(None, tz='utc') - pd.Timestamp('now', tz='utc')
Traceback (most recent call last):
File "<ipython-input-2-5e0738cec5fa>", line 1, in <module>
pd.Timestamp(None, tz='utc') - pd.Timestamp('now', tz='utc')
File "pandas\tslib.pyx", line 1099, in pandas.tslib._NaT.__sub__ (pandas\tslib.c:21618)
File "pandas\tslib.pyx", line 1026, in pandas.tslib._Timestamp.__sub__ (pandas\tslib.c:20036)
TypeError: Timestamp subtraction must have the same timezones or no timezones
```
Subtracting a timezone unaware timestamp from a NaT timestamp is no problem. Also subtracting a NaT from a timezone aware timestamp works:
``` python
In [3]: pd.Timestamp(None) - pd.Timestamp('now')
Out[3]: NaT
In [4]: pd.Timestamp('now', tz='utc') - pd.Timestamp(None, tz='utc')
Out[4]: NaT
In [5]: pd.Timestamp('now', tz='utc') - pd.Timestamp(None)
Out[5]: NaT
```
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| null | 3 | 2015-11-29T19:03:45Z | 2016-02-05T14:53:24Z | null | NONE | null | ### Expected
Regardless of the arguments specified, the return of `DataFrame.join` always has the left side's index values and name. In the case of `how='right'`, there should be `np.nan` in the index when the right side does not 'match' a row on the left.
In this case:
``` python
>>> pd.DataFrame([], columns=['gene'], index=pd.Index([], name='family')).join(s), how='right', on='gene')
gene gene_id
family
NaN NaN 1
```
### Actual / reproduce
Right index is returned for empty dataframe:
``` python
>>> import pandas as pd
>>> s = pd.Series([1], name='gene_id', index=pd.Index([1], name='gene')
>>> pd.DataFrame([], columns=['gene'], index=pd.Index([], name='family')).join(s), how='right', on='gene')
gene gene_id
gene
1 NaN 1
```
Note that it doesn't happen with non-empty frames:
``` python
>>> pd.DataFrame([1], columns=['gene'], index=pd.Index([1], name='family')).join(s), how='right', on='gene')
gene gene_id
family
1 1 1
```
### Version
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.5-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: None
pip: 7.1.2
setuptools: 18.5
Cython: None
numpy: 1.10.1
scipy: 0.16.1
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.0b1
pymysql: None
psycopg2: None
```
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} | 15 | 2015-11-30T06:20:28Z | 2020-04-10T02:46:42Z | null | CONTRIBUTOR | null | Copying dataframe with to_clipboard and Qt gives different results: when pasted to Excel there's one more empty line. It may be unexpected and overwrite important data.
When looking into clipboard with Free Clipboard Viewer we can see that Qt uses CR+LF linebreaks:
and pandas uses LF:
Maybe this is the source of problem.
(This isn't fixed by #9693)
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| null | 1 | 2015-11-30T08:24:32Z | 2015-11-30T11:42:49Z | 2015-11-30T11:42:49Z | NONE | null | {
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| null | 12 | 2015-11-30T09:32:43Z | 2015-11-30T16:07:16Z | 2015-11-30T15:58:29Z | NONE | null | I have a similiar issue with BUG #11697 but not same.
This issue is on win64. But my macos is ok.
Version:
python:2.7.10
pandas:0.17.1
numpy: 1.10.1
I use the @ajspeck example.
I got the same error
```
File "pandas\algos.pyx", line 1586, in pandas.algos.roll_median_c (pandas\algos.c:36408)
MemoryError: skiplist_insert failed
```
when I used the follow code:
```
import numpy as np
import pandas as pd
n = 20000
pd.rolling_median(pd.Series(np.random.randn(n)), window=2, center=False)
pd.rolling_median(pd.Series(np.random.randn(n)), window=2, center=False)
```
while
```
import numpy as np
import pandas as pd
n=20000
pd.rolling_median(pd.Series(np.random.randn(n)), window=2, center=False)
```
works fine.
I found sum of n must less than 25200
I don't know the issue is the same to ajspeck's.And the change couldn't fix my issue.
I guess the node is not destoried.
Thanks!
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} | 11 | 2015-11-30T12:17:38Z | 2021-05-25T17:29:48Z | null | NONE | null | When I work with Pandas DataFrames, I prefer to keep the full column names for clarity. So when I print out the `head`, or use describe, I get a meaningful table. However, this also means I have column names like "Time of Sale" that become annoying to type out.
A nice compromise seems like it would be to have short "aliases" for column names. For instance, I can define the `tos` average for the above, perhaps like so:
```
df = pd.read_csv(...)
df.set_alias({'Time of Sale' : 'tos'})
```
Then, the `__get_attribute__` method can look up aliases in addition to column names, so I can refer to that column simply as `df.tos`. But for all other purposes, the columns name is still the descriptive full name.
Would this make sense?
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| null | 3 | 2015-11-30T13:18:39Z | 2015-11-30T14:30:47Z | 2015-11-30T13:49:39Z | NONE | null | Hello
I am not sure, maybe this is the expected behaviour, but it was a big surprise for me (and a source of some bugs).
As the title states, dropping rows (whether inplace or not) does not delete corresponding index entries (when the frame has a multiIndex). If one later uses index values directly it causes really unexpected behaviour.
An obvious work-around is to reset index and re-build it, but it is not really elegant code...
Simple example:
``` python
>>> import pandas as pd
>>> df = pd.DataFrame([['A', 'a', 1], ['B', 'b', 2], ['C', 'c', 3]], columns=['upper', 'lower', 'num'])
>>> df = df.set_index(['upper', 'lower'])
>>> df = df.drop('A')
>>> df.index
MultiIndex(levels=[['A', 'B', 'C'], ['a', 'b', 'c']],
labels=[[1, 2], [1, 2]],
names=['upper', 'lower'])
>>> df.index.levels[0]
Index(['A', 'B', 'C'], dtype='object', name='upper')
```
Even though the row was deleted correctly.
show_versions():
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-18-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: None
setuptools: 18.4
Cython: 0.23.3
numpy: 1.10.1
scipy: 0.14.1
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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| null | 4 | 2015-11-30T13:29:09Z | 2015-11-30T14:38:25Z | 2015-11-30T13:51:42Z | CONTRIBUTOR | null | I just used `pd.to_datetime` on a string column and inspected the head of the dataframe and it looked good. Unfortunately, I had german dates (day.month.year) with high days in the first rows. These were converted correctly, but then I got dates (of the next month) with low days and the conversion switched to american style (month.day.year). I would expect that I at least get a warning that the date formats are different or that the first found date format is used for all dates:
```
>>> pd.to_datetime(["29.01.1945","1.3.1945", "02.03.1945"])
DatetimeIndex(['1945-01-29', '1945-01-03', '1945-02-03'], dtype='datetime64[ns]', freq=None)
```
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} | 5 | 2015-11-30T13:30:58Z | 2021-05-12T13:27:55Z | 2021-05-12T13:27:55Z | NONE | null | *edit: behavior with DataFrame[sparse]*
```
In [68]: a=np.zeros(shape=(5,5))
In [69]: pd.DataFrame(a).apply(lambda x: pd.SparseArray(x, fill_value=0)).drop_duplicates()
Out[69]:
0 1 2 3 4
0 NaN NaN NaN NaN NaN
```
~~~
It seems as if pd.SparseDataFrame().duplicated consistently fails when SparseDataFrame contains the value provided by `default_fill_value` in the data.
When SpraseDataFrame does not contain the default_fill_value everything works fine.
to reproduce, default_fill_value can be anything, either the default NaN or a manually specified one (I chose 0).
Reproduction:
```
In [2]: import pandas as pd
In [3]: import numpy as np
In [4]: a=np.zeros(shape=(5,5))
In [5]: df=pd.SparseDataFrame(a)
In [6]: df.drop_duplicates() # <- This is good
Out[6]:
0 1 2 3 4
0 0 0 0 0 0
In [8]: df=pd.SparseDataFrame(a, default_fill_value=0)
In [9]: df.drop_duplicates() # <- This should work similarly to [6] but fails instead
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-daf275b6788b> in <module>()
----> 1 df.drop_duplicates()
/usr/local/lib/python2.7/dist-packages/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
87 else:
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(*args, **kwargs)
90 return wrapper
91 return _deprecate_kwarg
/usr/local/lib/python2.7/dist-packages/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
87 else:
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(*args, **kwargs)
90 return wrapper
91 return _deprecate_kwarg
/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in drop_duplicates(self, subset, keep, inplace)
3003 deduplicated : DataFrame
3004 """
-> 3005 duplicated = self.duplicated(subset, keep=keep)
3006
3007 if inplace:
/usr/local/lib/python2.7/dist-packages/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
87 else:
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(*args, **kwargs)
90 return wrapper
91 return _deprecate_kwarg
/usr/local/lib/python2.7/dist-packages/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
87 else:
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(*args, **kwargs)
90 return wrapper
91 return _deprecate_kwarg
/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in duplicated(self, subset, keep)
3056
3057 ids = get_group_index(labels, shape, sort=False, xnull=False)
-> 3058 return Series(duplicated_int64(ids, keep), index=self.index)
3059
3060 #----------------------------------------------------------------------
/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc in __init__(self, data, index, dtype, name, copy, fastpath)
225 raise_cast_failure=True)
226
--> 227 data = SingleBlockManager(data, index, fastpath=True)
228
229 generic.NDFrame.__init__(self, data, fastpath=True)
/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc in __init__(self, block, axis, do_integrity_check, fastpath)
3734 block = make_block(block,
3735 placement=slice(0, len(axis)),
-> 3736 ndim=1, fastpath=True)
3737
3738 self.blocks = [block]
/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc in make_block(values, placement, klass, ndim, dtype, fastpath)
2452
2453 return klass(values, ndim=ndim, fastpath=fastpath,
-> 2454 placement=placement)
2455
2456
/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc in __init__(self, values, placement, ndim, fastpath)
85 raise ValueError('Wrong number of items passed %d,'
86 ' placement implies %d' % (
---> 87 len(self.values), len(self.mgr_locs)))
88
89 @property
ValueError: Wrong number of items passed 0, placement implies 5
```
show_versions():
```
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.1
statsmodels: None
IPython: 4.0.0
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
```
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} | 5 | 2015-11-30T14:15:33Z | 2016-02-06T19:49:44Z | 2016-02-06T19:49:44Z | MEMBER | null | http://stackoverflow.com/questions/33995707/attributeerror-unknown-property-color-cycle
```
In [1]: matplotlib.__version__
Out[1]: '1.5.0'
In [2]: df = pd.DataFrame(np.random.randn(5,5))
In [4]: df.plot()
Out[4]: <matplotlib.axes._subplots.AxesSubplot at 0x7bc5c50>
In [5]: pd.set_option('display.mpl_style', 'default')
In [6]: df.plot()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
....
AttributeError: Unknown property color_cycle
In [7]: pd.__version__
Out[7]: '0.17.1'
```
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| null | 3 | 2015-11-30T19:45:43Z | 2015-12-03T00:05:10Z | 2015-11-30T21:20:16Z | NONE | null | I want to store a dataframe with hierarchical column index in a CSV. The index contains both string and integer levels. If I'm correct this should be done like that:
```
import numpy as np
import pandas as pd
n = 10
p = 6
names = ['alphabetic', 'numeric']
levels = [['a', 'b'], [0, 1, 2]]
column_index = pd.MultiIndex.from_product(levels, names=names)
data = np.random.rand(n, p)
df = pd.DataFrame(data=data, columns=column_index)
df.to_csv('test.csv')
read_df = pd.io.parsers.read_csv('test.csv', header=[0, 1], index_col=0)
# This assertion fails
assert(np.all(df.columns == read_df.columns))
```
As mentioned, the assertion fails. The problem is that in `read_df`, the second level ('numeric') contains strings ('0', '1', '2') instead of numbers.
Versions:
```
>>> pd.util.print_versions.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-68-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0+git8-gcac4ad2
nose: 1.3.1
pip: 1.5.4
setuptools: 3.3
Cython: 0.20.1post0
numpy: 1.8.2
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 1.2.1
sphinx: 1.2.2
patsy: 0.4.1
dateutil: 1.5
pytz: 2012c
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.4.3
matplotlib: 1.3.1
openpyxl: 2.3.0-b2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 1 | 2015-11-30T22:45:16Z | 2015-12-12T14:04:00Z | 2015-12-12T14:04:00Z | NONE | null | In the Dataframes.to_string function there is a option to not print the index:
index : bool, optional
```
whether to print index (row) labels, default True
```
Can this be added to the Series function to_string?
http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.Series.to_string.html
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| null | 7 | 2015-12-01T04:13:36Z | 2015-12-01T14:11:51Z | 2015-12-01T12:48:26Z | NONE | null | I have a dataframe with some empty strings and some NaNs. I want to convert both of those to None.
I'm calling applymap to convert the empty strings into None, which works; but it has the side-effect of converting all the existing Nones into NaN!
Using pandas version 0.16:
```
import pandas
import numpy
p = pandas.DataFrame({ 'x' : [ numpy.NaN, 1, 2 ], 'y' : [ '', 'hello', '' ] })
#This line fixes the NaN in the first column but doesn't do anything
#about the empty strings in the second column.
p = p.where(pandas.notnull(p), None)
#This line fixes the empty strings in the second column, but it messed up the first column!
#It turns my None back into a Nan again! But why? Looks like the column was dtype 'object'
#before and this is converting it to dtype=float, but why? I never asked it to do that.
p = p.applymap(lambda x: None if isinstance(x, str) and x is not None and len(x) <= 0 else x)
```
Is there any way to convert both the NaNs and the empty strings into None?
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| null | 1 | 2015-12-01T10:23:17Z | 2015-12-01T11:47:54Z | 2015-12-01T11:47:38Z | NONE | null | I have been using an older version of pandas (0.13.1) and serialized several timedeltas in a data frame. These timedeltas ended up like this in the generated CSV file:
```
-1 days +23:30:00.000000000
```
This format cannot be parsed anymore. It seems that the + sign in front of the hours causes the parsing to fail:
```
In [24]: pd.to_timedelta('-1 days 23:30:00.000000000')
Out[24]: numpy.timedelta64(-171000000000000,'ns')
In [25]: pd.to_timedelta('-1 days +23:30:00.000000000')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-25-79414e44ea89> in <module>()
----> 1 pd.to_timedelta('-1 days +23:30:00.000000000')
/usr/lib/python2.7/dist-packages/pandas/tseries/timedeltas.pyc in to_timedelta(arg, box, unit)
60
61 # ...so it must be a scalar value. Return scalar.
---> 62 return _coerce_scalar_to_timedelta_type(arg, unit=unit)
63
64 _short_search = re.compile(
/usr/lib/python2.7/dist-packages/pandas/tseries/timedeltas.pyc in _coerce_scalar_to_timedelta_type(r, unit)
76
77 # we are already converting to nanoseconds
---> 78 converter = _get_string_converter(r, unit=unit)
79 r = converter()
80 unit='ns'
/usr/lib/python2.7/dist-packages/pandas/tseries/timedeltas.pyc in _get_string_converter(r, unit)
140
141 # no converter
--> 142 raise ValueError("cannot create timedelta string converter")
143
144 def _possibly_cast_to_timedelta(value, coerce=True):
ValueError: cannot create timedelta string converter
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11732 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11732/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11732/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11732/events | https://github.com/pandas-dev/pandas/issues/11732 | 119,704,980 | MDU6SXNzdWUxMTk3MDQ5ODA= | 11,732 | API: change .resample to be a groupby-like API | {
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} | 7 | 2015-12-01T12:17:44Z | 2016-02-02T15:16:18Z | 2016-02-02T15:16:18Z | CONTRIBUTOR | null | similar to #11603
this would transform:
`s.resample('D',how='max')`
to
`s.resample('D').max()`
This would be a breaking API change, as the default is `how='mean'`, meaning, that `s.resample('D')` returns the `mean` of the resampled data. However it would be visible at the very least and not simply change working code.
This would bring `.resample` (which is just a groupby type operation under the hood anyhow) into the API syntax for `.groupby` and `.rolling` et. al.
Furthermore this would allow geitem / aggregate type operations with minimal effort
e.g.
`s.resample('D').agg(['min','max'])`
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} | 18 | 2015-12-01T12:29:45Z | 2018-11-18T21:42:59Z | 2018-11-18T21:42:59Z | MEMBER | null | From SO: http://stackoverflow.com/questions/34020061/excel-to-pandas-dataframe-using-first-column-as-index
@chris-b1 another one on the multi-index excel issues .. :-)
Small test case: content of excel file:
| A | A | B | B |
| --- | --- | --- | --- |
| key | val | key | val |
| 1 | 2 | 3 | 4 |
| 1 | 2 | 3 | 4 |
gives:
```
In [2]: pd.read_excel("test_excel_index_col.xlsx", header=[0,1], index_col=None)
Out[2]:
A A B
key val key val
1 2 3 4
1 2 3 4
```
It's not super clear in the formatting of the dataframe, but the [1, 1] is the index and [A, key] are seen as the level names of the multi-indexed columns.
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| null | 2 | 2015-12-01T14:18:32Z | 2015-12-01T14:36:01Z | 2015-12-01T14:25:51Z | NONE | null | With a tab separated file with 'T' as a column heading a ValueError is generated when using this column label to identify the column. This does not happen with other letters including 'F'
```
test.tsv
A B T F
1 0 0 0
0 1 1 1
1 1 1 1
```
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame.from_csv('test.tsv', index_col=False, sep='\t')
In [3]: len(df[df.A])
Out[3]: 3
In [4]: len(df[df.F])
Out[4]: 3
In [5]: len(df[df.T])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-102a0305e214> in <module>()
----> 1 len(df[df.T])
/home/alistair/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
1963 return self._getitem_array(key)
1964 elif isinstance(key, DataFrame):
-> 1965 return self._getitem_frame(key)
1966 elif is_mi_columns:
1967 return self._getitem_multilevel(key)
/home/alistair/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_frame(self, key)
2035 def _getitem_frame(self, key):
2036 if key.values.dtype != np.bool_:
-> 2037 raise ValueError('Must pass DataFrame with boolean values only')
2038 return self.where(key)
2039
ValueError: Must pass DataFrame with boolean values only
```
pd.show_versions():
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-68-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.2.3
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.0.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.6
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: None
Jinja2: None
```
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| null | 2 | 2015-12-01T16:28:17Z | 2015-12-28T16:57:29Z | 2015-12-28T16:57:29Z | NONE | null | Using pandas 0.17.1 with matplotlib 1.5:
``` python
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
data = pd.DataFrame({'1': [1, 2, 3], '2': [3, 5, 1]})
data.plot(kind='bar')
plt.savefig('test.png', dpi=300)
```
Results in
Which uses the colors of matplotlibs default style, not of `ggplot`.
Using matplotlib 1.4.3 the correct colors are used:
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```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'x': ["2015-05-27 19:05:17.178000-02:00"]})
In [3]: pd.DatetimeIndex(df.x)
Out[3]: DatetimeIndex(['2015-05-27 19:05:17.178000-02:00'], dtype='datetime64[ns]', freq=None, tz='tzoffset(None, -7200)')
```
In 0.17.0, it's now ignoring timezones:
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'x': ["2015-05-27 19:05:17.178000-02:00"]})
In [3]: pd.DatetimeIndex(df.x)
Out[3]: DatetimeIndex(['2015-05-27 21:05:17.178000'], dtype='datetime64[ns]', freq=None)
```
This slipped thru since the tests are very simple and don't cover many cases:
https://github.com/pydata/pandas/blob/cda9da82a68aed42421f7f788fb0c1e949e7a60c/pandas/tests/test_format.py#L3342
I'll attempt a patch later but if someone can give pointers where the breaking change happened I appreciate.
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| null | 1 | 2015-12-02T06:43:10Z | 2015-12-02T11:37:43Z | 2015-12-02T11:37:04Z | NONE | null | Suppose I have a series:
``` python
In [31]: pd.__version__
Out[31]: u'0.17.1'
In [32]: ser = pd.Series(range(4), index=['Aa', 'Ab', 'Ba', 'Bb'])
In [33]: ser
Out[33]:
Aa 0
Ab 1
Ba 2
Bb 3
dtype: int64
```
I can change the index as following:
``` python
In [34]: ser.rename(lambda i: tuple(i))
Out[34]:
A a 0
b 1
B a 2
b 3
dtype: int64
In [35]: ser.index.name = 'First', 'Second'
In [36]: ser.rename(lambda i: tuple(i))
Out[36]:
First Second
A a 0
b 1
B a 2
b 3
dtype: int64
```
But `rename` failed if the index's name is a single str:
``` python
In [37]: ser.index.name = 'Title'
In [38]: ser.rename(lambda i: tuple(i))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-38-72ab91ddd5a1> in <module>()
----> 1 ser.rename(lambda i: tuple(i))
-> 4371 'Length of names must match number of levels in MultiIndex.')
4372
4373 if level is None:
ValueError: Length of names must match number of levels in MultiIndex.
In [39]:
```
It will be good if the `rename` method has option like `indexNames` and can be used like
``` python
In [39]: ser.rename(lambda i: tuple(i), indexNames=['First', 'Second'])
```
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} | 3 | 2015-12-02T11:17:47Z | 2015-12-07T11:11:21Z | 2015-12-07T11:11:21Z | CONTRIBUTOR | null | end_time of a Period with multiplied freq is the square of the multiplication
``` python
>>> pd.__version__
u'0.17.0'
>>> p = pd.Period('2015-08-01', freq='3D')
>>> p.end_time, p.to_timestamp(how='E')
(Timestamp('2015-08-09 23:59:59.999999999'), Timestamp('2015-08-03 00:00:00'))
>>> p = pd.Period('2015-08-01', freq='5D')
>>> p.end_time, p.to_timestamp(how='E')
(Timestamp('2015-08-25 23:59:59.999999999'), Timestamp('2015-08-05 00:00:00'))
```
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| null | 2 | 2015-12-02T16:10:09Z | 2015-12-02T16:18:04Z | 2015-12-02T16:15:58Z | NONE | null | According to the latest doc `header` parameter can be
> boolean or list of string, default True
However, the following code
```
import pandas as pd
pd.read_csv('test.csv', header=True)
```
Produces this error:
> TypeError: Passing a bool to header is invalid. Use header=None for no header or header=int or list-like of ints to specify the row(s) making up the column names.
Not sure what is expected behaviour but the code and the doc don't seem to agree with each other?
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-238.9.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.17.1
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| null | 6 | 2015-12-02T16:23:10Z | 2021-04-21T05:07:42Z | null | CONTRIBUTOR | null | When you supply `drop` with an item that implements `__getitem__`, `drop` doesn't drop it. It does when supplied with the item within a list.
That's because instead of checking `is_list_like` ([here](https://github.com/pydata/pandas/blob/master/pandas/core/common.py#L2329)), `drop` calls `_index_labels_to_array` which does [this](https://github.com/pydata/pandas/blob/master/pandas/core/common.py#L2023). That returns an empty list when supplied with an object that implements `__getitem__`. (if you want the technicals, that's because `list(obj) == []`, as `__getitem__` used to be the way of implementing iteration. `is_list_like` ignores that case, I think correctly).
Should `_index_labels_to_array` instead call `is_list_like`?
``` python
In [182]:
class GetItemObj(object):
def __init__(self, n):
self.n = n
def __getitem__(self, item):
if item == 'a':
return self.n
else:
raise IndexError
def __hash__(self):
return self.n
def __repr__(self):
return 'Item: {}'.format(self.n)
In [183]:
index=pd.Index([GetItemObj(i) for i in range(2)])
index
Out[183]:
Index([Item: 0, Item: 1], dtype='object')
In [186]:
index.drop(index[0])
Out[186]:
Index([Item: 0, Item: 1], dtype='object')
In [187]:
index.drop([index[0]])
Out[187]:
Index([Item: 1], dtype='object')
```
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} | 3 | 2015-12-02T16:37:39Z | 2016-01-19T15:50:56Z | 2016-01-17T03:43:25Z | NONE | null | Do single-row dataframes (not series) get special treatment in some way? It seems you can do arbitrary groupby operations on them on non-existent columns without errors.
```
df1 = pd.DataFrame(np.random.randn(1,4), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.randn(2,4), columns=list('ABCD'))
In [3]: df1.groupby('asdf')
Out[3]: pandas.core.groupby.DataFrameGroupBy object at 0x1139acb10
In [4]: df2.groupby('asdf')
KeyError: 'asdf'
```
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} | 3 | 2015-12-02T16:53:20Z | 2016-04-01T13:13:27Z | 2016-04-01T13:13:27Z | CONTRIBUTOR | null | If I group a `DataFrame` by a column of dates, the return type varies depending on whether I just group or whether I also apply a frequency in the `Grouper`.
Grouping without resampling dates returns a `DataFrame` when I apply a function which returns a labeled `Series`, or a `Series` if the function returns a scalar:
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'], 'value': [10, 13]})
In [3]: def sumfunc(x):
...: return pd.Series([x['value'].sum()], ('sum',))
...:
In [4]: df.groupby(pd.Grouper(key='date')).apply(sumfunc)
Out[4]:
sum
date
10/10/2000 10
11/10/2000 13
In [5]: type(df.groupby(pd.Grouper(key='date')).apply(sumfunc))
Out[5]: pandas.core.frame.DataFrame
In [17]: df.groupby(pd.Grouper(key='date')).apply(lambda x: x.value.sum())
Out[17]:
date
2000-10-10 10
2000-11-10 13
dtype: int64
In [18]: type(df.groupby(pd.Grouper(key='date')).apply(lambda x: x.value.sum()))
Out[18]: pandas.core.series.Series
```
If I apply a frequency in the `Grouper`, I get a `Series` with a multi-index when the function returns a labeled `Series`, or a `TypeError` when it returns a scalar.
```
In [6]: df['date'] = pd.to_datetime(df['date'])
In [7]: df.groupby(pd.Grouper(freq='M', key='date')).apply(sumfunc)
Out[7]:
date
2000-10-31 sum 10
2000-11-30 sum 13
dtype: int64
In [8]: type(df.groupby(pd.Grouper(freq='M', key='date')).apply(sumfunc))
Out[8]: pandas.core.series.Series
In [16]: df.groupby(pd.Grouper(freq='M', key='date')).apply(lambda x: x.value.sum())
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-16-ad73d0ebc475> in <module>()
----> 1 df.groupby(pd.Grouper(freq='M', key='date')).apply(lambda x: x.value.sum())
/Users/shoover/.py35/lib/python3.5/site-packages/pandas/core/groupby.py in apply(self, func, *args, **kwargs)
713 # ignore SettingWithCopy here in case the user mutates
714 with option_context('mode.chained_assignment',None):
--> 715 return self._python_apply_general(f)
716
717 def _python_apply_general(self, f):
/Users/shoover/.py35/lib/python3.5/site-packages/pandas/core/groupby.py in _python_apply_general(self, f)
720
721 return self._wrap_applied_output(keys, values,
--> 722 not_indexed_same=mutated)
723
724 def aggregate(self, func, *args, **kwargs):
/Users/shoover/.py35/lib/python3.5/site-packages/pandas/core/groupby.py in _wrap_applied_output(self, keys, values, not_indexed_same)
3253 # Handle cases like BinGrouper
3254 return self._concat_objects(keys, values,
-> 3255 not_indexed_same=not_indexed_same)
3256
3257 def _transform_general(self, func, *args, **kwargs):
/Users/shoover/.py35/lib/python3.5/site-packages/pandas/core/groupby.py in _concat_objects(self, keys, values, not_indexed_same)
1271 group_names = self.grouper.names
1272 result = concat(values, axis=self.axis, keys=group_keys,
-> 1273 levels=group_levels, names=group_names)
1274 else:
1275
/Users/shoover/.py35/lib/python3.5/site-packages/pandas/tools/merge.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
810 keys=keys, levels=levels, names=names,
811 verify_integrity=verify_integrity,
--> 812 copy=copy)
813 return op.get_result()
814
/Users/shoover/.py35/lib/python3.5/site-packages/pandas/tools/merge.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
866 for obj in objs:
867 if not isinstance(obj, NDFrame):
--> 868 raise TypeError("cannot concatenate a non-NDFrame object")
869
870 # consolidate
TypeError: cannot concatenate a non-NDFrame object
```
Since in this example, assigning dates to months still leaves the same groups, I would have expected identical results whether I set `freq='M'` or not. I'm guessing that the difference is that the `freq='M'` causes an extra `groupby` to happen under the hood, yes? When I ran into this, what I expected to happen was for `pd.Grouper(freq='M', key='date')` to do a single `groupby`, combining rows where dates happened to fall into the same month.
Pandas version:
```
In [9]: pd.__version__
Out[9]: '0.17.1+22.g0c43fcc'
```
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| null | 10 | 2015-12-02T19:16:05Z | 2015-12-29T19:47:05Z | 2015-12-29T19:46:58Z | NONE | null | Noticing some strange behaviour when using the `query` method. I'm expecting empty results from the query but once in a while I'm getting a non-empty dataframe returned.
``` python
import pandas as pd
testdf = pd.DataFrame(pd.np.random.randint(100, size=(10000, 2)))
testdf.columns = ['a', 'b']
missing_value = -2000
print missing_value in testdf['a'].values
>>>False
```
Doing this 10000 times with using the classical way:
``` python
found_count = 0
for i in xrange(10000):
qq = testdf[testdf['a']==missing_value]
if not qq.empty:
print "Value found"
found_count += 1
print "Times found: {}".format(found_count)
>>>0
```
Using query:
``` python
found_count = 0
for i in xrange(1000):
qq = testdf.query('a==-2000')
if not qq.empty:
print "Value found"
found_count += 1
print "Times found: {}".format(found_count)
>>>Value found
>>>Value found
>>>Times found: 2
```
or
``` python
found_count = 0
for i in xrange(1000):
qq = testdf.query('a==@missing_value')
if not qq.empty:
print "Value found"
found_count += 1
print "Times found: {}".format(found_count)
>>>Value found
>>>Times found: 1
```
The number of non-empty results varies but tends to happen 0.2% of the time. Is this a bug or have I misunderstood what `query` is supposed to to? Version is `0.17.0`.
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} | 4 | 2015-12-02T19:20:32Z | 2016-01-02T19:16:00Z | 2016-01-02T19:16:00Z | NONE | null | http://pandas.pydata.org/pandas-docs/version/0.17.1/whatsnew.html and http://pandas.pydata.org/commercialsupport.html has a link to the NumFocus announcement. The link points to http://numfocus.org/news/2015/10/09/numfocus-announces-new-fiscally-sponsored-project-pandas/, which is broken. The correct link is:
http://www.numfocus.org/blog/numfocus-announces-new-fiscally-sponsored-project-pandas
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} | 14 | 2015-12-02T23:25:07Z | 2017-04-02T22:55:23Z | 2017-04-02T22:55:23Z | CONTRIBUTOR | null | When using SAS or Stata data, dates are represented as the number of days since 1/1/1960, and other statistical software uses different [origin dates](http://blog.stata.com/2011/01/05/using-dates-and-times-from-other-software/). With that in mind, it would be nice to have an origin date that can be specified. See also, #3969.
It's a relatively simple thing, and not hard to work around, of course. However, I end up dealing with date formatting on just about every data set I import, and I imagine that lots of others do, too.
Currently, I do something like this:
``` python
import pandas as pd
import datetime
EPOCH1960 = datetime.date(1970, 1, 1) - datetime.date(1960, 1, 1)
data = pd.read_stata('./data.dta')
data['date'] = pd.to_datetime(data['date'],unit='D') - EPOCH1960
```
In R, the `as.Date()` function takes an origin parameter for numeric types (see, [manual](https://stat.ethz.ch/R-manual/R-devel/library/base/html/as.Date.html)). So, in R, the date part would simply be:
``` r
data$date <- as.Date(data$date, origin = '1960-01-01')
```
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} | 7 | 2015-12-02T23:32:45Z | 2015-12-04T23:03:27Z | 2015-12-04T23:02:59Z | CONTRIBUTOR | null | After playing with R a bunch, I started feeling like the explanation of `SettingWithCopy` wasn't getting to the core of the matter, which is actually an essential consequence of python slice assignment semantics. Here's how python handles chained assignment:
``` python
df['foo']['bar'] = quux
# becomes
df.__getitem__('foo').__setitem__('bar', quux)
```
whereas in R, it's this:
``` R
df["foo"]["bar"] <- quux
# becomes
df["foo"] <- `[<-`(df["foo"], "bar", quux)
# becomes
df <- `[<-`(df, "foo", `[<-`(`[`(df, "foo"), "bar", quux))
```
That last is a lot of line noise, though the R method names ``[`` and ``[<-`` are more concise than `__getitem__` and `__setitem__`! But imagine that you could call `__setitem__` with a kwarg `inplace=False` that would cause it to return a modified copy instead of modifying the original object. Then the R version would translate to this in python:
``` python
df = df.__setitem__('foo',
df.__getitem__('foo')
.__setitem__('bar', quux, inplace=False),
inplace=False)
```
This is incredibly awkward, but it has the advantage of making `SettingWithCopy` unnecessary— _everything_ is a copy, and yet things get set nonetheless.
So this commit is an attempt to explain this without requiring the reader to know R.
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} | 5 | 2015-12-02T23:59:46Z | 2021-05-12T13:27:55Z | 2021-05-12T13:27:55Z | CONTRIBUTOR | null | in py3, the following should correctly raise an `IndexError`
```
In [1]: s = Series(range(5),index=list('aacde'))
In [2]: s[5]
IndexError
```
however
a positional indexer that is in the range should work
`s[3]`
Further more, this must be a monotonic increasing duplicated index to trigger
```
In [8]: Series(range(5),index=list('aabcd'))[3]
IndexError: 3
In [9]: Series(range(5),index=list('aadcb'))[3]
Out[9]: 3
```
simple change needed [here](https://github.com/pydata/pandas/blob/master/pandas/core/index.py#L1849)
in that if its an integer and in the range of the index it can return that value.
Though _maybe_ we should just detect this in the cython code (`index.pyx/Index/_get_loc_duplicated`)
needless to say, no tests for this :<
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| null | 1 | 2015-12-03T00:38:30Z | 2015-12-03T00:41:51Z | 2015-12-03T00:41:16Z | NONE | null | According to the [API](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.ptp.html?highlight=ptp#pandas.Series.ptp), the `ptp` method of Series should be able to handle 'index' as an argument for the `axis` keyword. But when I try the following code:
```
x = pd.Series(range(15,20), index=range(5,10))
x.ptp(axis='index')
```
I get an error: "TypeError: an integer is required for the axis"
I'm using Pandas 0.17.0
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| null | 1 | 2015-12-03T00:54:10Z | 2015-12-03T01:07:43Z | 2015-12-03T01:07:22Z | NONE | null | I want to slice log files to extract only values within a certain time range. This works well with all but one log file. _"Must be the log file then!"_. However, the log file is well-formed like all the others and I don't see the reason my code fails:
``` python
import pandas as pan
import datetime as dt
time_f = '%Y-%m-%d %H:%M:%S.%f'
def get_sublog_by_index(log, t_stime, t_etime):
s_idx, e_idx = log.index.slice_locs(t_stime, t_etime)
return log.ix[s_idx: e_idx + 1]
if __name__ == '__main__':
#http://pastebin.com/tQ6MFhgN
df_good = pan.read_csv('dstat_good', header=True, parse_dates=True, index_col=0)
#http://pastebin.com/M9r59Seq
df_bad = pan.read_csv('dstat_bad', header=True, parse_dates=True, index_col=0)
#manual construction just to be shure; why does pan.tslib.Timestamp.strptime fail?
s_t_g = pan.tslib.Timestamp(dt.datetime.strptime('2015-04-23 03:46:37.030256', time_f))
s_e_g = pan.tslib.Timestamp(dt.datetime.strptime('2015-04-23 04:46:40.560409', time_f))
s_t_b = pan.tslib.Timestamp(dt.datetime.strptime('2015-04-23 02:44:49.310807', time_f))
s_e_b = pan.tslib.Timestamp(dt.datetime.strptime('2015-04-23 03:44:52.922636', time_f))
sublog_g = get_sublog_by_index(df_good, s_t_g, s_e_g)
sublog_b = get_sublog_by_index(df_bad, s_t_b, s_e_b)
```
I have two log files to test, one working, the other not:
http://pastebin.com/tQ6MFhgN (working)
http://pastebin.com/M9r59Seq (not working)
At closer inspection you will notice that the start and end times (`s_t*` and `s_e*`) are within the range of the logs' timestamps. You will also notice that the _bad_ log has no odd data or anything else that gives a hint why the code is not working in that case. This is truly odd and seems to origin from the deep. Or I'm struck with utter blindness?
For completeness, this is what I get if I try to slice the _bad_ log file:
```
...
File "/usr/local/bin/dstatlogsplit", line 54, in get_sublog_by_index
s_idx, e_idx = log.index.slice_locs(t_stime, t_etime)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/index.py", line 2484, in slice_locs
start_slice = self.get_slice_bound(start, 'left', kind)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/index.py", line 2432, in get_slice_bound
raise err
KeyError: 1429757089310807000L
```
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} | 6 | 2015-12-03T04:46:43Z | 2015-12-13T03:09:33Z | 2015-12-12T14:04:00Z | CONTRIBUTOR | null | Fixes #11729
Please Review .
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| null | 6 | 2015-12-03T05:52:31Z | 2016-01-06T17:35:57Z | 2016-01-06T17:21:07Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/11740
Let me know what tests would be helpful for these types of 'cleaning' PRs, and whether a "what's new" is needed.
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shiny new fast version of avro might be interesting: https://github.com/MaxPoint/cyavro/issues/1
by @mariusvniekerk
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| null | 1 | 2015-12-03T15:02:06Z | 2015-12-03T16:32:45Z | 2015-12-03T16:32:45Z | NONE | null | When using [Series.ptp()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.ptp.html?highlight=ptp#pandas.Series.ptp) with `axis='index'` as an argument, I would expect the function to return max(index) - min(index). Instead, it appears as if the operation is performed on the data held in the Series (as if the `axis='index'` argument were not provided at all).
```
x = pd.Series(np.linspace(0,5,6)*2, index=np.linspace(0,5,6))
print x.ptp() #expect 10, get 10
print x.ptp(axis=0) #expect 10, get 10
print x.ptp(axis='index') #expect 5, get 10
print x.index.max()-x.index.min() #expect 5, get 5
```
I'm using Pandas 0.17.1.
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} | 23 | 2015-12-03T18:18:37Z | 2020-04-08T09:55:40Z | 2020-04-08T09:55:40Z | NONE | null | ```
# error
In [27]: pd.DataFrame([[1, 1]], columns=['x','x']).iat[0,0]
TypeError: len() of unsized object
# ok
In [26]: pd.DataFrame([[1, 1]], columns=['x','y']).iat[0,0]
Out[26]: 1
```
I have some weird issue in a DataFrame I'm creating from a row-based array.
Using python3 and pandas 0.17.1 (from debian unstable), I get:
```
df = pandas.DataFrame(data=data[1:], columns=data[0])
df.iat[0, 0]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3/dist-packages/pandas/core/indexing.py", line 1555, in __getitem__
return self.obj.get_value(*key, takeable=self._takeable)
File "/usr/lib/python3/dist-packages/pandas/core/frame.py", line 1808, in get_value
series = self._iget_item_cache(col)
File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 1116, in _iget_item_cache
lower = self.take(item, axis=self._info_axis_number, convert=True)
File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 1371, in take
convert=True, verify=True)
File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 3628, in take
axis=axis, allow_dups=True)
File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 3510, in reindex_indexer
indexer, fill_tuple=(fill_value,))
File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 3536, in _slice_take_blocks_ax0
slice_or_indexer, self.shape[0], allow_fill=allow_fill)
File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 4865, in _preprocess_slice_or_indexer
return 'fancy', indexer, len(indexer)
TypeError: len() of unsized object
```
Interestingly, I can otherwise manage the dataframe just fine.
The same code, running under python2.7 shows no issue.
What could be the cause of such error?
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} | 9 | 2015-12-03T21:23:04Z | 2016-02-10T18:45:38Z | 2016-02-10T18:45:38Z | CONTRIBUTOR | null | ``` python
import pandas as pd
x = pd.Series(pd.date_range('20151124 10:00', '20151124 11:00', freq = '1h', tz = "UTC") )
y = pd.Series(pd.date_range('2012-01-01', '2012-01-03'))
pd.concat([x,y])
```
AttributeError: 'numpy.ndarray' object has no attribute 'tz_localize'
Is it a bug or not supported ?
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} | 22 | 2015-12-03T21:36:37Z | 2016-02-08T06:37:41Z | 2016-02-08T06:11:21Z | CONTRIBUTOR | null | closes #11555
Updated IO Tools documentation for read_csv() and read_table() to be consistent with the doc-string.
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} | 30 | 2015-12-04T13:33:41Z | 2016-08-25T22:20:12Z | 2016-08-25T22:20:12Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/34078316/tz-aware-datetime-series-producing-utc-based-date-output-in-pandas-series-app/34078346#34078346)
```
In [1]: s = Series(pd.to_datetime([1396670200, 1405733045, 1448248441],unit='s')).dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
In [2]: s
Out[2]:
0 2014-04-04 23:56:40-04:00
1 2014-07-18 21:24:05-04:00
2 2015-11-22 22:14:01-05:00
dtype: datetime64[ns, US/Eastern]
```
These are correct as they are in the local timezone, but we are not propogating the tz here and just returning a naive date (in local zone)
```
In [3]: s.dt.date
Out[3]:
0 2014-04-04
1 2014-07-18
2 2015-11-22
dtype: object
In [4]: s.dt.date[0]
Out[4]: datetime.date(2014, 4, 4)
```
These are converted to UTC, then returning a naive date
These should be local & with a timezone attached
```
In [5]: s.apply(lambda x: x.date())
Out[5]:
0 2014-04-05
1 2014-07-19
2 2015-11-23
dtype: object
```
further, test
`s.apply(lambda x: x)`
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} | 9 | 2015-12-04T13:40:16Z | 2016-04-30T18:47:41Z | 2016-04-30T18:47:41Z | NONE | null | xref #11760
Parsing unix epoch timestamps give NaT with `errors='coerce'` while they can be parsed correctly without it:
``` python
>>> pd.to_datetime(1420043460, errors='coerce', unit='s')
NaT
>>> pd.to_datetime(1420043460, unit='s')
Timestamp('2014-12-31 16:31:00')
```
pandas: 0.17.1
numpy: 1.10.1
Python 2.7.7
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} | 10 | 2015-12-04T13:55:40Z | 2018-02-10T18:02:11Z | 2018-02-10T18:02:10Z | CONTRIBUTOR | null | ```
In [19]: df = DataFrame({'a':['A1', 'A1', 'A1'], 'b':['B1','B1','B2'], 'c':1})
In [20]: df.set_index('a').groupby('b').rank(method='first')
Out[20]:
c
a
A1 1
A1 2
A1 1
In [21]: df.set_index('a').groupby('c').rank(method='first')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-21-6b8d4cae9d91> in <module>()
----> 1 df.set_index('a').groupby('c').rank(method='first')
/home/nicolas/Git/pandas/pandas/core/groupby.pyc in rank(self, axis, numeric_only, method, na_option, ascending, pct)
/home/nicolas/Git/pandas/pandas/core/groupby.pyc in wrapper(*args, **kwargs)
618 # mark this column as an error
619 try:
--> 620 return self._aggregate_item_by_item(name, *args, **kwargs)
621 except (AttributeError):
622 raise ValueError
/home/nicolas/Git/pandas/pandas/core/groupby.pyc in _aggregate_item_by_item(self, func, *args, **kwargs)
3076 # GH6337
3077 if not len(result_columns) and errors is not None:
-> 3078 raise errors
3079
3080 return DataFrame(result, columns=result_columns)
TypeError: rank() got an unexpected keyword argument 'numeric_only'
```
I'm trying to obtain what I would get with a `row_number()` in SQL...
Notice that if I replace the value in the `'c'` column with the string `'1'`, then even `df.set_index('a').groupby('b').rank(method='first')` fails.
Am I doing something wrong?
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} | 2 | 2015-12-04T14:53:02Z | 2016-05-08T14:06:06Z | 2016-05-08T14:05:59Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/34078114/convert-pandas-time-series-unix-epoch-to-datetime/34078557#34078557)
xref to #11758
We should force int conversions when `unit` is passed (as by definition these are int-like things).
```
In [21]: s = pd.Series(["1440643875", "1440644191", "1440645638", "1440998720"], dtype=object)
In [22]: pd.to_datetime(s,unit='s')
ValueError: year is out of range
In [23]: pd.to_datetime(s.astype(int),unit='s')
Out[23]:
0 2015-08-27 02:51:15
1 2015-08-27 02:56:31
2 2015-08-27 03:20:38
3 2015-08-31 05:25:20
dtype: datetime64[ns]
```
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} | 2 | 2015-12-04T16:21:11Z | 2015-12-07T11:33:53Z | 2015-12-07T11:33:53Z | NONE | null | When duplicated columns exists, DataFrame.info() returns broken output:
```
pd.DataFrame([[1, 1]], columns=['x','x']).info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1 entries, 0 to 0
Data columns (total 2 columns):
x 1 non-null x int64
x int64
dtype: object
x 1 non-null x int64
x int64
dtype: object
dtypes: int64(2)
memory usage: 24.0 bytes
```
See #11754
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} | 9 | 2015-12-04T16:25:23Z | 2021-04-21T05:16:09Z | 2021-04-21T05:16:08Z | NONE | null | ```
df1 = pd.DataFrame([[1, 1]], columns=['x','x'])
df2 = pd.DataFrame([[1, 1]], columns=['x','y'])
pd.merge(df1, df2, on='x')
```
Results in:
```
Traceback (most recent call last):
File "test.py", line 5, in <module>
m = pd.merge(df1, df2, on='x')
File "/usr/lib/python3/dist-packages/pandas/tools/merge.py", line 35, in merge
return op.get_result()
File "/usr/lib/python3/dist-packages/pandas/tools/merge.py", line 196, in get_result
join_index, left_indexer, right_indexer = self._get_join_info()
File "/usr/lib/python3/dist-packages/pandas/tools/merge.py", line 324, in _get_join_info
sort=self.sort, how=self.how)
File "/usr/lib/python3/dist-packages/pandas/tools/merge.py", line 516, in _get_join_indexers
llab, rlab, shape = map(list, zip( * map(fkeys, left_keys, right_keys)))
File "/usr/lib/python3/dist-packages/pandas/tools/merge.py", line 681, in _factorize_keys
llab = rizer.factorize(lk)
File "pandas/hashtable.pyx", line 850, in pandas.hashtable.Int64Factorizer.factorize (pandas/hashtable.c:15601)
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
```
See #11754
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} | 8 | 2015-12-04T17:58:48Z | 2015-12-15T22:51:56Z | 2015-12-15T22:51:56Z | CONTRIBUTOR | null | Is there some philosophical reason that round(Series) and round(Dataframe) is not defined? See the example below. Being able to write round() as a function of a Dataframe or Series, as opposed to doing it afterwards would be nice. I'm willing to implement `__round__()` to make it work, but don't want to go down that path if there is some reason `__round__()` is not implemented.
Example:
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])
print (df.A.round())
print (round(df))
```
Produces
```
0 -2
1 1
2 -1
3 -1
4 2
5 0
6 0
7 0
8 -2
9 0
Name: A, dtype: float64
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-9d2be9cbdc70> in <module>()
3 df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'])
4 print (df.A.round())
----> 5 print (round(df))
TypeError: type DataFrame doesn't define __round__ method
```
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} | 4 | 2015-12-04T19:38:01Z | 2015-12-05T15:13:23Z | 2015-12-05T15:13:23Z | NONE | null | **infer_types** is indicated in the "IO Tools page" (http://pandas.pydata.org/pandas-docs/stable/io.html#io-read-html), but not in the API Reference page (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_html.html) and library v0.17.1
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Closes #11764
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} | 0 | 2015-12-05T14:07:43Z | 2015-12-05T16:37:39Z | 2015-12-05T16:37:39Z | CONTRIBUTOR | null | Also included an example.
Side-note, the API could probably be a lot cleaner here. Accept `*args` instead of a list e.g.
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} | 3 | 2015-12-05T16:12:27Z | 2015-12-07T11:24:56Z | 2015-12-07T11:24:53Z | CONTRIBUTOR | null | use typed `iNaT` in Cython code when possible, remove checking that is almost always `False`
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} | 1 | 2015-12-05T17:53:05Z | 2015-12-08T01:44:52Z | 2015-12-08T01:44:52Z | CONTRIBUTOR | null | Unfinished at the moment. Just wanted to push something up since there are other PRs that could build on this.
This moves the logic of "what value should I display for this cell?" from the Template to the `Styler` class itself. The two biggest benefits are
1. User extensibility: Users provide formatting functions (via an API that's TBD) which are called upon the data values when we `translate`.
2. Maintainability: Instead of complex logic in the template, we have (slightly) less complex logic in the class itself.
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} | 8 | 2015-12-05T19:56:19Z | 2015-12-15T12:01:56Z | 2015-12-15T12:01:56Z | CONTRIBUTOR | null | @kawochen just updated the appveyor support (was not really running correctly before).
in [here](https://github.com/pydata/pandas/commit/5b84e49e44cb378cee211436f0966e53d89b08b1)
2.7 fully passes and 3.5 does except for some of these odd MemoryErrors, must be some sort of recursion issue, see build [here](https://ci.appveyor.com/project/jreback/pandas/build/1.0.1526/job/0meym8xg4j0bh9ea)
I cannot reproduce these locally, only on appveyor, any thoughts?
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} | 3 | 2015-12-06T04:18:03Z | 2015-12-08T16:14:35Z | 2015-12-08T15:20:46Z | CONTRIBUTOR | null | - import constants from matplolib.dates
- do not re-implement AutoDateFormatter functionality that
is already upstream
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} | 3 | 2015-12-06T06:13:56Z | 2015-12-07T11:05:39Z | 2015-12-07T03:06:22Z | CONTRIBUTOR | null | closes #11738
1. Added a new test with the multiple of a frequency.
2. Rearranged some tests to improve readability of the code.
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} | 3 | 2015-12-06T12:46:27Z | 2016-05-16T12:09:51Z | 2016-05-16T12:09:51Z | CONTRIBUTOR | null | According to the Enhancements text for 17.1:
> pd.read_\* functions can now also accept pathlib.Path, or py._path.local.LocalPath objects for the filepath_or_buffer argument. (GH11033)
all read_\* functions should support pathlib.Path now. It works for me with `read_csv` but not for `read_hdf`.
Interestingly, I find in the squashed PR for this issue, 0d3dcbb, only a comment regarding `read_csv` and `read_table`, while the merge 82f0033 talks about `read_*`. Is there something missing for `read_hdf` ?
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} | 4 | 2015-12-06T12:49:21Z | 2015-12-07T00:34:07Z | 2015-12-07T00:33:58Z | CONTRIBUTOR | null | Closes #11708
Minutes needed to be added to the hour offset rather than subtracted.
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| null | 1 | 2015-12-06T16:24:59Z | 2015-12-06T16:39:50Z | 2015-12-06T16:36:11Z | CONTRIBUTOR | null | replace (and by extension other masking operations) fail on Series with tz-aware data. Observing the following behavior:
``` python
# non-tz aware
In [23]: ser = pd.Series([pd.NaT, pd.Timestamp('2015/01/01')])
In [24]: ser
Out[24]:
0 NaT
1 2015-01-01
dtype: datetime64[ns]
In [25]: ser.replace(pd.NaT, pd.Timestamp.min)
Out[25]:
0 1677-09-22 00:12:43.145225
1 2015-01-01 00:00:00.000000
dtype: datetime64[ns]
# timezone-aware
In [26]: ser = pd.Series([pd.NaT, pd.Timestamp('2015/01/01', tz='UTC')])
In [27]: ser.replace(pd.NaT, pd.Timestamp.min)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-27-cfc55c339a93> in <module>()
----> 1 ser.replace(pd.NaT, pd.Timestamp.min)
/home/env/local/lib/python2.7/site-packages/pandas/core/generic.py in replace(self, to_replace, value, inplace, limit, regex, method, axis)
2994 elif not com.is_list_like(value): # NA -> 0
2995 new_data = self._data.replace(to_replace=to_replace, value=value,
-> 2996 inplace=inplace, regex=regex)
2997 else:
2998 msg = ('Invalid "to_replace" type: '
/home/env/local/lib/python2.7/site-packages/pandas/core/internals.py in replace(self, **kwargs)
2760
2761 def replace(self, **kwargs):
-> 2762 return self.apply('replace', **kwargs)
2763
2764 def replace_list(self, src_list, dest_list, inplace=False, regex=False):
/home/env/local/lib/python2.7/site-packages/pandas/core/internals.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
2709 copy=align_copy)
2710
-> 2711 applied = getattr(b, f)(**kwargs)
2712
2713 if isinstance(applied, list):
/home/env/local/lib/python2.7/site-packages/pandas/core/internals.py in replace(self, to_replace, value, inplace, filter, regex)
567 return [self]
568 return [self.copy()]
--> 569 return self.putmask(mask, value, inplace=inplace)
570
571 def setitem(self, indexer, value):
/home/env/local/lib/python2.7/site-packages/pandas/core/internals.py in putmask(self, mask, new, align, inplace, axis, transpose)
723
724 print
--> 725 np.putmask(new_values, mask, new)
726
727 # maybe upcast me
TypeError: putmask() argument 1 must be numpy.ndarray, not DatetimeIndex
```
Believe this was introduced in 0.17.0, and might be related to https://github.com/pydata/pandas/issues/11693 - however, the patch in that issue doesn't appear to fix this.
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} | 2 | 2015-12-06T18:08:34Z | 2015-12-10T12:10:45Z | 2015-12-10T12:10:45Z | CONTRIBUTOR | null | when `pd.to_numeric` is called with `errors='coerce'` on a `DataFrame`, it doesn't raise and just returns the original `DataFrame`.
This may be related to the discussion here https://github.com/pydata/pandas/issues/11221 as this function currently doesn't support anything more than 1-d.
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'a': [1, 2, 'foo'], 'b': [2.3, -1, 'bar']})
In [3]: df
Out[3]:
a b
0 1 2.3
1 2 -1
2 foo bar
In [4]: pd.to_numeric(df)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-9febd95a7c0a> in <module>()
----> 1 pd.to_numeric(df)
/Users/mortada_mehyar/code/github/pandas/pandas/tools/util.py in to_numeric(arg, errors)
94 conv = lib.maybe_convert_numeric(arg,
95 set(),
---> 96 coerce_numeric=coerce_numeric)
97 except:
98 if errors == 'raise':
/Users/mortada_mehyar/code/github/pandas/pandas/src/inference.pyx in pandas.lib.maybe_convert_numeric (pandas/lib.c:52369)()
518 cdef int64_t iINT64_MIN = <int64_t> INT64_MIN
519
--> 520 def maybe_convert_numeric(object[:] values, set na_values,
521 bint convert_empty=True, bint coerce_numeric=False):
522 '''
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
In [5]: pd.to_numeric(df, errors='coerce')
Out[5]:
a b
0 1 2.3
1 2 -1
2 foo bar
```
Note that the last expression doesn't raise but the previous one does.
Seems like we should either
1. make `pd.to_numeric` work with `DataFrame` or `NDFrame` in general
2. simply raise here too if a `DataFrame` or something more than 1-d is passed
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} | 7 | 2015-12-06T20:57:55Z | 2015-12-10T12:26:09Z | 2015-12-10T12:26:09Z | CONTRIBUTOR | null | closes #11406 - makes `QuarterBegin` for `n=0` match Month/Year.
xref #11427 - once this is merged the failures there should pass - there is also a little more documentation about general offset semantics in that PR.
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} | 23 | 2015-12-06T21:16:17Z | 2018-06-19T09:41:27Z | 2015-12-19T17:14:51Z | CONTRIBUTOR | null | Hello, this is my very first pull request on github. I added latex/pdf conversion capabilities to the dataframe when used in a Jupyter/Ipython notebook. The method simply uses the existing to_latex method of the Dataframe class. I believe teh following Issues or PR have already attempted this, but they seem closed: #11399 and #9821
The _repr_latex_ method looks for newly created options:
- display.latex_escape
- display.latex_longtable
which are passed as arguments to the to_latex method.
a simple test has been added to check if the conversion works. However, if the
implementation of the to_latex method changes, the test function would also have to be updated.
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} | 1 | 2015-12-07T03:36:26Z | 2015-12-07T18:26:48Z | 2015-12-07T11:11:04Z | CONTRIBUTOR | null | closes #11738
1. Added a new test with the multiple of a frequency.
2. Rearranged some tests to improve readability of the code.
3. Added bug fix to whatsnew
4. Added a comment in the test
Didn't follow the contributing guidelines carefully in PR #11771 and hence this new PR.
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} | 5 | 2015-12-07T05:22:33Z | 2015-12-10T12:10:49Z | 2015-12-10T12:10:45Z | CONTRIBUTOR | null | closes #11776
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| null | 4 | 2015-12-07T09:40:35Z | 2015-12-09T15:43:36Z | 2015-12-09T15:34:53Z | NONE | null | Pandas version I am using: 0.16.2
My initial chart is produced like this:
```
combined['fraud_count'].unstack(-2).unstack(-1).fillna(0).plot()
```
which looks very neat and perfect, as expected.
In the next step, I am trying to use the secondary_y feature like this:
```
combined['fraud_count'].unstack(-2).unstack(-1).fillna(0).plot()
combined['fraud_amount'].unstack(-2).unstack(-1).fillna(0).plot(secondary_y=True)
```
which doesn't produce a secondary axis. So, I do it this way:
```
ax = combined['fraud_count'].unstack(-2).unstack(-1).fillna(0).plot()
ax2 = combined['fraud_amount'].unstack(-2).unstack(-1).fillna(0).plot(secondary_y=True, ax = ax)
```
which produces the chart I want.
**However, the labels on the x-axis are not rotated any more but written horizontally and overlapping each other.**
I was trying some ways to get this fixed but none of them really seemed to work.
I believe this is a quite common thing to do, so I'd be happy to hear how this could be done. Any help is welcome!
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} | 4 | 2015-12-07T11:01:23Z | 2018-06-01T14:29:48Z | 2016-02-11T13:04:13Z | MEMBER | null | Matplotlib now has support for enabling different styles, we don't use this anymore in our docs, and it only gives problems to keep supporting this with newer matplotlib versions (see eg #11727).
So to raise @TomAugspurger idea there in an issue: let's just deprecate it?
@sinhrks @jreback
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} | 3 | 2015-12-07T11:19:47Z | 2018-07-08T16:00:34Z | 2018-07-08T16:00:33Z | CONTRIBUTOR | null | reorg on the html options, which I think we are going to need some more e.g. `df.style`.
This will make it more consistent with the option config style
need to deprecate the original and add new.
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} | 3 | 2015-12-07T14:30:38Z | 2015-12-16T14:05:40Z | 2015-12-16T14:02:19Z | CONTRIBUTOR | null | Copied examples from the reStructuredText [tutorial](http://pandas.pydata.org/pandas-docs/stable/timedeltas.html) on timedeltas.
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} | 4 | 2015-12-07T16:12:34Z | 2016-01-19T20:02:06Z | 2016-01-19T20:02:06Z | CONTRIBUTOR | null | The following script causes a segfault on my machine
``` python
from io import BytesIO
from multiprocessing.pool import ThreadPool
import pandas as pd
# Make many fake CSV files in memory
bytes = ['\n'.join(['%d,%d,%d' % (i,i,i) for i in range(10000)]).encode()
for j in range(100)]
files = [BytesIO(b) for b in bytes]
# Read all files in many threads
pool = ThreadPool(8)
pool.map(pd.read_csv, files)
```
```
$ python script.py
Segmentation fault (core dumped)
```
Python 3.4, Pandas 0.17.1, Ubuntu 14.04
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} | 12 | 2015-12-07T16:57:28Z | 2021-04-21T05:18:56Z | null | CONTRIBUTOR | null | The generic function `.mad()` calculates the mean absolute difference of a set of data, but in some cases the median absolute difference is more appropriate. In `R`, the `mad()` function accepts a `center` argument to specify how the average absolute difference should be calculated. I propose to add the same to the _pandas_ function.
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| null | 9 | 2015-12-07T17:24:11Z | 2019-01-11T13:25:30Z | 2016-01-30T15:45:05Z | NONE | null | supersedes #10243
closes #6245
Here is the code as it stands now. Appending MultiIndex axes now works. The one test that still fails is test_append_frame_column_oriented: writing the DateTimeIndex as columns fails because the index is converted to an array of timestamps so np.array creates an object array. If it could be passed down unmolested np.array() would do the right thing. I don't yet have a good idea how to fix that.
I added a test for reading the old format, let me know if you think it's sufficient coverage. I've been testing the reading more broadly against my own data.
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} | 2 | 2015-12-07T18:15:20Z | 2015-12-15T19:46:03Z | 2015-12-09T15:20:41Z | CONTRIBUTOR | null | Update the reStructuredText tutorial on timedeltas to include
context, similar to edits suggested for #11785.
Also, add a space after various commas (PEP8), and periods/colons
at the end of sentences for consistency.
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} | 17 | 2015-12-07T18:42:04Z | 2016-01-27T13:56:55Z | 2016-01-19T20:02:06Z | CONTRIBUTOR | null | closes #11786
Fixed an issue with thread safety when calling read_csv with a StringIO object.
The issue was caused by a misplaced PyGilSate_Ensure()
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| null | 8 | 2015-12-07T22:47:45Z | 2015-12-08T15:48:30Z | 2015-12-08T15:36:09Z | NONE | null | When using python 3.4.3 or 3.5.0 and pandas 0.16.2, this simple example works
```
import pandas as pd
import multiprocessing as mp
def squareIt(row):
return row[0]**2, row[1]**2
df = pd.DataFrame({'a': range(100), 'b': range(100)})
with mp.Pool(2) as pool:
sq = pool.imap(squareIt, df.itertuples(False), chunksize=10)
for x in sq:
print(x)
```
when upgrading to 0.17.1 there is this error.
```
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
File "/opt/Python-3.4.3/lib/python3.4/multiprocessing/pool.py", line 295, in <genexpr>
return (item for chunk in result for item in chunk)
File "/opt/Python-3.4.3/lib/python3.4/multiprocessing/pool.py", line 689, in next
raise value
File "/opt/Python-3.4.3/lib/python3.4/multiprocessing/pool.py", line 383, in _handle_tasks
put(task)
File "/opt/Python-3.4.3/lib/python3.4/multiprocessing/connection.py", line 206, in send
self._send_bytes(ForkingPickler.dumps(obj))
File "/opt/Python-3.4.3/lib/python3.4/multiprocessing/reduction.py", line 50, in dumps
cls(buf, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <class 'pandas.core.frame.Pandas'>: attribute lookup Pandas on pandas.core.frame failed
```
Is there a better way to do some simple multi-threaded processing of a dataframe. This had worked very well for me in the past. I would rather not rewrite all of my analysis, but if there is a better way I could be convinced.
Thanks.
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} | 2 | 2015-12-07T23:58:06Z | 2018-06-28T10:23:02Z | 2018-06-28T10:23:02Z | CONTRIBUTOR | null | Seeing the following behavior in 0.17.1 with the patch from https://github.com/pydata/pandas/pull/11715 applied (would raise without the patch):
``` python
In [29]: ser = pd.Series([pd.NaT, pd.Timestamp('2015/01/01', tz='UTC')])
# works
In [30]: ser.replace(pd.NaT, pd.Timestamp.min)
Out[30]:
0 1677-09-22 00:12:43.145225
1 2015-01-01 00:00:00+00:00
dtype: object
# doesn't work
In [31]: ser.replace([np.nan, pd.NaT], pd.Timestamp.min)
Out[31]:
0 NaT
1 2015-01-01 00:00:00+00:00
dtype: datetime64[ns, UTC]
# works without timezone-aware datetimes
In [32]: ser = pd.Series([pd.NaT, pd.Timestamp('2015/01/01')])
In [33]: ser.replace([np.nan, pd.NaT], pd.Timestamp.min)
Out[33]:
0 1677-09-22 00:12:43.145225
1 2015-01-01 00:00:00.000000
dtype: datetime64[ns]
```
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| null | 6 | 2015-12-08T02:47:16Z | 2019-10-13T00:27:48Z | 2019-10-13T00:27:47Z | NONE | null | Here is my repro script:
``` python
import pandas as pd
import sys
for df in pd.read_csv(sys.argv[1], chunksize=1000):
print(df[['sum']].sum())
```
and I am attaching [small.csv.gz](https://github.com/pydata/pandas/files/54833/small.csv.gz) as the smallest data set I know reproduces this segfault. Running `python repro.py small.csv.gz` reproduces the segfault in 0.17.1 on OSX Yosemite. I can't reproduce with 0.13.1 or 0.17.1 on Ubuntu 14.04. Removing `chunksize` works normally with that file.
I tried my best to narrow it down. You can edit this file down to under 2000 lines and the segfault does not occur. Once it goes over 2000 lines I start to see the segfault. I can add lines 1000 at a time and notice the segfault is intermittent (I see it again at 6002 lines). It seems like to me if there are a multiple of `chunksize` items in the file the segfault does not occur.
I installed via `pip install pandas`. I also repro'd this on latest master (43edd83) on OSX Yosemite.
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.4
pip: 1.5.6
setuptools: 8.2.1
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.15.1
statsmodels: None
IPython: 2.3.1
sphinx: 1.1.2
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
```
Exception Type: EXC_BAD_ACCESS (SIGSEGV)
Exception Codes: KERN_INVALID_ADDRESS at 0x0000000110bbf0bf
VM Regions Near 0x110bbf0bf:
MALLOC_LARGE 0000000110b3f000-0000000110bbf000 [ 512K] rw-/rwx SM=PRV
-->
MALLOC_LARGE 0000000110cae000-0000000110e2e000 [ 1536K] rw-/rwx SM=PRV
Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0 parser.so 0x000000011069af3f __pyx_f_6pandas_6parser_10TextReader__convert_with_dtype + 2191
1 parser.so 0x00000001106977ed __pyx_f_6pandas_6parser_10TextReader__convert_tokens + 3293
2 parser.so 0x00000001106c47fe __pyx_pf_6pandas_6parser_10TextReader_16_convert_column_data + 3006
3 parser.so 0x000000011069572b __pyx_f_6pandas_6parser_10TextReader__read_rows + 1371
4 parser.so 0x0000000110693f65 __pyx_f_6pandas_6parser_10TextReader__read_low_memory + 869
5 parser.so 0x00000001106c2b9e __pyx_pw_6pandas_6parser_10TextReader_9read + 174
6 org.python.python 0x000000010e1f77e6 PyEval_EvalFrameEx + 14392
7 org.python.python 0x000000010e1f3d7a PyEval_EvalCodeEx + 1409
8 org.python.python 0x000000010e1fa59d fast_function + 117
9 org.python.python 0x000000010e1f7400 PyEval_EvalFrameEx + 13394
10 org.python.python 0x000000010e1f3d7a PyEval_EvalCodeEx + 1409
11 org.python.python 0x000000010e1fa59d fast_function + 117
12 org.python.python 0x000000010e1f7400 PyEval_EvalFrameEx + 13394
13 org.python.python 0x000000010e18f67a gen_send_ex + 193
14 org.python.python 0x000000010e1f4525 PyEval_EvalFrameEx + 1399
15 org.python.python 0x000000010e1f3d7a PyEval_EvalCodeEx + 1409
16 org.python.python 0x000000010e1f37f3 PyEval_EvalCode + 54
17 org.python.python 0x000000010e2138a2 run_mod + 53
18 org.python.python 0x000000010e213945 PyRun_FileExFlags + 133
19 org.python.python 0x000000010e2134e2 PyRun_SimpleFileExFlags + 769
20 org.python.python 0x000000010e224c5b Py_Main + 3051
21 libdyld.dylib 0x00007fff8c26c5c9 start + 1
```
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} | 0 | 2015-12-08T09:36:53Z | 2015-12-08T20:14:58Z | 2015-12-08T20:14:58Z | CONTRIBUTOR | null | deep copies of Series with timezone-aware datetimes are shallow in 0.17.1:
``` python
In [12]: import pandas as pd
In [13]: pd.__version__
Out[13]: u'0.17.1'
In [14]: ser = pd.Series([pd.Timestamp('2012/01/01', tz='UTC')])
In [15]: ser2 = ser.copy(deep=True)
In [18]: ser2[0] = pd.Timestamp('1999/01/01', tz='UTC')
# unexpected mutation
In [19]: ser
Out[19]:
0 1999-01-01 00:00:00+00:00
dtype: datetime64[ns, UTC]
```
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| null | 4 | 2015-12-08T14:03:45Z | 2015-12-08T15:12:56Z | 2015-12-08T14:45:34Z | CONTRIBUTOR | null | ERROR: type should be string, got "https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=arm64&ver=0.17.1-2&stamp=1449515678\nso this one is not even big endian afaik\n\n```\nERROR: test_timestamp_compare (pandas.tests.test_frame.TestDataFrame)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_frame.py\", line 5749, in test_timestamp_compare\n expected = left_f(df, Timestamp('20010109'))\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/ops.py\", line 1116, in f\n return res.fillna(True).astype(bool)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/generic.py\", line 2632, in astype\n dtype=dtype, copy=copy, raise_on_error=raise_on_error, **kwargs)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/internals.py\", line 2864, in astype\n return self.apply('astype', dtype=dtype, **kwargs)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/internals.py\", line 2823, in apply\n applied = getattr(b, f)(**kwargs)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/internals.py\", line 430, in astype\n values=values, **kwargs)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/internals.py\", line 2001, in _astype\n return super(DatetimeBlock, self)._astype(dtype=dtype, **kwargs)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/internals.py\", line 472, in _astype\n values = com._astype_nansafe(values.ravel(), dtype, copy=True)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/common.py\", line 2433, in _astype_nansafe\n (arr.dtype, dtype))\nTypeError: cannot astype a datetimelike from [datetime64[ns]] to [bool]\n\n======================================================================\nFAIL: test_NaT_cast (pandas.tseries.tests.test_timeseries.TestSeriesDatetime64)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tseries/tests/test_timeseries.py\", line 3516, in test_NaT_cast\n assert_series_equal(result, expected)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 946, in assert_series_equal\n check_less_precise, obj='{0}'.format(obj))\n File \"pandas/src/testing.pyx\", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)\n File \"pandas/src/testing.pyx\", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 819, in raise_assert_detail\n raise AssertionError(msg)\nAssertionError: Series are different\n\nSeries values are different (100.0 %)\n[left]: [1970-01-01T00:00:00.000000000+0000]\n[right]: [NaT]\n\n======================================================================\nFAIL: test_where_datetime (pandas.tests.test_series.TestSeries)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_series.py\", line 2191, in test_where_datetime\n assert_series_equal(rs, expected)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 946, in assert_series_equal\n check_less_precise, obj='{0}'.format(obj))\n File \"pandas/src/testing.pyx\", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)\n File \"pandas/src/testing.pyx\", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 819, in raise_assert_detail\n raise AssertionError(msg)\nAssertionError: Series are different\n\nSeries values are different (50.0 %)\n[left]: [1970-01-01T00:00:00.000000010+0000, 1970-01-01T00:00:00.000000000+0000]\n[right]: [1970-01-01T00:00:00.000000010+0000, NaT]\n\n======================================================================\nFAIL: test_where_timedelta (pandas.tests.test_series.TestSeries)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_series.py\", line 2212, in test_where_timedelta\n assert_series_equal(rs, expected)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 946, in assert_series_equal\n check_less_precise, obj='{0}'.format(obj))\n File \"pandas/src/testing.pyx\", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)\n File \"pandas/src/testing.pyx\", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 819, in raise_assert_detail\n raise AssertionError(msg)\nAssertionError: Series are different\n\nSeries values are different (50.0 %)\n[left]: [10 nanoseconds, 0 nanoseconds]\n[right]: [10 nanoseconds, NaT]\n```\n" | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11796 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11796/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11796/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11796/events | https://github.com/pandas-dev/pandas/pull/11796 | 121,021,053 | MDExOlB1bGxSZXF1ZXN0NTI5ODg1MjY= | 11,796 | BUG: bug in deep copy of datetime tz-aware objects, #11794 | {
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} | 0 | 2015-12-08T14:42:16Z | 2015-12-08T20:14:58Z | 2015-12-08T20:14:58Z | CONTRIBUTOR | null | closes #11794
cleanups in copy / remove warnings
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| null | 6 | 2015-12-08T20:18:29Z | 2015-12-09T06:04:09Z | 2015-12-08T20:34:34Z | CONTRIBUTOR | null | Can you explain the behavior of the following :
<!-- language: lang-python -->
```
rows = pd.DataFrame([
(0, np.nan, np.nan, 'toto'),
(1, np.nan, np.nan, 'tata')
])
dico = {}
for row in rows.itertuples():
k = (row[2], row[3])
v = row[4]
print('hash(k) : {0}'.format(hash(k)))
v_prev = dico.setdefault(k, None)
if v_prev is None:
dico[k] = v
print('dico : {0}'.format(dico))
```
Result :
```
hash(k) : 3713080549408328131
hash(k) : 3713080549408328131
dico : {(nan, nan): 'toto', (nan, nan): 'tata'}
```
I manage to create a dict with the same key `(np.nan, np.nan)`. I have got this behavior only with `np.nan`. With `None` or whatever else, I get the usual result with only one key.
You can find some hints in my SO question : http://stackoverflow.com/questions/34163497/wrong-behavior-dict-with-the-same-key-based-on-tuple
It seems to be linked to `RichCompare`. The comparison operator `is` is not used in this case
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https://api.github.com/repos/pandas-dev/pandas/issues/11798 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11798/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11798/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11798/events | https://github.com/pandas-dev/pandas/pull/11798 | 121,119,877 | MDExOlB1bGxSZXF1ZXN0NTMwNTIwNTc= | 11,798 | Fix import-time DeprecationWarning on Python 3.5 | {
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} | 3 | 2015-12-08T22:57:18Z | 2015-12-10T17:04:35Z | 2015-12-10T01:30:31Z | CONTRIBUTOR | null | Fixes the following:
```
$ python3.5 -Wall
Python 3.5.0 (default, Sep 18 2015, 09:31:01)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-11)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas.tslib
pandas/__init__.py:7: DeprecationWarning: bad escape \s
from pandas import hashtable, tslib, lib
```
In Python 3.5 re.sub replacement patterns containing unrecognized character escapes are deprecated. This raw string format is needed to substitute `\s` literally.
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https://api.github.com/repos/pandas-dev/pandas/issues/11799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11799/events | https://github.com/pandas-dev/pandas/issues/11799 | 121,140,700 | MDU6SXNzdWUxMjExNDA3MDA= | 11,799 | Option to suppress automatic conversion of tuples to MultiIndex | {
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| null | 14 | 2015-12-09T01:33:49Z | 2015-12-13T20:53:46Z | 2015-12-09T14:20:59Z | NONE | null | Right now we have:
```
>>> pd.DataFrame({(1,2):pd.Series([2.3])}).columns
MultiIndex(levels=[[1], [2]],
labels=[[0], [0]])
>>> pd.Index([(1,2)])
MultiIndex(levels=[[1], [2]],
labels=[[0], [0]])
```
Could we have an option to suppress this behavior? One problem this causes is that the `.rename` method is not uniform and does not work if a tuple is silently converted:
```
>>> pd.DataFrame({(1,2):pd.Series([2.3])}).rename(columns={(1,2):(1,3)})
1
2
0 2.3
```
To see why such behavior is problematic consider the following unintuitive example:
```
>>> a = ('a', 1)
>>> b = ('b', 2)
>>> pd.DataFrame({a:pd.Series([2,3])})
a
1
0 2
1 3
>>> pd.DataFrame({a:pd.Series([2,3])}).rename(columns={a:b})
a
1
0 2
1 3
```
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} | 2 | 2015-12-09T09:05:56Z | 2016-02-06T18:19:36Z | 2015-12-12T14:05:13Z | CONTRIBUTOR | null | apply seems to lose timezone awareness. Would expect the following to be invariant:
``` python
In [9]: import pandas as pd
In [10]: pd.__version__
Out[10]: u'0.17.1'
In [11]: ser = pd.Series([pd.Timestamp('2015/01/01', tz='UTC'), pd.Timestamp('2015/01/02', tz='UTC')])
In [12]: ser
Out[12]:
0 2015-01-01 00:00:00+00:00
1 2015-01-02 00:00:00+00:00
dtype: datetime64[ns, UTC]
In [13]: ser.apply(lambda x: x)
Out[13]:
0 2015-01-01
1 2015-01-02
dtype: datetime64[ns]
```
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| null | 3 | 2015-12-09T11:59:48Z | 2015-12-09T14:11:36Z | 2015-12-09T13:45:19Z | NONE | null | When you save MultiIndex DataFrame to csv and then loading it with no additional parameters pandas not able to read multiindices properly and reconstructed dataframe is incorrect, however all MultiIndices are stored. Creating MultiIndex DataFrame:
``` python
>>> import pandas as pd
>>> arrays = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']),
np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])]
>>> tuples = list(zip(*arrays))
>>> index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
>>> df = pd.DataFrame(np.random.randn(6, 6), index=index[:6], columns=index[:6])
```
``` python
>>> df
first bar baz foo
second one two one two one two
first second
bar one 0.608879 -1.128122 -0.238610 1.089921 0.521766 1.116998
two 1.161840 0.876528 -1.417781 -1.150305 -1.043776 0.458298
baz one 1.761382 0.634688 -1.146492 -0.082379 0.414079 -0.520011
two 1.079547 0.667917 0.122468 -1.112053 0.408232 0.703068
foo one -1.031256 1.439310 0.604550 -0.275087 -0.316610 -0.589309
two 1.730061 -1.213560 -0.581617 -0.260879 0.069140 0.970397
```
Now saving to .csv
``` python
>>> df.to_csv('test.csv')
```
File content:
```
first,,bar,bar,baz,baz,foo,foo
second,,one,two,one,two,one,two
first,second,,,,,,
bar,one,-0.218970051706,-0.37813933444,-0.7634843365,-0.704262374956,-0.839464555892,0.541422908532
bar,two,0.681850126209,-0.12138626844,0.361273042684,0.143076786043,0.89542332917,-1.4010526032
baz,one,-0.818985292735,-1.09454292831,0.162462638192,-1.03231385607,0.553385654032,0.585854960842
baz,two,-1.54054784208,1.00696690064,0.976815889071,-1.39618300884,0.283526891003,-0.942377368698
foo,one,1.08113073993,-0.29494275034,0.738820962724,-1.62865612926,1.10227198603,-0.628396328368
foo,two,0.135391399079,0.410272615409,0.59806563553,0.172448276698,0.11403085441,1.47679237362
```
Loading file:
``` python
>>> df = pd.read_csv('test.csv')
```
```
first Unnamed: 1 bar bar.1 baz \
0 second NaN one two one
1 first second NaN NaN NaN
2 bar one 0.608879221531 -1.12812151143 -0.238610193059
3 bar two 1.16184022363 0.876527615412 -1.41778054545
4 baz one 1.76138211998 0.634687797737 -1.14649190565
5 baz two 1.0795474718 0.667917354209 0.122468371132
6 foo one -1.03125632895 1.43930972259 0.604550496274
7 foo two 1.73006141389 -1.21356047236 -0.581617481203
baz.1 foo foo.1
0 two one two
1 NaN NaN NaN
2 1.08992073325 0.521766323704 1.11699823915
3 -1.15030481312 -1.04377642582 0.458297920053
4 -0.0823790312557 0.414078987249 -0.520010569472
5 -1.11205302607 0.40823221543 0.703068488788
6 -0.27508709062 -0.316610246209 -0.589308900542
7 -0.260878563358 0.0691397545038 0.9703965012
```
Yes, I know that I need to specify columns and index parameters, but why I should do this? All information needed to restore original DataFrame with MultiIndex are already in the file. Is it possible to make it default to parse MultiIndices in csv, for example, using multiple separators in a row:
```
first,second,,,,,,
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11802 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11802/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11802/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11802/events | https://github.com/pandas-dev/pandas/issues/11802 | 121,252,448 | MDU6SXNzdWUxMjEyNTI0NDg= | 11,802 | Sorting within groupby causes duplication of index level | {
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} | 1 | 2015-12-09T14:28:14Z | 2021-04-21T05:21:43Z | null | CONTRIBUTOR | null | When I run this code on 0.17.0 on Python 3.5.0
```
import pandas as pd
d = pd.DataFrame(data=[[2, 1]])
print(d.index)
print((d.
groupby(level=0).
apply(lambda d:
d.sort_values(0)
)
).index)
```
I get
```
Int64Index([0], dtype='int64')
MultiIndex(levels=[[0], [0]],
labels=[[0], [0]])
```
The index gets duplicated (and turned into a multiindex) as a result of the `sort` within the `groupby`.
Appears to be a bug to me.
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| null | 5 | 2015-12-09T16:09:58Z | 2015-12-10T14:46:40Z | 2015-12-10T12:06:50Z | NONE | null | This is related to the old memory issues that pandas had when reading csv files. Now the situation is much better, but I've found that when the csv file has many string columns the memory consumption is increased a lot. Having numeric dtypes is handled correctly.
In pandas/io/parsers.py - TextFileReader.read method:
``` python
ret = self._engine.read(nrows)
if self.options.get('as_recarray'):
return ret
# May alter columns / col_dict
index, columns, col_dict = self._create_index(ret)
df = DataFrame(col_dict, columns=columns, index=index)
```
The first line reads the file correctly with linear memory usage, but the final line constructing the DataFrame has a memory peak which -in my case- seems to near double the memory needed.
I've traced the issue down to the internals.py - block manager - form_blocks method. When treating the np.object dtypes is copying the data, and thus the memory peak.
Any ideas about how this can be mitigated?
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