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| null | 5 | 2015-09-05T18:24:40Z | 2015-09-05T18:35:45Z | 2015-09-05T18:28:27Z | CONTRIBUTOR | null | When I run tests nosetests pandas/io/tests/test_excel.py, I got a lot of errors but the code is just pulled from master without any changes. Could anyone try it on your end? Anything I missed?
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} | 11 | 2015-09-05T18:56:36Z | 2015-10-10T00:23:39Z | 2015-10-10T00:23:39Z | MEMBER | null | On master, as a small example:
```
In [1]: df = pd.DataFrame([[1,2,3,4],[5,6,7,8]], columns=['A','B','A','B'])
In [2]: df
Out[2]:
A B A B
0 1 2 3 4
1 5 6 7 8
In [4]: df.to_excel('test_excel_duplicate_columns.xlsx')
```
gives:
So the values of columns 2 and 3 are swapped (not the column names)
BTW, this happens both with .xlsx as .xls (openpyxl / xlsxwriter / xlwt)
Possibly related: #10982, #10970
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} | 1 | 2015-09-05T19:09:03Z | 2015-09-21T15:35:26Z | 2015-09-11T14:52:10Z | CONTRIBUTOR | null | {
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| null | 1 | 2015-09-05T20:12:55Z | 2015-09-05T20:20:06Z | 2015-09-05T20:20:06Z | CONTRIBUTOR | null | There was one small thing overlooked when merging PR #10964: the url of the renamed test file was still pointing to my repo instead of pydata/pandas.
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} | 3 | 2015-09-05T20:58:39Z | 2015-09-08T00:30:05Z | 2015-09-08T00:30:05Z | CONTRIBUTOR | null | xref #10625
before this patch:
```
In [1]: from string import ascii_lowercase
In [2]: np.random.seed(2718281)
In [3]: n = 1 << 21
In [4]: dr = date_range('2015-08-30', periods=n // 10, freq='T')
In [5]: df = DataFrame({
...: '1st':np.random.choice(list(ascii_lowercase), n),
...: '2nd':np.random.randint(0, 5, n),
...: '3rd':np.random.choice(dr, n)})
In [6]: df.loc[np.random.choice(n, n // 10), '3rd'] = np.nan
In [7]: gr = df.groupby(['1st', '2nd'])
In [8]: %timeit gr.count()
The slowest run took 21.22 times longer than the fastest. This could mean that an intermediate result is being cached
1 loops, best of 3: 13.3 ms per loop
In [9]: %timeit gr.count()
100 loops, best of 3: 13.8 ms per loop
In [10]: pd.__version__
Out[10]: '0.16.2+521.g207efc2'
```
with this patch:
```
In [8]: %timeit gr.count()
1 loops, best of 3: 144 ms per loop
In [9]: %timeit gr.count()
10 loops, best of 3: 149 ms per loop
In [10]: pd.__version__
Out[10]: '0.16.2+522.g9c2d1a6'
```
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} | 8 | 2015-09-05T21:24:50Z | 2015-09-09T15:04:00Z | 2015-09-09T14:55:53Z | CONTRIBUTOR | null | Fixed issue #10888
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| null | 3 | 2015-09-05T22:06:11Z | 2015-09-05T22:13:34Z | 2015-09-05T22:13:34Z | NONE | null | I have 3 csv files, which could be linked together via different columns. I want to merge all the 3 csv files in 1 dataframe.
```
import pandas as pd
a_df = pd.read_csv(a_file, sep=';', index_col=['booking_id'])
b_df = pd.read_csv(b_file, sep=';', index_col=['ga:adwordsCreativeID', 'ga:date', 'ga:transactionId'])
c_df = pd.read_csv(c_file, sep=';', index_col=['Advertentie-ID', 'Dag'])
df = b.merge(a,b, left_index=True, right_index=True)
```
But some of the columns need some altering before the linking can occur.
Alterations:
- I need to get the first 7 characters of the `ga:transactionId` column so it's equal to `booking_id`.
- I need to parse the `ga:date` string to a timestamp (format = yyyymmdd) so i can use it with `ga:adwordsCreativeID` to form an unique key.
- I need to parse the `Dag` string to a timestamp (format = dd-mm-yyyy) so i can use it with `Advertentie-ID` to form the same unique key.
I assume that somewhere in the read_csv code is iterated through the csv data and added to the dataframe. Why not invoke a function callback so that during import the alterations can be applied?
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} | 3 | 2015-09-07T00:34:38Z | 2015-09-07T20:32:13Z | 2015-09-07T20:32:13Z | NONE | null | In Pandas 0.16.2 an attempt to assign a boolean array (created by pd.notnull) to a Panel fails with "ValueError: could not broadcast input array from shape...". The same code is successful using Pandas 0.15.2.
``` python
>>> import pandas as pd
>>> import numpy as np
>>>
>>> # create a test panel
... dates = pd.date_range('20150101', periods=6)
>>> df1 = pd.DataFrame([0,-.1,.2,np.nan,.23,np.nan], index=dates, columns=list('A'))
>>> df2 = pd.DataFrame([.3,np.nan,.3,np.nan,.6,.1], index=dates, columns=list('A'))
>>> data = {'Item1' : df1, 'Item2': df2}
>>> panel = pd.Panel(data)
>>>
>>> # check the shape
... panel.loc[:, :, 'A'].shape
(6, 2)
>>>
>>> # create a dataframe with boolean values indicating not null data values
... notnull = pd.notnull(panel.loc[:, :, 'A'])
>>> notnull.shape
(6, 2)
>>>
>>> # attempt to store it in the panel
... panel.loc[:, :, 'NotNull'] = notnull
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/edb/anaconda2/lib/python2.7/site-packages/pandas/core/indexing.py", line 115, in __setitem__
self._setitem_with_indexer(indexer, value)
File "/Users/edb/anaconda2/lib/python2.7/site-packages/pandas/core/indexing.py", line 445, in _setitem_with_indexer
setter(item, v)
File "/Users/edb/anaconda2/lib/python2.7/site-packages/pandas/core/indexing.py", line 411, in setter
s._data = s._data.setitem(indexer=pi, value=v)
File "/Users/edb/anaconda2/lib/python2.7/site-packages/pandas/core/internals.py", line 2483, in setitem
return self.apply('setitem', **kwargs)
File "/Users/edb/anaconda2/lib/python2.7/site-packages/pandas/core/internals.py", line 2459, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/edb/anaconda2/lib/python2.7/site-packages/pandas/core/internals.py", line 611, in setitem
values[indexer] = value
ValueError: could not broadcast input array from shape (2) into shape (6)
>>>
```
pd.show_versions() output for the failing version:
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
>>>
```
pd.show_versions() for the working version:
``` python
pd.show_versions()
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: 0.15.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
>>>
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11015 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11015/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11015/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11015/events | https://github.com/pandas-dev/pandas/pull/11015 | 105,143,718 | MDExOlB1bGxSZXF1ZXN0NDQzMTkxNDY= | 11,015 | BUG: .ix with PeriodIndex is fixed | {
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} | 3 | 2015-09-07T04:21:55Z | 2015-12-08T03:52:34Z | 2015-09-09T21:40:50Z | CONTRIBUTOR | null | Solves https://github.com/pydata/pandas/issues/4125
It doesn't feel like an elegant solution, but I think it works.
I think when Period gets its own dtype, these will all go away?
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} | 3 | 2015-09-07T14:27:19Z | 2015-09-09T12:39:53Z | 2015-09-09T11:17:16Z | NONE | null | Using pandas from master (0.16.2+590.g81b647f) in Python 3.4.2, the following code gives an `IndexError: index out of bounds`:
```
import pandas as pd, numpy as np
df = pd.DataFrame(dict(a=[np.nan]*3, b=[1,2,3]))
g = df.groupby(('a', 'b'))
len(g) # IndexError
```
The same problem occurs when calling `list(g)` instead. Since NaN values are skipped according to the documentation, I guess the correct answer would be zero for `len(g)` and an empty list for `list(g)`.
Strangely, iteration works, so `for x in g: pass` (or `[x for x in g]`) does not give an error (and iterates zero times). Also, `g.count()`, `g.sum()` etc. work (and return an empty DataFrame).
To add to the confusion, `g.groups` gives the dictionary `{(nan, 1): [0], (nan, 2): [1], (nan, 3): [2]}`. Shouldn’t this be empty because group keys with NaNs are dropped?
Grouping only by column 'a' or 'b' works and results in a length of 0 or 3, respectively.
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} | 10 | 2015-09-07T14:46:47Z | 2015-09-11T14:36:06Z | 2015-09-11T14:24:25Z | MEMBER | null | Fixed 2 problems:
- Specified `dtype` is not applied to other iterables.
```
import numpy as np
import pandas as pd
pd.Index([1, 2, 3], dtype=int)
# Index([1, 2, 3], dtype='object')
```
- Specifying `category` to ndarray-like results in `TypeError`
```
pd.Index(np.array([1, 2, 3]), dtype='category')
# TypeError: data type "category" not understood
```
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} | 1 | 2015-09-07T15:44:46Z | 2015-09-07T20:23:10Z | 2015-09-07T20:19:58Z | MEMBER | null | Fixed 2 issues:
- `date_range` can't handle negative calendar-based freq (like `A`, `Q` and `M`).
```
import pandas as pd
# OK
pd.date_range('2011-01-01', freq='-1D', periods=3)
# DatetimeIndex(['2011-01-01', '2010-12-31', '2010-12-30'], dtype='datetime64[ns]', freq='-1D')
# NG
pd.date_range('2011-01-01', freq='-1M', periods=3)
# DatetimeIndex([], dtype='datetime64[ns]', freq='-1M')
```
- Unable to infer negative freq.
```
pd.DatetimeIndex(['2011-01-05', '2011-01-03', '2011-01-01'], freq='infer')
# DatetimeIndex(['2011-01-05', '2011-01-03', '2011-01-01'], dtype='datetime64[ns]', freq=None)
pd.TimedeltaIndex(['-1 days', '-3 days', '-5 days'], freq='infer')
# TimedeltaIndex(['-1 days', '-3 days', '-5 days'], dtype='timedelta64[ns]', freq=None)
```
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} | 1 | 2015-09-07T15:55:08Z | 2021-04-18T23:34:07Z | null | NONE | null | I suspect this is related to #6710.
```
import pandas
from io import StringIO
t = "a,b,c,d\n1970-01-01,2,3,4"
s2 = "a,b,c\n1970-01-01,2,3\n1980-01-01,2,3,4"
s = "a,b,c\n1970-01-01,2,3,4"
pandas.read_csv(StringIO(t), parse_dates=['a'])
pandas.read_csv(StringIO(s2), parse_dates=['a'])
pandas.read_csv(StringIO(s), parse_dates=['a'])
```
Calling `read_csv` with `parse_dates=['a']` does the correct thing for t, raises a CParserError for s2, and raises `TypeError: 'NoneType' object has no attribute '__getitem__'` for s.
I would expect a CParserError for s as well.
Pandas version: 0.16.2
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https://api.github.com/repos/pandas-dev/pandas/issues/11020 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11020/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11020/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11020/events | https://github.com/pandas-dev/pandas/issues/11020 | 105,247,285 | MDU6SXNzdWUxMDUyNDcyODU= | 11,020 | pandas.read_csv(..., skiprows=2, engine='c') with unix-style line breaks crashes python on windows | {
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} | 5 | 2015-09-07T16:59:35Z | 2020-01-04T17:25:15Z | 2020-01-04T17:25:15Z | NONE | null | The following makes python crash ("Kernel died, restarting" in IPython) on my windows 7 machine:
```
import pandas
myfile = open("test.csv", "w", newline="\n")
myfile.write("blah\n\ncol_1,col_2,col_3\n\n")
myfile.close()
dat = pandas.read_csv("test.csv", skiprows=2, encoding="utf-8", engine="c")
```
Note the unix-style line breaks.
The test case seems to be pretty precise, since changing about anything leads to working code. I tried, e.g.,
- with Windows-style line breaks ('\r\n' instead of '\n')
- without the two initial lines and the `skiprows` parameter
- with two empty initial lines
- with just one initial line, containing text
- with the 'python' engine,
and everything worked.
Here is the output of `pandas.show_versions()`:
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
```
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} | 1 | 2015-09-07T18:33:05Z | 2015-09-19T00:38:09Z | 2015-09-07T20:16:06Z | CONTRIBUTOR | null | Fixes GH #11014
This isn't actually a recent regression. The bug was introduced in 0.15.0 with this commit: https://github.com/pydata/pandas/commit/30246a7d2b113b7115fd0708655ae8e1c3f2e6c5; it was just made more common (i.e., it happens on panels without mixed dtype) in 0.16.2 by this commit: https://github.com/pydata/pandas/commit/5c394677e10dac38fe3375e2564bb76cf8bbffca.
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} | 3 | 2015-09-07T19:05:11Z | 2016-11-30T15:30:51Z | 2015-09-07T20:14:54Z | NONE | null | Hello all,
I'm trying to convert a `datetime64[ns]` object to a unix timestamp without an `apply`.
I was wondering if it was the desired behavior to throw an error when trying to do:
``` python
import pandas as pd
import datetime as dt
import numpy as np
my_time = np.datetime64(dt.datetime.utcnow())
my_df = pd.DataFrame({"time": [my_time]*3})
my_df.time = my_df.time.astype("datetime64[s]")
```
The error:
``` python
TypeError Traceback (most recent call last)
<ipython-input-150-0b63b4529623> in <module>()
----> 1 my_df.time = my_df.time.astype("datetime64[s]")
/home/thomas/anaconda3/lib/python3.4/site-packages/pandas/core/generic.py in astype(self, dtype, copy, raise_on_error, **kwargs)
2409
2410 mgr = self._data.astype(
-> 2411 dtype=dtype, copy=copy, raise_on_error=raise_on_error, **kwargs)
2412 return self._constructor(mgr).__finalize__(self)
2413
/home/thomas/anaconda3/lib/python3.4/site-packages/pandas/core/internals.py in astype(self, dtype, **kwargs)
2502
2503 def astype(self, dtype, **kwargs):
-> 2504 return self.apply('astype', dtype=dtype, **kwargs)
2505
2506 def convert(self, **kwargs):
/home/thomas/anaconda3/lib/python3.4/site-packages/pandas/core/internals.py in apply(self, f, axes, filter, do_integrity_check, **kwargs)
2457 copy=align_copy)
2458
-> 2459 applied = getattr(b, f)(**kwargs)
2460
2461 if isinstance(applied, list):
/home/thomas/anaconda3/lib/python3.4/site-packages/pandas/core/internals.py in astype(self, dtype, copy, raise_on_error, values, **kwargs)
371 def astype(self, dtype, copy=False, raise_on_error=True, values=None, **kwargs):
372 return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,
--> 373 values=values, **kwargs)
374
375 def _astype(self, dtype, copy=False, raise_on_error=True, values=None,
/home/thomas/anaconda3/lib/python3.4/site-packages/pandas/core/internals.py in _astype(self, dtype, copy, raise_on_error, values, klass, **kwargs)
401 if values is None:
402 # _astype_nansafe works fine with 1-d only
--> 403 values = com._astype_nansafe(self.values.ravel(), dtype, copy=True)
404 values = values.reshape(self.values.shape)
405 newb = make_block(values,
/home/thomas/anaconda3/lib/python3.4/site-packages/pandas/core/common.py in _astype_nansafe(arr, dtype, copy)
2699 elif dtype != _NS_DTYPE:
2700 raise TypeError("cannot astype a datetimelike from [%s] to [%s]" %
-> 2701 (arr.dtype, dtype))
2702 return arr.astype(_NS_DTYPE)
2703 elif is_timedelta64_dtype(arr):
TypeError: cannot astype a datetimelike from [datetime64[ns]] to [datetime64[s]]
```
But if I only take the values and work on this numpy array:
``` python
print(my_df.time.values.astype("datetime64[s]").dtype)
datetime64[s]
my_df.time = my_df.time.values.astype("datetime64[s]")
print(my_df.time)
0 2015-09-07 18:56:44
1 2015-09-07 18:56:44
2 2015-09-07 18:56:44
Name: time, dtype: datetime64[ns]
```
The type is correctly changed and pandas reconvert it to a `datetime64[ns]` object.
Could we control this behavior?
So I can convert my `datetime64[ns]` object into a unix timestamp like this:
``` python
my_df.time = my_df.time.values.astype("datetime64[s]").astype(int)
```
Second question why not have the same behavior for `pandas.Series`?
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} | 1 | 2015-09-07T22:43:56Z | 2015-09-08T00:30:05Z | 2015-09-08T00:30:05Z | CONTRIBUTOR | null | closes #11010
```
In [10]: %timeit gr['3rd'].count()
100 loops, best of 3: 15.1 ms per loop
In [11]: %timeit gr['3rd'].min()
100 loops, best of 3: 8.4 ms per loop
In [12]: %timeit gr['3rd'].max()
100 loops, best of 3: 8.47 ms per loop
In [13]: %timeit gr['3rd'].first()
100 loops, best of 3: 10.5 ms per loop
In [14]: %timeit gr['3rd'].last()
100 loops, best of 3: 10.3 ms per loop
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11024/events | https://github.com/pandas-dev/pandas/pull/11024 | 105,376,756 | MDExOlB1bGxSZXF1ZXN0NDQ0Mjk2NzA= | 11,024 | BUG: DatetimeIndex.freq can defer by ufunc | {
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} | 1 | 2015-09-08T13:07:49Z | 2015-09-09T08:47:50Z | 2015-09-09T08:47:32Z | MEMBER | null | Follow up of #10638. `DatetimeIndex.freq` also can be changed when other arg is an ndarray.
As the same as #10638, freq is re-infered only when it exists. Otherwise, leave it as `None` even if the result can be on specific freq. This is based on other functions behavior as below:
```
idx = pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'])
idx
# DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], dtype='datetime64[ns]', freq=None)
idx.take([0, 2])
# DatetimeIndex(['2011-01-01', '2011-01-03'], dtype='datetime64[ns]', freq=None)
idx = pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], freq='D')
idx.take([0, 2])
# DatetimeIndex(['2011-01-01', '2011-01-03'], dtype='datetime64[ns]', freq='2D')
```
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} | 1 | 2015-09-08T13:51:54Z | 2015-09-08T20:35:25Z | 2015-09-08T15:21:31Z | MEMBER | null | Related to #11013. Define `GroupBy.count` for API doc and added docstring.
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} | 15 | 2015-09-08T16:40:44Z | 2020-04-17T03:20:19Z | null | NONE | null | Would be really cool if would be implemented to quickly find and retrieve rows from DataFrame. Currently I previously sort a field and the use "**searchsorted**" function in order to get the index of the selection... but this is limited to one field only and still doesn't handle multiple searches. It would be really similar to the "query" functionality but will take advantages from using some sort of "internal" precomputed index in order to reduce latency.
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| null | 1 | 2015-09-08T17:22:31Z | 2015-09-08T17:28:01Z | 2015-09-08T17:27:36Z | CONTRIBUTOR | null | documenation -> documentation
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} | 9 | 2015-09-08T20:34:19Z | 2015-10-27T20:25:50Z | 2015-10-27T20:25:50Z | NONE | null | closes https://github.com/pydata/pandas/issues/9540
for example:
``` python
import pandas as pd
d = {'one' : ['A', 'A', 'B', 'C'],
'two' : [4., 3., 2., 1.],
'three' : [10., 8., 5., 7.]}
df = pd.DataFrame(d)
df.hist('two', by='one', weights='three', bins=range(0, 10))
```
does not seem to break anything, but this is my first meddling in the pandas library, so a review would be nice
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| null | 2 | 2015-09-08T21:22:16Z | 2015-09-08T21:57:46Z | 2015-09-08T21:44:41Z | NONE | null | There seems to be a bug in the `filter` method when grouping by multiple columns, where one of the grouping columns has type `datetime64[ns]`. Here is an example:
``` python
data = [
('a', pd.Timestamp('2015-08-01'), 0),
('a', pd.Timestamp('2015-08-01'), 0),
('a', pd.Timestamp('2015-08-02'), 1),
('b', pd.Timestamp('2015-08-01'), 2),
('b', pd.Timestamp('2015-08-02'), 3),
('c', pd.Timestamp('2015-08-01'), 4),
('c', pd.Timestamp('2015-08-02'), 5),
('c', pd.Timestamp('2015-08-02'), 5),
]
data = pd.DataFrame(data, columns=('A', 'B', 'group_number'))
groups = data.groupby(['A', 'B'])
assert len(groups) == 6 # OK
filtered = groups.filter(lambda x: len(x) > 1)
assert len(filtered) == 4 # AssertionError
```
Now, if I change the timestamps to strings, `filter` works as expected:
``` python
data2 = data.copy()
data2['B'] = data2['B'].astype(str)
groups2 = data2.groupby(['A', 'B'])
filtered2 = groups2.filter(lambda x: len(x) > 1)
assert len(filtered2) == 4 # OK
```
Also, `filter` works as expected when grouping by _only_ column B, regardless of whether it contains timestamps or strings.
What's going on here? Am I missing something or is this actually a bug? Thanks!
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| null | 3 | 2015-09-09T01:18:09Z | 2015-09-09T01:52:44Z | 2015-09-09T01:52:42Z | CONTRIBUTOR | null | Fixed #11016
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} | 6 | 2015-09-09T01:51:42Z | 2015-09-09T12:40:45Z | 2015-09-09T11:16:52Z | CONTRIBUTOR | null | This is the new PR for Fixed issue #11016
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| null | 1 | 2015-09-09T03:25:01Z | 2015-09-09T03:37:18Z | 2015-09-09T03:37:18Z | CONTRIBUTOR | null | When I try and run `head()` or `query()` on a dataframe while working in ipython, I'm getting the following error on a DataFrame that seems pretty normal (850 rows, 159 columns, mixed type (lots of strings).
```
/Users/Nick/github/pandas/pandas/core/format.py:2052: RuntimeWarning: invalid value encountered in greater has_large_values = (abs_vals > 1e8).any()
/Users/Nick/github/pandas/pandas/core/format.py:2053: RuntimeWarning: invalid value encountered in less has_small_values = ((abs_vals < 10 ** (-self.digits)) &
/Users/Nick/github/pandas/pandas/core/format.py:2054: RuntimeWarning: invalid value encountered in greater (abs_vals > 0)).any()
```
Oddly, it comes up _before_ i finish the command or hit enter, and I can still run the command if I type it out in an editor and execute the line -- it only comes up when I'm typing interactively.
Is this a bug? If not, any idea what it is? Not sure how to recreate with other data.
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} | 9 | 2015-09-09T11:12:13Z | 2015-10-18T08:37:00Z | 2015-10-17T14:45:29Z | CONTRIBUTOR | null | i’d like `pd.read_table(some_path / 'filename.tsv')` to work without going through `str()`
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} | 3 | 2015-09-09T13:43:38Z | 2015-09-09T21:09:28Z | 2015-09-09T21:09:28Z | CONTRIBUTOR | null | Always returns true for equality and inequality:
``` python
In [168]: t = pd.Timestamp('2000-01-29 01:59:00')
In [169]: a = pd.Series([t])
In [170]: b = pd.Series([t])
In [171]: a
Out[171]:
0 2000-01-29 01:59:00
dtype: datetime64[ns]
In [172]: a == b
Out[172]:
0 True
dtype: bool
In [173]: a != b
Out[173]:
0 True
dtype: bool
In [174]: t == t
Out[174]: True
In [175]: t != t
Out[175]: False
```
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| null | 5 | 2015-09-09T15:56:00Z | 2015-09-09T16:27:26Z | 2015-09-09T16:03:25Z | NONE | null | This issue arose when using the interpolate method to extend an existing dataframe using the procedure in the snippet. Most interpolated values seem to behave correctly, but occasionally there is some kind of rounding error which is not visible to ordinary inspection. Trying to access a row in the dataframe using one of the interpolated values as the key sometimes fails even though the value appears to be correct when the data is viewed. The snippet provides an example:
``` python
import pandas as pd
import numpy as np
# Setup
df = pd.DataFrame({'values': [0,1,2,3,4,5]})
df2 = pd.DataFrame(index = np.linspace(0, 5, 501))
df3 = pd.concat([df, df2], axis=1)
df4 = df3.interpolate()
# The results of this statement appear correct
print df4.iloc[113:116]
# This statement works as intended: demonstrates correct syntax
print df4[df4.values == 1.13]
# This statement returns an empty dataframe despite everything else looking right
print df4[df4.values == 1.14]
```
The problem can be solved by replacing the interpolated column with one which has been rounded to the same level of precision. Viewing the data shows no change, but the previous errors disappear.
INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.2
nose: 1.3.4
Cython: None
numpy: 1.8.1
scipy: 0.14.1rc1
statsmodels: None
IPython: 2.3.1
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 1 | 2015-09-09T17:05:23Z | 2015-09-09T21:09:17Z | 2015-09-09T21:09:17Z | MEMBER | null | closes #11034
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} | 1 | 2015-09-09T20:14:32Z | 2015-09-09T21:37:01Z | 2015-09-09T21:10:44Z | CONTRIBUTOR | null | One location referencing `display.max_colwidth` was incorrectly spelled as `display.max_columnwidth`.
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} | 3 | 2015-09-09T23:35:42Z | 2019-10-21T19:52:15Z | null | CONTRIBUTOR | null | ``` python
>>> df
1st 2nd
0 1 NaN
1 2 3
2 1 NaN
3 2 4
>>> df.groupby('1st')['2nd'].nth(0, dropna=True)
1st
1 3
2 4
Name: 2nd, dtype: float64
```
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} | 34 | 2015-09-09T23:38:43Z | 2016-04-25T14:22:20Z | 2016-04-25T14:21:45Z | CONTRIBUTOR | null | closes #12839
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_ngroups_10000_cumcount | 3.9790 | 74.9559 | 0.0531 |
groupby_ngroups_100_cumcount | 0.6043 | 0.9940 | 0.6079 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [2a1c935] : PERF: improves performance in GroupBy.cumcount
Base [5b1f3b6] : reverts 'from .pandas_vb_common import *'
```
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} | 4 | 2015-09-10T03:57:40Z | 2015-09-21T12:41:12Z | 2015-09-10T10:33:31Z | NONE | null | > > > import pandas as pd
> > > text = 'this is "out text"'
> > > df = pd.DataFrame(index=['1'],columns=['1','2'])
> > > df.loc['1','1']=123
> > > df.loc['1','2']=text
> > > df.to_csv('foo.txt',index=False,header=False,quoting=3,sep=' ')
---
Error Traceback (most recent call last)
/nas02/home/v/a/valenal/scrpts/<ipython-input-198-6da754b0e15c> in <module>()
----> 1 df.to_csv('foo.txt',index=False,header=False,quoting=3,sep=' ')
/nas01/depts/ie/cempd/apps/python/python2.6/lib/python2.6/site-packages/pandas-0.16.0-py2.6-linux-x86_64.egg/pandas/core/frame.pyc in to_csv(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, quoting, quotechar, line_terminator, chunksize, tupleize_cols, date_format, doublequote, escapechar, decimal, **kwds)
1183 escapechar=escapechar,
1184 decimal=decimal)
-> 1185 formatter.save()
1186
1187 if path_or_buf is None:
/nas01/depts/ie/cempd/apps/python/python2.6/lib/python2.6/site-packages/pandas-0.16.0-py2.6-linux-x86_64.egg/pandas/core/format.pyc in save(self)
1430
1431 else:
-> 1432 self._save()
1433
1434 finally:
/nas01/depts/ie/cempd/apps/python/python2.6/lib/python2.6/site-packages/pandas-0.16.0-py2.6-linux-x86_64.egg/pandas/core/format.pyc in _save(self)
1530 break
1531
-> 1532 self._save_chunk(start_i, end_i)
1533
1534 def _save_chunk(self, start_i, end_i):
/nas01/depts/ie/cempd/apps/python/python2.6/lib/python2.6/site-packages/pandas-0.16.0-py2.6-linux-x86_64.egg/pandas/core/format.pyc in _save_chunk(self, start_i, end_i)
1553 date_format=self.date_format)
1554
-> 1555 lib.write_csv_rows(self.data, ix, self.nlevels, self.cols, self.writer)
1556
1557 # from collections import namedtuple
/nas01/depts/ie/cempd/apps/python/python2.6/lib/python2.6/site-packages/pandas-0.16.0-py2.6-linux-x86_64.egg/pandas/lib.so in pandas.lib.write_csv_rows (pandas/lib.c:16804)()
Error: need to escape, but no escapechar set
## INSTALLED VERSIONS
commit: None
python: 2.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-238.12.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.iso885915
pandas: 0.16.0
nose: 0.11.1
Cython: 0.19.2
numpy: 1.7.0
scipy: 0.14.0
statsmodels: 0.6.0
IPython: 0.12.1
sphinx: 1.0.7
patsy: 0.3.0
dateutil: 2.1
pytz: 2013b
bottleneck: None
tables: None
numexpr: 2.1
matplotlib: 1.3.1
openpyxl: 1.5.8
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: 3.0.1
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: 0.6.1.None
## psycopg2: 2.5.5 (dt dec pq3 ext)
I am trying to output to a text file using to_csv without extra quotes.
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} | 6 | 2015-09-10T04:44:15Z | 2015-09-11T14:12:48Z | 2015-09-11T14:12:48Z | NONE | null | using pandas 0.16.2
minimum example here:
```
import numpy as np
import pandas as pd
index = pd.MultiIndex.from_product([range(10), [0, 1]])
data = pd.DataFrame(np.arange(100).reshape(-1, 20), columns=index)
data.groupby(level=0, axis=1).filter(lambda x: x.iloc[0, 0] > 10)
```
expected: get all filtered (grouped) columns as a DataFrame
result: `IndexError`
when change `np.arange(100)` to `np.arange(1000)` (more rows than columns)
results data frame with missing rows.
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} | 1 | 2015-09-10T05:59:15Z | 2019-10-23T05:23:37Z | null | MEMBER | null | As a follow up on #9321:
We should start adding optional arguments that are common between multiple plot types to the signatures for plotting sub-methods, such as `figsize` and `axis`. Currently, these are only documented on the main `.plot()` methods and we use `**kwargs` for the rest. This would be nice because we could clarify for users which optional arguments work for which types of plots -- this is not at all obvious right now.
@JanSchulz [suggests](https://github.com/pydata/pandas/pull/9321#discussion_r39086063) we could perhaps do this dynamically by updating the `__signature__` object. However, I don't know yet if this will generate the correct API documentation -- maybe if Sphinx is run using Python 3?
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} | 3 | 2015-09-10T06:01:50Z | 2016-02-12T03:10:54Z | 2016-02-12T03:10:54Z | MEMBER | null | Another followup to #9321:
Most of the documentation examples in visualization.rst (the [main plotting docs](http://pandas.pydata.org/pandas-docs/stable/visualization.html)) currently use the `kind` argument. We should probably switch these to use the plotting submethods instead, to push users in that direction.
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} | 3 | 2015-09-10T06:15:34Z | 2019-10-22T16:15:12Z | null | NONE | null | Why string comparison return 'False'? How I can compare strings?
```
In [3]: import pandas as pd
In [4]: df = pd.DataFrame( {'col1':[1,0]} )
In [5]: external_var = 'yes'
In [6]: df.eval( '(@external_var == "yes") == col1' )
Out[6]:
0 False
1 True
dtype: bool
In [7]: df.eval( '(@external_var == "yes")')
Out[7]: False
In [8]: df.eval( '(1 == 1)')
Out[8]: True
In [9]: df.eval( '("yes" == "yes")')
Out[9]: False
In [11]: import numexpr as ne
In [12]: pd.__name__, pd.__version__, ne.__name__, ne.__version__
Out[12]: ('pandas', '0.16.2', 'numexpr', '2.4.3')
```
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| null | 3 | 2015-09-10T07:36:20Z | 2015-09-10T09:40:20Z | 2015-09-10T09:40:20Z | NONE | null | We have lots of objects allocated in memory and the explicit call to `gc.collect(2)` in `generic.py` is killing our app's performance (taking ~0.5s _each time_!)
https://github.com/pydata/pandas/blob/master/pandas/core/generic.py#L1278
Ideally pandas shouldn't call the garbage collector manually, or at the very list there should be a way to disable this call.
Thanks!
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} | 1 | 2015-09-10T08:50:49Z | 2015-09-10T10:57:48Z | 2015-09-10T10:57:47Z | MEMBER | null | Small correction, QUOTE_NONE and QUOTE_NONNUMERIC have to be switched:
```
In [1]: import csv
In [2]: csv.QUOTE_NONNUMERIC
Out[2]: 2
In [3]: csv.QUOTE_NONE
Out[3]: 3
```
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| null | 3 | 2015-09-10T09:01:48Z | 2015-09-10T11:43:23Z | 2015-09-10T11:43:23Z | NONE | null | I want to upload a file from csv to mysql with the following commands
```
df = pandas.read_csv('test.csv', encoding = 'utf-8')
engine = sqlalchemy.create(<Mysql-Connection-URI>, encoding = 'utf-8')
df.to_sql('testtable', con = engine)
```
mysql has utf-8 as global encoding. If I upload the file manually to the database it goes without problems.
using to_sql throws the following errror:
`UnicodeEncodeError: 'latin-1' codec can't encode character '\u015e'`etc...
Any ideas, where Latin-1 becomes part of the games and how this could be fixed? Thanks for any advice!
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} | 3 | 2015-09-10T11:42:38Z | 2015-09-10T22:12:02Z | 2015-09-10T22:11:55Z | CONTRIBUTOR | null | closes #10645
closes #10692
replaces #10675
Do I need `@slow` for these tests?
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| null | 2 | 2015-09-10T12:15:57Z | 2015-10-08T19:23:35Z | 2015-09-10T12:24:04Z | NONE | null | There appears to be a memory leak in the DataFrame.dropna(inplace=True) function. Please see the ipython session:
```
[1]:
import ipython_memory_usage.ipython_memory_usage as imu
imu.start_watching_memory()
import pandas as pd
import numpy as np
pd.__version__
Out[1]: '0.16.2'
In [1] used 23.6875 MiB RAM in 0.70s, peaked 0.00 MiB above current, total RAM usage 59.76 MiB
In [2]:
df = pd.DataFrame(np.ones(1e7))
df.info(memory_usage=True)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10000000 entries, 0 to 9999999
Data columns (total 1 columns):
0 float64
dtypes: float64(1)
memory usage: 152.6 MB
In [2] used 153.3477 MiB RAM in 0.14s, peaked 0.00 MiB above current, total RAM usage 213.11 MiB
In [3]:
df.dropna(inplace=True)
df.head(2)
Out[3]:
0
0 1
1 1
In [3] used 0.1758 MiB RAM in 0.60s, peaked 385.92 MiB above current, total RAM usage 213.29 MiB
In [4]:
df.dropna(inplace=True)
df.head(2)
Out[4]:
0
0 1
1 1
In [4] used 152.9375 MiB RAM in 0.61s, peaked 182.86 MiB above current, total RAM usage 366.22 MiB
In [5]:
df.dropna(inplace=True)
df.head(2)
Out[5]:
0
0 1
1 1
In [5] used 152.9297 MiB RAM in 0.58s, peaked 272.79 MiB above current, total RAM usage 519.15 MiB
```
Continuing to run cells 3,4 and 5 will add around 150MB to the memory usage each time. The behaviour is only materialised when the df.head() command is run after the dropna inplace.
The behaviour can also be seen in a cell which is not dropping na in place:
```
df = df.dropna()
df.head(2)
```
but this only happens if the df.dropna(inplace=True) has been run within the same cell already.
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} | 11 | 2015-09-10T12:59:11Z | 2015-10-17T14:46:11Z | 2015-10-17T14:45:16Z | CONTRIBUTOR | null | fixes #11033
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} | 17 | 2015-09-10T13:57:34Z | 2019-12-27T16:58:05Z | 2019-12-27T16:58:05Z | NONE | null | Similar to `to_latex` and `to_html`.
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| null | 12 | 2015-09-10T21:29:48Z | 2021-07-12T01:35:40Z | null | MEMBER | null | This came up in the PR adding plot submethods like `.plot.hist`: https://github.com/pydata/pandas/pull/9321#issuecomment-139203762
As long as API differences can be reconciled, I think it would be a nice idea to deprecate (and eventually remove) `DataFrame.hist`, `DataFrame.boxplot` and `Series.hist` in favor of the accessor mtehods.
This old issue discusses API differences (possibly no longer accurate/relevant?): https://github.com/pydata/pandas/issues/413
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} | 2 | 2015-09-11T02:15:01Z | 2015-09-11T14:15:01Z | 2015-09-11T14:12:16Z | CONTRIBUTOR | null | Fixed #11041
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| null | 4 | 2015-09-11T04:39:54Z | 2015-09-11T13:43:41Z | 2015-09-11T12:33:48Z | NONE | null | When slicing a data frame with MultiIndex, the returned dataframes 'index' attribute is not sliced. It remains equal to the index of the unsliced object.
``` python
# Setup test DF
df = pd.DataFrame(np.random.randint(0, 10, size=(10,5)))
df['date'] = pd.date_range(start='2015-01-01', periods=10)
df.loc[0:4, 'id'] = 'ABC'
df.loc[4:, 'id'] = 'DEF'
df.set_index(['date', 'id'], inplace=True)
df = pd.DataFrame(np.random.randint(0, 10, size=(10,5)))
df['date'] = pd.date_range(start='2015-01-01', periods=10)
df.loc[0:4, 'id'] = 'ABC'
df.loc[4:, 'id'] = 'DEF'
df.set_index(['date', 'id'], inplace=True)
```
Formatting is a bit screwed up here. The columns are 0-4
``` python
In [5]: df
Out[5]:
0 1 2 3 4
date id
2015-01-01 ABC 4 3 2 9 7
2015-01-02 ABC 6 4 7 6 6
2015-01-03 ABC 5 6 9 2 5
2015-01-04 ABC 7 6 7 4 1
2015-01-05 DEF 0 5 9 0 2
2015-01-06 DEF 9 6 4 3 5
2015-01-07 DEF 0 9 2 4 4
2015-01-08 DEF 8 7 8 9 7
2015-01-09 DEF 2 2 1 4 7
2015-01-10 DEF 2 7 7 3 2
```
Filter a date range and look at the index
``` python
In [6]:df.loc['2015-01-01':'2015-01-05'].index.levels[0]
Out[6]:
DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04', '2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08', '2015-01-09', '2015-01-10'], dtype='datetime64[ns]', name=u'date', freq=None, tz=None)
```
The current workaround involves resetting the index and setting it again.
Surprised no one else has encountered this. We are encountering this very frequently. Is there a cleaner workaround others are using?
``` python
pandas: 0.16.2
```
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} | 16 | 2015-09-11T05:26:28Z | 2015-09-13T20:27:43Z | 2015-09-13T20:16:22Z | CONTRIBUTOR | null | `DataFrameFormatter` does not have this parameter: `force_unicode_docstring`. This change uses the doc-string re-design in #11011
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} | 5 | 2015-09-11T07:04:32Z | 2020-04-22T21:26:00Z | 2020-04-22T21:26:00Z | NONE | null | this is from a stackoverflow question [here](http://stackoverflow.com/questions/32441403/pandas-concat-produces-all-nan), you can download serialized object [here](https://www.evernote.com/l/AH4AdfgOJJROuZSfGfDR_jZvA0zEpIHgyq0) and reproduce using **python 2.7** and **pandas 0.16.2**.
I'm trying to concat two series with multiindex using `pd.concat([a, b], axis=1)` like so:
``` python
>>>payed_orders.head()
dt product_id
2015-01-15 10001 1
10007 1
10016 14
10022 1
10023 1
Name: payed_orders, dtype: int64
>>>refund_orders.head()
dt product_id
2015-01-15 10007 1
10016 4
10030 1
2015-01-16 10007 3
10008 1
Name: refund_orders, dtype: int64
>>>pd.concat([payed_orders.head(), refund_orders.head()], axis=1, ignore_index=False)
payed_orders refund_orders
dt product_id
2015-01-15 10001 NaN NaN
10007 NaN NaN
10016 NaN NaN
10022 NaN NaN
10023 NaN NaN
10030 NaN NaN
2015-01-16 10007 NaN NaN
10008 NaN NaN
```
I've checked the index type and many other stuff to make sure no obvious were made, I've read the docs to learn that concatenating and merging series and dataframes may introduce `NaN`, but I didn't find anything in the docs to explain this behavior.
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| null | 1 | 2015-09-11T09:20:20Z | 2015-09-11T11:14:09Z | 2015-09-11T11:14:04Z | MEMBER | null | Mainly some rearranging (eg the datetime tz was somewhere in the middle of the API changes, moved it to in front of the new features)
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} | 0 | 2015-09-11T14:20:16Z | 2019-01-03T00:40:12Z | 2019-01-03T00:40:12Z | CONTRIBUTOR | null | I have seen these errors spuriously: https://travis-ci.org/jreback/pandas/jobs/79852164
but they go away when re-run...any ideas?
```
======================================================================
FAIL: test_to_html_truncate (pandas.tests.test_format.TestDataFrameFormatting)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/test_format.py", line 999, in test_to_html_truncate
self.assertEqual(result, expected)
AssertionError: u'<div style="max-width:1500px;overflow:auto;">\n<table border="1" class="datafr [truncated]... != u'<div>\n<table border="1" class="dataframe">\n <thead>\n <tr style="text-al [truncated]...
Diff is 1759 characters long. Set self.maxDiff to None to see it.
======================================================================
FAIL: test_to_html_truncate_multi_index (pandas.tests.test_format.TestDataFrameFormatting)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/test_format.py", line 1115, in test_to_html_truncate_multi_index
self.assertEqual(result, expected)
AssertionError: u'<div style="max-width:1500px;overflow:auto;">\n<table border="1" class="datafr [truncated]... != u'<div>\n<table border="1" class="dataframe">\n <thead>\n <tr>\n <th></ [truncated]...
Diff is 2107 characters long. Set self.maxDiff to None to see it.
======================================================================
FAIL: test_to_html_truncate_multi_index_sparse_off (pandas.tests.test_format.TestDataFrameFormatting)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/test_format.py", line 1225, in test_to_html_truncate_multi_index_sparse_off
self.assertEqual(result, expected)
AssertionError: u'<div style="max-width:1500px;overflow:auto;">\n<table border="1" class="datafr [truncated]... != u'<div>\n<table border="1" class="dataframe">\n <thead>\n <tr>\n <th></ [truncated]...
Diff is 1877 characters long. Set self.maxDiff to None to see it.
----------------------------------------------------------------------
Ran 9032 tests in 947.494s
```
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} | 2 | 2015-09-11T14:34:54Z | 2015-09-12T13:16:30Z | 2015-09-12T13:16:30Z | NONE | null | variant of #10907
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'a' : [1],'b':[2]})
In [3]: df
Out[3]:
a b
0 1 2
In [4]: df['c'] = 3.5
In [5]: df.shift(axis=1)
Out[5]:
a b c
0 NaN 1 NaN
```
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} | 1 | 2015-09-11T14:51:15Z | 2021-09-09T14:14:19Z | 2021-09-09T14:14:19Z | NONE | null | Exploring [in-lining plots](https://github.com/iiSeymour/sparkline-nb/blob/master/sparkline-nb.ipynb) in DataFrames I found it would be useful to have a display option for setting HTML escaping. This would avoid the work around of `HTML(df.to_html(escape=False))` every time you want to render the DataFrame.
I propose adding the option `display.escape_notebook_repr_html`.
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| null | 3 | 2015-09-11T14:59:20Z | 2015-11-02T12:08:44Z | 2015-11-02T12:08:44Z | NONE | null | Adds the option `display.escape_notebook_repr_html` for controlling HTML escaping when rendering DataFrames in IPython notebooks. Unset, this argument does not change the default behaviour.
Fixes #11062
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| null | 2 | 2015-09-11T15:10:54Z | 2015-09-11T15:27:35Z | 2015-09-11T15:26:12Z | NONE | null | This is more of a request rather than a call for help I believe, as I think there isn't a way pandas can currently help me with this. Here's my stackoverflow thread relating the issue. http://stackoverflow.com/questions/32523083/iterator-to-iterate-over-excel-file-in-python
I work with large excel files which I use as databases. Most of the time, I need the raw data from the database only once but with read_excel() method, I have to load it all into my memory even though I'll need that data only once. Sad thing is, the amount of time I take for calculations and data arrangement is far shorter than the time my script takes to read excel data.
It would be pretty nice if pandas had a feature which would allow us to have iterator objects that iterate over certain row/column ranges. As I have stated in my stackoverflow thread, people would be able to make their own iterators if pandas had a method to return a single cell value. Or you could just pass a range of columns or rows to it and it could return a generator.
If these are already all there or there are methods for this kind of applications, or anything that can help me deal with large excel files, I would like to know. Thank you.
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} | 7 | 2015-09-11T16:38:44Z | 2015-11-15T21:09:40Z | 2015-11-15T21:09:28Z | NONE | null | This is the issue: https://github.com/pydata/pandas/issues/10407
I just added a bunch of try-catch AttributeError around the offending code - it seemed to do the trick for me.
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} | 3 | 2015-09-11T20:40:57Z | 2018-05-18T07:44:24Z | 2018-05-18T07:44:24Z | MEMBER | null | NaT and NaN get infilled in some strange way:
```
In [16]: pd.MultiIndex.from_product([pd.to_datetime(['2000-01-01', 'NaT', '2000-01-05'])])
Out[16]: DatetimeIndex(['2000-01-01', '2000-01-05', '2000-01-05'], dtype='datetime64[ns]', freq=None)
In [25]: pd.MultiIndex.from_product([[1.0, np.nan, 2.0]])
Out[25]: Float64Index([1.0, 2.0, 2.0], dtype='float64')
In [28]: pd.MultiIndex.from_product([[1.0, np.nan]])
Out[28]: Float64Index([1.0, 1.0], dtype='float64')
```
If the single array is all nulls, an error is raised:
```
In [26]: pd.MultiIndex.from_product([[np.nan, np.nan]])
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-26-a74dc8f094a4> in <module>()
----> 1 pd.MultiIndex.from_product([[np.nan, np.nan]])
/Users/shoyer/dev/pandas/pandas/core/index.py in from_product(cls, iterables, sortorder, names)
4708
4709 return MultiIndex(levels=[c.categories for c in categoricals],
-> 4710 labels=labels, sortorder=sortorder, names=names)
4711
4712 @property
/Users/shoyer/dev/pandas/pandas/core/index.py in __new__(cls, levels, labels, sortorder, names, copy, verify_integrity, _set_identity, name, **kwargs)
3898 else:
3899 name = None
-> 3900 return Index(levels[0], name=name, copy=True).take(labels[0])
3901
3902 result = object.__new__(MultiIndex)
/Users/shoyer/dev/pandas/pandas/core/index.py in take(self, indices, axis, allow_fill, fill_value)
1223
1224 indices = com._ensure_platform_int(indices)
-> 1225 taken = self.values.take(indices)
1226 return self._shallow_copy(taken)
1227
IndexError: cannot do a non-empty take from an empty axes.
```
The treatment of nulls seems to work correctly if the result has multiple levels.
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| null | 1 | 2015-09-11T22:33:26Z | 2015-09-11T23:05:56Z | 2015-09-11T23:00:48Z | CONTRIBUTOR | null | Adds missing static files that didn't get pushed in https://github.com/pydata/pandas/pull/10967
@jreback
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} | 1 | 2015-09-12T09:05:48Z | 2015-09-12T10:45:07Z | 2015-09-12T09:22:34Z | MEMBER | null | Reorganized release note for 0.17. There are some descriptions which looks the same level, but one in a highlights and the other is not.
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| null | 13 | 2015-09-12T10:14:21Z | 2016-01-08T23:59:46Z | 2015-11-20T18:44:08Z | MEMBER | null | Continued in #12002
---
Closes #6100
Closes #3800
Just to check how this takes on travis
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} | 4 | 2015-09-12T13:24:59Z | 2015-09-15T13:34:03Z | 2015-09-15T13:23:57Z | CONTRIBUTOR | null | I frequently find myself interacting with CSV files stored in Amazon's S3 service, and have run into a few areas where I think small improvements in `read_csv` could be a big help.
- [x] #11073 Allow streaming reads<br>
This is the most important improvement for me. The current pandas code downloads the entire file from S3 before passing it into the parser. If I have a 6 GB file in S3, it's much better to not need to download the entire thing just to check the first few rows with the "nrows" keyword to `read_csv`. Or perhaps I want to process the file one chunk at a time using "chunksize". We can iterate through a file on disk in these ways, but not currently with a file in S3.
- [x] #11074 Infer compression type from S3 filenames<br>
If an S3 filename ends with ".gz" or ".bz2", the parser should be able to infer the compression type, just as with a file on disk.
- [x] #11072 Streaming bz2 reads, C parser bz2 reads<br>
Currently, the C parser refuses open bz2-compressed file objects entirely, and the Python parser decompresses the entire file before continuing, which runs into the same problem with needing to read in a potentially large file before doing any work.
- [x] #11071 Recognize "s3n" EDIT: and "s3a"<br>
I've only run into this when using Spark, and I admit I don't fully understand the difference. It seems that S3 files can be accessed via "s3://" or "s3n://" ("S3 native"). It would be useful for pandas to recognize both. Some notes I found: https://wiki.apache.org/hadoop/AmazonS3 http://notes.mindprince.in/2014/08/01/difference-between-s3-block-and-s3-native-filesystem-on-hadoop.html
I will open PRs to address each of these.
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} | 8 | 2015-09-12T13:32:12Z | 2015-09-14T20:12:22Z | 2015-09-13T14:52:53Z | CONTRIBUTOR | null | This is the one modification related to issue #11070 which affects non-S3 interactions with `read_csv`. The Python 3 standard library has an improved capability for handling bz2 compression, so a simple change will let `read_csv` stream bz2-compressed files.
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} | 20 | 2015-09-12T13:51:09Z | 2015-09-14T22:32:55Z | 2015-09-14T22:24:49Z | CONTRIBUTOR | null | File reading from AWS S3: Modify the `get_filepath_or_buffer` function such that it only opens the connection to S3, rather than reading the entire file at once. This allows partial reads (e.g. through the `nrows` argument) or chunked reading (e.g. through the `chunksize` argument) without needing to download the entire file first.
I wasn't sure what the best place was to put the `OnceThroughKey`. (Suggestions for better names welcome.) I don't like putting an entire class inside a function like that, but this keeps the `boto` dependency contained.
The `readline` function, and modifying `next` such that it returns lines, was necessary to allow the Python engine to read uncompressed CSVs.
The Python 2 standard library's `gzip` module needs a `seek` and `tell` function on its inputs, so I reverted to the old behavior there.
Partially addresses #11070 .
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} | 11 | 2015-09-12T16:09:22Z | 2015-09-15T13:29:18Z | 2015-09-15T13:23:42Z | CONTRIBUTOR | null | When reading CSVs, if `compression='infer'`, check the input before calling `get_filepath_or_buffer` in the `_read` function. This way we can catch compresion extensions on S3 files. Partially resolves issue #11070 .
Checking for the file extension in the `_read` function should make the checks inside the parsers redundant. When I tried to remove them, however, I discovered that there's tests which assume the parsers can take an "infer" compression, so I left their checks.
I also discovered that the URL-reading code has a test which reads a URL ending in "gz" but which appears not to be gzip encoded, so this PR attempts to preserve its verdict in that case.
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} | 1 | 2015-09-12T16:21:30Z | 2015-09-13T19:10:41Z | 2015-09-13T19:10:41Z | CONTRIBUTOR | null | I don't really use SAS any more, but I've got some SAS -> pandas notes I was thinking of expanding to a doc section (like the R & SQL ones) as long as people don't think it's too out of scope for the pandas docs.
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| null | 1 | 2015-09-12T16:51:35Z | 2021-04-18T23:42:14Z | null | NONE | null | Info:
matplotlib (1.4.3)
pandas (0.16.2)
Python 3.4.2
Example:
```
dataframe.plot(secondary_y=['error'])
```
Yields:
The gridlines appear on top of the `power` data and there are no lines parallel to the y-axis.
Expected:
Which was obtained by the following code:
```
ax = dataframe.plot(y=['power'])
ax2 = dataframe.plot(y=['error'], secondary_y=True, mark_right=False, ax=ax)
ax.grid(True)
ax2.grid(False)
ax2.yaxis.set_major_locator(LinearLocator(len(ax.get_yticks())))
ax2.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: '{:0.2f}'.format(x)))
```
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} | 14 | 2015-09-12T18:04:09Z | 2015-09-14T22:09:01Z | 2015-09-14T22:09:01Z | MEMBER | null | The following code works in 0.16.2 and not in latest master:
``` python
data = pd.DataFrame(
[[100, 1, 'Alice'],
[200, 2, 'Bob'],
[300, 3, 'Charlie'],
[-400, 4, 'Dan'],
[500, 5, 'Edith']],
columns=['amount', 'id', 'name']
)
expected = data.groupby(['id', 'amount'])['name'].nunique()
```
Going to bisect this today unless someone beats me to it.
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} | 1 | 2015-09-12T20:52:47Z | 2015-10-14T10:56:26Z | 2015-10-14T10:56:26Z | CONTRIBUTOR | null | We don't keep the sortedness state in an index per-se, but it is rather cheap to check
- `is_monotonic_increasing` or `is_monotonic_decreasing` on a reg-index
- MultiIndex should check `is_lexsorted` (this might be done already)
```
In [8]: df = DataFrame(np.random.randn(1000000,2),columns=list('AB'))
In [9]: %timeit df.sort_index()
10 loops, best of 3: 37.1 ms per loop
In [10]: %timeit -n 1 -r 1 df.index.is_monotonic_increasing
1 loops, best of 1: 2.01 ms per loop
In [11]: %timeit -n 1 -r 1 df.index.is_monotonic_increasin^C
KeyboardInterrupt
In [11]: %timeit df.set_index('A').sort_index()
10 loops, best of 3: 175 ms per loop
In [12]: %timeit -n 1 -r 1 df.set_index('A').index.is_monotonic_increasing
1 loops, best of 1: 9.54 ms per loop
```
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} | 0 | 2015-09-12T22:46:57Z | 2015-09-12T23:36:12Z | 2015-09-12T23:36:12Z | CONTRIBUTOR | null | {
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} | 1 | 2015-09-12T22:50:09Z | 2016-04-10T14:22:42Z | 2016-04-10T14:22:42Z | MEMBER | null | ```
import numpy as np
import pandas as pd
s1 = pd.Series([1, 2, 3], name='x')
s2 = pd.Series(name='y')
# NG, no columns is added
pd.concat([s1, s2], axis=1)
# x
#0 1
#1 2
#2 3
# If padded by value, OK
pd.concat([s1, pd.Series([np.nan]*3, name='z')], axis=1)
# x z
#0 1 NaN
#1 2 NaN
#2 3 NaN
# If converted to frame, OK.
pd.concat([s1.to_frame(), s2.to_frame()], axis=1)
# x y
#0 1 NaN
#1 2 NaN
#2 3 NaN
```
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} | 1 | 2015-09-12T23:25:09Z | 2015-09-13T00:23:54Z | 2015-09-12T23:35:09Z | MEMBER | null | This was not building properly.
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} | 3 | 2015-09-13T00:08:24Z | 2020-01-01T16:02:30Z | 2020-01-01T15:55:38Z | CONTRIBUTOR | null | http://pydata.github.io/pandas/# is a view since 0.14 (its not every tag, but a sampling).
The regressions pages is now working [here](http://pydata.github.io/pandas/#regressions?sort=3&dir=desc)
- [ ] http://pydata.github.io/pandas/#groupby.groupby_indices.time_groupby_indices
- [ ] http://pydata.github.io/pandas/#frame_ctor.frame_ctor_nested_dict_int64.time_frame_ctor_nested_dict_int64 (#11158)
- [ ] http://pydata.github.io/pandas/#index_object.index_int64_intersection.time_index_int64_intersection
- [ ] http://pydata.github.io/pandas/#groupby.groupby_ngroups_100_cumprod.time_groupby_ngroups_100_cumprod
- [ ] http://pydata.github.io/pandas/#frame_methods.frame_get_dtype_counts.time_frame_get_dtype_counts
- [ ] http://pydata.github.io/pandas/#ctors.index_from_series_ctor.time_index_from_series_ctor
- [ ] http://pydata.github.io/pandas/#binary_ops.frame_multi_and_st.time_frame_multi_and_st
- [ ] http://pydata.github.io/pandas/#indexing.series_loc_list_like.time_series_loc_list_like
timeseries / period related:
- [x] http://pydata.github.io/pandas/#plotting.plot_timeseries_period.time_plot_timeseries_period (#11194)
- [ ] http://pydata.github.io/pandas/#timeseries.timeseries_iter_periodindex.time_timeseries_iter_periodindex
- [ ] http://pydata.github.io/pandas/#timeseries.period_setitem.time_period_setitem
- [ ] http://pydata.github.io/pandas/#timeseries.datetimeindex_normalize.time_datetimeindex_normalize
- [ ] http://pydata.github.io/pandas/#timeseries.dataframe_resample_max_string.time_dataframe_resample_max_string
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| null | 14 | 2015-09-13T00:28:26Z | 2019-02-14T05:23:07Z | 2015-09-14T02:01:07Z | CONTRIBUTOR | null | Fixed #10355
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} | 1 | 2015-09-13T10:52:27Z | 2015-09-21T02:35:53Z | 2015-09-21T02:35:53Z | CONTRIBUTOR | null | When creating the union of an index with itself, the frequency may end up being reset. I would have expected the call to union in the following example to be invariant and return the original index:
```
In [56]: index = pd.bdate_range('20150101', periods=10)
In [57]: index.freq = None
In [58]: index
Out[58]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-01-01, ..., 2015-01-14]
Length: 10, Freq: None, Timezone: None
In [59]: index.union(index)
Out[59]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-01-01, ..., 2015-01-14]
Length: 10, Freq: B, Timezone: None
```
This then propagates through to e.g. `combine_first`, which is also not invariant. This might be desirable behavior in some cases but definitely surprising imo.
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} | 11 | 2015-09-13T14:17:11Z | 2015-09-13T21:43:33Z | 2015-09-13T19:10:41Z | CONTRIBUTOR | null | closes #11075
xref #4052
There doesn't seem to be a straightforward way to syntax highlight the SAS code, so it will format as generic code blocks.
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} | 0 | 2015-09-13T17:23:53Z | 2016-08-31T12:47:53Z | 2016-08-31T12:47:53Z | CONTRIBUTOR | null | Certain BigQuery integration tests are being skipped because they require a project id.
Valid BigQuery credentials are required to properly test the gbq module on travis.
See PR #10857
```
---------------------------------------------------------------------------------------------------------------------------------------------------------
#18 pandas.io.tests.test_gbq.TestGBQConnectorIntegration.test_should_be_able_to_get_a_bigquery_service: Cannot run integration tests without a project id
#19 pandas.io.tests.test_gbq.TestGBQConnectorIntegration.test_should_be_able_to_get_results_from_query: Cannot run integration tests without a project id
#20 pandas.io.tests.test_gbq.TestGBQConnectorIntegration.test_should_be_able_to_get_schema_from_query: Cannot run integration tests without a project id
#21 pandas.io.tests.test_gbq.TestGBQConnectorIntegration.test_should_be_able_to_get_valid_credentials: Cannot run integration tests without a project id
#22 pandas.io.tests.test_gbq.TestGBQConnectorIntegration.test_should_be_able_to_make_a_connector: Cannot run integration tests without a project id
---------------------------------------------------------------------------------------------------------------------
#23 <nose.suite.ContextSuite context=TestReadGBQIntegration>:setup: Cannot run integration tests without a project id
#24 <nose.suite.ContextSuite context=TestToGBQIntegration>:setup: Cannot run integration tests without a project id
---------------------------------------------------------------------------------------------------------------
```
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} | 3 | 2015-09-13T19:09:12Z | 2015-09-14T19:51:21Z | 2015-09-14T19:51:21Z | CONTRIBUTOR | null | While working on another issue, I noticed the following error in the build test log on travis.
```
-------------------------------------------------------------
#15 nose.failure.Failure.runTest: need httplib2 and auth libs
-------------------------------------------------------------
```
I'm not familar with the ga module, but 'll try to fix it
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} | 3 | 2015-09-13T20:15:02Z | 2015-09-14T20:23:13Z | 2015-09-14T19:51:21Z | CONTRIBUTOR | null | closes #11090
This commit should resolve the following error in the travis build log.
```
-------------------------------------------------------------
#15 nose.failure.Failure.runTest: need httplib2 and auth libs
-------------------------------------------------------------
```
Valid Google credentials are required to run the ga unit tests.
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} | 1 | 2015-09-14T12:21:30Z | 2015-09-14T13:10:51Z | 2015-09-14T13:10:48Z | MEMBER | null | Updated ["enhancing performance"](http://pandas.pydata.org/pandas-docs/stable/enhancingperf.html#enhancingperf-eval) doc based on #10953.
CC @sklam
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} | 3 | 2015-09-14T15:25:50Z | 2015-09-16T23:02:45Z | 2015-09-16T23:02:08Z | CONTRIBUTOR | null | The current test for `test_to_html_unicode` only checks for whether the function works but does not check for its correctness.
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} | 0 | 2015-09-14T19:41:10Z | 2015-09-27T14:10:24Z | 2015-09-27T14:10:24Z | CONTRIBUTOR | null | xref #10572
cc @parthea
now that the testing infrastructure is more sound, I think this only requires a newish version of the apiclient (e.g. > 1.4)
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} | 12 | 2015-09-14T20:27:25Z | 2015-09-21T15:33:55Z | 2015-09-21T15:32:17Z | CONTRIBUTOR | null | Fixed #7692
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} | 1 | 2015-09-14T22:33:24Z | 2015-09-15T00:43:33Z | 2015-09-15T00:43:33Z | CONTRIBUTOR | null | xref https://github.com/numpy/numpy/issues/4464
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} | 0 | 2015-09-14T22:39:53Z | 2015-09-15T23:25:24Z | 2015-09-15T23:25:24Z | CONTRIBUTOR | null | A few changes are needed: https://travis-ci.org/jreback/pandas/jobs/80331174 (since merged to master)
so the Call node as changed
need to do something like this: https://bitbucket.org/pytest-dev/pytest/pull-requests/296/astcall-signature-changed-on-35/diff
If this is fixed I think everything will pass. I guess this is an API change in python.
```
======================================================================
ERROR: test_df_use_case (pandas.computation.tests.test_eval.TestMathNumExprPandas)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/computation/tests/test_eval.py", line 1488, in test_df_use_case
parser=self.parser)
File "/home/travis/build/jreback/pandas/pandas/core/frame.py", line 2102, in eval
return _eval(expr, **kwargs)
File "/home/travis/build/jreback/pandas/pandas/computation/eval.py", line 230, in eval
truediv=truediv)
File "/home/travis/build/jreback/pandas/pandas/computation/expr.py", line 656, in __init__
self.terms = self.parse()
File "/home/travis/build/jreback/pandas/pandas/computation/expr.py", line 673, in parse
return self._visitor.visit(self.expr)
File "/home/travis/build/jreback/pandas/pandas/computation/expr.py", line 314, in visit
return visitor(node, **kwargs)
File "/home/travis/build/jreback/pandas/pandas/computation/expr.py", line 320, in visit_Module
return self.visit(expr, **kwargs)
File "/home/travis/build/jreback/pandas/pandas/computation/expr.py", line 314, in visit
return visitor(node, **kwargs)
File "/home/travis/build/jreback/pandas/pandas/computation/expr.py", line 498, in visit_Assign
return self.visit(node.value, **kwargs)
File "/home/travis/build/jreback/pandas/pandas/computation/expr.py", line 314, in visit
return visitor(node, **kwargs)
File "/home/travis/build/jreback/pandas/pandas/computation/expr.py", line 543, in visit_Call
args = [self.visit(targ) for targ in node.args]
File "/home/travis/build/jreback/pandas/pandas/computation/expr.py", line 543, in <listcomp>
args = [self.visit(targ) for targ in node.args]
File "/home/travis/build/jreback/pandas/pandas/computation/expr.py", line 314, in visit
return visitor(node, **kwargs)
File "/home/travis/build/jreback/pandas/pandas/computation/expr.py", line 545, in visit_Call
if node.starargs is not None:
AttributeError: 'Call' object has no attribute 'starargs'
```
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} | 5 | 2015-09-14T23:54:06Z | 2015-09-17T17:34:49Z | 2015-09-17T17:27:23Z | CONTRIBUTOR | null | Some doc-strings are outdated, in a sense that there are missing newly added optional parameters and default values.
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| null | 12 | 2015-09-15T02:29:11Z | 2015-10-11T15:43:28Z | 2015-10-11T15:43:28Z | CONTRIBUTOR | null | Added support for BigQuery's new user defined function (UDF) feature. See https://cloud.google.com/bigquery/user-defined-functions#api for details.
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| null | 3 | 2015-09-15T06:25:42Z | 2020-04-30T05:49:21Z | 2020-04-30T05:49:21Z | NONE | null | Is there a way to pass a list or dict of colors to a pandas barplot with subplot=True? The colors are used per plot instead of colors for individual bars. I would like to plot bar plots that have a different color per bar.
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| null | 1 | 2015-09-15T08:47:46Z | 2015-09-15T10:18:44Z | 2015-09-15T10:18:44Z | CONTRIBUTOR | null | ``` python
>>> import pandas as pd
>>> pd.__version__
u'0.17.0rc1'
>>> df = pd.DataFrame({'A': [0, 0, 1, 1], 'B': range(4)})
>>> df.groupby('A').count()['B']
A
0 2
1 2
Name: B, dtype: int64
>>> df.groupby('A')['B'].count()
Traceback (most recent call last):
...
TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'
```
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} | 23 | 2015-09-15T12:38:20Z | 2015-10-03T22:24:44Z | 2015-10-03T14:41:03Z | MEMBER | null | Closes #2612. Added `display.unicode.east_asian_width` options, which calculate text width considering East Asian Width. Enabling this option affects to a performance as width must be calculated per characters.
#### Current results (captured)
- [x] Basic impl and test
- [x] Series / DataFrame truncation
- [x] Perf test
- [x] Doc / Release note
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