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} | 2 | 2016-06-08T20:03:40Z | 2017-02-06T00:10:54Z | 2016-06-08T20:32:54Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
a = pd.DataFrame({'numbers': np.arange(10), 'categories': list('abcabcabcd')})
a['categories'] = a['categories'].astype('category')
b = pd.DataFrame({'numbers': np.arange(10)})
print a.dtypes
print a.categories.cat.categories
print
merged = pd.merge(a, b, left_index=True, right_index=True)
print merged.dtypes
print merged['categories'].cat.categories
print 'Merge ok!'
print
merged = pd.merge(a, b, on=['numbers'], how='left')
print merged.dtypes
try:
print merged['categories'].cat.categories #crashes
except:
print 'Merge not ok!'
print
```
#### Expected Output
The try block should print "categories" 's categories the same way as above, with:
`Index([u'a', u'b', u'c', u'd'], dtype='object')`
However, the data type is replaced to object/string.
This is not fixed by the v0.18.2 release, which fixes some of the merge issues where int's would get casted to floats when merging.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.23.3.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 20.3.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: None
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} | 3 | 2016-06-08T22:39:31Z | 2016-06-09T11:35:14Z | 2016-06-08T23:54:15Z | NONE | null | when there are lots of columns of index levels it is more convenient to specify the column or index level that should be merged rather than all of the other columns or index levels
for example a multiindex dataframe
```
df.mean(level=['year', 'month', 'day', 'hour', 'min', 'trial'])
```
can be replaced with
```
df.mean(level='rep', reverse_level=True)
```
or for a flat index dataframe (where the data is in a column named 'result')
```
df.groupby(['year', 'month', 'day', 'hour', 'min', 'trial']).mean()
```
can be replaced with
```
df.groupby(['rep', 'result'], reverse=True)
```
http://stackoverflow.com/questions/16808682/is-there-in-pandas-operation-complementary-opposite-to-groupby
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} | 12 | 2016-06-08T23:31:14Z | 2016-08-06T22:54:20Z | 2016-08-06T22:53:22Z | CONTRIBUTOR | null | Closes #10153 (at least partly)
Adds the ability to directly parse a `Categorical` through the `dtype` parameter to `read_csv`. Currently just uses whatever is there as the categories, a possible enhancement would be to allow and enforce user-specified categories, through not quite sure what the api would be.
This only parses string categories - originally I had an impl that did type inference on the categories, but it added a lot of complication without much benefit, now the recommendation in the docs is to convert after parsing.
Here's an example timing. For reasonably sparse data, a slightly worse than 2x speedup is what I'm typically seeing, along with much better memory usage.
``` python
group1 = ['aaaaa', 'bbbbb', 'cccccc', 'ddddddd', 'eeeeeeee']
df = pd.DataFrame({'a': np.random.choice(group1, 10000000).astype('object'),
'b': np.random.choice(group1, 10000000).astype('object'),
'c': np.random.choice(group1, 10000000).astype('object')})
df.to_csv('strings.csv', index=False)
In [14]: %timeit pd.read_csv('strings.csv').apply(pd.Categorical)
1 loops, best of 3: 6.66 s per loop
In [13]: %timeit pd.read_csv('strings.csv', dtype='category')
1 loop, best of 3: 3.68 s per loop
```
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} | 4 | 2016-06-09T00:03:39Z | 2018-03-25T23:55:22Z | 2018-03-25T23:55:22Z | NONE | null | When a DataFrame column is constructed from timezone-aware datetime objects, its `values` attribute returns a `pandas.DatetimeIndex` instead of a 2D numpy array. This is problematic because the datetime index does not support all operations that a numpy array does.
#### Code Sample, a copy-pastable example if possible
``` python
import datetime
import dateutil
import pandas
import numpy as np
df = pandas.DataFrame()
df['Time'] = [datetime.datetime(2015,1,1,tzinfo=dateutil.tz.tzutc())]
df.dropna(axis=0) # raises ValueError: 'axis' entry is out of bounds
```
Also, `print df.values` returns `DatetimeIndex(['2015-01-01'], dtype='datetime64[ns, UTC]', freq=None)`.
#### Expected Output
The `df.dropna` call should be a no-op.
Compare this to the case when constructed using `df['Time'] = [datetime.datetime(2015,1,1)]`. In that case, `df.dropna` works as expected, and `df.values` is `array([['2014-12-31T16:00:00.000000000-0800']], dtype='datetime64[ns]')`.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.0.2
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.1
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13408 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13408/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13408/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13408/events | https://github.com/pandas-dev/pandas/issues/13408 | 159,313,816 | MDU6SXNzdWUxNTkzMTM4MTY= | 13,408 | fillna() is incomplete after performing resample() | {
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} | 4 | 2016-06-09T01:54:59Z | 2016-06-09T11:54:26Z | 2016-06-09T11:48:47Z | NONE | null | #### Code Sample
Make data
```
>>> dates = pd.date_range('01-01-2015','01-07-2015', freq='B')
>>> df = pd.DataFrame({'a':[1,2,np.nan,4,5],'b':[6,7,8,9,np.nan]}, dates)
>>> print(df)
a b
2015-01-01 1.0 6.0
2015-01-02 2.0 7.0
2015-01-05 NaN 8.0
2015-01-06 4.0 9.0
2015-01-07 5.0 NaN
```
Then re-sample
```
>>> df = df.resample('D').fillna(method='ffill')
>>> print(df)
a b
2015-01-01 1.0 6.0
2015-01-02 2.0 7.0
2015-01-03 2.0 7.0
2015-01-04 2.0 7.0
2015-01-05 NaN 8.0
2015-01-06 4.0 9.0
2015-01-07 5.0 NaN
```
#### Expected Output
`fillna()` should have filled all of the `NaNs`, but instead it seems to miss those that existed before `resample()` was called.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 15 Stepping 11, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 22.0.5
Cython: 0.23.1
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 3.2.1
sphinx: None
patsy: 0.3.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: None
pandas_datareader: None
```
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} | 1 | 2016-06-09T12:16:22Z | 2016-06-09T12:22:08Z | 2016-06-09T12:21:49Z | NONE | null | The [Advanced reindexing and alignment](http://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced-reindexing-and-alignment) section of the documentation describes a method for filling data based on a partial match of indexes.
In this example, one level of a multiindex is dropped and data is copied based on a match of the remaining levels. This example only works because the original multiindex only had 2 levels so that when one level was dropped, the remaining index is no longer a multiindex. Unfortunately in practice you might end up wanting to align or reindex two multiindex based on the remaining levels (as I do), but it seems this behavior is not supported..
```
midx = pd.MultiIndex(levels=[['zero', 'one'], ['x','y'], ['a', 'b']], labels=[[1]*4+[0]*4,[1,1,0,0]*2,[1,0]*4], names=['first','second','third'])
df = pd.DataFrame(np.random.randn(8,2), index=midx)
df2 = df.mean(level=[k for k in df.index.names if k != 'third'])
df2.reindex(df.index, level=[0, 1])
```
the result of the last command is `TypeError: Join on level between two MultiIndex objects is ambiguous`
Particularly with named index levels, it not clear how this is ambiguous..
When you attempt an `align` where level is specified you end up with the same `TypeError` and when you attempt
```
df.align(df2)
```
you get a `NotImplementedError`
Based on the example in the documentation you would expect that dropping a multiindex level then reindexing in order to duplicate some data across the dropped index level would work. It is surprising that this doesn't work. Is there a way to implement this functionality?
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} | 10 | 2016-06-09T12:49:14Z | 2021-05-01T22:10:01Z | null | CONTRIBUTOR | null | xref #13361
- [x] support union w `Series/CategoricalIndex` as well as `Categorical` #14199
- [x] add `ignore_order` to ignore the raising on an ordered Categorical (and just have it work) #15219
- [ ] do we want to put this in the `pd` namespace (or change its name). Consider `Categorical.from_union(...)`
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} | 16 | 2016-06-09T13:18:35Z | 2016-06-17T17:10:32Z | 2016-06-17T16:39:07Z | MEMBER | null | 1) Add significant testing to quoting in `read_csv`
2) Fix bug in C engine in which a NULL `quotechar` would raise even though `quoting=csv.QUOTE_NONE`.
3) Fix bug in C engine in which `quoting=csv.QUOTE_NONNUMERIC` wouldn't cause non-quoted fields to be cast to `float`. Relevant definitions can be found in the Python docs <a href="https://docs.python.org/3.5/library/csv.html">here</a>.
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} | 5 | 2016-06-09T14:18:36Z | 2017-03-13T13:16:45Z | 2016-12-06T11:35:03Z | CONTRIBUTOR | null | The function below has been incorrectly implemented. If the frame has an index with duplicated values, you will get a result with more than `n` rows and not properly sorted. So `nsmallest` and `nlargest` for DataFrame doesn't return a correct frame in this particular case.
```
def _nsorted(self, columns, n, method, keep):
if not com.is_list_like(columns):
columns = [columns]
columns = list(columns)
ser = getattr(self[columns[0]], method)(n, keep=keep)
ascending = dict(nlargest=False, nsmallest=True)[method]
return self.loc[ser.index].sort_values(columns, ascending=ascending,
kind='mergesort')
```
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} | 3 | 2016-06-09T18:15:56Z | 2016-06-09T19:43:58Z | 2016-06-09T19:16:27Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
… for windows and linux
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| null | 5 | 2016-06-09T20:01:07Z | 2016-06-09T22:55:25Z | 2016-06-09T22:08:35Z | NONE | null | Right now I have to do the following:
``` python
ix=None
for ixi in [res[col].str.contains('string') for col in res.columns]:
if ix is not None:
ix = ix | ixi
else:
ix = ixi
res[ix]
```
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/33"
} | 2 | 2016-06-09T22:06:00Z | 2016-06-10T09:48:37Z | 2016-06-10T09:48:32Z | CONTRIBUTOR | null | I previously posted this as a question (not knowing it was a bug) here: http://stackoverflow.com/questions/37732403/pandas-dataframe-from-multiindex-and-numpy-structured-array-recarray
First I create a two-level MultiIndex:
```
import numpy as np
import pandas as pd
ind = pd.MultiIndex.from_product([('X','Y'), ('a','b')])
```
I can use it like this:
```
pd.DataFrame(np.zeros((3,4)), columns=ind)
```
Which gives:
```
X Y
a b a b
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
```
But now I'm trying to do this:
```
dtype = [('Xa','f8'), ('Xb','i4'), ('Ya','f8'), ('Yb','i4')]
pd.DataFrame(np.zeros(3, dtype), columns=ind)
```
But that gives me an empty DataFrame!
```
Empty DataFrame
Columns: [(X, a), (X, b), (Y, a), (Y, b)]
Index: []
```
I expected it to do the same thing as this:
```
df = pd.DataFrame(np.zeros(3, dtype))
df.columns = ind
df
```
Which is:
```
X Y
a b a b
0 0.0 0 0.0 0
1 0.0 0 0.0 0
2 0.0 0 0.0 0
```
---
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-86-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
pip: 8.1.1
setuptools: 20.7.0
numpy: 1.10.0
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.2.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.4.3
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} | 5 | 2016-06-09T22:13:27Z | 2021-05-01T22:12:08Z | null | CONTRIBUTOR | null | xref #13992
Some of the issues and inconsistencies I noticed.
(Apologies for a bit lengthy input.)
##### Settings:
``` python
df = pd.DataFrame({"A": [2, 1, 2, 2],
"B": [3, 3, 4, 4],
"C1": pd.Categorical([5, 6, 6, 6], [5, 6, 7]),
"C2": pd.Categorical(list("aaab"), list("abc")),
"D": pd.date_range('2011-11-11', periods=4),
"E": [10, 20, 30, 40]})
df
Out[7]:
A B C1 C2 D E
0 2 3 5 a 2011-11-11 10
1 1 3 6 a 2011-11-12 20
2 2 4 6 a 2011-11-13 30
3 2 4 6 b 2011-11-14 40
df.dtypes
Out[8]:
A int64
B int64
C1 category
C2 category
D datetime64[ns]
E int64
dtype: object
df_ac = df[['A', 'C1', 'C2']]
df_ad = df[['A', 'D']]
df_abc = df[['A', 'B', 'C1', 'C2']]
df_abd = df[['A', 'B', 'D']]
df_abe = df[['A', 'B', 'E']]
df_acd = df[['A', 'C1', 'C2', 'D']]
df_abcd = df[['A', 'B', 'C1', 'C2', 'D']]
```
Usually, non-numeric types are skipped in aggregation functions:
``` python
df.groupby('A').mean()
Out[16]:
B E
A
1 3.000000 20.000000
2 3.666667 26.666667
df.groupby('A').sum()
Out[17]:
B E
A
1 3 20
2 11 80
df.groupby(['A', 'B']).mean()
Out[18]:
E
A B
1 3 20
2 3 10
4 35
df.groupby(['A', 'B'], as_index=False).sum()
Out[19]:
A B E
0 1 3 20
1 2 3 10
2 2 4 70
```
#### Issues:
But if there are no numeric types, an output varies. (I use here subframes `df_xxx` of the original data frame.)
``` python
# .mean() always raises.
df_ac.groupby('A').mean() # (1)
pandas.core.base.DataError: No numeric types to aggregate
# .sum() adds categoricals
df_ac.groupby('A').sum() # (2)
Out[21]:
C1 C2
A
1 6 a
2 17 aab
# and tries to do something with datetimes.
df_ad.groupby('A').sum() # (3)
Out[22]:
D
A
1 2011-11-12
2 NaT
df_acd.groupby('A').sum() # (4)
Out[23]:
C1 C2 D
A
1 6.0 a 2011-11-12 00:00:00
2 NaN NaN NaN
# It's even worse for multiple groupers.
df_abcd.groupby(['A', 'B']).sum() # (5)
Out[24]:
A B
1 3 C1 6
C2 a
D 2011-11-12 00:00:00
2 3 C1 5
C2 a
D 2011-11-11 00:00:00
dtype: object
df_abcd.groupby(['A', 'B'], as_index=False).sum() # (6)
Out[25]:
0 A 1
B 3
C1 6
C2 a
D 2011-11-12 00:00:00
1 A 2
B 3
C1 5
C2 a
D 2011-11-11 00:00:00
2 A 4
B 8
dtype: object
# Additionally, the index is not reset here and the grouper columns are transformed.
df_abc.groupby(['A', 'B'], as_index=False).sum() # (7)
Out[26]:
A B C1 C2
A B
1 3 1 3 6 a
2 3 2 3 5 a
4 4 8 12 ab
# Sometimes an empty data frame is returned (a reasonable output):
df_abd.groupby(['A', 'B']).sum() # (8)
Out[27]:
Empty DataFrame
Columns: []
Index: []
# but not always:
df_abd.groupby(['A', 'B'], as_index=False).sum() # (9)
Out[28]:
0 A 1
B 3
D 2011-11-12 00:00:00
1 A 2
B 3
D 2011-11-11 00:00:00
2 A 4
B 8
dtype: object
df_abcd.groupby(['A', 'B']).sum() # (10)
Out[29]:
A B
1 3 C1 6
C2 a
D 2011-11-12 00:00:00
2 3 C1 5
C2 a
D 2011-11-11 00:00:00
dtype: object
```
##### Some other issues:
Multiple groupers with a categrical one (it's already addressed in #13204).
``` python
df.groupby(['A', 'C1'], as_index=False).sum() # (11)
Out[36]:
A C1 B E
A C1
1 5 NaN NaN NaN NaN
6 NaN NaN NaN NaN
7 NaN NaN NaN NaN
2 5 NaN NaN NaN NaN
6 NaN NaN NaN NaN
7 NaN NaN NaN NaN
```
`apply`
``` python
# mean() as expected:
df.groupby(['A', 'B']).apply(lambda x: np.mean(x))
Out[30]:
A B E
A B
1 3 1.0 3.0 20.0
2 3 2.0 3.0 10.0
4 2.0 4.0 35.0
# sum() not:
df.groupby(['A', 'B']).apply(lambda x: np.sum(x)) # (12)
Out[31]:
A B
1 3 A 1
B 3
C1 6
C2 a
D 2011-11-12 00:00:00
E 20
2 3 A 2
B 3
C1 5
C2 a
D 2011-11-11 00:00:00
E 10
4 A 4
B 8
E 70
dtype: object
```
`transform`
``` python
# it's ok with numeric types only
df_abe.groupby(['A', 'B']).transform('sum')
Out[32]:
E
0 10
1 20
2 70
3 70
# Doesn't transform anything with mixed types:
df.groupby(['A', 'B']).transform('sum') # (13)
Out[33]:
C1 C2 D E
0 5 a 2011-11-11 10
1 6 a 2011-11-12 20
2 6 a 2011-11-13 30
3 6 b 2011-11-14 40
# but someimtes transforms categoricals
df_abc.groupby(['A', 'B']).transform('sum') # (14)
Out[34]:
C1 C2
0 5 a
1 6 a
2 12 ab
3 12 ab
# and sometimes not:
df_abc.groupby(['A', 'B'], as_index=False).transform('sum') # (15)
Out[35]:
C1 C2
0 5 a
1 6 a
2 6 a
3 6 b
```
#### What should be the expected output?
Some ideas for aggregation (with `sum()`) when there's no numeric types (1)-(10):
1. always raise (as `.mean()` does)
2. return an empty DataFrame as in (8)
(but (a) should `.mean()` do the same? (b) should groupers be returned when `as_index=False`?)
3. ???
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
machine: x86_64
pandas: 0.18.1
Cython: 0.24
numpy: 1.11.0
IPython: 4.0.1
```
Exactly the same output with:
```
pandas: 0.18.1+119.gd405bf2
```
##### Just a thought about a possible approach:
A call to `_python_apply_general` inside `_python_agg_general`
https://github.com/pydata/pandas/blob/master/pandas/core/groupby.py#L795
seems to trigger some of the issues. (It doesn't exclude grouper columns when `as_index=False`, treats categoricals as their underlying types, and possibly just replicates some actions from earlier part of `_python_agg_general`.)
The following change affects (and possibly solves, at least partially)
issues (5), (6), (7), (9), (10), (14):
``` python
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index bea62e9..0d401e2 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -791,10 +791,7 @@ class _GroupBy(PandasObject, SelectionMixin):
except TypeError:
continue
- if len(output) == 0:
- return self._python_apply_general(f)
-
- if self.grouper._filter_empty_groups:
+ if len(output) > 0 and self.grouper._filter_empty_groups:
mask = counts.ravel() > 0
for name, result in compat.iteritems(output):
```
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| null | 1 | 2016-06-09T22:50:21Z | 2016-06-09T22:54:44Z | 2016-06-09T22:54:44Z | NONE | null | Has anyone seen any method how to detect index column(s) in dataframe based on uniqueness of rows?
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} | 4 | 2016-06-10T05:22:02Z | 2016-06-16T12:32:47Z | 2016-06-16T12:32:24Z | MEMBER | null | Closes #12922: "bug" traced to #12194 via bisection, where the float formatting was unconditionally casting everything to string.
I say "bug" (with quotations) because the changes to `get_result_as_array` did **correctly** cast everything to string as per the documentation (i.e. it had inadvertently patched a bug itself even though it was just a cleaning PR). However, the changes had overlooked the impact it would have on `to_csv`.
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} | 8 | 2016-06-10T06:05:26Z | 2016-07-10T21:50:54Z | 2016-07-10T21:50:36Z | MEMBER | null | Title is self-explanatory.
Internal code and #6581 indicate that this was long overdue.
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} | 4 | 2016-06-10T10:17:09Z | 2019-08-07T13:57:33Z | 2019-08-07T13:57:33Z | NONE | null | While attempting to use `pd.qcut` (which returned a Categorical) to bin some data in groups for plotting, I encountered the following error. The idea is to group a DataFrame by columns (`axis=1`) using a Categorical.
#### Minimal breaking example
```
>>> import pandas
>>> df = pandas.DataFrame({'a':[1,2,3,4], 'b':[-1,-2,-3,-4], 'c':[5,6,7,8]})
>>> df
a b c
0 1 -1 5
1 2 -2 6
2 3 -3 7
3 4 -4 8
>>> grp = pandas.Categorical([1,0,1])
>>> df.groupby(grp, axis=1).mean()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ntawolf/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 3778, in groupby
**kwargs)
File "/home/ntawolf/anaconda3/lib/python3.5/site-packages/pandas/core/groupby.py", line 1427, in groupby
return klass(obj, by, **kwds)
File "/home/ntawolf/anaconda3/lib/python3.5/site-packages/pandas/core/groupby.py", line 354, in __init__
mutated=self.mutated)
File "/home/ntawolf/anaconda3/lib/python3.5/site-packages/pandas/core/groupby.py", line 2390, in _get_grouper
raise ValueError("Categorical dtype grouper must "
ValueError: Categorical dtype grouper must have len(grouper) == len(data)
```
#### Expected behaviour
Same as
```
>>> df.T.groupby(grp, axis=0).mean().T
0 1
0 -1 3
1 -2 4
2 -3 5
3 -4 6
```
So, it works as expected when doubly transposed. This makes it appear as a bug to me.
#### Proposed solution
In [`if is_categorical_dtype(gpr) and len(gpr) != len(obj):`](https://github.com/pydata/pandas/blob/master/pandas/core/groupby.py#L2406), change `len(obj)` to `obj.shape[axis]`. This assumes that `len(obj) == obj.shape[0]` for all `obj`.
So, supposing you agree that this is a bug, should a test be put in [`test_groupby_categorical`](https://github.com/pydata/pandas/blob/master/pandas/tests/test_groupby.py#L3968)?
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-59-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 22.0.5
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.1
xlsxwriter: 0.8.9
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
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} | 17 | 2016-06-10T13:39:38Z | 2016-06-10T20:13:30Z | 2016-06-10T18:10:02Z | CONTRIBUTOR | null | I filed #13415 in which it was said that `DataFrame(recarray, columns=MultiIndex)` does reindexing and so only selects matching columns to be in the resultant frame. I can see how this might be a backward compatibility constraint. However, I have discovered a similar but different case which still seems broken:
```
arr = np.zeros(3, [('q', [('x',float), ('y',int)])])
ind = pd.MultiIndex.from_tuples([('q','x'),('q','y')])
pd.DataFrame(arr, columns=ind)
```
This creates a 3x2 array of zeros, but results in a 3x2 DataFrame of NaNs. Note that the column names basically match: the NumPy array has a top-level `q` with subitems `x` and `y`, and so does the `MultiIndex`. If the top-level name in the MultiIndex is changed to something other than `q` it results in an empty DataFrame, meaning that there is some recognized correspondence between the input data and the requested columns. But the data is lost nevertheless, putting NaNs where should be zeros.
Either the columns are considered non-matching, in which case the result should be an empty DataFrame, or they do match, in which case the result should be a DataFrame with contents from the input array.
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} | 1 | 2016-06-10T14:44:13Z | 2016-06-10T15:28:04Z | 2016-06-10T14:50:23Z | NONE | null | I have a dataframe like this:
```
seasonname Core-no season Fall/Winter Spring/Summer Summery Core True Summer
date_utc
2015-06-01 199.7825 111.1363 180.3559 166.6217 66.3297
2015-06-02 201.9999 111.9228 158.2303 153.7611 54.7868
2015-06-03 169.8468 108.4291 137.1497 118.4648 55.1780
2015-06-04 164.2676 108.7186 160.1299 140.5675 46.6883
2015-06-05 144.8806 71.8517 113.8878 111.8050 31.0500
2015-06-06 122.1885 76.7588 111.0260 92.0495 26.9652
2015-06-07 79.8287 32.7678 41.0086 54.5739 19.5470
2015-06-08 193.2663 125.5355 172.7237 145.4507 47.1553
2015-06-09 175.1136 89.9360 164.2935 144.5485 52.4961
2015-06-10 167.5729 88.9686 125.5094 153.6020 46.3585
```
When I apply the ggplot pandas theme, the graph loses its legend and axes and otherwise looks distorted. Without the theme, it displays fine.
`df['2015-06':].plot(figsize=(12,10))`
**Problem**: `pd.options.display.mpl_style = 'default'` creates a distorted graph.
**Solution**: `plt.style.use('ggplot')` works
You can find more information from the StackOverflow question [here](http://stackoverflow.com/questions/37733968/odd-pandas-line-chart-using-ggplot-theme).
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} | 1 | 2016-06-10T19:22:40Z | 2016-06-11T12:14:21Z | 2016-06-11T12:14:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Something goes wrong when I try to set a NaN value using the iloc syntax. Instead of just the entry being set, the entire row gets set.
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(1,5).reshape(2,2), columns=["A"]*2)
# this works like expected
df.iloc[0,0] = -1
df
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>A</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>-1</td>
<td>2</td>
</tr>
<tr>
<th>1</th>
<td>3</td>
<td>4</td>
</tr>
</tbody>
</table>
</div>
``` python
# this sets the entire row to NaN
df.iloc[1,0] = np.nan
df
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>A</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>-1.0</td>
<td>2.0</td>
</tr>
<tr>
<th>1</th>
<td>NaN</td>
<td>NaN</td>
</tr>
</tbody>
</table>
</div>
``` python
# It only seems to be an issue when indexing the first repeated column
df.iloc[0,1] = np.nan
df
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>A</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>-1.0</td>
<td>NaN</td>
</tr>
<tr>
<th>1</th>
<td>NaN</td>
<td>NaN</td>
</tr>
</tbody>
</table>
</div>
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 21.2.2
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
```
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} | 12 | 2016-06-10T20:16:26Z | 2016-10-21T18:57:28Z | 2016-10-21T18:57:25Z | NONE | null | line 2549 of parsers.py should be
``` python
self.delimiter = b'\r\n' + bytes(delimiter, 'utf8') if delimiter else b'\n\r\t '
```
instead of
``` python
self.delimiter = '\r\n' + delimiter if delimiter else '\n\r\t '
```
otherwise an exception is going to be raised on line 2605 of the same module, as it expects `delimiter` to be `bytes` not `str`
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-12-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: pt_BR.UTF-8
pandas: 0.18.1
nose: 1.2.1
pip: 8.1.2
setuptools: 22.0.5
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.2.0
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.7.2.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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} | 95 | 2016-06-10T23:53:24Z | 2016-07-10T21:15:11Z | 2016-07-10T21:12:56Z | MEMBER | null | Title is self-explanatory. Closes #13352.
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- [x] whatsnew entry
follow up for #13080 to use `_coerce_indexer_dtype`.
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} | 6 | 2016-06-11T19:06:37Z | 2021-05-17T15:17:29Z | 2021-05-17T15:17:29Z | NONE | null | #### Code Sample, a copy-pastable example if possible
> > > df = pd.DataFrame({"a" : [0,1,2], "b" : [1,2,3]})
> > > df[["a", "a"]].apply(lambda x: x[0] + x[1], axis = 1)
> > > #### Expected Output
> > >
> > > 0 0
> > > 1 2
> > > 2 4
> > > dtype: int64
> > > #### output of `pd.show_versions()`
> > >
> > > Traceback (most recent call last):
> > > File "C:\Users\Alexander\Anaconda3\lib\site-packages\pandas\indexes\base.py", line 1980, in get_value
> > > tz=getattr(series.dtype, 'tz', None))
> > > File "pandas\index.pyx", line 103, in pandas.index.IndexEngine.get_value (pandas\index.c:3332)
> > > File "pandas\index.pyx", line 111, in pandas.index.IndexEngine.get_value (pandas\index.c:3035)
> > > File "pandas\index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas\index.c:3955)
> > > File "pandas\index.pyx", line 169, in pandas.index.IndexEngine._get_loc_duplicates (pandas\index.c:4236)
> > > TypeError: unorderable types: str() > int()
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\Alexander\Anaconda3\lib\site-packages\pandas\core\frame.py", line 4061, in apply
return self._apply_standard(f, axis, reduce=reduce)
File "C:\Users\Alexander\Anaconda3\lib\site-packages\pandas\core\frame.py", line 4157, in _apply_standard
results[i] = func(v)
File "<stdin>", line 1, in <lambda>
File "C:\Users\Alexander\Anaconda3\lib\site-packages\pandas\core\series.py", line 583, in __getitem__
result = self.index.get_value(self, key)
File "C:\Users\Alexander\Anaconda3\lib\site-packages\pandas\indexes\base.py", line 2000, in get_value
raise IndexError(key)
IndexError: (0, 'occurred at index 0')
This is the error I get when running on Python 3.5.1/Pandas 0.18.1 on Windows. I get the expected output on Linux running Python 2.7.11/Pandas 0.18.1.
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https://api.github.com/repos/pandas-dev/pandas/issues/13428 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13428/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13428/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13428/events | https://github.com/pandas-dev/pandas/pull/13428 | 159,806,832 | MDExOlB1bGxSZXF1ZXN0NzM0ODU3OTk= | 13,428 | BUG: in _nsorted for frame with duplicated values index | {
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| null | 10 | 2016-06-12T04:11:50Z | 2016-10-28T15:59:24Z | 2016-10-28T15:59:24Z | CONTRIBUTOR | null | - [x] closes #13412
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 7 | 2016-06-12T11:42:51Z | 2021-05-15T03:56:33Z | 2021-05-15T03:56:33Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
`DataFrame.loc` supports partial string selection when it has `DatetimeIndex`. But it can't work if index is `PeriodIndex`.
```
ddf = pd.DataFrame(np.random.randn(10, 3),
index=pd.date_range('2011-01-01', freq='H', periods=10))
ddf.loc['2011-01-01']
# OK, returns DataFrame using partial string selection
pdf = pd.DataFrame(np.random.randn(10, 3),
index=pd.period_range('2011-01-01', freq='H', periods=10))
pdf.loc['2011-01-01']
#0 0.114496
#1 -1.900340
#2 0.870142
# Name: 2011-01-01 00:00, dtype: float64
```
#### Expected Output
Should return `DataFrame` using partial string selection, like `__getitem__`.
#### output of `pd.show_versions()`
current master
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} | 3 | 2016-06-12T12:18:24Z | 2016-06-14T21:26:17Z | 2016-06-14T21:26:02Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Related to #12759. `PeirodIndex` can be created from list/array which contains `pd.NaT`.
currently it raises:
```
pd.PeriodIndex([pd.NaT])
# ValueError: freq not specified and cannot be inferred from first element
pd.PeriodIndex([pd.Period('2011-01', freq='M'), pd.NaT])
# AttributeError: 'NaTType' object has no attribute 'ordinal'
```
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} | 12 | 2016-06-12T14:13:48Z | 2017-04-07T15:18:25Z | 2017-04-07T15:18:25Z | CONTRIBUTOR | null | Inspired by some bug reports around multiindex sortedness (http://stackoverflow.com/questions/31427466/ensuring-lexicographical-sort-in-pandas-multiindex, #10651, #9212), I found that `sort_index()` sometimes can't make a multiindex ready for slicing, but `sort_index(level=0)` (so does `sortlevel()`) can.
``` python
In [119]: pd.__version__
Out[119]: u'0.18.1'
In [120]: df = pd.DataFrame({'col1': ['b','d','b','a'], 'col2': [3,1,1,2], 'data':['one','two','three','four']})
In [121]: df2 = df.set_index(['col1','col2'])
In [122]: df2.index.set_levels(['b','d','a'], level='col1', inplace=True)
In [123]: df2.index.set_labels([0,1,0,2], level='col1', inplace=True)
In [124]: df2.sortlevel()
Out[124]:
data
col1 col2
b 1 three
3 one
d 1 two
a 2 four
In [125]: df2.sort_index()
Out[125]:
data
col1 col2
a 2 four
b 1 three
3 one
d 1 two
In [126]: df2.sort_index(level=0)
Out[126]:
data
col1 col2
b 1 three
3 one
d 1 two
a 2 four
```
While `df2.sort_index()` does give a visually lexicographically sorted output, it DOES NOT support slicing.
``` python
df2.sort_index().loc['b':'d']
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
# a lot of lines omitted here.
/Users/yimengzh/miniconda/envs/cafferc3/lib/python2.7/site-packages/pandas/indexes/multi.py in _partial_tup_index(self, tup, side)
1488 raise KeyError('Key length (%d) was greater than MultiIndex'
1489 ' lexsort depth (%d)' %
-> 1490 (len(tup), self.lexsort_depth))
1491
1492 n = len(tup)
KeyError: 'Key length (1) was greater than MultiIndex lexsort depth (0)'
```
So I have two questions.
1. Is this the intended behavior? I thought `level=0` and `level=None` are synonyms to me, but they are not. Looking at the code, https://github.com/pydata/pandas/blob/4de83d25d751d8ca102867b2d46a5547c01d7248/pandas/core/frame.py#L3245-L3247 indeed there's a special processing when `level` is not `None`.
2. What does "lexicographically sorted" mean? I think it should mean sorted in terms of levels, not labels. Is this what "lexicographically sorted" means in the doc for Advanced Indexing? If this is true, then I think `make_index(level=0)` is correct, yet `make_index()` is not.
Thanks.
#### output of `pd.show_versions()`
``` python
In [130]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.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: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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} | 17 | 2016-06-13T00:09:39Z | 2021-05-17T15:17:29Z | 2021-05-17T15:17:29Z | CONTRIBUTOR | null | Issues:
- [x] `Index.difference` and `symmetric_difference` raise for mixed types (solved by #13514)
- [x] `Index._get_consensus_name` incorrectly returns an empty index (solved by #13514)
- [ ] bug in `Index.union` for non-unique indexes (see [comment](#issuecomment-226665776))
- [ ] bug in `Index.intersection` for non-unique indexes (same [comment](#issuecomment-226665776))
- [ ] inconsistent sorting in of mixed-int Indexes in python2 (see [comment](#issuecomment-233520619))
---
### `Index.difference` and `symmetric_difference` raise for mixed types
#### Code Sample, a copy-pastable example if possible
``` python
idx1 = pd.Index([0, 1, 'A', 'B'])
idx2 = pd.Index([0, 2, 'A', 'C'])
idx1.difference(idx2)
...
File "/usr/local/lib64/python3.5/site-packages/pandas/indexes/base.py", line 1861, in difference
theDiff = sorted(set(self) - set(other))
TypeError: unorderable types: str() < int()
idx1.symmetric_difference(idx2)
...
File "/usr/local/lib64/python3.5/site-packages/pandas/indexes/base.py", line 1861, in difference
theDiff = sorted(set(self) - set(other))
TypeError: unorderable types: str() < int()
```
But `union` and `intersection` work:
``` python
idx1.union(idx2)
Out[14]: Index([0, 1, 'A', 'B', 2, 'C'], dtype='object')
idx1.intersection(idx2)
Out[15]: Index([0, 'A'], dtype='object')
```
#### Expected Output
``` python
idx1.difference(idx2)
Out[]: Index([1, 'B'], dtype='object')
idx1.symmetric_difference(idx2)
Out[]: Index([1, 'B', 2, 'C'], dtype='object')
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: 4a6621fcaa9a3b98172b90a69de574ec94b108df
python: 3.5.1.final.0
python-bits: 64
OS: Linux
machine: x86_64
byteorder: little
pandas: 0.18.1
...
```
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} | 7 | 2016-06-13T12:02:06Z | 2021-05-01T22:17:05Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
[data.zip](https://github.com/pydata/pandas/files/311709/data.zip)
With the attached data (0.18.1 msgpack format), read the contained data frame and inspect the dtypes:
```
df = pd.read_msgpack('path/to/test.msg')
print df.dtypes
phases end datetime64[ns]
name object
start datetime64[ns]
parameters scanFrequency float64
tideFile object
dtype: object
```
After assigning a column group to itself, the dtypes of this column changes:
```
df['parameters'] = df['parameters']
print df.dtypes
phases end datetime64[ns]
name object
start datetime64[ns]
parameters scanFrequency object
tideFile object
dtype: object
```
#### Expected Output
I would expect that dtypes are preserved. Otherwise I have a hard time of manually recreating them.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.18.1-tf1-ll
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.7
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
```
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} | 2 | 2016-06-13T13:24:04Z | 2017-03-29T23:28:23Z | 2017-03-29T23:28:23Z | NONE | null | The `read_html()` function seems to treat every `<th>` in a table as a column, even if they occur in separate `<tr>`s. This means that it breaks even on simple tables generated by pandas' `to_html()` function.
#### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame(
columns=["Name", "Age", "Party"],
data = [("Hillary", 68, "D"), ("Bernie", 74, "D"), ("Donald", 69, "R")])
df = df.set_index("Name")
html = df.to_html()
df2 = pd.read_html(html)[0]
print df2
```
This is the value of `html`, generated by the `to_html()` function on the original data frame:
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Age</th>
<th>Party</th>
</tr>
<tr>
<th>Name</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>Hillary</th>
<td>68</td>
<td>D</td>
</tr>
<tr>
<th>Bernie</th>
<td>74</td>
<td>D</td>
</tr>
<tr>
<th>Donald</th>
<td>69</td>
<td>R</td>
</tr>
</tbody>
</table>
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Age</th>
<th>Party</th>
</tr>
<tr>
<th>Name</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>Hillary</th>
<td>68</td>
<td>D</td>
</tr>
...
</tbody>
</table>
```
And this is the printed output of the newly-parsed dataframe `df2`:
```
Unnamed: 0 Age Party Name Unnamed: 4 Unnamed: 5
0 Hillary 68 D NaN NaN NaN
1 Bernie 74 D NaN NaN NaN
2 Donald 69 R NaN NaN NaN
```
What happens is that the `to_html()` function produces an html table with two header rows, one for the column names and one with the index name. However the `read_html()` parser interprets each individual `th` cell as an expected column, resulting in twice the number of columns. Even worse, this produces a column with the same name as the original index but without any data.
#### Expected Output
The `read_html` parser could either treat the multi-row header fully correctly:
```
Age Party
Name
Hillary 68 D
Bernie 74 D
Donald 69 R
```
Or it could just ignore any rows after the first one:
```
Unnamed: 0 Age Party
0 Hillary 68 D
1 Bernie 74 D
2 Donald 69 R
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_IE.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 21.0.0
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.1
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.1.2
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.3.5
bs4: 4.4.1
html5lib: 1.0b3
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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} | 4 | 2016-06-13T23:28:16Z | 2016-06-18T15:25:37Z | 2016-06-18T15:25:16Z | CONTRIBUTOR | null | - [ ] closes #13354
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2016-06-14T03:31:30Z | 2016-06-15T02:00:54Z | 2016-06-15T01:48:21Z | CONTRIBUTOR | null | closes #13166, closes #13335
Using exactly the approach suggested by @ruoyu0088
significant changes in asv below
```
before after ratio
- 8.88s 78.12ms 0.01 indexing.float_loc.time_float_loc
- 13.11s 78.12ms 0.01 groupby.groupby_float32.time_groupby_sum
```
Factor of 10 smaller benches
```
before after ratio
- 171.88ms 43.29ms 0.25 indexing.float_loc.time_float_loc
- 1.42s 11.23ms 0.01 groupby.groupby_float32.time_groupby_sum
```
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file "setup,py" , line 33 , in <module>
ver =Cython. **version**
atrribute error: 'module' object has no attribute ' **version** '
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} | 4 | 2016-06-14T12:14:00Z | 2016-06-16T21:02:21Z | 2016-06-16T21:02:21Z | CONTRIBUTOR | null | This should raise; `None` is not an allowed option for the replacement.
```
In [2]: s = pd.Series(['a', 'b', None])
In [3]: s
Out[3]:
0 a
1 b
2 None
dtype: object
In [4]: s.str.replace('a', None)
Out[4]:
0 NaN
1 NaN
2 NaN
dtype: float64
```
cc @kwsmith
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| null | 12 | 2016-06-14T13:27:25Z | 2021-05-01T22:17:29Z | null | NONE | null | Situation:
.xlsx file having a column with label and hyperlinks (see screenshot collage)
In Excel, label: Python (that has a hyperlink to i.e.: www.python.org)
#### Code Sample, a copy-pastable example if possible
data = pd.read_excel('Book1.xlsx')
column has just "Python" as content. Hyperlink information is lost.
(see screenshot)
#### Expected Output, Proposal:
Add an argument for example named hyperlinkparser to read_excel()
that offers options: "label", "hyperlink", "both"
label ... just the label (this is how it is handled now)
hyperlink ... just the hyperlink, drop the label
split ... created two separate columns for label and hyperlink each
with example it could look like:
| MyLinks | MyLinks_hyperlink
0 | Python | www.python.org
btw. openpyxl 2.4.+ has this feature now included (was broken in previous versions). Maybe this helps for implementation.
Thank you!
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| null | 14 | 2016-06-14T14:45:52Z | 2016-07-09T14:23:15Z | 2016-07-09T12:55:28Z | MEMBER | null | - [x] closes #13365
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
@jreback take this just as a wild guess of how to fix #13365 . If the approach makes sense, I will add tests, whatsnew and some more docs.
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} | 2 | 2016-06-14T16:02:36Z | 2016-06-21T10:09:36Z | 2016-06-21T10:09:29Z | CONTRIBUTOR | null | - [x] closes #13383
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Added functionality in validate function of Rolling to ensure that window size is non-negative.
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| null | 4 | 2016-06-14T16:13:17Z | 2019-04-10T00:44:30Z | 2019-04-10T00:44:30Z | CONTRIBUTOR | null | Based on a sqlite database [my_database.zip](https://github.com/pydata/pandas/files/314395/my_database.zip) created from the file [source.zip](https://github.com/pydata/pandas/files/314358/source.zip) in a table `my_table`. I get different results when parsing the dates in the method `read_sql_query` between version 0.18.0 and 0.18.1.
The date format looks like this: `"2015-05-01 00:00:00.000000"`
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
from sqlalchemy import create_engine
engine = create_engine('sqlite:///my_database.db')
result = pd.read_sql_query('SELECT * from my_table;', engine, parse_dates={"RDD": 'D'})
```
#### Expected Output (0.18.0)
| RDD | QTY |
| --- | --- |
| 2015-05-01 | 1.0 |
| 2015-05-04 | 1.0 |
| 2013-12-03 | 1.0 |
| 2011-09-02 | 1.0 |
| 2010-09-01 | 20.0 |
#### Current Output (0.18.1)
| RDD | QTY |
| --- | --- |
| 2115-12-30 20:53:38.800017408 | 1.0 |
| 2115-12-30 20:53:38.800017408 | 1.0 |
| 2115-12-30 20:53:38.800017408 | 1.0 |
| 2115-12-30 20:53:38.800017408 | 1.0 |
| 2116-08-08 09:38:37.216342016 | 20.0 |
#### output of `pd.show_versions()`
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 11 | 2016-06-14T18:53:04Z | 2018-12-05T19:06:01Z | 2018-12-05T19:06:01Z | CONTRIBUTOR | null | The boat may have long sailed on this, but just for consideration.
I semi-frequently get the `.levels` and `.labels` of a `MultiIndex` backwards. Maybe it's just me, but I think `labels` is the culprit, because in other pandas contexts, "labels" refer to the actual value of the thing. E.g.
- `.loc` indexes by "labels" (values)
- the values inside of a single row index or columns are the "labels" for those items
So, consistent with `Categorical`, would it make sense for the integer mapping inside a `MultiIndex` to also be called `.codes`?
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} | 1 | 2016-06-14T21:40:00Z | 2016-06-17T00:37:55Z | 2016-06-17T00:37:39Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/commit/8f3229227856af4feedb5894da9ccd4168a4e2a0
https://github.com/pydata/pandas/commit/eefe71e27131bc4848e549e3688ef6700b57b73a (partial revert)
fixing to 4.0.8
seems 4.1 breaks things.
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} | 5 | 2016-06-15T07:37:50Z | 2016-08-16T10:15:30Z | 2016-08-16T10:15:29Z | NONE | null | I read dataframe from hdf5 file and rank it. but It rise to a SIGSEGV.
Maybe it's a numpy or table bug..
#### Code Sample, a copy-pastable example if possible
import pandas as pd
def test_df_ranks(f):
df = pd.read_hdf(f, key="t")
print (df.shape)
print (type(df))
print (df)
s=df.non_current_asset_to_total_asset
#s.rank() # rank() work properly
s.rank(ascending=False) #rank(ascending=False) crash
#### Expected Output
I expected work well, but rise to me SIGSEGV error
the stacks as follows.
#7 OBJECT_compare (ip1=0x47a3ef4b2e420, ip2=0x7f5c5413f128, __NPY_UNUSED_TAGGEDap=0x7f5cd0100760) at numpy/core/src/multiarray/arraytypes.c.src:2753
#8 0x00007f5d0142c50e in npy_aquicksort (vv=vv@entry=0x7f5c5413f060, tosort=tosort@entry=0x7f5c5413cc80, num=num@entry=52, varr=varr@entry=0x7f5cd0100760) at numpy/core/src/npysort/quicksort.c.src:480
#9 0x00007f5d0139a78a in _new_argsortlike (op=op@entry=0x7f5cd0100760, axis=0, argsort=argsort@entry=0x7f5d0142c310 <npy_aquicksort>, argpart=argpart@entry=0x0, kth=kth@entry=0x0, nkth=nkth@entry=0)
at numpy/core/src/multiarray/item_selection.c:1035
#10 0x00007f5d0139dd7b in PyArray_ArgSort (op=op@entry=0x7f5cd0100760, axis=0, which=<optimized out>) at numpy/core/src/multiarray/item_selection.c:1309
#11 0x00007f5d013dd012 in array_argsort (self=0x7f5cd0100760, args=<optimized out>, kwds=<optimized out>) at numpy/core/src/multiarray/methods.c:1278
#12 0x00007f5cf4eef28f in __Pyx_PyObject_Call (func=0x7f5cd1a1acc8, arg=0x7f5d0f900048, kw=0x0) at pandas/algos.c:201388
#13 0x00007f5cf504e006 in __pyx_pf_6pandas_5algos_8rank_1d_generic (__pyx_v_in_arr=__pyx_v_in_arr@entry=0x7f5cd0100620, __pyx_v_retry=1, __pyx_v_ties_method=0x7f5cf6999768, __pyx_v_ascending=0x7f5d0f6bd700 <_Py_FalseStruct>,
__pyx_v_na_option=<optimized out>, __pyx_v_pct=0x7f5d0f6bd700 <_Py_FalseStruct>, __pyx_self=<optimized out>) at pandas/algos.c:14942
#14 0x00007f5cf5050481 in __pyx_pw_6pandas_5algos_9rank_1d_generic (__pyx_self=<optimized out>, __pyx_args=<optimized out>, __pyx_kwds=0x7f5cd8659488) at pandas/algos.c:14439
#15 0x00007f5d0f3b9477 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#16 0x00007f5d0f3b9f3e in PyEval_EvalCodeEx () from /lib64/libpython3.4m.so.1.0
#17 0x00007f5d0f3b7a12 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#18 0x00007f5d0f3b9f3e in PyEval_EvalCodeEx () from /lib64/libpython3.4m.so.1.0
#19 0x00007f5d0f3b7a12 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#20 0x00007f5d0f3b8e40 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#21 0x00007f5d0f3b9f3e in PyEval_EvalCodeEx () from /lib64/libpython3.4m.so.1.0
#22 0x00007f5d0f3b7a12 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#23 0x00007f5d0f3b9f3e in PyEval_EvalCodeEx () from /lib64/libpython3.4m.so.1.0
#24 0x00007f5d0f32a4b3 in function_call () from /lib64/libpython3.4m.so.1.0
#25 0x00007f5d0f301dcc in PyObject_Call () from /lib64/libpython3.4m.so.1.0
#26 0x00007f5d0f3b57c9 in PyEval_EvalFrameEx () from /lib64/libpython3.4m.so.1.0
#### output of `pd.show_versions()`
pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 14.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: 0.6.7.None
psycopg2: None
Jinja2: None
The h5 data in my github
[https://github.com/HaoXJ/codefail/blob/master/data/test.h5](url)
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https://api.github.com/repos/pandas-dev/pandas/issues/13446 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13446/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13446/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13446/events | https://github.com/pandas-dev/pandas/issues/13446 | 160,372,771 | MDU6SXNzdWUxNjAzNzI3NzE= | 13,446 | Behaviour of .sum() inconsistent between Series and DataFrame when summing Decimals | {
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} | 2 | 2016-06-15T09:05:42Z | 2016-06-16T08:09:38Z | 2016-06-15T11:56:45Z | CONTRIBUTOR | null | Using `sum()` on a Series of Decimal datatypes returns a Decimal. However, using `sum()` on a DataFrame of Decimal datatypes returns floats.
I think it would be more consistent for the DataFrame `sum()` to return Decimals.
#### Code Sample
``` python
import pandas as pd
from decimal import Decimal
df = pd.DataFrame({'a':[Decimal('1.0'), Decimal('2.0')],
'b':[Decimal('3.0'), Decimal('4.0')]})
type(df['a'].sum()), type(df.sum()[0])
Out[1]: (decimal.Decimal, numpy.float64)
```
#### Expected Output
``` python
Out[1]: (decimal.Decimal, decimal.Decimal)
```
#### output of `pd.show_versions()`
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: x86
processor: x86 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
```
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| null | 3 | 2016-06-15T10:24:04Z | 2016-06-15T15:34:34Z | 2016-06-15T11:26:06Z | CONTRIBUTOR | null | Trying to print anything containing a V\* (void) type throws in `isnull`:
``` python
>>> import numpy as np
>>> import pandas as pd
>>> arr = np.array([b'foo'], dtype='V')
>>> arr
array([[102 111 111]],
dtype='|V3')
>>> s = pd.Series(arr)
>>> s[0]
[102 111 111]
>>> s
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could
not be safely coerced to any supported types according to the casting rule ''safe''
```
From the looks of it, `pandas.core.common._isnull_ndarraylike` (and maybe some other methods?) should take care of this, e.g. return an array filled with False.
Here's the relevant part of the traceback:
```
pandas/formats/format.py in _format_strings(self)
2057 vals = vals._values
2058
-> 2059 is_float = lib.map_infer(vals, com.is_float) & notnull(vals)
2060 leading_space = is_float.any()
2061
pandas/core/common.py in notnull(obj)
248 pandas.isnull : boolean inverse of pandas.notnull
249 """
--> 250 res = isnull(obj)
251 if lib.isscalar(res):
252 return not res
pandas/core/common.py in isnull(obj)
89 pandas.notnull: boolean inverse of pandas.isnull
90 """
---> 91 return _isnull(obj)
92
93
pandas/core/common.py in _isnull_new(obj)
99 raise NotImplementedError("isnull is not defined for MultiIndex")
100 elif isinstance(obj, (gt.ABCSeries, np.ndarray, pd.Index)):
--> 101 return _isnull_ndarraylike(obj)
102 elif isinstance(obj, gt.ABCGeneric):
103 return obj._constructor(obj._data.isnull(func=isnull))
pandas/core/common.py in _isnull_ndarraylike(obj)
190 result = values.view('i8') == tslib.iNaT
191 else:
--> 192 result = np.isnan(values)
193
194 # box
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could
not be safely coerced to any supported types according to the casting rule ''safe''
```
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} | 4 | 2016-06-15T11:30:26Z | 2016-06-16T12:13:47Z | 2016-06-16T12:13:47Z | CONTRIBUTOR | null | xref #13436
so this is failing on windows py2.7 (3.5 works fine).
```
pandas\algos.c(113380) : warning C4244: '=' : conversion from 'int' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(114441) : warning C4244: '=' : conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(114538) : warning C4244: '=' : conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(114647) : warning C4244: '=' : conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(114744) : warning C4244: '=' : conversion from '__pyx_t_5numpy_int64_t' to '__pyx_t_5numpy_float64_t', possible loss of data
pandas\algos.c(116387) : warning C4244: '=' : conversion from 'npy_double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(118081) : warning C4244: '=' : conversion from 'npy_double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(119587) : warning C4244: '=' : conversion from 'npy_double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(119609) : warning C4244: '/=' : conversion from 'double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(121324) : warning C4244: '=' : conversion from 'npy_double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(124112) : warning C4244: '=' : conversion from 'npy_double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(126309) : warning C4244: '=' : conversion from 'npy_double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(128960) : warning C4244: '=' : conversion from 'npy_double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(131798) : warning C4244: '=' : conversion from 'npy_double' to '__pyx_t_5numpy_float32_t', possible loss of data
pandas\algos.c(133366) : warning C4244: 'function' : conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(134168) : warning C4244: '=' : conversion from 'long' to 'char', possible loss of data
pandas\algos.c(139210) : warning C4244: '=' : conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(163983) : warning C4244: 'function' : conversion from 'Py_ssize_t' to 'int', possible loss of data
pandas\algos.c(164199) : warning C4244: 'function' : conversion from 'npy_int64' to 'int', possible loss of data
error: command 'C:\\Program Files (x86)\\Microsoft Visual Studio 9.0\\VC\\BIN\\amd64\\cl.exe' failed with exit status 2
```
would be ok with just having this change affect py3 instead
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} | 1 | 2016-06-15T13:15:00Z | 2016-06-15T13:25:16Z | 2016-06-15T13:25:13Z | CONTRIBUTOR | null | Really small documentation fix, that rather confused me as I was first reading it (given the documentation contradicts itself). I believe this is the correct resolution.
Let me know if I need to actually compile/test anything.
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} | 2 | 2016-06-15T13:58:27Z | 2016-06-16T12:18:44Z | 2016-06-16T12:18:43Z | CONTRIBUTOR | null | - [x] ~~closes #xxxx~~ There is no issue for this.
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixes the error message when the argument to `to_datetime` doesn't match the format specified.
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} | 1 | 2016-06-15T14:36:41Z | 2016-06-17T22:04:34Z | 2016-06-17T22:04:34Z | CONTRIBUTOR | null | #### Code sample
```
df = pd.DataFrame({'year': [2015, 2016],
'month': [2, 3],
'day': [4, 5]})
pd.to_datetime(df.astype('int16'))
```
#### Expected Output
```
0 2015-02-04
1 2016-03-05
dtype: datetime64[ns]
```
#### output of `pd.show_versions()`
``````
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None```
``````
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} | 3 | 2016-06-15T15:55:35Z | 2016-06-16T07:51:48Z | 2016-06-16T07:51:44Z | NONE | null | df = pd.DataFrame({'A' : [1, 1, 3, 4], 'B': [7, 7, 7, 8], 'C': [5, 6, 7, 8], 'D': [5, 6, 7, 8]})
print(df)
print("")
print(df.groupby(["A", "B"]).rank())
print("")
print(df.groupby(["A", "B"]).sum())
print("")
print(df.groupby(["A", "B"]).count())
print("")
#### Expected Output
Would expect all three functions to show similar output. Sum and count work as expected but rank does not show A/B.
#### output of `pd.show_versions()`
A B C D
0 1 7 5 5
1 1 7 6 6
2 3 7 7 7
3 4 8 8 8
```
C D
```
0 1.0 1.0
1 2.0 2.0
2 1.0 1.0
3 1.0 1.0
```
C D
```
A B
1 7 11 11
3 7 7 7
4 8 8 8
```
C D
```
A B
1 7 2 2
3 7 1 1
4 8 1 1
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} | 9 | 2016-06-15T19:05:16Z | 2017-02-16T14:28:55Z | 2017-02-16T14:28:47Z | CONTRIBUTOR | null | xref addtl example in #13795
Pandas `Resampler.nunique` appears to be putting the same data in multiple bins:
``` python
import pandas as pd
# Create a series with a datetime index
index = pd.date_range('1-1-2000', '2-15-2000', freq='h')
index2 = pd.date_range('4-15-2000', '5-15-2000', freq='h')
index3 = index.append(index2)
s = pd.Series(range(len(index3)), index=index3)
# Since all elements are unique, `count` and `nunique` should give the same result
count = s.resample('M').count()
nunique = s.resample('M').nunique()
```
In pandas 0.18.1 and 0.18.0 these don't give the same results, when they should
```
In [3]: count
Out[3]:
2000-01-31 744
2000-02-29 337
2000-03-31 0
2000-04-30 384
2000-05-31 337
Freq: M, dtype: int64
In [4]: nunique
Out[4]:
2000-01-31 337
2000-02-29 744
2000-03-31 0
2000-04-30 744
2000-05-31 337
Freq: M, dtype: int64
```
In pandas 0.17.0 and 0.17.1 (adjusting to old style resample syntax), the `nunique` one fails due to a "`ValueError: Wrong number of items passed 4, placement implies 5`" somewhere in the depths of `internals.py`. If I go back to 0.16.2, I do get the same result for each.
I'm not sure what's going on here. Since the `nunique` results sum to larger than the length, it appears data is being counted more than once.
---
``` python
In [19]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
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
```
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} | 1 | 2016-06-15T20:38:26Z | 2016-07-07T07:26:12Z | 2016-07-07T07:26:12Z | CONTRIBUTOR | null | I ran into the following exception when using the pandas `read_gbq()` function.
```
1 import pandas as pd
----> 2 pd.read_gbq('select * from SampleDataset.SampleTable', project_id='xxxxxxx')
/usr/local/lib/python2.7/dist-packages/pandas/io/gbq.pyc in read_gbq(query, project_id, index_col, col_order, reauth, verbose)
425 job_id = uuid.uuid4().hex
426 rows = []
--> 427 remaining_rows = len(dataframe)
428
429 if self.verbose:
/usr/local/lib/python2.7/dist-packages/pandas/io/gbq.pyc in __init__(self, project_id, reauth)
111 not match the schema of the destination
112 table in BigQuery.
--> 113 """
114 pass
115
/usr/local/lib/python2.7/dist-packages/pandas/io/gbq.pyc in test_google_api_imports(self)
127 Raised when BigQuery reports a streaming insert error.
128 For more information see `Streaming Data Into BigQuery
--> 129 <https://cloud.google.com/bigquery/streaming-data-into-bigquery>`__
130 """
131
ImportError: Missing module required for Google BigQuery support: No module named discovery
```
An exception occurs in `test_google_api_imports()` . I believe the issue is on line 49 in https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py .
We use
```
from apiclient.discovery import build
```
instead of
```
from googleapiclient.discovery import build
```
`apiclient` is an alias for `googleapiclient` according to https://github.com/google/google-api-python-client/blob/master/apiclient/__init__.py#L1
A similar issue is reported here: https://github.com/google/google-api-python-client/issues/13
`apiclient.__version__` fails
```
>> import apiclient
>> apiclient.__version__
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-90-662a0f56e34b> in <module>()
1 import apiclient
----> 2 apiclient.__version__
AttributeError: 'module' object has no attribute '__version__'
```
`googleapiclient.__version__` works
```
import googleapiclient
googleapiclient.__version__
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: None
pandas: None
nose: 1.3.7
pip: 1.5.6
setuptools: 5.5.1
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 11 | 2016-06-15T21:15:06Z | 2016-06-17T12:49:24Z | 2016-06-16T12:16:55Z | NONE | null | The feature I'd like to suggest/request is pretty simple, it has workarounds but it seems very odd to me that this is not a native behavior. Specially because the workarounds seem so slow.
I imagine it as a simple parameter for the `groupby` function.
The feature is: the ability to group a level (of a MultiIndex dataframe) but still retain that level's labels. I think it will be clear with the example below.
Note that this came from a question I asked in [stack overflow](http://stackoverflow.com/questions/37845306/pandas-multiindex-groupby-retaining-index-levels/37845567?noredirect=1#37845567)
#### Code Sample
```
df = pd.DataFrame([['A', 1, 3],
['A', 2, 4],
['A', 3, 6],
['B', 1, 9],
['B', 2, 10],
['B', 4, 6]],
columns=pd.Index(['Name', 'Date', 'Value'], name='ColumnName')
).set_index(['Name', 'Date'])
df.groupby(level=('Name')).last()
```
#### Intended Output
```
ColumnName Value
Name Date
A 3 6
B 4 6
```
#### output of `pandas 0.18.0`
```
ColumnName Value
Name
A 6
B 6
```
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} | 2 | 2016-06-15T22:39:24Z | 2016-06-16T12:13:47Z | 2016-06-16T12:13:47Z | CONTRIBUTOR | null | closes #13448
This built locally (on Windows) for py27 and py35.
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} | 1 | 2016-06-15T23:21:50Z | 2019-10-21T20:03:53Z | null | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/37845884/pandas-setting-column-subset-slow)
I'm guessing there's a lot of inference / checks along this path, but this is slower than I'd expect.
``` python
In [66]: df = pd.DataFrame(np.random.randint(0,1000, size=(10000, 1026)))
...: f = list(range(1024))
...:
In [67]: %time df[f] = df[f].astype(float)
Wall time: 10.4 s
```
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} | 9 | 2016-06-16T02:16:25Z | 2016-07-07T07:26:19Z | 2016-07-07T07:26:12Z | CONTRIBUTOR | null | - [ x ] closes #13454
- [ x ] tests added / passed
- [ x ] passes `git diff upstream/master | flake8 --diff`
- [ x ] whatsnew entry
All gbq tests pass locally
```
tony@tonypc:~/parthea-pandas/pandas/io/tests$ nosetests test_gbq.py -v
test_read_gbq_with_corrupted_private_key_json_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_read_gbq_with_empty_private_key_file_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_read_gbq_with_empty_private_key_json_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_read_gbq_with_invalid_private_key_json_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_read_gbq_with_no_project_id_given_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_read_gbq_with_private_key_json_wrong_types_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_return_bigquery_booleans_as_python_booleans (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_return_bigquery_floats_as_python_floats (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_return_bigquery_integers_as_python_floats (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_return_bigquery_strings_as_python_strings (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_return_bigquery_timestamps_as_numpy_datetime (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_that_parse_data_works_properly (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_to_gbq_should_fail_if_invalid_table_name_passed (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_to_gbq_with_no_project_id_given_should_fail (pandas.io.tests.test_gbq.GBQUnitTests) ... ok
test_should_be_able_to_get_a_bigquery_service (pandas.io.tests.test_gbq.TestGBQConnectorIntegration) ... ok
test_should_be_able_to_get_results_from_query (pandas.io.tests.test_gbq.TestGBQConnectorIntegration) ... ok
test_should_be_able_to_get_schema_from_query (pandas.io.tests.test_gbq.TestGBQConnectorIntegration) ... ok
test_should_be_able_to_get_valid_credentials (pandas.io.tests.test_gbq.TestGBQConnectorIntegration) ... ok
test_should_be_able_to_make_a_connector (pandas.io.tests.test_gbq.TestGBQConnectorIntegration) ... ok
test_should_be_able_to_get_a_bigquery_service (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyContentsIntegration) ... ok
test_should_be_able_to_get_results_from_query (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyContentsIntegration) ... ok
test_should_be_able_to_get_schema_from_query (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyContentsIntegration) ... ok
test_should_be_able_to_get_valid_credentials (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyContentsIntegration) ... ok
test_should_be_able_to_make_a_connector (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyContentsIntegration) ... ok
test_should_be_able_to_get_a_bigquery_service (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyPathIntegration) ... ok
test_should_be_able_to_get_results_from_query (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyPathIntegration) ... ok
test_should_be_able_to_get_schema_from_query (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyPathIntegration) ... ok
test_should_be_able_to_get_valid_credentials (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyPathIntegration) ... ok
test_should_be_able_to_make_a_connector (pandas.io.tests.test_gbq.TestGBQConnectorServiceAccountKeyPathIntegration) ... ok
test_bad_project_id (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_bad_table_name (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_column_order (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_column_order_plus_index (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_download_dataset_larger_than_200k_rows (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_index_column (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_malformed_query (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_arbitrary_timestamp (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_empty_strings (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_false_boolean (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_null_boolean (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_null_floats (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_null_integers (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_null_strings (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_null_timestamp (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_timestamp_unix_epoch (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_true_boolean (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_valid_floats (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_valid_integers (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_properly_handle_valid_strings (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_read_as_service_account_with_key_contents (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_should_read_as_service_account_with_key_path (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_unicode_string_conversion_and_normalization (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_zero_rows (pandas.io.tests.test_gbq.TestReadGBQIntegration) ... ok
test_create_dataset (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_create_table (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_dataset_does_not_exist (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_dataset_exists (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_delete_dataset (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_delete_table (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_generate_schema (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_google_upload_errors_should_raise_exception (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_list_dataset (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_list_table (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_list_table_zero_results (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_table_does_not_exist (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_upload_data (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_upload_data_if_table_exists_append (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_upload_data_if_table_exists_fail (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_upload_data_if_table_exists_replace (pandas.io.tests.test_gbq.TestToGBQIntegration) ... ok
test_upload_data_as_service_account_with_key_contents (pandas.io.tests.test_gbq.TestToGBQIntegrationServiceAccountKeyContents) ... ok
test_upload_data_as_service_account_with_key_path (pandas.io.tests.test_gbq.TestToGBQIntegrationServiceAccountKeyPath) ... ok
pandas.io.tests.test_gbq.test_requirements ... ok
pandas.io.tests.test_gbq.test_generate_bq_schema_deprecated ... ok
----------------------------------------------------------------------
Ran 73 tests in 435.170s
OK
```
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} | 7 | 2016-06-16T08:49:44Z | 2016-06-21T12:43:02Z | 2016-06-21T12:43:02Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
nosetests pandas/tseries/tests/test_timezones.py:TestTimeZones.test_normalize_tz
F
======================================================================
FAIL: test_normalize_tz (pandas.tseries.tests.test_timezones.TestTimeZones)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/ravikumarnimmi/work/pandas/pandas/tseries/tests/test_timezones.py", line 1398, in test_normalize_tz
self.assertTrue(result.is_normalized)
AssertionError: False is not true
----------------------------------------------------------------------
Ran 1 test in 0.026s
FAILED (failures=1)
```
#### Expected Output
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: f7528866e07048f051f46f621ca76947730c7d32
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1+129.gf752886
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
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
```
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} | 2 | 2016-06-16T09:26:20Z | 2016-06-16T21:02:28Z | 2016-06-16T21:02:21Z | CONTRIBUTOR | null | - [x] closes #13438
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
.str.replace now raises TypeError when replacement is invalid
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} | 3 | 2016-06-16T11:07:55Z | 2016-07-21T14:16:50Z | 2016-07-21T14:16:50Z | CONTRIBUTOR | null | I am trying to parse some HTML and would love to have `converters` (https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L1582-L1586) exposed to the `read_html` function.
Is there a reason why more parsing/all parsing options aren't exposed to `TextParser` at https://github.com/pydata/pandas/blob/master/pandas/io/html.py#L631-L634 ?
If not, would it be of interest to have a PR that adds this functionality?
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} | 10 | 2016-06-16T13:19:19Z | 2017-02-27T19:45:10Z | 2017-02-27T19:44:37Z | CONTRIBUTOR | null | Fix a bug when using DataFrame.to_records with unicode column names in python 2
- [x] closes #11879
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 4 | 2016-06-16T15:51:59Z | 2018-03-07T18:04:24Z | 2018-03-07T18:04:24Z | NONE | null | to_msgpack() fails when the data has "period". This works in 0.16.2
#### ISSUE
```
import pandas as pd
pd.DataFrame({'C': pd.period_range('2015-01-01', periods=3)}).to_msgpack()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-65-8e5ad9cd6036> in <module>()
----> 1 pd.DataFrame({'C': pd.period_range('2015-01-01', periods=3)}).to_msgpack()
/auto/energymdl2/anaconda/envs/commod_20160516_pd18/lib/python2.7/site-packages/pandas/core/generic.pyc in to_msgpack(self, path_or_buf, encoding, **kwargs)
1120 from pandas.io import packers
1121 return packers.to_msgpack(path_or_buf, self, encoding=encoding,
-> 1122 **kwargs)
1123
1124 def to_sql(self, name, con, flavor='sqlite', schema=None, if_exists='fail',
/auto/energymdl2/anaconda/envs/commod_20160516_pd18/lib/python2.7/site-packages/pandas/io/packers.pyc in to_msgpack(path_or_buf, *args, **kwargs)
152 elif path_or_buf is None:
153 buf = compat.BytesIO()
--> 154 writer(buf)
155 return buf.getvalue()
156 else:
/auto/energymdl2/anaconda/envs/commod_20160516_pd18/lib/python2.7/site-packages/pandas/io/packers.pyc in writer(fh)
145 def writer(fh):
146 for a in args:
--> 147 fh.write(pack(a, **kwargs))
148
149 if isinstance(path_or_buf, compat.string_types):
/auto/energymdl2/anaconda/envs/commod_20160516_pd18/lib/python2.7/site-packages/pandas/io/packers.pyc in pack(o, default, encoding, unicode_errors, use_single_float, autoreset, use_bin_type)
699 use_single_float=use_single_float,
700 autoreset=autoreset,
--> 701 use_bin_type=use_bin_type).pack(o)
702
703
pandas/msgpack/_packer.pyx in pandas.msgpack._packer.Packer.pack (pandas/msgpack/_packer.cpp:3434)()
pandas/msgpack/_packer.pyx in pandas.msgpack._packer.Packer.pack (pandas/msgpack/_packer.cpp:3276)()
pandas/msgpack/_packer.pyx in pandas.msgpack._packer.Packer._pack (pandas/msgpack/_packer.cpp:2433)()
pandas/msgpack/_packer.pyx in pandas.msgpack._packer.Packer._pack (pandas/msgpack/_packer.cpp:3006)()
pandas/msgpack/_packer.pyx in pandas.msgpack._packer.Packer._pack (pandas/msgpack/_packer.cpp:2433)()
pandas/msgpack/_packer.pyx in pandas.msgpack._packer.Packer._pack (pandas/msgpack/_packer.cpp:3006)()
pandas/msgpack/_packer.pyx in pandas.msgpack._packer.Packer._pack (pandas/msgpack/_packer.cpp:3088)()
/auto/energymdl2/anaconda/envs/commod_20160516_pd18/lib/python2.7/site-packages/pandas/io/packers.pyc in encode(obj)
510 return {u'typ': u'period',
511 u'ordinal': obj.ordinal,
--> 512 u'freq': u(obj.freq)}
513 elif isinstance(obj, BlockIndex):
514 return {u'typ': u'block_index',
/auto/energymdl2/anaconda/envs/commod_20160516_pd18/lib/python2.7/site-packages/pandas/compat/__init__.pyc in u(s)
262
263 def u(s):
--> 264 return unicode(s, "unicode_escape")
265
266 def u_safe(s):
TypeError: coercing to Unicode: need string or buffer, Day found
```
#### Expected Output
Works on 0.16.2
```
In [30]: pd.DataFrame({'C': pd.period_range('2015-01-01', periods=3)}).to_msgpack()
Out[30]: '\x84\xa6blocks\x91\x86\xa5items\x86\xa4name\xc0\xa5dtype\x11\xa8compress\xc0\xa4data\x91\xa1C\xa5klass\xa5Index\xa3typ\xa5index\xa8compress\xc0\xa5shape\x92\x01\x03\xa6values\x93\x83\xa7ordinal\xcd@4\xa4freq\xa1D\xa3typ\xa6period\x83\xa7ordinal\xcd@5\xa4freq\xa1D\xa3typ\xa6period\x83\xa7ordinal\xcd@6\xa4freq\xa1D\xa3typ\xa6period\xa5klass\xabObjectBlock\xa5dtype\x11\xa4axes\x92\x86\xa4name\xc0\xa5dtype\x11\xa8compress\xc0\xa4data\x91\xa1C\xa5klass\xa5Index\xa3typ\xa5index\x86\xa4name\xc0\xa5dtype\x07\xa8compress\xc0\xa4data\xb8\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\xa5klass\xaaInt64Index\xa3typ\xa5index\xa3typ\xadblock_manager\xa5klass\xa9DataFrame'
```
#### INSTALLED VERSIONS
```
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-573.7.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.4.3
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
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} | 6 | 2016-06-16T17:23:06Z | 2016-06-17T22:04:40Z | 2016-06-17T22:04:34Z | CONTRIBUTOR | null | - [x] closes #13451
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixes #13451
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} | 3 | 2016-06-16T18:24:04Z | 2020-05-11T00:54:29Z | null | NONE | null | This was an issue that was fixed in 0.14.0, but has re-appeared in 0.18.1. The series s should have a mulit-level index, but in 18.1 it has a single-level index with a tuple for each value in the index.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
from numpy.random import randn
a = [['bar', 'bar', 'foo', 'foo'],['one', 'two', 'one', 'two']]
t = list(zip(*a))
ind = pd.MultiIndex.from_tuples(t, names=['first', 'second'])
dat = pd.Series(randn(4), index=ind)
s = pd.Series()
s = s.append(dat)
s.index.levels
```
#### Expected Output
FrozenList([['bar', 'foo'], ['one', 'two']])
#### output of `pd.show_versions()`
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 22.0.5
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.1
xlsxwriter: 0.8.9
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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} | 1 | 2016-06-16T19:50:16Z | 2016-06-16T20:03:01Z | 2016-06-16T20:02:49Z | NONE | null | Dot product of empty dataframe resulted in dataframe filled with zeros.
I thought the expected ones would be empty or NaN?
In [3]: df = pd.DataFrame(index = ['a','b'])
In [4]: srs = pd.Series()
In [5]: df
Out[5]:
Empty DataFrame
Columns: []
Index: [a, b]
In [6]: srs
Out[6]: Series([], dtype: float64)
In [7]: df.dot(srs)
Out[7]:
a 0
b 0
dtype: float64
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} | 5 | 2016-06-16T21:24:07Z | 2016-07-11T07:31:10Z | 2016-07-11T07:31:10Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
`Index`/`Series` results in `datetime64` dtype if all inputs are `pd.NaT`.
```
pd.Index([pd.NaT, pd.NaT])
# DatetimeIndex(['NaT', 'NaT'], dtype='datetime64[ns]', freq=None)
pd.Series([pd.NaT, pd.NaT])
#0 NaT
#1 NaT
# dtype: datetime64[ns]
```
However, it coerces to `timedelta64` if `np.nan` is included.
```
pd.Index([pd.NaT, np.nan])
# TimedeltaIndex([NaT, NaT], dtype='timedelta64[ns]', freq=None)
pd.Series([pd.NaT, np.nan])
#0 NaT
#1 NaT
# dtype: timedelta64[ns]
```
This looks to be caused by following internals:
```
pd.tslib.is_timestamp_array(np.array([pd.NaT, np.nan]))
# False
pd.lib.is_timedelta_array(np.array([pd.NaT, np.nan]))
# True
```
#### Expected Output
`datetime64` dtype?
#### output of `pd.show_versions()`
on current master
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} | 10 | 2016-06-16T22:46:45Z | 2021-05-01T22:22:40Z | null | MEMBER | null | I know `iterrows` is not the most recommended function, but I noticed a strange behaviour (triggered by a problem of a geopandas user: https://github.com/geopandas/geopandas/issues/348). When using `iterrows` on a df with mixed dtypes (so the resulting series is of object dtype), the numeric values are converted to python types, while with `loc/iloc` the numpy types are preserved:
```
In [254]: df = pd.DataFrame({'int':[0,1], 'float':[0.1,0.2], 'str':['a','b']})
In [255]: df
Out[255]:
float int str
0 0.1 0 a
1 0.2 1 b
In [256]: row1 = df.iloc[0]
In [257]: i, row2 = next(df.iterrows())
In [258]: row3= next(df.itertuples())
In [260]: type(row1['float'])
Out[260]: numpy.float64
In [261]: type(row2['float'])
Out[261]: float
In [269]: type(row3.float)
Out[269]: numpy.float64
```
Is this intentional? (it's a consequence of using `self.values` in the implementation, and numpy does this conversion to python types in an object array) And if so, is this worth documenting?
(note it was actually the numpy types in an object dtyped series that caused an issue for the geopandas user, because fiona couldn't handle those numpy scalars in an object dtyped column, but that's not an issue to blame pandas)
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} | 3 | 2016-06-17T00:12:31Z | 2016-06-17T13:19:04Z | 2016-06-17T13:03:59Z | CONTRIBUTOR | null | xref #13358
- [ ] need full signature on deprecated `ordered_merge`
- [ ] `pd.merge_asof()` for asof-style time-series joining, see :ref:`here <whatsnew_0182.enhancements.asof_merge>` for highlites
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} | 4 | 2016-06-17T00:54:22Z | 2016-06-17T08:09:42Z | 2016-06-17T00:59:56Z | NONE | null | Hello the Pandas team,
It's me again ;-)
Consider the following:
```
rng = pd.date_range('1/1/2011', periods=5, freq='T')
ts = pd.DataFrame({'A' : np.random.randn(len(rng))}, index=rng)
ts.drop(ts.index[[2,3]],inplace=True)
# note how I delete some rows so that the index contains holes
ts
Out[37]:
A
2011-01-01 00:00:00 0.873475
2011-01-01 00:01:00 0.035362
2011-01-01 00:04:00 -0.903640
```
I can shift my data in many different ways
```
ts.A.shift(1)
ts.A.shift(1, freq='T')
ts['index']=ts.index.shift(1, freq='T')
```
but I am unable to generate a variable that contains the lagged values, say, **1 minute ago** (which is different from one observation ago as in `ts.A.shift(1)`).
In other words, is it possible to get the following output?
```
A
2011-01-01 00:00:00 NaN
2011-01-01 00:01:00 0.873475
2011-01-01 00:04:00 NaN
```
I get a `NaN` at row 3 because there is no data at `2011-01-01 00:03:00` (1 minute ago)
I thought a timeindex was able to understand the time component of the index. Am I missing something here?
Thanks!
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| null | 4 | 2016-06-17T01:04:36Z | 2021-06-23T15:41:07Z | 2016-06-17T01:08:33Z | NONE | null | By default `rolling()` sets labels (resulting values) at the "right edge" of the window. The right edge is where the window has the greatest index (biggest number, most recent date, etc.). `rolling()` does have an argument to center labels, but there doesn't seem to be a way to set the label at the left edge or anywhere else. Why can't `rolling()` have an argument that allows labels to be set at any position within the window? Something like this:
```
> df = pd.DataFrame([1,2,3,4,5])
> df
0
0 1
1 2
2 3
3 4
4 5
> df.rolling(window=3,position=0).sum()
0
0 NaN
1 NaN
2 6.0
3 9.0
4 12.0
> df.rolling(window=3,position=2).sum()
0
0 6.0
1 9.0
2 12.0
3 NaN
4 NaN
```
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} | 0 | 2016-06-17T01:11:56Z | 2016-06-17T22:04:34Z | 2016-06-17T22:04:34Z | CONTRIBUTOR | null | https://ci.appveyor.com/project/jreback/pandas-465/build/job/ic8ytmkwcc8nfucc
Current are
- `conda==4.1.1` is good
- `conda-build==1.21.0` seems buggy
a working version is:
- `conda=4.0.8`
- `conda-build=1.20.3`
may need to fix the version if conda-build not fixed soon
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} | 10 | 2016-06-17T17:26:19Z | 2020-06-17T01:29:17Z | 2019-03-05T22:15:20Z | CONTRIBUTOR | null | Is there a reason that `.rename` doesn't raise if keys are supplied that don't exist? This caught me out a couple of times:
``` python
In [14]: import pandas as pd
In [15]: df=pd.DataFrame({'a': [1,2], 'b': [3,4]})
In [16]: df
Out[16]:
a b
0 1 3
1 2 4
In [17]: df.rename(columns={'a': 'c'})
Out[17]:
c b
0 1 3
1 2 4
# ok
In [18]: df.rename(columns={'d': 'c'})
Out[18]:
a b
0 1 3
1 2 4
# this should raise?
```
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} | 2 | 2016-06-17T17:53:54Z | 2021-05-01T22:23:42Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [2]: df = pd.DataFrame(0, index=range(2), columns=pd.MultiIndex.from_product([[1], [2]]))
In [3]: df['oth'] = 1
In [4]: df.loc[0, 'oth']
Out[4]:
1
Name: 0, dtype: int64
In [5]: df.loc[0, 'oth'] += 1
In [6]: df.loc[0, 'oth']
Out[6]:
NaN
Name: 0, dtype: float64
```
#### Expected Output
```
In [6]: df.loc[0, 'oth']
Out[6]:
2
Name: 0, dtype: float64
```
#### output of `pd.show_versions()`
```
In [7]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: ab286f0ea605bec7dc96631ac8493278846a637d
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
pandas: 0.18.1+123.gab286f0.dirty
nose: 1.3.7
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+111ddc0
xarray: None
IPython: 5.0.0.dev
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: 1.1.0dev
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.1.2
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.0
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.1
```
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} | 20 | 2016-06-17T18:17:26Z | 2020-08-07T22:22:57Z | 2020-08-07T22:22:56Z | NONE | null | xref #13742 for addl cases.
```
In [23]: df1 = pd.DataFrame({'a':[1,2]}, index=pd.Index(['a', 'b'], name='idx'))
In [24]: df2 = pd.DataFrame({'b':[2,3]}, index=pd.Index(['b', 'c'], name='idx'))
In [26]: pd.concat([df1, df2], axis=1)
Out[26]:
a b
a 1.0 NaN
b 2.0 2.0
c NaN 3.0
In [27]: print pd.concat([df1, df2], axis=1).index.name
None
```
So the issue seems to be with a string index that is not equal, as when the index of the two frames is equal (no NaNs are introduced), the name is kept and also when using numerical indexes, see https://github.com/pydata/pandas/issues/13475#issuecomment-232310977
---
When I use the concat function with input dataframes that have index.name assigned, sometimes the resulting dataframe has the index.name assigned, sometimes it does not.
I ran the code below from the python interpreter, using a conda environment with pandas-0.18.1
I don't see any odd / extra characters around the "pert_well" column in the files between the files.
#### Code Sample, a copy-pastable example if possible
``` python
import pandas
a_data = """x_amount_mg x_annotation x_mmoles_per_liter mfc_plate_name x_avg_mol_weight x_volume_ul pert_mfc_desc pert_iname x_purity pert_id_vendor pert_well pert_vehicle pert_mfc_id x_smiles x_mg_per_ml pert_dose_unit pert_dose pert_id pert_plate pert_type
0.04784 ACCEPT 10.0 B-REPO-01-B64-101 405.4084 11 Taltirelin Taltirelin 86.52 HY-B0596 C18 DMSO BRD-K93869735-001-01-1 CN1C(=O)C[C@H](NC1=O)C(=O)N[C@@H](Cc1cnc[nH]1)C(=O)N1CCC[C@H]1C(N)=O 4.054084 um 20.0 BRD-K93869735 PMEL008 trt_cp"""
b_data = """pert_well pert_2_type pert_2_id pert_2_mfc_id pert_2_mfc_desc pert_2_id_vendor pert_2_iname pert_2_dose pert_2_dose_unit pert_2_vehicle pert_3_type pert_3_idpert_3_mfc_id pert_3_mfc_desc pert_3_id_vendor pert_3_iname pert_3_dose pert_3_dose_unit pert_3_vehicle
A01 ctl_vehicle DMSO DMSO DMSO -666 DMSO -666 -666 -666 ctl_untrt CMAP-000 -666 UnTrt -666 -666 -666 -666 -666"""
d_data = """x_amount_mg x_annotation x_mmoles_per_liter mfc_plate_name x_avg_mol_weight x_volume_ul pert_mfc_desc pert_iname x_purity pert_id_vendor pert_well pert_vehicle pert_mfc_id x_smiles x_mg_per_ml pert_dose_unit pert_dose pert_id pert_plate pert_type
0.0 -666 -666 B-REPO-01-B64-107 -666 0 -666 -666 -666 -666 A01 -666 -666 -666 -666 -666 -666 CMAP-000 PMEL001 ctl_untrt"""
a = pandas.read_csv(StringIO(a_data), sep="\t", index_col="pert_well")
b = pandas.read_csv(StringIO(b_data), sep="\t", index_col="pert_well")
c = pandas.concat([a,b], axis=1)
c.index
d = pandas.read_csv(StringIO(d_data), sep="\t", index_col="pert_well")
e = pandas.concat([d,b], axis=1)
e.index
```
results:
``` python
Index([u'A01', u'A02', u'A03', u'A04', u'A05', u'A06', u'A07', u'A08', u'A09',
u'A10',
...
u'P15', u'P16', u'P17', u'P18', u'P19', u'P20', u'P21', u'P22', u'P23',
u'P24'],
dtype='object', length=384)
Index([u'A01', u'A02', u'A03', u'A04', u'A05', u'A06', u'A07', u'A08', u'A09',
u'A10',
...
u'P15', u'P16', u'P17', u'P18', u'P19', u'P20', u'P21', u'P22', u'P23',
u'P24'],
dtype='object', name=u'pert_well', length=384)
```
#### Expected Output
c.index.name should be "pert_well"
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-573.7.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
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
[PMEL_input_files_for_pandas_issue.zip](https://github.com/pydata/pandas/files/321051/PMEL_input_files_for_pandas_issue.zip)
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} | 1 | 2016-06-17T19:34:15Z | 2016-06-17T19:36:40Z | 2016-06-17T19:36:25Z | NONE | null | #### Code Sample, a copy-pastable example if possible
In 0.18.1, a single NaN in a column will cause the reported percentiles to all be NaN. In 0.18.0 and according to the documentation, NaNs are excluded.
```
import pandas as pd
import numpy as np
df = pd.DataFrame(data={'blah': range(10) + [np.nan]})
df.describe(percentiles=[0.1, 0.5, 0.9])
```
```
blah
count 10.00000
mean 4.50000
std 3.02765
min 0.00000
10% NaN
50% NaN
90% NaN
max 9.00000
```
#### Expected Output
```
blah
count 10.00000
mean 4.50000
std 3.02765
min 0.00000
10% 0.90000
50% 4.50000
90% 8.10000
max 9.00000
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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} | 14 | 2016-06-18T04:12:59Z | 2016-07-11T07:31:21Z | 2016-07-11T07:31:10Z | MEMBER | null | - [x] closes #13467
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
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} | 2 | 2016-06-18T06:11:42Z | 2016-06-19T12:46:58Z | 2016-06-18T10:51:52Z | NONE | null | #### Code Sample, a copy-pastable example if possible
dtw[(x <= dtw.id < y)]
#### Expected Output
same as dtw[(x <= dtw.id) & (dtw.id < y)] or dtw.query('{}<=id<{}'.format(x, y)), but these solutions are less Pythonic
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| null | 1 | 2016-06-18T08:56:17Z | 2016-06-18T14:46:08Z | 2016-06-18T14:45:59Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Hello,
At the moment I have to call two methods, if I want to extract a numpy array from a
DataFrame with a specific type.
Let's assume for this example that `example_frame` contains only integers.
If I want to force `int64` numbers, I have to write:
```
example_frame.get_values().astype('int64')
```
#### Expected Output
It would be a bit nicer if, `get_values(dtype=None)` works per default as before,
but allows to write:
```
example_frame.get_values(dtype='int64')
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-73-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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} | 29 | 2016-06-18T09:49:19Z | 2019-06-19T01:05:34Z | 2019-06-19T01:05:34Z | MEMBER | null | From https://github.com/pydata/pandas/issues/13443#issuecomment-226037416
The current MultiIndex representation looks like this:
```
In [15]: mi = pd.MultiIndex.from_tuples([('A', 1), ('A', 2),
...: ('B', 3), ('B', 4)])
In [16]: mi
Out[16]:
MultiIndex(levels=[[u'A', u'B'], [1, 2, 3, 4]],
labels=[[0, 0, 1, 1], [0, 1, 2, 3]])
```
So this shows the underlying `labels` and `levels`. Personally, I don't find this a very good repr, because:
- The shown `level` and `labels` are actually (internal) concepts of MultiIndex a lot of user do not have to think about.
- It also does not give you the best idea how the MultiIndex looks like at a glance IMO (since you have to look at the values of the labels to know what the order of the levels will be).
**So the question is: is there a better alternative?**
We could show tuples:
```
MultiIndex([('A', 1), ('A', 2), ('B', 3), ('B', 4)])
```
Or the individual levels:
```
MultiIndex([['A', 'A', 'B', 'B'], [1, 2, 3, 4]])
```
or ..
Historical note: in older versions, there was a difference between the `repr` and `str` of a MultiIndex:
```
In [1]: mi = pd.MultiIndex.from_tuples([('A', 1), ('A', 2),
...: ('B', 3), ('B', 4)])
In [2]: mi
Out[2]:
MultiIndex(levels=[[u'A', u'B'], [1, 2, 3, 4]],
labels=[[0, 0, 1, 1], [0, 1, 2, 3]])
In [3]: print mi
A 1
2
B 3
4
In [6]: pd.__version__
Out[6]: '0.14.1'
```
but seems to been disappeared (on purpose or not).
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} | 22 | 2016-06-18T16:09:28Z | 2016-06-22T10:21:40Z | 2016-06-22T10:21:28Z | CONTRIBUTOR | null | - [ x] closes #13398
- [ x] no tests added -> could not find location for IO tests?
- [ x] passes `git diff upstream/master | flake8 --diff`
Change the way of reading back to readline (consistent with the test before entering the function)
One failure on Windows 10 (Python 3.5), but expected to fail actually (should probably tag it as well?)
```
======================================================================
FAIL: test_next (pandas.io.tests.test_common.TestMMapWrapper)
----------------------------------------------------------------------
Traceback (most recent call last):
File "G:\Informatique\pandas\pandas\io\tests\test_common.py", line 139, in test_next
self.assertEqual(next_line, line)
AssertionError: 'a,b,c\r\n' != 'a,b,c\n'
- a,b,c
? -
+ a,b,c
```
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} | 1 | 2016-06-18T17:53:56Z | 2016-06-18T21:19:02Z | 2016-06-18T21:18:08Z | NONE | null | #### Existing Code
In the pandas core module `base.py`, the `__sizeof__` member of the `PandasObject` class uses the following line:
```
return super(self, PandasObject).__sizeof__()
```
#### Expected Code
This is an incorrect use of `super`, which is supposed to have the type argument before the object argument ([reference](https://docs.python.org/3/library/functions.html#super)). This also results in a known problem with the memory tracking tool `pympler` ([reference](http://stackoverflow.com/a/37681457)). The corrected line should instead be:
```
return super(PandasObject, self).__sizeof__()
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 15 Stepping 11, GenuineInte
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 22.0.5
Cython: 0.23.1
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 3.2.1
sphinx: None
patsy: 0.3.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.3
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: None
pandas_datareader: None
```
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} | 2 | 2016-06-18T18:36:24Z | 2021-02-24T16:39:01Z | 2021-02-24T16:39:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'a': ['a1', 'a2', 'a3'], 'b': [1,2,3], 'c':['c1', 'c2', 'c3']})
In [3]: x = df[df['c'] == 'c1']
In [4]: y = x.pivot_table(index='a', columns='c', values='b')
In [5]: y.columns
Out[5]: Index([u'c1'], dtype='object', name=u'c')
In [6]: x = df[df['c'] == 'c4']
In [7]: y = x.pivot_table(index='a', columns='c', values='b')
In [8]: y.columns # returns a multi-index, should not
Out[8]:
MultiIndex(levels=[[u'b'], []],
labels=[[], []],
names=[None, u'c'])
# a check was added for empty dataframe that is causing the behavior, works in 0.16.2
> /auto/energymdl2/anaconda/envs/commod_20160516_pd18/lib/python2.7/site-packages/pandas/tools/pivot.py(161)pivot_table()
159
160 # discard the top level
--> 161 if values_passed and not values_multi and not table.empty:
162 table = table[values[0]]
163
```
#### Expected Output
```
In [5]: y.columns
Out[5]: Index([], dtype='object', name=u'c')
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-573.7.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.4.3
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
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} | 6 | 2016-06-19T03:02:19Z | 2016-06-21T12:43:08Z | 2016-06-21T12:43:02Z | CONTRIBUTOR | null | - [x] closes #13459
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
is_normalized returned False for normalized date_range
in case of local timezone 'Asia/Kolkata'
Fixes: #13459
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| null | 7 | 2016-06-19T05:52:13Z | 2016-11-16T22:27:09Z | 2016-11-16T22:27:09Z | NONE | null | - [ ] closes #13434
- [ ] read_html() handles tables with multiple header rows
Now it produces the following output for the issue
Unnamed: 0 Age Party
0 Hillary 68 D
1 Bernie 74 D
2 Donald 69 R
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} | 4 | 2016-06-19T07:16:05Z | 2018-05-29T00:28:41Z | 2018-05-29T00:28:41Z | NONE | null | this is really bad for me since almost all linux logfiles contain zone offset.
As far as I can see there is not workaround without parsing the timestamp twice.
#### Code Sample, a copy-pastable example if possible
_parse common Apache access log timestamp_
```
pd.to_datetime(['28/Jul/2006:10:22:04 -0300'], format='%d/%b/%Y:%H:%M:%S %z')
...
/usr/lib/python2.7/dist-packages/pandas/tseries/tools.pyc in _convert_listlike(arg, box, format, name)
381 return DatetimeIndex._simple_new(values, name=name, tz=tz)
382 except (ValueError, TypeError):
--> 383 raise e
384
385 if arg is None:
ValueError: 'z' is a bad directive in format '%d/%b/%Y:%H:%M:%S %z'
```
#### Expected Output
_should parse timestamps with zone offset_
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.2.0
sphinx: 1.3.6
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2014.10
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.1
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
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| null | 3 | 2016-06-19T13:23:43Z | 2017-04-05T02:06:51Z | 2016-06-19T14:31:41Z | CONTRIBUTOR | null | The build [here](https://travis-ci.org/pydata/pandas/jobs/138633677#L1866) didn't succeed. Had `nbconvert` in the debs, but not `nbformat`.
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} | 3 | 2016-06-19T17:52:18Z | 2021-05-01T22:26:22Z | null | NONE | null | It appears that `fill_value` option for the `df.add()` method is not actually implemented in the source code. This was brought up in a[ StackOverflow question](http://stackoverflow.com/questions/37887796/pandas-dataframe-add-fill-value-notimplementederror).
[piRSquared](http://stackoverflow.com/users/2336654/pirsquared) is the user that identified it.
The following are all copied from that SO post.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
np.random.seed([3,1415])
df = pd.DataFrame(np.random.choice((1, 2, np.nan), (5, 5)))
s = pd.Series(range(5))
df.add(s, fill_value=0)
>>>
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-18-ce2809aeda79> in <module>()
----> 1 df.add(s, fill_value=0)
/Users/daddy30000/anaconda/lib/python2.7/site-packages/pandas/core/ops.pyc in f(self, other, axis, level, fill_value)
1060 return self._combine_frame(other, na_op, fill_value, level)
1061 elif isinstance(other, ABCSeries):
-> 1062 return self._combine_series(other, na_op, fill_value, axis, level)
1063 elif isinstance(other, (list, tuple)):
1064 if axis is not None and self._get_axis_name(axis) == 'index':
/Users/daddy30000/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _combine_series(self, other, func, fill_value, axis, level)
3511 else:
3512 return self._combine_match_columns(other, func, level=level,
-> 3513 fill_value=fill_value)
3514 return self._combine_series_infer(other, func, level=level,
3515 fill_value=fill_value)
/Users/daddy30000/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _combine_match_columns(self, other, func, level, fill_value)
3542 if fill_value is not None:
3543 raise NotImplementedError("fill_value %r not supported" %
-> 3544 fill_value)
3545
3546 new_data = left._data.eval(func=func, other=right,
NotImplementedError: fill_value 0 not supported
```
#### Expected Output
```
0 1 2 3 4
0 1.0 1.0 2.0 3.0 4.0
1 2.0 3.0 2.0 4.0 4.0
2 1.0 1.0 3.0 4.0 4.0
3 1.0 1.0 2.0 4.0 6.0
4 1.0 3.0 4.0 3.0 5.0
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 21.2.1
Cython: 0.22.1
numpy: 1.11.0
scipy: 0.15.1
statsmodels: 0.6.1
xarray: None
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
jinja2: 2.7.3
boto: 2.38.0
pandas_datareader: None
```
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| null | 5 | 2016-06-20T00:10:52Z | 2021-05-01T22:27:27Z | null | NONE | null | #### Code Sample:
Dataframe:
```
gre gpa prestige
0 380.0 3.61 3.0
1 660.0 3.67 3.0
2 800.0 4.00 1.0
3 640.0 3.19 4.0
4 0520.0 2.93 4.0
```
`df.plot.hist(figsize=(20, 10), layout=(2,3), subplots=True, sharex=False, bins=10);`
Result:
Note that scales are all off
#### Expected Output
Independent axis scale for subplots
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.2.2
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13490 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13490/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13490/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13490/events | https://github.com/pandas-dev/pandas/issues/13490 | 161,140,831 | MDU6SXNzdWUxNjExNDA4MzE= | 13,490 | Symmetric difference on equal MultiIndexes raises TypeError | {
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} | 2 | 2016-06-20T08:24:08Z | 2017-05-31T11:56:52Z | 2017-05-31T11:56:52Z | NONE | null | Calling `symmetric_difference` on two equal multiindices results in a TypeError rather than an empty MultiIndex. This is surprising since calling `difference` on the same multiindices results in the expected empty MultiIndex.
#### Code Sample, a copy-pastable example if possible
```
a = pandas.MultiIndex.from_product([['a', 'b'], [0, 1]])
b = pandas.MultiIndex.from_product([['a', 'b'], [0, 1]])
print(a.symmetric_difference(b))
```
Which gives the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Miniconda3\envs\balancedbeta\lib\site-packages\pandas\indexes\base.py", line 1674, in __xor__
return self.symmetric_difference(other)
File "C:\Miniconda3\envs\balancedbeta\lib\site-packages\pandas\indexes\base.py", line 1911, in symmetric_difference
return self._shallow_copy_with_infer(the_diff, **attribs)
File "C:\Miniconda3\envs\balancedbeta\lib\site-packages\pandas\indexes\multi.py", line 387, in _shallow_copy_with_infer
return self._shallow_copy(values, **kwargs)
File "C:\Miniconda3\envs\balancedbeta\lib\site-packages\pandas\indexes\multi.py", line 396, in _shallow_copy
return MultiIndex.from_tuples(values, **kwargs)
File "C:\Miniconda3\envs\balancedbeta\lib\site-packages\pandas\indexes\multi.py", line 883, in from_tuples
raise TypeError('Cannot infer number of levels from empty list')
TypeError: Cannot infer number of levels from empty list
```
#### Expected Output
```
MultiIndex(levels=[[], []], labels = [[], []])
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: DA
pandas: 0.18.1
nose: None
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.23
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13491 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13491/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13491/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13491/events | https://github.com/pandas-dev/pandas/pull/13491 | 161,183,892 | MDExOlB1bGxSZXF1ZXN0NzQ0MzUyNjg= | 13,491 | DOC: find kernelspec | {
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} | 9 | 2016-06-20T12:29:13Z | 2017-04-05T02:06:52Z | 2016-06-20T14:34:09Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/pull/13487
Problem was the notebook was written with a python3 kernel, but the doc build is python2.
There are potentially problems here if you have non-default kernel names in your environment...
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} | 12 | 2016-06-20T21:15:19Z | 2017-06-04T10:44:26Z | 2017-06-04T10:44:26Z | NONE | null | #### Code Sample
```
idx = pd.Index(pd.to_datetime([datetime.date(2000, 1, 1), datetime.date(2000, 1, 2)]), name='cols')
idx1 = pd.Index(pd.to_datetime([datetime.date(2010, 1, 1), datetime.date(2010, 1, 2)]), name='rows')
s = pd.DataFrame(np.arange(4).reshape(2,2), columns=idx, index=idx1)
print type(s.index.name)
# The type is str
<type 'str'>
s.reset_index()
cols rows 2000-01-01 00:00:00 2000-01-02 00:00:00
0 2010-01-01 0 1
1 2010-01-02 2 3
with pd.HDFStore("/logs/tmp/test.h5", "w") as store:
store.put("test", s, "fixed")
# When reading the data from HDF5, the index name comes back as a numpy.string_
with pd.HDFStore("/logs/tmp/test.h5", "r") as store:
s1 = store["test"]
type(s1.index.name)
numpy.string_
# numpy.concatenate throws a ValueError,
# which the code does not catch to convert the column to type object from DatetimeIndex, and fails
s1.reset_index()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-93-f61766d7f5c1> in <module>()
----> 1 s1.reset_index()
/auto/energymdl2/anaconda/envs/commod_20160516/lib/python2.7/site-packages/pandas/core/frame.pyc in reset_index(self, level, drop, inplace, col_level, col_fill)
2731 name = tuple(name_lst)
2732 values = _maybe_casted_values(self.index)
-> 2733 new_obj.insert(0, name, values)
2734
2735 new_obj.index = new_index
/auto/energymdl2/anaconda/envs/commod_20160516/lib/python2.7/site-packages/pandas/core/frame.pyc in insert(self, loc, column, value, allow_duplicates)
2228 value = self._sanitize_column(column, value)
2229 self._data.insert(
-> 2230 loc, column, value, allow_duplicates=allow_duplicates)
2231
2232 def assign(self, **kwargs):
/auto/energymdl2/anaconda/envs/commod_20160516/lib/python2.7/site-packages/pandas/core/internals.pyc in insert(self, loc, item, value, allow_duplicates)
3100 self._blknos = np.insert(self._blknos, loc, len(self.blocks))
3101
-> 3102 self.axes[0] = self.items.insert(loc, item)
3103
3104 self.blocks += (block,)
/auto/energymdl2/anaconda/envs/commod_20160516/lib/python2.7/site-packages/pandas/tseries/index.pyc in insert(self, loc, item)
1505 item = _to_m8(item, tz=self.tz)
1506 try:
-> 1507 new_dates = np.concatenate((self[:loc].asi8, [item.view(np.int64)],
1508 self[loc:].asi8))
1509 if self.tz is not None:
ValueError: new type not compatible with array.
# The exception caluse does not catch ValueError
.../pandas/tseries/index.py
1720 freq = None
1721
1722 if isinstance(item, (datetime, np.datetime64)):
1723 self._assert_can_do_op(item)
1724 if not self._has_same_tz(item):
1725 raise ValueError(
1726 'Passed item and index have different timezone')
1727 # check freq can be preserved on edge cases
1728 if self.size and self.freq is not None:
1729 if ((loc == 0 or loc == -len(self)) and
1730 item + self.freq == self[0]):
1731 freq = self.freq
1732 elif (loc == len(self)) and item - self.freq == self[-1]:
1733 freq = self.freq
1734 item = _to_m8(item, tz=self.tz)
1735 try:
1> 1736 new_dates = np.concatenate((self[:loc].asi8, [item.view(np.int64)],
1737 self[loc:].asi8))
1738 if self.tz is not None:
1739 new_dates = tslib.tz_convert(new_dates, 'UTC', self.tz)
1740 return DatetimeIndex(new_dates, name=self.name, freq=freq,
1741 tz=self.tz)
1742
1743 except (AttributeError, TypeError):
1744
1745 # fall back to object index
1746 if isinstance(item, compat.string_types):
1747 return self.asobject.insert(loc, item)
1748 raise TypeError(
1749 "cannot insert DatetimeIndex with incompatible label")
```
#### Expected Output
```
cols rows 2000-01-01 00:00:00 2000-01-02 00:00:00
0 2010-01-01 0 1
1 2010-01-02 2 3
```
#### output of `pd.show_versions()`
**_# Problem occurs in 0.16.2 and 0.18.1_**
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-573.7.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.4.3
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
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} | 10 | 2016-06-21T01:05:58Z | 2017-04-05T02:06:58Z | 2016-06-23T13:28:02Z | CONTRIBUTOR | null | Just want to see the output log from the pull request.
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} | 3 | 2016-06-21T08:58:04Z | 2016-06-22T10:08:30Z | 2016-06-22T10:08:30Z | MEMBER | null | Turns out Windows errors differently when an invalid `fileno` is passed into the `mmap` constructor, so there's no need to skip the test (xref: 9670b31).
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| null | 1 | 2016-06-21T12:52:03Z | 2016-06-21T12:56:30Z | 2016-06-21T12:56:30Z | NONE | null | #### Test code
[20160621 eval problem.txt](https://github.com/pydata/pandas/files/325599/20160621.eval.problem.txt)
#### Expected Output
Raises Value Error when using 'from pandas import *'. Executes as expected using 'import pandas'.
Issue was raised originally at bugs.Python.org - Issue: 27361 but from there pointed to pandas. I'm not clear whether the bug is in pandas, or eval or import.
I ran this under anaconda (condo version 4.0.7) with Python 3.4.4 under OS X 10.11.5
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 4.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
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} | 21 | 2016-06-22T07:15:59Z | 2016-07-11T18:47:08Z | 2016-07-11T18:21:06Z | CONTRIBUTOR | null | Before taking a stab at #10806, I looked at the relevant tests and they didn't make much sense to me.
This commit fixes:
- Some expected results were obtained by running code identical to what's being tested. (i.e. test could never fail)
- Removed duplicate assertions testing the same functionality both in `test_sort_index` and in `test_sort_values`.
- Switched the names `test_sort_index` and `test_sort_values` based on the (remaining) assertions being tested in their bodies.
Still confusing, unfixed:
- The `DataFrame.sort_index` docstring doesn't specify what happens when `level` is unspecified and `sort_remaining=False`. However there is a legacy test using this case: it's not clear to me what result or behavior is being tested.
First time pandas contributor, so I'd appreciate a good critical review!
- [X] tests pass
- [X] passes `git diff upstream/master | flake8 --diff`
- [x] ensure default options in docstrings
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| null | 6 | 2016-06-22T20:42:58Z | 2016-06-23T08:17:44Z | 2016-06-22T23:49:39Z | CONTRIBUTOR | null | Consider the following script:
import pandas as PD
df = PD.read_csv('sample.csv',
encoding='utf8')
print(df.columns[:5])
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| null | 4 | 2016-06-23T11:49:47Z | 2016-06-23T21:09:03Z | 2016-06-23T21:09:03Z | NONE | null | Hey everyone,
I was working on a project that involves working with data acquired via the world bank pandas api (i.e. `pandas.io.wb`). This library has a function called `pandas.io.wb.search(str)` which is very useful. In data transformation terms, it's basically does this- given a string 's', filter the dataframe by retaining only the tuples where a string match occurs with the given string 's'.
This operation occurs in my workflow on a regular basis, and I was wondering if anyone else feels the need to have this function natively in pandas. Would be a huge time saver in my opinion!
Was thinking of a function that looks like this:
`DataFrame.search(search_str, is_ignore_case=True, is_match_full_word=False, **kwargs)`
Would love to hear from the community! :)
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} | 0 | 2016-06-23T12:38:50Z | 2016-06-23T15:37:45Z | 2016-06-23T15:37:45Z | MEMBER | null | closes #12161
With this change, the links in the api summary to the accessor methods should work again.
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} | 1 | 2016-06-23T13:42:17Z | 2016-08-31T13:13:55Z | 2016-08-31T13:13:55Z | CONTRIBUTOR | null | Idea after seeing the gitter between @ssanderson and @jorisvandenbossche
Consider this data:
```
In [13]: df = pd.DataFrame({'date': pd.date_range('2015-01-01', freq='W', periods=5),
'a': np.arange(5)},
index=pd.MultiIndex.from_arrays([
[1,2,3,4,5],
pd.date_range('2015-01-01', freq='W', periods=5)],
names=['v','d']))
In [14]: df
Out[14]:
a date
v d
1 2015-01-04 0 2015-01-04
2 2015-01-11 1 2015-01-11
3 2015-01-18 2 2015-01-18
4 2015-01-25 3 2015-01-25
5 2015-02-01 4 2015-02-01
```
Right now to resample by `MultiIndex` level `'d'` or column `'date'` it would be:
```
In [15]: df.groupby(pd.TimeGrouper(key='date', freq='M')).sum()
Out[15]:
a
date
2015-01-31 6
2015-02-28 4
In [16]: df.groupby(pd.TimeGrouper(level='d', freq='M')).sum()
Out[16]: <same>
```
What if we instead allowed this as a convenience?
``` python
In [17]: df.resample('M', level='d').sum()
In [17]: df.resample('M', key='date').sum()
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13501 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13501/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13501/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13501/events | https://github.com/pandas-dev/pandas/issues/13501 | 161,930,304 | MDU6SXNzdWUxNjE5MzAzMDQ= | 13,501 | Partial indexing with a list and hierarchical index | {
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} | 4 | 2016-06-23T14:00:53Z | 2021-05-17T15:17:29Z | 2021-05-17T15:17:29Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Teaching a pandas course. Attendee just came across this. Note that we index with a list instead of a tuple at the bottom.
``` python
frame = pd.DataFrame(np.arange(12).reshape(( 4, 3)),
index =[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],
columns =[['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']])
frame.index.names = ['key1', 'key2']
frame.columns.names = ['state', 'color']
frame
frame.loc[['b', 2], 'Colorado']
frame.loc[['b', 1], 'Colorado']
```
Returns
```
color Green
key1 key2
b 1 8
2 11
```
in both cases on pandas 0.18.1
#### Expected Output
Error?
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.1
setuptools: 20.3
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.0.3
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
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} | 0 | 2016-06-23T15:30:18Z | 2016-06-24T12:04:12Z | 2016-06-24T12:04:12Z | MEMBER | null | Up to a clean doc build :-)
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} | 7 | 2016-06-23T16:15:24Z | 2019-04-28T16:45:40Z | 2019-04-28T16:45:40Z | CONTRIBUTOR | null | addtl example on #13753
#### Code Sample, a copy-pastable example if possible
``` python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'x': range(5), 'y': range(5)})
In [3]: df
Out[3]:
x y
0 0 0
1 1 1
2 2 2
3 3 3
4 4 4
In [4]: df.rolling(2, axis='columns').sum() # meets expectations
Out[4]:
x y
0 NaN 0.0
1 NaN 2.0
2 NaN 4.0
3 NaN 6.0
4 NaN 8.0
In [5]: df.rolling(2, axis='columns').count() # appears to be the same as axis='rows'
Out[5]:
x y
0 1.0 1.0
1 2.0 2.0
2 2.0 2.0
3 2.0 2.0
4 2.0 2.0
In [6]: df.rolling(2, axis='rows').count() # yeah, exactly the same
Out[6]:
x y
0 1.0 1.0
1 2.0 2.0
2 2.0 2.0
3 2.0 2.0
4 2.0 2.0
```
#### output of `pd.show_versions()`
``` python
In [7]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
Arose from tests in https://github.com/dask/dask/pull/1280
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