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TST: print skipped tests files
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| 0 | 2017-02-15T13:31:47Z | 2017-02-15T15:23:55Z | 2017-02-15T15:23:55Z |
CONTRIBUTOR
| null |
xref #15341
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| 2 | 2017-02-15T14:12:05Z | 2017-02-15T15:17:35Z | 2017-02-15T15:17:14Z |
NONE
| null |
Trying to reproduce example `[_85]` from [the docs](http://pandas.pydata.org/pandas-docs/stable/computation.html#applying-multiple-functions-at-once):
```python
from pandas import DataFrame, Timestamp
c = pandas.DataFrame({u'ul_payload': {('a', Timestamp('2016-11-01 06:15:00')): 5, ('a', Timestamp('2016-11-01 07:45:00')): 8, ('a', Timestamp('2016-11-01 09:00:00')): 9, ('a', Timestamp('2016-11-01 07:15:00')): 6, ('a', Timestamp('2016-11-01 07:30:00')): 7, ('a', Timestamp('2016-11-01 06:00:00')): 4}, u'dl_payload': {('a', Timestamp('2016-11-01 06:15:00')): 15, ('a', Timestamp('2016-11-01 07:45:00')): 18, ('a', Timestamp('2016-11-01 09:00:00')): 19, ('a', Timestamp('2016-11-01 07:15:00')): 16, ('a', Timestamp('2016-11-01 07:30:00')): 17, ('a', Timestamp('2016-11-01 06:00:00')): 14}})
In [27]: c
Out[27]:
dl_payload ul_payload
a 2016-11-01 06:00:00 14 4
2016-11-01 06:15:00 15 5
2016-11-01 07:15:00 16 6
2016-11-01 07:30:00 17 7
2016-11-01 07:45:00 18 8
2016-11-01 09:00:00 19 9
In [30]: c.groupby(level=0).rolling(window=3).agg([np.sum, np.max])
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
/Users/olevinkr/repos/research/src/son/research/apps/deep_forecast/data.py in <module>()
----> 1 c.groupby(level=0).rolling(window=3).agg([np.sum, np.max])
/Users/olevinkr/anaconda/lib/python2.7/site-packages/pandas/core/window.pyc in aggregate(self, arg, *args, **kwargs)
1063 @Appender(SelectionMixin._agg_doc)
1064 def aggregate(self, arg, *args, **kwargs):
-> 1065 return super(Rolling, self).aggregate(arg, *args, **kwargs)
1066
1067 agg = aggregate
/Users/olevinkr/anaconda/lib/python2.7/site-packages/pandas/core/window.pyc in aggregate(self, arg, *args, **kwargs)
319
320 def aggregate(self, arg, *args, **kwargs):
--> 321 result, how = self._aggregate(arg, *args, **kwargs)
322 if result is None:
323 return self.apply(arg, args=args, kwargs=kwargs)
/Users/olevinkr/anaconda/lib/python2.7/site-packages/pandas/core/base.pyc in _aggregate(self, arg, *args, **kwargs)
566 return result, True
567 elif hasattr(arg, '__iter__'):
--> 568 return self._aggregate_multiple_funcs(arg, _level=_level), None
569 else:
570 result = None
/Users/olevinkr/anaconda/lib/python2.7/site-packages/pandas/core/base.pyc in _aggregate_multiple_funcs(self, arg, _level)
611 try:
612 colg = self._gotitem(col, ndim=1, subset=obj[col])
--> 613 results.append(colg.aggregate(arg))
614 keys.append(col)
615 except (TypeError, DataError):
/Users/olevinkr/anaconda/lib/python2.7/site-packages/pandas/core/window.pyc in aggregate(self, arg, *args, **kwargs)
1063 @Appender(SelectionMixin._agg_doc)
1064 def aggregate(self, arg, *args, **kwargs):
-> 1065 return super(Rolling, self).aggregate(arg, *args, **kwargs)
1066
1067 agg = aggregate
/Users/olevinkr/anaconda/lib/python2.7/site-packages/pandas/core/window.pyc in aggregate(self, arg, *args, **kwargs)
319
320 def aggregate(self, arg, *args, **kwargs):
--> 321 result, how = self._aggregate(arg, *args, **kwargs)
322 if result is None:
323 return self.apply(arg, args=args, kwargs=kwargs)
/Users/olevinkr/anaconda/lib/python2.7/site-packages/pandas/core/base.pyc in _aggregate(self, arg, *args, **kwargs)
566 return result, True
567 elif hasattr(arg, '__iter__'):
--> 568 return self._aggregate_multiple_funcs(arg, _level=_level), None
569 else:
570 result = None
/Users/olevinkr/anaconda/lib/python2.7/site-packages/pandas/core/base.pyc in _aggregate_multiple_funcs(self, arg, _level)
595 for a in arg:
596 try:
--> 597 colg = self._gotitem(obj.name, ndim=1, subset=obj)
598 results.append(colg.aggregate(a))
599
/Users/olevinkr/anaconda/lib/python2.7/site-packages/pandas/core/base.pyc in _gotitem(self, key, ndim, subset)
682 for attr in self._attributes])
683 self = self.__class__(subset,
--> 684 groupby=self._groupby[key],
685 parent=self,
686 **kwargs)
/Users/olevinkr/anaconda/lib/python2.7/site-packages/pandas/core/base.pyc in __getitem__(self, key)
328 def __getitem__(self, key):
329 if self._selection is not None:
--> 330 raise Exception('Column(s) %s already selected' % self._selection)
331
332 if isinstance(key, (list, tuple, ABCSeries, ABCIndexClass,
Exception: Column(s) dl_payload already selected
```
Aggregating by a single function doesn't throw the exception (xref https://github.com/pandas-dev/pandas/issues/14013):
```python
In [29]: c.groupby(level=0).rolling(window=3).agg(np.sum)
Out[29]:
dl_payload ul_payload
a a 2016-11-01 06:00:00 NaN NaN
2016-11-01 06:15:00 NaN NaN
2016-11-01 07:15:00 45.0 15.0
2016-11-01 07:30:00 48.0 18.0
2016-11-01 07:45:00 51.0 21.0
2016-11-01 09:00:00 54.0 24.0
```
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 28.6.1
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.9.1
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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allow flexible convert_dates parameter in pandas.read_json
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[] | null | 2 | 2017-02-15T14:39:35Z | 2017-02-15T15:14:52Z | 2017-02-15T14:48:28Z |
CONTRIBUTOR
| null |
Currently, when using pandas.read_json to convert a json structure into a DataFrame, it is difficult to parse on the fly datetime except with convert_dates that allows rules:
```
convert_dates : boolean, default True
List of columns to parse for dates; If True, then try to parse datelike columns default is True; a column label is datelike if
it ends with '_at',
it ends with '_time',
it begins with 'timestamp',
it is 'modified', or
it is 'date'
```
It would be more flexible to allow to specify either a boolean (and keep the current rules) or to specify some (simplified) list of regexp pattern like:
- to replicate current rules: `convert_dates=("*_at","*_time","timestamp*","modified","date")`
- to allow alternatives:`convert_dates=("*_at","*_on","datetime_*","start","end")`
Haven't yet looked at the implementation, just sharing a feature request...
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DOC: use shared_docs for Index.get_indexer, get_indexer_non_unique
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STYLE: flake8 upgraded to 3.3 on conda
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| 0 | 2017-02-15T17:39:59Z | 2017-02-15T17:59:12Z | 2017-02-15T17:59:12Z |
CONTRIBUTOR
| null |
fixes for E305, 2 blank lines after a class definition
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Cannot set value correctly
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[] | null | 7 | 2017-02-15T18:01:26Z | 2021-05-08T01:03:07Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# It's pretty simple but it doesn't work.
# Here stocks is inside a class which type is pd.Series.
# I tried to print money / price * (1-r) and found it was not zero.
# However, self.stocks[ticker] is never changed which confuses me
self.stocks[ticker] += money / price * (1 - r)
```
#### Problem description
See the comments above. It only happens when I tried to use a certain set of data but works for other situations. I've tried to change the value of self.stocks[name] and something more strange happened.
``` python
# this works
self.stocks[ticker] = 6
self.stocks[ticker] += money / price * (1 - r)
# I was able to see the different value printed after the line
# but if I change it to
if self.stocks[ticker] == 0:
self.stocks[ticker] = 0
self.stocks[ticker] += money / price* (1 - r)
# it repeated the unchanged result which drove me mad
```
#### Expected Output
self.stocks[ticker] should be changed
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: zh_CN.UTF-8
LOCALE: zh_CN.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 32.2.0
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.2
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.7.2
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTA2MzcyMDk4
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BUG: Accept Generic Array-Like for .where
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| 11 | 2017-02-15T18:36:17Z | 2017-02-25T22:55:28Z | 2017-02-24T20:05:19Z |
MEMBER
| null |
Setup:
~~~python
>>> s = Series([1, 2, 3])
>>> cond = [False, True, True]
~~~
Before:
~~~python
>>> s.where(cond)
...
ValueError: where requires an ndarray like object for its condition
~~~
After:
~~~python
>>> s.where(cond)
0 NaN
1 2.0
2 3.0
dtype: float64
~~~
Note that `Index` objects can accept array-like objects as conditionals, and I wrote tests to confirm.
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| 0 | 2017-02-15T19:01:20Z | 2017-02-15T20:21:24Z | 2017-02-15T20:21:24Z |
CONTRIBUTOR
| null |
change 3.6 build to use numpy=1.12 & add back xlwt (was not on defaults for a while)
|
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MDExOlB1bGxSZXF1ZXN0MTA2NDA0MjY1
| 15,416 |
TST: convert yield based test_pickle.py to parametrized to remove warnings
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| 1 | 2017-02-15T21:35:02Z | 2017-02-16T14:12:51Z | 2017-02-16T14:12:51Z |
CONTRIBUTOR
| null |
xref #15341
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Join on level between two UNIQUE MultiIndex objects with unique level broadcast
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| 2 | 2017-02-15T21:44:41Z | 2020-04-03T04:25:01Z | 2020-04-03T04:25:00Z |
NONE
| null |
Performing simple arithmetic operations between two `DataFrames` that have a MultiIndex on the columns is disabled because the join on level is deemed ambiguous. This is not the case when the labels in both `DataFrame`s are unique and there is a unique mapping between the two and the MultiIndex levels are uniquely labeled.
First, let's just construct the DataFrame.
```
>>> data = {
('b', 'x', ''): {0: 0.5, 1: 0.59999999999999998, 2: 0.69999999999999996},
('b', 'y', ''): {0: -0.25, 1: -0.26000000000000001, 2: 0.27000000000000002},
('b', 'z', ''): {0: 0.29999999999999999, 1: 0.40000000000000002, 2: -0.69999999999999996},
('v', 'x', 'a'): {0: 125.0, 1: 250.0, 2: 375.0},
('v', 'x', 'p1'): {0: 100.0, 1: 200.0, 2: 300.0},
('v', 'x', 'p2'): {0: 150.0, 1: 300.0, 2: 450.0},
('v', 'y', 'a'): {0: 250.0, 1: 375.0, 2: 750.0},
('v', 'y', 'p1'): {0: 200.0, 1: 300.0, 2: 600.0},
('v', 'y', 'p2'): {0: 300.0, 1: 450.0, 2: 900.0},
('v', 'z', 'a'): {0: 500.0, 1: 750.0, 2: 1000.0},
('v', 'z', 'p1'): {0: 400.0, 1: 600.0, 2: 800.0},
('v', 'z', 'p2'): {0: 600.0, 1: 900.0, 2: 1200.0}}
>>> data = pd.DataFrame.from_dict(data, orient="columns").sort_index(axis=1)
>>> data.columns.names = ["Measurement", "Component", "Species"]
>>> data.T
0 1 2
Measurement Component Species
b x 0.50 0.60 0.70
y -0.25 -0.26 0.27
z 0.30 0.40 -0.70
v x a 125.00 250.00 375.00
p1 100.00 200.00 300.00
p2 150.00 300.00 450.00
y a 250.00 375.00 750.00
p1 200.00 300.00 600.00
p2 300.00 450.00 900.00
z a 500.00 750.00 1000.00
p1 400.00 600.00 800.00
p2 600.00 900.00 1200.00
```
In the case that there is a unique mapping between the two `DataFrames` such that there is a single level over which to broadcast, a `TypeError` is currently raised.
```
>>> mag = data.pow(2).sum(axis=1, level=["Measurement", "Species"]).pipe(np.sqrt)
>>> mag
Measurement b v
Species a p1 p2
0 0.634429 572.821962 458.257569 687.386354
1 0.766551 875.000000 700.000000 1050.000000
2 1.026109 1305.038314 1044.030651 1566.045976
>>> data.divide(mag, axis=1, level="Component").T
Traceback (most recent call last)
...
TypeError: Join on level between two MultiIndex objects is ambiguous
```
This solution provides a simple tool for many users, removing the necessity of a common for loop and manual index selection. The expected output would be something like
```
>>> data.divide(mag, axis=1, level="Component").T
0 1 2
Measurement Component Species
b x 0.788110 0.782727 0.682189
y -0.394055 -0.339182 0.263130
z 0.472866 0.521818 -0.682189
v x a 0.218218 0.285714 0.287348
p1 0.218218 0.285714 0.287348
p2 0.218218 0.285714 0.287348
y a 0.436436 0.428571 0.574696
p1 0.436436 0.428571 0.574696
p2 0.436436 0.428571 0.574696
z a 0.872872 0.857143 0.766261
p1 0.872872 0.857143 0.766261
p2 0.872872 0.857143 0.766261
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 26.1.1
Cython: None
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
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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| 207,961,594 |
MDExOlB1bGxSZXF1ZXN0MTA2NDIyNTY5
| 15,418 |
Ensure the right values are set in SeriesGroupBy.nunique
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| 7 | 2017-02-15T23:24:23Z | 2017-02-16T14:29:05Z | 2017-02-16T14:28:48Z |
CONTRIBUTOR
| null |
We only need to use the group boundaries as the index for `res` so that
the dimensions match those of `out`. Fixes #13453
- [x] closes #13453
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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MDU6SXNzdWUyMDgwMDYxNDI=
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Using a string as an index of dataframe
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| 1 | 2017-02-16T03:40:59Z | 2017-02-16T08:52:26Z | 2017-02-16T08:52:14Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
temp = "['A', 'B']"
transactions[temp]
#### Problem description
I am trying to use a string as an index of dataframe. Here if I write **transactions[['A', 'B']]** the code runs ,but when I write the same thing into a string and pass it as an index it does not work.
#### Expected Output
I want to display a dataframe including both the columns.
|
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MDU6SXNzdWUyMDgwMDkxNDU=
| 15,420 |
rank incorrectly orders ordered categories
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| 1 | 2017-02-16T04:01:20Z | 2017-02-24T19:57:32Z | 2017-02-24T19:57:32Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
a = pd.DataFrame(['first', 'second', 'third', 'fourth', 'fifth', 'sixth'], columns=['A'])
a['A'] = a['A'].astype('category', ).cat.set_categories(
['first', 'second', 'third', 'fourth', 'fifth', 'sixth'], ordered=True)
a['A'].rank()
# outputs:
# 0 2.0
# 1 4.0
# 2 6.0
# 3 3.0
# 4 1.0
# 5 5.0
```
#### Problem description
rank seems to be ignoring the order of ordered categories.
#### Expected Output
```
0 1.0
1 2.0
2 3.0
3 4.0
4 5.0
5 6.0
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 34.2.0
Cython: None
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
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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MDU6SXNzdWUyMDgxMDg4NDY=
| 15,421 |
Unexpected string->float conversion in DataFrame.groupby().apply()
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| 1 | 2017-02-16T12:33:52Z | 2017-03-16T11:52:16Z | 2017-02-16T13:24:50Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
df = pd.DataFrame({'A':[10, 20, 30], 'B': [ 'foo', '3', '4'], 'T': [pd.Timestamp("12:31:22")]*3})
def get_B(g):
return g.iloc[0][['B']]
print df.groupby('A').apply(get_B)
# Observed output:
B
A
10 NaN
20 3.0
30 4.0
```
#### Problem description
groupby.apply() does an unexpected conversion from string to float for column 'B' in the example above. The bug is triggered only when both of the following happen:
1. A column ('B' in the example above) has string values, some of which are parseable as numbers and some which are not.
2. Another column ('T' in the example above) in the dataframe has timestamps.
#### Expected Output
```
B
A
10 foo
20 3
30 4
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8.1
boto: 2.45.0
pandas_datareader: 0.2.1
</details>
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| 10 | 2017-02-16T14:05:04Z | 2017-02-25T10:55:45Z | 2017-02-24T19:57:32Z |
CONTRIBUTOR
| null |
check for categorical, and then pass the underlying integer codes.
- [ ] closes #15420
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0MTA2NTQyMjQ2
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BUG: to_sql convert index name to string (#15404)
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| 6 | 2017-02-16T14:58:28Z | 2017-02-17T13:16:17Z | 2017-02-17T13:09:21Z |
CONTRIBUTOR
| null |
…rs with numeric index name - needs conversion to string
- [x] closes #15404
- [x] tests passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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Indexing MultiIndex with NDFrame indexer fails if index of indexer does not contain 0
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| 7 | 2017-02-16T15:39:14Z | 2017-02-20T14:44:01Z | 2017-02-20T14:44:01Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [2]: d = pd.DataFrame([[1, 1, 3],
...: [1, 2, 4],
...: [2, 2, 5]], columns=['a', 'b', 'c'])
In [3]: d.set_index(['a', 'b']).loc[pd.Series([1, 2])]
Out[3]:
c
a b
1 1 3
2 4
2 2 5
In [4]: d.set_index(['a', 'b']).loc[pd.Series([1, 2], index=[2,0])]
Out[4]:
c
a b
1 1 3
2 4
2 2 5
In [5]: d.set_index(['a', 'b']).loc[pd.Series([1, 2], index=[2,3])]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-5-a23ad44827f6> in <module>()
----> 1 d.set_index(['a', 'b']).loc[pd.Series([1, 2], index=[2,3])]
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
1339 else:
1340 key = com._apply_if_callable(key, self.obj)
-> 1341 return self._getitem_axis(key, axis=0)
1342
1343 def _is_scalar_access(self, key):
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1527 if isinstance(labels, MultiIndex):
1528 if (not isinstance(key, tuple) and len(key) > 1 and
-> 1529 not isinstance(key[0], tuple)):
1530 if isinstance(key, ABCSeries):
1531 # GH 14730
/home/pietro/nobackup/repo/pandas/pandas/core/series.py in __getitem__(self, key)
604 key = com._apply_if_callable(key, self)
605 try:
--> 606 result = self.index.get_value(self, key)
607
608 if not is_scalar(result):
/home/pietro/nobackup/repo/pandas/pandas/indexes/base.py in get_value(self, series, key)
2304 try:
2305 return self._engine.get_value(s, k,
-> 2306 tz=getattr(series.dtype, 'tz', None))
2307 except KeyError as e1:
2308 if len(self) > 0 and self.inferred_type in ['integer', 'boolean']:
/home/pietro/nobackup/repo/pandas/pandas/index.pyx in pandas.index.IndexEngine.get_value (pandas/index.c:3992)()
/home/pietro/nobackup/repo/pandas/pandas/index.pyx in pandas.index.IndexEngine.get_value (pandas/index.c:3689)()
/home/pietro/nobackup/repo/pandas/pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4688)()
/home/pietro/nobackup/repo/pandas/pandas/src/hashtable_class_helper.pxi in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:13370)()
/home/pietro/nobackup/repo/pandas/pandas/src/hashtable_class_helper.pxi in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:13308)()
KeyError: 0
```
#### Problem description
The index of the indexer should not matter at all... and indeed there is no bug when indexing a (non-``Multi``)``Index``:
```
In [6]: d.set_index(['a']).loc[pd.Series([1, 2], index=[2,3])]
Out[6]:
b c
a
1 1 3
1 2 4
2 2 5
```
#### Expected Output
The same as ``d.set_index(['a', 'b']).loc[pd.Series([1, 2])]``.
A PR is on its way.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.19.0+464.g0b28e5a
pytest: None
pip: 8.1.2
setuptools: 28.0.0
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.18.1
xarray: None
IPython: 5.1.0.dev
sphinx: 1.4.8
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.0
feather: None
matplotlib: 2.0.0rc2
openpyxl: None
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: None
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: 0.2.1
</details>
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Mi indexing
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| 8 | 2017-02-16T15:44:48Z | 2017-02-21T01:00:44Z | 2017-02-20T14:44:02Z |
MEMBER
| null |
- [x] closes #15424
- [x] closes #15434
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
This also fixes indexing with a ``DataFrame``, which was actually not mentioned in #15424 (but it's a twoliner)
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| 10 | 2017-02-16T15:48:12Z | 2017-02-27T15:46:31Z | 2017-02-27T15:46:31Z |
NONE
| null |
xref #10668 (for more examples)
Hello!
I'm running into some odd behavior trying to group rows of a pandas dataframe by ID and then selecting out max/min datetimes (w/ timezones). This is with python 2.7, pandas 0.18.1 and numpy 1.11.1 (I saw in earlier posts a similar problem was apparently fixed w/ pandas 0.15).
Specifically, if I try:
`print orders.groupby('OrderID')['start_time'].agg(np.min).iloc[:5]`
I get:
```
OrderID
O161101XVS100000044 2016-11-01 12:03:12.920000-04:00
O161101XVS100000047 2016-11-01 12:03:36.693000-04:00
O161101XVS100000098 2016-11-01 12:09:08.330000-04:00
O161101XVS100000122 2016-11-01 12:09:59.950000-04:00
O161101XVS100000152 2016-11-01 12:11:29.790000-04:00
Name: start_time, dtype: datetime64[ns, US/Eastern]
```
Where the raw data had times closer to 8 am (US/Eastern). In other words, it reverted back to UTC times, even though it says it's eastern times, and has UTC-4 offset.
But if I instead try:
`print orders.groupby('OrderID')['start_time'].agg(lambda x: np.min(x)).iloc[:5]`
I now get:
```
OrderID
O161101XVS100000044 2016-11-01 08:03:12.920000-04:00
O161101XVS100000047 2016-11-01 08:03:36.693000-04:00
O161101XVS100000098 2016-11-01 08:09:08.330000-04:00
O161101XVS100000122 2016-11-01 08:09:59.950000-04:00
O161101XVS100000152 2016-11-01 08:11:29.790000-04:00
Name: start_time, dtype: datetime64[ns, US/Eastern]
```
Which is the behavior I intended. This second method is vastly slower, and I would have assumed the two approaches would yield identical results ...
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| 208,218,475 |
MDExOlB1bGxSZXF1ZXN0MTA2NTk0NjYw
| 15,427 |
TST: remove yielding tests from test_msgpacks.py
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| 1 | 2017-02-16T19:17:40Z | 2017-02-17T14:51:47Z | 2017-02-17T14:51:47Z |
CONTRIBUTOR
| null |
xref #15341
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MDU6SXNzdWUyMDgyNTg3NDY=
| 15,428 |
BUG: pd.cut with bins=1 and input all 0s
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| 1 | 2017-02-16T21:53:44Z | 2017-03-08T13:43:24Z | 2017-03-08T13:43:24Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> pd.__version__
u'0.19.2'
>>> s = pd.Series([0,0,0])
>>> pd.cut(s, 1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/luca/.local/lib/python2.7/site-packages/pandas/tools/tile.py", line 119, in cut
include_lowest=include_lowest)
File "/home/luca/.local/lib/python2.7/site-packages/pandas/tools/tile.py", line 194, in _bins_to_cuts
raise ValueError('Bin edges must be unique: %s' % repr(bins))
ValueError: Bin edges must be unique: array([ 0., 0.])
>>> s = pd.Series([-1,-1,-1])
>>> pd.cut(s, 1)
0 (-1.001, -0.999]
1 (-1.001, -0.999]
2 (-1.001, -0.999]
>>> s = pd.Series([1,1,1])
>>> pd.cut(s, 1)
0 (0.999, 1.001]
1 (0.999, 1.001]
2 (0.999, 1.001]
```
#### Problem description
The special case of an input containing all 0s raises a ValueError. I had a look at the [code](https://github.com/pandas-dev/pandas/blob/master/pandas/tools/tile.py#L107) and it is straightforward to fix. I can provide a PR if requested.
#### Expected Output
```
>>> pd.cut(s, 1)
0 (-0.001, 0.001]
1 (-0.001, 0.001]
2 (-0.001, 0.001]
```
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.0-36-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 8.1.1
setuptools: 32.3.1
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8.1
boto: None
pandas_datareader: 0.2.1
</details>
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MDU6SXNzdWUyMDgyNjE1NjY=
| 15,429 |
BUG: groupby.transform ValueError: Could not convert object to NumPy datetime
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| 1 | 2017-02-16T22:03:40Z | 2017-02-17T14:32:59Z | 2017-02-17T14:32:51Z |
CONTRIBUTOR
| null |
#### Problem description
As can be seen [here](http://stackoverflow.com/questions/42260764/pandas-timestamp-difference-in-groupby-transform), the error `ValueError: Could not convert object to NumPy datetime` is being thrown by:
```python
df = DataFrame(dict(A=Timestamp('20130101'), B=np.arange(5)))
result = df.groupby('A')['A'].transform(lambda x: x.max() - x.min())
```
NOTE: I will be submitting a PR shortly.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
pandas: 0.19.2
nose: 1.3.7
pip: 8.1.2
setuptools: 18.2
Cython: None
numpy: 1.12.0
dateutil: 2.6.0
pytz: 2016.6.1
</details>
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MDExOlB1bGxSZXF1ZXN0MTA2NjI1OTE0
| 15,430 |
BUG: GH15429 transform result of timedelta from datetime
|
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| 9 | 2017-02-16T22:13:26Z | 2017-02-27T21:38:46Z | 2017-02-27T21:38:08Z |
CONTRIBUTOR
| null |
The transform() operation needs to return a like-indexed. To facilitate this, transform starts with a copy of the original series. Then, after the computation for each group, sets the appropriate elements of the copied series equal to the result. At that point is does a type comparison, and discovers that the timedelta is not cast-able to a datetime.
- [x] closes #10972
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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BUG: pd.qcut with q=1 and input with identical values
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| 2 | 2017-02-16T22:24:42Z | 2017-02-17T14:51:34Z | 2017-02-17T14:45:26Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
>>> pd.__version__
u'0.19.2'
>>> s = pd.Series([1,1,1])
>>> pd.qcut(s, 1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/luca/.local/lib/python2.7/site-packages/pandas/tools/tile.py", line 175, in qcut
precision=precision, include_lowest=True)
File "/home/luca/.local/lib/python2.7/site-packages/pandas/tools/tile.py", line 194, in _bins_to_cuts
raise ValueError('Bin edges must be unique: %s' % repr(bins))
ValueError: Bin edges must be unique: array([1, 1])
>>> s = pd.Series([1,1,2])
>>> pd.qcut(s, 1)
0 [1, 2]
1 [1, 2]
2 [1, 2]
>>> s = pd.Series([0,1,2])
>>> pd.qcut(s, 1)
0 [0, 2]
1 [0, 2]
2 [0, 2]
```
#### Problem description
If the input contains identical values pd.qcut fails even with q=1. I had a look at the [code](https://github.com/pandas-dev/pandas/blob/master/pandas/tools/tile.py#L209) and I can try to relax the check to allow this specific case to pass and avoid the exception, but then I have to make sure the code can handle a single edge.
#### Expected Output
```
>>> s = pd.Series([1,1,1])
>>> pd.qcut(s, 1)
0 [0, 2]
1 [0, 2]
2 [0, 2]
```
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.0-36-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 8.1.1
setuptools: 32.3.1
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.1.4
pymysql: None
psycopg2: None
jinja2: 2.8.1
boto: None
pandas_datareader: 0.2.1
</details>
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MDU6SXNzdWUyMDgyNzIxODc=
| 15,432 |
Resample convention='start' not functioning properly
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[] | null | 1 | 2017-02-16T22:45:14Z | 2017-07-16T15:55:34Z | 2017-07-16T15:55:34Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'values': [2, 3]}, index='1986-01-31', '1986-2-28')
df.resample('M', convention='s').sum()
```
#### Problem description
The convention argument does not seem to have any function on start on end when Month is passed as the resample period for a datetime index.
#### Expected Output
| |values|
|------------|--|
|1986-01-01|2|
|1986-02-01|3|
#### Actual Output
| |values|
|-------------|--|
|1986-01-31|2|
|1986-02-28|3|
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: 0.9.2
apiclient: 1.6.1
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTA2NjM2MzIy
| 15,433 |
BUG: GH15426 timezone lost in groupby-agg with cython functions
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| 4 | 2017-02-16T23:19:19Z | 2017-02-27T15:46:38Z | 2017-02-27T15:46:31Z |
CONTRIBUTOR
| null |
- [x] closes #15426
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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MDU6SXNzdWUyMDgyOTQwMTk=
| 15,434 |
MultiIndex can't be indexed with an np.array
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| 1 | 2017-02-17T00:28:39Z | 2017-02-20T14:44:02Z | 2017-02-20T14:44:01Z |
MEMBER
| null |
From #15425
#### Code Sample, a copy-pastable example if possible
```python
In [3]: mi = pd.MultiIndex.from_product([[1, 2], ['a', 'b']])
In [4]: pd.Series(range(4)).loc[np.array([1, 2])]
Out[4]:
1 1
2 2
dtype: int64
In [5]: pd.Series(range(4), index=mi).loc[np.array([1, 2])]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in _get_label(self, label, axis)
99 try:
--> 100 return self.obj._xs(label, axis=axis)
101 except:
/home/pietro/nobackup/repo/pandas/pandas/core/generic.py in xs(self, key, axis, level, drop_level)
1842 loc, new_index = self.index.get_loc_level(key,
-> 1843 drop_level=drop_level)
1844 else:
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in get_loc_level(self, key, level, drop_level)
1932 else:
-> 1933 return partial_selection(key)
1934 else:
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in partial_selection(key, indexer)
1897 if indexer is None:
-> 1898 indexer = self.get_loc(key)
1899 ilevels = [i for i in range(len(key))
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in get_loc(self, key, method)
1807 start, stop = (self.slice_locs(lead_key, lead_key)
-> 1808 if lead_key else (0, len(self)))
1809
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in slice_locs(self, start, end, step, kind)
1713 # happens in get_slice_bound method), but it adds meaningful doc.
-> 1714 return super(MultiIndex, self).slice_locs(start, end, step, kind=kind)
1715
/home/pietro/nobackup/repo/pandas/pandas/indexes/base.py in slice_locs(self, start, end, step, kind)
3346 if start is not None:
-> 3347 start_slice = self.get_slice_bound(start, 'left', kind)
3348 if start_slice is None:
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in get_slice_bound(self, label, side, kind)
1684 label = label,
-> 1685 return self._partial_tup_index(label, side=side)
1686
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in _partial_tup_index(self, tup, side)
1727
-> 1728 if lab not in lev:
1729 if not lev.is_type_compatible(lib.infer_dtype([lab])):
/home/pietro/nobackup/repo/pandas/pandas/indexes/base.py in __contains__(self, key)
1495 def __contains__(self, key):
-> 1496 hash(key)
1497 # work around some kind of odd cython bug
TypeError: unhashable type: 'numpy.ndarray'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-5-bf8891f2016d> in <module>()
----> 1 pd.Series(range(4), index=mi).loc[np.array([1, 2])]
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
1339 else:
1340 key = com._apply_if_callable(key, self.obj)
-> 1341 return self._getitem_axis(key, axis=0)
1342
1343 def _is_scalar_access(self, key):
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1550 # fall thru to straight lookup
1551 self._has_valid_type(key, axis)
-> 1552 return self._get_label(key, axis=axis)
1553
1554
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in _get_label(self, label, axis)
100 return self.obj._xs(label, axis=axis)
101 except:
--> 102 return self.obj[label]
103 elif isinstance(label, tuple) and isinstance(label[axis], slice):
104 raise IndexingError('no slices here, handle elsewhere')
/home/pietro/nobackup/repo/pandas/pandas/core/series.py in __getitem__(self, key)
645 key = check_bool_indexer(self.index, key)
646
--> 647 return self._get_with(key)
648
649 def _get_with(self, key):
/home/pietro/nobackup/repo/pandas/pandas/core/series.py in _get_with(self, key)
658 if isinstance(key, tuple):
659 try:
--> 660 return self._get_values_tuple(key)
661 except:
662 if len(key) == 1:
/home/pietro/nobackup/repo/pandas/pandas/core/series.py in _get_values_tuple(self, key)
706
707 # If key is contained, would have returned by now
--> 708 indexer, new_index = self.index.get_loc_level(key)
709 return self._constructor(self._values[indexer],
710 index=new_index).__finalize__(self)
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in get_loc_level(self, key, level, drop_level)
1931 return partial_selection(key)
1932 else:
-> 1933 return partial_selection(key)
1934 else:
1935 indexer = None
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in partial_selection(key, indexer)
1896 def partial_selection(key, indexer=None):
1897 if indexer is None:
-> 1898 indexer = self.get_loc(key)
1899 ilevels = [i for i in range(len(key))
1900 if key[i] != slice(None, None)]
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in get_loc(self, key, method)
1806 lead_key, follow_key = key[:i], key[i:]
1807 start, stop = (self.slice_locs(lead_key, lead_key)
-> 1808 if lead_key else (0, len(self)))
1809
1810 if start == stop:
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in slice_locs(self, start, end, step, kind)
1712 # This function adds nothing to its parent implementation (the magic
1713 # happens in get_slice_bound method), but it adds meaningful doc.
-> 1714 return super(MultiIndex, self).slice_locs(start, end, step, kind=kind)
1715
1716 def _partial_tup_index(self, tup, side='left'):
/home/pietro/nobackup/repo/pandas/pandas/indexes/base.py in slice_locs(self, start, end, step, kind)
3345 start_slice = None
3346 if start is not None:
-> 3347 start_slice = self.get_slice_bound(start, 'left', kind)
3348 if start_slice is None:
3349 start_slice = 0
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in get_slice_bound(self, label, side, kind)
1683 if not isinstance(label, tuple):
1684 label = label,
-> 1685 return self._partial_tup_index(label, side=side)
1686
1687 def slice_locs(self, start=None, end=None, step=None, kind=None):
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in _partial_tup_index(self, tup, side)
1726 section = labs[start:end]
1727
-> 1728 if lab not in lev:
1729 if not lev.is_type_compatible(lib.infer_dtype([lab])):
1730 raise TypeError('Level type mismatch: %s' % lab)
/home/pietro/nobackup/repo/pandas/pandas/indexes/base.py in __contains__(self, key)
1494
1495 def __contains__(self, key):
-> 1496 hash(key)
1497 # work around some kind of odd cython bug
1498 try:
TypeError: unhashable type: 'numpy.ndarray'
```
#### Problem description
The ``array`` should be accepted as it is accepted for the (non-``Multi``)``Index``
#### Expected Output
``` python
In [6]: pd.Series(range(4), index=mi).loc[np.array([1, 2])]
Out[6]:
1 a 0
b 1
2 a 2
b 3
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.19.0+466.g5a8883b
pytest: 3.0.6
pip: 8.1.2
setuptools: 28.0.0
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.18.1
xarray: None
IPython: 5.1.0.dev
sphinx: 1.4.8
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.0
feather: None
matplotlib: 2.0.0rc2
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: 0.2.1
</details>
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| 208,300,251 |
MDU6SXNzdWUyMDgzMDAyNTE=
| 15,435 |
Boolean operations in groupby objects are extremely slow compared to numpy counterpart.
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| 3 | 2017-02-17T01:03:15Z | 2018-03-01T01:30:19Z | 2018-03-01T01:30:19Z |
NONE
| null |
```python
%timeit -n 10000 np.random.choice([True, False], 10000).any()
%timeit -n 10000 np.random.choice([True, False], 10000).sum().astype(bool)
```
```
10000 loops, best of 3: 83.7 µs per loop
10000 loops, best of 3: 106 µs per loop
```
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({
"a": np.random.randint(0,20, 10000),
"b": np.random.randint(0,20, 10000),
"c": np.random.choice([True, False], 10000)
})
%timeit -n 100 df.groupby(["a", "b"])["c"].any()
%timeit -n 100 df.groupby(["a", "b"])["c"].sum().astype(bool)
```
```
100 loops, best of 3: 40.9 ms per loop
100 loops, best of 3: 1.46 ms per loop
```
#### Problem description
The issue here is that the `any` method for `groupby` objects seams to be freakishly slow. It is actually better to sum up all the boolean values and do a typecast with `.astype(bool)`. In numpy the operations have similar benchmarks. The method with `any` is actually faster!.
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-53-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 8.1.1
setuptools: None
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.2.2
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: 2.45.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTA2NjU5Mjcx
| 15,436 |
Documents touch-up for DataFrame.to_excel() freeze_panes option
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| 1 | 2017-02-17T02:54:35Z | 2017-02-17T08:17:39Z | 2017-02-17T08:17:39Z |
CONTRIBUTOR
| null |
Follow-up to @jorisvandenbossche 's comments on pull request #15291.
Only doc strings and external documentation modified.
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BUG: pd.cut with bins=1 and input all 0s
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| 12 | 2017-02-17T14:35:00Z | 2017-03-08T13:51:17Z | 2017-03-08T13:43:24Z |
CONTRIBUTOR
| null |
The special case of running pd.cut() qith bins=1 an input containing all
0s raises a ValueError
- [x] closes #15428
closes #15431
- [X] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
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[] | null | 4 | 2017-02-17T15:41:38Z | 2021-05-08T01:06:00Z | null |
MEMBER
| null |
(From #15425 )
Currently, (non-``Multi``)``Index``es can be indexed with ``Series`` indexers. And this actually also applies to ``MultiIndex``es, of which you would be selecting from the first level. Hence, it seems a natural consequence for ``MultiIndex``es to be indexed with ``DataFrame`` indexers.
Moreover, once #15434 is fixed, we will have a bi-dimensional object (``MultiIndex``) which can be indexed with ``np.array``s... but only one-dimensional ones! This is also strange.
The feature per se is certainly useful. As a simple real world example, I am currently working with a ``subjects`` ``DataFrame`` to which I must attribute two columns from ``design``, another ``DataFrame``, depending on a ``group`` and ``time`` columns of ``subjects``, which are also levels of the ``MultiIndex`` of ``design``. I would like to just do
``` python
subjects[design.columns] = design.loc[subjects[["group", "time"]]]
```
Now, I know this could be solved by ``.join``ing the two ``DataFrames``... but this is conceptually more complicated (I even currently ignore whether I can join one ``DataFrame`` on columns and the other on index levels... but this is OT), to the point that I'm rather doing:
```python
to_mi = lambda df : df.set_index(list(df.columns)).index
subjects[design.columns] = design.loc[to_mi(subjects[["group", "time"]])]
```
@jorisvandenbossche [suggests](https://github.com/pandas-dev/pandas/pull/15425#issuecomment-280500783) this feature would add complexity to indexing, _"eg, should the column names align on the level names?"_. I'm personally fine with both answers:
- **Yes**: then we just use something like ``to_mi`` above (transforming a ``DataFrame`` in ``MultiIndex``, and then using it to actually index)
- **No**: then it's really really simple (we just transform the ``DataFrame`` into tuples - I had actually already done this in #15425 before rolling back)
"**Yes**" is probably the cleanest answer (possibly together with allowing indexing with bi-dimensional ``np.array``s, to obtain the equivalent of the "**No**" answer). In any case, once we decide, I can take care of this.
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BUG: Categorical.unique() preserves categories
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| 6 | 2017-02-17T16:06:45Z | 2017-02-22T18:55:31Z | 2017-02-22T18:50:40Z |
CONTRIBUTOR
| null |
- [X] closes #13179
- [x] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
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| 208,494,657 |
MDU6SXNzdWUyMDg0OTQ2NTc=
| 15,440 |
Adding Timedelta parameter to time-based .rolling()
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| 6 | 2017-02-17T16:42:36Z | 2017-03-05T21:26:00Z | 2017-02-18T12:08:54Z |
CONTRIBUTOR
| null |
Starting with a simple DataFrame:
```python
n = 10
df = pd.DataFrame({'value': np.arange(n)},
index=pd.date_range('2015-12-24', periods=n, freq="D"))
```
I can invoke a time-based `.rolling()` with a string for the offset:
```python
df.rolling('3d').sum()
```
I would like to invoke it with a `Timedelta` since it is easier to arithmetically change the window length:
```python
df.rolling(pd.Timedelta(days=3)).sum()
```
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[ENH] read_table(usecols = int | str) to produce squeezed Series
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| 4 | 2017-02-17T17:28:07Z | 2018-07-06T20:21:16Z | 2017-02-18T14:22:16Z |
NONE
| null |
#### Problem description
This is a feature request for usecols to accept integer value and string. If integer/string is provided, flag `squeeze` is to be turned on, thus producing a Series.
#### Rationale
+ consistency with `index_col` which accepts both integer/string and lists
+ shortcut
#### Expected Output
```python
# test
testfile = "pandas/tests/io/data/tips.csv"
df = pd.read_table(testfile, sep=",", usecols="tip")
assert isinstance(df, pd.Series)
df = pd.read_table(testfile, sep=",", usecols=2)
assert isinstance(df, pd.Series)
df = pd.read_table(testfile, sep=",", usecols=[2])
assert isinstance(df, pd.DataFrame)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.3.2.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-642.13.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: 1.3.0
pip: 8.1.2
setuptools: 20.9.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0.dev
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.3.1-dev0
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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COMPAT: .unique() should be type preserving
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| 1 | 2017-02-17T18:04:14Z | 2019-11-14T14:47:49Z | 2019-11-14T14:47:49Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/15439/files#r101811209
```
In [1]: Series([1, 2, 2], dtype='int8').unique()
Out[1]: array([1, 2])
In [3]: Series([1, 2, 2], dtype='int32').unique()
Out[3]: array([1, 2])
In [4]: Series([1, 2, 2], dtype='int64').unique()
Out[4]: array([1, 2])
In [5]: Series([1, 2, 2], dtype='float32').unique()
Out[5]: array([ 1., 2.])
```
these should all be the original dtype.
We only create hashtables (and use them in unique) for ``int64``, ``float64`` and ``uint64``, should do this for other dtypes.
https://github.com/pandas-dev/pandas/blob/master/pandas/src/hashtable_class_helper.pxi.in#L222
I thought there was an issue about this, but guess not.
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BUG: rolling not accepting Timedelta-like window args (#15440)
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| 6 | 2017-02-17T20:05:22Z | 2017-12-20T02:04:10Z | 2017-02-18T12:08:54Z |
MEMBER
| null |
- [x] closes #15440
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Allow `rolling()` to accept `pd.Timedelta` or `datetime.timedelta` as a `window` argument.
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MDExOlB1bGxSZXF1ZXN0MTA2ODAyNzM1
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Add __copy__ and __deepcopy__ to NDFrame
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| 15 | 2017-02-17T20:05:32Z | 2017-02-20T19:24:40Z | 2017-02-20T19:24:11Z |
CONTRIBUTOR
| null |
- [ ] closes #15370
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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BUG: testing on windows
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| 1 | 2017-02-17T20:17:43Z | 2020-04-01T20:21:34Z | 2017-02-18T17:05:35Z |
CONTRIBUTOR
| null |
- we are passing builds which actually have an error
- fix the small dtype issue
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MDU6SXNzdWUyMDg1NTI5NzE=
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Should str.replace accept a compiled expression?
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| 4 | 2017-02-17T20:55:17Z | 2017-03-06T00:16:20Z | 2017-03-05T11:23:58Z |
NONE
| null |
The current solution is to call `str.replace(<compiled_re>.pattern, flags=<compiled_re>.flags)` which is relatively ugly and verbose in my opnion.
Here's a contrived example of removing stopwords and normalizing whitespace afterwards:
```python
import pandas as pd
import re
some_names = pd.Series(["three weddings and a funeral", "the big lebowski", "florence and the machine"])
stopwords = ["the", "a", "and"]
stopwords_re = re.compile(r"(\s+)?\b({})\b(\s+)?".format("|".join(stopwords), re.IGNORECASE)
whitespace_re = re.compile(r"\s+")
# desired code:
# some_names.str.replace(stopwords_re, " ").str.strip().str.replace(whitespace_re, " ")
# actual code:
some_names.\
str.replace(stopwords_re.pattern, " ", flags=stopwords_re.flags).\
str.strip().str.replace(whitespace_re.pattern, " ", flags=whitespace_re.flags)
```
Why do I think this is better?
1. It's [nice](http://stackoverflow.com/a/453568/2954547) to have commonly used regular expressions compiled and to carry their flags around with them (and also allows the use of ["verbose" regular expressions](http://stackoverflow.com/a/29159014/2954547))
2. It's not that compiled regular expressions should quack like strings... it's that in this case we're making strings quack like compiled regular expressions, but at the same time not letting those compiled regular expressions quack their own quack.
Is there a good reason _not_ to implement this?
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MDU6SXNzdWUyMDg1Njg0MDE=
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Wrong result of pandas.sparse.series.SparseSeries.loc with indexer of length 1
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| 1 | 2017-02-17T21:55:02Z | 2017-03-07T21:17:20Z | 2017-03-07T21:17:20Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [3]: idx = pd.MultiIndex.from_tuples([('A', 0), ('A', 1), ('B', 0), ('C', 0), ('C', 1)])
In [4]: orig = pd.Series([1, np.nan, np.nan, 3, np.nan], index=idx)
In [5]: sparse = orig.to_sparse()
In [6]: sparse.loc[['A']]
Out[6]:
A NaN
dtype: float64
BlockIndex
Block locations: array([], dtype=int32)
Block lengths: array([], dtype=int32)
```
#### Problem description
It should return as below
#### Expected Output
``` python
In [7]: orig.loc[['A']].to_sparse()
Out[7]:
A 0 1.0
1 NaN
dtype: float64
BlockIndex
Block locations: array([0], dtype=int32)
Block lengths: array([1], dtype=int32)
```
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: f65a6415f15d438432cc6954ead61b052c5d4d60
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.19.0+473.gf65a641
pytest: 3.0.6
pip: 8.1.2
setuptools: 28.0.0
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.18.1
xarray: None
IPython: 5.1.0.dev
sphinx: 1.4.8
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.0
feather: None
matplotlib: 2.0.0rc2
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: 0.2.1
</details>
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BUG: Trivial fix to #15447
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[] | null | 3 | 2017-02-17T22:20:36Z | 2017-02-21T01:01:29Z | 2017-02-17T22:32:24Z |
MEMBER
| null |
- [x] closes #15447
- [ ] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
This is not really meant for merging because I don't understand that piece code and don't have time to understand it. But if you want to merge it anyway I can add a test and a whatsnew entry.
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Week Start/End Resamping: WS/WE frequencies
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[] | null | 6 | 2017-02-17T22:43:49Z | 2019-04-29T17:28:32Z | 2017-05-13T21:37:43Z |
CONTRIBUTOR
| null |
This is just some initial work to start the conversation.
- [ ] closes #7049
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
|
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DOC: correct rpy2 examples (GH15142)
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| 2 | 2017-02-17T22:51:06Z | 2018-04-10T11:35:37Z | 2017-02-18T10:52:02Z |
MEMBER
| null |
To fix the current doc build errors (https://github.com/pandas-dev/pandas/pull/15142#issuecomment-274346255)
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DOC: Link CONTRIBUTING.md to contributing.rst
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| 1 | 2017-02-18T04:00:31Z | 2017-02-21T22:26:13Z | 2017-02-21T22:26:04Z |
MEMBER
| null |
Cuts down `CONTRIBUTING.md` to summarize `contributing.rst` and links to relevant sections.
Closes #15349.
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Incoherence between Index and MultiIndex when labels in list are not found
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| 14 | 2017-02-18T05:22:09Z | 2021-05-24T18:55:28Z | 2021-05-24T18:55:28Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [2]: s = pd.Series(range(4), index=pd.MultiIndex.from_product([[1,2], ['a', 'b']]))
In [3]: s.loc[['not', 'found']]
Out[3]: Series([], dtype: int64)
```
#### Problem description
With regular ``Index``es, looking for a list of labels of which none is present will raise ``KeyError`` (see below). We should be coherent (and while I would prefer the empty result to the exception, this was already discussed in #7999).
#### Expected Output
Compare to
``` python
In [4]: s.reset_index().loc[['not', 'found']]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-4-e13655430320> in <module>()
----> 1 s.reset_index().loc[['not', 'found']]
/home/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
1339 else:
1340 key = com._apply_if_callable(key, self.obj)
-> 1341 return self._getitem_axis(key, axis=0)
1342
1343 def _is_scalar_access(self, key):
/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1539 raise ValueError('Cannot index with multidimensional key')
1540
-> 1541 return self._getitem_iterable(key, axis=axis)
1542
1543 # nested tuple slicing
/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
1049 def _getitem_iterable(self, key, axis=0):
1050 if self._should_validate_iterable(axis):
-> 1051 self._has_valid_type(key, axis)
1052
1053 labels = self.obj._get_axis(axis)
/home/nobackup/repo/pandas/pandas/core/indexing.py in _has_valid_type(self, key, axis)
1429
1430 raise KeyError("None of [%s] are in the [%s]" %
-> 1431 (key, self.obj._get_axis_name(axis)))
1432
1433 return True
KeyError: "None of [['not', 'found']] are in the [index]"
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: f65a6415f15d438432cc6954ead61b052c5d4d60
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.19.0+473.gf65a641
pytest: 3.0.6
pip: 8.1.2
setuptools: 28.0.0
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.18.1
xarray: None
IPython: 5.1.0.dev
sphinx: 1.4.8
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.0
feather: None
matplotlib: 2.0.0rc2
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: 0.2.1
</details>
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| 208,642,270 |
MDU6SXNzdWUyMDg2NDIyNzA=
| 15,453 |
BUG: int64 overflow/wrap around with sum()
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| 11 | 2017-02-18T13:36:10Z | 2017-10-20T13:24:03Z | 2017-10-20T13:23:39Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: s = pd.Series([2**31])
In [3]: print(s.dtype, s.sum())
(dtype('int64'), -2147483648)
In [4]: pd.Series([2**31 - 1, 1]).sum()
Out[4]: -2147483648
In [5]: pd.Series([2**31 - 1, 1]).astype('int32').sum()
Out[5]: 2147483648
```
#### Problem description
negative values in [3] and [4]
#### Expected Output
see [5]
#### Output of ``pd.show_versions()``
<details>
```python
In [6]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 34.2.0
Cython: None
numpy: 1.12.0
scipy: 0.19.0rc1
statsmodels: 0.8.0
xarray: None
IPython: 5.2.2
sphinx: 1.5.2
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: None
numexpr: 2.6.2
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.3
html5lib: 0.999999999
httplib2: 0.10.3
apiclient: 1.6.2
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.5
boto: None
pandas_datareader: None
```
</details>
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| 1 | 2017-02-18T18:23:10Z | 2020-01-21T18:26:46Z | 2020-01-21T18:26:46Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
In [26]: df = pd.DataFrame(0, index=range(2), columns=pd.MultiIndex.from_product([[1], [2]]))
...:
...:
In [27]: df
Out[27]:
1
2
0 0
1 0
In [28]: df[[]]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-28-13353c6d99d3> in <module>()
----> 1 df[[]]
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas/pandas/core/frame.py in __getitem__(self, key)
2014 if isinstance(key, (Series, np.ndarray, Index, list)):
2015 # either boolean or fancy integer index
-> 2016 return self._getitem_array(key)
2017 elif isinstance(key, DataFrame):
2018 return self._getitem_frame(key)
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas/pandas/core/frame.py in _getitem_array(self, key)
2058 return self.take(indexer, axis=0, convert=False)
2059 else:
-> 2060 indexer = self.loc._convert_to_indexer(key, axis=1)
2061 return self.take(indexer, axis=1, convert=True)
2062
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas/pandas/core/indexing.py in _convert_to_indexer(self, obj, axis, is_setter)
1216 # this is not the most robust, but...
1217 if (isinstance(labels, MultiIndex) and
-> 1218 not isinstance(objarr[0], tuple)):
1219 level = 0
1220 _, indexer = labels.reindex(objarr, level=level)
IndexError: index 0 is out of bounds for axis 0 with size 0
```
#### Problem description
This is inconsistent with our other indexers, which all return empty DataFrames / series
1. Regular index in the columns or index
```python
In [30]: df.T[[]]
Out[30]:
Empty DataFrame
Columns: []
Index: [(1, 2)]
```
```python
In [32]: df.loc[[]]
Out[32]:
Empty DataFrame
Columns: [(1, 2)]
Index: []
```
2. MultiIndex in the index with .loc
```python
In [34]: df.T.loc[[]]
Out[34]:
Empty DataFrame
Columns: [0, 1]
Index: []
```
A probably related bug: while `df.loc[:, []]` works as expected, `__setitem__` raises with the same error:
```python
In [65]: df.loc[:, []] # OK
Out[65]:
Empty DataFrame
Columns: []
Index: [0, 1]
```
```python
In [66]: df.loc[:, []] = 10 # should be a no-op really
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-66-6cd48eddd6a3> in <module>()
----> 1 df.loc[:, []] = 10
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas/pandas/core/indexing.py in __setitem__(self, key, value)
146 else:
147 key = com._apply_if_callable(key, self.obj)
--> 148 indexer = self._get_setitem_indexer(key)
149 self._setitem_with_indexer(indexer, value)
150
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas/pandas/core/indexing.py in _get_setitem_indexer(self, key)
125 if isinstance(key, tuple):
126 try:
--> 127 return self._convert_tuple(key, is_setter=True)
128 except IndexingError:
129 pass
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas/pandas/core/indexing.py in _convert_tuple(self, key, is_setter)
192 if i >= self.obj.ndim:
193 raise IndexingError('Too many indexers')
--> 194 idx = self._convert_to_indexer(k, axis=i, is_setter=is_setter)
195 keyidx.append(idx)
196 return tuple(keyidx)
/Users/tom.augspurger/Envs/py3/lib/python3.6/site-packages/pandas/pandas/core/indexing.py in _convert_to_indexer(self, obj, axis, is_setter)
1216 # this is not the most robust, but...
1217 if (isinstance(labels, MultiIndex) and
-> 1218 not isinstance(objarr[0], tuple)):
1219 level = 0
1220 _, indexer = labels.reindex(objarr, level=level)
IndexError: index 0 is out of bounds for axis 0 with size 0
```
#### Expected Output
```python
In [47]: pd.DataFrame([], index=[0, 1])
Out[47]:
Empty DataFrame
Columns: []
Index: [0, 1]
```
I *think* that's right, all the columns should be dropped.
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: 0ceb40fde46ec705a6a7d0a7e53477e65ad2c891
python: 3.6.0.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0+466.g0ceb40fde.dirty
pytest: 3.0.5
pip: 9.0.1
setuptools: 32.3.0
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
xarray: None
IPython: 5.2.2
sphinx: 1.5.2
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: None
numexpr: 2.6.1
feather: None
matplotlib: 2.0.0
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.5
s3fs: 0.0.8
pandas_datareader: None
</details>
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| 208,669,178 |
MDExOlB1bGxSZXF1ZXN0MTA2ODc1NDA2
| 15,455 |
DOC: DataFrame.get_value
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[] | null | 12 | 2017-02-18T20:26:02Z | 2017-05-13T21:37:09Z | 2017-05-13T21:37:09Z |
CONTRIBUTOR
| null |
Basic examples for working with DataFrame's ``get_value`` method. I do not understand how the ``takeable`` option is supposed to work. If someone can explain it to me, I'd be happy to add an example for that as well.
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ENH: str.replace accepts a compiled expression
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| 19 | 2017-02-19T22:20:09Z | 2017-03-05T22:44:45Z | 2017-03-05T11:23:58Z |
CONTRIBUTOR
| null |
.str.replace now accepts a compiled regular expression.
See #15446
- [x] closes #15446
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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| 9 | 2017-02-19T23:52:17Z | 2018-08-12T08:16:21Z | 2018-08-12T08:16:21Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [2]: df = pd.DataFrame([[2, 1], [4, (1,2)]]).set_index([0, 1])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/pietro/nobackup/repo/pandas/pandas/core/categorical.py in __init__(self, values, categories, ordered, name, fastpath)
288 try:
--> 289 codes, categories = factorize(values, sort=True)
290 except TypeError:
/home/pietro/nobackup/repo/pandas/pandas/core/algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
360 uniques, labels = safe_sort(uniques, labels, na_sentinel=na_sentinel,
--> 361 assume_unique=True)
362
/home/pietro/nobackup/repo/pandas/pandas/core/algorithms.py in safe_sort(values, labels, na_sentinel, assume_unique)
258 # unorderable in py3 if mixed str/int
--> 259 ordered = sort_mixed(values)
260 else:
/home/pietro/nobackup/repo/pandas/pandas/core/algorithms.py in sort_mixed(values)
251 dtype=bool)
--> 252 nums = np.sort(values[~str_pos])
253 strs = np.sort(values[str_pos])
/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py in sort(a, axis, kind, order)
821 a = asanyarray(a).copy(order="K")
--> 822 a.sort(axis=axis, kind=kind, order=order)
823 return a
TypeError: unorderable types: tuple() > int()
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-2-b560307721b0> in <module>()
----> 1 df = pd.DataFrame([[2, 1], [4, (1,2)]]).set_index([0, 1])
/home/pietro/nobackup/repo/pandas/pandas/core/frame.py in set_index(self, keys, drop, append, inplace, verify_integrity)
2907 arrays.append(level)
2908
-> 2909 index = MultiIndex.from_arrays(arrays, names=names)
2910
2911 if verify_integrity and not index.is_unique:
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in from_arrays(cls, arrays, sortorder, names)
1085 from pandas.core.categorical import _factorize_from_iterables
1086
-> 1087 labels, levels = _factorize_from_iterables(arrays)
1088 if names is None:
1089 names = [getattr(arr, "name", None) for arr in arrays]
/home/pietro/nobackup/repo/pandas/pandas/core/categorical.py in _factorize_from_iterables(iterables)
2082 # For consistency, it should return a list of 2 lists.
2083 return [[], []]
-> 2084 return map(list, lzip(*[_factorize_from_iterable(it) for it in iterables]))
/home/pietro/nobackup/repo/pandas/pandas/core/categorical.py in <listcomp>(.0)
2082 # For consistency, it should return a list of 2 lists.
2083 return [[], []]
-> 2084 return map(list, lzip(*[_factorize_from_iterable(it) for it in iterables]))
/home/pietro/nobackup/repo/pandas/pandas/core/categorical.py in _factorize_from_iterable(values)
2054 codes = values.codes
2055 else:
-> 2056 cat = Categorical(values, ordered=True)
2057 categories = cat.categories
2058 codes = cat.codes
/home/pietro/nobackup/repo/pandas/pandas/core/categorical.py in __init__(self, values, categories, ordered, name, fastpath)
293 # raise, as we don't have a sortable data structure and so
294 # the user should give us one by specifying categories
--> 295 raise TypeError("'values' is not ordered, please "
296 "explicitly specify the categories order "
297 "by passing in a categories argument.")
TypeError: 'values' is not ordered, please explicitly specify the categories order by passing in a categories argument.
```
#### Problem description
I would understand the ``unorderable types`` error - after all, I'm asking to have different types in the index, and pandas maybe wants to compare them, and it can't (this error obviously only affects Python3). But then,
``` python
In [3]: pd.DataFrame([[2, 'a'], [4, (1,2)]]).set_index([0, 1])
Out[3]:
Empty DataFrame
Columns: []
Index: [(2, a), (4, (1, 2))]
```
works fine, (even though ``'a' > (1,2)`` raises ``TypeError``), and even allows me to sort the resulting index! Moreover, [I understand this should be supported](https://github.com/pandas-dev/pandas/issues/11565#issuecomment-157691996).
#### Expected Output
``` python
Empty DataFrame
Columns: []
Index: [(4, 1), (2, (1, 2))]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.19.0+473.gf65a641
pytest: 3.0.6
pip: 8.1.2
setuptools: 28.0.0
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.18.1
xarray: None
IPython: 5.1.0.dev
sphinx: 1.4.8
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.0
feather: None
matplotlib: 2.0.0rc2
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: 0.2.1
</details>
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DOC: DataFrame.plot options are not documented in docstring form
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| 7 | 2017-02-20T02:44:45Z | 2020-01-22T08:46:07Z | 2020-01-22T08:46:00Z |
MEMBER
| null |
These can be seen by inspecting the `__call__` attribute, but they are referenced from the particular plotting methods, but not easily learned about in IPython/Jupyter.
https://github.com/pandas-dev/pandas/blob/master/pandas/tools/plotting.py#L3781
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| 208,776,457 |
MDU6SXNzdWUyMDg3NzY0NTc=
| 15,459 |
pandas0.19.2 make django1.10.4 not work
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| 2 | 2017-02-20T03:36:29Z | 2017-02-20T14:09:22Z | 2017-02-20T14:09:22Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
#coding=utf-8
from django.forms.models import model_to_dict
from django.db import connection
import os,sys
import time,datetime
from .models import RegistrationTable
import pandas as pd
def insert_db(data,SupplierPaymentDict,company_name,chinese_name):
return 0
```
#### Problem description
if i import pandas 0.19.2 into the programe ，only type
import pandas will make the error。
the error is ，if i use mod_wsgi4.4+apache2.4+django1.10.4 to run a website，i will got “127.0.0.1 is respording”，but if i use django‘runserver like “ python manage.py runserver "，it will work well。
but if i roll back to 0.18.0，all is ok
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: 0.7.9.None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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MDU6SXNzdWUyMDg4NTkwNDg=
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Quantile fails when only NaNs on some rows/columns
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| 1 | 2017-02-20T11:55:31Z | 2017-02-20T14:08:49Z | 2017-02-20T14:08:24Z |
NONE
| null |
Quantile method on DataFrame fails when there are NaNs in some specific way. Some cases it handles properly. I guess it's somehow related to cases when some rows and/or columns have only NaNs.
First, some working examples:
```python
>>> pd.DataFrame({"a": [1, 2, 3], "b": [np.nan, np.nan, np.nan]}).quantile(axis=0)
a 2.0
b NaN
Name: 0.5, dtype: float64
```
```python
>>> pd.DataFrame({"a": [1, 2, 3], "b": [np.nan, np.nan, np.nan]}).quantile(axis=1)
0 1.0
1 2.0
2 3.0
Name: 0.5, dtype: float64
```
#### Code Sample, a copy-pastable example if possible
If I change the second element on the first column to NaN, it all breaks. Now there's one row and one column with only NaNs:
```python
>>> pd.DataFrame({"a": [1, np.nan, 3], "b": [np.nan, np.nan, np.nan]}).quantile(axis=0)
a 1.0
b 3.0
Name: 0.5, dtype: float64
```
```python
>>> pd.DataFrame({"a": [1, np.nan, 3], "b": [np.nan, np.nan, np.nan]}).quantile(axis=1)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-a62c307ef5d1> in <module>()
----> 1 pd.DataFrame({"a": [1, np.nan, 3], "b": [np.nan, np.nan, np.nan]}).quantile(axis=1)
/.../lib/python3.5/site-packages/pandas/core/frame.py in quantile(self, q, axis, numeric_only, interpolation)
5153 axis=1,
5154 interpolation=interpolation,
-> 5155 transposed=is_transposed)
5156
5157 if result.ndim == 2:
/.../lib/python3.5/site-packages/pandas/core/internals.py in quantile(self, **kwargs)
3142
3143 def quantile(self, **kwargs):
-> 3144 return self.reduction('quantile', **kwargs)
3145
3146 def setitem(self, **kwargs):
/.../lib/python3.5/site-packages/pandas/core/internals.py in reduction(self, f, axis, consolidate, transposed, **kwargs)
3071 for b in self.blocks:
3072 kwargs['mgr'] = self
-> 3073 axe, block = getattr(b, f)(axis=axis, **kwargs)
3074
3075 axes.append(axe)
/.../lib/python3.5/site-packages/pandas/core/internals.py in quantile(self, qs, interpolation, axis, mgr)
1325 values = _block_shape(values[~mask], ndim=self.ndim)
1326 if self.ndim > 1:
-> 1327 values = values.reshape(result_shape)
1328
1329 from pandas import Float64Index
ValueError: total size of new array must be unchanged
```
#### Problem description
In the first case, the quantiles are incorrect and in the second case, the quantile computation should *not* raise an error.
#### Expected Output
```python
>>> pd.DataFrame({"a": [1, np.nan, 3], "b": [np.nan, np.nan, np.nan]}).quantile(axis=0)
a 2.0
b NaN
Name: 0.5, dtype: float64
```
```python
>>> pd.DataFrame({"a": [1, np.nan, 3], "b": [np.nan, np.nan, np.nan]}).quantile(axis=1)
0 1.0
1 NaN
2 3.0
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.9-gnu-1
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0.post20161106
Cython: None
numpy: 1.11.1
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: 1.0.13
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTA3MDU4ODYy
| 15,461 |
BUG: fix SparseSeries reindex by using Series implementation
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| 21 | 2017-02-20T18:37:34Z | 2017-03-07T23:19:52Z | 2017-03-07T21:17:20Z |
MEMBER
| null |
- [x] closes #15447
- [x] tests added / passed
- [x] passes ``git diff master | flake8 --diff``
- [x] whatsnew entry
I abunded a bit with the tests for ``.reindex()`` (for ``.loc`` there were already quite a bit).
This is completely independent from the approach in #15448 , which also made sense but on which I will come back in a different PR. In the current PR, I'm just exploiting the fact that sparse ``SparseBlock``s "know what to do", and the ``SparseSeries`` itself has nothing particular to do for reindexing.
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BUG: read_table doesn't properly read uint64
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| 2 | 2017-02-20T22:37:24Z | 2017-02-20T23:00:00Z | 2017-02-20T23:00:00Z |
NONE
| null |
#### Code Sample
```python
import pandas as pd
import numpy as np
import StringIO
tmp = StringIO.StringIO(buf="id\n" + "9"*19 + "\n")
pd.read_table(tmp, dtype={'id': np.uint64})
```
#### Problem description
A string of 19 nines is about 2^{63.11} and fits in a np.uint64, e.g., `np.array([9999999999999999999])` has dtype uint64. `pd.read_table` without the dtype option will simply coerce this column to `object` dtype, which is reasonable.
#### Expected Output
A data frame with dtype np.uint64, or perhaps a warning that `read_table` doesn't support uint64.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 16.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 32.1.0
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.2.2
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
</details>
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Series.rolling(3).quantile(10) segmentation fault
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| 3 | 2017-02-20T23:27:52Z | 2017-02-23T13:24:28Z | 2017-02-23T13:24:28Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> pd.Series(np.arange(10)).rolling(3).quantile(10)
[1] 20214 segmentation fault
```
#### Problem description
The value 10 is over the fraction of 1 which .quantile() works with, but I guess it still shouldn't crash my interpreter.
#### Expected Output
```text
ValueError: quantile value 10 not in [0, 1]
```
#### Output of ``pd.show_versions()``
pandas 0.19.0+479.g4842bc7
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| 3 | 2017-02-21T00:59:03Z | 2017-02-21T22:32:37Z | 2017-02-21T02:39:52Z |
CONTRIBUTOR
| null |
this adds support for using CircleCI; configured to put 4 of our builds (3.4, 3.4-slow, 2.7 compat, and 3.5 ascii), they are still on Travis ATM. They are built/tested simultaneously on CircleCI (as we get 4 containers for open-source).
CircleCI is a PITA. It has parallelism, but no build-matrix. So this took a while to get right. Don't currently use pytest-xdist as was getting some failures, but maybe can turn on at some point.
I'll come back later and remove the builds from TravisCI when everything's all connected.
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| 4 | 2017-02-21T02:54:08Z | 2017-02-22T22:14:41Z | 2017-02-22T15:01:49Z |
CONTRIBUTOR
| null |
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GH6322, Bug on reset_index for a MultiIndex of all NaNs
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| 2 | 2017-02-21T04:13:48Z | 2017-02-21T13:41:32Z | 2017-02-21T13:38:57Z |
CONTRIBUTOR
| null |
The reset_index function fails for a MultiIndex if at
least one column of the index is all NaN.
- [x] closes #6322
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
As per the comment in the issue, I tried other solutions such as putting the `take` under an `if len(values)` call, unfortunately that broke other issues downstream.
I started to investigate and it seems like a `MultiIndex` makes a category that ignores `NaN`, so the `values` in the `reset_index` is empty. After trying a few options, the `if mask.all()` seemed like the simplest change.
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| 1 | 2017-02-21T09:34:43Z | 2017-02-21T14:10:43Z | 2017-02-21T14:10:23Z |
CONTRIBUTOR
| null |
```python
import pandas as pd
df = pd.DataFrame()
df['a'] = ['hello world', 'hi']
df['b'] = 0
print(df.to_string(index=False))
```
#### Problem description
It fails to align the first column header:
a b
hello world 0
hi 0
#### Expected Output
a b
hello world 0
hi 0
#### Output of ``pd.show_versions()``
pandas: 0.19.2
python: 3.5.1
OS: Linux
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
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PERF: fast non-unique indexing
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CONTRIBUTOR
| null |
closes #15364
This is essentially the same pull request as https://github.com/pandas-dev/pandas/pull/15372 but now from a proper branch.
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| 4 | 2017-02-21T17:28:35Z | 2017-03-10T09:25:12Z | 2017-03-09T14:26:57Z |
NONE
| null |
The Exponentially Weighted Windows page is not rendering the formulas in LaTeX format.
http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows
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MDU6SXNzdWUyMDkyNzAzNTE=
| 15,470 |
On Series with CategoricalIndex, __getitem__ not equal to .loc
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| 3 | 2017-02-21T20:57:39Z | 2021-05-08T01:11:06Z | null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> s = pd.Series([2, 1, 0], index=pd.CategoricalIndex([2, 1, 0]))
>>> s.index
CategoricalIndex([2, 1, 0], categories=[0, 1, 2], ordered=False, dtype='category')
>>> s[[0, 1, 2]].equals(s.loc[[0, 1, 2]]) # Should be True
False
>>> s[[0, 1, 2]].equals(s.iloc[[0, 1, 2]]) # Should be False
True
```
#### Problem description
For Series with CategoricalIndex, `__getitem__` indexing behaves differently than `.loc`. On other-indexed series, these accessors return the same result:
```py
>>> s = pd.Series([2, 1, 0], index=[2, 1, 0])
>>> s[[0, 1, 2]].equals(s.loc[[0, 1, 2]]) # Should be True
True
>>> s[[0, 1, 2]].equals(s.iloc[[0, 1, 2]]) # Should be True
False
```
#### Expected Output
```python
>>> s = pd.Series([2, 1, 0], index=pd.CategoricalIndex([2, 1, 0]))
>>> s[[0, 1, 2]].equals(s.loc[[0, 1, 2]])
True
```
#### Output of ``pd.show_versions()``
0.19.0+479.git
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| 15,471 |
ERR: better error message when using invalid indexer in iloc
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| 3 | 2017-02-21T22:23:03Z | 2020-03-30T19:45:22Z | 2020-03-30T19:45:22Z |
MEMBER
| null |
Not the most important isssue, but I find the error message when doing label based indexing with strings in `iloc` not very clear:
```
In [12]: s = pd.Series(range(5), index=pd.date_range("2012-01-01", periods=5))
In [15]: s.loc["2012-01-02":"2012-01-04"]
Out[15]:
2012-01-02 1
2012-01-03 2
2012-01-04 3
Freq: D, dtype: int64
In [16]: s.iloc["2012-01-02":"2012-01-04"]
...
TypeError: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with
these indexers [2012-01-02] of <class 'str'>
```
So I by accident used iloc instead of loc, but the error message did not directly make my error clear to me. I think it should just say something about that strings are not allowed in iloc. Because I *can* do slice indexing with strings on a DatetimeIndex (as the error message says I cannot), only not with iloc :-)
(on pandas 0.19.2)
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| 15,472 |
Indexing a MultiIndex with a (Multi)Index
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[] | null | 3 | 2017-02-22T01:04:08Z | 2021-05-08T01:12:01Z | null |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [2]: s = pd.Series(range(8), index=pd.MultiIndex.from_product([[1,2], [3,4], [3,4]],
names=['a', 'b', 'c']))
In [3]: s.loc[s.index] # Works as expected
Out[3]:
a b c
1 3 3 0
4 1
4 3 2
4 3
2 3 3 4
4 5
4 3 6
4 7
dtype: int64
In [4]: s.loc[s.iloc[2:-1].index] # Works as expected
Out[4]:
a b c
1 4 3 2
4 3
2 3 3 4
4 5
4 3 6
dtype: int64
In [5]: s.loc[s.index.droplevel('c')] # Just reindexes... weird
Out[5]:
1 3 NaN
3 NaN
4 NaN
4 NaN
2 3 NaN
3 NaN
4 NaN
4 NaN
dtype: float64
In [6]: s.loc[s.index.droplevel(['b', 'c']), :] # Works (flat index)
Out[6]:
a b c
1 3 3 0
4 1
4 3 2
4 3
2 3 3 4
4 5
4 3 6
4 7
dtype: int64
In [7]: s.loc[s.index.droplevel(['b', 'c'])] #... but fails if I use the shortened notation!
[...]
TypeError: unhashable type: 'Int64Index'
In [8]: s.loc[s.swaplevel('b', 'c')] # Works
Out[8]:
a b c
1 3 3 0
4 1
4 3 2
4 3
2 3 3 4
4 5
4 3 6
4 7
dtype: int64
In [9]: s.loc[s.index.swaplevel('b', 'c')] # Different result! (reindexes)
Out[9]:
a c b
1 3 3 0
4 3 2
3 4 1
4 4 3
2 3 3 4
4 3 6
3 4 5
4 4 7
dtype: int64
In [10]: s.loc[pd.MultiIndex.from_product([[1,2], [3], [4]],
names=['a', 'c', 'b'])] # Does not respect column names!
Out[10]:
a c b
1 3 4 1
2 3 4 5
dtype: int64
```
#### Problem description
This clearly needs a unified approach (and I can try).
#### Expected Output
I guess most expected outputs above are obvious, except for ``In [10]:`` (and maybe ``In [5]:``, which however is already discussed [elsewhere](15452)). That is: it is not obvious whether level names in the indexer should be matched to level names in the indexed, when both are set ([see this comment](https://github.com/pandas-dev/pandas/pull/15425#issuecomment-280500783)). It would probably be more ``pandas``-ish if they were.
In other terms, while there is no doubt that
``` python
Out[10]:
a c b
1 3 4 1
2 3 4 5
dtype: int64
```
is wrong, we must decide whether we want
``` python
Out[10]:
a b c
1 3 4 1
2 3 4 5
dtype: int64
```
or
``` python
Out[10]:
a b c
1 4 3 2
2 4 3 6
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.19.0+478.g12f2c6a
pytest: 3.0.6
pip: 8.1.2
setuptools: 28.0.0
Cython: 0.23.4
numpy: 1.12.0
scipy: 0.18.1
xarray: None
IPython: 5.1.0.dev
sphinx: 1.4.8
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.0
feather: None
matplotlib: 2.0.0rc2
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_datareader: 0.2.1
</details>
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Pandas isin with empty dataframe produces epoch value on datetime type instead of boolean
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| 1 | 2017-02-22T11:38:02Z | 2017-03-05T01:45:21Z | 2017-03-05T01:45:21Z |
NONE
| null |
I've noticed that doing an isin on a DataFrame which contains datetime types where the operand is an empty DataFrame produces epoch datetime values (i.e. 1970-01-01), instead of 'False'. It seems unlikely that this is correct?
The following code demonstrates this:
```python
import pandas as pd
data = {'date': ['2014-05-01 18:47:05.069722', '2014-05-01 18:47:05.119994', '2014-05-02 18:47:05.178768']}
data2 = {'date': ['2014-05-01 18:47:05.069722', '2014-05-01 18:47:05.119994']}
df = pd.DataFrame(data, columns = ['date'])
df['date'] = pd.to_datetime(df['date'])
df2 = pd.DataFrame(data2, columns = ['date'])
df2['date'] = pd.to_datetime(df2['date'])
df3 = pd.DataFrame([], columns = ['date'])
df4 = pd.DataFrame()
print df.isin(df2)
print df.isin(df3)
print df.isin(df4)
```
This outputs:
```
date
0 True
1 True
2 False
date
0 1970-01-01
1 1970-01-01
2 1970-01-01
date
0 1970-01-01
1 1970-01-01
2 1970-01-01
```
I would normally expect a list of False values instead of '1970-01-01'? I notice that with pandas = 0.16.2 and numpy = 1.9.2, df.isin(df3) produces the more expected:
```
date
0 False
1 False
2 False
```
But df.isin(df4) is as previous.
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-279.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 2.2
Cython: None
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: 5.2.2
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
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: 1.1.5
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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| 209,433,080 |
MDU6SXNzdWUyMDk0MzMwODA=
| 15,474 |
VIS/BUG DatetimeIndex with freq=None plot return rotated xtickslabels
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[] | null | 3 | 2017-02-22T11:57:15Z | 2020-04-30T06:00:36Z | 2020-04-30T06:00:35Z |
NONE
| null |
#### Code Sample
```python
file = 'test.csv' #with a "date" column
df1 = pd.read_csv(file, index_col="date", parse_dates=["date"])
df1.plot()
idx =pd.date_range('2000-01-01','2003-01-01', freq="6d")
df2 = pd.DataFrame(index=idx, data=np.random.randn(len(idx),1))
df2.plot()
```
#### Problem description
When plotting a time indexed series, we expect no rotation in x labels. It does not work when importing data with
```
df = pd.read_csv(file, index_col="date", parse_dates=["date"])
```
where the rotation occurs. Moreover, set ```rot=0``` gives a strange behavior with label not centered on ticks.
The only difference is that ```freq=None``` in the first case.
#### Expected Output
No rotation by default.
Here is what we have for ```df1.plot()``` (Problem) :
Here is what we have for ```df1.plot(rot=0)``` (Problem) :
Here is what we have for ```df2.plot()``` (Ok) :
### Details
May be related to :
- https://github.com/pandas-dev/pandas/issues/8150
- https://github.com/pandas-dev/pandas/issues/15025
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.9-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8
pandas: 0.19.2
nose: None
pip: None
setuptools: 34.2.0
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.10.3
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUyMDk0ODM5MzM=
| 15,475 |
Groupby all levels except the specified ones
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| 4 | 2017-02-22T15:16:28Z | 2018-11-03T13:56:52Z | 2018-11-03T13:56:52Z |
CONTRIBUTOR
| null |
Inspired by
> http://stackoverflow.com/questions/25606478/grouping-by-everything-except-for-one-index-column-in-pandas
I was thinking if it would be useful to add a parameter to the groupby function (e.g. allbut=True) so that pandas will group all levels except the specified ones) .
I suggest so because in a multilevel index with several levels, eliminating just one level requires a quite verbose approach
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| 15,476 |
The roll_quantile function now throws an exception instead of causing a segfault
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| 9 | 2017-02-22T16:29:15Z | 2017-02-23T13:28:59Z | 2017-02-23T13:24:28Z |
CONTRIBUTOR
| null |
The roll_quantile function in window.pyx now raises a ValueError when the quantile value is not in [0.0, 1.0]. Also added tests for the new exception and extended test cases for the roll_quantile function.
- [x] closes #15463
- [x] tests added / passed
- [ ] does not pass ``git diff upstream/master | flake8 --diff`` because of some cdef code blocks in the file but my code does not generate any errors
- [x] two EOL spaces removed by my editor
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CLN: split off frozen (immutable) data structures into pandas/indexes/frozen.py
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| 1 | 2017-02-22T19:15:18Z | 2017-02-24T12:14:43Z | 2017-02-24T12:14:43Z |
CONTRIBUTOR
| null |
should make it a bit easier to work with these; and no reason to be in pandas/core/base.py
|
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BUG: GH #12223, GH #15262. Allow ints for names in MultiIndex
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| 11 | 2017-02-22T22:25:10Z | 2017-02-28T15:43:50Z | 2017-02-24T20:18:20Z |
CONTRIBUTOR
| null |
- [X] closes #12223 and #15262
- [X] tests added / passed
- `pandas/tests/frame/test_combine_concat.py::TestDataFrameConcatCommon::test_concat_numerical_names`
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
See discussion in #10461 . Now if we internally know we need to call `get_level_values` for a specific level number, there is an internal function for doing that, which should allow better support for when people create indices with names that are integers.
|
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MDExOlB1bGxSZXF1ZXN0MTA3NTY0MjY0
| 15,479 |
Fixed LatexFormatter with MultIndex index
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[] | null | 13 | 2017-02-23T06:38:46Z | 2017-08-17T10:25:50Z | 2017-08-17T10:25:50Z |
CONTRIBUTOR
| null |
Used `MultiIndex.formatter()` to correctly sparsify the index for `to_latex()`.
I was having an issue with printing Latex tables. This can be seen in the comparison of `to_string()` and `to_latex()` for the following table. Notice that the first row of `Vol8` should not be missing any labels because it marks a new label in the outermost level. This is correct in `to_string` and incorrect in `to_latex`.
Output of `to_string()`:
```
Volunteer Visit Stim Marker
Vol6 2 Long CD137+ 96
IFNg+ 192
Short CD154+ 96
14 Long IFNg+ 288
Short CD137+ 192
Vol7 2 Long CD69- 96
IFNg+ 96
Short CD137+ 96
4 Short CD137+ 192
IFNg+ 96
14 Short CD137+ 96
CD154+ 96
IFNg+ 96
Vol8 2 Short CD137+ 288
4 Long IFNg+ 192
```
Output of `to_latex()`:
```
\begin{tabular}{llllr}
\toprule
& & & & Total cells \\
Volunteer & Visit & Stim & Marker & \\
\midrule
Vol6 & 2 & Long & CD137+ & 96 \\
& & & IFNg+ & 192 \\
& & Short & CD154+ & 96 \\
& 14 & Long & IFNg+ & 288 \\
& & Short & CD137+ & 192 \\
Vol7 & 2 & Long & CD69- & 96 \\
& & & IFNg+ & 96 \\
& & Short & CD137+ & 96 \\
& 4 & & & 192 \\
& & & IFNg+ & 96 \\
& 14 & & CD137+ & 96 \\
& & & CD154+ & 96 \\
& & & IFNg+ & 96 \\
Vol8 & 2 & & CD137+ & 288 \\
& 4 & Long & IFNg+ & 192 \\
\bottomrule
\end{tabular}
```
I've tested the relevant pieces of this code using this example, but I would be grateful for help with adding tests to the repository. I'm not set up to develop the `pandas` codebase currently and am not exactly sure how to add the right tests in the right places. Thanks!
- [x] Addresses issue #15480
- [x] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
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MDU6SXNzdWUyMDk2Nzc3MTE=
| 15,480 |
Bug with sparsify in DataFrame.to_latex()
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[] | null | 2 | 2017-02-23T06:42:44Z | 2018-06-10T18:58:36Z | 2018-06-10T18:58:36Z |
CONTRIBUTOR
| null |
Please see PR #15479
I was having an issue with printing Latex tables with a MultiIndex and wanting a sparse index displayed. This can be seen in the comparison of `to_string()` and `to_latex()` for the following table. Notice that the first row of `Vol8` should not be missing any labels because it marks a new label in the outermost level. This is correct in `to_string` and incorrect in `to_latex`.
Output of `to_string()`:
```
Volunteer Visit Stim Marker
Vol6 2 Long CD137+ 96
IFNg+ 192
Short CD154+ 96
14 Long IFNg+ 288
Short CD137+ 192
Vol7 2 Long CD69- 96
IFNg+ 96
Short CD137+ 96
4 Short CD137+ 192
IFNg+ 96
14 Short CD137+ 96
CD154+ 96
IFNg+ 96
Vol8 2 Short CD137+ 288
4 Long IFNg+ 192
```
Output of `to_latex()`:
```
\begin{tabular}{llllr}
\toprule
& & & & Total cells \\
Volunteer & Visit & Stim & Marker & \\
\midrule
Vol6 & 2 & Long & CD137+ & 96 \\
& & & IFNg+ & 192 \\
& & Short & CD154+ & 96 \\
& 14 & Long & IFNg+ & 288 \\
& & Short & CD137+ & 192 \\
Vol7 & 2 & Long & CD69- & 96 \\
& & & IFNg+ & 96 \\
& & Short & CD137+ & 96 \\
& 4 & & & 192 \\
& & & IFNg+ & 96 \\
& 14 & & CD137+ & 96 \\
& & & CD154+ & 96 \\
& & & IFNg+ & 96 \\
Vol8 & 2 & & CD137+ & 288 \\
& 4 & Long & IFNg+ & 192 \\
\bottomrule
\end{tabular}
```
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ENH: BusinessHour to support multiple opening and closing times per day
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| 3 | 2017-02-23T09:45:43Z | 2019-06-28T14:10:21Z | 2019-06-28T14:10:21Z |
CONTRIBUTOR
| null |
BusinessHour supports arbitrary start and end times. But some businesses open and close more than once per day. Some examples:
* Restaurants, who might want to analyze foot traffic or revenue, but excluding times they don't open.
* Stock markets, several of which have a lunch break.
* Mexico, where standard business hours are 07:00-14:00 and 16:00-18:00.
Many use cases could be satisfied by just two opens and two closes per day, but it might be just as well to support any amount.
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MDU6SXNzdWUyMDk3MzY1NDY=
| 15,482 |
BUG: DataFrame.consolidate throws TypeError with bytes blocks
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| 4 | 2017-02-23T11:33:44Z | 2017-03-05T14:41:32Z | 2017-03-05T14:41:26Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
x = pd.DataFrame([['a']]).astype('S1')
y = pd.concat([x]*2, 1)
y.consolidate()
```
#### Problem description
This throws the following error:
```python
File "bug.py", line 3, in <module>
y.consolidate()
File "/home/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 2822, in consolidate
cons_data = self._protect_consolidate(f)
File "/home/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 2790, in _protect_consolidate
result = f()
File "/home/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 2821, in <lambda>
f = lambda: self._data.consolidate()
File "/home/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 3526, in consolidate
bm._consolidate_inplace()
File "/home/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 3531, in _consolidate_inplace
self.blocks = tuple(_consolidate(self.blocks))
File "/home/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 4523, in _consolidate
_can_consolidate=_can_consolidate)
File "/home/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 4543, in _merge_blocks
new_values = _vstack([b.values for b in blocks], dtype)
File "/home/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 4584, in _vstack
if dtype == _NS_DTYPE or dtype == _TD_DTYPE:
TypeError: data type "bytes8" not understood
```
#### Expected Output
No error should be thrown, y should be consolidated.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.8-100.fc24.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.8.0
xarray: 0.9.1
IPython: 4.2.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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MDU6SXNzdWUyMDk3NTYxODI=
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DEPR: .consolidate() should be non-public
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| 3 | 2017-02-23T13:07:16Z | 2017-02-28T10:43:23Z | 2017-02-28T10:43:23Z |
CONTRIBUTOR
| null |
no user reason to use this, and will eventually disappear in pandas 2.0 anyhow.
I would rename to ``_consolidate()`` and then provide a deprecation warning for ``.consolidate()``. Would need to change some internal code to use the renamed version.
xref https://github.com/pandas-dev/pandas/issues/15482#issuecomment-281986233
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CLN: remove pandas/io/gbq.py and tests and replace with pandas-gbq
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| 9 | 2017-02-23T13:14:15Z | 2017-02-27T20:48:07Z | 2017-02-27T20:48:07Z |
CONTRIBUTOR
| null |
closes #15347
- going to have to migrate the docs as well, but need some work on pandas-gbq side for this.
- should change the tests to check a bit more (a more comprehensive check)
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DOC: docstrings of is_type_factory and is_instance_factory in config.py not accurate.
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| 5 | 2017-02-23T16:03:22Z | 2017-02-24T19:53:56Z | 2017-02-24T19:53:43Z |
CONTRIBUTOR
| null |
The validators returned by the functions don't return True, contrary to the docstrings' descriptions.
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ENH: fill_value argument for shift
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| 5 | 2017-02-23T17:35:25Z | 2018-12-26T01:29:55Z | 2018-12-26T01:29:55Z |
CONTRIBUTOR
| null |
#### Feature Request
At the moment, the Series/DataFrame `shift` methods always fill with nans, which causes an upcast for boolean/integer types. In cases where there is an appropriate non-nan fill value, it would be useful to provide this value in `shift`, rather than having to correctly downcast the result. This would presumably also be more efficient than `.shift().fillna()` in those cases where no upcast is required.
#### Example Code
```python
>>> pd.Series([True]).shift(fill_value=False)
0 False
dtype: bool
```
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support CategoricalIndex for read_msgpack
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| 1 | 2017-02-23T17:35:55Z | 2017-02-24T20:41:46Z | 2017-02-24T20:41:46Z |
CONTRIBUTOR
| null |
#### The following code fails:
```
import pandas as pd
pdf = pd.DataFrame(dict(A=[1,1,1,2,2,2], B = [1,2,3,4,5,6]))
pdf['A'] = pdf['A'].astype('category')
pdf.set_index('A', inplace = True)
pdf.to_msgpack('/some/path')
pdf2 = pd.read_msgpack('/some/path')
```
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-6-cab186b6bdcd> in <module>()
4 pdf.set_index('A', inplace = True)
5 pdf.to_msgpack(/some/path')
----> 6 pdf2 = pd.read_msgpack('/some/path')
/.../Anaconda2/lib/python2.7/site-packages/pandas/io/packers.pyc in read_msgpack(path_or_buf, encoding, iterator, **kwargs)
200 if exists:
201 with open(path_or_buf, 'rb') as fh:
--> 202 return read(fh)
203
204 # treat as a binary-like
/.../Anaconda2/lib/python2.7/site-packages/pandas/io/packers.pyc in read(fh)
185
186 def read(fh):
--> 187 l = list(unpack(fh, encoding=encoding, **kwargs))
188 if len(l) == 1:
189 return l[0]
pandas/msgpack/_unpacker.pyx in pandas.msgpack._unpacker.Unpacker.__next__ (pandas/msgpack/_unpacker.cpp:5618)()
pandas/msgpack/_unpacker.pyx in pandas.msgpack._unpacker.Unpacker._unpack (pandas/msgpack/_unpacker.cpp:4602)()
/.../Anaconda2/lib/python2.7/site-packages/pandas/io/packers.pyc in decode(obj)
557 data = unconvert(obj[u'data'], dtype,
558 obj.get(u'compress'))
--> 559 return globals()[obj[u'klass']](data, dtype=dtype, name=obj[u'name'])
560 elif typ == u'range_index':
561 return globals()[obj[u'klass']](obj[u'start'],
KeyError: u'CategoricalIndex'
```
#### Problem description
`read_msgpack` apparently does not seem to support a CategoricalIndex, however, it is possible to save a dataframe with a CategoricalIndex using `to_msgpack`.
Background: I am currently using the to_msgpack method to save a dask dataframe, where the index is (something like) a time stamp. It is not unique. I am overall very satisfied with the performance of `to_msgpack`, however when it comes to space efficency, having a categorical index would probably provide a significant improvement.
Or maybe it works, but I am using it wrong?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.16.60-0.42.5-smp
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
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: 5.2.2
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: 1.4.4
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: 0.9.2
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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Repr-ing SparseDataFrame after setting a value
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| 1 | 2017-02-23T18:36:33Z | 2017-03-07T13:28:16Z | 2017-03-07T13:28:16Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> sdf = pd.SparseDataFrame(index=[0, 1], columns=[0, 1])
>>> sdf[0][0] = 1 # This warns; ignore
>>> sdf
AttributeError: 'NoneType' object has no attribute 'type'
```
#### Problem description
After some value is set on a copy of a SparseSeries, the enclosing SparseDataFrame can no longer be printed (raises AttributeError).
#### Expected Output
```py
>>> sdf
0 1
0 NaN NaN
1 NaN NaN
```
#### Output of ``pd.show_versions()``
0.19.0+479.git
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BUG: repr SparseDataFrame after setting a value
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| 4 | 2017-02-23T18:41:06Z | 2017-03-07T13:28:23Z | 2017-03-07T13:28:16Z |
CONTRIBUTOR
| null |
- [x] closes #15488
- [x] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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| 15,490 |
BUG: assignment with .at or .loc modifies dataframe when it fails
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| 6 | 2017-02-23T19:01:54Z | 2021-05-08T01:12:44Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
d = pd.DataFrame()
try:
d.set_value(0, "c", [1,2,4])
except Exception:
print "Caught"
print d # c is now in d
```
#### Problem description
The above call set_value (correctly) fails and throw an exception. However, the dataframe has been modified. It has a new 'c' column of type float. Because the operation failed, it probably should have remained unchanged.
#### Expected Output
print d should print an empty dataframe.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-41-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.1
pip: 1.5.4
setuptools: 3.3
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.1
sphinx: None
patsy: 0.2.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 1.0.15
pymysql: None
psycopg2: 2.4.5 (dt dec mx pq3 ext)
jinja2: 2.8
boto: 2.41.0
pandas_datareader: None
</details>
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| 1 | 2017-02-23T19:17:21Z | 2017-08-03T14:12:41Z | 2017-02-23T19:22:59Z |
NONE
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ERR: better message for HDFStore date query with non date columns
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| 2 | 2017-02-23T20:27:33Z | 2017-03-03T15:07:47Z | 2017-03-03T15:07:47Z |
CONTRIBUTOR
| null |
When querying a `HDFStore` with a timestamp, on a column that (in my case, inadvertently) isn't a datetime, the error message isn't helpful; I think we can trap and show something better here.
```python
df = pd.DataFrame({'date': ['2014-01-01', '2014-01-02'], 'values': [1, 2]})
df.to_hdf('store.h5', 'df', format='t', data_columns=['date'])
ts = pd.Timestamp('2014-01-01')
In [18]: pd.read_hdf('store.h5', 'df', where='date > ts')
File "<string>", line 1
(date > 1970-01-01 00:00:01.388556)
^
SyntaxError: invalid token
```
pandas 0.19.2
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msgpack supports CategoricalIndex
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| 1 | 2017-02-23T21:33:50Z | 2017-02-24T20:41:47Z | 2017-02-24T20:41:47Z |
CONTRIBUTOR
| null |
- [x] closes #15487
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
This allows `read_msgpack` to read DataFrames with a categorical index. I'd assume that my testcase needs some improvement. Where can I find some information where and how to add the "whatsnew entry"?
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MDU6SXNzdWUyMDk5MzY5Njc=
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BUG: dt.strftime causes TypeError when there is only one row in DataFrame
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| 5 | 2017-02-24T01:43:47Z | 2018-01-10T00:17:29Z | 2018-01-10T00:17:29Z |
NONE
| null |
Code below outlines the issue, if I have a DataFrame with a date column and want to transfer the datetime to string using df.loc[:,'date'] = df['date'].dt.strftime('%Y-%m-%d'), it works fine as long as there is more than 1 row in the Data Frame. If there is only one row then you get a Type Error (see below for full error). If, on the other hand, I use df['date']=df['date'].dt.strftime('%Y-%m-%d') then it works fine for one or more rows. This was observed on both 0.17.1 and 0.19.2.
----- Error Generated -----
Traceback (most recent call last):
File "/usr/local/lib/python3.4/site-packages/pandas/core/internals.py", line 2265, in _try_coerce_args
other = other.astype('i8', copy=False).view('i8')
ValueError: invalid literal for int() with base 10: '2017-02-23'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "./testpd.py", line 35, in <module>
do_stuff(d1)
File "./testpd.py", line 17, in do_stuff
df.loc[:,'date'] = df['date'].dt.strftime('%Y-%m-%d')
File "/usr/local/lib/python3.4/site-packages/pandas/core/indexing.py", line 141, in __setitem__
self._setitem_with_indexer(indexer, value)
File "/usr/local/lib/python3.4/site-packages/pandas/core/indexing.py", line 533, in _setitem_with_indexer
setter(labels[0], value)
File "/usr/local/lib/python3.4/site-packages/pandas/core/indexing.py", line 473, in setter
s._data = s._data.setitem(indexer=pi, value=v)
File "/usr/local/lib/python3.4/site-packages/pandas/core/internals.py", line 3168, in setitem
return self.apply('setitem', **kwargs)
File "/usr/local/lib/python3.4/site-packages/pandas/core/internals.py", line 3056, in apply
applied = getattr(b, f)(**kwargs)
File "/usr/local/lib/python3.4/site-packages/pandas/core/internals.py", line 668, in setitem
values, _, value, _ = self._try_coerce_args(self.values, value)
File "/usr/local/lib/python3.4/site-packages/pandas/core/internals.py", line 2270, in _try_coerce_args
raise TypeError
TypeError
----- Code to Reproduce Issue -----
```
#!/usr/bin/env python3
""" Demonstrate Pandas issue """
import datetime as dt
import pandas as pd
def do_stuff(d):
df = pd.DataFrame(d)
df['date'] = df['date'].dt.strftime('%Y-%m-%d')
df['id'] = df['id'].astype(str)
df['fl'] = df['fl'].astype(str)
print("df[df]: " + str(df))
df = pd.DataFrame(d)
df.loc[:,'id'] = df['id'].astype(str)
df.loc[:,'fl'] = df['fl'].astype(str)
df.loc[:,'date'] = df['date'].dt.strftime('%Y-%m-%d')
print("df.loc[df]: " + str(df))
""" With 2 rows in Data Frame, works fine """
d2 = { 'date': pd.Series([dt.datetime.today(), dt.datetime.today()]),
'bsym': pd.Series(['blah', 'blah2']),
'id': pd.Series([1, 2]),
'fl': pd.Series([1.5, 3]) }
print("D2...")
do_stuff(d2)
""" With 1 row in Data Frame, exception occurs on date column """
d1 = { 'date': pd.Series([dt.datetime.today()]),
'bsym': pd.Series(['blah']),
'id': pd.Series([1]),
'fl': pd.Series([1.5]) }
print("D1...")
do_stuff(d1)
```
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MDU6SXNzdWUyMDk5NDQ0ODI=
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Update plotting functions to use the new Matplotlib default styles
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| 11 | 2017-02-24T02:40:04Z | 2017-09-24T10:11:29Z | 2017-09-24T10:11:25Z |
NONE
| null |
In version 2.0.0, Matplotlib introduced several changes in it's default style. Pandas plotting functions should be updated to use the new style.
http://matplotlib.org/users/dflt_style_changes.html
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BUG: reindex_like after shape comparison
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| 5 | 2017-02-24T08:27:50Z | 2017-02-27T19:50:46Z | 2017-02-27T19:50:03Z |
CONTRIBUTOR
| null |
in assert_frame_equal, if check_like, the former code reindex_like before shape comparison.
for example:
if left.shape=(2,2), right.shpae=(2.0), after reindex_like, left.shape=(2,0),right.shape=(2,0),then the shape comparison will not find out that the two dataframes are different. For that, the assert_frame_equal will not raise assertion errors. But in fact it should raise.
@jreback
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ENH: Native conversion from/to scipy.sparse matrix to SparseDataFrame
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| 27 | 2017-02-24T10:28:38Z | 2018-10-23T16:34:39Z | 2017-03-10T14:36:34Z |
CONTRIBUTOR
| null |
- [x] closes #4343
- [x] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
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PERF: categorical rank
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| 5 | 2017-02-24T13:38:39Z | 2017-03-01T21:56:17Z | 2017-03-01T21:56:17Z |
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/15422#discussion_r102941126
easy enough after #15422 to rank the categories themselves rather than using expanded values; prob most relevant for ``object`` dtypes.
```
In [15]: s = Series(tm.makeCategoricalIndex(100000))
In [16]: res = Series(np.array(s.cat.rename_categories(Series(s.cat.categories).rank()))).rank()
In [17]: res2 = s.rank()
In [18]: res.equals(res2)
Out[18]: True
In [19]: %timeit Series(np.array(s.cat.rename_categories(Series(s.cat.categories).rank()))).rank()
100 loops, best of 3: 4.39 ms per loop
In [20]: %timeit s.rank()
10 loops, best of 3: 132 ms per loop
```
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DOC: Fix to docstrings of is_type_factory and is_instance_factory
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| 2 | 2017-02-24T15:07:46Z | 2017-02-25T04:40:24Z | 2017-02-24T19:53:44Z |
CONTRIBUTOR
| null |
- [X] closes #15485
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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PeriodIndex becomes DatetimeIndex on multiple BDay resampling
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| 1 | 2017-02-24T19:18:59Z | 2017-02-24T19:29:09Z | 2017-02-24T19:28:55Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
series = pd.Series(1., pd.period_range('2015', '2016', freq='B'))
resampled = series.resample('3B').sum()
print type(series.index)
print type(resampled.index)
```
#### Problem description
A `PeriodIndex` when resampled by a multiple of BusinessDays becomes a `DatetimeIndex`.
#### Expected Output
I would expect it to be a 3B `PeriodIndex`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-36-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.24
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 4.1.1
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: None
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.5.0
bs4: 4.5.1
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.45.0
pandas_datareader: None
</details>
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| 15,501 |
DEPR: rename to _consolidate and create deprecation warning
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[] | null | 5 | 2017-02-25T04:51:58Z | 2017-03-05T08:04:55Z | 2017-02-28T10:43:23Z |
CONTRIBUTOR
| null |
- [x] closes #15483
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Test output:
```
running: pytest --skip-slow --skip-network pandas
============================= test session starts ==============================
platform linux2 -- Python 2.7.13, pytest-3.0.6, py-1.4.32, pluggy-0.4.0
rootdir: /home/sfoo/pandas, inifile: setup.cfg
collected 11882 items / 4 skipped
10147 passed, 1662 skipped, 77 xfailed, 2084 pytest-warnings in 407.18 seconds
```
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Allow Enums in place of certain parameters values
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[] | null | 5 | 2017-02-25T09:38:03Z | 2020-02-11T22:28:01Z | 2020-02-11T22:28:01Z |
CONTRIBUTOR
| null |
I have a suggestion for how to use Enums to codify less transparent parameter values. Since 3.4, Python comes with built-in support for Enums, which could be useful in certain instances for Pandas.
One example is allowing enums in place of integers for axis values, which frankly to this day I cannot seem to remember which is which. These could for instance be stored in an attribute in each function. This could potentially work like this:
import pandas as pd
df = pd.DataFrame({'a': [1,2,3], 'b': [2,3,4]})
df.sum(axis=df.sum.axis.COLUMNWISE)
a 6
b 9
dtype: int64
Another example is having support for enums in place of string values. This would be useful as a way to codify all supported string values outside of documentation, and also enabling autocomplete support in REPLs. Another example:
import pandas as pd
from pandas import axis
df = pd.DataFrame({'a': [1,2,3,5], 'b': [2,3,4,None]})
df.sort_values('b', na_position=df.sort_values.na_position.FIRST)
a b
3 5 NaN
0 1 2.0
1 2 3.0
2 3 4.0
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PERF/API: fast paths for product MultiIndex?
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| 5 | 2017-02-25T12:52:38Z | 2017-03-05T22:11:29Z | 2017-03-05T22:11:28Z |
CONTRIBUTOR
| null |
#### Feature Proposal
At the moment, we have a few different methods for storing indexed higher-dimensional arrays:
- DataFrame/Series with a product MultiIndex (**PMI**), which can be slow
- Panel, which is fast, but less flexible, and may be deprecated soon (#13563)
- xarray, which is designed for this, but has a smaller API
For some datasets, I've found the PMI to be the best option, together with occasional workarounds for performance bottlenecks. Operations which are slow for a general MultiIndex, like `unstack()` or `swaplevel().sortlevel()`, can be sped up for PMIs (see below).
It would be great if we could do something like this more generally, with fast paths for PMIs. We could maybe have `MultiIndex.from_product()` return a PMI object, which would upcast to MultiIndex when necessary. We could also have `stack()` and `unstack()` create PMI objects where possible, and perhaps add an argument to `concat()` and `set_index()` to create PMIs. Slow MultiIndex operations could then have a fast path for PMI objects.
#### Code Sample
```python
import numpy as np
import pandas as pd
m = 100
n = 1000
levels = np.arange(m)
index = pd.MultiIndex.from_product([levels]*2)
columns = np.arange(n)
values = np.arange(m*m*n).reshape(m*m, n)
df = pd.DataFrame(values, index, columns)
# >>> timeit slow_unstack()
# 1 loop, best of 3: 363 ms per loop
def slow_unstack():
return df.unstack()
# >>> timeit fast_unstack()
# 10 loops, best of 3: 55 ms per loop
def fast_unstack():
columns = pd.MultiIndex.from_product([df.columns, levels])
values = df.values.reshape(m, m, n).swapaxes(1, 2).reshape(m, m*n)
return pd.DataFrame(values, levels, columns)
# >>> timeit slow_swaplevel_sortlevel()
# 1 loop, best of 3: 213 ms per loop
def slow_swaplevel_sortlevel():
return df.swaplevel().sortlevel()
# >>> timeit fast_swaplevel_sortlevel()
# 10 loops, best of 3: 38.7 ms per loop
def fast_swaplevel_sortlevel():
values = df.values.reshape(m, m, n).swapaxes(0, 1).reshape(m*m, n)
index = df.index.swaplevel().sortlevel()[0]
return pd.DataFrame(values, index, df.columns)
```
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PERF: improve iloc list indexing
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| 11 | 2017-02-25T17:20:11Z | 2017-10-16T09:45:07Z | 2017-04-01T21:51:44Z |
MEMBER
| null |
Did a profile of `iloc` using a list indexer, and noticed a few parts that seemingly can be optimized rather easily:
- convert early to array, and keep as array
- do not validate the input for out of bounds (this validation is also (and much faster) done in `np.take`)
- do not validate kwargs (for numpy compat) (-> could define a `_take` method that does not do this for internal usage?)
- use fastpath for resulting Series creation
The failing tests are currently just the ones that checked for the kwarg validation (and one failing test due to the fastpath taken where object datetimes are not coerced to datetime64).
Compared to master, I get up to 40 to 60% improvement on some simple tests:
PR:
```
In [1]: s = pd.Series(np.random.randn(10000))
In [2]: s.iloc[10]
Out[2]: 0.91268983379322421
In [3]: %timeit s.iloc[[10]]
The slowest run took 14.18 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 47.3 µs per loop
In [4]: indexer = list(range(10, 30)) + list(range(50,80))
In [5]: %timeit s.iloc[indexer]
The slowest run took 5.92 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 49.8 µs per loop
In [6]: indexer = list(range(10, 30)) + list(range(50,800))
In [7]: %timeit s.iloc[indexer]
10000 loops, best of 3: 104 µs per loop
```
master:
```
In [1]: s = pd.Series(np.random.randn(10000))
In [2]: s.iloc[10]
Out[2]: -1.4152272792497846
In [3]: %timeit s.iloc[[10]]
The slowest run took 6.70 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 103 µs per loop
In [4]: indexer = list(range(10, 30)) + list(range(50,80))
In [5]: %timeit s.iloc[indexer]
The slowest run took 4.12 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 131 µs per loop
In [6]: indexer = list(range(10, 30)) + list(range(50,800))
In [7]: %timeit s.iloc[indexer]
The slowest run took 17.01 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 247 µs per loop
```
There are some details to work out, but already putting it up here for discussion (and need to run the benchmark suite).
From the remaining time that iloc takes, a large part is spent in the creation of the actual result (the new series): creating a new index (~ 17%), creating a new series based on the values and index (~ 32%). So in total almost half of the time, and thus wondering if this is not possible to speedup.
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MDExOlB1bGxSZXF1ZXN0MTA3OTc1OTE5
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DOC: fix doc build warnings
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| 3 | 2017-02-25T20:35:27Z | 2017-02-25T21:38:57Z | 2017-02-25T21:38:57Z |
MEMBER
| null |
Bunch of small fixes that caused warnings/errors
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MDExOlB1bGxSZXF1ZXN0MTA3OTc2NTQ3
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Added FrozenList subtraction
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| 17 | 2017-02-25T20:52:16Z | 2018-10-28T06:47:43Z | 2017-03-02T13:25:17Z |
CONTRIBUTOR
| null |
- [x] closes #15475
- [x] tests added / passed
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- [x] whatsnew entry
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| 210,263,356 |
MDExOlB1bGxSZXF1ZXN0MTA3OTc3ODA3
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BUG: Fix nansum overflow on Windows with bottleneck
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NONE
| null |
Bottleneck used in nansum produces overflow error on Windows. Temporary fix that could be undone once the error in bottleneck is fixed and released, to avoid platform specificity.
- [x] closes #15453
- [x] tests added / passed
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