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MDU6SXNzdWUxODI2NTM5OTc=
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PERF: to_json very slow with lines=True
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| 1 | 2016-10-12T22:53:20Z | 2016-10-15T20:02:41Z | 2016-10-15T20:02:41Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
N = 100000
C = 5
In [6]: df = DataFrame(dict([('float{0}'.format(i), np.random.randn(N)) for i in range(C)]))
In [7]: df.to_json('foo.json',orient='records',lines=True)
In [8]: %timeit df.to_json('foo.json',orient='records',lines=True)
1 loop, best of 3: 3.66 s per loop
In [9]: %timeit df.to_json('foo.json',orient='records')
10 loops, best of 3: 98.8 ms per loop
```
As discussed in https://github.com/pydata/pandas/pull/14391
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DOC: pydata/pandas -> pandas-dev/pandas
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| 3 | 2016-10-12T23:46:34Z | 2016-10-13T19:59:31Z | 2016-10-13T19:59:21Z |
CONTRIBUTOR
| null |
as main repo was updated
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ENH: Do not exclude Boolean columns from DataFrame.describe()
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| 1 | 2016-10-12T23:48:42Z | 2016-10-12T23:57:48Z | 2016-10-12T23:57:12Z |
NONE
| null |
Sometimes, it is desired to know the percentage of true values in a Boolean column. Like when a DataFrame is composed of results from fitting many models, knowing up front the distribution of number of successful fits is helpful.
```
In [1]: df = pd.DataFrame({'c1' : list('abcdef'),
'c2' : [True, False, True, True, False, False],
'c3' : np.random.randn(6),
'c4' : np.arange(6)})
df.describe()
Out[1]:
c3 c4
count 6.000000 6.000000
mean 0.088425 2.500000
std 1.633860 1.870829
min -2.337804 0.000000
25% -0.973194 1.250000
50% 0.487471 2.500000
75% 1.231870 3.750000
max 1.873493 5.000000
```
#### Expected Output
```
In [2]: df['c2'] = df['c2'].astype('int64')
df.describe()
Out[2]:
c2 c3 c4
count 6.000000 6.000000 6.000000
mean 0.500000 0.088425 2.500000
std 0.547723 1.633860 1.870829
min 0.000000 -2.337804 0.000000
25% 0.000000 -0.973194 1.250000
50% 0.500000 0.487471 2.500000
75% 1.000000 1.231870 3.750000
max 1.000000 1.873493 5.000000
```
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Behavior change in Index.equals from 0.18.0 to 0.19.0 -- intentional?
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| 2 | 2016-10-13T04:22:42Z | 2019-06-28T19:54:50Z | 2019-06-28T19:54:50Z |
NONE
| null |
Traditionally, we have:
``` python
In [3]: pandas.__version__
Out[3]: '0.18.0'
In [4]: pandas.Index([1, 2]).equals([1, 2])
Out[4]: True
```
But this changed
``` python
In [4]: pandas.__version__
Out[4]: '0.19.0'
In [5]: pandas.Index([1, 2]).equals([1, 2])
Out[5]: False
```
Looks like it might not be intentional so FYI.
(Noticed because it broke a few tests in the patsy test suite.)
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Bug: Made it so that 0 was included in uint8
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| 3 | 2016-10-13T04:25:29Z | 2016-10-22T08:49:42Z | 2016-10-21T23:55:53Z |
NONE
| null |
- [ ] closes #14401
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Decided to restart. Sorry for the inconvenience. -> #14472
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| 0 | 2016-10-13T07:41:20Z | 2016-10-13T07:49:57Z | 2016-10-13T07:45:11Z |
NONE
| null |
#### A small, complete example of the issue
``` python
# Your code here
```
#### Expected Output
#### Output of `pd.show_versions()`
<details>
# Paste the output here
</details>
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Cannot initialize a data frame with None
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| 1 | 2016-10-13T12:51:03Z | 2016-10-13T13:13:39Z | 2016-10-13T13:13:29Z |
NONE
| null |
Hi all,
just figured out that the following code
```
pd.DataFrame({'x':None}, index=[0])
```
works in 0.18.1 but not 0.19.0, is that the expected behavior? Thanks!
Best,
Jie
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Inconsistent behavior in Timestamp for large negative values
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| 2 | 2016-10-13T14:35:43Z | 2016-10-20T13:46:01Z | 2016-10-20T13:46:01Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
# Your code here
In [1]: import numpy as np; import pandas as pd
In [3]: pd.Timestamp(np.iinfo(np.int64).min + 80000000000000)
Out[3]: Timestamp('2262-04-11 22:26:03.145224192')
In [4]: pd.Timestamp(np.iinfo(np.int64).min + 90000000000000)
Out[4]: Timestamp('1677-09-22 01:12:43.145224192')
In [5]: pd.Timestamp(np.iinfo(np.int64).min + 85000000000000)
---------------------------------------------------------------------------
OutOfBoundsDatetime Traceback (most recent call last)
<ipython-input-5-47bf9ab2ad0f> in <module>()
----> 1 pd.Timestamp(np.iinfo(np.int64).min + 85000000000000)
pandas/tslib.pyx in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:9932)()
pandas/tslib.pyx in pandas.tslib.convert_to_tsobject (pandas/tslib.c:26453)()
pandas/tslib.pyx in pandas.tslib._check_dts_bounds (pandas/tslib.c:30034)()
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2262-04-11 23:49:23
```
#### Expected Output
I would expect all the values listed above to evaluate to dates in the 1600s. The behavior here looks like there might be a signed integer overflow happening in C somewhere, which is a little nerve-wracking since that's undefined behavior unless pandas is compiled with `-fwrapv`.
#### Output of `pd.show_versions()`
<details>
# Paste the output here
In [8]: pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-16-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 28.3.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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| 182,807,873 |
MDExOlB1bGxSZXF1ZXN0ODkyMTUyMDM=
| 14,416 |
Concat with axis rows
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| 6 | 2016-10-13T14:37:04Z | 2016-10-19T13:50:07Z | 2016-10-15T19:59:53Z |
CONTRIBUTOR
| null |
- [x] closes #14369
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
I closed #14389 and opened with one in hopes of the new commits showing up.
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MDU6SXNzdWUxODI4NjI1Mzg=
| 14,417 |
BUG: index.max() not matching index.levels[0].max() for multiindex on sliced dataframe
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| 1 | 2016-10-13T18:19:09Z | 2016-10-13T19:01:54Z | 2016-10-13T19:01:39Z |
NONE
| null |
Small example:
``` python
df = pd.DataFrame(index=pd.MultiIndex.from_product([['A','B','C'],['a','b']])
```
Then:
``` python
df.loc[:'B'].index.max()
```
gives `('B', 'b')` as expected.
But:
``` python
df.loc[:'B'].index.levels[0].max()
```
gives `'C'` in conflict with the previous result.
#### Expected Functionality
The max index of each level matches the max index of the full multiindex
(Apologies if this is not actually intended functionality)
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| 182,874,273 |
MDU6SXNzdWUxODI4NzQyNzM=
| 14,418 |
read_csv and others derived from _read close user-provided filehandles
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| 12 | 2016-10-13T19:09:25Z | 2020-10-08T12:35:07Z | 2016-11-02T11:27:40Z |
NONE
| null |
I believe the "regression" was introduced on [this line](https://github.com/pandas-dev/pandas/commit/4a805216d99b37955c97625d304980eff10cab56#diff-777d7549579ddc0c6e67596ad87e0d27R1447). That being said, tracking which filehandles a library owns vs what a user provided is _hard_, and I can't fault you guys if this is considered correct behavior from now on. Just wanted to bring it to your attention. Thanks!
#### A small, complete example of the issue
``` pytb
In [1]: import pandas as pd
In [2]: import io
In [3]: fh = io.StringIO('a,b\n1,2\n')
In [4]: fh.closed
Out[4]: False
In [5]: pd.read_csv(fh)
Out[5]:
a b
0 1 2
In [6]: fh.closed
Out[6]: True
```
#### Expected Output
``` pytb
In [6]: fh.closed
Out[6]: False
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-96-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 20.1.1
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.5a2
patsy: None
dateutil: 2.5.3
pytz: 2016.4
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
</details>
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MDExOlB1bGxSZXF1ZXN0ODkyNzQyNDI=
| 14,419 |
Period factorization
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| 2 | 2016-10-13T20:17:07Z | 2016-10-15T19:27:19Z | 2016-10-15T19:27:11Z |
CONTRIBUTOR
| null |
- [x] closes #14338
- [x] tests not needed / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
asv
```
before after ratio
[c41c6511] [96b364a4]
- 2.44s 46.28ms 0.02 groupby.groupby_period.time_groupby_sum
```
Continuation of #14348
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DataFrame.groupby(level=...).rolling(n).mean()
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| 3 | 2016-10-13T20:58:07Z | 2016-10-13T21:12:13Z | 2016-10-13T21:11:55Z |
NONE
| null |
Using the groupby().rolling() object seems to duplicate a level of the index.
```
In [9]: d.groupby(level='ticker').rolling(30).mean()
Out[9]:
ticker ticker date
BMO BMO 2006-01-02 NaN
2006-01-03 NaN
TD TD 2016-09-22 57.139340
2016-09-23 57.171864
In [10]: d.groupby(level='ticker').apply(pd.rolling_mean, 30)
Out[10]:
ticker date
BMO 2006-01-02 NaN
2006-01-03 NaN
TD 2016-09-22 57.139340
2016-09-23 57.171864
In [11]: d.groupby(level='ticker').apply(lambda x: x.rolling(30).mean())
Out[11]:
ticker date
BMO 2006-01-02 NaN
2006-01-03 NaN
TD 2016-09-22 57.139340
2016-09-23 57.171864
```
I would expect the output to be the same in all three cases.
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cryptic DataFrame.agg error when using dictionaries
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| 18 | 2016-10-13T23:23:18Z | 2016-10-14T19:33:06Z | 2016-10-13T23:30:19Z |
NONE
| null |
Not sure if this is a bug. This works:
``` python
(
pd.DataFrame({"u": [2,1,4,2,5], "a": ["a", "a", "b", "a", "b"]})
.groupby("a")
.agg(lambda x: np.mean(x)/np.std(x))
)
```
while this returns an error:
``` python
(
pd.DataFrame({"u": [2,1,4,2,5], "a": ["a", "a", "b", "a", "b"]})
.groupby("a")
.agg({"blah": lambda x: np.mean(x)/np.std(x)})
)
```
error: KeyError: 'blah'
<details>
## LONG ERROR MESSAGE
``````
KeyError Traceback (most recent call last)
/opt/local/lib/python3.6/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)
2103 try:
-> 2104 return self._engine.get_loc(key)
2105 except KeyError:
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4160)()
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4024)()
pandas/src/hashtable_class_helper.pxi in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13161)()
pandas/src/hashtable_class_helper.pxi in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13115)()
KeyError: 'blah'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-100-4f93bf630ec4> in <module>()
2 pd.DataFrame({"u": [2,1,4,2,5], "a": ["a", "a", "b", "a", "b"]})
3 .groupby("a")
----> 4 .agg({"blah": lambda x: np.mean(x)/np.std(x)})
5 )
/opt/local/lib/python3.6/site-packages/pandas/core/groupby.py in aggregate(self, arg, *args, **kwargs)
3697 @Appender(SelectionMixin._agg_doc)
3698 def aggregate(self, arg, *args, **kwargs):
-> 3699 return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
3700
3701 agg = aggregate
/opt/local/lib/python3.6/site-packages/pandas/core/groupby.py in aggregate(self, arg, *args, **kwargs)
3195
3196 _level = kwargs.pop('_level', None)
-> 3197 result, how = self._aggregate(arg, _level=_level, *args, **kwargs)
3198 if how is None:
3199 return result
/opt/local/lib/python3.6/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
547
548 try:
--> 549 result = _agg(arg, _agg_1dim)
550 except SpecificationError:
551
/opt/local/lib/python3.6/site-packages/pandas/core/base.py in _agg(arg, func)
498 result = compat.OrderedDict()
499 for fname, agg_how in compat.iteritems(arg):
--> 500 result[fname] = func(fname, agg_how)
501 return result
502
/opt/local/lib/python3.6/site-packages/pandas/core/base.py in _agg_1dim(name, how, subset)
477 aggregate a 1-dim with how
478 """
--> 479 colg = self._gotitem(name, ndim=1, subset=subset)
480 if colg.ndim != 1:
481 raise SpecificationError("nested dictionary is ambiguous "
/opt/local/lib/python3.6/site-packages/pandas/core/groupby.py in _gotitem(self, key, ndim, subset)
3724 elif ndim == 1:
3725 if subset is None:
-> 3726 subset = self.obj[key]
3727 return SeriesGroupBy(subset, selection=key,
3728 grouper=self.grouper)
/opt/local/lib/python3.6/site-packages/pandas/core/frame.py in __getitem__(self, key)
2055 return self._getitem_multilevel(key)
2056 else:
-> 2057 return self._getitem_column(key)
2058
2059 def _getitem_column(self, key):
/opt/local/lib/python3.6/site-packages/pandas/core/frame.py in _getitem_column(self, key)
2062 # get column
2063 if self.columns.is_unique:
-> 2064 return self._get_item_cache(key)
2065
2066 # duplicate columns & possible reduce dimensionality
/opt/local/lib/python3.6/site-packages/pandas/core/generic.py in _get_item_cache(self, item)
1384 res = cache.get(item)
1385 if res is None:
-> 1386 values = self._data.get(item)
1387 res = self._box_item_values(item, values)
1388 cache[item] = res
/opt/local/lib/python3.6/site-packages/pandas/core/internals.py in get(self, item, fastpath)
3518
3519 if not isnull(item):
-> 3520 loc = self.items.get_loc(item)
3521 else:
3522 indexer = np.arange(len(self.items))[isnull(self.items)]
/opt/local/lib/python3.6/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)
2104 return self._engine.get_loc(key)
2105 except KeyError:
-> 2106 return self._engine.get_loc(self._maybe_cast_indexer(key))
2107
2108 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4160)()
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4024)()
pandas/src/hashtable_class_helper.pxi in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13161)()
pandas/src/hashtable_class_helper.pxi in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13115)()
KeyError: 'blah'```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.alpha.3
python-bits: 64
OS: Linux
OS-release: 3.14.32-xxxx-grs-ipv6-64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_IE.UTF-8
LOCALE: en_IE.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 28.3.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0b3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
``````
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MDU6SXNzdWUxODI5NDc5OTM=
| 14,422 |
BUG: to_numeric doesn't work uint64 numbers
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| 4 | 2016-10-14T02:15:33Z | 2019-11-05T17:19:53Z | 2019-11-05T17:19:53Z |
NONE
| null |
uint64 isn't very well supported right now but something to consider.
``` python
In [10]: pd.to_numeric(pd.Series([0, 9223372036854775808]), downcast = 'unsigned')
---------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
<ipython-input-10-6e8272095758> in <module>()
----> 1 pd.to_numeric(pd.Series([0, 9223372036854775808]), downcast = 'unsigned')
/home/verhalenn/Documents/Open-Source/pandas/pandas/tools/util.py in to_numeric(arg, errors, downcast)
193 coerce_numeric = False if errors in ('ignore', 'raise') else True
194 values = lib.maybe_convert_numeric(values, set(),
--> 195 coerce_numeric=coerce_numeric)
196
197 except Exception:
/home/verhalenn/Documents/Open-Source/pandas/pandas/src/inference.pyx in pandas.lib.maybe_convert_numeric (pandas/lib.c:53043)()
667 seen_float = True
668 elif util.is_integer_object(val):
--> 669 floats[i] = ints[i] = val
670 seen_int = True
671 elif util.is_bool_object(val):
OverflowError: Python int too large to convert to C long
```
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| 182,959,741 |
MDU6SXNzdWUxODI5NTk3NDE=
| 14,423 |
BUG: unwanted numeric coercion after groupby-apply
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| 2 | 2016-10-14T04:13:53Z | 2017-03-16T12:08:59Z | 2017-03-16T12:08:59Z |
NONE
| null |
xref #14873 (boolean casts)
xref #14849 (datetime)
#### A small, complete example of the issue
``` python
import pandas as pd
def predictions(tool):
out = pd.Series(index=['p1', 'p2', 'useTime'], dtype=object)
if 'step1' in list(tool.State):
out['p1'] = str(tool[tool.State == 'step1'].Machine.values[0])
if 'step2' in list(tool.State):
out['p2'] = str(tool[tool.State == 'step2'].Machine.values[0])
out['useTime'] = str(tool[tool.State == 'step2'].oTime.values[0])
return out
df1 = pd.DataFrame({'Key': ['B', 'B', 'A', 'A'],
'State': ['step1', 'step2', 'step1', 'step2'],
'oTime': ['', '2016-09-19 05:24:33', '', '2016-09-19 23:59:04'],
'Machine': ['23', '36L', '36R', '36R']})
df2 = df1.copy()
df2.oTime = pd.to_datetime(df2.oTime)
pred1 = df1.groupby('Key').apply(predictions)
pred2 = df2.groupby('Key').apply(predictions)
print(pred1)
print(pred2)
```
#### Actual Output:
```
p1 p2 useTime
Key
A 36R 36R 2016-09-19 23:59:04
B 23 36L 2016-09-19 05:24:33
p1 p2 useTime
Key
A NaN 36R 2016-09-19T23:59:04.000000000
B 23.0 36L 2016-09-19T05:24:33.000000000
```
#### Expected Output
pred1 and pred2 should have the same values in column p1.
pred1 is correct whereas pred2 is changing type to float64.
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.0
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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BUG: String indexing against object dtype may raise AttributeError
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| 3 | 2016-10-14T11:21:48Z | 2016-10-22T09:54:41Z | 2016-10-22T09:54:33Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
String indexing may raise `AttributeError`, rather than `KeyError`.
```
df = pd.DataFrame([1], pd.Index([pd.Timestamp('2011-01-01')], dtype=object))
df.index.is_all_dates
# True
df['2011']
# AttributeError: 'Index' object has no attribute '_get_string_slice'
```
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MDU6SXNzdWUxODMwMjg5MTA=
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incorrect calculation of centered moving averages for even length series
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| 7 | 2016-10-14T11:39:06Z | 2018-07-17T08:42:19Z | 2016-10-14T20:45:43Z |
NONE
| null |
For even numbered periods the centered moving average is calculated incorrectly. Suppose the period length is 5. Then the center of 5 periods is 3. However if the period length is 4 then the center of the period is 2.5. The value at index 3 should be the average of the values at 2.5 and 3.5. Pandas is showing the 2.5 value at 3 which is incorrect.
EXAMPLE:
a=pd.Series(range(1,6), index=range(1,6))
a.rolling(4, center=True).mean()
1 NaN
2 NaN
3 2.5
4 3.5
5 NaN
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ENH: merge_asof() has left_index/right_index and left_by/right_by (#14253)
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[] | null | 3 | 2016-10-14T15:08:45Z | 2016-10-28T17:44:18Z | 2016-10-28T17:42:29Z |
CONTRIBUTOR
| null |
- [x] closes #14253
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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| 5 | 2016-10-14T20:40:05Z | 2019-09-18T14:02:16Z | 2019-09-18T14:02:16Z |
CONTRIBUTOR
| null |
As discussed in https://github.com/pandas-dev/pandas/pull/14384#discussion_r82538778.
#### A small, complete example of the issue
``` python
import pandas as pd
df1 = pd.DataFrame({'a': [1, 2, 3]})
df2 = pd.DataFrame({'b': [2,3,4]})
df = pd.concat([df1, df1, df2], axis=1).to_sparse()
df.index = [1, 2, 3]
df.loc[1, 'a'] = 3
```
errors with
``` python
AttributeError Traceback (most recent call last)
<ipython-input-6-0a670748626a> in <module>()
4 df = pd.concat([df1, df1, df2], axis=1).to_sparse()
5 df.index = [1, 2, 3]
----> 6 df.loc[1, 'a'] = 3
/Users/bkandel/.virtualenvs/pandas_19/lib/python2.7/site-packages/pandas/core/indexing.pyc in __setitem__(self, key, value)
138 key = com._apply_if_callable(key, self.obj)
139 indexer = self._get_setitem_indexer(key)
--> 140 self._setitem_with_indexer(indexer, value)
141
142 def _has_valid_type(self, k, axis):
/Users/bkandel/.virtualenvs/pandas_19/lib/python2.7/site-packages/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
545 # scalar
546 for item in labels:
--> 547 setter(item, value)
548
549 else:
/Users/bkandel/.virtualenvs/pandas_19/lib/python2.7/site-packages/pandas/core/indexing.pyc in setter(item, v)
453
454 def setter(item, v):
--> 455 s = self.obj[item]
456 pi = plane_indexer[0] if lplane_indexer == 1 else plane_indexer
457
/Users/bkandel/.virtualenvs/pandas_19/lib/python2.7/site-packages/pandas/sparse/frame.pyc in __getitem__(self, key)
345 return self._getitem_array(key)
346 else:
--> 347 return self._get_item_cache(key)
348
349 @Appender(DataFrame.get_value.__doc__, indents=0)
/Users/bkandel/.virtualenvs/pandas_19/lib/python2.7/site-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
1385 if res is None:
1386 values = self._data.get(item)
-> 1387 res = self._box_item_values(item, values)
1388 cache[item] = res
1389 res._set_as_cached(item, self)
/Users/bkandel/.virtualenvs/pandas_19/lib/python2.7/site-packages/pandas/core/frame.pyc in _box_item_values(self, key, values)
2392 items = self.columns[self.columns.get_loc(key)]
2393 if values.ndim == 2:
-> 2394 return self._constructor(values.T, columns=items, index=self.index)
2395 else:
2396 return self._box_col_values(values, items)
AttributeError: 'BlockManager' object has no attribute 'T'
```
#### Expected Output
``` python
a a b
1 3 3 2
2 2 2 3
3 3 3 4
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 28.3.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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Bug: Grouping by index and column fails on DataFrame with single index (GH14327)
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| 7 | 2016-10-14T23:39:26Z | 2016-10-15T19:25:48Z | 2016-10-15T19:25:41Z |
CONTRIBUTOR
| null |
- [x] closes #14327
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This PR is a continuation of #14333. See discussion there for explanation of why this new PR was needed.
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| 4 | 2016-10-14T23:53:09Z | 2016-10-15T20:03:48Z | 2016-10-15T20:03:48Z |
CONTRIBUTOR
| null |
closes #14408
master
```
In [1]: np.random.seed(1234)
In [2]: N = 100000
...: C = 5
...: df = DataFrame(dict([('float{0}'.format(i), np.random.randn(N)) for i in range(C)]))
In [3]: %timeit df.to_json('foo.json',orient='records',lines=True)
1 loop, best of 3: 3.67 s per loop
```
PR (with proper encoding/decoding, IOW work directly on the bytes and minimize copies)
```
In [5]: %timeit df.to_json('foo.json',orient='records',lines=True)
10 loops, best of 3: 137 ms per loop
```
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MDExOlB1bGxSZXF1ZXN0ODk1MDU2MDM=
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DOC: correct DataFrame.pivot docstring
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| 2 | 2016-10-15T20:29:41Z | 2016-10-22T09:50:20Z | 2016-10-22T09:50:20Z |
MEMBER
| null |
The mention of panels that are created is not correct. You get a multi-index, I noticed
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MDExOlB1bGxSZXF1ZXN0ODk1MDg2MTQ=
| 14,431 |
BUG: Fix issue with inserting duplicate columns in a dataframe (#14291)
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| 4 | 2016-10-15T22:24:41Z | 2016-10-24T22:52:00Z | 2016-10-24T22:51:29Z |
CONTRIBUTOR
| null |
- [x] closes #14291
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Resubmitting PR (@jorisvandenbossche)
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| 183,238,446 |
MDExOlB1bGxSZXF1ZXN0ODk1MDk3MTA=
| 14,432 |
ENH: Allow the groupby by param to handle columns and index levels (GH5677)
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| 13 | 2016-10-15T23:13:33Z | 2017-06-05T11:28:15Z | 2016-12-14T20:39:39Z |
CONTRIBUTOR
| null |
- [x] closes #5677
- [x] tests added
- [x] rebase after #14342 is merged to fix #14334
- [x] tests pass (requires rebase above)
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
- [x] updates existing documentation
- [x] add example to whatsnew 0.20 and groupby.rst
Change to allow strings passed as the `by` parameter to `df.groupby` to reference columns (existing behavior) or index level names if no column match is found. Columns take precedence in the case of ambiguity to maintain backward compatibility.
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BUG: underflow on Timestamp creation
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| 3 | 2016-10-16T12:03:44Z | 2016-10-20T13:46:01Z | 2016-10-20T13:46:01Z |
CONTRIBUTOR
| null |
- [x] closes #14415
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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| 183,279,933 |
MDExOlB1bGxSZXF1ZXN0ODk1MzI5NTk=
| 14,434 |
Checks for left_index and right_index merge parameters
|
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| 9 | 2016-10-16T17:46:40Z | 2016-10-20T10:42:51Z | 2016-10-20T10:32:06Z |
CONTRIBUTOR
| null |
Hi,
I just committed an error when I was doing an analysis using pandas and this motivated me to implement two checks which in my opinion are necessary.
I was trying to perform a merge and I confused the parameters "left_on" and "right_on" for "left_index" and "right_index". I ran the code and it did not raised me any error. It produced a table which seem to be fine in terms of shape. I suspect that what happened is that the tables got merged by the index of the data frame. I think it would be a great idea to check if right_index and left_index are of type bool, if not, raise an error. This way we will avoid that more people got the same error as mine :D.
Tests passed
Thanks!
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HDF Store with multi-index problems when using data_columns=True
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| 1 | 2016-10-16T21:24:19Z | 2016-12-04T17:22:51Z | 2016-12-04T17:22:51Z |
NONE
| null |
####
Not sure if this is supposed to be like this. When I store a multi-index along with the option `data_column=True`, I cannot query along the dataframe indexes only the columns are valid references. However, it works when I store them with the option `data_columns=[...]`.
``` python
import pandas as pd
import numpy as np
#adjust the path
file_path = 'D:\\test_store.h5'
np.random.seed(1234)
pd.set_option('display.max_rows',4)
# simulate some data
index = pd.MultiIndex.from_product([np.arange(10000,10200),
pd.date_range('19800101',periods=500)],
names=['id','date'])
df = pd.DataFrame(dict(id2=np.random.randint(0, 1000, size=len(index)),
w=np.random.randn(len(index))),
index=index).reset_index().set_index(['id', 'date'])
# store the data
store = pd.HDFStore(file_path,mode='a',complib='blosc', complevel=9)
store.append('df_dc_None', df, data_columns=None)
store.append('df_dc_explicit', df, data_columns=['id2', 'w'])
store.append('df_dc_True', df, data_columns=True)
store.close()
# query the data
start = '19810201'
print(pd.read_hdf(file_path,'df_dc_None', where='date>start & id=10000'))
print(pd.read_hdf(file_path,'df_dc_True', where='id2>500'))
print(pd.read_hdf(file_path,'df_dc_explicit', where='date>start & id2>500'))
try:
print(pd.read_hdf(file_path,'df_dc_True', where='date>start & id2>500'))
except ValueError as err:
print(err)
```
#### Output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.2
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
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: 0.7.9.None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: 0.2.1
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WIP: Rework IndexEngine to better support hashtable-free algorithms.
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| 6 | 2016-10-17T01:33:58Z | 2017-02-27T21:06:03Z | 2017-02-27T16:00:28Z |
CONTRIBUTOR
| null |
Initial work toward #14273. This makes `__contains__` behave uniformly across all index types by making it simply defer to `get_loc` (we can and probably should go back and put in a fast path for the case that a hash table has already been populated). Assuming this general path seems reasonable, the next step would be to update `get_indexer` to do binary-search-based lookups on large indices.
A few high-level design questions:
- I haven't benchmarked yet to see how big the difference is, but theoretically these changes are trading slower lookups for a smaller memory footprint. Depending on users' usage patterns and resource constraints, the tradeoffs made here may or may not be an improvement for them. I added an `avoid_hashtable` keyword to IndexEngine to make it easier to test the new behavior interactively without having to work with giant indices. Do we want to support a user-configurable toggle like this for cases where people know that they really do or don't want a hash table? If so, where would they pass that option? Alternatively, should _SIZE_CUTOFF be configurable, perhaps via `pandas.set_option`?
- One odd behavior I ran into while working on this is that `Int64Index.__contains__` silently truncates floats to integers before doing lookups, while `Int64Index.get_loc` does not. On this branch, that behavior is only preserved for integral-valued floats (the behavior of coercing integral floats to ints was explicitly tested). This also happens for `DatetimeIndex`. Is there any concern about "breaking" the behavior of `Int64Index.__contains__` for non-integral floats?
<details>
```
In [11]: pd.__version__
Out[11]: u'0.19.0'
In [12]: i
Out[12]: Int64Index([1, 2, 3, 5], dtype='int64')
In [13]: 1.5 in i
Out[13]: True
In [14]: i.get_loc(1.5)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-14-c37f93fbd72a> in <module>()
----> 1 i.get_loc(1.5)
/home/ssanderson/.virtualenvs/pandas-19/local/lib/python2.7/site-packages/pandas/indexes/base.pyc in get_loc(self, key, method, tolerance)
2104 return self._engine.get_loc(key)
2105 except KeyError:
-> 2106 return self._engine.get_loc(self._maybe_cast_indexer(key))
2107
2108 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4160)()
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3983)()
pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7773)()
KeyError: 1.5
```
</details>
- [ ] closes #14273
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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| 3 | 2016-10-17T07:26:02Z | 2016-10-17T10:51:20Z | 2016-10-17T10:18:36Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
ix = pd.Index([0,0,1,2])
sr = pd.Series([0,0,1,2])
ix.unique()
Int64Index([0, 1, 2], dtype='int64')
sr.unique()
array([0, 1, 2], dtype=int64)
```
#### Expected Output
I'd expect that unique returns the original datatype or always an array.
I'd prefer the original data_type since arrays do not provide a to_series function but indexes do.
So when working with pipelines, this is much more convenient
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 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: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
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: 1.1.0
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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MDU6SXNzdWUxODM0MDQ0MjI=
| 14,438 |
BUG: concat with dataframes heterogeneous in length and tuple keys
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| 1 | 2016-10-17T12:36:37Z | 2016-10-20T10:28:27Z | 2016-10-20T10:25:35Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
import numpy as np
import pandas as pd
df1 = pd.DataFrame(np.random.rand(4).reshape((2, 2)), columns=list('AB'))
df2 = pd.DataFrame(np.random.rand(6).reshape((3, 2)), columns=list('AB'))
print(pd.concat([df1, df2], keys=[('bee', 'bah'), ('bee', 'bouh')], names=['zig', 'zag', None]))
```
#### Expected Output (works w/ 0.18.1)
```
A B
zig zag
bee bah 0 0.689411 0.364650
1 0.143786 0.415858
bouh 0 0.557942 0.646434
1 0.551938 0.695175
2 0.991975 0.880451
```
#### 0.19 raises
```
/path/to/lib/python3.4/site-packages/pandas/tools/merge.py in _make_concat_multiindex(indexes, keys, levels, names)
1673 else:
1674 codes, categories = _factorize_from_iterable(concat_index)
-> 1675 levels.append(categories)
1676 label_list.append(codes)
1677
AttributeError: 'tuple' object has no attribute 'append'
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.4.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-74-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG:
LOCALE: None.None
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
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
</details>
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| 14,439 |
DEV: Update merge-pr.py script given repo rename
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| 1 | 2016-10-17T13:14:33Z | 2016-10-20T14:35:19Z | 2016-10-19T11:58:54Z |
MEMBER
| null |
This is still pointing to pydata/pandas.git -- it is working fine presumably because of magic redirection
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MDU6SXNzdWUxODM0MTk2OTA=
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Floating point accuracy problems in `DatetimeIndex.round`
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| 1 | 2016-10-17T13:42:57Z | 2017-03-05T16:26:59Z | 2017-03-05T16:26:59Z |
NONE
| null |
#### A small, complete example of the issue
There is a slight problem when using the rounding methods of `DatetimeIndex` (`round, floor, ceil`) to high frequencies as illustrated by this example:
``` python
pd.DatetimeIndex(['2016-10-17 12:00:00.0015']).round('ms')
DatetimeIndex(['2016-10-17 12:00:00.001999872'], dtype='datetime64[ns]', freq=None)
```
The problem is here in the `TimelikeOps._round` method:
```
result = (unit * rounder(values / float(unit))).astype('i8')
```
`rounder(values / float(unit))` returns an array of `floats` containing the multiples of `unit` required. However, although the values look like `ints`, when multiplied by `unit` the result can be off due to floating point accuracy. Replacing it with
```
result = (unit * rounder(values / float(unit)).astype('i8'))
```
Should fix the problem. I'm willing to do a PR to fix it.
#### Output of `pd.show_versions()`
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.7.6.None
psycopg2: None
jinja2: 2.8
boto: None
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| 183,443,939 |
MDExOlB1bGxSZXF1ZXN0ODk2NDMxODU=
| 14,441 |
Update unpack_template.h
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| 4 | 2016-10-17T15:12:36Z | 2016-10-20T22:44:57Z | 2016-10-20T22:44:39Z |
CONTRIBUTOR
| null |
- [x ] closes #14373
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
USE_CASE_RANGE is a GNU C feature. This change will activate
USE_CASE_RANGE on any platform when using GNU C and not on any platform
when a different compiler is being used.
closes #14373
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DOC: Expand on reference docs for read_json
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| 3 | 2016-10-17T17:15:12Z | 2016-10-27T13:51:10Z | 2016-10-27T13:51:10Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [] whatsnew entry
Continuation of closed PR #14284
Documentation update for `read_json()`.
expanded reference document for pandas.read_json(), especially concentrating on the orient parameter. Also added some example usage code and explicitly mention to_json() as a source of valid JSON strings.
<div class="section" id="pandas-read-json">
<h1>pandas.read_json<a class="headerlink" href="#pandas-read-json" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="pandas.read_json">
<code class="descclassname">pandas.</code><code class="descname">read_json</code><span class="sig-paren">(</span><em>path_or_buf=None</em>, <em>orient=None</em>, <em>typ='frame'</em>, <em>dtype=True</em>, <em>convert_axes=True</em>, <em>convert_dates=True</em>, <em>keep_default_dates=True</em>, <em>numpy=False</em>, <em>precise_float=False</em>, <em>date_unit=None</em>, <em>encoding=None</em>, <em>lines=False</em><span class="sig-paren">)</span><a class="reference external" href="http://github.com/pandas-dev/pandas/blob/master/pandas/io/json.py#L112-L286"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pandas.read_json" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert a JSON string to pandas object</p>
<table class="docutils field-list" frame="void" rules="none">
<colgroup><col class="field-name">
<col class="field-body">
</colgroup><tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>path_or_buf</strong> : a valid JSON string or file-like, default: None</p>
<blockquote>
<div><p>The string could be a URL. Valid URL schemes include http, ftp, s3, and
file. For file URLs, a host is expected. For instance, a local file
could be <code class="docutils literal"><span class="pre">file://localhost/path/to/table.json</span></code></p>
</div></blockquote>
<p><strong>orient</strong> : string,</p>
<blockquote>
<div><p>Indication of expected JSON string format.
Compatible JSON strings can be produced by <code class="docutils literal"><span class="pre">to_json()</span></code> with a
corresponding orient value.
The set of possible orients is:</p>
<blockquote>
<div><ul class="simple">
<li><code class="docutils literal"><span class="pre">'split'</span></code> : dict like
<code class="docutils literal"><span class="pre">{index</span> <span class="pre">-></span> <span class="pre">[index],</span> <span class="pre">columns</span> <span class="pre">-></span> <span class="pre">[columns],</span> <span class="pre">data</span> <span class="pre">-></span> <span class="pre">[values]}</span></code></li>
<li><code class="docutils literal"><span class="pre">'records'</span></code> : list like
<code class="docutils literal"><span class="pre">[{column</span> <span class="pre">-></span> <span class="pre">value},</span> <span class="pre">...</span> <span class="pre">,</span> <span class="pre">{column</span> <span class="pre">-></span> <span class="pre">value}]</span></code></li>
<li><code class="docutils literal"><span class="pre">'index'</span></code> : dict like <code class="docutils literal"><span class="pre">{index</span> <span class="pre">-></span> <span class="pre">{column</span> <span class="pre">-></span> <span class="pre">value}}</span></code></li>
<li><code class="docutils literal"><span class="pre">'columns'</span></code> : dict like <code class="docutils literal"><span class="pre">{column</span> <span class="pre">-></span> <span class="pre">{index</span> <span class="pre">-></span> <span class="pre">value}}</span></code></li>
<li><code class="docutils literal"><span class="pre">'values'</span></code> : just the values array</li>
</ul>
</div></blockquote>
<p>The allowed and default values depend on the value
of the <cite>typ</cite> parameter.</p>
<ul class="simple">
<li>when <code class="docutils literal"><span class="pre">typ</span> <span class="pre">==</span> <span class="pre">'series'</span></code>,<ul>
<li>allowed orients are <code class="docutils literal"><span class="pre">{'split','records','index'}</span></code></li>
<li>default is <code class="docutils literal"><span class="pre">'index'</span></code></li>
<li>The Series index must be unique for orient <code class="docutils literal"><span class="pre">'index'</span></code>.</li>
</ul>
</li>
<li>when <code class="docutils literal"><span class="pre">typ</span> <span class="pre">==</span> <span class="pre">'frame'</span></code>,<ul>
<li>allowed orients are <code class="docutils literal"><span class="pre">{'split','records','index',</span>
<span class="pre">'columns','values'}</span></code></li>
<li>default is <code class="docutils literal"><span class="pre">'columns'</span></code></li>
<li>The DataFrame index must be unique for orients <code class="docutils literal"><span class="pre">'index'</span></code> and
<code class="docutils literal"><span class="pre">'columns'</span></code>.</li>
<li>The DataFrame columns must be unique for orients <code class="docutils literal"><span class="pre">'index'</span></code>,
<code class="docutils literal"><span class="pre">'columns'</span></code>, and <code class="docutils literal"><span class="pre">'records'</span></code>.</li>
</ul>
</li>
</ul>
</div></blockquote>
<p><strong>typ</strong> : type of object to recover (series or frame), default ‘frame’</p>
<p><strong>dtype</strong> : boolean or dict, default True</p>
<blockquote>
<div><p>If True, infer dtypes, if a dict of column to dtype, then use those,
if False, then don’t infer dtypes at all, applies only to the data.</p>
</div></blockquote>
<p><strong>convert_axes</strong> : boolean, default True</p>
<blockquote>
<div><p>Try to convert the axes to the proper dtypes.</p>
</div></blockquote>
<p><strong>convert_dates</strong> : boolean, default True</p>
<blockquote>
<div><p>List of columns to parse for dates; If True, then try to parse
datelike columns default is True; a column label is datelike if</p>
<ul class="simple">
<li>it ends with <code class="docutils literal"><span class="pre">'_at'</span></code>,</li>
<li>it ends with <code class="docutils literal"><span class="pre">'_time'</span></code>,</li>
<li>it begins with <code class="docutils literal"><span class="pre">'timestamp'</span></code>,</li>
<li>it is <code class="docutils literal"><span class="pre">'modified'</span></code>, or</li>
<li>it is <code class="docutils literal"><span class="pre">'date'</span></code></li>
</ul>
</div></blockquote>
<p><strong>keep_default_dates</strong> : boolean, default True</p>
<blockquote>
<div><p>If parsing dates, then parse the default datelike columns</p>
</div></blockquote>
<p><strong>numpy</strong> : boolean, default False</p>
<blockquote>
<div><p>Direct decoding to numpy arrays. Supports numeric data only, but
non-numeric column and index labels are supported. Note also that the
JSON ordering MUST be the same for each term if numpy=True.</p>
</div></blockquote>
<p><strong>precise_float</strong> : boolean, default False</p>
<blockquote>
<div><p>Set to enable usage of higher precision (strtod) function when
decoding string to double values. Default (False) is to use fast but
less precise builtin functionality</p>
</div></blockquote>
<p><strong>date_unit</strong> : string, default None</p>
<blockquote>
<div><p>The timestamp unit to detect if converting dates. The default behaviour
is to try and detect the correct precision, but if this is not desired
then pass one of ‘s’, ‘ms’, ‘us’ or ‘ns’ to force parsing only seconds,
milliseconds, microseconds or nanoseconds respectively.</p>
</div></blockquote>
<p><strong>lines</strong> : boolean, default False</p>
<blockquote>
<div><p>Read the file as a json object per line.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.19.0.</span></p>
</div>
</div></blockquote>
<p><strong>encoding</strong> : str, default is ‘utf-8’</p>
<blockquote>
<div><p>The encoding to use to decode py3 bytes.</p>
<div class="versionadded">
<p><span class="versionmodified">New in version 0.19.0.</span></p>
</div>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"><strong>result</strong> : Series or DataFrame, depending on the value of <cite>typ</cite>.</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Examples</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">],</span> <span class="p">[</span><span class="s1">'c'</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">]],</span>
<span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'row 1'</span><span class="p">,</span> <span class="s1">'row 2'</span><span class="p">],</span>
<span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'col 1'</span><span class="p">,</span> <span class="s1">'col 2'</span><span class="p">])</span>
</pre></div>
</div>
<p>Encoding/decoding a Dataframe using <code class="docutils literal"><span class="pre">'split'</span></code> formatted JSON:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s1">'split'</span><span class="p">)</span>
<span class="go">'{"columns":["col 1","col 2"],</span>
<span class="go"> "index":["row 1","row 2"],</span>
<span class="go"> "data":[["a","b"],["c","d"]]}'</span>
<span class="gp">>>> </span><span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">_</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">'split'</span><span class="p">)</span>
<span class="go"> col 1 col 2</span>
<span class="go">row 1 a b</span>
<span class="go">row 2 c d</span>
</pre></div>
</div>
<p>Encoding/decoding a Dataframe using <code class="docutils literal"><span class="pre">'index'</span></code> formatted JSON:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span>
<span class="go">'{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}'</span>
<span class="gp">>>> </span><span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">_</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span>
<span class="go"> col 1 col 2</span>
<span class="go">row 1 a b</span>
<span class="go">row 2 c d</span>
</pre></div>
</div>
<p>Encoding/decoding a Dataframe using <code class="docutils literal"><span class="pre">'records'</span></code> formatted JSON.
Note that index labels are not preserved with this encoding.</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s1">'records'</span><span class="p">)</span>
<span class="go">'[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]'</span>
<span class="gp">>>> </span><span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">_</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">'records'</span><span class="p">)</span>
<span class="go"> col 1 col 2</span>
<span class="go">0 a b</span>
<span class="go">1 c d</span>
</pre></div>
</div>
</dd></dl>
</div>
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MDU6SXNzdWUxODM1MDY5OTY=
| 14,443 |
duplicated removing items that are not duplicates in 0.18
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| 3 | 2016-10-17T19:36:17Z | 2016-10-18T23:41:35Z | 2016-10-18T11:47:00Z |
NONE
| null |
I wish I could say more about this, but it is with proprietary data. Basically, I have a DataFrame of 10 columns and around 7000 rows. When I call "duplicated" on 4 of the rows (3 have strings, 1 has a float), I get around 10 items being flagged as duplicate (5 pairs) when in fact, they are different.
This is sample text of what is being flagged:
```
Type Line LineString \
1885 else 832.0 (&temp32)->byte1 = (&temp32)->byte4
1895 do while 832.0 do while(0)
4515 enum 122.0 enum {QQ_ON_UNASSERTED = 0, QQ_ON_ASSERTED = 1}
4521 enum 167.0 enum {FIELD_RESET = 1, FIELD_TRIPPED = 0}
Parameter Filename
1885 temp32 arinc.c
1895 arinc.c
4515 QQ_ON_ASSERTED eeprom.c
4521 FIELD_TRIPPED eeprom.c
```
I know that duplicates are checked through hashing, but is there some way to, I don't know, compare a checksum or a more robust measurement to ensure that only duplicates are flagged? What is the chance that two items could be hashed to the same value?
By adding more of the fields to check the data I am able to prevent false duplicate flags.
I am using Pandas 0.18 from WinPython-64bit-3.4.4.2Qt5.
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Series `sort_index` missing `kind` keyword
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| 2 | 2016-10-17T22:17:38Z | 2016-10-20T10:38:09Z | 2016-10-17T23:26:55Z |
NONE
| null |
#### A small, complete example of the issue
``` python
>>> import pandas as pd
>>> pd.Series(index=[3,2,1,4,3]).sort_index(kind='mergesort')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: sort_index() got an unexpected keyword argument 'kind'
```
#### Expected Output
A `Series` sorted by the index, i.e.:
``` python
>>> pd.Series(index=[3,2,1,4,3]).sort_index()
1 NaN
2 NaN
3 NaN
3 NaN
4 NaN
dtype: float64
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-42-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.1
pip: 1.5.4
setuptools: 3.3
Cython: 0.20.1post0
numpy: 1.11.0
scipy: 0.13.3
statsmodels: None
xarray: None
IPython: 1.2.1
sphinx: 1.2.2
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.8.7
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.7.2
boto: None
pandas_datareader: None
</details>
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ENH: support kind and na_position kwargs in Series.sort_index
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| 15 | 2016-10-17T23:22:47Z | 2016-12-04T20:33:03Z | 2016-12-04T20:32:42Z |
CONTRIBUTOR
| null |
closes #13589
closes #13729
I'm waiting on adding the whatsnew entry. I'm not sure if this is a BUG (the [documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_index.html) says these parameters exist) or an ENH (we're adding support for these parameters). It depends on how you look at it!
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Fix index for datetime64 conversion. Fixes #13937
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| 15 | 2016-10-18T12:38:41Z | 2017-03-03T10:07:39Z | 2017-03-02T13:44:58Z |
CONTRIBUTOR
| null |
- [x] closes #13937
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] 0.20.0
Fix index for datetime64 conversion
This PR fixes index for datetime64 conversion. Also,
update tests - pandas/pandas/tests/tests_convert_to.py
Fixes https://github.com/pydata/pandas/issues/13937
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BUG: Rename pydata to pandas-dev in merge-pr.py
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| 2 | 2016-10-18T14:17:52Z | 2016-10-20T14:27:52Z | 2016-10-19T11:58:54Z |
MEMBER
| null |
Title is self-explanatory. Closes #14439.
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DOC/BUG: pd.to_datetime example produces different results from documentation
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| 4 | 2016-10-18T22:19:12Z | 2016-10-24T22:28:17Z | 2016-10-24T22:28:17Z |
NONE
| null |
#### A small, complete example of the issue
referring too http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html
The example provides different results.
``` python
# Your code here
import pandas as pd
pd.to_datetime('13000101', format='%Y%m%d')
```
raises...
```
Traceback (most recent call last):
File "C:\Miniconda3\lib\site-packages\pandas\tseries\tools.py", line 404, in _convert_listlike
values, tz = tslib.datetime_to_datetime64(arg)
File "pandas\tslib.pyx", line 1754, in pandas.tslib.datetime_to_datetime64 (pandas\tslib.c:30557)
TypeError: Unrecognized value type: <class 'str'>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Miniconda3\lib\site-packages\pandas\util\decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "C:\Miniconda3\lib\site-packages\pandas\tseries\tools.py", line 424, in to_datetime
return _convert_listlike(np.array([arg]), box, format)[0]
File "C:\Miniconda3\lib\site-packages\pandas\tseries\tools.py", line 407, in _convert_listlike
raise e
File "C:\Miniconda3\lib\site-packages\pandas\tseries\tools.py", line 374, in _convert_listlike
errors=errors)
File "pandas\tslib.pyx", line 3721, in pandas.tslib.array_strptime (pandas\tslib.c:60980)
File "pandas\tslib.pyx", line 3716, in pandas.tslib.array_strptime (pandas\tslib.c:60878)
File "pandas\tslib.pyx", line 1718, in pandas.tslib._check_dts_bounds (pandas\tslib.c:30034)
pandas.tslib.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1300-01-01 00:00:00
```
#### Expected Output
(according to example in docs)
```
datetime.datetime(1300, 1, 1, 0, 0)
```
#### Output of `pd.show_versions()`
<details>
> > > pd.show_versions()
## 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 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 28.0.0
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: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: 1.4.1
bottleneck: 1.1.0
tables: None
numexpr: 2.6.0
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: 4.5.0
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.0
pymysql: 0.7.9.None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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Type levels as list in _make_concat_multiindex (fixes #14438)
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| 5 | 2016-10-19T06:20:15Z | 2016-10-20T10:25:40Z | 2016-10-20T10:25:35Z |
CONTRIBUTOR
| null |
- [x] closes #14438
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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| 3 | 2016-10-19T09:04:00Z | 2016-10-19T10:29:26Z | 2016-10-19T10:29:13Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
df = pd.DataFrame({"data": np.arange(0, 10, 0.1)})
v = pd.cut(df.data, [0, 1, 2, 5, 10], include_lowest=True).rename("cuts")
df.join(pd.get_dummies(v))
```
#### Expected Output
A data frame similar to the input dataframe with 4 additional variables for the cut categories.
#### What happens
```
----> 3 df.join(pd.get_dummies(v))
/home/pwaller/.local/lib/python3.5/site-packages/pandas/core/frame.py in join(self, other, on, how, lsuffix, rsuffix, sort)
4534 # For SparseDataFrame's benefit
4535 return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,
-> 4536 rsuffix=rsuffix, sort=sort)
4537
4538 def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='',
/home/pwaller/.local/lib/python3.5/site-packages/pandas/core/frame.py in _join_compat(self, other, on, how, lsuffix, rsuffix, sort)
4548 return merge(self, other, left_on=on, how=how,
4549 left_index=on is None, right_index=True,
-> 4550 suffixes=(lsuffix, rsuffix), sort=sort)
4551 else:
4552 if on is not None:
/home/pwaller/.local/lib/python3.5/site-packages/pandas/tools/merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator)
57 right_index=right_index, sort=sort, suffixes=suffixes,
58 copy=copy, indicator=indicator)
---> 59 return op.get_result()
60 if __debug__:
61 merge.__doc__ = _merge_doc % '\nleft : DataFrame'
/home/pwaller/.local/lib/python3.5/site-packages/pandas/tools/merge.py in get_result(self)
505 result_data = concatenate_block_managers(
506 [(ldata, lindexers), (rdata, rindexers)],
--> 507 axes=[llabels.append(rlabels), join_index],
508 concat_axis=0, copy=self.copy)
509
/home/pwaller/.local/lib/python3.5/site-packages/pandas/indexes/base.py in append(self, other)
1440 # if any of the to_concat is category
1441 from pandas.indexes.category import CategoricalIndex
-> 1442 return CategoricalIndex._append_same_dtype(self, to_concat, name)
1443
1444 if len(typs) == 1:
/home/pwaller/.local/lib/python3.5/site-packages/pandas/indexes/category.py in _append_same_dtype(self, to_concat, name)
578 ValueError if other is not in the categories
579 """
--> 580 to_concat = [self._is_dtype_compat(c) for c in to_concat]
581 codes = np.concatenate([c.codes for c in to_concat])
582 result = self._create_from_codes(codes, name=name)
/home/pwaller/.local/lib/python3.5/site-packages/pandas/indexes/category.py in <listcomp>(.0)
578 ValueError if other is not in the categories
579 """
--> 580 to_concat = [self._is_dtype_compat(c) for c in to_concat]
581 codes = np.concatenate([c.codes for c in to_concat])
582 result = self._create_from_codes(codes, name=name)
AttributeError: 'Index' object has no attribute '_is_dtype_compat'
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.1
setuptools: 28.6.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.2
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 183,916,492 |
MDU6SXNzdWUxODM5MTY0OTI=
| 14,451 |
pandas performance hotspots
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| 9 | 2016-10-19T09:54:52Z | 2016-10-24T15:11:19Z | 2016-10-20T23:08:27Z |
NONE
| null |
#### Context
I'm writing a text mining toolkit, and decided to try pandas because it offered convenience row-and-column labelled access. Numpy doesn't, and structured numpy arrays only have named columns.
#### Performance Problem
Indexing text, and manipulating dataframes of word co-occurrence/relevance appeared to be very slow, so I profiled my code using %time, cProfile and pprofile.
The following 2 blogs detail this work:
[http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/10/profiling-indexing-relevance-and-co.html](http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/10/profiling-indexing-relevance-and-co.html)
[http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/10/profiling-indexing-relevance-and-co_16.html](http://makeyourowntextminingtoolkit.blogspot.co.uk/2016/10/profiling-indexing-relevance-and-co_16.html)
#### Key Findings
It seems that some operations are **really** slow:
- growing a pandas dataframe using df.ix[a,b] = value ... 96% time of a profiled function is in indexing.py __setitem__
- performing cell calculations eg df.ix[a,b] += something .. one example saw 60% of a function doing an in-place increment!
- performing calculations on rows eg df.loc[a] = somethng ... 37% of a function is spent doing df.loc[a] = df.loc[a] \* factor
#### Solution?
I know the optimal use-case for pandas is working with whole-arrays and columns at a time .. but this performance seems pretty bad.
Does the pandas project consider it a priority to improve this?
My initial testing with pure numpy and also h5py (vs pandas.HD5) seems to show massive improvements .. but at a loss of convenience.
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DOC:updated docstring in .to_datetime()
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| 5 | 2016-10-19T15:13:56Z | 2016-10-29T17:11:45Z | 2016-10-24T22:28:17Z |
CONTRIBUTOR
| null |
- [x] closes #14448
- [ ] tests added / passed
- [ ] passes git diff upstream/master | flake8 --diff
- [ ] whatsnew entry
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BUG: Catch overflow in both directions for checked add
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| 9 | 2016-10-19T16:13:28Z | 2016-10-24T23:51:04Z | 2016-10-22T09:49:40Z |
MEMBER
| null |
Port of #14324 after project name change.
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Unable to store category data in panels
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| 3 | 2016-10-19T16:23:39Z | 2018-07-06T20:21:11Z | 2016-10-20T23:07:57Z |
NONE
| null |
#### Create a dataframe with a variable of dtype category and then convert to a panel
``` python
# A dataframe with a category variable
df = pd.DataFrame({ 'day' : [1,2,3], 'month' : ['a','b','c'], 'dow' : ['mon','mon','thur' ]})
df = df.set_index(['day','month'])
df.dow = df.dow.astype('category')
# Transform to panel and check the dtype
df.to_panel().dtypes
#### Expected Output
dow category
dtype: object
```
#### 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: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
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: 1.5.1
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
# The variable was converted from a category to an object when put into a panel
dow object
dtype: object
</details>
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ERR: qcut uniquess checking
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| 9 | 2016-10-19T18:02:55Z | 2016-12-28T14:39:53Z | 2016-12-27T22:24:56Z |
CONTRIBUTOR
| null |
- [x] closes #7751
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Add option to drop non-unique bins.
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DOC: fix pd.to_datetime documentation (issue #14448)
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| 2 | 2016-10-19T19:56:33Z | 2016-10-20T10:22:18Z | 2016-10-20T01:12:15Z |
NONE
| null |
Dupe of #14452
- [x] closes #14448
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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groupby transform producing different results depending on whether a lambda function was passed or a numpy function was passed
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| 0 | 2016-10-19T21:40:43Z | 2016-10-25T10:55:04Z | 2016-10-25T10:55:04Z |
NONE
| null |
stackoverflow
http://stackoverflow.com/q/40141881/2336654
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(dict(A=['a', 'a'], B=[0, 1]))
print(df.groupby('A').transform(np.mean))
# is no the same as
print(df.groupby('A').transform(lambda x: np.mean(x)))
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
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: 0.2.1
</details>
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MDExOlB1bGxSZXF1ZXN0OTAxMTAwODY=
| 14,458 |
BUG: Set frequency for empty Series
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| 8 | 2016-10-19T23:36:04Z | 2017-03-03T13:17:14Z | 2017-03-03T13:16:14Z |
CONTRIBUTOR
| null |
- [x] closes #14320
- [x] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Alternative to #14340
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Conflict b/w skiprows and default quotechar kwargs to pandas.read_table
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| 14 | 2016-10-20T12:13:41Z | 2016-10-31T20:39:59Z | 2016-10-31T20:39:59Z |
NONE
| null |
#### A small, complete example of the issue
while trying to open a data file similar to
```
a
b"
c
d
e"
a,b,c
1,2,3
4,5,6
7,8,9
```
i expect the following code
```
python -c "import pandas; df = pandas.read_table('quoted_data.csv', skiprows=5, sep=','); print df"
```
#### Expected Output
```
a b c
0 1 2 3
1 4 5 6
2 7 8 9
```
#### Observed Output
```
Empty DataFrame
Columns: [7,8,9]
Index: []
```
### Further Insight
```
python -c "import pandas; df = pandas.read_table('quoted_data.csv', skiprows=2, sep=','); print df"
```
surprisingly works. also,
```
python -c "import pandas; df = pandas.read_table('quoted_data.csv', skiprows=5, quotechar="?", sep=','); print df"
```
works
The behavior changed between `pandas` `0.18.0` and `0.18.1`. we suspect changes made in https://github.com/pandas-dev/pandas/pull/12900 to be causing this.
Note that the difference in `skiprows` values that works (2) and that doesn't (5) is the same as the number of lines in the file between quote chars.
Apologies for the noise if this has already been reported or is being addressed.
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 16.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 23.1.0
Cython: 0.24
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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| 184,213,943 |
MDU6SXNzdWUxODQyMTM5NDM=
| 14,460 |
Strip columns/column names in data frame of white spaces
|
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open
| false | null |
[] | null | 9 | 2016-10-20T12:19:37Z | 2021-05-02T00:33:43Z | null |
NONE
| null |
This is more of a question than a bug.
#### A small, complete example of the issue
while opening a data file similar to
```
a , b , c , d
1 , 2 , 3 , 4
5 , 6 , 7 , 8
```
using
``` python
python -c "import pandas; df = pandas.read_table('unstripped_data.csv', sep=','); print df.columns"
```
#### Observed Output
`Index([u' a ', u' b ', u' c ', u' d '], dtype='object')`
#### Expected Output
`Index([u'a', u'b', u'c', u'd'], dtype='object')`
We expected that the column names/columns be stripped of white spaces.
Apologies for the noise if this has already been reported or is being addressed.
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 16.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 23.1.0
Cython: 0.24
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
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pd.Series.asof performance regression: 14x slower
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| 9 | 2016-10-20T16:05:57Z | 2016-10-26T08:16:57Z | 2016-10-26T08:16:57Z |
CONTRIBUTOR
| null |
#### pd.Series.asof takes 14x longer in 0.19.0 than 0.18.1
``` python
In[14]: pd.__version__
Out[14]: '0.18.1'
In[15]: s = pd.Series([1, 1, 1, np.nan, np.nan])
In[16]: %timeit s.asof(4)
The slowest run took 8.25 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 8.96 µs per loop
In[8]: pd.__version__
Out[8]: '0.19.0'
In[9]: s = pd.Series([1, 1, 1, np.nan, np.nan])
In[10]: %timeit s.asof(4)
10000 loops, best of 3: 125 µs per loop
```
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Alternative serialization for text data
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| 4 | 2016-10-20T19:27:26Z | 2020-09-22T10:22:28Z | null |
CONTRIBUTOR
| null |
Pandas DataFrames containing text columns are expensive to serialize. This affects dask.dataframe performance in multiprocessing or distributed settings.
### Pickle is expensive
In particular the current solution of using `pickle.dumps` for object dtype columns can be needlessly expensive when all of the values in the column are text. In this case fairly naive solutions, like `msgpack` can be much much faster.
Here is an old blogpost on the topic: http://matthewrocklin.com/blog/work/2015/03/16/Fast-Serialization
And an image
### Alternatives
There are naive solutions like msgpack (already in pandas) or encoding the text directly.
There are more sophisticated solutions as well that would provide efficient packing, including with repeated elements.
### But sometimes objects are objects
One concern here is that sometimes the Python objects aren't text. I propose that Pandas does a check each time or asks for forgiveness on an exception.
Anyway, I would find this valuable. It would help to reduce bottlenecks in dask.dataframe in some situations.
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ExtractAll on a single pattern
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| 1 | 2016-10-20T20:46:27Z | 2016-10-21T10:41:59Z | 2016-10-21T10:41:43Z |
NONE
| null |
``` python
s = pd.DataFrame(["I love you", "Pandas", "<3"], index=["A", "B", "C"],columns=['a'])
io=r"\b(Pandas)\b"
s['a'].str.extractall(io)
```
#### Expected Output
```
0
```
match
B 0 Pandas
#### Output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-27-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.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: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
<details>
# Paste the output here
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ian/anaconda3/lib/python3.5/site-packages/pandas/core/strings.py", line 1621, in extractall
return str_extractall(self._orig, pat, flags=flags)
File "/home/ian/anaconda3/lib/python3.5/site-packages/pandas/core/strings.py", line 716, in str_extractall
result = DataFrame(match_list, index, columns)
File "/home/ian/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 263, in __init__
arrays, columns = _to_arrays(data, columns, dtype=dtype)
File "/home/ian/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 5352, in _to_arrays
dtype=dtype)
File "/home/ian/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 5431, in _list_to_arrays
coerce_float=coerce_float)
File "/home/ian/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 5489, in _convert_object_array
'columns' % (len(columns), len(content)))
AssertionError: 1 columns passed, passed data had 6 columns
</details>
I am new to extractall() and the google resources are somewhat scarce.
I am trying to find the rows with the most hits of the words in a string so I am using normal regex syntax to construct a pattern to look for all whole words. It works fine if I look for more than one word:
> > > io=r"\b(I)\b|\b(love)\b|\b(you)\b"
> > > s['a'].str.extractall(io)
> > > 0 1 2
> > > match
> > > A 0 I NaN NaN
> > > 1 NaN love NaN
> > > 2 NaN NaN you
But for one word it fails, if use just extract then it works.
> > > io=r"\b(Pandas)\b"
> > > s['a'].str.extract(io)
> > > A NaN
> > > B Pandas
> > > C NaN
I then obviously have to change my logic based on if it is a single or multi word.
Unless I add a bogus lookup!
> > > io=r"\b(Pandas)\b|\b(blahblahblah)\b"
> > > s['a'].str.extractall(io)
> > > 0 1
> > > match
> > > B 0 Pandas NaN
Am I doing something daft? Is this a bug or maybe a feature if so could this be changed to work on a single lookup?
Thanks
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Allow DataFrame.apply() to accept dict of funcs
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| 2 | 2016-10-21T01:08:01Z | 2017-10-27T19:06:18Z | 2017-04-14T15:37:04Z |
CONTRIBUTOR
| null |
When using DataFrame.apply(), it would be helpful to be able to apply different functions to different columns. There's a nice workaround on [StackOverflow](http://stackoverflow.com/questions/26434123/pandas-apply-different-functions-to-different-columns):
``` python
import numpy as np
import pandas as pd
def multi_func(functions):
def f(col):
return functions[col.name](col)
return f
df = pd.DataFrame(np.random.random((10, 2)), columns=['A', 'B'])
results = df.apply(multi_func({'A': np.mean, 'B': np.sum}))
results
A 0.401456
B 6.845529
dtype: float64
```
My guess is that the changes would be made in [pandas.core.frame.DataFrame](https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py), potentially adding a new `_apply_X` method. Any thoughts/advice?
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MDExOlB1bGxSZXF1ZXN0OTAzMjgzNTA=
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MAINT: Use check_output when merging.
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| 2 | 2016-10-21T06:18:41Z | 2016-10-24T22:10:15Z | 2016-10-24T22:10:14Z |
MEMBER
| null |
Since we don't support Python 2.6 anymore, the `check_output` method from `subprocess` is
at our disposal.
Follow-up to #14447.
xref <a href="https://github.com/pandas-dev/pandas/issues/14439#issuecomment-254522055"> #14439 (comment)</a>
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MDExOlB1bGxSZXF1ZXN0OTAzNjMxMDc=
| 14,466 |
BUG: incorrect broadcasting that could casuse dtype coercion in a groupby-transform
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| 0 | 2016-10-21T10:53:50Z | 2016-10-25T10:55:04Z | 2016-10-25T10:55:04Z |
CONTRIBUTOR
| null |
closes #14457
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Dataframe constructor misinterprets `columns` argument if nested list is passed in as the `data` parameter.
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| 8 | 2016-10-21T18:33:45Z | 2021-05-19T12:53:57Z | 2021-05-19T12:53:57Z |
NONE
| null |
This issue is based on Stack Overflow question http://stackoverflow.com/q/40182072/2988730.
#### A small, complete example of the issue
``` python
df = pd.DataFrame([[1, 2, 3], [4, 5, 6]],
index=[['gibberish']*2, [0, 1]],
columns=[['baldersash']*3, [10, 20, 30]])
```
The result is
```
File "<ipython-input-321-2695882ac68b>", line 3, in <module>
columns=[['baldersash']*3, [10, 20, 30]])
File "/home/jfoxrabi/miniconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 263, in __init__
arrays, columns = _to_arrays(data, columns, dtype=dtype)
File "/home/jfoxrabi/miniconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 5352, in _to_arrays
dtype=dtype)
File "/home/jfoxrabi/miniconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 5431, in _list_to_arrays
coerce_float=coerce_float)
File "/home/jfoxrabi/miniconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 5489, in _convert_object_array
'columns' % (len(columns), len(content)))
AssertionError: 2 columns passed, passed data had 3 columns
```
#### Expected Output
```
baldersash
10 20 30
gibberish 0 1 2 3
1 4 5 6
```
The surprising thing here is that any of the following seem to work just fine:
1. Supplying a numpy array as `data`:
``` python
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6]]),
index=[['gibberish']*2, [0, 1]],
columns=[['baldersash']*3, [10, 20, 30]])
```
Results in
```
baldersash
10 20 30
gibberish 0 1 2 3
1 4 5 6
```
2. Reducing the size of the input array to have two columns:
``` python
df = pd.DataFrame([[1, 2], [3, 4]],
index=[['gibberish']*2, [0, 1]],
columns=[['baldersash']*2, [10, 20]])
```
Results in
```
baldersash
10 20
gibberish 0 1 2
1 3 4
```
3. Omitting the `columns` argument:
``` python
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6]]),
index=[['gibberish']*2, [0, 1]])
```
Results in
```
0 1 2
gibberish 0 1 2 3
1 4 5 6
```
4. Using a single-level list for the `columns` argument:
``` python
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6]]),
index=[['gibberish']*2, [0, 1]],
columns=[10, 20, 30])
```
Results in
```
10 20 30
gibberish 0 1 2 3
1 4 5 6
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.29.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.5
xlrd: None
xlwt: None
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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adding static type checking with mypy
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| 38 | 2016-10-21T18:56:32Z | 2019-08-05T20:39:47Z | 2019-03-26T15:45:07Z |
CONTRIBUTOR
| null |
http://blog.zulip.org/2016/10/13/static-types-in-python-oh-mypy/
might be interesting if someone is looking for a project :>
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| 3 | 2016-10-21T21:04:48Z | 2016-10-22T08:33:13Z | 2016-10-22T08:33:07Z |
NONE
| null |
#### A small, complete example of the issue
pd.read_html() not working showing error in _html5lib.py,line 70 class TreeBuilderForHtml5lib
``` python
import pandas as pd
import html5lib
import lxml
data = pd.read_html('https://blockchain.info/blocks')
```
gives
```
File "C:\Anaconda2\lib\site-packages\bs4\builder\_html5lib.py", line 70, in <module>
class TreeBuilderForHtml5lib(html5lib.treebuilders._base.TreeBuilder):
AttributeError: 'module' object has no attribute '_base'
```
#### Output of `pd.show_versions()`
<details>
# Paste the output here
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.2
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.7
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: None
html5lib: 0.999999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
</details>
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DOC: update readme for repo move
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| 1 | 2016-10-22T00:00:25Z | 2016-10-22T09:07:31Z | 2016-10-22T00:37:02Z |
CONTRIBUTOR
| null |
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DataFrame Operations on Large Frames Seems To Crash Python Kernel on MacOS Yosemite At Random
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[] | null | 3 | 2016-10-22T00:21:31Z | 2018-02-13T12:02:02Z | 2018-02-13T12:02:02Z |
NONE
| null |
Apologies for not following the requested format, but there is no output for this error. Running the script from command line will occasionally execute without error, but often python will crash without error. This occurs with both python 3.5 and 2.7, and with pandas 0.18.1 and 0.19.0.
The code came directly from a windows machine where it ran fine and now it does not run reliably on Mac... In one instance it happens when merging two large frame and in another it occurs when using groupby.
I know this is extremely obscure but does anyone have any ideas?
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BUG: downcast = 'unsigend' on 0 would would not downcast to unsigned.
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| 7 | 2016-10-22T00:47:20Z | 2016-11-02T10:47:10Z | 2016-10-25T10:47:51Z |
NONE
| null |
Continuation of #14412
- [ ] closes #14401
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Once again I had some very bizarre issues with git. Hope this does it this time.
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ERR: Fix GH13139: better error message on invalid pd.eval and df.query input
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| 3 | 2016-10-22T01:35:07Z | 2016-10-24T22:13:28Z | 2016-10-24T22:13:24Z |
CONTRIBUTOR
| null |
- [x] closes #13139
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Added test case to check for invalid input(empy string) on pd.eval('') and df.query('').
Used existing helper function(_check_expression)
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RLS: v0.19.1
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| 10 | 2016-10-22T09:56:32Z | 2016-11-07T20:04:30Z | 2016-11-07T20:04:30Z |
MEMBER
| null |
Been busy with other things last week, but next week I should have some more time for pandas and I could prepare for a 0.19.1 release (eg somewhere by the end of next week/beginning week after). Sounds OK?
Open issues/PRs: https://github.com/pandas-dev/pandas/milestones/0.19.1
Some issues still that would be nice to get fixed next week before releasing.
@pandas-dev/pandas-core
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BLD: don't require cython on sdist install
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| 3 | 2016-10-22T11:49:26Z | 2016-10-24T22:24:19Z | 2016-10-24T22:24:18Z |
CONTRIBUTOR
| null |
- [x] closes #14204
- [x] tests not needed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
We could just make a cython a build-time requirement, but given we already create a sdists with `.c` files, probably makes sense to continue to support it.
I tested this locally, applying this patch on the latest source [release](https://pypi.python.org/packages/58/2c/62ba69f6cf16693fc341df084e8262ebb89f678ddc534164099fc7ddeb0b/pandas-0.19.0.zip#md5=6243f3abeaed035720bd77237bdade53) and it installed into a an environment without cython fine.
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PERF: performance regression in Series.asof
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| 19 | 2016-10-22T16:21:02Z | 2016-10-26T08:17:08Z | 2016-10-26T08:16:57Z |
CONTRIBUTOR
| null |
Fix performance regression in Series.asof by avoiding pre-computing nulls and returning value by indexing the underlying ndarray.
- [x] closes #14461
- [x] add asv benchmark
|
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| 184,665,418 |
MDU6SXNzdWUxODQ2NjU0MTg=
| 14,477 |
read_csv incompatible with newstr and future
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| 8 | 2016-10-23T01:24:45Z | 2018-08-29T23:46:55Z | 2016-10-26T22:31:40Z |
NONE
| null |
When upgrading the pandas-0.19 I have several tests failing on a package I maintain. These packages are using several imports from future to work with both py2 and py3. It seems there is an issue with using `from __future__ import unicode_literals`
#### A small, complete example of the issue
``` python
import pandas as pd
pd.read_csv('simple.txt', quotechar='"')
from __future__ import unicode_literals
pd.read_csv('simple.txt', quotechar='"')
```
The first reading works the second does not and throws the stack trace attached. ("TypeError: "quotechar" must be string, not unicode")
The example file
[simple.txt](https://github.com/pandas-dev/pandas/files/546124/simple.txt)
#### Expected Output
#### Output of `pd.show_versions()`
<details>
## 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: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 26.0.0
Cython: None
numpy: 1.11.2
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
```
TypeError Traceback (most recent call last)
<ipython-input-2-6e275a5a7598> in <module>()
1 from __future__ import unicode_literals
----> 2 pd.read_csv('/Users/lom/simple.csv', quotechar='"')
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
643 skip_blank_lines=skip_blank_lines)
644
--> 645 return _read(filepath_or_buffer, kwds)
646
647 parser_f.__name__ = name
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
386
387 # Create the parser.
--> 388 parser = TextFileReader(filepath_or_buffer, **kwds)
389
390 if (nrows is not None) and (chunksize is not None):
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, f, engine, **kwds)
727 self.options['has_index_names'] = kwds['has_index_names']
728
--> 729 self._make_engine(self.engine)
730
731 def close(self):
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _make_engine(self, engine)
920 def _make_engine(self, engine='c'):
921 if engine == 'c':
--> 922 self._engine = CParserWrapper(self.f, **self.options)
923 else:
924 if engine == 'python':
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, src, **kwds)
1387 kwds['allow_leading_cols'] = self.index_col is not False
1388
-> 1389 self._reader = _parser.TextReader(src, **kwds)
1390
1391 # XXX
pandas/parser.pyx in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4411)()
pandas/parser.pyx in pandas.parser.TextReader._set_quoting (pandas/parser.c:6535)()
TypeError: "quotechar" must be string, not unicode
```
</details>
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| 184,704,997 |
MDU6SXNzdWUxODQ3MDQ5OTc=
| 14,478 |
df.to_hdf() blocks some supported pytables compression types ‘blosc:lz4’, ‘blosc:lz4hc’, ‘blosc:snappy’, ‘blosc:zlib’ and ‘blosc:zstd’
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| 5 | 2016-10-23T18:10:06Z | 2017-05-11T22:55:10Z | 2017-05-11T22:55:09Z |
NONE
| null |
`df.to_hdf()` blocks access to the following compressors offered in pytables 3.3.0: ‘blosc:lz4’, ‘blosc:lz4hc’, ‘blosc:snappy’, ‘blosc:zlib’ and ‘blosc:zstd’.
I would like to try blosc:lz4 compression for some of the bigger data I have to compare size and speed to LZO.
``` python
df.to_hdf(path, 'df', complib='blosc:lz4')
D:\Python27\lib\site-packages\pandas\io\pytables.pyc in __init__(self, path, mode, complevel, complib, fletcher32, **kwargs)
434
435 if complib not in (None, 'blosc', 'bzip2', 'lzo', 'zlib'):
--> 436 raise ValueError("complib only supports 'blosc', 'bzip2', lzo' "
437 "or 'zlib' compression.")
438
ValueError: complib only supports 'blosc', 'bzip2', lzo' or 'zlib' compression.
```
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API: Rename CParserError to ParserError
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| 27 | 2016-10-23T20:07:05Z | 2016-11-18T13:38:45Z | 2016-11-18T13:38:11Z |
MEMBER
| null |
Title is self-explanatory. Partially resolves #12665.
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Strip whitespace from column names when usecols in read_csv
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| 6 | 2016-10-24T09:09:09Z | 2016-10-24T10:39:00Z | 2016-10-24T10:15:26Z |
NONE
| null |
#### A small, complete example of the issue
when loading a file of the type, where headers have a trailing whitespace,
```
a ,b ,c
1,2,3
4,5,6
```
I would expect the following code to work and give the result
``` python
pandas.read_table('test_data.csv', sep=',', usecols=['a', 'b'])
```
#### Expected Output
```
a b
0 1 2
1 4 5
```
#### Actual Output
Neither the `c` nor the `python` engine produce the expected result.
the tracebacks have been concatenated for brevity.
``` python
/Users/rahulporuri/Github/pandas/pandas/io/parsers.pyc in __init__(self, src, **kwds)
1431
1432 if len(self.names) < len(self.usecols):
-> 1433 raise ValueError("Usecols do not match names.")
1434
1435 self._set_noconvert_columns()
ValueError: Usecols do not match names.
```
``` python
/Users/rahulporuri/Github/pandas/pandas/io/parsers.pyc in _handle_usecols(self, columns, usecols_key)
2199 for u in self.usecols:
2200 if isinstance(u, string_types):
-> 2201 col_indices.append(usecols_key.index(u))
2202 else:
2203 col_indices.append(u)
ValueError: 'a' is not in list
```
This is related to an issue reported earlier https://github.com/pandas-dev/pandas/issues/14460 on stripping columns/column names of whitespaces.
On a side note, if the file has column names with leading whitespaces instead of trailing whitespaces, adding the `skipinitialspace=True` kwarg to `pandas.read_table` produces the expected result.
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: 794f79295484298525dbc8dd3b8ab251ad065e61
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 16.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0+27.g794f792
nose: None
pip: 8.1.2
setuptools: 23.1.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: None
xarray: 0.7.2
IPython: 4.1.2
sphinx: 1.4.1
patsy: None
dateutil: 2.5.2
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
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
</details>
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MDExOlB1bGxSZXF1ZXN0OTA1ODk1ODU=
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BUG: fix empty intersection of RangeIndex (GH14364)
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| 3 | 2016-10-24T10:54:53Z | 2016-10-24T22:21:12Z | 2016-10-24T22:20:45Z |
MEMBER
| null |
closes #14364
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fillna on a string column
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| 2 | 2016-10-24T13:40:51Z | 2016-10-24T14:46:05Z | 2016-10-24T14:06:02Z |
NONE
| null |
Using pandas version 0.16.0.
I read a CSV file that has a string column with some missing values, and pandas loads those missing string values as NaN. Pandas won't let me group on that string value, so I'm trying to fillna() with the value "<blank>". But that doesn't work either, pandas overwrites the good values then too.
Yes, I understand fillna is apparently trying to convert the strings to numbers. But then there's no intuitive way to handle missing string values. Can we change fillna to handle missing string values?
``` python
import pandas
import numpy
#The real use case is reading a CSV with missing values, but this demos the issue
df = pandas.DataFrame({ 'x': ['first', numpy.nan, 'third'], 'y' : [1, 2, 3]})
df #Shows we have a missing value in the string column
#I want to group by 'x'. The following won't work because pandas doesn't
#correctly sum the value for the missing row (it ought to be "2" but shows blank).
#Yes, I understand NaN != NaN, so I get why it fails, but then I need a workaround.
df.groupby('x').sum() #shows blank for the missing value, instead of "2".
#OK, so I'll fillna the missing value and then group.
df['x'] = df['x'].fillna('<blank>', inplace=True)
df #BAD! That overwrote all my good string values!!!
```
Since the column was already an object (with just strings) and I'm fillna with a string, it should not have to drop values.
pandas.show_versions()
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.0
nose: None
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numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: None
numexpr: 2.4.3
matplotlib: 1.4.3
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xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.7.2
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.3
pymysql: None
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Enhancement: add "sum" to pandas.describe()
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| 25 | 2016-10-24T14:06:12Z | 2017-01-03T20:43:45Z | 2016-11-23T13:06:09Z |
NONE
| null |
Feature request: can we add "sum" to the pandas.describe() method? Not every dataset cares about sum, of course, but enough datasets do care about it that it seems worth adding to the default describe() method. (For instance, financial applications where you have multiple columns representing different scenarios, and you want to see how much profit would be earned of each scenario).
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| 14,484 |
BUG: to_latex outputs string with missing second index level values
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| 3 | 2016-10-24T18:55:28Z | 2017-12-08T11:11:44Z | 2017-12-08T11:11:44Z |
NONE
| null |
I am using pandas to generate a LaTeX string using the to_latex() method on a DataFrame, which is indexed using a MultiIndex object. Running the code snippet produces an incorrect list of strings: the LaTeX table is missing two index numbers.
#### Code Snippet
``` python
import pandas as pd
outliers_lst = [(23240, 0),
(23240, 15),
(23240, 23),
(23240, 31),
(23240, 85),
(38661, 85),
(41231, 85),
(41231, 92),
(46371, 0)]
headers = (['max', 'EC 1', 'S'],
['max', 'EC 1', 'A'],
['max', 'EC 2', 'S'])
table = pd.DataFrame("",index = pd.MultiIndex.from_tuples(sorted(outliers_lst)), columns = pd.MultiIndex.from_tuples(headers))
table.to_latex(index = True, longtable = True, column_format = 'c'*5).split('\n')
```
#### Incorrect Output
```
[u'\\begin{longtable}{cccccccccccccccccccccccccc}',
u'\\toprule',
u' & & max & & \\\\',
u' & & EC 1 & & EC 2 \\\\',
u' & & S & A & S \\\\',
u'\\midrule',
u'\\endhead',
u'\\midrule',
u'\\multicolumn{3}{r}{{Continued on next page}} \\\\',
u'\\midrule',
u'\\endfoot',
u'',
u'\\bottomrule',
u'\\endlastfoot',
u'23240 & 0 & & & \\\\',
u' & 15 & & & \\\\',
u' & 23 & & & \\\\',
u' & 31 & & & \\\\',
u' & 85 & & & \\\\',
u'38661 & & & & \\\\',
u'41231 & & & & \\\\',
u' & 92 & & & \\\\',
u'46371 & 0 & & & \\\\',
u'\\end{longtable}',
u'']
```
#### Correct Output
```
[u'\\begin{longtable}{cccccccccccccccccccccccccc}',
u'\\toprule',
u' & & max & & \\\\',
u' & & EC 1 & & EC 2 \\\\',
u' & & S & A & S \\\\',
u'\\midrule',
u'\\endhead',
u'\\midrule',
u'\\multicolumn{3}{r}{{Continued on next page}} \\\\',
u'\\midrule',
u'\\endfoot',
u'',
u'\\bottomrule',
u'\\endlastfoot',
u'23240 & 0 & & & \\\\',
u' & 15 & & & \\\\',
u' & 23 & & & \\\\',
u' & 31 & & & \\\\',
u' & 85 & & & \\\\',
u'38661 & 85 & & & \\\\',
u'41231 & 85 & & & \\\\',
u' & 92 & & & \\\\',
u'46371 & 0 & & & \\\\',
u'\\end{longtable}',
u'']
```
Note that in the correct output LaTeX strings, the rows with indices (38661, 85) and (41231, 85) correctly include the second index (the number 85), but in the incorrect LaTeX strings the rows do not include the number 85.
Could this be because the row (23240, 85) above (38661, 85) includes 85 in its second index?
<details>
commit: None
python: 2.7.12.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
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
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: 1.5.1
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: 0.7.6.None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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| 184,938,738 |
MDU6SXNzdWUxODQ5Mzg3Mzg=
| 14,485 |
Concatenation of list columns fails when grouping on multiple columns
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| 1 | 2016-10-24T19:56:35Z | 2016-10-24T22:05:54Z | 2016-10-24T22:05:53Z |
NONE
| null |
Assume we have a simple DF like this
```
x = pd.DataFrame({'a':[1,1,2,2],'b':['a','a','a','b'],'c':[[1,2],[3,4],[5,6],[7,8]]})
a b c
0 1 a [1, 2]
1 1 a [3, 4]
2 2 a [5, 6]
3 2 b [7, 8]
```
and we want to do a groupby operation that concatenates the lists in column `c` together. This works fine when grouping on one column:
```
x.groupby('b')['c'].sum()
b
a [1, 2, 3, 4, 5, 6]
b [7, 8]
dtype: object
x.groupby('a')['c'].sum()
a
1 [1, 2, 3, 4]
2 [5, 6, 7, 8]
dtype: object
```
But fails when grouping on multiple columns (i.e. `x.groupby(['a','b'])['c'].sum()`) with `ValueError: Function does not reduce`.
This is based on this SO question: https://stackoverflow.com/questions/40224793/pandas-concatenate-list-columns-with-groupby-when-grouping-on-multiple-columns/40226389#40226389
#### Output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
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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
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 17.1.1
Cython: 0.22.1
numpy: 1.11.2
scipy: 0.18.1
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apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: 2.33.0
pandas_datareader: None
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Funky labels in groupby().myserie.plot.line()
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| 0 | 2016-10-24T22:00:16Z | 2021-05-02T00:39:15Z | null |
NONE
| null |
#### A small, complete example of the issue
I am not sure I understand how label generation work in pandas and I've had several problems in the past. I tried to find some documentation without success so here is my pb. I open a ticket here since I was told on gitter it might be a bug but it might just be me misunderstanding
``` python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({ 'A' : 1.,
'B' : pd.Timestamp('20130102'),
'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
'D' : np.array([3] * 4,dtype='int32'),
'E' : pd.Categorical(["test","train","test","train"]),
'F' : 'foo' })
fig = plt.figure()
axes = fig.gca()
g = df.groupby(['B','F'])
print("Nb of %d" % len(g))
g.plot.line(ax=axes,
legend=True,
x="D",
y="C"
)
plt.show()
```
#### Expected Output
I would expect to see only one label per group there is one group but instead I see 2 labels (only one line plotted).
The real example (in a more complex program), I have 2 groups with a multiindex of 3 keys and it generates 6 labels while it plots 2 lines.
Hence it seems there are as many labels as (nb of groups)\* (nb of keys in multindex)
#### Output of `pd.show_versions()`
<details>
# Paste the output here
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.0-26-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8
```
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1
Cython: 0.24.1
numpy: 1.11.1rc1
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.5
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.5
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
</details>
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API: to_datetime inconsistent returning of datetime.datetime
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| 5 | 2016-10-24T22:56:44Z | 2021-05-02T00:39:44Z | null |
MEMBER
| null |
Copying from https://github.com/pandas-dev/pandas/issues/14448#issuecomment-255068330 (as the issue is closed now):
```
In [1]: pd.to_datetime('13000101', errors='ignore')
Out[1]: '13000101'
In [2]: pd.to_datetime('13000101', errors='ignore', format='%Y%m%d')
Out[2]: datetime.datetime(1300, 1, 1, 0, 0)
```
The above inconsistency (which is also not documented at all I think), is that something we want to fix?
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Adding optional pickle protocol version argument to pandas.to_pickle()
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| 1 | 2016-10-25T10:02:35Z | 2017-05-18T10:30:53Z | 2017-05-18T10:30:53Z |
NONE
| null |
Sometimes pickle files saved in Python v3.x are needed to read in Python v2.x. It would be nice if one can easily set pickle protocol version in `to_pickle()`.
It can be done with the following little changes:
In file `/pandas/io/pickle.py`:
```
def to_pickle(obj, path, protocol=pkl.HIGHEST_PROTOCOL):
"""
Pickle (serialize) object to input file path
Parameters
----------
obj : any object
path : string
File path
"""
with open(path, 'wb') as f:
pkl.dump(obj, f, protocol=protocol)
```
In file `/pandas/core/generic.py`:
```
def to_pickle(self, path, protocol):
"""
Pickle (serialize) object to input file path.
Parameters
----------
path : string
File path
"""
from pandas.io.pickle import to_pickle
return to_pickle(self, path, protocol)
```
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CI: numpy dev started failing
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| 3 | 2016-10-25T10:08:45Z | 2016-10-26T22:19:16Z | 2016-10-26T22:19:16Z |
CONTRIBUTOR
| null |
https://travis-ci.org/pandas-dev/pandas/jobs/169382808
IIRC some issues w.r.t. int *\* int. unrelated to this PR.
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| 1 | 2016-10-25T14:32:05Z | 2016-10-26T10:21:26Z | 2016-10-26T10:21:03Z |
CONTRIBUTOR
| null |
I am getting "time out" errors when trying to read from a large CSV (2GB) in S3:
``` python
df = pd.read_csv('path_to_2GB_file.csv')
```
The file is hosted privately so unfortunately can't make it accessible. I'll be happy to try reading from an open/public S3 repository if anyone knows where to get CSVs of a similar size. Unfortunately I couldn't find any on a quick search.
#### Output
``` python
TimeoutError Traceback (most recent call last)
<ipython-input-4-0470fac7215b> in <module>()
----> 1 winsdf = pd.read_csv('s3://dxusers-useast1/sys-opt/flight_creative_exchange_spend_hour/2016-10-10.csv')
/Users/amelio/anaconda/envs/py35/lib/python3.5/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squ
eeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose,
skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quote
char, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarr
ay, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
643 skip_blank_lines=skip_blank_lines)
644
--> 645 return _read(filepath_or_buffer, kwds)
646
647 parser_f.__name__ = name
/Users/amelio/anaconda/envs/py35/lib/python3.5/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
398 return parser
399
--> 400 data = parser.read()
401 parser.close()
402 return data
/Users/amelio/anaconda/envs/py35/lib/python3.5/site-packages/pandas/io/parsers.py in read(self, nrows)
936 raise ValueError('skipfooter not supported for iteration')
937
--> 938 ret = self._engine.read(nrows)
939
940 if self.options.get('as_recarray'):
/Users/amelio/anaconda/envs/py35/lib/python3.5/site-packages/pandas/io/parsers.py in read(self, nrows)
1503 def read(self, nrows=None):
1504 try:
-> 1505 data = self._reader.read(nrows)
1506 except StopIteration:
1507 if self._first_chunk:
pandas/parser.pyx in pandas.parser.TextReader.read (pandas/parser.c:9884)()
pandas/parser.pyx in pandas.parser.TextReader._read_low_memory (pandas/parser.c:10142)()
pandas/parser.pyx in pandas.parser.TextReader._read_rows (pandas/parser.c:10870)()
pandas/parser.pyx in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:10741)()
pandas/parser.pyx in pandas.parser.raise_parser_error (pandas/parser.c:25721)()
/Users/amelio/anaconda/envs/py35/lib/python3.5/socket.py in readinto(self, b)
573 while True:
574 try:
--> 575 return self._sock.recv_into(b)
576 except timeout:
577 self._timeout_occurred = True
/Users/amelio/anaconda/envs/py35/lib/python3.5/ssl.py in recv_into(self, buffer, nbytes, flags)
927 "non-zero flags not allowed in calls to recv_into() on %s" %
928 self.__class__)
--> 929 return self.read(nbytes, buffer)
930 else:
931 return socket.recv_into(self, buffer, nbytes, flags)
/Users/amelio/anaconda/envs/py35/lib/python3.5/ssl.py in read(self, len, buffer)
789 raise ValueError("Read on closed or unwrapped SSL socket.")
790 try:
--> 791 return self._sslobj.read(len, buffer)
792 except SSLError as x:
793 if x.args[0] == SSL_ERROR_EOF and self.suppress_ragged_eofs:
/Users/amelio/anaconda/envs/py35/lib/python3.5/ssl.py in read(self, len, buffer)
573 """
574 if buffer is not None:
--> 575 v = self._sslobj.read(len, buffer)
576 else:
577 v = self._sslobj.read(len)
TimeoutError: [Errno 60] Operation timed out
```
#### Output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1.post20160901
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
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: 1.1.1
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
```
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| 185,188,781 |
MDU6SXNzdWUxODUxODg3ODE=
| 14,491 |
ERR: improve error message with unalignable label indexing
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| 4 | 2016-10-25T17:55:05Z | 2016-12-09T16:36:30Z | 2016-12-09T16:36:29Z |
NONE
| null |
#### A small, complete example of the issue
Using a boolean index in a `pandas.Series` on a data frame with an string index fails with `Unalignable boolean Series key provided`:
``` python
import pandas as pd
# DF without string index
df = pd.DataFrame({"A": [0, 1, 2]})
df.loc[[True, True, True], :] # works
df.loc[pd.Series([True, True, True]), :] # works
# DF with string index
df = pd.DataFrame({"A": [0, 1, 2]}, index=["a", "b", "c"])
df.loc[[True, True, True], :] # works
df.loc[pd.Series([True, True, True]), :] # fails
```
#### Expected Output
Boolean indexing should work with a `pandas.Series` like it does with regular Python lists and boolean numpy arrays.
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-98-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: None
numexpr: 2.6.1
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
</details>
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| 14,492 |
BUG: Accept unicode quotechars again in pd.read_csv
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| 10 | 2016-10-25T18:54:17Z | 2016-10-27T07:05:58Z | 2016-10-26T22:31:40Z |
MEMBER
| null |
Title is self-explanatory. Affects Python 2.x only. Closes #14477.
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ENH: Add sparse option in pivot_table / pivot / unstack
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| 7 | 2016-10-25T20:25:09Z | 2020-04-20T01:02:55Z | null |
NONE
| null |
#### A small, complete example of the issue
``` python
# Your code here
sparse_df = df.pivot(index='a', columns='b', values='c', aggfunc=lambda x: len(x), sparse=True)
```
#### Expected Output
#### Output of `pd.show_versions()`
<details>
# Paste the output here
</details>
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BUG: don't allow users to move from an interned string
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| 10 | 2016-10-25T20:53:43Z | 2016-11-16T21:02:11Z | 2016-11-16T20:57:42Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
I was thinking about code paths that give away borrowed references to strings and realized that interned strings can be accessed even when their refcount is 1. This is because the intern table holds a "weak ref" to the string. This allows the string to be garbage collected normally; however, if a user creates an interned string with exactly one reference and feeds it to move, you could later get a second reference to the **same** string object by calling intern with a copy of the old string. The data backing the old string may have been mutated so you will get very strange results. I am almost certain that no on has run into this since `move_into_mutable_buffer` was added but it doesn't hurt to add this quick check to prevent broken usage like this in the future.
This only affects python 2 because bytes objects cannot be interned in python 3.
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COMPAT: bottleneck 1.2.0
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| 1 | 2016-10-25T22:41:39Z | 2017-01-28T12:46:08Z | 2017-01-28T12:46:08Z |
CONTRIBUTOR
| null |
just need to verify things, should hopefully be ok.
https://pypi.python.org/pypi/Bottleneck
its not up on conda-forge (or defaults ATM). so need to watch for that and test.
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Use old Cython build_ext until includes are fixed.
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| 5 | 2016-10-26T05:19:08Z | 2016-10-26T22:34:45Z | 2016-10-26T22:29:04Z |
CONTRIBUTOR
| null |
This change in Cython 0.25
The distutils extension Cython.Distutils.build_ext has now been updated to use cythonize which properly handles dependencies. The old extension can still be found in Cython.Distutils.old_build_ext and is now deprecated.
seems to have broken the include of auto-generated algos_common_helper.pxi. As a temporary workaround, the old build_ext can be used.
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TST: do not use int to negative int power in tests (GH14489)
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[] | null | 3 | 2016-10-26T10:31:00Z | 2016-10-26T10:39:19Z | 2016-10-26T10:39:19Z |
MEMBER
| null |
Closes #14489
Numpy starts to error on those cases from 1.12 https://github.com/numpy/numpy/pull/8127
This just fixes the test for now, but other option is to coerce to floats (but that is certainly not for 0.19.1)
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COMPAT/TST: fix test for range testing of negative integers to neg powers
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| 4 | 2016-10-26T10:32:03Z | 2017-01-09T15:52:12Z | 2016-10-26T22:19:16Z |
CONTRIBUTOR
| null |
xref https://github.com/numpy/numpy/pull/8127
closes #14489
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Panel.ffill ignores axis parameter and fill along axis=1
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| 7 | 2016-10-26T11:01:38Z | 2017-01-20T12:16:29Z | 2017-01-20T12:16:29Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
a = pd.Panel({'a':np.arange(15).reshape((5,3)), 'b':np.arange(15,30).reshape((5,3))})
a['a'].loc[1,1] = np.nan
a['a']
Out[36]:
0 1 2
0 0 1.0 2
1 3 NaN 5
2 6 7.0 8
3 9 10.0 11
4 12 13.0 14
a.ffill(axis=2)['a']
Out[38]:
0 1 2
0 0.0 1.0 2.0
1 3.0 1.0 5.0
2 6.0 7.0 8.0
3 9.0 10.0 11.0
4 12.0 13.0 14.0
```
#### Expected Output
`a.at['a', 1, 1] == 3`
If you ffill along axis=2, you should get `3` instead of `1`
#### Output of `pd.show_versions()`
Pandas 0.19.0
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MDU6SXNzdWUxODUzNjYzODQ=
| 14,500 |
BUG: index.map() deletes index.name
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| 3 | 2016-10-26T11:25:01Z | 2016-10-27T12:57:47Z | 2016-10-27T12:57:47Z |
NONE
| null |
This is related to https://github.com/pandas-dev/pandas/issues/12766
Possibly a duplicate.
#### A small, complete example of the issue
```
DD = pd.DataFrame({'A':[1,2,3,4,5]})
DD.index.name = 'Test'
print DD.index.name
DD.index = DD.index.map(unicode)
print DD.index.name
```
#### Expected Output
The mapping operation deletes the index name.
However mapping a function on the index means modifying an existing index, so one would expect the name of the index to stay the same as opposed to when choosing a completely new index.
#### Output of `pd.show_versions()`
```
python: 2.7.12.final.0
pandas: 0.19.0
```
#### EDIT:
Doing a `set_index(column)` does NOT set the column name as the index name, I think it would be common sense that the name of the column which becomes the new index be automatically set as the index name.
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BUG: fix DatetimeIndex._maybe_cast_slice_bound for empty index (GH14354)
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| 2 | 2016-10-26T12:27:56Z | 2016-10-27T13:53:09Z | 2016-10-27T13:53:09Z |
MEMBER
| null |
Closes #14354
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| 3 | 2016-10-26T13:17:49Z | 2016-10-27T07:11:44Z | 2016-10-27T07:11:41Z |
MEMBER
| null |
Addresses my remaining comment from https://github.com/pandas-dev/pandas/pull/14346#issuecomment-255520873
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API: Expand read_csv dtype for categoricals
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| 2 | 2016-10-26T16:47:34Z | 2021-05-02T00:40:16Z | null |
CONTRIBUTOR
| null |
In https://github.com/pandas-dev/pandas/pull/13406 Chris added support for `read_csv(..., dtype={'col': 'category'})` (thanks!). This issue is for expanding that syntax to allow a more complete specification of the resulting categorical.
``` python
# Your code here
df = pd.read_csv(path, dtype={'col': pd.Categorical(['a', 'b', 'c'], ordered=True})
df = pd.read_csv(path, dtype={'col': ['a', 'b', 'c']}) # shorthand, but unordered only
# we would still accept `dtype={'col': 'category'}` as well, to infer categories
```
Implementation-wise, I think we can keep all the parsing logic as is, and simply loop over `dtype` and call `set_categories` (and maybe `as_ordered`) on all the categoricals just before returning to the user.
This would help a bit in dask, where their category type inference can fail if the first partition doesn't contain all the categories (see https://github.com/dask/dask/issues/1705). This is why it'd be preferable to do it as an option to `read_csv`, rather than putting in on the user to followup with a `set_categories`.
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BUG: to_numeric downcast = 'unsigned' would not un-sign a 0 value.
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| 8 | 2016-10-26T17:42:43Z | 2017-02-24T01:36:34Z | 2016-11-17T13:10:59Z |
NONE
| null |
- [ ] closes #14401
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Have to figure out whats going wrong with the build still. Will let you know as soon as I do.
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| 20 | 2016-10-26T20:29:50Z | 2016-11-04T11:20:47Z | 2016-11-04T11:20:14Z |
CONTRIBUTOR
| null |
Continued in https://github.com/pandas-dev/pandas/pull/14583
---
When using pandas.io.json.json_normalize to parse a nested json and convert it to a dataframe, the meta parameter can be used to use fields as metadata for each record in resulting table. In some cases, not all items may contain all of the specified meta fields. This change will avoid throwing an error and output np.nan instead.
… meta parameter that don't always occur in every item of the list
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closed
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| 22 | 2016-10-26T21:18:10Z | 2016-12-18T00:09:10Z | 2016-12-16T23:26:00Z |
CONTRIBUTOR
| null |
- [x] closes #12766
- [x] closes #12798
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
This is a follow on to #12798.
|
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|
https://api.github.com/repos/pandas-dev/pandas/issues/14507
|
https://api.github.com/repos/pandas-dev/pandas
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https://api.github.com/repos/pandas-dev/pandas/issues/14507/labels{/name}
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https://api.github.com/repos/pandas-dev/pandas/issues/14507/comments
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https://api.github.com/repos/pandas-dev/pandas/issues/14507/events
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https://github.com/pandas-dev/pandas/pull/14507
| 185,519,117 |
MDExOlB1bGxSZXF1ZXN0OTEwOTUwNDQ=
| 14,507 |
[BUG] Handle trailing escaped backslash in line delimited json
|
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|
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closed
| false | null |
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/33"
}
| 0 | 2016-10-26T21:46:25Z | 2016-10-27T08:53:17Z | 2016-10-26T21:50:06Z |
CONTRIBUTOR
| null |
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry (This is a bugfix for https://github.com/pandas-dev/pandas/pull/14429 and https://github.com/pandas-dev/pandas/pull/14391)
|
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| null | 0 |
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