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} | 8 | 2014-10-07T16:45:26Z | 2014-10-10T12:17:40Z | 2014-10-10T12:17:40Z | CONTRIBUTOR | null | More specific exception allows for a safer, more explicit catch by a caller.
ConcatenationError follows the pattern of MergeError which is used in the same file when something is wrong with a caller's inputs to merge().
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} | 0 | 2014-10-07T17:55:47Z | 2014-10-07T19:57:11Z | 2014-10-07T19:57:11Z | CONTRIBUTOR | null | see here: https://ci.appveyor.com/project/jreback/pandas/build/1.0.455/job/2avadnw54tpv3lob
so it appears that matplotlib changed the version string in 1.4.0 to be `unicode` (and not a string).
(to be honest I would call that a bug, separate issue).
BUT our tests now fail because of this, I guess
`str(matploblib.__version__) > LooseVersion('1.4.0')` is now necessary
weird
@TomAugspurger
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} | 5 | 2014-10-07T17:59:08Z | 2014-10-08T14:56:20Z | 2014-10-07T18:05:53Z | NONE | null | Hi,
I discovered the SparseDataFrame very recently and ran into the following:
``` python
In [19]: %paste
In [6]: df = DataFrame(randn(10000, 4))
In [7]: df.ix[:9998] = np.nan
In [8]: sdf = df.to_sparse()
## -- End pasted text --
In [20]: sdf.values.nbytes == randn(10000,4).nbytes
Out[20]: True
In [21]: sdf.density
Out[21]: 0.0001
```
It looks to me that the SparseDataFrame is using the same amount of memory as
would a dense numpy array. What am I missing? I'm very excited about the idea of having sparse data support in pandas.
Best,
Immanuel
``` python
In [22]: %paste
>>> from pandas.util.print_versions import show_versions
>>> show_versions()
## -- End pasted text --
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-32-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
```
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} | 0 | 2014-10-07T19:18:10Z | 2014-10-07T19:57:11Z | 2014-10-07T19:57:11Z | CONTRIBUTOR | null | closes #8502
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} | 2 | 2014-10-08T06:34:28Z | 2015-08-08T19:46:01Z | 2015-08-08T19:46:01Z | NONE | null | While trying to get handle on duplicated records I stumble upon [this](http://stackoverflow.com/questions/26244309/how-to-analyze-all-duplicate-entries-in-this-pandas-dataframe) which lead to conclusion that `.duplicated(take_last=True)` seem to be taking the first of duplicate rows and `.duplicate(take_last=False)` takes the last rows.
Here is an illustration:
```
import pandas as pd
data = { 'key1':[1,2,3,1,2,3,2,2,2],
'key2':[2,2,1,2,2,2,2,2,2],
'dup':['d1_1','d2_1', 'n_d','d1_2','d2_2', 'n_d','d2_3','d2_4','d2_5']}
df = pd.DataFrame(data,columns=['key1','key2','dup'])
print df
key1 key2 dup
0 1 2 d1_1
1 2 2 d2_1
2 3 1 n_d
3 1 2 d1_2
4 2 2 d2_2
5 3 2 n_d
6 2 2 d2_3
7 2 2 d2_4
8 2 2 d2_5
```
Now with `take_last=False` it would be fair to expect dn_1s to be in output, but this is not the case:
```
c1 = df.duplicated(['key1', 'key2'], take_last=False)
df[c1]
key1 key2 dup
3 1 2 d1_2
4 2 2 d2_2
6 2 2 d2_3
7 2 2 d2_4
8 2 2 d2_5
```
And `take_last=True` outputs the first rows:
```
c2 = df.duplicated(['key1', 'key2'], take_last=True)
df[c2]
key1 key2 dup
0 1 2 d1_1
1 2 2 d2_1
4 2 2 d2_2
6 2 2 d2_3
7 2 2 d2_4
```
Unless I am misunderstanding the doc:
```
take_last : boolean, default False
Take the last observed row in a row. Defaults to the first row
```
it does feel that `.duplicated()` could be improved by fixing this behavior.
And additionally it would have one more parameter:
```
take_all : boolean, default False
Take all observed rows. Overrides take_last
```
or alternatively a keyword parameter:
```
take : 'last', 'first', 'all', default 'last'
Sets which observed duplicated rows to take. Default: take last observed rows.
```
Right now trying to get all observed duplicates requires applying two above conditions `c1 | c2`.
This was done with pandas 0.14.1.
Thank you.
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| null | 0 | 2014-10-08T09:37:35Z | 2014-10-08T09:57:25Z | 2014-10-08T09:57:25Z | NONE | null | Hello,
The following code ran perfectly fine on 0.14.1, but breaks on 0.15 RC1, raising
ValueError: cannot reindex from a duplicate axis.
Is it a bug or an intended change of behaviour? Thanks.
``` python
import pandas as pd
import numpy as np
d = {'Quantity' : pd.Series([1., 2., 3., 4., 5.],
index=['foo', 'foo', 'foo', 'foo', 'bar']),
'Group' : pd.Series([1, 2, 1, 2, 1], index=['foo', 'foo', 'foo', 'foo', 'bar'])}
df = pd.DataFrame(d)
groups = df.groupby([df.index, 'Group'], sort=False, as_index=True)
temp = groups['Quantity'].apply(lambda x: x+np.roll(x, 1))
df['QuantityApplied'] = temp
```
Pandas 0.15.0 RC1
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} | 14 | 2014-10-08T10:58:17Z | 2014-10-13T14:21:33Z | 2014-10-13T14:20:11Z | CONTRIBUTOR | null | {
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} | 19 | 2014-10-08T15:48:29Z | 2019-12-17T18:14:09Z | 2019-12-17T18:14:09Z | CONTRIBUTOR | null | It would be really great if `to_(filetype)` supported writing to S3.
Here is an example upload to s3 function that takes in a local file and places it on a s3 bucket.
``` python
def upload_to_s3(local_file_path, file_name, bucket_name, s3_directory):
"""
Returns
----------
Uploads local file to appropriate s3 key, and prints status
Parameters
----------
local_file_path : str
ex. 'my/local/path'
file_name : str
ex. 'cleaned_data.csv'
bucket_name : str
ex. 'dsapp-edu-data'
s3_directory : str
ex. 'NC-Cabarrus/cleaned_data'
"""
def percent_cb(complete, total):
"""
Helper function that prints progress
"""
sys.stdout.write('.')
sys.stdout.flush()
conn = boto.connect_s3()
bucket = conn.get_bucket(bucket_name)
full_key_name = os.path.join(s3_directory, file_name)
k = bucket.new_key(full_key_name)
full_filepath = os.path.join(local_file_path, file_name)
k.set_contents_from_filename(full_filepath, cb=percent_cb, num_cb=10)
return None
```
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} | 9 | 2014-10-08T19:42:39Z | 2014-10-30T11:19:44Z | 2014-10-08T20:06:42Z | MEMBER | null | From the example in the v0.13.1 whatsnew docs (http://pandas.pydata.org/pandas-docs/version/0.15.0/whatsnew.html#id12):
```
df = DataFrame({'col':['foo', 0, np.nan]}).sort()
df2 = DataFrame({'col':[np.nan, 0, 'foo']}, index=[2,1,0])
df.equals(df)
```
gives:
```
In [3]: df.equals(df)
C:\Anaconda\lib\site-packages\numpy\core\numeric.py:2367: FutureWarning: numpy e
qual will not check object identity in the future. The comparison did not return
the same result as suggested by the identity (`is`)) and will change.
return bool(asarray(a1 == a2).all())
Out[3]: True
```
The example in basics.rst section does not have this warning (http://pandas.pydata.org/pandas-docs/version/0.15.0/basics.html#comparing-if-objects-are-equivalent):
```
df = DataFrame({'one' : Series(randn(3), index=['a', 'b', 'c']),
'two' : Series(randn(4), index=['a', 'b', 'c', 'd']),
'three' : Series(randn(3), index=['b', 'c', 'd'])})
(df+df).equals(df*2)
```
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} | 2 | 2014-10-08T19:55:26Z | 2014-10-09T13:36:59Z | 2014-10-09T13:36:59Z | CONTRIBUTOR | null | Hi, thanks to all of you who spend time on this project, it is invaluable to me.
Unfortuatnely, i think I have run into an indexing-type issue, either that or my expectations are incorrect. Consider the following script:
``` python
import random
import pandas
import numpy as np
columns = list('abcdefg')
subcols = columns[1:-1]
X = pandas.DataFrame(0.0, columns=columns, index=range(100))
Z = pandas.DataFrame(np.random.randn(100,len(subcols)), columns=subcols, index=range(100))
block1 = list(subcols)
random.shuffle(block1)
X[block1] -= Z
print X.corrwith(Z)
X = pandas.DataFrame(0.0, columns=columns, index=range(100))
X[block1] -= Z[block1]
print X.corrwith(Z)
print pandas.__version__
```
The output i get is something like this (obviously depends on the random inputs)
a NaN
b -0.127386
c -0.073521
d -0.073521
e -0.127386
f -1.000000
g NaN
dtype: float64
a NaN
b -1
c -1
d -1
e -1
f -1
g NaN
dtype: float64
0.15.0rc1-10-g215569a
where the second result is what i would have expected.
I was surprised to find that I have to "reindex" Z by block1 to get a correct result. Is my expectation incorrect?
Many thanks
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} | 53 | 2014-10-08T20:55:57Z | 2014-10-30T11:20:22Z | 2014-10-10T01:17:56Z | CONTRIBUTOR | null | Fixes #7065
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} | 12 | 2014-10-08T21:22:27Z | 2015-09-03T15:25:02Z | 2014-12-05T20:16:33Z | CONTRIBUTOR | null | Determines whether vertical lines will be added to parallel_coordinates plot. I was using parallel coordinates to make a seasonality chart - plotting years on top of each other (days/months of year on x-axis). With that many xticks, the vertical lines were overwhelming. It's possible to remove the lines after the fact, but without specific line identifiers, it isn't a straight forward process. However, preventing them from being added is a one-liner...
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} | 3 | 2014-10-08T21:25:21Z | 2014-11-29T16:48:19Z | 2014-11-29T16:48:19Z | MEMBER | null | Just wondering if this is a bug, from http://stackoverflow.com/questions/26246864/restoring-the-default-display-context-in-pandas
If you use `pd.option_context` accidentally without `with`, this changes the default option values.
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} | 4 | 2014-10-08T21:30:17Z | 2017-09-21T18:15:44Z | 2014-10-08T22:49:43Z | NONE | null | After doing
```
conda create -n dummy_python python=2.7 pandas=0.14 ipython=2.0
```
and then activating dummy_python, I get the following error with a simple .xlsx file that has 3 sheets:
``` python
In [4]: pandas.read_excel('candidate_test.xlsx', sheetname='prices')
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-4-ad7ce8e2fc77> in <module>()
----> 1 pandas.read_excel('candidate_test.xlsx', sheetname='prices')
/home/ely/anaconda/envs/dummy_python/lib/python2.7/site-packages/pandas/io/excel.pyc in read_excel(io, sheetname, **kwds)
111 engine = kwds.pop('engine', None)
112
--> 113 return ExcelFile(io, engine=engine).parse(sheetname=sheetname, **kwds)
114
115
/home/ely/anaconda/envs/dummy_python/lib/python2.7/site-packages/pandas/io/excel.pyc in __init__(self, io, **kwds)
129 def __init__(self, io, **kwds):
130
--> 131 import xlrd # throw an ImportError if we need to
132
133 ver = tuple(map(int, xlrd.__VERSION__.split(".")[:2]))
ImportError: No module named xlrd
```
But with pandas 0.13.1, the same sheet parses with no error from the same command:
``` python
In [2]: pandas.version.version
Out[2]: '0.13.0'
In [3]: pandas.read_excel('candidate_test.xlsx', sheetname='prices')
Out[3]:
date time millis bid offer
0 2012.09.30 22:00:15 1349042415003 1.28490 1.28495
1 2012.09.30 22:00:16 1349042416203 1.28485 1.28495
2 2012.09.30 22:00:18 1349042418302 1.28480 1.28495
3 2012.09.30 22:03:37 1349042617304 1.28480 1.28500
4 2012.09.30 22:05:17 1349042717103 1.28485 1.28500
5 2012.09.30 22:06:05 1349042765403 1.28490 1.28500
6 2012.09.30 22:06:06 1349042766404 1.28495 1.28500
7 2012.09.30 22:06:53 1349042812903 1.28495 1.28505
8 2012.09.30 22:06:53 1349042813004 1.28495 1.28500
9 2012.09.30 22:07:59 1349042879103 1.28485 1.28500
10 2012.09.30 22:08:03 1349042882703 1.28490 1.28500
11 2012.09.30 22:08:04 1349042883903 1.28495 1.28500
12 2012.09.30 22:08:05 1349042885304 1.28485 1.28490
13 2012.09.30 22:08:07 1349042886503 1.28480 1.28490
14 2012.09.30 22:08:13 1349042892702 1.28480 1.28485
15 2012.09.30 22:08:20 1349042900103 1.28475 1.28485
16 2012.09.30 22:08:20 1349042900204 1.28475 1.28480
17 2012.09.30 22:08:38 1349042917604 1.28425 1.28470
18 2012.09.30 22:08:38 1349042917702 1.28460 1.28470
19 2012.09.30 22:08:38 1349042918005 1.28460 1.28480
20 2012.09.30 22:08:39 1349042919103 1.28425 1.28480
21 2012.09.30 22:08:39 1349042919202 1.28450 1.28480
22 2012.09.30 22:08:40 1349042919503 1.28440 1.28480
23 2012.09.30 22:08:49 1349042928702 1.28440 1.28485
24 2012.09.30 22:08:49 1349042929003 1.28440 1.28480
25 2012.09.30 22:10:12 1349043011803 1.28440 1.28475
26 2012.09.30 22:10:12 1349043012004 1.28440 1.28480
27 2012.09.30 22:10:17 1349043017204 1.28440 1.28465
28 2012.09.30 22:11:37 1349043096604 1.28440 1.28450
29 2012.09.30 22:11:40 1349043099503 1.28430 1.28440
30 2012.09.30 22:11:40 1349043099604 1.28430 1.28465
31 2012.09.30 22:11:46 1349043105703 1.28430 1.28440
32 2012.09.30 22:11:46 1349043106105 1.28430 1.28465
33 2012.09.30 22:12:58 1349043178004 1.28430 1.28460
34 2012.09.30 22:13:13 1349043192503 1.28430 1.28465
35 2012.09.30 22:13:40 1349043220003 1.28425 1.28465
36 2012.09.30 22:13:43 1349043222903 1.28430 1.28465
37 2012.09.30 22:15:32 1349043331903 1.28460 1.28465
38 2012.09.30 22:15:34 1349043334303 1.28440 1.28465
39 2012.09.30 22:15:35 1349043334503 1.28430 1.28465
40 2012.09.30 22:15:35 1349043334603 1.28460 1.28465
41 2012.09.30 22:15:36 1349043335902 1.28425 1.28465
42 2012.09.30 22:15:36 1349043336004 1.28440 1.28465
43 2012.09.30 22:15:38 1349043337703 1.28440 1.28460
44 2012.09.30 22:15:38 1349043337803 1.28425 1.28460
45 2012.09.30 22:15:38 1349043337902 1.28430 1.28460
46 2012.09.30 22:15:40 1349043339603 1.28430 1.28465
47 2012.09.30 22:15:55 1349043354903 1.28470 1.28485
48 2012.09.30 22:15:57 1349043356702 1.28450 1.28485
49 2012.09.30 22:16:57 1349043417003 1.28450 1.28480
50 2012.09.30 22:17:09 1349043429302 1.28450 1.28475
51 2012.09.30 22:17:17 1349043436702 1.28425 1.28475
52 2012.09.30 22:17:17 1349043436802 1.28440 1.28475
53 2012.09.30 22:17:23 1349043442802 1.28470 1.28475
54 2012.09.30 22:17:24 1349043443702 1.28425 1.28475
55 2012.09.30 22:17:24 1349043443803 1.28450 1.28475
56 2012.09.30 22:17:48 1349043468303 1.28425 1.28475
57 2012.09.30 22:17:48 1349043468404 1.28440 1.28475
58 2012.09.30 22:17:57 1349043476904 1.28450 1.28475
59 2012.09.30 22:18:10 1349043490303 1.28450 1.28465
... ... ... ... ...
[34650 rows x 5 columns]
```
So the question is, why was a dependency on XLRD introduced, but the library is not installed as a dependency when pandas is installed through conda?
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} | 1 | 2014-10-08T22:00:26Z | 2014-10-10T22:32:42Z | 2014-10-10T22:32:42Z | MEMBER | null | Would it be appropriate to add [xray](https://github.com/xray/xray) to the ecosystem docs?
In brief, the goal of xray is to provide N-dimensional variants of the core pandas data structure (e.g., [as more flexible alternatives to pd.Panel](http://xray.readthedocs.org/en/stable/faq.html)).
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| null | 21 | 2014-10-09T03:35:17Z | 2019-05-31T23:45:20Z | 2015-10-20T23:14:36Z | NONE | null | I'm working on some language analysis and using pandas to munge the data and grab some descriptive stats. This is just an illustrative example, I'm doing all kinds of slighty different things.
Suppose I have a series containing chunks of text, and I want to turn the line into multiple lines, preserving the index values. Here are the naive results:
``` python
In [53]: s=pd.Series(['This is text No 1.', 'and here is no. 2','and 3'],index=['Alice','Bob','Alice'])
...: s
Out[53]:
Alice This is text No 1.
Bob and here is no. 2
Alice and 3
dtype: object
In [54]: s.map(lambda x: x.split(' '))
Out[54]:
Alice [This, is, text, No, 1.]
Bob [and, here, is, no., 2]
Alice [and, 3]
dtype: object
In [55]: s.apply(lambda x: pd.Series(x.split(' ')))
Out[55]:
0 1 2 3 4
Alice This is text No 1.
Bob and here is no. 2
Alice and 3 NaN NaN NaN
```
What I'd like is to be able to do is (Made up example):
``` python
In [67]: s.flatmap(lambda x: x.split(' '))
Out[67]:
Alice This
Alice is
Alice text
Alice No
Alice 1.
Bob and
Bob here
Bob is
Bob no.
Bob 2
Alice and
Alice 3
dtype: object
```
In general, I'd like to be able to explode a single row in a dataframe into multiple rows, by transforming one column value into multiple values, each becoming a new row with the value of other columns prserved, for example:
``` python
In [69]: df=pd.DataFrame([['2014-01-01','Alice',"A B"],['2014-01-02','Bob','C D']],columns=['dt','name','text'])
...: df
Out[69]:
dt name text
0 2014-01-01 Alice A B
1 2014-01-02 Bob C D
In [70]: df.flatmap(lambda x: x.split(),on='text')
dt name text
0 2014-01-01 Alice A
1 2014-01-01 Alice B
2 2014-01-01 Bob C
3 2014-01-01 Bob D
```
Perhaps there's another way to do this, but that's how my natural instict suggests this should be done, flatmap is a fairly universal concept.
Groupby already does similar things based on return type, It doesn't have to be limited to groupby though.
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https://api.github.com/repos/pandas-dev/pandas/issues/8518 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8518/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8518/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8518/events | https://github.com/pandas-dev/pandas/issues/8518 | 45,344,939 | MDU6SXNzdWU0NTM0NDkzOQ== | 8,518 | BUG: read_pickle and categorical data | {
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} | 1 | 2014-10-09T09:27:40Z | 2014-10-10T12:50:13Z | 2014-10-10T12:50:13Z | NONE | null | I can pickle dataframes with categoricals but not import them again. Since HDF is officially not supported yet: is there any working alternative for persistence of categoricals?
``` python
df = pd.DataFrame({'a':['x','y','x']})
df.to_pickle('/tmp/df.pkl')
pd.read_pickle('/tmp/df.pkl')
df['a'] = df['a'].astype('category')
df.to_pickle('/tmp/dfcat.pkl')
pd.read_pickle('/tmp/dfcat.pkl')
```
```
TypeError Traceback (most recent call last)
<ipython-input-21-57b10ac045a6> in <module>()
7
8 df.to_pickle('/tmp/dfcat.pkl')
----> 9 pd.read_pickle('/tmp/dfcat.pkl')
//anaconda/envs/pd15/lib/python2.7/site-packages/pandas-0.15.0rc1_10_g215569a-py2.7-macosx-10.5-x86_64.egg/pandas/io/pickle.pyc in read_pickle(path)
58
59 try:
---> 60 return try_read(path)
61 except:
62 if PY3:
//anaconda/envs/pd15/lib/python2.7/site-packages/pandas-0.15.0rc1_10_g215569a-py2.7-macosx-10.5-x86_64.egg/pandas/io/pickle.pyc in try_read(path, encoding)
55 except:
56 with open(path, 'rb') as fh:
---> 57 return pc.load(fh, encoding=encoding, compat=True)
58
59 try:
//anaconda/envs/pd15/lib/python2.7/site-packages/pandas-0.15.0rc1_10_g215569a-py2.7-macosx-10.5-x86_64.egg/pandas/compat/pickle_compat.pyc in load(fh, encoding, compat, is_verbose)
114 up.is_verbose = is_verbose
115
--> 116 return up.load()
117 except:
118 raise
//anaconda/envs/pd15/lib/python2.7/pickle.pyc in load(self)
856 while 1:
857 key = read(1)
--> 858 dispatch[key](self)
859 except _Stop, stopinst:
860 return stopinst.value
//anaconda/envs/pd15/lib/python2.7/pickle.pyc in load_build(self)
1215 setstate = getattr(inst, "__setstate__", None)
1216 if setstate:
-> 1217 setstate(state)
1218 return
1219 slotstate = None
//anaconda/envs/pd15/lib/python2.7/site-packages/pandas-0.15.0rc1_10_g215569a-py2.7-macosx-10.5-x86_64.egg/pandas/core/internals.pyc in __setstate__(self, state)
2311 self.blocks = tuple(
2312 unpickle_block(b['values'], b['mgr_locs'])
-> 2313 for b in state['blocks'])
2314 else:
2315 # discard anything after 3rd, support beta pickling format for a
//anaconda/envs/pd15/lib/python2.7/site-packages/pandas-0.15.0rc1_10_g215569a-py2.7-macosx-10.5-x86_64.egg/pandas/core/internals.pyc in <genexpr>((b,))
2311 self.blocks = tuple(
2312 unpickle_block(b['values'], b['mgr_locs'])
-> 2313 for b in state['blocks'])
2314 else:
2315 # discard anything after 3rd, support beta pickling format for a
//anaconda/envs/pd15/lib/python2.7/site-packages/pandas-0.15.0rc1_10_g215569a-py2.7-macosx-10.5-x86_64.egg/pandas/core/internals.pyc in unpickle_block(values, mgr_locs)
2303 if values.dtype == 'M8[us]':
2304 values = values.astype('M8[ns]')
-> 2305 return make_block(values, placement=mgr_locs)
2306
2307 if (isinstance(state, tuple) and len(state) >= 4
//anaconda/envs/pd15/lib/python2.7/site-packages/pandas-0.15.0rc1_10_g215569a-py2.7-macosx-10.5-x86_64.egg/pandas/core/internals.pyc in make_block(values, placement, klass, ndim, dtype, fastpath)
2075
2076 return klass(values, ndim=ndim, fastpath=fastpath,
-> 2077 placement=placement)
2078
2079
//anaconda/envs/pd15/lib/python2.7/site-packages/pandas-0.15.0rc1_10_g215569a-py2.7-macosx-10.5-x86_64.egg/pandas/core/internals.pyc in __init__(self, values, placement, fastpath, **kwargs)
1583 super(CategoricalBlock, self).__init__(_maybe_to_categorical(values),
1584 fastpath=True, placement=placement,
-> 1585 **kwargs)
1586
1587 @property
//anaconda/envs/pd15/lib/python2.7/site-packages/pandas-0.15.0rc1_10_g215569a-py2.7-macosx-10.5-x86_64.egg/pandas/core/internals.pyc in __init__(self, values, placement, ndim, fastpath)
1073 # kludgetastic
1074 if ndim is None:
-> 1075 if len(placement) != 1:
1076 ndim = 1
1077 else:
TypeError: object of type 'slice' has no len()
```
## INSTALLED VERSIONS
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
pandas: 0.15.0rc1-10-g215569a
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https://api.github.com/repos/pandas-dev/pandas/issues/8519 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8519/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8519/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8519/events | https://github.com/pandas-dev/pandas/pull/8519 | 45,354,535 | MDExOlB1bGxSZXF1ZXN0MjI0ODYzNjU= | 8,519 | BUG: fix CategoricalBlock pickling | {
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} | 12 | 2014-10-09T11:21:56Z | 2014-10-10T13:17:54Z | 2014-10-10T12:50:13Z | CONTRIBUTOR | null | This should fix #8518.
Apparently, `NonConsolidatableMixIn.__init__` was copied from a pre-`BlockPlacement` version of `SparseBlock.__init__` which was incompatible with placement being a slice. The current one is, so I moved it to NonConsolidatableMixIn class almost verbatim.
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} | 0 | 2014-10-09T12:06:03Z | 2014-10-09T13:36:59Z | 2014-10-09T13:36:59Z | CONTRIBUTOR | null | closes #8511
closes #5104
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} | 8 | 2014-10-09T14:19:41Z | 2020-05-10T15:52:55Z | 2020-05-10T15:52:55Z | NONE | null | (edit):
```python
import pandas as pd
import datetime as dt
import numpy as np
datetime_start = dt.datetime(2014, 9, 1, 9, 30)
datetime_end = dt.datetime(2014, 9, 1, 16, 0)
tt = pd.date_range(datetime_start, datetime_end, freq='1Min')
df = pd.DataFrame(np.arange(len(tt)), index=tt, columns=['A'])
```
The first item is at 9:30, which is not divisible by 8 minutes. We'd like `df.resample('8T', base='start').first()` to be equivalent to `df.resample('8T', base=2)`.
```python
In [13]: df.resample("8T", base=2).first()
Out[13]:
A
2014-09-01 09:30:00 0
2014-09-01 09:38:00 8
2014-09-01 09:46:00 16
2014-09-01 09:54:00 24
2014-09-01 10:02:00 32
```
---
It seems that for 1Min bar data, resample() with sampling frequency of any multiple of 8 has a bug. The code below illustrates the bug when resampling is done at [3, 5, 6, 8, 16] Min. For both 3 and 5 frequency, the first entry of the resampled dataframe index starts at the base timestamp (9:30 in this case) while for frequencies 8 and 16, the resampled index starts at 9:26 and 9:18 respectively.
``` Python
import pandas as pd
import datetime as dt
import numpy as np
datetime_start = dt.datetime(2014, 9, 1, 9, 30)
datetime_end = dt.datetime(2014, 9, 1, 16, 0)
tt = pd.date_range(datetime_start, datetime_end, freq='1Min')
df = pd.DataFrame(np.arange(len(tt)), index=tt, columns=['A'])
for freq in [3, 5, 6, 8, 16]:
print(freq)
print(df.resample(str(freq) + 'Min', how='first', base=30).head(2))
```
produces the following output:
```
3
A
2014-09-01 09:30:00 0
2014-09-01 09:33:00 3
5
A
2014-09-01 09:30:00 0
2014-09-01 09:35:00 5
6
A
2014-09-01 09:30:00 0
2014-09-01 09:36:00 6
8
A
2014-09-01 09:26:00 0
2014-09-01 09:34:00 4
16
A
2014-09-01 09:18:00 0
2014-09-01 09:34:00 4
```
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} | 5 | 2014-10-09T16:53:56Z | 2014-10-10T08:36:26Z | 2014-10-09T18:32:16Z | NONE | null | The following code works fine with pandas 0.14.1
``` python
import pandas as pd
pd.Series([1, 1], index=[True, False]).drop(True)
```
Output:
``` python
False 1
dtype: int64
```
In pandas 0.15rc1, the code blows up and produces the following error:
```
ValueError Traceback (most recent call last)
<ipython-input-35-f9a275b6ce2c> in <module>()
----> 1 pd.Series([1, 1], index=[True, False]).drop(True)
/home/d/.virtualenvs/test/local/lib/python2.7/site-packages/pandas-0.15.0rc1-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in drop(self, labels, axis, level, inplace, **kwargs)
1531 axis, axis_ = self._get_axis(axis), axis
1532
-> 1533 if axis.is_unique:
1534 if level is not None:
1535 if not isinstance(axis, MultiIndex):
/home/d/.virtualenvs/test/local/lib/python2.7/site-packages/pandas-0.15.0rc1-py2.7-linux-x86_64.egg/pandas/lib.so in pandas.lib.cache_readonly.__get__ (pandas/lib.c:38943)()
/home/d/.virtualenvs/test/local/lib/python2.7/site-packages/pandas-0.15.0rc1-py2.7-linux-x86_64.egg/pandas/core/index.pyc in is_unique(self)
582 def is_unique(self):
583 """ return if the index has unique values """
--> 584 return self._engine.is_unique
585
586 def is_integer(self):
/home/d/.virtualenvs/test/local/lib/python2.7/site-packages/pandas-0.15.0rc1-py2.7-linux-x86_64.egg/pandas/index.so in pandas.index.IndexEngine.is_unique.__get__ (pandas/index.c:4449)()
/home/d/.virtualenvs/test/local/lib/python2.7/site-packages/pandas-0.15.0rc1-py2.7-linux-x86_64.egg/pandas/index.so in pandas.index.IndexEngine._do_unique_check (pandas/index.c:4901)()
/home/d/.virtualenvs/test/local/lib/python2.7/site-packages/pandas-0.15.0rc1-py2.7-linux-x86_64.egg/pandas/index.so in pandas.index.IndexEngine._ensure_mapping_populated (pandas/index.c:5106)()
/home/d/.virtualenvs/test/local/lib/python2.7/site-packages/pandas-0.15.0rc1-py2.7-linux-x86_64.egg/pandas/index.so in pandas.index.IndexEngine.initialize (pandas/index.c:5203)()
/home/d/.virtualenvs/test/local/lib/python2.7/site-packages/pandas-0.15.0rc1-py2.7-linux-x86_64.egg/pandas/hashtable.so in pandas.hashtable.PyObjectHashTable.map_locations (pandas/hashtable.c:12440)()
ValueError: Does not understand character buffer dtype format string ('?')
```
Is this the expected behaviour in the new release?
cheers
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/28"
} | 0 | 2014-10-09T17:11:37Z | 2014-10-09T18:32:16Z | 2014-10-09T18:32:16Z | CONTRIBUTOR | null | closes #8522
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} | 16 | 2014-10-09T21:50:24Z | 2019-10-21T20:06:54Z | null | NONE | null | Hi,
I'm working with two colleagues (@FrancescElies & @JaviBi4) on seeing if we can add groupby to bcolz, what we did first was to see if we can make Pandas' excellent klib implementation more library independent. See https://github.com/CarstVaartjes/khash
A while back khash (https://github.com/attractivechaos/klib) was updated to 0.2.8:
2013-05-02 (0.2.8):
```
* Use quadratic probing. When the capacity is power of 2, stepping function
i*(i+1)/2 guarantees to traverse each bucket. It is better than double
hashing on cache performance and is more robust than linear probing.
In theory, double hashing should be more robust than quadratic probing.
However, my implementation is probably not for large hash tables, because
the second hash function is closely tied to the first hash function,
which reduce the effectiveness of double hashing.
Reference: http://research.cs.vt.edu/AVresearch/hashing/quadratic.php
```
We updated the klib to it (which was something of a headache to be honest), but the quadratic probing seems to be quite faster which might make it interesting for Pandas to retrofit this into the general Pandas master. See the example:
```
import numpy as np
import random
import pandas as pd
from khash.hashtable import Int64HashTable, StringHashTable
def new_klib_int(input_array):
ht = Int64HashTable(len(input_array))
return ht.get_labels_groupby(input_array)
def new_klib_str(input_array):
ht = StringHashTable(len(input_array))
return ht.factorize(input_array)
def new_klib_float(input_array):
ht = Float64HashTable(len(input_array))
return ht.factorize(input_array)
a = np.random.randint(100, size=10000000)
b = np.fromiter((random.choice(['NY', 'IL', 'OH', 'CA']) for _ in xrange(10000000)), dtype='S2')
b = pd.Series(b).values
c = np.random.random_sample(10000000) * 10000
%timeit pd.factorize(a)
%timeit new_klib_int(a)
%timeit pd.factorize(b)
%timeit new_klib_str(b)
%timeit pd.factorize(c)
%timeit new_klib_float(c)
In [20]: %timeit pd.factorize(a)
10 loops, best of 3: 122 ms per loop
In [21]: %timeit new_klib_int(a)
10 loops, best of 3: 101 ms per loop
In [22]: %timeit pd.factorize(b)
1 loops, best of 3: 496 ms per loop
In [23]: %timeit new_klib_str(b)
10 loops, best of 3: 165 ms per loop
In [36]: %timeit pd.factorize(c)
1 loops, best of 3: 1.61 s per loop
In [37]: %timeit new_klib_float(c)
1 loops, best of 3: 1.44 s per loop
```
So a 20%ish improvement for int64 and a 65% increase for strings in this example. Just thought to give you a heads up about this, as you might have missed 0.2.8 (and it's a bit of a pain to adjust everything so this might help to make Pandas even faster)
Edit: added float64 example too (10-15% improvement)
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| null | 2 | 2014-10-10T00:21:53Z | 2015-10-20T23:12:51Z | 2015-10-20T23:12:51Z | MEMBER | null | I didn't find any search results for this particular Exception:
```
>>> df2 = pandas.DataFrame(df_data, index=df_ind, columns=df_cols)
python(86000,0x7fff7a7aa310) malloc: *** mach_vm_map(size=200447690256384) failed (error code=3)
*** error: can't allocate region
*** set a breakpoint in malloc_error_break to debug
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 243, in __init__
arrays, columns = _to_arrays(data, columns, dtype=dtype)
File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 4639, in _to_arrays
dtype=dtype)
File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 4760, in _list_of_dict_to_arrays
content = list(lib.dicts_to_array(data, list(columns)).T)
File "lib.pyx", line 443, in pandas.lib.dicts_to_array (pandas/lib.c:9308)
MemoryError
```
If it matters, df_data is a list of dictionaries, on the order of 10^4 entries, with about 10 keys apiece. df_ind and df_cols are lists of datetime objects and integers, respectively. Before this error was thrown, Activity Monitor registered this process as taking about 200 MB of memory. I can recreate the contents of these lists if needbe.
Shortly afterwards I tried the same thing without passing columns:
`>>> df3 = pandas.DataFrame(df_data, index=df_ind)`
This crashed my system, but I got a screenshot before rebooting:


Immediately before this command, PID 86000 was at ~200 MB and kernel_task was ~1 GB. After I killed the process, kernel_task fell pretty quickly.
Possibly relevant version information:
```
>>> import pandas
>>> from pandas.util.print_versions import show_versions
>>> show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20.2
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: None
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2013b
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: 3.3.5
bs4: 4.3.2
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
Let me know if I can provide any more useful information.
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} | 21 | 2014-10-10T02:56:48Z | 2014-11-22T14:47:11Z | 2014-11-21T23:20:24Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/7724
closes https://github.com/pydata/pandas/issues/2646
on current master:
```
>>> df
jolia
jim joe jolie joline
1 z 2014-10-14 a 30
y 2014-10-13 b 3
x 2014-10-12 c 15
0 z 2014-10-11 d 35
y 2014-10-10 e 43
x 2014-10-09 f 36
```
False negative if the key length exceeds lexsort depth:
```
>>> (0,) in df.index
False
>>> (0, 'z') in df.index
False
>>> (0, 'z', '2014-10-11') in df.index
False
>>> (0, 'z', Timestamp('2014-10-11')) in df.index
False
>>> (0, 'z', '2014-10-11', 'd') in df.index
False
```
only ones which work:
```
>>> 0 in df.index
True
>>> (0, 'z', Timestamp('2014-10-11'), 'd') in df.index
True
```
which take a different code paths. The last one only works if the index is unique:
```
>>> (0, 'z', Timestamp('2014-10-11'), 'd') in pd.concat([df, df]).index
False
```
for all of the false negative cases above, obviously `df.loc[key]` fails:
```
>>> df.loc[(0, 'z', Timestamp('2014-10-11'))]
KeyError: 'Key length (3) was greater than MultiIndex lexsort depth (0)'
```
_Some of these issues persist even if the index is lexically sorted:_
```
>>> df.sort_index(inplace=True)
>>> df # lexically sorted
jolia
jim joe jolie joline
0 x 2014-10-09 f 36
y 2014-10-10 e 43
z 2014-10-11 d 35
1 x 2014-10-12 c 15
y 2014-10-13 b 3
z 2014-10-14 a 30
```
date-time indexing with a full-key fails if index is unique:
```
>>> (0, 'x', '2014-10-09') in df.index # partial key, works!
True
>>> (0, 'x', '2014-10-09', 'f') in df.index # full key, unique index, breaks!
False
```
also, non-unique lexically sorted index always returns false positive with any full key:
```
>>> xdf = pd.concat([df, df]).sort_index()
>>> xdf
jolia
jim joe jolie joline
0 x 2014-10-09 f 36
f 36
y 2014-10-10 e 43
e 43
z 2014-10-11 d 35
d 35
1 x 2014-10-12 c 15
c 15
y 2014-10-13 b 3
b 3
z 2014-10-14 a 30
a 30
>>> (0, '$', '2014-10-09') in xdf.index # partial key works
False
>>> (0, '$', '2014-10-09', '#') in xdf.index # full key always `True`
True
>>> xdf.loc[(0, '$', '2014-10-09', '#')]
KeyError: 'the label [$] is not in the [columns]'
```
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} | 13 | 2014-10-10T09:36:32Z | 2014-10-17T07:22:32Z | 2014-10-17T07:22:32Z | NONE | null | I am converting a Stata dataset to a dataframe, then multiply two columns to create a third one. The chosen datatype for the two columns is int8 and for some reason the new column blindly follows that.
For instance, this code
``` Python
df = pd.read_stata(file)
df['w_age_educ'] = df['w_age'] * df['weduc']
print(df[['w_age', 'weduc', 'w_age_educ']].dtypes)
print(df[['w_age', 'weduc', 'w_age_educ']][:3])
```
would give me
``` Python
w_age int8
weduc int8
w_age_educ int8
dtype: object
w_age weduc w_age_educ
0 44 14 104
1 34 13 -70
2 33 18 82
```
Is this a bug or intended behavior? If the latter, how can I get my desired product column?
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| null | 2 | 2014-10-10T09:58:33Z | 2015-10-20T23:13:01Z | 2015-10-20T23:13:01Z | CONTRIBUTOR | null | when doing dataframe.plot(kind='barh',rot=45), then the labels of the y axis are rotated. Generally rotation is applied to avoid label overlapping, and that's a problem mostly for the x axis.
Should not therefore the keyword apply to the x axis, even though technically the y axis is the "category axis" in this case?
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| null | 1 | 2014-10-10T10:24:40Z | 2014-10-11T14:10:23Z | 2014-10-11T14:10:23Z | CONTRIBUTOR | null | This parameter apparently only affects the main axis. Should it not apply also to the other axis, and possibly to other elements as well, such as legend and labels?
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} | 6 | 2014-10-10T10:55:54Z | 2018-07-08T14:32:51Z | 2018-07-06T23:03:34Z | NONE | null | The behaviour below occurs in versions `'0.15.0rc1-21-g32c5016'` and `'0.14.1'`.
When the `label` passed to the `drop` method of a `Series` is not in the index:
(a) if the index is not all booleans then an error is raised
(b) if the index is all booleans and has length > 1 then no error is raised; the original series is returned
(c) if the index is all booleans and has length == 1 then an error is raised
I propose that:
1. the difference between the behaviour in (a) and (b) should be documented
2. the behaviour in (c) should be changed to match that in (b)
Examples of current behaviour:
(a)
``` python
>>> pd.Series([1, 1], index=['a', True]).drop(False)
ValueError: labels [False] not contained in axis
```
(b)
``` python
>>> pd.Series([1, 1], index=[True, True]).drop(False)
True 1
True 1
dtype: int64
```
(c)
``` python
>>> pd.Series([1], index=[True]).drop(False)
ValueError: labels [False] not contained in axis
```
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} | 29 | 2014-10-10T11:36:13Z | 2018-06-18T22:34:30Z | 2018-06-18T22:34:30Z | NONE | null | Works with datetime.datetime, does not work with pandas.tslib.Timestamp.
Encounter the issue when trying to resample hourly a series.
```
import pandas as pd
from datetime import datetime as dt
import pytz
tz = 'Europe/Berlin'
d = dt(2014,10,26,02,00)
tz = pytz.timezone(tz)
tz.localize(d)
x = pd.Series(0., [d]).index[0]
x.tz_localize(tz)
```
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} | 5 | 2014-10-10T15:05:59Z | 2015-03-17T00:18:39Z | 2015-03-17T00:18:39Z | MEMBER | null | Overview of the issues:
- [ ] Number of rows is incorrect (#7508)
- [ ] Setting `pd.options.display.max_rows` to larger values has no effect (https://github.com/pydata/pandas/issues/7508#issuecomment-58669898)
- [ ] formatting seems to occur seperately for both parts (this issue #8532, see two exaples below)
---
When a Series gets truncated, it seems that both parts are formatted separately, with the consequence that eg:
- the float formatting can be different in both parts
- the alignment (depending on the width of the index) is different in both parts
Note that this is not the case with a DataFrame
```
In [44]: s = pd.Series([1,1,1,1,1,1,1,1,1,1,0.9999,1,1]*10)
In [45]: pd.options.display.max_rows = 10
In [46]: s
Out[46]:
0 1
1 1
2 1
...
127 0.9999
128 1.0000
129 1.0000
Length: 130, dtype: float64
In [47]: s.to_frame()
Out[47]:
0
0 1.0000
1 1.0000
2 1.0000
3 1.0000
4 1.0000
.. ...
125 1.0000
126 1.0000
127 0.9999
128 1.0000
129 1.0000
[130 rows x 1 columns]
```
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} | 1 | 2014-10-10T15:59:57Z | 2015-05-12T08:33:59Z | 2015-05-12T08:33:50Z | CONTRIBUTOR | null | Currently `pd.read_sql_query()` accepts an `sqlalchemy.engine.base.Engine` instance as the `con` parameter. Using that, it presumably creates a new `sqlalchemy.engine.base.Connection` to connect to the DB server and issue the query.
However, as most (all?) DB servers associate a session with a connection, this precludes issuing queries for an existing session/connection.
For example, it is common to break up large queries into a series of steps that create TEMPorary intermediate result tables, possibly joining some into further intermediate tables, and then querying for the final result. The lifetime and scope of a TEMP table is that of the session/connection.
Hence, something like:
``` python
con = engine.connect()
con.execute('CREATE TEMP TABLE tmptable ...')
result = pd.read_sql_query('SELECT * FROM tmptable ..', engine)
```
will fail as the tmptable isn't available to the connection/session created by read_sql_query().
Concatenating the SQL statements into one query string with ';' statement seperator, or using the SQLAlchemy `text` class to attempt to create the table and query it in one SQL _query_ also fails as leads to SQLAlchemy claiming the query doesn't return results (perhaps because it doesn't being with "SELECT"?)
This, and other similar use-cases involving multiple queries within a single session/connection context, could be easily supported by allowing a `sqlalchemy.engine.base.Connection` to be passed to the `read_sql_query()` and similar functions, whereby they would use the supplied connection rather than creating a new one. An added benefit would be the reduction in overhead of repeatedly creating and tearing down connections to the DB server and also allowing users to implement connection pooling when necessary.
(Issue created at suggestion of @joris in this [StackOverflow question](http://stackoverflow.com/questions/26286615/how-can-pandas-read-sql-query-query-a-temp-table) )
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} | 3 | 2014-10-10T18:11:45Z | 2014-10-10T22:38:00Z | 2014-10-10T22:32:42Z | MEMBER | null | Fixes #8516
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| null | 6 | 2014-10-11T02:48:43Z | 2015-02-09T00:43:14Z | 2014-10-13T02:19:33Z | CONTRIBUTOR | null | This is a follow on PR as a result of [seeing travis and sphinx render ~30 in line examples for the cookbook](https://github.com/pydata/pandas/pull/8288#issuecomment-58734824).
Continues to address #6918
Initial PR: https://github.com/pydata/pandas/pull/8288
PS - apologies for wanting to rush the first PR. My evening opened up unexpectedly. Feel free to merge this latest, or provide more feedback for me to work on later. I'm going off grid for 2+ days starting oct 10th 11 PM EST.
The latest rendering is [here](https://cloud.githubusercontent.com/assets/6062071/4601203/f3dcdc76-50f0-11e4-8466-4db698bdc3b4.png):
Edit : Removed inline view of humongous rendering, still available for viewing at link above.
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} | 8 | 2014-10-11T12:40:15Z | 2017-04-05T02:06:09Z | 2014-10-11T19:27:25Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8491
Building the docs now to check that everything looks ok.
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} | 4 | 2014-10-11T14:41:45Z | 2015-10-18T23:52:37Z | 2015-10-18T23:52:37Z | CONTRIBUTOR | null | This continues a discussion started at the end of GH 8512
Summary: NumPy is changing the behavior of `==`. It no longer checks for `is` relationships between elements. Therefore,
In older versions of NumPy:
> > > array([nan], dtype=object) == array([nan], dtype=object)
> > > array([ True], dtype=bool)
In future NumPy, I think this will happen:
> > > array([nan], dtype=object) == array([nan], dtype=object)
> > > array([ False], dtype=bool)
In current NumPy version 1.9, a DeprecationWarning or FutureWarning is issued.
One place where this issue affects Pandas is
nosetests -v pandas/tests/test_series.py:TestSeries.test_replace
The problem originates in internals.py/_possibly_compare which is called like this:
```
_possibly_compare(np.array([1, np.nan], dtype='O'), 'foo', operater.eq)
_possibly_compare(np.array(['2000-01-02T19:00'], dtype='datetime64[ns]'), 1, operater.eq)
```
Both of these raise DeprecationWarnings.
`a` is always an array, `b` is a scalar. AFAICT, the intent of
`_possibly_compare` is to return a boolean mask where elements in `a` equal `b`.
Since we want a boolean mask, `array_equivalent` is not a help here since that
just returns a single bool.
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} | 1 | 2014-10-11T15:23:35Z | 2018-07-08T14:32:59Z | 2018-07-06T23:01:49Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/26295861/pandas-tshift-slow-in-groups
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| null | 5 | 2014-10-11T17:33:24Z | 2014-10-13T11:51:28Z | 2014-10-11T18:34:21Z | NONE | null | I have a Series with a MultiIndex like this:
```
In [174]: a
Out[174]:
hour year weekday workingday
0 2011 0 0 count 4.000000
mean 76.750000
std 53.368998
min 17.000000
25% 44.000000
50% 75.000000
75% 107.750000
max 140.000000
1 count 28.000000
mean 29.500000
std 31.792732
min 4.000000
25% 15.000000
50% 24.500000
75% 35.500000
...
23 2012 5 0 mean 152.687500
std 55.711490
min 20.000000
25% 119.000000
50% 148.000000
75% 187.750000
max 239.000000
6 0 count 34.000000
mean 71.617647
std 32.471913
min 23.000000
25% 46.000000
50% 68.500000
75% 104.250000
max 123.000000
Length: 3456, dtype: float64
```
And I'd like to get, for example, the rows where `hour=1, year=2011, weekday=[1,2,3,4]`
Intuitively, I would do this: `a.loc[1,2011,1:4]` which would work in a 4-dimensional numpy array. Unfortunately it raises the error:
``` pytb
/usr/lib/python2.7/dist-packages/pandas/core/index.pyc in get_loc_level(self, key, level, drop_level)
3214 continue
3215 else:
-> 3216 raise TypeError(key)
3217
3218 if indexer is None:
TypeError: (0, 2011, slice(0, 4, None))
```
Even If I try suppling the index list instead of a slice: `a.loc[0,2011,[1,2,3,4]]` I get an error: `unhashable type: 'list'`
Is there a reason why this syntax is not supported? How can I select labels slices in a MultiIndex?
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} | 15 | 2014-10-11T18:28:48Z | 2020-01-20T23:49:14Z | 2020-01-20T23:49:14Z | NONE | null | openpyxl and xlrd do not support XLSB. I'm curious if anyone has taken a look at integrating (more like creating) the functionality into Pandas. Looks like it could be a Python package in it self.
Spec from Microsoft:
http://msdn.microsoft.com/en-us/library/cc313133(v=office.12).aspx
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} | 2 | 2014-10-11T19:51:12Z | 2017-04-05T02:06:11Z | 2014-10-28T03:56:19Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/8494
I took the easy solution and just copied the series.
Alternatively we could attach a label property to the MPLObject, and check that wherever we check the index name.
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} | 9 | 2014-10-11T22:16:59Z | 2016-04-29T13:40:30Z | 2016-04-29T13:40:30Z | NONE | null | The following code seems to raise an error, since the result object does not make sense (well, at least to me):
```
In [61]: import datetime
In [62]: import pandas as pd
In [63]: df = pd.DataFrame.from_records ( [[datetime.datetime(2014,9,10),1234,"start"],
[datetime.datetime(2013,10,10),1234,"start"]], columns = ["date", "change", "event"] )
In [64]: df
Out[64]:
date change event
0 2014-09-10 1234 start
1 2013-10-10 1234 start
In [65]: ts = df.set_index('date')
In [66]: ts
Out[66]:
change event
date
2014-09-10 1234 start
2013-10-10 1234 start
In [67]: byperiod = ts.groupby([pd.TimeGrouper(freq="M"), "event"], as_index=False)
In [68]: byperiod.groups
Out[68]:
{<pandas.tseries.resample.TimeGrouper at 0xab6bcaec>: [Timestamp('2014-09-10 00:00:00')],
'event': [Timestamp('2013-10-10 00:00:00')]}
```
I would expect, for Out[68], two groups, one for each (date, event) pair.
Am I wring, or this is a bug?
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} | 1 | 2014-10-12T00:59:45Z | 2014-10-13T11:36:56Z | 2014-10-13T11:36:56Z | CONTRIBUTOR | null | A MultiIndex with a DatetimeIndex level is slower than a similar index with numeric levels:
```
lev1 = range(10000)
lev2 = range(100)
mi = pd.MultiIndex.from_product([lev1, lev2])
%time mi.values
```
CPU times: user 571 ms, sys: 41 ms, total: 612 ms
Wall time: 612 ms
```
lev1 = range(10000)
lev2 = pd.date_range('1/1/2014', periods=100)
mi = pd.MultiIndex.from_product([lev1, lev2])
%time mi.values
```
CPU times: user 2.51 s, sys: 68 ms, total: 2.58 s
Wall time: 2.58 s
The overhead is in boxing the level values when generating the tuples for the `values` property. The overhead can be minimized if we do the boxing once for each distinct value rather than for each occurrence of that value in the tuples.
I can send in a PR shortly.
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} | 2 | 2014-10-12T01:56:02Z | 2014-10-13T15:32:55Z | 2014-10-13T11:36:39Z | CONTRIBUTOR | null | Addresses #8543. The benefits are most pronounced when there are a small number of distinct datetimes relative to the number of rows in the index.
vbench results with `-r multi`:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
multi_with_datetime_level | 1.5767 | 46.8633 | 0.0336 |
reindex_multiindex | 1.1919 | 1.2113 | 0.9840 |
groupby_transform_multi_key4 | 121.5626 | 123.5036 | 0.9843 |
frame_multi_and_st | 33.3813 | 33.5990 | 0.9935 |
groupby_multi_series_op | 11.2731 | 11.3403 | 0.9941 |
groupby_multi_count | 7.2227 | 7.2653 | 0.9941 |
groupby_multi_different_functions | 9.5117 | 9.5397 | 0.9971 |
groupby_multi_size | 19.4443 | 19.4850 | 0.9979 |
frame_multi_and_no_ne | 55.7680 | 55.8747 | 0.9981 |
groupby_multi_different_numpy_functions | 9.5197 | 9.5037 | 1.0017 |
groupby_transform_multi_key1 | 65.6123 | 65.4763 | 1.0021 |
read_table_multiple_date | 148.9107 | 148.2257 | 1.0046 |
join_dataframe_index_multi | 15.1207 | 15.0447 | 1.0051 |
stat_ops_level_frame_sum_multiple | 6.7097 | 6.6660 | 1.0065 |
groupby_multi_cython | 12.6956 | 12.5817 | 1.0091 |
frame_multi_and | 19.6610 | 19.4677 | 1.0099 |
read_table_multiple_date_baseline | 68.1753 | 67.3580 | 1.0121 |
multiindex_from_product | 10.0873 | 9.9500 | 1.0138 |
groupby_transform_multi_key2 | 45.2770 | 44.5527 | 1.0163 |
groupby_multi_python | 122.5337 | 119.1190 | 1.0287 |
stat_ops_level_series_sum_multiple | 5.2770 | 5.1050 | 1.0337 |
groupby_transform_multi_key3 | 788.8023 | 736.1453 | 1.0715 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
```
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} | 17 | 2014-10-12T04:37:14Z | 2021-02-03T21:23:26Z | null | CONTRIBUTOR | null | http://pandas.pydata.org/developers.html
- https://mail.python.org/mailman/listinfo/pandas-dev
Examples:
- https://en.wikipedia.org/wiki/Responsible_disclosure
- https://docs.djangoproject.com/en/dev/internals/security/#reporting-security-issues
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| null | 5 | 2014-10-13T04:46:47Z | 2014-10-13T11:41:39Z | 2014-10-13T11:39:25Z | NONE | null | ``` python
In [93]: ts= pd.Timestamp(datetime.datetime(2014, 1,1)).tz_localize(
...: 'America/New_York')
...: df=pd.DataFrame([1],index=[ts])
...: df
Out[93]:
0
2014-01-01 00:00:00-05:00 1
In [94]: df.to_excel('/home/yank/2.xls')
TypeError: can't subtract offset-naive and offset-aware datetimes
```
I'm using 0.15RC1, dateutil: 2.2, pytz: 2014.7 and xlsxwriter 0.5.5 all fresh pip.
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} | 5 | 2014-10-13T08:18:02Z | 2015-05-09T16:07:17Z | 2015-05-09T16:07:17Z | CONTRIBUTOR | null | This should close #8524.
The idea is that quadratic probing `(i**2 + i)/2` is faster than double hashing and can be shown to traverse all elements if `nbuckets == 2**N`.
This branch compiles and passes tests, but I haven't done any benchmarks yet.
## TODO
(probably in other issues)
- [ ] see if the "flag compression" hack can be removed, it complicates syncing and
I'm not sure if there was any noticeable performance increase
- [x] request upstream klib maintainers to add a way to override "kh_inline",
e.g. by wrapping its definition with `#ifndef kh_inline`, so that
`PANDAS_INLINE` flag can be moved away from khash.h and specified in
`khash_python.h" with
``` c
#define kh_inline PANDAS_INLINE
#include "khash.h"
```
- [ ] I don't like it that pandas makes `khint64_t` definition signed. In
upstream it is unsigned which -- I agree -- is inconsistent, but bit shifting
operations on signed integers have a) undefined behaviour according to the
standard and b) different semantics which might cause performance degradation
on some hash functions:
``` c
int main(int argc, char *argv[])
{
int64_t foo = -1;
uint64_t ufoo = *((uint64_t*)&foo);
printf("foo: %lx; foo>>1: %lx\n", foo, foo >> 1);
printf("ufoo: %lx; ufoo>>1: %lx\n", ufoo, ufoo >> 1);
return 0;
}
```
```
$ ./a.out
foo: ffffffffffffffff; foo>>1: ffffffffffffffff
ufoo: ffffffffffffffff; ufoo>>1: 7fffffffffffffff
```
- [ ] memory allocation functions `kmalloc/krealloc/kfree` should be overridden to
use PyMem interface to be shown in tracemalloc output. `PyMem_Calloc` is
[only provided for 3.5](http://bugs.python.org/issue21233), so it may require more work than just defining a macro
rename.
- [ ] khash-0.2.8 API introduces `-1` return values for operations that have failed,
most likely because of memory allocation errors. these should be handled and
reported as `MemoryError`s to the interpreter
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} | 10 | 2014-10-13T12:31:11Z | 2014-11-15T20:38:38Z | 2014-11-15T19:01:57Z | NONE | null | Issue #8212 (first part): pandas.read_excel accepts an optional argument "converters" (which is passed down to PythonParser) to convert single cells in columns with a conversion function. I documented this feature and added a try/except block to make it work in case some cells contain NaNs.
What's still missing is the full "dtype" argument, a la read_csv. That patch is somewhat orthogonal because it only works with the C parser, so I plan to implement it in a second step.
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} | 1 | 2014-10-13T16:14:02Z | 2014-10-15T18:21:27Z | 2014-10-15T18:21:23Z | CONTRIBUTOR | null | Corrects some deficiencies noted here: https://github.com/pydata/pandas/pull/8544#issuecomment-58909397. Adds a test case and an additional vbench test.
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} | 13 | 2014-10-13T17:39:48Z | 2014-11-11T14:33:25Z | 2014-11-11T14:32:45Z | CONTRIBUTOR | null | closes #8302
- Move any/all implementations from DataFrame to NDFrame, adding support for Series
- Special case single dimension case in NDFrame, to handle numpy.any/all specific arguments
- Add assorted NotImplementedErrors in cases where arguments are ignored (including in preexisting _reduce functions)
- Move the other any/all implementations from IndexOpsMixin to Index, outside of Series’ inheritance tree
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} | 32 | 2014-10-13T21:58:19Z | 2015-02-09T00:43:38Z | 2014-10-28T00:25:36Z | CONTRIBUTOR | null | This PR adds ISO 3-digit country codes, support for World Bank Country Code exceptions, by changing the error handling and warning logic and introducing an `error` argument which toggles between ignore/warn/raise . Validation no longer filters bad country codes, prior to submitting the API request. Instead, it attempts to catch errors in the WB response - which is a very hairy thing. See notes added in remote_data.tst for issues.
During this PR, we attempted to clean up the JSON response, and make the module as simple as possible, without restricting the user. In the end, it proved somewhat awkward, but the solution is a good one given the constraints of the WB API.
closes #8482 .
Updated right before merging to remove noise, and provide a summary.
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} | 18 | 2014-10-13T22:17:09Z | 2014-12-06T21:13:56Z | 2014-12-06T17:09:37Z | CONTRIBUTOR | null | read_html neglected table footers. Although rare, some sites use the footer for data. First time pull request so hopefully not messed up.
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} | 2 | 2014-10-14T15:22:41Z | 2018-07-06T20:21:05Z | 2017-07-11T09:54:58Z | CONTRIBUTOR | null | Currently `pd.read_excel` only allows reading in `DataFrame`s. To read a Panel back in (after `pnl.to_excel(fl)`) one has to loop over the sheets.
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} | 4 | 2014-10-14T20:31:39Z | 2018-08-14T10:49:11Z | 2018-08-14T10:49:11Z | NONE | null | Subtracting a scalar datetime from a series returns timedeltas, but doesn't do the same for a dataframe.
```
import pandas as pd
series = pd.Series(pd.date_range('2000-1-1', periods=100), index=range(0, 100))
df = pd.DataFrame(series)
date = pd.Timestamp('1995-1-1')
series - date
#0 1826 days
#1 1827 days
#2 1828 days
# ....
df - date
# 0
#0 1975-01-01
#1 1975-01-02
#2 1975-01-03
# ....
```
It seems like both should return timedeltas.
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| null | 4 | 2014-10-14T21:57:33Z | 2014-10-15T13:41:14Z | 2014-10-14T22:13:05Z | CONTRIBUTOR | null | When using DataFrame.loc with a list of indices, the index name is missing from the resultant DataFrame unless the list of indices includes all of the indices in the original DataFrame.
Example:
``` python
import pandas as pd
d=pd.DataFrame(
[
[1,2,3],
[4,5,6],
[7,8,9]
],
columns=['i','j','k']
).set_index('i')
```
When I use .loc and ask for all of the rows, I get (as desired) the correct, named index out the other end:
``` python
d.loc[[1,4,7]]
# j k
# i
#1 2 3
#4 5 6
#7 8 9
d.loc[[1,4,7]].index.name
# 'i'
```
When I ask for a list of indices that is not all of them, I also expect to get a named index out the other end. However, the index values are preserved but the index name is lost:
``` python
d.loc[[1,4]]
# j k
#1 2 3
#4 5 6
d.loc[[1,4]].index.name
# None
```
Suggesting that this is a bug because the index naming should be the same no matter whether I `.loc` for for all of the rows or just some of them.
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} | 6 | 2014-10-15T00:14:08Z | 2014-10-15T01:35:43Z | 2014-10-15T01:24:00Z | MEMBER | null | Hi folks, it's been a while. I would like to draw your attention to:
```
dr = pd.date_range('20000101', freq='D', periods=100000)
%time dr._is_dates_only
```
This takes about 500ms on my machine. Scales linearly with the size of the index (I interrupted a repr on a 5mm-length index and discovered this). Probably a good idea to fix this for 0.15.0 (this is on master)
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} | 0 | 2014-10-15T00:56:51Z | 2014-10-15T01:24:00Z | 2014-10-15T01:24:00Z | CONTRIBUTOR | null | closes #8556
low hanging fruit (and now is pretty scale independent).
```
In [1]: dr = pd.date_range('20000101', freq='D', periods=100000)
In [2]: %timeit dr._is_dates_only
10000000 loops, best of 3: 87.6 ns per loop
In [3]: pd.__version__
Out[3]: '0.15.0rc1-38-g857042f'
```
I do recall this slipping in sometime maybe in 0.14.0 was the changing in how the default display would be handled for dates with and w/o times.
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} | 0 | 2014-10-15T01:40:20Z | 2014-11-16T14:09:36Z | 2014-11-16T14:09:36Z | CONTRIBUTOR | null | ```
n [1]: df = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
In [2]: df.index.name = 'The Index'
In [5]: df.plot(use_index=False)
Out[5]: <matplotlib.axes._subplots.AxesSubplot at 0x10b1a1438>
```

The x axis label
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} | 30 | 2014-10-15T07:14:22Z | 2020-09-08T04:13:12Z | 2018-07-06T22:49:02Z | NONE | null | `Series.value_counts()` also shows categories with count 0.
Thought this would be a bug but according to [doc](http://pandas.pydata.org/pandas-docs/version/0.15.0/categorical.html#operations) it is intentional.
This makes the output of value_counts inconsistent when switching between category and non-category dtype. Apart from that it blows up the value_counts output for series with many categories.
I would prefer to hide counts (i.e. zero) for non-occuring categories by default and rather consider a parameter `dropzero=True` similar to `dropna` (see also #5569).
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} | 13 | 2014-10-15T07:55:55Z | 2020-08-13T19:05:50Z | null | NONE | null | ``` python
In [44]: df=pd.DataFrame([[0,10],[1,20]],columns=['cat','count'])
...: df
Out[44]:
cat count
0 0 10
1 1 20
In [45]: df.query('cat')
Out[45]:
cat count
0 0 10
1 1 20
```
Expected the first row where cat==0 to be dropped since 0 is Falsey.
This unhelpful exception is how I stumbled over this
```
In [46]: df.query('cat & count > 10')
NotImplementedError: couldn't find matching opcode for 'and_blb'
```
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| null | 1 | 2014-10-15T16:26:19Z | 2014-10-15T17:35:45Z | 2014-10-15T17:35:37Z | CONTRIBUTOR | null | There was one repleacement to many: in R it's still "levels" and not "categories".
-> one word replacement in the docs
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| null | 1 | 2014-10-15T19:23:20Z | 2014-10-24T11:16:24Z | 2014-10-24T11:16:24Z | MEMBER | null | `\n` -> `\\n` to render correclty (and some other small things)
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} | 5 | 2014-10-15T22:24:29Z | 2014-11-12T15:51:49Z | 2014-10-17T07:22:32Z | CONTRIBUTOR | null | Added upcasting of numeric types which will default numeric data to either
int64 or float64. This option has been enabled by default.
closes #8527
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} | 3 | 2014-10-15T22:57:07Z | 2018-07-08T14:33:13Z | 2018-07-06T22:40:10Z | NONE | null | Would it make sense for IndexingError to subclass IndexError to facilitate duck-typing as well as catching IndexError exceptions?
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} | 4 | 2014-10-16T00:58:38Z | 2014-10-28T07:35:24Z | 2014-10-16T16:12:20Z | MEMBER | null | In #7780, I added colormap support to scatter, but choose to override the
default rainbow colormap (https://github.com/pydata/pandas/pull/7780#issuecomment-49533995).
I'm now regreting choosing 'RdBu' as the default colormap. I think a simple
black-white colormap is a more conservative and better default choice for
coloring scatter plots -- binary colormaps really only make sense if the data
is signed. 'Greys' is also the default colormap set by the seaborn package,
and @mwaskom has done far more thinking about colormaps than I have.
If possible, I would love to get this in before 0.15 is released, so we don't
have to worry about changing how anyone's plots look.
CC @sinhrks
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| null | 1 | 2014-10-16T01:59:17Z | 2014-10-18T12:42:19Z | 2014-10-16T12:49:02Z | CONTRIBUTOR | null | Added a warning about the new default behavior of the `ewm*()` differing from prior versions.
Also cleaned up some formatting.
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} | 9 | 2014-10-16T13:29:13Z | 2014-10-18T12:56:00Z | 2014-10-18T12:56:00Z | NONE | null | ``` python
In [13]: df=pd.DataFrame([[0,10],[1,20]],columns=['cat','count'])
...: df
Out[13]:
cat count
0 0 10
1 1 20
In [14]: df.query('cat > 0')
Out[14]:
cat count
1 1 20
In [15]: df.query('~(cat > 0)')
Out[15]:
cat count
1 1 20
0 0 10
In [16]: df.query(' not (cat > 0)')
Out[16]:
cat count
1 1 20
0 0 10
```
I expected the two last results to return only the first row.
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} | 7 | 2014-10-16T19:54:37Z | 2017-06-08T10:47:33Z | 2017-06-08T10:47:32Z | NONE | null | I tried to use series.get(x) (where x is np.nan in some instance) and the result was an empty series. It seems like it should just return None if it can't be found.
```
import pandas as pd
import numpy as np
series = pd.Series(range(0, 10))
series.get(np.nan)
# returns None
series = series.reindex(pd.Float64Index(range(0, 10)))
series.get(10)
# This correctly returns None
series.get(np.nan)
# returns empty series with empty Float64Index
series.get(np.nan, np.nan)
# same thing
```
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} | 0 | 2014-10-17T00:44:36Z | 2014-10-17T01:19:54Z | 2014-10-17T01:19:54Z | CONTRIBUTOR | null | xref #8512
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} | 14 | 2014-10-17T10:59:55Z | 2018-01-15T14:27:17Z | 2018-01-15T12:36:38Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26421221/propogation-of-metadata-from-dataframe-to-series/26423596#26423596)
this allows `copy.copy(df)` to work properly
by definition 'always' deep
```
__copy__ = copy
__deepcopy__ = copy
```
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} | 11 | 2014-10-17T11:04:25Z | 2021-04-11T05:20:00Z | null | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26421221/propogation-of-metadata-from-dataframe-to-series/26423596#26423596)
xref #2485
xref #7868
- [ ] documentation (maybe in the cookbook) to start.
- [ ] add _metadata to Index
- [ ] top-level metadata control (maybe)
```
def unit_meta(self, other, method=None):
# combine and return a new unit for this
if self.unit == other.unit:
return self.unit
# coerce unit
return coerced_unit
pd.set_metadata({ 'unit' : unit_meta })
```
This last will require a bit of change in `__finalize__` to handle a metadata finalizer for a specific name (but straightforward)
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} | 4 | 2014-10-17T13:33:26Z | 2016-04-19T12:03:05Z | 2016-04-19T12:03:05Z | NONE | null | When trying to resample timestamps into 5 minute time slots grouping on an id column (tried both counting and summing aggregation in 'how' parameter). In a DataFrame with TimeSeries data I get a memory crash, i.e. Segmentation Fault.
I reduced the DataFrame as far as I could in reproducing the crash. Also noted that it will not cause a segfault if I sort the index (don't know if this is needed for resample() function could not find such documentation).
``` python
import datetime
import pandas as pd
all_wins_and_wagers =\
[(1L, datetime.datetime(2013, 10, 1, 16, 20), 1L, 0L),
(2L, datetime.datetime(2013, 10, 1, 16, 10), 1L, 0L),
(2L, datetime.datetime(2013, 10, 1, 18, 15), 1L, 0L),
(2L, datetime.datetime(2013, 10, 1, 16, 10, 31), 1L, 0L)]
df = pd.DataFrame.from_records(all_wins_and_wagers, columns=("ID", "timestamp", "A", "B")).set_index("timestamp")
df_resampled = df.groupby("ID").resample("5min", "sum")
```
Tried on following setups of pandas.
## INSTALLED VERSIONS
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.16-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.1
nose: 1.3.4
Cython: None
numpy: 1.9.0
scipy: None
statsmodels: None
IPython: 2.3.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.7
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.0.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
## INSTALLED VERSIONS
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.16-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: None
numpy: 1.8.0
scipy: None
statsmodels: None
IPython: 2.3.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 1.5
pytz: 2012c
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.0.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: 0.999
bq: None
apiclient: None
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} | 9 | 2014-10-17T14:11:51Z | 2018-07-08T14:33:20Z | 2018-07-06T22:38:27Z | CONTRIBUTOR | null | Currently it is easy to apply functions to the data in a row or column using apply. However, when important parameters for the analysis is contained in the multiindex for the row or column being analyzed (that is, the column index when analyzing along a column or the row index when analyzing along a row), there is currently no easy way to make use of these parameters.
I think it would be useful if users could provide a dictionary that maps index levels to function arguments to use when applying a function to a pandas Series, DataFrame, Panel, or Grouper.
Some specific examples:
1. One of the multiindex levels is sampling frequency, which varies across rows. You want to do the fft of each row, but you need the sampling frequency for that.
2. One of the multiindex levels is the total duration of each recording, and you want to do a count of events per unit of time.
3. Each element is a time point. One of the multiindex levels is the minimum allow of the recording and another is the stop time, and you want to slice the results to only include values between the start and stop.
These are all easy if the values are the same for all rows or columns, but become much harder currently if they vary.
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} | 2 | 2014-10-17T17:17:08Z | 2018-07-17T21:28:49Z | 2014-10-20T00:58:25Z | CONTRIBUTOR | null | As a performance junkie I benchmark a lot and that involves frequent creation of temporary arrays of significant sizes. I couldn't but notice that `tm.makeStringIndex` is somewhat slow in that regard and this PR should fix that.
It also does:
```
* add tm.rands_array(nchars, size) function to generate string arrays
* add tm.randu_array(nchars, size) function to generate unicode arrays
* replace [rands(N) for _ in range(X)] idioms:
- with makeStringIndex in benchmarks to maintain backward compatibility
- with rands_array in tests to maintain 1-to-1 type correspondence
```
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} | 2 | 2014-10-18T04:29:06Z | 2014-11-12T15:51:47Z | 2014-10-20T11:03:40Z | CONTRIBUTOR | null | Allow columns to be selected when importing Stata data files.
Closes #7935
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} | 18 | 2014-10-18T13:25:11Z | 2014-10-30T11:19:44Z | 2014-10-22T06:18:18Z | NONE | null | Doubling the string size does not change the reported memory use:
``` python
In [64]: d=pd.DataFrame(["%06d" %x for x in range(1000000)])
...: d.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1000000 entries, 0 to 999999
Data columns (total 1 columns):
0 1000000 non-null object
dtypes: object(1)
memory usage: 15.3 MB
In [65]: d=pd.DataFrame(["%012d" %x for x in range(1000000)])
...: d.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1000000 entries, 0 to 999999
Data columns (total 1 columns):
0 1000000 non-null object
dtypes: object(1)
memory usage: 15.3 MB
```
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| null | 5 | 2014-10-19T06:48:18Z | 2014-10-28T13:58:15Z | 2014-10-28T13:58:15Z | NONE | null | ``` python
In [10]: df=pd.DataFrame([range(100)]*100)
...: df.transpose
Out[10]:
<bound method DataFrame.transpose of 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
0 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
1 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
2 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
3 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
4 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
5 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
6 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
7 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
8 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
9 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
10 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
11 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
12 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
13 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
14 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
15 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
16 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
17 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
18 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
19 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
.. .. .. .. .. .. .. .. .. .. .. ... .. .. .. .. .. .. .. .. .. ..
80 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
81 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
82 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
83 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
84 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
85 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
86 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
87 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
88 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
89 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
90 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
91 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
92 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
93 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
94 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
95 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
96 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
97 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
98 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
99 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
[100 rows x 100 columns]>
In [11]: ndarray = df.values
...: ndarray.transpose
Out[11]: <function transpose>
```
Maybe this is a result of all the refactoring in last versions?
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} | 8 | 2014-10-19T13:24:31Z | 2014-10-30T11:19:44Z | 2014-10-20T22:18:25Z | NONE | null | Hi,
``` python
from pandas.io import gbq
```
gives the following error:
``` python
>>> from pandas.io import gbq
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/site-packages/pandas/__init__.py", line 45, in <module>
from pandas.io.api import *
File "/usr/local/lib/python2.7/site-packages/pandas/io/api.py", line 15, in <module>
from pandas.io.gbq import read_gbq
File "/usr/local/lib/python2.7/site-packages/pandas/io/gbq.py", line 39, in <module>
if LooseVersion(_GOOGLE_API_CLIENT_VERSION >= '1.2.0'):
File "/usr/local/Cellar/python/2.7.8_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/version.py", line 265, in __init__
self.parse(vstring)
File "/usr/local/Cellar/python/2.7.8_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/version.py", line 274, in parse
self.component_re.split(vstring))
TypeError: expected string or buffer
```
The error is in [gbq.py](https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py) line 46:
``` python
if LooseVersion(_GOOGLE_API_CLIENT_VERSION >= '1.2.0'):
```
Correct this to:
``` python
if LooseVersion(_GOOGLE_API_CLIENT_VERSION) >= '1.2.0':
```
to return true when the google api client has the correct version.
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} | 0 | 2014-10-19T15:18:41Z | 2014-10-30T11:19:44Z | 2014-10-28T00:04:25Z | CONTRIBUTOR | null | ```
>>> df = pd.DataFrame(np.random.randint(0, 100, (50, 2)), columns=['jim', 'joe'])
>>> ts = pd.Series(np.random.randint(0, 3, 50))
>>> df.groupby(ts, as_index=False).idxmax()
jim joe
0 25 36
1 22 5
2 3 4
>>> df.groupby(ts, as_index=False).max()
NaN jim joe
0 0 93 94
1 1 94 89
2 2 85 93
```
`max` adds the grouper to the result frame, whereas `idxmax` doesn't.
The API documentation [says](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html#pandas.DataFrame.groupby):
> as_index=False is effectively “SQL-style” grouped output
so i would assume, it should not inject a new column with `NaN` name.
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} | 5 | 2014-10-19T20:57:14Z | 2014-11-17T15:24:10Z | 2014-11-17T15:24:10Z | MEMBER | null | Related #7770
Using the example of the docs (http://pandas.pydata.org/pandas-docs/stable/reshaping.html#multiple-levels):
```
columns = MultiIndex.from_tuples([('A', 'cat', 'long'), ('B', 'cat', 'long'), ('A', 'dog', 'short'), ('B', 'dog', 'short')],
names=['exp', 'animal', 'hair_length'])
df = DataFrame(randn(4, 4), columns=columns)
```
CONTEXT: `df.stack(level=['animal', 'hair_length'])` and `df.stack(level=[1, 2])` are equivalent (feature introduced in #7770). Mixing integers location and string names (eg `df.stack(level=['animal', 2])`) gives a ValueError.
**But** if you have level names of mixed types, some different (and wrong things) happen:
- With a total different number, it still works as it should:
```
df.columns.names = ['exp', 'animal', 10]
df.stack(level=['animal', 10])
```
- With the number 1, it treats the 1 as a level number instead of the level name, leading to a wrong result (two times the same level unstacked):
```
In [42]: df.columns.names = ['exp', 'animal', 1]
In [43]: df.stack(level=['animal', 1])
Out[43]:
exp A B
animal animal
0 cat cat -1.006065 0.401136
dog dog 0.526734 -1.753478
1 cat cat -0.718401 -0.400386
dog dog -0.951336 -1.074323
2 cat cat 1.119843 -0.606982
dog dog 0.371467 -1.837341
3 cat cat -1.467968 1.114524
dog dog -0.040112 0.240026
```
- With the number 0, it gives a strange error:
```
In [46]: df.columns.names = ['exp', 'animal', 0]
In [47]: df.stack(level=['animal', 0])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-47-4e9507e0708f> in <module>()
----> 1 df.stack(level=['animal', 0])
/home/joris/scipy/pandas/pandas/core/frame.pyc in stack(self, level, dropna)
3390
3391 if isinstance(level, (tuple, list)):
-> 3392 return stack_multiple(self, level, dropna=dropna)
3393 else:
3394 return stack(self, level, dropna=dropna)
....
/home/joris/scipy/pandas/pandas/core/index.pyc in _partial_tup_index(self, tup, side)
3820 raise KeyError('Key length (%d) was greater than MultiIndex'
3821 ' lexsort depth (%d)' %
-> 3822 (len(tup), self.lexsort_depth))
3823
3824 n = len(tup)
KeyError: 'Key length (2) was greater than MultiIndex lexsort depth (0)'
```
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} | 11 | 2014-10-19T21:00:29Z | 2014-12-07T13:23:35Z | 2014-10-28T00:04:25Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/7115
closes https://github.com/pydata/pandas/issues/8112
closes https://github.com/pydata/pandas/issues/8582
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} | 9 | 2014-10-19T21:04:51Z | 2014-10-30T11:20:23Z | 2014-10-27T14:42:02Z | MEMBER | null | Closes #8477
What did I change:
- some general clean-up (blank lines, ..)
- put the different new feature sections in front (as also in the highlights section)
- the 2-level menu structure as discussed in #8477
- API section: move some entries to the 'enhancements' section (entries that were not 'real' (in sense of breaking) API changes):
- describe keywords include/exclude
- new 'level' keyword for Index.set_names/set_levels
- new 'level' keyword for Index isin
- histogram/boxplot also via `plot`
- index new methods duplicated and drop_duplicates
Still work in progress!
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} | 6 | 2014-10-20T00:50:35Z | 2014-10-30T11:20:23Z | 2014-10-20T12:23:30Z | CONTRIBUTOR | null | closes #8581
Version comparison had a formatting error resulting in "TypeError: expected string or buffer" and preventing Spyder from loading (and probably other issues). Fixed.
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} | 0 | 2014-10-20T12:11:05Z | 2015-09-05T22:20:25Z | 2015-09-05T22:20:25Z | CONTRIBUTOR | null | clarify / doc / test (explicity) on passing `sort=True|False` to a `groupby/resample`
from [SO](http://stackoverflow.com/questions/26456125/python-pandas-is-order-preserved-when-using-groupby-and-agg/26465555#26465555resample forces a sort)
groupby only sorts if sort is True and the index is NOT monotonic.
just like to test / validate and write a doc not about this
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} | 2 | 2014-10-20T17:20:06Z | 2014-10-30T11:19:44Z | 2014-10-24T18:36:38Z | CONTRIBUTOR | null | Works in 0.14.1:
``` python
In [11]: s = pd.Series(np.random.randn(100))
In [12]: pd.cut(s, 10, retbins=True)
Out[12]:
( (0.993, 1.376]
(-0.924, -0.541]
(-0.158, 0.226]
(-1.695, -1.308]
(-0.924, -0.541]
(-0.541, -0.158]
(-0.158, 0.226]
(-0.541, -0.158]
(0.609, 0.993]
(0.226, 0.609]
(0.226, 0.609]
(0.609, 0.993]
(0.609, 0.993]
...
(0.993, 1.376]
(-0.541, -0.158]
(0.226, 0.609]
(0.226, 0.609]
(-0.158, 0.226]
(0.226, 0.609]
(-0.158, 0.226]
(-0.924, -0.541]
(-1.695, -1.308]
(0.993, 1.376]
(-0.158, 0.226]
(-0.924, -0.541]
(0.993, 1.376]
Levels (10): Index(['(-1.695, -1.308]', '(-1.308, -0.924]',
'(-0.924, -0.541]', '(-0.541, -0.158]',
'(-0.158, 0.226]', '(0.226, 0.609]',
'(0.609, 0.993]', '(0.993, 1.376]', '(1.376, 1.759]',
'(1.759, 2.143]'], dtype=object)
Length: 100,
array([-1.69479925, -1.30760325, -0.92424086, -0.54087848, -0.1575161 ,
0.22584629, 0.60920867, 0.99257106, 1.37593344, 1.75929582,
2.14265821]))
```
but does not work in 0.15.0/current master:
```
In [13]: pd.cut(s, 10, retbins=True)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-13-958e04d69222> in <module>()
----> 1 pd.cut(s, 10, retbins=True)
/home/andrew/git/pandas-rosnfeld/pandas/tools/tile.pyc in cut(x, bins, right, labels, retbins, precision, include_lowest)
113 include_lowest=include_lowest)
114 if isinstance(x, Series):
--> 115 res = Series(res, index=x.index)
116 return res
117
/home/andrew/git/pandas-rosnfeld/pandas/core/series.pyc in __init__(self, data, index, dtype, name, copy, fastpath)
210 raise_cast_failure=True)
211
--> 212 data = SingleBlockManager(data, index, fastpath=True)
213
214 generic.NDFrame.__init__(self, data, fastpath=True)
/home/andrew/git/pandas-rosnfeld/pandas/core/internals.pyc in __init__(self, block, axis, do_integrity_check, fastpath)
3324 block = make_block(block,
3325 placement=slice(0, len(axis)),
-> 3326 ndim=1, fastpath=True)
3327
3328 self.blocks = [block]
/home/andrew/git/pandas-rosnfeld/pandas/core/internals.pyc in make_block(values, placement, klass, ndim, dtype, fastpath)
2058
2059 return klass(values, ndim=ndim, fastpath=fastpath,
-> 2060 placement=placement)
2061
2062
/home/andrew/git/pandas-rosnfeld/pandas/core/internals.pyc in __init__(self, values, ndim, fastpath, placement)
1372 super(ObjectBlock, self).__init__(values, ndim=ndim,
1373 fastpath=fastpath,
-> 1374 placement=placement)
1375
1376 @property
/home/andrew/git/pandas-rosnfeld/pandas/core/internals.pyc in __init__(self, values, placement, ndim, fastpath)
73 raise ValueError('Wrong number of items passed %d,'
74 ' placement implies %d' % (
---> 75 len(self.values), len(self.mgr_locs)))
76
77 @property
ValueError: Wrong number of items passed 2, placement implies 100
```
The same goes for qcut. These work if retbins=False.
I found this while testing out the new release, wanted to take a look at the Categorical type. I should have done this with the RC and caught it sooner...
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} | 42 | 2014-10-21T01:06:54Z | 2015-05-14T15:52:50Z | 2015-05-09T16:08:56Z | NONE | null | closes #8489
The pandas google bigquery connector can be difficult to use from an ipython notebook: when no authentication token exists, it fails silently (and can hang the notebook). This happens because the oauth2client library is trying to bring up a browser on the console, to request an auth token.
This PR provides an improved mechanism: an optional `use_gcloud_auth` argument which specifies that the user wants to employ a pre-existing authentication token, generated using [gcloud auth login](https://cloud.google.com/sdk/gcloud/reference/auth/login). An error is raised if the credentials file is missing.
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} | 2 | 2014-10-21T01:12:19Z | 2014-10-31T20:18:44Z | 2014-10-31T20:18:44Z | NONE | null | Using the documentation's own CustomBusinessDay example, you can no longer save objects with a CustomBusinessDay frequency to an HDF store in Pandas 15.0 Final (but worked in 14.1):
``` python
import pandas as pd
import numpy as np
from pandas.tseries.offsets import CustomBusinessDay
from datetime import datetime
weekmask_egypt = 'Sun Mon Tue Wed Thu'
holidays = ['2012-05-01', datetime(2013, 5, 1), np.datetime64('2014-05-01')]
bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt)
dt = datetime(2013, 4, 30)
dts = pd.date_range(dt, periods=5, freq=bday_egypt)
s = (pd.Series(dts.weekday, dts).map(pd.Series('Mon Tue Wed Thu Fri Sat Sun'.split())))
store = pd.HDFStore('test.hdf')
store.put('test',s)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/site-packages/pandas/io/pytables.py", line 831, in put
self._write_to_group(key, value, append=append, **kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas/io/pytables.py", line 1280, in _write_to_group
s.write(obj=value, append=append, complib=complib, **kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas/io/pytables.py", line 2557, in write
self.write_index('index', obj.index)
File "/usr/local/lib/python2.7/site-packages/pandas/io/pytables.py", line 2316, in write_index
node._v_attrs.freq = index.freq
File "/usr/local/lib/python2.7/site-packages/tables/attributeset.py", line 455, in __setattr__
self._g__setattr(name, value)
File "/usr/local/lib/python2.7/site-packages/tables/attributeset.py", line 397, in _g__setattr
self._g_setattr(self._v_node, name, stvalue)
File "hdf5extension.pyx", line 700, in tables.hdf5extension.AttributeSet._g_setattr (tables/hdf5extension.c:6623)
File "/usr/local/Cellar/python/2.7.8/Frameworks/Python.framework/Versions/2.7/lib/python2.7/copy_reg.py", line 70, in _reduce_ex
raise TypeError, "can't pickle %s objects" % base.__name__
TypeError: can't pickle busdaycalendar objects
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.15.0
nose: 1.3.1
Cython: 0.20.2
numpy: 1.8.1
scipy: 0.14.0
statsmodels: None
IPython: 2.2.0
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2014.4
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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| null | 2 | 2014-10-21T11:57:10Z | 2021-04-11T05:21:06Z | null | CONTRIBUTOR | null | - [ ] `pd.Summary`
- [x] dict-of-dict #9052
- [ ] named functions #10100
From [SO](http://stackoverflow.com/questions/26456125/python-pandas-is-order-preserved-when-using-groupby-and-agg/26465555?noredirect=1#comment41593401_26465555)
```
In [100]: df = pd.DataFrame({'A': ['group1', 'group1', 'group2', 'group2', 'group3', 'group3'],
'B': [10, 12, 10, 25, 10, 12],
'C': [100, 102, 100, 250, 100, 102]})
In [101]: df
Out[101]:
A B C
0 group1 10 100
1 group1 12 102
2 group2 10 100
3 group2 25 250
4 group3 10 100
5 group3 12 102
In [102]: df.groupby('A').agg(['mean',lambda x: x.iloc[1]])
Out[102]:
B C
mean <lambda> mean <lambda>
A
group1 11.0 12 101 102
group2 17.5 25 175 250
group3 11.0 12 101 102
```
Would be nice to be able to use `lambda x: x.nth(1)` or maybe `'nth(1)'`
`In [103]: df.groupby('A').agg(['mean','nth(1')])`
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} | 2 | 2014-10-21T12:28:00Z | 2019-12-26T13:35:09Z | 2019-12-26T13:35:09Z | NONE | null | The behaviour below occurs in versions `'0.15.0rc1-21-g32c5016'` and `'0.14.1'`.
When the `label` passed to the `drop` method of a `Series` is not in the axis:
(a) if the index is not a `MultiIndex` then an exception is raised (in most cases; see issue #8530)
(b) if the index is a `MultiIndex` then an exception is not raised; the original series is returned
Examples of current behaviour:
(a)
``` python
>>> pd.Series([10, 20, 30], [1, 2, 3]).drop(7)
ValueError: labels [7] not contained in axis
```
(b)
``` python
>>> pd.Series(
[10, 20, 30],
pd.MultiIndex(
levels=[[1, 2, 3], [4, 5, 6]],
labels=[[0, 1, 2], [0, 1, 2]],
names=['a', 'b'])
).drop(7, level='a')
a b
1 4 10
2 5 20
3 6 30
dtype: int64
```
I propose that in (b) an exception be raised. Furthermore, an exception should be raised in (b) if (for instance) the label passed to `drop` had been `4`, because although `4` is in the axis, it is not in the specified level.
The exception message in (b) would have to refer to the level, e.g.:
``` python
ValueError: labels [7] not contained in level
```
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} | 11 | 2014-10-21T13:26:30Z | 2018-07-06T22:19:16Z | 2018-07-06T22:19:16Z | NONE | null | it would be nice to have number in front of all labels. put number like 00, 01, 02 in front of labels so that it would order appropriately.
Lib\site-packages\pandas\tools\tile.py
```
def _format_levels(bins, prec, right=True,
include_lowest=False):
fmt = lambda v: _format_label(v, precision=prec)
cnter=0
if right:
levels = []
for a, b in zip(bins, bins[1:]):
fa, fb = fmt(a), fmt(b)
if a != b and fa == fb:
raise ValueError('precision too low')
formatted = '%02d: (%s, %s]' % (cnter, fa, fb)
cnter=cnter +1
levels.append(formatted)
if include_lowest:
levels[0] = '[' + levels[0][1:]
else:
levels = ['[%s, %s)' % (fmt(a), fmt(b))
for a, b in zip(bins, bins[1:])]
return levels
```
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} | 18 | 2014-10-21T18:38:30Z | 2020-03-15T11:20:13Z | 2016-05-27T12:41:32Z | CONTRIBUTOR | null | ```
import pandas as pd
df1 = pd.DataFrame({'key': [1,2,3,4], 'val1': [1,2,3,4]})
df2 = pd.DataFrame({'key': [1,2,3,5], 'val2': [1,2,3,4]})
df = df1.merge(df2, how='outer')
```
Was expecting `key` to stay int64, since a merge can't introduce missing key values if none were present in the inputs.
```
print df.dtypes
key float64
val1 float64
val2 float64
dtype: object
```
Version 0.15.0-6-g403f38d
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} | 3 | 2014-10-21T19:50:20Z | 2018-07-08T14:33:28Z | 2018-07-06T22:16:18Z | CONTRIBUTOR | null | A lot of functions in pandas ignore any additional keyword arguments in `**kwargs`. This makes it very easy for unnoticed mistakes in the code, without getting any error; I have personally came across both cases below in my code:
```
>>> def foo(df):
... df.drop('joe', axis=1, inplce=True) # mis-spelling a keyword
... df.reindex([1, 0], inplace=True) # specifying an unsupported keyword
... return df
>>> df
jim joe jolie
0 71 22 92
1 78 55 77
>>> foo(df)
jim joe jolie
0 71 22 92
1 78 55 77
```
Another example that this may happen unnoticed is with legacy code, once there is an api change; for example once `cols` argument is dropped form [`.drop_duplicates`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop_duplicates.html#pandas.DataFrame.drop_duplicates). I think, at least at the public api level, the code should check if after `.pop`ing supported key-word arguments, anything is left in `**kwargs`. [example from scipy project](https://github.com/scipy/scipy/blob/5cf30815cbf2bf9e66cea8e289fa09213e8161fc/scipy/optimize/optimize.py#L654).
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} | 1 | 2014-10-21T21:40:19Z | 2014-10-30T11:20:23Z | 2014-10-21T23:20:26Z | CONTRIBUTOR | null | The pointer to `read_sql_query` is not specified correctly in `doc/source/io.rst` .
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} | 1 | 2014-10-22T07:36:49Z | 2014-10-30T11:20:23Z | 2014-10-22T10:54:40Z | MEMBER | null | This should resolve @kay1793's complaint in #8578.
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} | 1 | 2014-10-22T10:57:00Z | 2021-04-11T05:22:09Z | 2021-04-11T05:22:09Z | CONTRIBUTOR | null | xref #8599, #8578
allow a nice UX and a more correct estimate of memory usage for object (string) columns
prob will require a cythonized evaluation
this would replace the need for the '+' on object dtypes for mem usage
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