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| 200,082,872 |
MDU6SXNzdWUyMDAwODI4NzI=
| 15,108 |
boxplot fontsize argument only works on x axis (and not on y axis)
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| 2 | 2017-01-11T12:40:22Z | 2017-01-20T17:12:58Z | 2017-01-20T17:12:58Z |
CONTRIBUTOR
| null |
#### Code Sample
```python
import pandas as pd
df = pd.DataFrame({"a": [0, 0, 0, 1, 1, 1], "b": [1, 2, 3, 4, 5, 6]})
df.boxplot("b", by="a", fontsize=20)
```
#### Problem description
The fontsize is only applied to the x axis and not to the y axis tick labels (see figure).

#### Expected Output
IMO this argument should change the font size of both axes tick labels. Side note: I've tried to use the new plot API, but I couldn't get the output to match the one shown above using `df.plot.box` (I couldn't figure out how to set the variable to plot in the first place - probably this only works on Series and not on DataFrames).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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MDU6SXNzdWUyMDAxMTMxNjU=
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BUG: groupby.cummin/max changing passed values to nan uncesessarily
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| 3 | 2017-01-11T14:50:40Z | 2017-01-25T19:05:23Z | 2017-01-25T19:05:22Z |
CONTRIBUTOR
| null |
after #15053 is failing. (puzzled why its not actually failing on master though).
This is local in osx (3.5), so same as the travis test (though that used numpy 1.10.4), its possible that is the cause.
```
(pandas) bash-3.2$ nosetests pandas/tests/groupby/test_groupby.py -s -m cummin_cummax
F
======================================================================
FAIL: test_cummin_cummax (test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/pandas/pandas/tests/groupby/test_groupby.py", line 5798, in test_cummin_cummax
tm.assert_frame_equal(result, expected)
File "/Users/jreback/pandas/pandas/util/testing.py", line 1313, in assert_frame_equal
obj='DataFrame.iloc[:, {0}]'.format(i))
File "/Users/jreback/pandas/pandas/util/testing.py", line 1154, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "/Users/jreback/pandas/pandas/util/testing.py", line 878, in assert_attr_equal
left_attr, right_attr)
File "/Users/jreback/pandas/pandas/util/testing.py", line 1018, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Attributes are different
Attribute "dtype" are different
[left]: float64
[right]: int64
----------------------------------------------------------------------
Ran 1 test in 0.046s
FAILED (failures=1)
```
because the inf values are == iNaT, and we convert these on exit from the function as we are using these to mark NaT in all int64, not just for datetimelike.
https://github.com/jreback/pandas/commit/2af580638483bce8ca35d32239d28997bdc230c2 fixes, but is kludgey. I pre-check for iNaT an if so, convert to float.
A better soln is to pass in the ``is_datetimelike`` flag so that the check could be done internally in cython. IOW, if is_datetimelike==True, then go ahead and treat iNaT as null, otherwise ignore it. Ideally we would actually be able to return a correctly dtyped array in the first place, but the templating code won't allow this, e.g. we have a 1-1 mapping between in-out and they are done a-priori.
part of this fix could also remove the need for passing in ``accum`` as its wholly internal to the group by cython functions (as part of this could remove the fused typed, ``numeric`` and replace with templated code).
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BUG: Fix for GH #14848 for groupby().describe() with tuples as the Index
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| 8 | 2017-01-11T15:17:59Z | 2017-01-13T17:38:20Z | 2017-01-13T17:38:20Z |
CONTRIBUTOR
| null |
- [x] closes #14848
- [x ] tests added / passed
- [x ] passes ``git diff upstream/master | flake8 --diff``
- [x ] whatsnew entry
This isn't the most elegant fix for the bug, but all the tests pass. There is probably a bigger issue to deal with here, which is to support tuples as an `Index` (as opposed to a `MultiIndex`) throughout, but at least this fix clears the bug.
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ENH: Dropping outliers
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[] | null | 20 | 2017-01-11T17:00:56Z | 2020-06-07T10:13:10Z | 2020-06-07T10:13:10Z |
CONTRIBUTOR
| null |
Create a new function to remove outliers.
http://stackoverflow.com/questions/23199796/detect-and-exclude-outliers-in-pandas-dataframe
I find myself using the code from SO quite often to remove outliers in a particular column when preprocessing data and it seems this is a common issue. It would be nice to have a function that operates on a Series to do this automatically.
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(np.random.randn(100, 3))
# from SO answer by tanemaki
from scipy import stats
df[(np.abs(stats.zscore(df)) < 3).all(axis=1)]
# instead, would prefer
df.drop_outliers(3)
```
Alternatively, instead of a new function, we could modify .clip(), though I think a new function makes more sense.
The implementation could be similar to the SO implementation.
If there is agreement that this would be useful and the implementation makes sense, happy to do the PR.
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| 200,173,048 |
MDU6SXNzdWUyMDAxNzMwNDg=
| 15,112 |
Aggregate with `pd.Series.nunique` in datetime column has weird result
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| 1 | 2017-01-11T18:36:54Z | 2017-01-11T18:43:30Z | 2017-01-11T18:43:11Z |
NONE
| null |
I have `DataFrame`:
import pandas as pd
import datetime as datetime
a = pd.DataFrame({'id': [1, 2, 3, 2],
'my_date': [datetime.datetime(2017, 1, i) for i in range(1, 4)] + [datetime.datetime(2017, 1, 1)],
'num': [2, 3, 1, 4]
})
print (a.dtypes)
id int64
my_date datetime64[ns]
num int64
dtype: object
I try aggreagate by `pd.Series.nunique`, but get wrong output for datetimes:
grouped_a = a.groupby('id').agg({'my_date': pd.Series.nunique,
'num': pd.Series.nunique}).reset_index()
grouped_a.columns = ['id', 'num_unique_num', 'num_unique_my_date']
print (grouped_a)
id num_unique_num num_unique_my_date
0 1 1 1970-01-01 00:00:00.000000001
1 2 2 1970-01-01 00:00:00.000000002
2 3 1 1970-01-01 00:00:00.000000001
My solution is use `nunique` which works nice.
grouped_a = a.groupby('id').agg({'my_date': 'nunique',
'num': 'nunique'}).reset_index()
grouped_a.columns = ['id', 'num_unique_num', 'num_unique_my_date']
print (grouped_a)
id num_unique_num num_unique_my_date
0 1 1 1
1 2 2 2
2 3 1 1
But why does not work first solution? Bug?
[SO question](http://stackoverflow.com/questions/41597179/pandas-counting-unique-datetime-values-in-group-by-gives-weird-values).
print (pd.show_versions())
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: sk_SK
LOCALE: None.None
pandas: 0.19.2+0.g825876c.dirty
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
None
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| 15,113 |
DEPR: deprecate .ix in favor of .loc/.iloc
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| 15 | 2017-01-12T01:02:27Z | 2017-01-18T16:12:01Z | 2017-01-18T16:12:01Z |
CONTRIBUTOR
| null |
closes #14218
closes #15116
This shows a pretty big deprecation message, though its instructive.
```
In [1]: df = pd.DataFrame({'A': [1, 2, 3],
...: 'B': [4, 5, 6]},
...: index=list('abc'))
...: df
...:
Out[1]:
A B
a 1 4
b 2 5
c 3 6
In [2]: df.ix[[0, 2], 'A']
/Users/jreback/miniconda3/envs/pandas/bin/ipython:1: FutureWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing
You an do multi-axes indexing like the following
>>> df = pd.DataFrame({'A': [1, 2, 3],
'B': [4, 5, 6]},
index=list('abc'))
>>> df
A B
a 1 4
b 2 5
c 3 6
From prior versions
>>> df.ix[[0, 2], 'A']
a 1
c 3
Name: A, dtype: int64
Using .loc
>>> df.loc[df.index[[0, 2]], 'A']
a 1
c 3
Name: A, dtype: int64
Using .iloc
>>> df.iloc[[0, 2]], df.columns.get_loc('A')]
a 1
c 3
Name: A, dtype: int64
```
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MDExOlB1bGxSZXF1ZXN0MTAxMTk4OTM3
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TST: Add another test for uneven CSV rows
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| 11 | 2017-01-12T05:30:30Z | 2017-01-12T22:38:24Z | 2017-01-12T22:33:49Z |
MEMBER
| null |
Title is self-explanatory.
xref #15066.
Closes #9549.
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BUG: Comparisons result in different dtypes for empty DataFrames #15077
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| 5 | 2017-01-12T09:50:47Z | 2017-01-21T15:32:29Z | 2017-01-21T15:32:28Z |
CONTRIBUTOR
| null |
- [x] closes #15077
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
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Reindex versus ix gotchas documentation text does not match example code
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| 2 | 2017-01-12T14:20:02Z | 2017-01-18T16:12:01Z | 2017-01-18T16:12:00Z |
NONE
| null |
In the gotchas documentation, in the section "Reindex versus ix gotchas", there is the following example:
```python
In [28]: s = pd.Series([1, 2, 3], index=['a', 0, 1])
In [29]: s
Out[29]:
a 1
0 2
1 3
dtype: int64
In [30]: s.ix[[0, 1]]
Out[30]:
0 2
1 3
dtype: int64
In [31]: s.reindex([0, 1])
Out[31]:
0 2
1 3
dtype: int64
```
The example shows both `ix` and `reindex` behaving the same. The subsequent text, however, states that they are expected to give different results:
> Because the index in this case does not contain solely integers, `ix` falls back on integer indexing. By contrast, `reindex` only looks for the values passed in the index, thus finding the integers `0` and `1`.
I am not sure if the indexing behaviour is correct and the documentation is wrong, or vice versa. It appears that in this case `ix` does not fall back on integer indexing while the text states that it does.
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| 200,388,019 |
MDExOlB1bGxSZXF1ZXN0MTAxMjc0Mjk0
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BUG: Fix to_json lines with escaped characters
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| 8 | 2017-01-12T14:56:47Z | 2017-01-13T20:25:17Z | 2017-01-13T20:10:08Z |
CONTRIBUTOR
| null |
Updates existing to_json methodology by adding is_escaping variable,
which ensures escaped chars are handled correctly.
Bug description: A simple check of whether the prior char is a backslash
is insufficient because the backslash may itself be escaped.
A test is also included (previously included in #14693).
xref #14693
xref #15096
- [x] closes #15096 (#14693 was previously closed by author)
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff`` (used ``pep8radius master --diff`` instead)
- [x] whatsnew entry (does not seem necessary, but let me know if I should add)
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MDU6SXNzdWUyMDAzOTc4MDM=
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API: Return type of DataFrame.asof
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| 2 | 2017-01-12T15:32:34Z | 2017-01-26T12:37:53Z | 2017-01-26T12:37:53Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
In [135]: df = pd.DataFrame({'a': range(10), 'b': range(10)},
index=pd.date_range('2014-01-01', periods=10))
In [136]: df.asof('2014-01-15')
Out[136]:
a 9
b 9
Name: 2014-01-10 00:00:00, dtype: int32
In [137]: df.asof('2013-01-15')
Out[137]: nan
```
#### Problem description
I got tripped up by `[137]` returning a scalar - it makes sense for a `Series`, but not sure it does with a `DataFrame` lookup? So maybe it should instead return an empty Series, e.g.
```
In [138]: df.asof('2013-01-15')
Out[138]:
a NaN
b NaN
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.3.1
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MDU6SXNzdWUyMDA0MTczNzk=
| 15,119 |
Inconsistent parsing for timestamp with timezone with read_sql_query
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| 6 | 2017-01-12T16:40:11Z | 2018-12-31T14:53:40Z | 2018-12-31T14:53:40Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
I'm creating a test table in postgresql as follows:
```sql
CREATE TABLE test(date timestamptz);
INSERT INTO test VALUES('2014-10-16 07:50:49-04'),('2014-11-06 07:42:06-05')
```
Notice that the dates are across the dst date, so that they have different offsets to utc.
```python
df1 = pd.read_sql_query("SELECT date FROM test", engine,
index_col='date')
df2 = pd.read_sql_query("SELECT date FROM test WHERE date<='2014-10-17'",
engine,
index_col='date')
df2bis = pd.read_sql_query("SELECT date FROM test WHERE date<='2014-10-17'",
engine,
index_col='date',
parse_dates=['date'])
df3 = pd.read_sql_query("SELECT date FROM test", engine,
index_col='date',
parse_dates=['date'])
```
#### Problem description
```
In [159]: df1.index
Out[159]:
Index([2014-10-16 07:50:49-04:00, 2014-11-06 07:42:06-05:00], dtype='object', name='date')
In [160]: df2.index
Out[160]:
DatetimeIndex(['2014-10-16 11:50:49+00:00'], dtype='datetime64[ns, UTC]', name='date', freq=None)
In [162]: df2bis.index
Out[162]:
DatetimeIndex(['2014-10-16 11:50:49+00:00'], dtype='datetime64[ns, UTC]', name='date', freq=None)
In [163]: df3.index
Out[163]:
DatetimeIndex(['2014-10-16 11:50:49', '2014-11-06 12:42:06'], dtype='datetime64[ns]', name='date', freq=None)
```
#### Expected Output
The current behavior if I understand correctly is that if there is only one timezone offset: timestamps are parsed automatically to timezone aware timestamps.
If dates are across different offsets, they are not parsed automatically. We can force parsing using parse_dates, but then they are not timezone aware. I can make the conversion using tz_localize, but it's not very consistent. I would expect to have everything parsed automatically to timezone aware timestamps.
This is related to issue #7364. If there is a consensus on what should be done, I can try to make the required changes to make it more regular.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.13-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.utf8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 33.0.0
Cython: 0.25.2
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0rc2
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.5
sqlalchemy: 1.1.4
pymysql: 0.7.9.None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
boto: None
pandas_datareader: None
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BUG: indexing changes to .loc for compat to .ix for several situations
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| 1 | 2017-01-12T20:31:40Z | 2017-01-12T22:43:12Z | 2017-01-12T22:43:12Z |
CONTRIBUTOR
| null |
handle iterator
handle NamedTuple
.loc retuns scalar selection dtypes correctly, closes #11617
xref #15113
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Fix typo in docstring
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| 1 | 2017-01-12T21:53:39Z | 2017-01-12T22:11:11Z | 2017-01-12T22:10:59Z |
CONTRIBUTOR
| null |
it -> int
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| 6 | 2017-01-12T22:56:08Z | 2021-05-28T16:58:21Z | 2021-05-28T16:58:21Z |
MEMBER
| null |
Currently `read_csv` has some ways to deal with "bad lines" (bad in the sense of too many or too few fields compared to the determined number of columns):
- by default, it will error for too many fields, and fill with NaNs for too few fields
- with `error_bad_lines=false` rows with too many fields will be dropped instead of raising an error (and in that case, `warn_bad_lines` controls to get a warning or not)
- with `usecols` you can select certain columns, and in this way deal with rows with too many fields.
Some possibilities are missing in this scheme:
- "process" bad lines with too many fields, i.e. drop the excessive fields instead of either raising an error or dropping the full row (discussed in #9549)
- getting a warning or error with too few fields instead of automatically filling with NaNs (asked for in #9729), or dropping those rows
Apart from that, https://github.com/pandas-dev/pandas/issues/5686 makes the request to be able to specify a custom function to process a bad line, to have even more control.
In https://github.com/pandas-dev/pandas/issues/9549#issuecomment-76498787 (and surrounding comments) there was some discussion about how to integrate this, and some idea from there from @jreback and @selasley:
Provide more fine grained control in a new keyword (and deprecate `error_bad_lines`):
```
bad_lines='error'|'warn'|'skip'|'process'
```
or leave out `'warn'` and keep `warn_bad_lines` to be able to combine a warning with both 'skip' and 'process'.
We should further think about whether we can integrate this with the case of too few fields and not only too many.
I think it would be nice to have some better control here, but we should think a bit about the best API for this.
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Why does it take so much time to retrieve one row?
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| 2 | 2017-01-13T12:49:34Z | 2017-01-13T13:40:33Z | 2017-01-13T13:40:17Z |
NONE
| null |
```python
# data.csv contains 10000*10 records which is depicted as follows:
# 1,1,1,1,1,1,1,1,1,1
# 2,2,2,2,2,2,2,2,2,2
# ...
# 10000,10000,10000,10000,10000,10000,10000,10000,10000,10000
import pandas as pd
import numpy as np
import time
data = pd.read_csv('data.csv')
data_matrix = data.as_matrix()
s = time.time()
for _ in range(10000):
v = data.iloc[_]
e = time.time()
print('Performance with DataFrame:'+str(e-s))
s = time.time()
for _ in range(10000):
v = data_matrix[_]
e = time.time()
print('Performance with Array: '+str(e-s))
#result
#performance with DataFrame:3.964857816696167
#performance with array: 0.015623092651367188
```
#### Problem description
As is shown in the coding above,locating an element while given the index takes much much much longer in the DataFrame than in a raw array.
In a common sense,it hardly takes any time to locate an element in an raw array or list as the index of that element is given and there is no reason to use a sequential search.
However,comparing to the array,the performance is quite lower while retrieving a specific row in a DataFrame.The row-index is already given,the DataFrame should be able to locate the row directly,could it be said that the DataFrame actually start a sequential search?
Any alternative methods that works like locating an element in an array?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 37 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 32.0.0
Cython: None
numpy: 1.10.2
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
#
</details>
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BUG: pandas.Series.dt.round inconsistent behaviour on NAT's with different arguments
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| 1 | 2017-01-13T15:09:45Z | 2017-01-14T18:24:39Z | 2017-01-14T18:24:27Z |
CONTRIBUTOR
| null |
- [ ] closes #14940
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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| 2 | 2017-01-13T16:40:13Z | 2017-01-15T16:11:15Z | 2017-01-15T16:11:15Z |
CONTRIBUTOR
| null |
#### Code Sample
```python
>>> %paste
import pandas as pd
x = pd.Series([1.0])
y = pd.Series(x, copy=True)
z = pd.Series(x, copy=True, dtype=float)
x[0] = 2.0
print(x[0])
print(y[0])
print(z[0])
## -- End pasted text --
2.0
1.0
2.0
```
#### Problem description
Constructing a Series with copy=True should return a copy of the original, but when the dtype argument agrees with the source dtype, no copy is performed.
#### Expected Output
```python
2.0
1.0
1.0
```
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.13-100.fc21.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTAxNDk5NjI4
| 15,126 |
BUG: catch overflow in timestamp + offset operations
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| 1 | 2017-01-13T19:08:46Z | 2017-01-14T17:02:20Z | 2017-01-14T17:02:20Z |
CONTRIBUTOR
| null |
xref https://github.com/statsmodels/statsmodels/issues/3374
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TST: tests.groupby needs to be a package to run tests
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| 2 | 2017-01-13T19:34:21Z | 2017-01-13T20:21:22Z | 2017-01-13T20:21:22Z |
CONTRIBUTOR
| null |
skip parts of cummin test, xref #15109
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BUG: Fix Series constructor when copy=True
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| 4 | 2017-01-13T19:52:18Z | 2017-01-15T16:11:16Z | 2017-01-15T16:11:15Z |
CONTRIBUTOR
| null |
Updates Series constructor to include copy argument when dtype argument
is also provided. Adds tests for copy parameter.
xref #15125
- [x] closes #15125
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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| 15,129 |
ENH: Added 'direction' parameter to merge_asof() (#14887)
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| 4 | 2017-01-13T20:57:14Z | 2017-01-18T16:08:10Z | 2017-01-18T16:08:10Z |
CONTRIBUTOR
| null |
- [x] closes #14887
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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ERR: too strict validation on groupby.rolling with time-aware freq
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| 0 | 2017-01-13T21:23:22Z | 2017-01-20T12:21:53Z | 2017-01-20T12:21:53Z |
CONTRIBUTOR
| null |
http://stackoverflow.com/questions/41642320/efficient-pandas-rolling-aggregation-over-date-range-by-group-python-2-7-windo/41643179?noredirect=1#comment70486923_41643179
```
In [1]: data = [
...: ['David', '1/1/2015', 100], ['David', '1/5/2015', 500], ['David', '5/30/2015', 50], ['David', '7/25/2015', 50],
...: ['Ryan', '1/4/2014', 100], ['Ryan', '1/19/2015', 500], ['Ryan', '3/31/2016', 50],
...: ['Joe', '7/1/2015', 100], ['Joe', '9/9/2015', 500], ['Joe', '10/15/2015', 50]
...: ]
...:
...: list_of_vals = []
...:
...: dates_df = pd.DataFrame(data=data, columns=['name', 'date', 'amount'], index=None)
...: dates_df['date'] = pd.to_datetime(dates_df['date'])
...:
```
This check doesn't need to occur when we are grouping
```
In [7]: dates_df.groupby('name').rolling('180D', on='date')['amount'].sum()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-7-8896cb99a66a> in <module>()
----> 1 dates_df.groupby('name').rolling('180D', on='date')['amount'].sum()
/Users/jreback/pandas/pandas/core/groupby.py in rolling(self, *args, **kwargs)
1148 """
1149 from pandas.core.window import RollingGroupby
-> 1150 return RollingGroupby(self, *args, **kwargs)
1151
1152 @Substitution(name='groupby')
/Users/jreback/pandas/pandas/core/window.py in __init__(self, obj, *args, **kwargs)
635 self._groupby.mutated = True
636 self._groupby.grouper.mutated = True
--> 637 super(GroupByMixin, self).__init__(obj, *args, **kwargs)
638
639 count = GroupByMixin._dispatch('count')
/Users/jreback/pandas/pandas/core/window.py in __init__(self, obj, window, min_periods, freq, center, win_type, axis, on, **kwargs)
76 self.win_type = win_type
77 self.axis = obj._get_axis_number(axis) if axis is not None else None
---> 78 self.validate()
79
80 @property
/Users/jreback/pandas/pandas/core/window.py in validate(self)
1030 formatted = self.on or 'index'
1031 raise ValueError("{0} must be "
-> 1032 "monotonic".format(formatted))
1033
1034 from pandas.tseries.frequencies import to_offset
ValueError: date must be monotonic
```
This is ok
```
In [9]: dates_df.groupby('name').apply(lambda x: x.rolling('180D', on='date')['amount'].sum())
Out[9]:
name
David 0 100.0
1 600.0
2 650.0
3 100.0
Joe 7 100.0
8 600.0
9 650.0
Ryan 4 100.0
5 500.0
6 50.0
Name: amount, dtype: float64
```
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MDU6SXNzdWUyMDA4MDYyNjM=
| 15,131 |
Pandas contains shebang pointing to /usr/bin/python which are problematic
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| 4 | 2017-01-14T14:17:13Z | 2017-01-15T16:09:21Z | 2017-01-15T16:09:02Z |
CONTRIBUTOR
| null |
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
We are packaging pandas pre-installed in a virtualenv using a rpm. RPM has a "convenient" feature to automatically scan for script files with shebang and adding those as requirements of the resulting rpm. For more information, see [this blog post](http://www.legendiary.at/2013/03/20/rpmbuild-shebang-auto-required-packages/).
Most of the pandas scripts use
````bash
#!/usr/bin/env python
````
which rpm resolves to whatever is in the PATH.
However, some of the packages point to hard-coded path of python:
````bash
$ grep '^#!' **/*.py|grep -v '/usr/bin/env python'
pandas/tests/test_config.py:#!/usr/bin/python
pandas/tests/test_testing.py:#!/usr/bin/python
````
#### Proposed solution
I would propose that the above scripts would be changed to use `#!/usr/bin/env python`.
This change would make the rpm process resolve python and bash using whatever is in the path. For example, with CentOS 6, the system python (in `/usr/bin/python`) is python 2.6, although the `python` in PATH could very well be `python2.7` or newer. In our case the virtualenv is activated during the build, and thus `#!/usr/bin/env python` would resolve to the correct interpreter.
The additional benefit is that the scripts are more cross-platform.
I'm willing to submit a pull requests if you agree with the fix?
Numpy issue regarding the same matter: https://github.com/numpy/numpy/issues/8472
Scipy issue regarding the same matter: https://github.com/scipy/scipy/issues/6961
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| 7 | 2017-01-14T22:02:23Z | 2017-01-19T13:54:35Z | 2017-01-19T13:54:35Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
{"errors":["This check-in does not exist, it may have been deleted."]},
{"list":{"id":487004,"description":"foo.”\r\n\r\n* “I am aware that I’m drafting an email responding to a complaint.”\r\n\r\n* “I am aware that I’m wondering who will win.”\r\n\r\nThe great thing about this exercise is that it is generalizable. You practice during the meditation, but then you use it for your own goals during your day to day."..........}
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 32.3.1
Cython: None
numpy: 1.11.3
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
```
#### Problem description
List has quotes inside the json data. Expected to read this data line by line, but we get a UnicodeDecodeError at the position of that inner quote in the description
#### Expected Output
read successful
#### Output:
Traceback (most recent call last):
data = pd.read_json(fileName, lines=True)
File "/usr/local/lib/python2.7/site-packages/pandas/io/json.py", line 275, in r
ead_json
json = u'[' + u','.join(lines) + u']'
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 4924: ordina
l not in range(128)
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| 200,866,138 |
MDU6SXNzdWUyMDA4NjYxMzg=
| 15,133 |
Some problems with list in read_excel()
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[] | null | 10 | 2017-01-15T12:32:06Z | 2021-05-08T01:07:20Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
train = read_excel(
'Wilson_Schistosome Esophagus_differentially expressed transcripts.xlsx',
sheetname=3, header=[0, 1], skip_footer=11)
```
OK
```python
train = read_excel(
'Wilson_Schistosome Esophagus_differentially expressed transcripts.xlsx',
sheetname=3, header=[0, 1], skip_footer=11, names=[0] + list(range(2, 12)))
```
ValueError: Length of new names must be 1, got 2
```python
train = read_excel('Wilson_Schistosome Esophagus_differentially expressed transcripts.xlsx',
sheetname=2, header=[0, 1], skip_footer=6)
```
ValueError: Length of new names must be 1, got 2
```python
train = read_excel('Wilson_Schistosome Esophagus_differentially expressed transcripts.xlsx',
sheetname=[1, 3], header=[0, 1], skip_footer=3)
```
ValueError: Length of new names must be 1, got 2
#### Problem description
I am trying to read excel file ( https://github.com/ArtyomKaltovich/ikantam/blob/master/lab1/Wilson_Schistosome%20Esophagus_differentially%20expressed%20transcripts.xlsx ). But sometimes pandas does not accept list as parameters for read_excel.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-21-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 32.3.1
Cython: None
numpy: 1.11.3
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTAxNTk0Njc0
| 15,134 |
MAINT: remove shebang from test modules
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| 2 | 2017-01-15T12:51:44Z | 2017-07-11T10:06:15Z | 2017-01-15T16:09:03Z |
CONTRIBUTOR
| null |
- [x] closes #15131
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| 15,135 |
merge_asof fails when using left_index/right_index with a tolerance parameter
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| 5 | 2017-01-15T12:53:52Z | 2017-01-17T13:43:35Z | 2017-01-17T13:43:35Z |
CONTRIBUTOR
| null |
#### Problem description
Using the DataFrames *trades* and *quotes* from the `merge_asof` [documentation](http://pandas.pydata.org/pandas-docs/version/0.19.0/generated/pandas.merge_asof.html) as an example, there's an issue when using `merge_asof` to merge on indexes using `left_index`/`right_index` (rather than column names) and additionally specifying a tolerance parameter.
Admittedly, the `left_index`/`right_index` parameters are not documented for this function (in the prototype description), but they are still valid parameters (and have an example in the docs). It could be seen as inconsistent that they sometimes work as expected and sometimes raise a cryptic error message.
For example, first set the 'time' column of each DataFrame as the index:
```python
>>> quotes = quotes.set_index('time')
>>> trades = trades.set_index('time')
```
Then `pd.merge_asof(trades, quotes, left_index=True, right_index=True, by='ticker')` works exactly as `pd.asof_merge(trades, quotes, on='time', by='ticker')` (as expected).
However, `pd.merge_asof(trades, quotes, left_index=True, right_index=True, by='ticker', tolerance=pd.Timedelta('2ms'))` (specifying a tolerance) raises an error:
```
/.../anaconda3/lib/python3.4/site-packages/pandas/tools/merge.py in _get_merge_keys(self)
1147
1148 else:
-> 1149 raise MergeError("key must be integer or timestamp")
1150
1151 # validate allow_exact_matches
MergeError: key must be integer or timestamp
```
I would have expected it work exactly as `pd.merge_asof(trades, quotes, on='time', by='ticker', tolerance=pd.Timedelta('2ms'))` works. Failing this, an error telling me that I cannot use a tolerance when merging on indexes would be welcome.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.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_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.19.2
nose: 1.3.4
pip: 9.0.1
setuptools: 31.0.1.post20161215
Cython: 0.23.4
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 5.1.0
sphinx: 1.2.3
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.6.7
lxml: 3.6.4
bs4: 4.3.2
html5lib: 0.99999
httplib2: None
apiclient: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.36.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTAxNTk1MTAy
| 15,136 |
ENH: Timedelta isoformat
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| 16 | 2017-01-15T13:08:17Z | 2017-04-05T02:07:21Z | 2017-01-20T12:25:37Z |
CONTRIBUTOR
| null |
Split from https://github.com/pandas-dev/pandas/pull/14904
A few questions before I finish all the docs / tests:
The [spec](https://en.wikipedia.org/wiki/ISO_8601#Durations) seems very permissive about what's allowed, so we need to choose
- Do we include components pandas can't represent like Years and Months with 0 `(P0Y0M...)` (no)
- Do we include components whose value is 0, e.g. `P0DT...` vs. `PT...` (yes)
- Do we 0 pad units, e.g. `...T5H` vs `T05H` (no)
- Do we strip trailing 0s in the seconds decimal: `...6.000000000S` vs. `6S` (yes)
We probably want a reader for this format as well, maybe in the Timedelta constructor. Not sure if I'll have time to get to that before 0.20 . I'd rather prioritize the JSON table schema PR.
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Timedelta to_json with date_format='iso'
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| 7 | 2017-01-15T15:56:56Z | 2020-05-03T21:13:54Z | 2020-05-03T21:13:53Z |
CONTRIBUTOR
| null |
Is this formatting for timdelta columns intended / correct?
```python
In [12]: df = pd.DataFrame({"A": pd.date_range("2016", periods=4, freq='D'),
...: "B": pd.timedelta_range('1D', periods=4, freq='D')})
In [13]: df.to_json(date_format='iso')
Out[13]: '{"A":{"0":"2016-01-01T00:00:00.000Z","1":"2016-01-02T00:00:00.000Z","2":"2016-01-03T00:00:00.000Z","3":"2016-01-04T00:00:00.000Z"},"B":{"0":"1970-01-02T00:00:00.000Z","1":"1970-01-03T00:00:00.000Z","2":"1970-01-04T00:00:00.000Z","3":"1970-01-05T00:00:00.000Z"}}'
```
So a `pd.Timedelta('1D')` goes to `"1970-01-02T00:00:00.000Z"`.
The expected might be a [duration](https://en.wikipedia.org/wiki/ISO_8601#Durations) like
`"P1DT0H0M0S"`.
It seems to be reusing the logic for datetime columns and treating it as the equivalent int -> iso format. Ideally `to_json` will have a new paramater `timedelta_format` that takes the same values as `date_format`.
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read_fwf 'infer' where first hundred lines differ from other lines
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| 5 | 2017-01-16T01:30:38Z | 2018-11-26T00:35:45Z | 2018-11-26T00:35:45Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
1 1 -13.120080 0.229 0.484 -0.378 -0.872
1 2 -1.902843 -0.090 0.256 1.791 0.967
1 3 -22.050698 -0.176 -0.394 0.922 -0.454
1 4 -30.349928 0.081 -0.194 -0.327 -0.981
1 5 -22.204160 -0.168 -0.197 0.984 -0.266
1 6 -28.001753 -0.065 0.597 -0.203 -0.802
1 7 -17.247524 0.108 0.194 0.474 0.774
1 8 -28.014811 0.017 0.994 0.493 0.112
1 9 -13.325491 0.259 0.189 -1.275 0.149
1 10 -10.063621 0.327 0.108 -1.784 0.061
...
115 18 5.697000 0.391 -0.027 0.252 1.000
115 19 8.324000 -0.283 0.132 0.227 -0.216
115 20 48.451000 0.070 -0.041 0.379 -0.082
115 21 0.146000 0.677 0.031 -0.561 -0.149
115 22 1.443000 -0.706 -0.033 -0.222 0.035
115 23 4.595000 0.654 -0.081 0.774 0.997
115 24 0.146000 -0.677 0.031 0.561 -0.149
115 25 4.595000 0.654 -0.081 0.774 0.997
115 26 6.769000 -0.363 -0.093 -0.298 0.996
115 27 24.157000 -0.280 -0.324 -0.142 -0.946
```
#### Problem description
I have a long [fixed-width file](https://paste.ubuntu.com/23806889/) (>100k lines) that whose head and tail are shown above. I want to read this file with pandas. I figure `pd.read_fwf` is the way to do this. The issue comes up because it reads the first hundred lines, which start with `' 1'` to say "lets start reading at [2]" whereas the last hundred lines start with `115`, so it skips the initial `11` and starts the line with `5`, so I lose data.
A couple of approaches to solving this issue come to mind, though I'm sure there are others:
- Don't infer until all lines are scanned
- Take as an argument the number of lines to be scanned before concluding the format, including the option to scan all (e.g. `infer_from_all`)
- Take as an argument which direction to scan -- top to bottom or bottom to top
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-107-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
</details>
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BUG: Allow tolerance when left_index/right_index enabled for merge_asof() (#15135)
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| 1 | 2017-01-16T01:50:42Z | 2017-01-17T13:43:35Z | 2017-01-17T13:43:35Z |
CONTRIBUTOR
| null |
- [x] closes #15135
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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MDU6SXNzdWUyMDA5MzMwODQ=
| 15,140 |
Segmentation fault with `read_csv(io.StringIO("a\na"), float_precision="round_trip")`
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| 4 | 2017-01-16T05:16:01Z | 2017-01-20T12:31:32Z | 2017-01-20T12:31:32Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import io, pandas
pandas.read_csv(io.StringIO("a\na"), float_precision="round_trip")
```
The input needs to be at least two lines and must contain non-numerical data.
Experienced this problem on Arch Linux with Python 3.
#### Problem description
*Why is the current behaviour a problem?* (1) I can't parse a CSV file containing text with `round_trip` precision (2) Possible security vulnerability (3) It fills up my hard drive with core dumps
#### Expected Output
Nothing.
#### Actual Output
<details>
<pre>
#0 0x00007ffff7440350 in PyErr_Restore () from /usr/lib/libpython3.6m.so.1.0
#1 0x00007ffff7440a62 in PyErr_FormatV () from /usr/lib/libpython3.6m.so.1.0
#2 0x00007ffff7440b24 in PyErr_Format () from /usr/lib/libpython3.6m.so.1.0
#3 0x00007ffff73c2c60 in PyOS_string_to_double ()
from /usr/lib/libpython3.6m.so.1.0
#4 0x00007fffe5359a73 in ?? ()
from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so
#5 0x00007fffe53675c6 in ?? ()
from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so
#6 0x00007fffe535c74a in ?? ()
from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so
#7 0x00007fffe537db89 in ?? ()
from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so
#8 0x00007ffff74220b6 in PyCFunction_Call () from /usr/lib/libpython3.6m.so.1.0
#9 0x00007fffe534fe29 in ?? ()
from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so
#10 0x00007fffe536f78e in ?? ()
from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so
#11 0x00007fffe5344cf4 in ?? ()
from /usr/lib/python3.6/site-packages/pandas/parser.cpython-36m-x86_64-linux-gnu.so
#12 0x00007ffff7421ddc in _PyCFunction_FastCallDict ()
from /usr/lib/libpython3.6m.so.1.0
#13 0x00007ffff740c21f in ?? () from /usr/lib/libpython3.6m.so.1.0
#14 0x00007ffff73cd067 in _PyEval_EvalFrameDefault ()
from /usr/lib/libpython3.6m.so.1.0
#15 0x00007ffff740ade9 in ?? () from /usr/lib/libpython3.6m.so.1.0
#16 0x00007ffff740bf9a in ?? () from /usr/lib/libpython3.6m.so.1.0
#17 0x00007ffff740c303 in ?? () from /usr/lib/libpython3.6m.so.1.0
#18 0x00007ffff73cd067 in _PyEval_EvalFrameDefault ()
from /usr/lib/libpython3.6m.so.1.0
#19 0x00007ffff740aaa1 in ?? () from /usr/lib/libpython3.6m.so.1.0
#20 0x00007ffff740bf9a in ?? () from /usr/lib/libpython3.6m.so.1.0
#21 0x00007ffff740c303 in ?? () from /usr/lib/libpython3.6m.so.1.0
#22 0x00007ffff73cd067 in _PyEval_EvalFrameDefault ()
from /usr/lib/libpython3.6m.so.1.0
#23 0x00007ffff740bd4a in ?? () from /usr/lib/libpython3.6m.so.1.0
#24 0x00007ffff740c303 in ?? () from /usr/lib/libpython3.6m.so.1.0
#25 0x00007ffff73cd067 in _PyEval_EvalFrameDefault ()
from /usr/lib/libpython3.6m.so.1.0
#26 0x00007ffff740ade9 in ?? () from /usr/lib/libpython3.6m.so.1.0
#27 0x00007ffff740bf9a in ?? () from /usr/lib/libpython3.6m.so.1.0
#28 0x00007ffff740c303 in ?? () from /usr/lib/libpython3.6m.so.1.0
#29 0x00007ffff73cde87 in _PyEval_EvalFrameDefault ()
from /usr/lib/libpython3.6m.so.1.0
#30 0x00007ffff740c757 in PyEval_EvalCodeEx () from /usr/lib/libpython3.6m.so.1.0
#31 0x00007ffff73ccd4b in PyEval_EvalCode () from /usr/lib/libpython3.6m.so.1.0
#32 0x00007ffff74ae112 in ?? () from /usr/lib/libpython3.6m.so.1.0
#33 0x00007ffff74b097d in PyRun_FileExFlags () from /usr/lib/libpython3.6m.so.1.0
#34 0x00007ffff74b0b67 in PyRun_SimpleFileExFlags ()
from /usr/lib/libpython3.6m.so.1.0
#35 0x00007ffff74a4a91 in Py_Main () from /usr/lib/libpython3.6m.so.1.0
#36 0x0000000000400a5d in main ()
</pre>
</details>
#### Output of ``pd.show_versions()``
<details>
<pre>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.13-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8<br/>
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 33.0.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0rc2+2914.g1fa4dd705.dirty
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.5
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</pre>
</details>
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MDU6SXNzdWUyMDA5ODM3MTY=
| 15,141 |
pd.groupby(pd.TimeGrouper('W')).groups returns Timestamps instead of Periods
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| 15 | 2017-01-16T10:36:48Z | 2017-01-17T19:08:33Z | 2017-01-17T13:05:51Z |
NONE
| null |
Lets consider the following DataFrame:
```
date_range = pd.date_range(dt(2010,1,1), dt(2010,1,31), freq='1D')
df = pd.DataFrame(data = np.random.rand(len(date_range),2), index = date_range)
```
If I group the datapoints by periods of 1 week and visualize the groups definition, I get:
```
In: [1]:df.groupby(pd.TimeGrouper('W')).groups
Out:[1]:
{Timestamp('2010-01-03 00:00:00', freq='W-SUN'): 3,
Timestamp('2010-01-10 00:00:00', freq='W-SUN'): 10,
Timestamp('2010-01-17 00:00:00', freq='W-SUN'): 17,
Timestamp('2010-01-24 00:00:00', freq='W-SUN'): 24,
Timestamp('2010-01-31 00:00:00', freq='W-SUN'): 31}
I retrieve the keys of that dictionary:
In: [2]: list(df.groupby(pd.TimeGrouper('W')).keys())
Out:[2]:
[Timestamp('2010-01-03 00:00:00', freq='W-SUN'),
Timestamp('2010-01-10 00:00:00', freq='W-SUN'),
Timestamp('2010-01-31 00:00:00', freq='W-SUN'),
Timestamp('2010-01-17 00:00:00', freq='W-SUN'),
Timestamp('2010-01-24 00:00:00', freq='W-SUN')]
```
However I am left with those funny variables such as Timestamp('2010-01-24 00:00:00', freq='W-SUN') that have the prefix Timestamp but are structured like Periods. I think this is not correct, and those variables should be Periods?
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MDExOlB1bGxSZXF1ZXN0MTAxNzEzOTIy
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CLN/DEPR: remove deprecated pandas.rpy module (GH9602)
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| 8 | 2017-01-16T15:47:42Z | 2018-04-10T11:35:47Z | 2017-01-22T12:15:10Z |
MEMBER
| null |
Closes #9602
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Categoricals hash consistently
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| 4 | 2017-01-16T22:48:30Z | 2017-01-18T14:35:46Z | 2017-01-18T14:35:44Z |
CONTRIBUTOR
| null |
Previously categorical values were hashed using just their codes. This
meant that the hash value depended on the ordering of the categories,
rather than on the values the series represented. This caused problems
in dask, where different partitions might have different categorical
mappings.
This PR makes the hashing dependent on the values the categorical
represents, rather than on the codes. The categories are first hashed,
and then the codes are remapped to the hashed values. This is slightly
slower than before (still need to hash the categories, where we didn't
before), but allows for more consistent hashing.
Related to this work in dask: https://github.com/dask/dask/pull/1877.
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| 201,285,620 |
MDU6SXNzdWUyMDEyODU2MjA=
| 15,144 |
read_fwf with passing converters and na_values at the same time
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| 9 | 2017-01-17T13:35:49Z | 2017-01-24T05:02:04Z | 2017-01-23T20:23:21Z |
NONE
| null |
Duplicate of #13302
---
When reading via read_fwf and passing both na_values and converters at the same time, na_value detection does not work:
#### Code Sample, a copy-pastable example if possible
Assume a fwf-style text file like this:
> A B C
> 10 -1 10
> 10 10 10
```python
In: read_fwf(file_path, converters={'B':lambda x: int(x)/10.}, na_values={'B':-1})
Out:
> A B C
> 0 10 -0.1 10
> 1 10 1.0 10
```
#### Expected Output
```
> A B C
> 0 10 NaN 10
> 1 10 1.0 10
```
It would be nice if this could be handled behind the scenes.
In pandas.io.parsers._clean_na_values, float representations of the na_values are produced by _floatify_na_values. Maybe passing the converters to this function might be a solution?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 23 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
(...)
</details>
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MDU6SXNzdWUyMDEyOTAxNDU=
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COMPAT: create UInt64Block
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| 8 | 2017-01-17T13:51:58Z | 2019-07-23T17:09:46Z | 2019-07-23T17:09:45Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/14937#issuecomment-272705717
a number of indexing / conversion issues arise because we are treating ``uint`` as a direct ``int``, rather than a sub-class. (e.g. if we make UIntBlock a sub-class of IntBlock), I think can easily handle some small overrides to, for instance check for negative values when indexing.
```
In [1]: df = pd.DataFrame({'A' : np.array([1,2,3],dtype='uint64'), 'B': range(3)})
In [2]: df
Out[2]:
A B
0 1 0
1 2 1
2 3 2
In [4]: df.dtypes
Out[4]:
A uint64
B int64
dtype: object
```
Buggy
```
In [5]: df.iloc[1] = -1
In [6]: df
Out[6]:
A B
0 1 0
1 18446744073709551615 -1
2 3 2
```
```
In [7]: df.iloc[1] = np.nan
In [8]: df
Out[8]:
A B
0 1.0 0.0
1 NaN NaN
2 3.0 2.0
```
This is correct
```
In [9]: df.A.astype('uint64')
---------------------------------------------------------------------------
ValueError: Cannot convert non-finite values (NA or inf) to integer
```
However, this is not
```
In [10]: df.iloc[1] = -1
In [11]: df
Out[11]:
A B
0 1.0 0.0
1 -1.0 -1.0
2 3.0 2.0
In [12]: df.dtypes
Out[12]:
A float64
B float64
dtype: object
In [13]: df.A.astype('uint64')
Out[13]:
0 1
1 18446744073709551615
2 3
Name: A, dtype: uint64
```
Construction with invalid values
```
In [1]: Series([-1], dtype='uint64')
Out[1]:
0 18446744073709551615
dtype: uint64
```
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DEPR: remove freq attribute of Timestamp?
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| 12 | 2017-01-17T14:41:03Z | 2021-06-07T16:47:33Z | 2021-06-07T16:47:33Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/issues/15141#issuecomment-273184725
In theory these should be completely fungible. If so, we should remove the ``freq`` attribute
of ``Timestamps`` and instead return ``Periods`` in those instances.
```
In [2]: t = Timestamp('2010-01-03 00:00:00', freq='W-SUN')
In [3]: t
Out[3]: Timestamp('2010-01-03 00:00:00', freq='W-SUN')
In [4]: t.to_period()
Out[4]: Period('2009-12-28/2010-01-03', 'W-SUN')
In [5]: t.to_period().to_timestamp(how='start')
Out[5]: Timestamp('2009-12-28 00:00:00')
In [6]: t.to_period().to_timestamp(how='end')
Out[6]: Timestamp('2010-01-03 00:00:00')
```
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| 201,307,610 |
MDU6SXNzdWUyMDEzMDc2MTA=
| 15,147 |
No error when mismatched vectors are assigned to pandas.DataFrame
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| 2 | 2017-01-17T14:49:20Z | 2017-01-17T14:56:12Z | 2017-01-17T14:55:49Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# toy data
x = [0, 1, 1, 1, 2, 3, 4]
y = pd.qcut(sorted(np.unique(x), reverse=True), 5, labels=np.arange(5, 0, -1)) # size 5 only!
table = pd.DataFrame()
table['col_1'] = x # size 7
table['col_2'] = y # size 5!
```
#### Problem description
I'm assigning vectors with mismatched sizes to the dataframe. I'm not even specifying the index to assign it. This became a problem because it didn't raise any error. I was expecting it to raise an error or a warning. This can be a problem for code using ```table```.
#### Expected Output
Raise some error upon this mismatched assignment. The error can be generated if I only call:
```
table
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/usr/local/lib/python3.4/dist-packages/IPython/core/formatters.py in __call__(self, obj)
309 method = get_real_method(obj, self.print_method)
310 if method is not None:
--> 311 return method()
312 return None
313 else:
/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in _repr_html_(self)
608
609 return self.to_html(max_rows=max_rows, max_cols=max_cols,
--> 610 show_dimensions=show_dimensions, notebook=True)
611 else:
612 return None
/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in to_html(self, buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, bold_rows, classes, escape, max_rows, max_cols, show_dimensions, notebook, decimal, border)
1606 decimal=decimal)
1607 # TODO: a generic formatter wld b in DataFrameFormatter
-> 1608 formatter.to_html(classes=classes, notebook=notebook, border=border)
1609
1610 if buf is None:
/usr/local/lib/python3.4/dist-packages/pandas/formats/format.py in to_html(self, classes, notebook, border)
698 border=border)
699 if hasattr(self.buf, 'write'):
--> 700 html_renderer.write_result(self.buf)
701 elif isinstance(self.buf, compat.string_types):
702 with open(self.buf, 'w') as f:
/usr/local/lib/python3.4/dist-packages/pandas/formats/format.py in write_result(self, buf)
1022 indent += self.indent_delta
1023 indent = self._write_header(indent)
-> 1024 indent = self._write_body(indent)
1025
1026 self.write('</table>', indent)
/usr/local/lib/python3.4/dist-packages/pandas/formats/format.py in _write_body(self, indent)
1181 self._write_hierarchical_rows(fmt_values, indent)
1182 else:
-> 1183 self._write_regular_rows(fmt_values, indent)
1184 else:
1185 for i in range(len(self.frame)):
/usr/local/lib/python3.4/dist-packages/pandas/formats/format.py in _write_regular_rows(self, fmt_values, indent)
1215 row = []
1216 row.append(index_values[i])
-> 1217 row.extend(fmt_values[j][i] for j in range(ncols))
1218
1219 if truncate_h:
/usr/local/lib/python3.4/dist-packages/pandas/formats/format.py in <genexpr>(.0)
1215 row = []
1216 row.append(index_values[i])
-> 1217 row.extend(fmt_values[j][i] for j in range(ncols))
1218
1219 if truncate_h:
IndexError: list index out of range
Out[33]:
col_1 col_2
0 0 1
1 1 2
2 1 3
3 1 4
4 2 5
5 3
6 4
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-101-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.1
pip: 9.0.1
setuptools: 32.3.1
Cython: None
numpy: 1.11.0
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.0
pytz: 2012c
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: 1.2.0
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8.1
boto: None
pandas_datareader: None
</details>
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| 201,402,226 |
MDExOlB1bGxSZXF1ZXN0MTAxOTQ0NDQ2
| 15,148 |
BUG: Segfault due to float_precision='round_trip'
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| 4 | 2017-01-17T20:54:01Z | 2017-01-20T12:31:43Z | 2017-01-20T12:31:32Z |
CONTRIBUTOR
| null |
`round_trip` calls back into Python, so the GIL must be held. It also fails to silence the Python exception, leading to spurious errors.
Closes #15140.
- [x] closes #xxxx
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff`` (*Most of them are false positives because flake8 doesn't understand Cython or it was code I didn't even touch*)
- [x] whatsnew entry
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| 15,149 |
BUG: unicode characters when reading JSON lines
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| 4 | 2017-01-18T05:56:12Z | 2017-01-19T13:54:35Z | 2017-01-19T13:54:35Z |
CONTRIBUTOR
| null |
Fixes UnicodeDecodeError bug when reading JSON lines input with Ascii decoder, which is often the default setting in Python 2.7. Avoids issues with mixing unicode and ascii strings.
xref #15132
- [x] closes #15132
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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| 201,503,636 |
MDU6SXNzdWUyMDE1MDM2MzY=
| 15,150 |
BUG: crosstab cannot normalize multiple columns for the index
|
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| 3 | 2017-01-18T08:04:03Z | 2020-01-21T18:26:47Z | 2020-01-21T18:26:47Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 6, 'B': ['A', 'B', 'C'] * 8, 'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4, 'D': np.random.randn(24), 'E': np.random.randn(24)})
pd.crosstab([df.A, df.B], df.C, values=df.D, aggfunc=np.sum, normalize=True, margins=True)
```
#### Problem description
This gives "ValueError: labels ['All'] not contained in axis"
Currently, crosstab only normalize single index.
I expect crosstab can normalize with multiple columns for the index, but I got ValueError: labels ['All'] not contained in axis.
#### Expected Output
normalized result
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-47-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
None
</details>
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MDU6SXNzdWUyMDE1ODYzNDI=
| 15,151 |
TypeError when subtracting from empty index
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| 1 | 2017-01-18T14:20:26Z | 2017-01-18T14:39:54Z | 2017-01-18T14:39:41Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
While `pd.DataFrame().index + 1` returns the unchanged index `Index([], dtype='object')`,
I see the following error with subtraction:
```python
In [71]: pd.DataFrame().index - 1
Traceback (most recent call last):
File "<ipython-input-71-cbb1aa99c0f9>", line 1, in <module>
pd.DataFrame().index - 1
File "C:\Program Files\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\pandas\indexes\base.py", line 1796, in __sub__
"{typ}".format(typ=type(self)))
TypeError: cannot perform __sub__ with this index type: <class 'pandas.indexes.base.Index'>
```
#### Problem description
I would like to have `pd.DataFrame().index - 1` return the unchanged index, too.
The current behavior makes my code not generalize well for the edge case of empty dataframes (which happen in my environment).
#### Expected Output
The same as in the summation case.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 28.7.1
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.8.0rc1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: 1.4.4
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
</details>
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MDU6SXNzdWUyMDE1OTA4NzU=
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DEPR: DeprecationWarning -> FutureWarning
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| 4 | 2017-01-18T14:37:14Z | 2019-05-19T17:15:07Z | 2019-05-19T17:15:07Z |
CONTRIBUTOR
| null |
- [x] ``.ix`` #15113 (#26438)
- [ ] ~~``.isnull/.notnull`` #16972 ``~~
Move to #6581 when converted to `FutureWarning`
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| 3 | 2017-01-18T16:00:18Z | 2017-01-18T18:36:04Z | 2017-01-18T16:19:00Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Given a directory with a large collection (> 1900) of files such as the attached sample ([sample_file.zip](https://github.com/pandas-dev/pandas/files/714335/sample_file.zip)), and the following pseudo-code "junk.py" (needs work to make it copy-pastable by defining dtypes and colnames, as well as generating thousands of similar files
```python
import os.path as osp
import pandas as pd
for f in ec_files:
print(osp.basename(f))
fdf = pd.read_csv(f, dtype=dtypes, header=None,
parse_dates=[0, 1], index_col=1, names=colnames,
na_values=["NAN"], true_values=["t"],
false_values=["f"], low_memory=False)
```
#### Problem description
Running this script stops at different files each time it is run with the following traceback:
```python
Traceback (most recent call last):
File "junk.py", line 9, in <module>
false_values=["f"], low_memory=False)
File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 645, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 400, in _read
data = parser.read()
File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 938, in read
ret = self._engine.read(nrows)
File "/usr/lib/python2.7/dist-packages/pandas/io/parsers.py", line 1505, in read
data = self._reader.read(nrows)
File "pandas/parser.pyx", line 849, in pandas.parser.TextReader.read (pandas/parser.c:9907)
File "pandas/parser.pyx", line 945, in pandas.parser.TextReader._read_rows (pandas/parser.c:11161)
File "pandas/parser.pyx", line 1047, in pandas.parser.TextReader._convert_column_data (pandas/parser.c:12536)
File "pandas/parser.pyx", line 1126, in pandas.parser.TextReader._convert_tokens (pandas/parser.c:13783)
ValueError: invalid literal for float(): 2016-07-13 18:54:03.9
```
The file where the error is thrown, as well as the particular ValueError vary with each run, i.e. it is never consistent. **Furthermore, every file where the error occurs reads without any problem with the very same read_csv command called on its own (not in a loop)**. Therefore, the read_csv command is correct for these files.
If the number of files to loop through is relatively small (<100) the process **sometimes** finishes successfully, but not always.
I initially reported this in [Stackoverflow](http://stackoverflow.com/questions/41664844/dtype-seemingly-ignored-in-read-csv-during-long-loop)
#### Expected Output
read_csv output and behaviour should be the same whether it is run in a loop or independently.
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: None.None
pandas: 0.19.0+git14-ga40e185
nose: 1.3.7
pip: 9.0.1
setuptools: 32.0.0
Cython: None
numpy: 1.12.0rc2
scipy: 0.18.1
statsmodels: 0.8.0rc1
xarray: None
IPython: 5.1.0
sphinx: 1.4.9
patsy: 0.4.1+dev
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0rc2
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.7.1
bs4: 4.5.3
html5lib: 0.999999999
httplib2: 0.9.2
apiclient: 1.5.5
sqlalchemy: None
pymysql: None
psycopg2: 2.6.2 (dt dec mx pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
```
</details>
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MDU6SXNzdWUyMDE2NDk0NjY=
| 15,154 |
CI: invalidate cache when .pxi.in only changes
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| 4 | 2017-01-18T18:04:54Z | 2017-01-19T02:22:15Z | 2017-01-19T02:22:14Z |
CONTRIBUTOR
| null |
In the following, *only* the ``.pxi.in`` were changed and ``setup.py`` correctly has dependencies on these, so local building is fine. However, on travis, the cython cache does not seem to be invalidation (and I had to resort to manually clearing it).
similar to my comment: https://github.com/pandas-dev/pandas/pull/14937#issuecomment-273270706
happened again on merging: #15139, see: https://travis-ci.org/pandas-dev/pandas/jobs/193092807
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Groupby level fails to enumerate groups
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| 1 | 2017-01-18T18:36:35Z | 2017-01-19T20:21:53Z | 2017-01-19T20:21:53Z |
CONTRIBUTOR
| null |
#### Code Sample
```python
test = pd.DataFrame(data=np.arange(2,22,2),
index=pd.MultiIndex(levels=[pd.CategoricalIndex(["a", "b"]), range(10)],
labels=[[0]*5 + [1]*5, range(10)],
names = ["Index1", "Index2"]))
testGroupedBy = test.groupby(level=["Index1"])
testGroupedBy.get_group("a")
```
#### Problem description
instead of returning the data as expected, an error is thrown:
```python
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-1466-65ba7dd9f763> in <module>()
----> 1 testGroupedBy.get_group("a")
python2.7/site-packages/pandas/core/groupby.pyc in get_group(self, name, obj)
614 obj = self._selected_obj
615
--> 616 inds = self._get_index(name)
617 if not len(inds):
618 raise KeyError(name)
python2.7/site-packages/pandas/core/groupby.pyc in _get_index(self, name)
461 def _get_index(self, name):
462 """ safe get index, translate keys for datelike to underlying repr """
--> 463 return self._get_indices([name])[0]
464
465 @cache_readonly
python2.7/site-packages/pandas/core/groupby.pyc in _get_indices(self, names)
428 return []
429
--> 430 if len(self.indices) > 0:
431 index_sample = next(iter(self.indices))
432 else:
python2.7/site-packages/pandas/core/groupby.pyc in __getattr__(self, attr)
527
528 raise AttributeError("%r object has no attribute %r" %
--> 529 (type(self).__name__, attr))
530
531 plot = property(GroupByPlot)
AttributeError: 'DataFrameGroupBy' object has no attribute 'indices'
```
#### Expected Output
I guess an alternative way to get this output is `test.loc["a"]` - which yields the expected output below:
```python
0
Index2
0 2
1 4
2 6
3 8
4 10
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.11.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_GB.utf8
LANG: en_GB.utf8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 32.3.1
Cython: None
numpy: 1.11.3
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUyMDE2NzQ5NzI=
| 15,156 |
Versioneer reported version of dev build is incorrect
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[] | null | 5 | 2017-01-18T19:51:03Z | 2017-10-24T12:41:21Z | 2017-10-24T12:41:21Z |
CONTRIBUTOR
| null |
Sometimes it's nice to install projects from master to get recent fixes (e.g. `pip install git+https://github.com/pandas-dev/pandas.git`). However, since pandas switched to using dev branches for `0.19.1+`, versioneer incorrectly reports the version of the dev build as `0.19.0`. This breaks checks for version, e.g. the following would report false for a build off master, when semantically master is after the 0.19.2 release:
```python
from distutils.version import LooseVersion
import pandas as pd
LooseVersion(pd.__version__) > '0.19.2'
```
I'm not sure if there is an easy/good fix for this, just opening this to discuss.
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BUG: MultiIndex.set_labels not correctly verifying
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| 8 | 2017-01-18T21:16:29Z | 2021-05-08T00:34:42Z | 2021-05-08T00:34:42Z |
NONE
| null |
I believe that
label_length = len(self.labels[0])
should actually be
label_length = len(labels[0])
I tested my proposed change and it fixed the problem I had in my code that started when I updated Pandas from 0.18.1 to 0.19.2.
def _verify_integrity(self, labels=None, levels=None):
"""
Parameters
----------
labels : optional list
Labels to check for validity. Defaults to current labels.
levels : optional list
Levels to check for validity. Defaults to current levels.
Raises
------
ValueError
* if length of levels and labels don't match or any label would
exceed level bounds
"""
# NOTE: Currently does not check, among other things, that cached
# nlevels matches nor that sortorder matches actually sortorder.
labels = labels or self.labels
levels = levels or self.levels
if len(levels) != len(labels):
raise ValueError("Length of levels and labels must match. NOTE:"
" this index is in an inconsistent state.")
label_length = len(self.labels[0])
for i, (level, label) in enumerate(zip(levels, labels)):
if len(label) != label_length:
raise ValueError("Unequal label lengths: %s" %
([len(lab) for lab in labels]))
if len(label) and label.max() >= len(level):
raise ValueError("On level %d, label max (%d) >= length of"
" level (%d). NOTE: this index is in an"
" inconsistent state" % (i, label.max(),
len(level)))
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MDExOlB1bGxSZXF1ZXN0MTAyMTg4MTIy
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DOC: fix doc-build for .ix deprecations in whatsnew
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| 1 | 2017-01-19T01:59:44Z | 2017-01-19T02:01:46Z | 2017-01-19T02:01:45Z |
CONTRIBUTOR
| null |
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MDExOlB1bGxSZXF1ZXN0MTAyMTg5OTg3
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CI: add .pxi.in to build caching
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| 0 | 2017-01-19T02:20:20Z | 2017-01-19T02:22:15Z | 2017-01-19T02:22:15Z |
CONTRIBUTOR
| null |
closes #15154
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MDU6SXNzdWUyMDE3NjQzNDM=
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to_excel should (optionally) freeze panes
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| 4 | 2017-01-19T04:51:05Z | 2017-02-16T20:35:29Z | 2017-02-16T17:47:46Z |
CONTRIBUTOR
| null |
`DataFrame.to_excel()` is currently helpful in styling column and row headers distinctly from values. One other simple feature Excel provides to manage headers is "freeze panes". Panes can be frozen such that the column and row headers are fixed. This is achieved in openpyxl with `worksheet.freeze_panes = top_left_value_cell` and in xlwt with `worksheet.freeze_panes(bottommost_header_row, leftmost_header_col)`.
I think making this available, as an option or by default, would give a valuable usability boost to the exported Excel spreadsheets.
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MDExOlB1bGxSZXF1ZXN0MTAyMjE5NTc2
| 15,161 |
Apply fontsize to both x- and y-axis tick labels
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[] | null | 5 | 2017-01-19T08:16:39Z | 2017-01-20T17:13:03Z | 2017-01-20T17:12:58Z |
CONTRIBUTOR
| null |
- [x] closes #15108
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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| 201,823,663 |
MDExOlB1bGxSZXF1ZXN0MTAyMjQxODcz
| 15,162 |
BUG, TST: Patch handling of uint64 in algorithms
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| 3 | 2017-01-19T10:45:04Z | 2017-01-22T15:49:54Z | 2017-01-22T15:49:36Z |
MEMBER
| null |
1) Patches handling of `uint64` in `value_counts`
2) Adds tests for handling of `uint64` in `factorize`
xref #14934
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MDExOlB1bGxSZXF1ZXN0MTAyMjUwNTQy
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Bug in groupby.get_group on categoricalindex
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| 2 | 2017-01-19T11:41:28Z | 2017-11-10T11:11:23Z | 2017-01-19T20:21:53Z |
CONTRIBUTOR
| null |
- [x] closes #15155
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MTAyMjYxNDk3
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DOC: Clarified wording of coerce_float
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| 2 | 2017-01-19T12:58:06Z | 2017-01-19T13:57:21Z | 2017-01-19T13:57:19Z |
NONE
| null |
I think this is the intended meaning (and it matches with use).
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MDU6SXNzdWUyMDE4NzIzMDA=
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read_csv() will throw a exception when csv file has str 'NaN'
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| 5 | 2017-01-19T14:17:35Z | 2017-01-19T14:33:08Z | 2017-01-19T14:32:59Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from bokeh.charts import Bar, Line
from bokeh.plotting import output_file,show
output_file("test.html")
wt = pd.read_csv('test.csv', dtype={
'word': str,
'times': int,
})
print(wt['word'])
line = Line(wt,
x='word',
y='times'
)
show(line)
```
The test.csv file showed below:
```csv
word,times
nan,1
```
#### Problem description
read_csv() will throw a exception when read csv file has str 'NaN'
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
I think it has a str 'nan' becuase it conflicts with python built-in words.
if i remove 'nan' string, it works good.
#### Output
```
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL:
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 32.3.0
Cython: None
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: 0.999999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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| 201,904,049 |
MDU6SXNzdWUyMDE5MDQwNDk=
| 15,166 |
BUG: .loc indexing not preserving Index type
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| 2 | 2017-01-19T16:13:29Z | 2019-11-13T01:48:02Z | 2019-11-13T01:48:02Z |
CONTRIBUTOR
| null |
####
```python
test = pd.DataFrame(data=np.arange(2,22,2),
index=pd.MultiIndex(levels=[pd.CategoricalIndex(["a", "b"]), range(10)],
labels=[[0]*5 + [1]*5, range(10)],
names = ["Index1", "Index2"]))
test.index.levels[0]
# CategoricalIndex([u'a', u'b'], categories=[u'a', u'b'], ordered=False, name=u'Index1', dtype='category')
test.loc[["a"]].index.levels[0]
# Index([u'a'], dtype='object', name=u'Index1')
```
#### Problem description
When selecting data through a categorical index, the categorical index is lost.
#### Expected Output
```
test.loc[["a"]].index.levels[0]
# CategoricalIndex([u'a'], categories=[u'a', u'b'], ordered=False, name=u'Index1', dtype='category')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.11.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_GB.utf8
LANG: en_GB.utf8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 32.3.1
Cython: None
numpy: 1.11.3
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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Bug: resample options with TimedeltaIndex not working
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| 1 | 2017-01-19T17:42:47Z | 2017-01-19T18:51:08Z | 2017-01-19T18:50:55Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
index = pd.timedelta_range('0', periods=4, freq='s')
series = pd.Series(range(4), index=index)
print series
print series.resample('2s', label='left').sum()
print series.resample('2s', label='right').sum()
print series.resample('2s', label='right', closed='right').sum()
```
#### The output
```
00:00:00 0
00:00:01 1
00:00:02 2
00:00:03 3
Freq: S, dtype: int64
00:00:00 1
00:00:02 5
Freq: 2S, dtype: int64
00:00:00 1
00:00:02 5
Freq: 2S, dtype: int64
00:00:00 1
00:00:02 5
Freq: 2S, dtype: int64
```
#### Problem description
When calling the function resample with a DatetimeIndex works as expected. However, if called with a TimedeltaIndex the options label and closed does not work.
#### Expected Output
```
00:00:00 0
00:00:01 1
00:00:02 2
00:00:03 3
Freq: S, dtype: int64
00:00:00 1
00:00:02 5
Freq: 2S, dtype: int64
00:00:02 1
00:00:04 5
Freq: 2S, dtype: int64
00:00:00 0
00:00:02 3
00:00:04 3
Freq: 2S, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-74-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.1
pip: 9.0.1
setuptools: 28.8.0
Cython: 0.24.1
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.5.0
xarray: None
IPython: 4.1.2
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.5.3
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.4.5 (dt dec mx pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
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to_sql & cx_oracle
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| 5 | 2017-01-19T17:53:16Z | 2017-01-28T22:42:11Z | 2017-01-28T22:42:04Z |
NONE
| null |
Hello,
I am having the following error:
<type 'exceptions.TypeError'>
('expecting string, unicode or buffer object',)
expecting string, unicode or buffer object
but the issue is that this error occur only when the 'chunksize' is greater than 1. I did try different value for this parameter without success. Let me know how I can help solving this issue. Last but not least it looks like that is linked with one of the recent libraries update... was working just fine before 1 or 2 weeks ago.
thanks in advance.
JM
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resample().apply not returning multiple columns like groupby(pd.Timegrouper()).apply
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| 5 | 2017-01-19T18:32:33Z | 2017-10-27T20:32:20Z | 2017-10-27T20:32:20Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [3]: date = pd.date_range('1-1-2015', '12-31-15', freq='D')
In [4]: df = pd.DataFrame(data={'col1':np.random.rand(len(date))}, index=date)
In [5]: def calc(x):
s = pd.Series([1,2], index=['a', 'b'])
return s
In [6]: df.resample('M').apply(calc)
Out[6]:
col1
2015-01-31 NaN
2015-02-28 NaN
2015-03-31 NaN
2015-04-30 NaN
2015-05-31 NaN
2015-06-30 NaN
2015-07-31 NaN
2015-08-31 NaN
2015-09-30 NaN
2015-10-31 NaN
2015-11-30 NaN
2015-12-31 NaN
In [7]: df.groupby(pd.TimeGrouper('M')).apply(calc)
Out[7]:
a b
2015-01-31 1 2
2015-02-28 1 2
2015-03-31 1 2
2015-04-30 1 2
2015-05-31 1 2
2015-06-30 1 2
2015-07-31 1 2
2015-08-31 1 2
2015-09-30 1 2
2015-10-31 1 2
2015-11-30 1 2
2015-12-31 1 2
```
#### Problem description
It is my understanding that ``resample`` with `apply` should work very similarly as ``groupby(pd.Timegrouper)`` with ``apply``. In a more complex example I was trying to return many aggregated results that are calculated with several columns. It seems `resample` with `apply` is unable to return anything but a Series that has the same index as the calling DataFrame columns.
#### Expected Output
Should look exactly like the output from ``df.groupby(pd.TimeGrouper('M')).apply(calc)``
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.7
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.7.5.None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: 0.2.1
</details>
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| 201,966,842 |
MDExOlB1bGxSZXF1ZXN0MTAyMzQyNDEy
| 15,170 |
CI: have 34_SLOW build use matplotlib latest from conda-forge
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| 0 | 2017-01-19T20:28:37Z | 2017-01-19T21:53:38Z | 2017-01-19T21:53:38Z |
CONTRIBUTOR
| null |
CI: change 34_SLOW to use numpy 1.10* as mpl 2.0 requires anyhow
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ENH: Display options for Decimal objects.
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| 2 | 2017-01-19T20:47:35Z | 2021-05-08T00:36:02Z | 2021-05-08T00:36:02Z |
NONE
| null |
This is a feature request
#### Code Sample, a copy-pastable example if possible
```python
import decimal
import pandas as pd
pd.set_option('display.precision', 3)
ctx = decimal.getcontext()
def D(number):
return ctx.create_decimal(str(number))
df = pd.DataFrame({
"decimals": [D(1/9), D(1/11), D(1/13)],
"floats": [1/9, 1/11, 1/13]
})
df.head()
```
#### Problem description
You cannot easily set the display options for `Decimal` objects. If you have lots of Decimal columns and you are working interactively, this can quickly get annoying.
#### Expected Output
IMHO at the very least, we should be able to set the display precision. Ideally we should be able to use a callable similar to `float_format`. If that is the case, we should then be able to use custom formatters like e.g. [moneyfmt](https://docs.python.org/3.6/library/decimal.html#recipes).
If no specific options for `Decimal` objects are present, I think that it makes sense to fall back to the float options. If that is not acceptable, I think that at least adhering to the precision option is reasonable.
#### Output of ``pd.show_versions()``
<details>INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.13-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 33.1.1
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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Issue Opening Pre 0.12 Pickles Import Error
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| 3 | 2017-01-19T21:12:19Z | 2017-01-19T22:40:26Z | 2017-01-19T21:17:00Z |
NONE
| null |
I am having an issue opening some old pickle files that I created a couple of years ago (July of 2014). These files were pickled using in the `cPickle` module in python and I am pretty sure using a version of pandas <=0.12. I was using `cPickle` to load the files and getting a `TypeError` until I found the following answers on stack overflow:
http://stackoverflow.com/questions/20444593/pandas-compiled-from-source-default-pickle-behavior-changed
http://stackoverflow.com/questions/27950991/pandas-backwards-compatibility-issue-with-pickle-0-14-1-and-0-15-2
The solution is to be to use `pd.read_pickle` to load these files instead of using the python module `cPickle`. However when I try using `pd.read_pickle` I get the following `ImportError`:
```python
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-8-451b9dbea93c> in <module>()
1 data_dir="J:\Pat's Projects\Dynamical Phase Transition\Mosaic Trajectories"
2 #fit_params=cPickle.load(open(data_dir+'circle_fitting_params_071614.pkl','r'))
----> 3 expt_data=pd.read_pickle(data_dir+'data_frames_071614.pkl')
4 #expt_list=cPickle.load(open(data_dir+'expt_list_071614.pkl','r'))
C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\site-packages\pandas\io\pickle.pyc in read_pickle(path)
63
64 try:
---> 65 return try_read(path)
66 except:
67 if PY3:
C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\site-packages\pandas\io\pickle.pyc in try_read(path, encoding)
60 except:
61 with open(path, 'rb') as fh:
---> 62 return pc.load(fh, encoding=encoding, compat=True)
63
64 try:
C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\site-packages\pandas\compat\pickle_compat.pyc in load(fh, encoding, compat, is_verbose)
115 up.is_verbose = is_verbose
116
--> 117 return up.load()
118 except:
119 raise
C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\pickle.pyc in load(self)
862 while 1:
863 key = read(1)
--> 864 dispatch[key](self)
865 except _Stop, stopinst:
866 return stopinst.value
C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\pickle.pyc in load_global(self)
1094 module = self.readline()[:-1]
1095 name = self.readline()[:-1]
-> 1096 klass = self.find_class(module, name)
1097 self.append(klass)
1098 dispatch[GLOBAL] = load_global
C:\Users\Scherer Lab E\Anaconda2\envs\170112\lib\pickle.pyc in find_class(self, module, name)
1128 def find_class(self, module, name):
1129 # Subclasses may override this
-> 1130 __import__(module)
1131 mod = sys.modules[module]
1132 klass = getattr(mod, name)
ImportError: No module named copy_reg
```
Like I said, I don't know exactly what version of pandas was used when these were pickled but I think it was <=0.12. I am using python 2.7 installed from the Anaconda distribution.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 2.0.0
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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| 201,990,029 |
MDExOlB1bGxSZXF1ZXN0MTAyMzU5MDAz
| 15,173 |
DOC: add subsequent_indent to wrapped text
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| 2 | 2017-01-19T22:09:04Z | 2017-01-22T11:35:24Z | 2017-01-22T11:35:13Z |
NONE
| null |
Hey guys,
This should fix the parsing error in the [read_csv documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html#pandas.read_csv) :

`fill` wraps the line to 70 characters (what's left after the beginning of the line) but it wasn't indenting the subsequent line, so the parser was marking it as a new parameter (in bold).
This PR adds the missing indent, which should fix the parsing.
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bound method NDFrame: df.sample(n=ExpectedSize) returns df.size!=ExpectedSize
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[] | null | 2 | 2017-01-20T00:08:40Z | 2017-01-20T00:34:15Z | 2017-01-20T00:34:15Z |
NONE
| null |
I have this code (see screenshot for it's output)
```
#prerequisite: var X_first_neutral_data gets filled via csv read
print("got a var of type: " + str(type(X_first_neutral_data)) + " that I want to take 10 samples from")
print("size before: " + str(X_first_neutral_data.size))
X_first_neutral_data_less_data=X_first_neutral_data.sample(n=10)
print("size should be 10 now, right?!?! -> " + str(X_first_neutral_data_less_data.size))
print("original var still same: " + str(X_first_neutral_data_less_data.size))
print()
print(X_first_neutral_data.head)
```
#### Problem description
Type of variable is pandas.core.frame.DataFrame calling myvar=myvar.sample(n=10) does change the size of the variable but not to 10.
I don't think that my code is wrong because this code works as expected:
```
df=pd.DataFrame()
df=pd.DataFrame(np.random.randint(0,100,size=(10, 7)), columns=list('ABCDEFG'))
print(type(df))
print(df)
df=df.sample(3)
print(df)
```
Documentation of sample [here](http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.DataFrame.sample.html) states:
n = Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.
#### Expected Output

I have the newest pandas version: pandas-0.19.2
Am I missing something or is this a bug?
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MDExOlB1bGxSZXF1ZXN0MTAyMzgxNjUy
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BUG: no need to validate monotonicity when groupby-rolling
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| 0 | 2017-01-20T01:04:55Z | 2017-01-20T12:21:53Z | 2017-01-20T12:21:53Z |
CONTRIBUTOR
| null |
closes #15130
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`axis` param for pd.Panel doesn't work
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| 1 | 2017-01-20T03:04:53Z | 2018-07-06T20:21:14Z | 2017-01-20T12:16:43Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
a = pd.Panel({"A":{"a":[1,2,3,4,5], "b":[10, 20, np.nan, 40, 50]})
a.ffill(axis=0)["A"]
a.ffill(axis=1)["A"]
a.ffill(axis=2)["A"]
```
#### Problem description
change to the param `axis` doesn't change the result. It all gives:
a b
0 1.0 10.0
1 2.0 20.0
2 3.0 20.0
3 4.0 40.0
4 5.0 50.0
#### Expected Output
a b
0 1.0 10.0
1 2.0 20.0
2 3.0 3.0
3 4.0 40.0
4 5.0 50.0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 32.1.3
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.7.9.None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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| 202,106,065 |
MDExOlB1bGxSZXF1ZXN0MTAyNDM4MDcx
| 15,177 |
BLD: Remove csv dialect warning in io.rst
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| 5 | 2017-01-20T10:29:53Z | 2017-01-20T20:56:12Z | 2017-01-20T20:53:11Z |
MEMBER
| null |
Title is self-explanatory.
xref <a href="https://github.com/pandas-dev/pandas/pull/14911#issuecomment-273896473">#14911 (comment)</a>.
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MDU6SXNzdWUyMDIxMTYyNjQ=
| 15,178 |
rolling window function with multiple arguments by group
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[] | null | 1 | 2017-01-20T11:19:28Z | 2017-01-20T12:13:03Z | 2017-01-20T12:12:56Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
## let's say we have the below DF
rand = np.random.RandomState(1)
dff = pd.DataFrame({'A' : np.arange(20),
'B' : rand.randint(100, 120, 20),
'C' : rand.randint(0, 2, 20)})
### Ideal solution, this does not work of course
#dff['tau'] = dff.groupby('C').sort_values('A').rolling(window = 5).apply(sp.stats.mstats.kendalltau, args = ({'x' : 'A', 'y' : 'B'))
## workaround - need to pass an index, use a global DF, and (?) hard-code columns to use, use a to-be-deprecated function etc etc
def my_tau_indx(indx):
x = dff.iloc[indx, 0]
y = dff.iloc[indx, 1]
tau = sp.stats.mstats.kendalltau(x, y)[0]
return tau
grp = dff.sort_values(['A', 'C']).groupby('C', group_keys=False)
func = lambda x: pd.Series(pd.rolling_apply(np.arange(len(x)), 5, my_tau_indx), x.index)
t = grp.apply(func)
dff.reindex(t.index).assign(tau=t)
```
#### Problem description
There is no simple way to run a scipy/custom function requiring multiple arguments (by group) in a rolling window.
In the code above, let's say that the 'C' column below is used for grouping.
I need to
- Group by column 'C'
- Within each group, sort by 'A'
- Withing each group, apply a rolling function taking two (or more) arguments, like kendalltau, to arguments 'A' and 'B'.
I asked this question here: http://stackoverflow.com/questions/41715814/python-pandas-rolling-function-with-two-arguments-in-a-grouped-dataframe/41719105#41719105 .
And the only viable solution I received uses a to-be-deprecated pd.rolling_apply.
#### Expected Output
See the link above for the expected results. See the code sample for what I'd like to write to get the result.
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
DEBUG:pip.vcs:Registered VCS backend: git
DEBUG:pip.vcs:Registered VCS backend: hg
DEBUG:pip.pep425tags:Config variable 'Py_DEBUG' is unset, Python ABI tag may be incorrect
DEBUG:pip.pep425tags:Config variable 'WITH_PYMALLOC' is unset, Python ABI tag may be incorrect
DEBUG:pip.pep425tags:Config variable 'Py_DEBUG' is unset, Python ABI tag may be incorrect
DEBUG:pip.pep425tags:Config variable 'WITH_PYMALLOC' is unset, Python ABI tag may be incorrect
DEBUG:pip.vcs:Registered VCS backend: svn
DEBUG:pip.vcs:Registered VCS backend: bzr
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.12.0rc2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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MDExOlB1bGxSZXF1ZXN0MTAyNDUyMjQ0
| 15,179 |
DOC: reorg dialect in io.rst
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| 0 | 2017-01-20T12:11:46Z | 2017-01-20T20:59:38Z | 2017-01-20T20:59:38Z |
CONTRIBUTOR
| null |
seems we had a section after 'Error Handling' in Parsing Options which is really basic, but out of place.
Also moved Dialect to a proper sub-section.
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Unable to control import of 1st column from Excel file (with multi-row header)
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| 2 | 2017-01-20T12:16:47Z | 2017-01-20T14:43:03Z | 2017-01-20T12:51:18Z |
NONE
| null |
Let's assume there are two excel files with almost identical content:
file_1.xlsx:

file_2.xlsx:

Then reading first file in pandas:
df1 = pd.read_excel('file_1.xlsx',
header=0, index_col=None,
converters={'A0': str, 'B0': str})
print(df1)
Would produce expected result:
A0 B0 C0 D0 E0
0 0001 0004 0.1 1 a
1 0002 0005 0.2 2 b
2 0003 0006 0.3 3 c
However trying the same with the second file:
df2 = pd.read_excel('file_2.xlsx',
header=[0,1], index_col=None,
converters={('A0', 'A1'): str,
('A0', 'B1'): str},
)
print(df2)
Would yield somewhat different and unexpected (in comparison with previous example) result:
A0 A0 C0 E0
A1 B1 C1 D1 E1
1 0004 0.1 1 a
2 0005 0.2 2 b
3 0006 0.3 3 c
Since it is not possible to use `has_index_names=False` as it has been depreciated since 0.16.2, there seems to be no way to have control over how pandas imports this first column (i.e. no way to convert values before original formatting is lost).
And there is no way to tell pandas DO_NOT_ASSIGN first column to index as it ignores `index_col=None` when `header` is a list.
So the question is what would be the sensible way to regain control over import process of first columns with multi-index header:
- revive or de-depreciate (would that be appreciate?) `has_index_names`;
- make `index_col` play a role in parsing header?
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MDExOlB1bGxSZXF1ZXN0MTAyNDU3ODM2
| 15,181 |
Relocate exceptions #14800
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[] | null | 4 | 2017-01-20T12:53:48Z | 2017-03-20T00:13:02Z | 2017-03-20T00:13:01Z |
CONTRIBUTOR
| null |
- [x] closes #14800
- [x] tests added / passed
- [x] passes ``git diff master | flake8 --diff``
- [x] whatsnew entry
Deprecation warnings will only be issued when exceptions/warnings are raised. I did think of
putting a deprecation warning in ``pandas.common.core`` indicating that these exceptions
were deprecated but these would be seen when *anything* was imported from ``pandas.core.common``. Please advise.
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| 15,182 |
CategoricalData is converted to Objects when merging dataframes
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| 3 | 2017-01-20T16:56:57Z | 2017-01-20T17:10:01Z | 2017-01-20T17:02:33Z |
CONTRIBUTOR
| null |
#### Code Sample
```python
testData = pd.DataFrame(data={"id" : range(10), "cat" : pd.Categorical(["a"]*5 + ["b"]*5)})
print(testData.cat.dtype)
# category
testData2 = pd.DataFrame(data={"id" : range(10), "other" : np.random.randint(1,24,10)})
merged = pd.merge(testData, testData2,how="left", on="id")
print(merged.cat.dtype)
# object
```
#### Problem description
When merging two dataframes, an otherwise untouched categorical column is converted to an ordinary column.
#### Expected Output
```python
print(merged.cat.dtype)
# category
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 742d4a52d4db55793f6ca20014b619b5979627b9
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.11.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_GB.utf8
LANG: en_GB.utf8
LOCALE: None.None
pandas: 0.19.0+346.g742d4a5
nose: 1.3.7
pip: 9.0.1
setuptools: 33.1.1
Cython: 0.25.2
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
pandas_datareader: None
</details>
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BUG: Timestamp array comparison may raise RecursionError
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| 2 | 2017-01-21T05:16:37Z | 2017-12-19T11:09:45Z | 2017-12-19T11:09:45Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
n1 = np.array([pd.Timestamp('2011-01-01 00:00:00'),
pd.Timestamp('2011-01-03 00:00:00-0500', tz='US/Eastern')], dtype=object)
pd.Timestamp('2011-01-03 00:00:00-0500', tz='US/Eastern') == n1
# RecursionError: maximum recursion depth exceeded
```
#### Problem description
Related to #12780. This issue causes datetime-like `.replace` to raise ``SystemError``. Recursion is caused by this line:
https://github.com/pandas-dev/pandas/blob/master/pandas/tslib.pyx#L1040
#### Expected Output
raises ``TypeError``.
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
</details>
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| 15,184 |
DEPS: require modern openpyxl
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| 0 | 2017-01-21T16:29:44Z | 2017-12-04T11:35:57Z | 2017-12-04T11:35:45Z |
CONTRIBUTOR
| null |
we currently support as old as 1.6.1
we should bump this to 2.3 (released mid-2015).
then can remove the compat for openpyxl < 2 code and fully support styles.
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MDExOlB1bGxSZXF1ZXN0MTAyNTg3ODMz
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DEPR: remove openpyxl deprecation warnings
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| 0 | 2017-01-21T16:31:05Z | 2017-01-21T16:49:22Z | 2017-01-21T16:49:22Z |
CONTRIBUTOR
| null |
closes #15090
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ERR: handle category dtype in .query with engine=numexpr
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| 3 | 2017-01-22T02:00:03Z | 2020-04-26T20:56:25Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
BANDS = ["{}AM".format(i) for i in range(0,10)]
df = pd.DataFrame({'Time': BANDS})
df.Time = df.Time.astype('category', ordered=True, categories=BANDS)
df.query("Time >= '5AM'" )
```
#### Problem description
The query fails with a ValueError: unknown type object.
Since Time is an ordered categorical and the query should return df[df.Time >= '5AM']
#### Expected Output
Time
5 5AM
6 6AM
7 7AM
8 8AM
9 9AM
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.41-36.55.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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COMPAT: clarify Index integer conversions when dtype is specified in construction
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| 29 | 2017-01-22T15:40:30Z | 2017-03-29T04:31:00Z | 2017-03-10T23:41:49Z |
CONTRIBUTOR
| null |
xref #15162
so [8], and [9] are our current model, IOW, we make an effort to convert to the specified type, but will coerce to an available type if the data is not valid for that dtype.
ideally we would also be consistent w.r.t. #15145, IOW Series construction with a specified dtype (not that we upcast to the available Index types but don't do this for Series, e.g. [18])
So we should be consistent on this.
```
In [8]: Index([np.nan],dtype='int64')
Out[8]: Float64Index([nan], dtype='float64')
In [9]: Index([np.nan],dtype='uint64')
Out[9]: Float64Index([nan], dtype='float64')
In [10]: Index([np.iinfo(np.int64).max-1],dtype='int64')
Out[10]: Int64Index([9223372036854775806], dtype='int64')
In [11]: Index([np.iinfo(np.int64).max-1],dtype='uint64')
Out[11]: UInt64Index([9223372036854775806], dtype='uint64')
# I guess this should convert to Float64Index?
In [12]: Index([np.iinfo(np.uint64).max-1],dtype='int64')
---------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
<ipython-input-12-f2ec7d3c38a4> in <module>()
----> 1 Index([np.iinfo(np.uint64).max-1],dtype='int64')
/Users/jreback/pandas/pandas/indexes/base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
318 # other iterable of some kind
319 subarr = _asarray_tuplesafe(data, dtype=object)
--> 320 return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs)
321
322 """
/Users/jreback/pandas/pandas/indexes/base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
199 inferred = lib.infer_dtype(data)
200 if inferred == 'integer':
--> 201 data = np.array(data, copy=copy, dtype=dtype)
202 elif inferred in ['floating', 'mixed-integer-float']:
203
OverflowError: Python int too large to convert to C long
In [13]: Index([np.iinfo(np.uint64).max-1],dtype='uint64')
Out[13]: UInt64Index([18446744073709551614], dtype='uint64')
In [14]: Index([-1], dtype='int64')
Out[14]: Int64Index([-1], dtype='int64')
# this looks wrong
In [15]: Index([-1], dtype='uint64')
Out[15]: UInt64Index([18446744073709551615], dtype='uint64')
# we do this type of same-dtype upcasting already (this is correct / good thing)
In [18]: Index([np.iinfo(np.int32).max+1], dtype='int64')
Out[18]: Int64Index([2147483648], dtype='int64')
```
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| 202,391,829 |
MDExOlB1bGxSZXF1ZXN0MTAyNjI1Njgz
| 15,188 |
CLN: remove build warnings in join_asof indexers
|
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MDExOlB1bGxSZXF1ZXN0MTAyNjI1NzAy
| 15,189 |
CI: add matplotlib, xlwt to build for 3.6
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| 1 | 2017-01-22T16:08:25Z | 2017-01-23T20:01:16Z | 2017-01-22T16:20:58Z |
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MDU6SXNzdWUyMDIzOTYwMzA=
| 15,190 |
BLD: remove warnings for building parser / hashtable extensions
|
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| 7 | 2017-01-22T17:18:09Z | 2017-01-31T08:18:08Z | 2017-01-30T14:15:09Z |
CONTRIBUTOR
| null |
https://travis-ci.org/jreback/pandas/jobs/194236743
see what we can do about some of these warnings, I think *some* are the result of the uint64 hashtable code. this would be after https://github.com/pandas-dev/pandas/pull/15188 (merging shortly)
in parser.pyx
```
cythoning pandas/parser.pyx to pandas/parser.c
building 'pandas.parser' extension
creating build/temp.linux-x86_64-3.5/pandas/src
creating build/temp.linux-x86_64-3.5/pandas/src/parser
gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/klib -Ipandas/src -I/home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include -I/home/travis/miniconda3/envs/pandas/include/python3.5m -c pandas/parser.c -o build/temp.linux-x86_64-3.5/pandas/parser.o -Wno-unused-function
In file included from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/ndarraytypes.h:1777:0,
from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/parser.c:437:
/home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
In file included from pandas/parser.c:439:0:
pandas/src/numpy_helper.h: In function ‘transfer_object_column’:
pandas/src/numpy_helper.h:131:19: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
In file included from pandas/parser.c:441:0:
pandas/src/klib/khash_python.h: In function ‘asint64’:
pandas/src/klib/khash_python.h:17:3: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]
pandas/parser.c: In function ‘__pyx_f_6pandas_6parser_10TextReader__get_header’:
pandas/parser.c:9109:49: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c:9177:49: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c:9209:49: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c:10249:45: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c:10308:23: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c:10401:34: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c:10437:34: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__string_box_factorize’:
pandas/parser.c:22294:35: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__string_box_utf8’:
pandas/parser.c:22743:35: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__string_box_decode’:
pandas/parser.c:23203:35: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__categorical_convert’:
pandas/parser.c:23670:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__apply_converter’:
pandas/parser.c:30308:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c:30446:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c:30558:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c: In function ‘__pyx_f_6pandas_6parser__fill_structured_column’:
pandas/parser.c:31598:35: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/parser.c: In function ‘__pyx_f_6pandas_6parser_10TextReader__get_header’:
pandas/parser.c:10283:61: warning: ‘__pyx_v_data_line’ may be used uninitialized in this function [-Wuninitialized]
gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/klib -Ipandas/src -I/home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include -I/home/travis/miniconda3/envs/pandas/include/python3.5m -c pandas/src/parser/tokenizer.c -o build/temp.linux-x86_64-3.5/pandas/src/parser/tokenizer.o -Wno-unused-function
pandas/src/parser/tokenizer.c: In function ‘tokenize_bytes’:
pandas/src/parser/tokenizer.c:747:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:762:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:785:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:827:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:854:29: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:863:25: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:900:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:948:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:1015:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:1033:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:1043:21: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:1046:25: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:1049:25: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:1060:25: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:1077:41: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c: In function ‘parser_consume_rows’:
pandas/src/parser/tokenizer.c:1165:15: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:1202:19: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c: In function ‘parser_trim_buffers’:
pandas/src/parser/tokenizer.c:1229:17: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:1252:17: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c:1280:17: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c: In function ‘_tokenize_helper’:
pandas/src/parser/tokenizer.c:1333:47: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/src/parser/tokenizer.c: In function ‘str_to_uint64’:
pandas/src/parser/tokenizer.c:1991:16: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/klib -Ipandas/src -I/home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include -I/home/travis/miniconda3/envs/pandas/include/python3.5m -c pandas/src/parser/io.c -o build/temp.linux-x86_64-3.5/pandas/src/parser/io.o -Wno-unused-function
pandas/src/parser/io.c: In function ‘buffer_mmap_bytes’:
pandas/src/parser/io.c:218:32: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
gcc -pthread -shared -L/home/travis/miniconda3/envs/pandas/lib -Wl,-rpath=/home/travis/miniconda3/envs/pandas/lib,--no-as-needed build/temp.linux-x86_64-3.5/pandas/parser.o build/temp.linux-x86_64-3.5/pandas/src/parser/tokenizer.o build/temp.linux-x86_64-3.5/pandas/src/parser/io.o -L/home/travis/miniconda3/envs/pandas/lib -lpython3.5m -o
```
in hashtable.pyx
```
cythoning pandas/hashtable.pyx to pandas/hashtable.c
building 'pandas.hashtable' extension
gcc -pthread -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -Ipandas/src/klib -Ipandas/src -I/home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include -I/home/travis/miniconda3/envs/pandas/include/python3.5m -c pandas/hashtable.c -o build/temp.linux-x86_64-3.5/pandas/hashtable.o -Wno-unused-function
In file included from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/ndarraytypes.h:1777:0,
from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/hashtable.c:436:
/home/travis/miniconda3/envs/pandas/lib/python3.5/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
In file included from pandas/hashtable.c:438:0:
pandas/src/klib/khash_python.h: In function ‘asint64’:
pandas/src/klib/khash_python.h:17:3: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]
In file included from pandas/hashtable.c:439:0:
pandas/src/numpy_helper.h: In function ‘transfer_object_column’:
pandas/src/numpy_helper.h:131:19: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/hashtable.c: In function ‘__pyx_f_6pandas_9hashtable_value_count_float64’:
pandas/hashtable.c:22504:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_2duplicated_float64’:
pandas/hashtable.c:23298:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/hashtable.c: In function ‘__pyx_f_6pandas_9hashtable_value_count_uint64’:
pandas/hashtable.c:23829:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_6duplicated_uint64’:
pandas/hashtable.c:24623:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/hashtable.c: In function ‘__pyx_f_6pandas_9hashtable_value_count_object’:
pandas/hashtable.c:25180:33: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_10duplicated_object’:
pandas/hashtable.c:25824:31: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/hashtable.c: In function ‘__pyx_f_6pandas_9hashtable_value_count_int64’:
pandas/hashtable.c:26337:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_14duplicated_int64’:
pandas/hashtable.c:27130:37: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_16mode_int64’:
pandas/hashtable.c:27456:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_18mode_uint64’:
pandas/hashtable.c:27831:39: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
pandas/hashtable.c: In function ‘__pyx_pf_6pandas_9hashtable_20mode_object’:
pandas/hashtable.c:28199:33: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
gcc -pthread -shared -L/home/travis/miniconda3/envs/pandas/lib -Wl,-rpath=/home/travis/miniconda3/envs/pandas/lib,--no-as-needed build/temp.linux-x86_64-3.5/pandas/hashtable.o -L/home/travis/miniconda3/envs/pandas/lib -lpython3.5m -o /home/travis/build/jreback/pandas/pandas/hashtable.cpython-35m-x86_64-linux-gnu.so
```
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BLD: remove more build warnings
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| 0 | 2017-01-22T17:36:03Z | 2017-01-22T21:42:57Z | 2017-01-22T21:42:57Z |
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| 15,192 |
Line breaks in Exception messages
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| 3 | 2017-01-22T18:41:04Z | 2018-07-06T20:21:14Z | 2017-01-24T18:58:24Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'a': [1, '2']})
df.to_hdf('tmp.h5', 'key', format='table')
```
#### Problem description
I wonder if there is a reason why the exception message contains a line break:
```
TypeError: Cannot serialize the column [a] because
its data contents are [mixed-integer] object dtype
```
The line breaks are explicitly introduced in module pandas/io/pytables.py:
```
raise TypeError(
"Cannot serialize the column [%s] because\n"
"its data contents are [%s] object dtype"
% (item, inferred_type)
)
```
It should be noted that besides this message, there are many others exception massages with line breaks in this module.
I make this question because I am logging the output and I think it would be better to have an event log per line.
#### Expected Output
```
TypeError: Cannot serialize the column [a] because its data contents are [mixed-integer] object dtype
```
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-107-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: ca_ES.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
</details>
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MDU6SXNzdWUyMDI0MTk0NzE=
| 15,193 |
pivot_table over Categorical columns
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| 4 | 2017-01-22T23:24:09Z | 2017-03-04T21:41:53Z | 2017-03-04T21:41:53Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
stations = ['Kings Cross Station', 'Newtown Station', 'Parramatta Station',
'Town Hall Station', 'Central Station', 'Circular Quay Station',
'Martin Place Station', 'Museum Station', 'St James Station',
'Bondi Junction Station', 'North Sydney Station']
df = pd.DataFrame({'Station': ['Kings Cross Station', 'Newtown Station', 'Parramatta Station',
'Kings Cross Station', 'Newtown Station', 'Parramatta Station',
'Kings Cross Station', 'Newtown Station', 'Parramatta Station'],
'Date': pd.DatetimeIndex(['1/1/2017', '1/1/2017', '1/1/2017',
'2/1/2017', '2/1/2017', '2/1/2017',
'3/1/2017', '3/1/2017', '3/1/2017',]),
'Exit': range(0, 9)})
df.Station = df.Station.astype('category', ordered=True, categories=stations)
df.pivot_table(index = 'Date', columns= 'Station', values = 'Exit', dropna=True)
```
#### Problem description
When the column is a Categorical the output of pivot_table
1. Includes columns that are all NaN which should not be the case as dropna=True
2. Includes columns for categories that aren't in the input Data Frame, which is strange.
3. Is not the same as when the output before the column is converted to a categorical
This was not the behaviour in earlier versions (18?)
#### Expected Output
Should be the same as the output when the column is not a categorical
Station Kings Cross Station Newtown Station Parramatta Station
Date
2017-01-01 0 1 2
2017-02-01 3 4 5
2017-03-01 6 7 8
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.41-36.55.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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| 202,426,704 |
MDU6SXNzdWUyMDI0MjY3MDQ=
| 15,194 |
`lines` kwarg in `pd.read_json`
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| 1 | 2017-01-23T01:12:15Z | 2017-01-23T01:14:55Z | 2017-01-23T01:14:42Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
with open("clean.json", "r+") as data:
df = pd.read_json(path_or_buf=data, lines=True)
```
#### Problem description
In the documentation, `lines` is a kwarg that can be used to specify whether the JSON file has multiple lines. This code worked a month ago, where `df` was a dataframe where each entry was a line of `clean.json`.
#### Output
Instead, I get `TypeError: read_json() got an unexpected keyword argument 'lines'`
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MDExOlB1bGxSZXF1ZXN0MTAyNjY0OTAy
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BUG: Fixed incorrect stream size check (#14125)
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| 7 | 2017-01-23T06:38:42Z | 2017-01-24T23:14:06Z | 2017-01-24T23:13:38Z |
CONTRIBUTOR
| null |
- [X] closes #14125
- [X] tests added / passed: test_large_difference_in_columns in c_parser_only.py, Other tests passed.
- [X] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry: Added to 0.20 whatsnew
Issue summary: Previously, self->stream_cap was copied into a local variable called maxstreamsize each time tokenize_bytes ran, and then this was checked in the PUSH_CHAR macro. However, there is one other place in the file where function make_stream_space() is called (in end_line()), and when this happens self->stream_cap is increased but maxstreamsize is not updated, making the check incorrect. In rare circumstances (see original issue or test case) this could cause a crash. The resolution is to just check self->stream_cap directly.
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Fix bug in window function count should count anything non-null
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| 4 | 2017-01-23T13:39:54Z | 2017-01-24T18:02:30Z | 2017-01-24T18:02:16Z |
CONTRIBUTOR
| null |
- [x] closes #12541
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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PERF: DataFrame.groupby.nunique is non-performant
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| 1 | 2017-01-23T17:22:32Z | 2017-01-24T14:20:30Z | 2017-01-24T14:20:30Z |
CONTRIBUTOR
| null |
xref #14376
```
# from the asv
In [10]: n = 10000
...: df = DataFrame({'key1': randint(0, 500, size=n),
...: 'key2': randint(0, 100, size=n),
...: 'ints': randint(0, 1000, size=n),
...: 'ints2': randint(0, 1000, size=n), })
...:
In [11]: %timeit df.groupby(['key1', 'key2']).nunique()
1 loop, best of 3: 4.25 s per loop
In [12]: result = df.groupby(['key1', 'key2']).nunique()
In [13]: g = df.groupby(['key1', 'key2'])
In [14]: expected = pd.concat([getattr(g, col).nunique() for col in g._selected_obj.columns], axis=1)
In [15]: result.equals(expected)
Out[15]: True
In [16]: %timeit pd.concat([getattr(g, col).nunique() for col in g._selected_obj.columns], axis=1)
100 loops, best of 3: 6.94 ms per loop
```
``Series.groupby.nunique`` has a very performant implementation, but the way the ``DataFrame.groupby.nunique`` is implemented (via ``.apply``) it ends up in a python loop over the groups, which nullifies this.
should be straightforward to fix this. need to make sure to test with ``as_index=True/False``
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API: .str ops on category should return category if result is non-boolean
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[] | null | 4 | 2017-01-23T18:23:00Z | 2021-05-08T00:37:51Z | null |
CONTRIBUTOR
| null |
In the PR implementing ``.str/.dt`` on Categoricals, https://github.com/pandas-dev/pandas/pull/11582.
This is perfectly reasonable. We perform the string op on the uniques. This routine is a boolean result, so we return a boolean result.
```
In [2]: s = pd.Series(list('aabb')).astype('category')
In [3]: s
Out[3]:
0 a
1 a
2 b
3 b
dtype: category
Categories (2, object): [a, b]
In [4]: s.str.contains("a")
Out[4]:
0 True
1 True
2 False
3 False
dtype: bool
```
However, I don't recall the rationale for: performing the op on the uniques (as its a categorical), but then returning an ``object`` dtype.
```
In [5]: s.str.upper()
Out[5]:
0 A
1 A
2 B
3 B
dtype: object
```
These are by-definition pure transforms, and so a new categorical makes sense. e.g. in this case
```
In [6]: pd.Series(pd.Categorical.from_codes(s.cat.codes, s.cat.categories.str.upper()))
Out[6]:
0 A
1 A
2 B
3 B
dtype: category
Categories (2, object): [A, B]
```
This will be way more efficient than actually converting to object.
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BLD: numpy-dev causing some issues
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| 3 | 2017-01-23T20:37:45Z | 2017-01-24T18:52:39Z | 2017-01-24T18:52:39Z |
CONTRIBUTOR
| null |
https://travis-ci.org/pandas-dev/pandas/jobs/194558937
https://github.com/numpy/numpy/pull/8279 was just merged, which broke our tests
:>
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COMPAT: don't rely on dtype being False after numpy >= 1.13
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| 1 | 2017-01-23T21:18:57Z | 2017-01-24T18:52:39Z | 2017-01-24T18:52:39Z |
CONTRIBUTOR
| null |
closes #15199
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PERF: DataFrame.groupby.nunique
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| 0 | 2017-01-23T22:15:25Z | 2017-01-24T14:20:30Z | 2017-01-24T14:20:30Z |
CONTRIBUTOR
| null |
closes #15197
```
In [2]: %timeit df.groupby(['key1', 'key2']).nunique()
100 loops, best of 3: 9.31 ms per loop
```
```
before after ratio
[be3f2aea] [6d026165]
- 473.67μs 427.27μs 0.90 groupby.GroupBySuite.time_nunique('float', 100)
- 4.41s 9.53ms 0.00 groupby.groupby_nunique.time_groupby_nunique
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
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MDExOlB1bGxSZXF1ZXN0MTAyODE3NjY5
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BUG: GH14233 resample().median() failed if duplicate column names wer…
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| 2 | 2017-01-23T22:48:25Z | 2017-02-28T15:43:52Z | 2017-01-24T18:46:27Z |
CONTRIBUTOR
| null |
Simple fix for median issue. Should use cython implementation.
- [X] closes #14233
- [X] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
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MDU6SXNzdWUyMDI2OTY5ODU=
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read_csv: Rows with extra columns set data in index
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[] | null | 10 | 2017-01-24T01:04:42Z | 2019-01-23T16:44:45Z | null |
CONTRIBUTOR
| null |
#### Code Sample
```python
from io import StringIO
import pandas as pd
# Notice that the second row has an extra column
data = """UNITID,XTUIT1,TUITION1
100636,"R",0,
"""
f = StringIO(data)
df = pd.read_csv(f)
print(df)
```
#### Actual Output
```
UNITID XTUIT1 TUITION1
100636 R 0
```
#### Problem description
This behavior shifts column names, where the first column of data no longer matches the first column name. It also sets the extra data as the index, leading to very unexpected results.
#### Expected Output
```
UNITID XTUIT1 TUITION1 Unnamed: 3
0 100636 R 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 34.0.1
Cython: None
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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ENH: Allow elementwise coloring in background_gradient with axis=None
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| 8 | 2017-01-24T05:19:20Z | 2018-07-12T12:38:38Z | 2018-07-12T12:38:38Z |
CONTRIBUTOR
| null |
At present the axis argument to `background_gradient` only accepts 0 or 1 so that the resulting "heatmap" can only be applied columnwise or rowwise rather than to the whole table.
```
Signature: df.style.background_gradient(cmap='PuBu', low=0, high=0, axis=0, subset=None)
Parameters
----------
cmap: str or colormap
matplotlib colormap
low, high: float
compress the range by these values.
axis: int or str
1 or 'columns' for colunwise, 0 or 'index' for rowwise
subset: IndexSlice
a valid slice for ``data`` to limit the style application to
```
I propose that `axis=None` be allowed to mean applying a color gradient elementwise.
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MDExOlB1bGxSZXF1ZXN0MTAyODcxNzg2
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BUG: groupby.cummin/cummax nans inf values for int64
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| 4 | 2017-01-24T08:22:38Z | 2017-12-20T02:04:04Z | 2017-01-25T19:05:23Z |
MEMBER
| null |
- [x] closes #15109
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
I migrated the `accum` portions of the code into cython and attempted to implement your suggested method to fix this issue @jreback. Let me know if this is along the right track.
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DEPS: drop numpy < 1.9
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| 6 | 2017-01-24T13:57:20Z | 2017-08-22T09:50:58Z | 2017-08-22T09:50:58Z |
CONTRIBUTOR
| null |
this would provide some code cleanup possibilities.
- [ ] use ``np.full`` (see https://github.com/pandas-dev/pandas/pull/17000 for example)
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| 202,841,292 |
MDU6SXNzdWUyMDI4NDEyOTI=
| 15,207 |
ValueError("Cannot describe a DataFrame without columns") ValueError: Cannot describe a DataFrame without columns
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| 3 | 2017-01-24T14:55:37Z | 2017-01-24T15:46:19Z | 2017-01-24T15:02:48Z |
NONE
| null |
This is my TestWorkbook.xlsx in same location as my python script:
```
Data Time Variable Value
08/01/17 06:00:30 Occupancy.1m.15s.mean 497.714
08/01/17 06:00:30 QueueTime.1m.15s.mean 698.999
08/01/17 06:00:30 QueueStatus.1m.15s.mean 42.8639
08/01/17 06:00:30 QueueTime.1m.15s.mean 113.139
```
```python
import pandas as pd
import numpy as np
import glob
all_data = pd.DataFrame()
res = glob.glob("*.xlsx")
for f in glob.glob("TestWorkbook.xlsx"):
df = pd.read_excel(f, sheetname='Sheet1')
all_data = all_data.append(df, ignore_index=True)
all_data.describe()
```
Not sure why it is returning error
raise ValueError("Cannot describe a DataFrame without columns")
ValueError: Cannot describe a DataFrame without columns
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