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https://api.github.com/repos/pandas-dev/pandas/issues/8501
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45,130,854
MDExOlB1bGxSZXF1ZXN0MjIzNjY1Mzg=
8,501
Raise more specific exception on concat([None])
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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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8,502
BUG: matplotlib.__version__ is unicode
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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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45,139,234
MDU6SXNzdWU0NTEzOTIzNA==
8,503
SparseDataFrame with 0.0001 density using full memory
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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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COMPAT: matplotlib 1.4.0 version compat (GH8502)
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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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8,505
Behavior of pandas.DataFrame.duplicated()
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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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MDU6SXNzdWU0NTIyMzIyOA==
8,506
groupby.apply raises ValueError when as_index=True on 0.15 RC1
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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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8,507
DOC: Update ecosystem.rst: Add IDE, IPython, qgrid, Spyder, API, quandl, Out-of-core, Blaze
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2014-10-08T10:58:17Z
2014-10-13T14:21:33Z
2014-10-13T14:20:11Z
CONTRIBUTOR
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45,260,662
MDU6SXNzdWU0NTI2MDY2Mg==
8,508
Allow writing to S3 paths
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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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8,509
numpy FutureWarning in df.equals
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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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45,287,572
MDExOlB1bGxSZXF1ZXN0MjI0NDkwMjE=
8,510
DOC: fix example sql chunksize
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2014-10-08T19:45:27Z
2014-10-08T20:14:52Z
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45,288,711
MDU6SXNzdWU0NTI4ODcxMQ==
8,511
Indexing problem with in-place operator
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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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8,512
BUG: Suppress FutureWarning when comparing object arrays
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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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8,513
ENH: axvlines - boolean option to parallel_coordinates plot
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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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pd.option_context without 'with' changes option values
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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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MDU6SXNzdWU0NTI5OTY4OQ==
8,515
Module xlrd is not installed with 0.14; creates an Import Error when using read_excel
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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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add xray to ecosystem docs?
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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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MDU6SXNzdWU0NTMyMzczMw==
8,517
Feature request: Series.flatmap, DataFrame.flatmap
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21
2014-10-09T03:35:17Z
2019-05-31T23:45:20Z
2015-10-20T23:14:36Z
NONE
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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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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
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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
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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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45,358,277
MDExOlB1bGxSZXF1ZXN0MjI0ODg0NDI=
8,520
BUG: Bug in inplace operations with column sub-selections on the lhs (GH8511)
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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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ENH: resample(..., base='start') for automaticly determining base.
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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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8,522
Error with all boolean indices for Series
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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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8,523
BUG/REGR: bool-like Indexes not properly coercing to object (GH8522)
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0
2014-10-09T17:11:37Z
2014-10-09T18:32:16Z
2014-10-09T18:32:16Z
CONTRIBUTOR
null
closes #8522
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Klib upgrade (factorizing performance increase)
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16
2014-10-09T21:50:24Z
2019-10-21T20:06:54Z
null
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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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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: ![dataframescreenshot](https://cloud.githubusercontent.com/assets/8078968/4586172/3e9e5332-5012-11e4-8a3c-e5d9be2975a0.png) ![activitymonitorscreenshot](https://cloud.githubusercontent.com/assets/8078968/4586167/39f9dfae-5012-11e4-892a-9b8cb9c253a6.png) 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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45,441,435
MDExOlB1bGxSZXF1ZXN0MjI1Mzc1NDE=
8,526
index into multi-index past the lex-sort depth
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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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45,463,071
MDU6SXNzdWU0NTQ2MzA3MQ==
8,527
Choose correct datatype when creating new dataframe column from old ones
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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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45,464,941
MDU6SXNzdWU0NTQ2NDk0MQ==
8,528
rot parameter for BARH plots
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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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45,466,872
MDU6SXNzdWU0NTQ2Njg3Mg==
8,529
fontsize parameter in .plot()
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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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MDU6SXNzdWU0NTQ2OTAwNQ==
8,530
Series.drop() with boolean index: inconsistent behaviour
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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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45,471,665
MDU6SXNzdWU0NTQ3MTY2NQ==
8,531
bug in tz_localize when summer/winter time change
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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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8,532
BUG: Series not correctly formatted in truncated output
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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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45,495,922
MDU6SXNzdWU0NTQ5NTkyMg==
8,533
Accept SQLAlchemy Connection as con param in pd.read_sql_query()
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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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8,534
Add xray to ecosystem docs
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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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45,539,292
MDExOlB1bGxSZXF1ZXN0MjI1OTgzNjY=
8,535
DOC: Clean up Sphinx Issues, fix tabs, in Cookbook
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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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8,536
BUG: Dont add None to plot legend.
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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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MDU6SXNzdWU0NTU1MjkwNw==
8,537
Deprecation/FutureWarnings raised by `==` on NumPy arrays in NumPy version1.9
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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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8,538
PERF: speed up tshift, possibly by caching offset ops
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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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Label slices on Series with MultiIndex
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2014-10-11T17:33:24Z
2014-10-13T11:51:28Z
2014-10-11T18:34:21Z
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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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8,540
Enhancement: XLSB support in read_excel()
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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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BUG: Plot with ``label`` would overwrite index name
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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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groupby, TimeGrouper error
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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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8,545
SEC: Add security disclosure process to developers page
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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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45,607,668
MDU6SXNzdWU0NTYwNzY2OA==
8,546
BUG - to_excel() fails using xlsxwriter if the index uses local timestamps
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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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PERF: upgrade khash lib to 0.2.8
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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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BUG: inconsistent and undocumented option "converters" to read_excel
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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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PERF: Slowness in multi-level indexes with datetime levels (part 2)
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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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ENH: Move any/all to NDFrame, support additional arguments for Series. GH8302
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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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ENH: Add ISO3 ctry codes and error arg. Fix tests, warn/exception logic #8482
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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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Added footer to read_html
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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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No roundtrip for pd.Panel when writing to excel.
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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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MDU6SXNzdWU0NTc5NjQ0Nw==
8,554
Inconsistent Timestamp arithmetic
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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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BUG: Index name lost when using .loc with list of indices
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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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DatetimeIndex very slow repr performance
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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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PERF: fixup datetime_index repr perf (GH8556)
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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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VIS: plot with `use_index=False` shouldn't plot the index name
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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> ``` ![gh](https://cloud.githubusercontent.com/assets/1312546/4639393/2e6af314-540c-11e4-9d07-6c5ab8250aae.png) The x axis label
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45,836,297
MDU6SXNzdWU0NTgzNjI5Nw==
8,559
Zero counts in Series.value_counts for categoricals
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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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8,560
df.query() doesn't follow python truthy-ness?
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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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8,561
DOC/FIX: In R it's still levels...
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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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API: Unify SQLTable code for fallback and SQLAlchemy mode and move differences into database class
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13
2014-10-15T19:03:15Z
2016-01-20T14:15:53Z
2016-01-20T14:15:53Z
CONTRIBUTOR
null
In discussion of the SQL API in #7960 , it has been suggested that it may be possible to factor out differences in SQLAlchemy and fallback backend . This is an initial attempt to do so. Now `SQLTable` is uncoupled from the backend. `PandasSQL` is base class for `SQLDatabase` (SQLAlchemy backend) and `SQLiteDatabase` (fallback backend). This allows for the removal of some duplicate code.
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DOC: fix formatting read_csv comment kwarg explanation
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2014-10-24T11:16:24Z
2014-10-24T11:16:24Z
MEMBER
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`\n` -> `\\n` to render correclty (and some other small things)
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8,564
ENH: Add type conversion to read_stata and StataReader
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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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MDU6SXNzdWU0NTkyOTk4NQ==
8,565
pandas.indexing.IndexingError subclass IndexError
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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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https://api.github.com/repos/pandas-dev/pandas/issues/8566
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8,566
Switch default colormap for scatterplot to 'Greys'
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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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DOC: ignore_na warning
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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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8,568
df.query doesn't handle negation properly
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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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8,569
BUG: Series.get with Float64Index and numpy nan returns empty Series
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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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MDExOlB1bGxSZXF1ZXN0MjI4OTczNDQ=
8,570
PERF: improve perf of array_equivalent_object (GH8512)
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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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API: define __copy__ NDFrame
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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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8,572
DOC/API: document how to use metadata
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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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Using resample() with groupby on this DataFrame causes Segmentation Fault
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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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8,574
ENH: Use multiindex levels for function arguments in apply
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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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TST/PERF: optimize tm.makeStringIndex
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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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8,576
BUG/FIX: Fix dtype inference issue in query
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2
2014-10-18T00:01:47Z
2015-04-30T01:56:20Z
2014-10-18T12:55:27Z
MEMBER
null
closes #8568
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8,577
ENH: Allow columns selection in read_stata
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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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8,578
df.info() incorrectly reports memory use for string data?
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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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8,579
DOC: fix example of GH8512 name clash with following examples
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2014-10-18T22:42:11Z
2014-10-18T22:42:28Z
2014-10-18T22:42:16Z
MEMBER
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46,198,567
MDU6SXNzdWU0NjE5ODU2Nw==
8,580
BUG: remove data dump from repr of methods on pandas objects
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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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BUG: Error in gbq.py
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2014-10-19T13:24:31Z
2014-10-30T11:19:44Z
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NONE
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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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46,208,111
MDU6SXNzdWU0NjIwODExMQ==
8,582
groupby with as_index=False, inconsistency between max and idxmax
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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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46,216,639
MDExOlB1bGxSZXF1ZXN0MjI5ODUzMjc=
8,583
DOC/REL: adapt make.py file to upload with other user than pandas
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2
2014-10-19T20:38:48Z
2014-10-30T11:20:23Z
2014-10-22T20:43:54Z
MEMBER
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46,217,212
MDU6SXNzdWU0NjIxNzIxMg==
8,584
BUG: Passing multiple levels to stack when having mixed integer/string level names
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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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MDExOlB1bGxSZXF1ZXN0MjI5ODU2MTU=
8,585
BUG: column name conflict & as_index=False breaks groupby ops
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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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MDExOlB1bGxSZXF1ZXN0MjI5ODU2ODU=
8,586
DOC/REL: clean-up and restructure v0.15.0 whatsnew file (GH8477)
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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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46,223,425
MDExOlB1bGxSZXF1ZXN0MjI5ODg2MTM=
8,587
BUG: LooseVersion was used incorrectly
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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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DOC/API: clarify groupby sorting behavior
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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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cut/qcut "retbins" broken in 0.15.0
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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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8,590
ENH: improve bigquery connector to optionally allow use of gcloud credentials
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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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8,591
Pandas 15.0: TypeError: can't pickle busdaycalendar objects
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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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8,592
ENH: Raise error in certain unhandled _reduce cases.
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22
2014-10-21T03:14:00Z
2014-10-30T11:20:23Z
2014-10-28T00:03:46Z
CONTRIBUTOR
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46,384,736
MDU6SXNzdWU0NjM4NDczNg==
8,593
API: specification of functions in .agg
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2014-10-21T11:57:10Z
2021-04-11T05:21:06Z
null
CONTRIBUTOR
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- [ ] `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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Series.drop() with MultiIndex: inconsistent behaviour
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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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46,392,842
MDU6SXNzdWU0NjM5Mjg0Mg==
8,595
API: cut interval formatting
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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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8,596
BUG: DataFrame outer merge changes key columns from int64 to float64
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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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46,436,496
MDU6SXNzdWU0NjQzNjQ5Ng==
8,597
Functions should raise on unexpected keyword arguments in **kwargs
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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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8,598
Function pointer misspecified in io documentaion
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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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46,480,802
MDExOlB1bGxSZXF1ZXN0MjMxMzk1MTk=
8,599
Use '+' to qualify memory usage in df.info() if appropriate
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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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MDU6SXNzdWU0NjQ5Njg4MA==
8,600
API/PERF: add a performance string-object memory computation
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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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