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DOC: add automatic content to 0.16.1 whatsnew file
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0
2015-03-22T22:35:50Z
2015-03-23T09:13:07Z
2015-03-23T09:13:06Z
MEMBER
null
Replaced the manual contents with the automatic (like now in 0.16.0), and a small rephrase (putting the bug fixes first)
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9,703
groupby with multiindex behaves differently for series and single-column dataframe
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2015-03-23T00:51:44Z
2015-03-23T01:26:44Z
2015-03-23T01:26:31Z
CONTRIBUTOR
null
This behavior seems strange to me. Starting with a multiindex dataframe with unbalanced levels: ``` python In [3]: pd.__version__ Out[3]: '0.15.2' In [4]: df = pd.read_csv(StringIO(''' A,B,x three,a,1 three,b,2 three,c,3 two,a,4 two,b,5 one,a,6'''), index_col=list('AB')) In [5]: df Out[5]: x A B three a 1 b 2 c 3 two a 4 b 5 one a 6 ``` Groupby aggregations on this dataframe seem to revert the index to the cross product of the levels, potentially leaving many NAs in the result: ``` python In [6]: df.groupby(level=[0,1]).mean() Out[6]: x A B one a 6 b NaN c NaN three a 1 b 2 c 3 two a 4 b 5 c NaN ``` But with the series this doesn't occur (no NAs in the result): ``` python In [7]: df.x.groupby(level=[0,1]).mean() Out[7]: A B one a 6 three a 1 b 2 c 3 two a 4 b 5 Name: x, dtype: int64 ``` I'm wondering if this is a bug or intended behavior? I haven't been able to find any mention of different behavior in the docs.
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BUG? DataFrame.groupby.apply result has unexpected index
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1
2015-03-23T04:20:16Z
2020-06-28T04:37:46Z
null
MEMBER
null
Compare the output of these three groupby/apply calls: ``` In [66]: df = pd.DataFrame({'a': [1, 1, 2, 2], 'b': range(4)}) In [67]: df Out[67]: a b 0 1 0 1 1 1 2 2 2 3 2 3 In [68]: df.groupby('a')['b'].apply(lambda x: 2 * x) Out[68]: 0 0 1 2 2 4 3 6 dtype: int64 In [69]: df.groupby('a').apply(lambda x: 2 * x)['b'] Out[69]: 0 0 1 2 2 4 3 6 Name: b, dtype: int64 In [70]: df.groupby('a').apply(lambda x: 2 * x['b']) Out[70]: a 1 0 0 1 2 2 2 4 3 6 Name: b, dtype: int64 ``` The first two are what I expected; the last is not -- I expected the same result as the previous two.
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9,705
DOC: add DataFrame.assign to API docs
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1
2015-03-23T05:32:39Z
2015-03-25T00:49:17Z
2015-03-24T10:36:05Z
MEMBER
null
@TomAugspurger does this look like the right place to put this? @jorisvandenbossche can we possibly merge this into the docs for v0.16?
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DOC: try to use matplotlib style (mpl >= 1.4) in all docs
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now this was only done in the plotting docs itself, with as a result that the actual style used is a mixture between the two
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9,707
PERF: add memory benchmarks?
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0
2015-03-23T10:48:33Z
2018-07-14T16:57:55Z
null
MEMBER
null
Seeing #9648, we could maybe start adding memory benchmarks as well (this would be possible with asv?)
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9,708
Allow to select index in drop_duplicates and duplicated
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8
2015-03-23T12:11:47Z
2020-03-11T13:58:00Z
2015-03-25T22:56:58Z
CONTRIBUTOR
null
there’s no way to drop rows with duplicated index using `drop_duplicates`. we’d have to add a copy of the index as column, or do this: ``` python df[np.logical_not(df.index.duplicated(take_last=True).values)] ```
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9,709
printing a dataframe ignores timezone information of columns containing datetime64
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1
2015-03-23T12:59:26Z
2015-03-23T14:49:18Z
2015-03-23T14:48:59Z
NONE
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I have a dataframe with a column containing dates (I do not want to use this column as the index of my dataframe). When printing the dataframe, the timezone information is lost. Directly accessing the _.values_ shows the correct timezone. This might be related to #6415 ``` python import pandas as pd print "pandas version : ", pd.__version__ series = pd.to_datetime([1426374000000, 1426415697429], unit='ms').tz_localize('UTC').tz_convert('Europe/Berlin') print '-- Printing series --' print series df = pd.DataFrame(series) print '-- Printing df --' print df print '-- Printing df.values --' print df.values ``` ``` pandas version : 0.16.0 -- Printing series -- <class 'pandas.tseries.index.DatetimeIndex'> [2015-03-15 00:00:00+01:00, 2015-03-15 11:34:57.429000+01:00] Length: 2, Freq: None, Timezone: Europe/Berlin -- Printing df -- 0 0 2015-03-14 23:00:00 1 2015-03-15 10:34:57.429000 -- Printing df.values -- [['2015-03-15T00:00:00.000000000+0100'] ['2015-03-15T11:34:57.429000000+0100']] ```
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9,710
read_csv skips rows with value 0 if having initial space
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4
2015-03-23T13:34:23Z
2015-04-28T01:02:30Z
2015-04-28T01:02:30Z
NONE
null
Hi, I have something like the following csv file: ``` MyColumn 0 1 0 1 ``` Note the initial space in each row. Upgrading from 0.14.1 to 0.16 I recognized that read_csv started throwing away the 0 rows ``` In [28]: import pandas In [29]: from StringIO import StringIO In [30]: data = 'MyColumn\n 0\n 1\n 0\n 1' In [31]: pandas.read_csv(StringIO(data)) Out[31]: MyColumn 0 1 1 1 ``` skipinitialspace=True did not help: ``` In [32]: pandas.read_csv(StringIO(data), skipinitialspace=True) Out[32]: MyColumn 0 1 1 1 ``` however, skip_blank_lines=False would help: ``` In [34]: pandas.read_csv(StringIO(data), skip_blank_lines=False) Out[34]: MyColumn 0 0 1 1 2 0 3 1 ``` Not sure if this is working as intended. Cheers, Alex PS: Having a second columns works as expected: ``` In [40]: data = 'MyColumn,SecondColumn\n 0, 2\n 1, 3\n 0, 0\n 1, 4' In [41]: pandas.read_csv(StringIO(data)) Out[41]: MyColumn SecondColumn 0 0 3 1 1 4 2 0 0 3 1 6 ``` UPDATE: Made code more reproducable.
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SAS xport file reader
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31
2015-03-23T13:45:08Z
2015-11-12T23:43:34Z
2015-08-14T15:40:16Z
CONTRIBUTOR
null
Here is an initial attempt to port Jack Cushman's xport file reader into Pandas. Partially addresses #4052 (but this only deals with xport files, not sas7bdat files)
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63,795,241
MDU6SXNzdWU2Mzc5NTI0MQ==
9,712
to_csv and bytes on Python 3.
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12
2015-03-23T18:21:21Z
2020-06-29T15:36:49Z
null
CONTRIBUTOR
null
Is this desired behavior and something I need to work around or a bug? Notice the byte type marker is written to disk so you can't round-trip the data. This works fine in Python 2 with unicode AFAICT. ``` In [1]: pd.__version__ Out[1]: '0.15.2-252-g0d35dd4' In [2]: pd.DataFrame.from_dict({'a': ['a', 'b', 'c']}).a.str.encode("utf-8").to_csv("tmp.csv") In [3]: !cat tmp.csv 0,b'a' 1,b'b' 2,b'c' ```
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On demand imports failing in pandas.io.gbq
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10
2015-03-23T20:19:33Z
2015-05-01T16:45:21Z
2015-05-01T16:45:21Z
CONTRIBUTOR
null
After upgrading to 0.16.0 BigQuery functionality is not working. Upon invoking pd.read_gbq(), the _test_imports() function is running, which in turn runs _importers(). Everything is all well and good up to here, but per pdb, once _importers() returns, the imported modules are not accessible and read_gbq() throws `NameError: global name 'OAuth2WebServerFlow' is not defined`.
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to_datetime 1000x slower for timezone-aware strings vs timezone-agnostic
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3
2015-03-24T00:39:48Z
2016-01-26T14:32:58Z
2016-01-26T14:32:58Z
NONE
null
When converting a string date column to datetime, if the string has a GMT timezone suffix (e.g. "-0800"), it takes 1000x longer to parse: ``` dates = pd.Series(pd.date_range('1/1/2000', periods=2000)) string_dates = dates.apply(lambda s: str(s)) tz_string_dates = string_dates.apply(lambda dt: dt + ' -0800') %timeit pd.to_datetime(string_dates) > 1000 loops, best of 3: 579 µs per loop %timeit pd.to_datetime(tz_string_dates) > 1 loops, best of 3: 562 ms per loop ``` Note microseconds vs milliseconds. 3 orders of magnitude... seems unnecessary. This can make loading CSVs into correctly-typed dataframes very, very, very slow for large datasets.
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27
2015-03-24T06:13:49Z
2015-08-19T00:44:21Z
2015-08-19T00:32:16Z
CONTRIBUTOR
null
@jorisvandenbossche Here's the initial pass I was referencing in our discussion in #9660; also relevant closes #8361. A few caveats: - The `eval` suite will have to be converted by hand, as my `ast` transformer isn't parsing the string in `eval("df1 + df2")` - Lots of test failures that I haven't even looked at - `vbench` tended to swallow failing tests, so maybe this is okay? - Uses `conda` instead of `virtualenv` as I'm on OSX and `pip install` fails on several packages. - Takes a while - haven't had a chance to compare against `vbench`. It's possible `asv` is doing a lot more iterations by default. - The conversion depends on a non-stdlib package called `astor`, which does `ast -> source` conversion. Not aware of a way to do this with the stdlib. - The code size blows up as I'm not de-duplicating the setup in any way. Probably something that will just have to be fixed by hand after the switch is made. Still a work-in-progress, but hopefully good enough that you (or someone else) can give this a shot.
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17
2015-03-24T09:50:21Z
2018-03-02T02:37:42Z
2018-01-19T22:11:23Z
NONE
null
Hi, Lately I have started to use pandas heavily with relatively big files. When the RAM becomes full I delete (with del and gc) the big objects (Data frames), though this doesn't clear the memory. Can some onr please advise how to clear variables from RAM, similar to R with rm(some_object) ; gc() Additionally did some experiments about this and here are the results : Started with ~600MB RAM: 1. Ran two queries that jumped the memorey to ~ 3900MB (query -> to Data frame) 2. Removing these Data frames with one of the followings : %xdel df %reset_selective -f df def malloc_trim(): import ctypes ctypes.CDLL('libc.so.6').malloc_trim(0) and then gc.collect(), reduced the RAM similarly (with all three methods) to ~3000MB 3. afterwards running %reset -f => most of the RAM was cleaned 4. The script do some data "Jiu Jitsu" a then store result to HD. Restarting IPython and loading this file barley uses RAM ! What would you recommend ? Regards , Boris
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63,987,780
MDExOlB1bGxSZXF1ZXN0MzE4MjI4MzI=
9,717
Documentation how to drop duplicate indices
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1
2015-03-24T12:50:10Z
2015-03-25T22:57:26Z
2015-03-25T22:57:26Z
CONTRIBUTOR
null
fixes #9708
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64,032,397
MDU6SXNzdWU2NDAzMjM5Nw==
9,718
to_csv keyword arguments missing?
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3
2015-03-24T15:54:16Z
2015-09-10T18:31:05Z
2015-03-24T15:57:34Z
NONE
null
I just upgraded to 0.16.0 from 0.15.2 and the following command stopped working ``` dflabels.to_csv('labels.do', encoding='utf-8', index=False, sep='\n', quoting=False, quotechar=' ') ``` I get the following error ``` TypeError Traceback (most recent call last) <ipython-input-20-332582a26117> in <module>() ----> 1 dflabels.to_csv('labels.do', encoding='utf-8', index=False, sep='\n', quoting=False, quotechar=' ') TypeError: to_csv() got an unexpected keyword argument 'quoting' ``` Any one else having this issue?
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64,039,775
MDU6SXNzdWU2NDAzOTc3NQ==
9,719
Cannot get Yahoo option data using Remote Data Access of Pandas
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4
2015-03-24T16:17:33Z
2015-03-25T08:46:06Z
2015-03-25T08:46:06Z
NONE
null
I am trying to download Yahoo option data using the remote data access function of Pandas, I installed Anaconda on my laptop and launch ipython-qtconsole. I ran the following commands and got error "AttributeError: 'str' object has no attribute 'month', do I miss a module? If so, how can I find it? from pandas.io.data import Options aapl = Options('aapl', 'yahoo') data = aapl.get_all_data() AttributeError Traceback (most recent call last) in () ----> 1 data = aapl.get_all_data() C:\Personal\Anaconda\lib\site-packages\pandas\io\data.pyc in get_all_data(self, call, put) 1109 1110 for month in months: -> 1111 m2 = month.month 1112 y2 = month.year 1113 AttributeError: 'str' object has no attribute 'month'
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64,046,925
MDU6SXNzdWU2NDA0NjkyNQ==
9,720
Problem subtracting two datetime64 columns with non unique index
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4
2015-03-24T16:57:07Z
2015-03-30T07:58:26Z
2015-03-30T07:58:26Z
NONE
null
Came a across this very odd problem while trying to get difference between two columns in a DataFrame of type datetime64. The following simple example illustrates the problem: ``` df = pd.DataFrame({'A': pd.date_range('20150301', '20150304'), 'B': pd.date_range('20150303', '20150306')}, index = [1, 2, 3, 3]) df A B 1 2015-03-01 2015-03-03 2 2015-03-02 2015-03-04 3 2015-03-03 2015-03-05 3 2015-03-04 2015-03-06 df.B - df.A 1 2 days 2 2 days 3 2 days 3 1 days 3 3 days 3 2 days ``` dtype: timedelta64[ns] What happens generally is that the output contains an entry for the difference in every pair in the Cartesian product of the rows containing the duplicate index. I learned this the hard way on a df with 30K+ rows and only 50 distinct indices ending up trying to compute a series with 385 million+ entries, quickly freezing my computer. On a related note, using the eval method to compute the difference throws the following error: ``` df.eval('B - A') ValueError: unkown type timedelta64[ns] ```
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MDExOlB1bGxSZXF1ZXN0MzE4NjgxNDM=
9,721
DOC: fix api documentation for accessors
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1
2015-03-24T20:46:41Z
2015-05-11T12:47:19Z
2015-05-11T12:28:04Z
MEMBER
null
Closes #9599 With this I updated the docs (assign docstring included in API, and the cat/str/dt accessors now documented correctly). The Categorical page without methods and attributes is not yet working. Will complete this for the v0.16.1 docs
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64,172,038
MDExOlB1bGxSZXF1ZXN0MzE4OTI5MjQ=
9,722
Moved imports into functions that use them
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16
2015-03-25T04:38:20Z
2015-05-01T16:45:03Z
2015-05-01T16:45:03Z
CONTRIBUTOR
null
closes #9713 closes #8327 Changes to be committed: modified: pandas/io/gbq.py Moved google api imports into functions that call them, removed unused imports. This fixes #9713
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64,245,180
MDExOlB1bGxSZXF1ZXN0MzE5MTI2NjA=
9,723
BUG: _retry_read_url retries connection even if successful
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1
2015-03-25T11:17:27Z
2015-03-25T11:22:46Z
2015-03-25T11:18:52Z
CONTRIBUTOR
null
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MDU6SXNzdWU2NDI0Njg5NQ==
9,724
Scatter plot colour incorrect for 3 scatter points.
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2015-03-25T11:26:11Z
2021-04-18T05:57:13Z
null
CONTRIBUTOR
null
Based on the Stack Overflow question [here](http://stackoverflow.com/q/29253027/3005188) I believe I've found a bug for when a scatter plot only has three points. The full details are in the question but I'll sketch out the details here. The code (below) will create a scatter plot where each of the points has a different colour, rather than a single consistent colour (as is expected). It happens particularly when `seaborn` is used because, by default, `seaborn` will modify the `rcParams['patch.facecolor']` to be an RGB tuple. If you don't use `seaborn` then this value defaults to a string `'b'` which indicates the colour blue. Of course, this bug would arise whenever a user may use a custom `patch.facecolor` that happens to be an RGB tuple, so it isn't specifically restricted to `seaborn`. The issue arises because when a sequence of numbers is passed for the `c` argument with matplotlib, matplotlib will try to map it to the current colormap. When there are only three scatter points the sequence lengths match (i.e. `len(data) == len(colors)`) and so the numbers are mapped and the resulting colours used in the plot. ### Reproducing code ``` python from pandas import DataFrame import matplotlib.pyplot as plt import seaborn as sns import numpy as np df = DataFrame({'x': np.random.uniform(0, 1, 3), 'y': np.random.uniform(0, 1, 3)}) df.plot(kind = 'scatter', x = 'x', y = 'y') plt.show() ``` ![Example plot](http://i.imgur.com/OJvQXsR.png) ### Versions ``` INSTALLED VERSIONS ------------------ commit: None python: 3.4.2.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel byteorder: little LC_ALL: None LANG: None pandas: 0.15.0 nose: 1.3.3 Cython: None numpy: 1.9.0 scipy: 0.14.0 statsmodels: 0.5.0 IPython: 2.4.1 sphinx: 1.2.2 patsy: 0.3.0 dateutil: 2.2 pytz: 2014.7 bottleneck: None tables: None numexpr: None matplotlib: 1.4.2 openpyxl: 2.0.5 xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: 4.3.2 html5lib: None httplib2: None apiclient: None rpy2: None sqlalchemy: None pymysql: None psycopg2: None ```
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9,725
TST: Fix dateutil version check
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2015-03-25T11:44:34Z
2015-03-25T22:48:08Z
2015-03-25T22:06:21Z
CONTRIBUTOR
null
This allows the test to pass when using dateutil version 2.4.0 ``` In [2]: '2.4.0' <= LooseVersion('2.4') Out[2]: False In [3]: '2.4.0' <= LooseVersion('2.4.0') Out[3]: True ```
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9,726
"c"-engine read_table sigsegv when chunksize close to multiple of file length?
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2015-03-25T11:58:31Z
2015-12-11T15:21:16Z
2015-12-11T15:21:10Z
NONE
null
I have the following script: ``` table_generator = pd.io.parsers.read_table(chromosome_file, sep="\t", engine="c", chunksize=50000, names=["Start", "End", "Strand"], usecols=[1, 2, 5]) for chunk in table_generator: print chunk print chromosome_file ``` I get an error when the file length is close to a multiple of the chunksize: ``` fish: 'python exorcised.py' terminated by signal SIGSEGV (Address boundary error) ``` Changing the chunksize or the lengths of the files allows me to avoid the error. The error seems to happen before or when the tiny leftover chunk is read (since the previous `print` was displayed.) ``` INSTALLED VERSIONS ------------------ commit: None python: 2.7.9.final.0 python-bits: 64 OS: Darwin OS-release: 14.1.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.15.2 nose: 1.3.4 Cython: 0.22 numpy: 1.9.2 scipy: 0.14.1 statsmodels: 0.6.1 IPython: 2.1.0 sphinx: 1.2.2 patsy: 0.3.0 dateutil: 2.4.1 pytz: 2015.2 bottleneck: None tables: 3.1.1 numexpr: 2.3.1 matplotlib: 1.4.2 openpyxl: 1.8.5 xlrd: 0.9.3 xlwt: 0.7.5 xlsxwriter: 0.5.5 lxml: 3.3.5 bs4: 4.3.1 html5lib: None httplib2: None apiclient: None rpy2: None sqlalchemy: 0.9.4 pymysql: None psycopg2: None ``` The files read look like the following: ``` chrY 59052460 59052659 Keratinocyte_H3K27me3_03 1 + chrY 246219 246418 Melanocyte_H3K27me3_02 0 - chrY 9978094 9978293 Melanocyte_H3K27me3_03 1 + chrY 2472778 2472977 Fibroblast_H3K27me3_01 0 + chrY 13266277 13266476 Keratinocyte_H3K27me3_03 1 - chrY 699049 699248 Fibroblast_H3K27me3_02 0 - chrY 23986624 23986823 Melanocyte_H3K27me3_01 0 - chrY 562143 562342 Fibroblast_H3K27me3_03 1 + chrY 23026706 23026905 Melanocyte_H3K27me3_01 0 - chrY 17509636 17509835 Melanocyte_H3K27me3_03 1 + ``` With special symbols showing: ``` chrY^I10500^I10699^IMelanocyte_H3K27me3_01^I0^I+$ ```
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64,261,842
MDExOlB1bGxSZXF1ZXN0MzE5MTkwMDU=
9,727
ENH: Add an axis parameter to DataFrame.diff
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2015-03-25T12:53:50Z
2015-06-10T13:41:27Z
2015-04-29T10:45:52Z
CONTRIBUTOR
null
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64,289,766
MDU6SXNzdWU2NDI4OTc2Ng==
9,728
iterrows turns some columns from int64 to float64
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2015-03-25T14:53:38Z
2015-09-16T11:25:53Z
2015-03-25T15:19:47Z
CONTRIBUTOR
null
``` python >>> import pandas as pd >>> pd.version.version '0.15.2' >>> df = pd.DataFrame({u'A': {123.0: 2.0}, u'B': {123.0: 1}}) >>> df A B 123 2 1 >>> df['B'].dtype dtype('int64') >>> type(df.iterrows().next()[1]['B']) <type 'numpy.float64'> ```
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64,324,421
MDU6SXNzdWU2NDMyNDQyMQ==
9,729
"Bad" lines with too few fields
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9
2015-03-25T17:18:20Z
2021-04-18T05:58:08Z
null
NONE
null
As per http://pandas.pydata.org/pandas-docs/stable/io.html#handling-bad-lines, records with too many fields cause (depending on error_bad_lines) an exception to be thrown, or stderr to be written to. Would it be possible to add an option, defaulting to False, that gave similar warnings/errors if there are _too few_ fields in a record (compared to all the other records. The current behaviour is just to insert NaN's - but there are cases where data integrity is important so knowing that some records are missing fields is important. Cheers ^
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MDU6SXNzdWU2NDM0NDg5NQ==
9,730
Pandas concat breaks with axis specified
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2
2015-03-25T18:45:57Z
2015-03-25T21:26:50Z
2015-03-25T21:26:50Z
NONE
null
I have an odd issue where if I specify the axis in concat it throws the following error: InvalidIndexError: Reindexing only valid with uniquely valued Index objects Pandas version: 0.16.0 OS: Ubuntu 14.04 LTS x64 Here's the pickle of the data structure causing this (it's rather large, ~60 megs, but it's my real use case which caused this error) https://www.dropbox.com/s/02dn99dbi6p14um/pandas_error.pickle?dl=0 ``` import pickle import pandas as pd l = pickle.load(open('pandas_error.pickle')) scan_query = '(index >= 624.306193 & index <= 624.326171) | (index >= 624.807981 & index <= 624.826795) | (index >= 625.309633 & index <= 625.32635) | (index >= 628.315655 & index <= 628.331508) | (index >= 628.815043 & index <= 628.83164)' # works pd.concat([i.dropna().to_frame().query(scan_query) for i in l]) # breaks #pd.concat([i.dropna().to_frame().query(scan_query) for i in l], axis=1) ```
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9,731
[] (__getitem__) boolean indexing assignment bug with nans
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0
2015-03-25T20:34:47Z
2015-04-02T21:25:39Z
2015-04-02T21:25:39Z
NONE
null
See repro below: ``` python import pandas as pd import numpy as np temp = pd.Series(np.random.randn(10)) temp[3:6] = np.nan temp[8] = np.nan nan_index = np.isnan(temp) # this works temp1 = temp.copy() temp1[nan_index] = [99, 99, 99, 99] temp1[nan_index] 3 99 4 99 5 99 8 99 dtype: float64 # this doesn't - values look like they're being assigned in a different order? temp2 = temp.copy() temp2[nan_index] = [99, 99, 99, np.nan] 3 NaN 4 99 5 99 8 99 dtype: float64 # ... but it works properly when using .loc temp2 = temp.copy() temp2.loc[nan_index] = [99, 99, 99, np.nan] 3 99 4 99 5 99 8 NaN dtype: float64 ``` output of show_versions(): ``` INSTALLED VERSIONS ------------------ commit: None python: 2.7.9.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel byteorder: little LC_ALL: None LANG: None pandas: 0.16.0 nose: 1.3.4 Cython: 0.21.2 numpy: 1.9.2 scipy: 0.14.0 statsmodels: 0.5.0 IPython: 3.0.0 sphinx: 1.2.3 patsy: 0.2.1 dateutil: 2.4.1 pytz: 2015.2 bottleneck: 0.8.0 tables: 3.1.1 numexpr: 2.3.1 matplotlib: 1.4.0 openpyxl: 2.0.2 xlrd: 0.9.3 xlwt: 0.7.5 xlsxwriter: 0.6.6 lxml: 3.4.2 bs4: 4.3.2 html5lib: 0.999 httplib2: 0.8 apiclient: None sqlalchemy: 0.9.8 pymysql: None psycopg2: None ```
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9,732
Inconsistent behavior of partial string indexing vs straight indexing for timeseries data
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2
2015-03-25T23:19:53Z
2021-06-26T22:40:16Z
null
NONE
null
I recently ran into an issue where pandas is not handling the "<=" logical operator when comparing dates in an interesting way. ``` import pandas as pd rng = pd.date_range('1/1/2011', periods=72, freq='H') df = pd.DataFrame(rng) df.columns=['date'] crit = (df['date'] >= '2011-01-01') & (df['date'] <= '2011-01-02') df.loc[crit, 'New_Col'] = 'First two days?' ``` Anything after 2011-01-02 00:00:00 is not included in the less than or equal to 2011-01-02. One would expect that less than or equal to mean that entire day. Is this by design, or is this a bug?
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MDU6SXNzdWU2NDUzNzYyOQ==
9,733
Series.sum has inconsistent return type
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5
2015-03-26T14:18:03Z
2018-07-06T22:49:29Z
2018-07-06T22:49:17Z
NONE
null
Series.sum returns a numpy type, except when it's empty, in which case it returns a python int of value "0": ``` In [2]: type(pd.Series([0]).sum()) Out[2]: numpy.int64 In [3]: type(pd.Series().sum()) Out[3]: int ``` This poses a problem when I do `1 / myserie.sum()` because I expect to obtain `np.inf` rather than a divison by 0 exception. I think the return type of `Series.sum()` for empty series should be inferred from the Series's dtype This way, `Series([], dtype='str').sum()` would return an empty string, and `Series([]).sum()` would return `np.float64(0)` since an empty series' default type seems to be float64. Tested with Pandas 0.16.0
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HDFStore: AttributeError: __exit__ in with statement
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7
2015-03-26T14:37:00Z
2015-03-27T13:57:41Z
2015-03-27T13:57:41Z
NONE
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I have this method in a class: ``` def save(self, filename, key='/'): """Saves to an HDF5 store.""" with pd.HDFStore(filename) as store: for g in self.GENDERS: df = getattr(self, g) store.put(key+g, df, data_columns=df.columns, format='table', encoding='utf-8', dropna=False) ``` and in my Unittest: ``` def test_save_to_hdf(self): td = TrackData() with tempfile.NamedTemporaryFile(suffix='.h5') as temp: td.save(temp.name) ``` When I run nosetests I get this: ``` ERROR: test_save_to_hdf (courtana.tracker.tests.test_trackdata.TestTrackData) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/hugo/projects/courtana/courtana/tracker/tests/test_trackdata.py", line 34, in test_save_to_hdf td.save(temp.name) File "/home/hugo/projects/courtana/courtana/tracker/trackdata.py", line 54, in save with pd.HDFStore(filename) as store: AttributeError: __exit__ ``` Why did this suddenly stop working?
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9,735
BUG: read_csv, engine='c' error
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8
2015-03-26T14:59:21Z
2015-09-17T23:48:15Z
2015-09-17T23:48:15Z
NONE
null
I am trying to read a file 57MB with `pandas.csv_read`. The file contains a header (5 rows), afterwads integer values and at the end float values: ``` info 2681087 53329 1287215 1287215 53328 RSA 53328 53328 1287215 0 (I14) (I14) (d25.15) (d25.15) F 1 5332 1 33 61 92 128 ... 165 205 239 272 0.112474585277959E+09 0.126110931411177E+09 0.515995872032845E+09 0.126110931411175E+09 -0.194634413074014E+09 0.112474585277950E+09 ... ``` When I read the txt file: import pandas as pd pd.read_csv(file, skiprows=5+n_int_values, header=None, engine='c', dtype=np.float, low_memory=False) The result is an error: ``` --------------------------------------------------------------------------- CParserError Traceback (most recent call last) <ipython-input-118-699921ac7a12> in <module>() ----> 1 a=pd.read_csv(loc, skiprows=5+n_coloums+n_rows, header=None, engine='c', low_memory=False, error_bad_lines=False) C:\Anaconda\lib\site-packages\pandas\io\parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines) 468 skip_blank_lines=skip_blank_lines) 469 --> 470 return _read(filepath_or_buffer, kwds) 471 472 parser_f.__name__ = name C:\Anaconda\lib\site-packages\pandas\io\parsers.pyc in _read(filepath_or_buffer, kwds) 254 return parser 255 --> 256 return parser.read() 257 258 _parser_defaults = { C:\Anaconda\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows) 713 raise ValueError('skip_footer not supported for iteration') 714 --> 715 ret = self._engine.read(nrows) 716 717 if self.options.get('as_recarray'): C:\Anaconda\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows) 1162 1163 try: -> 1164 data = self._reader.read(nrows) 1165 except StopIteration: 1166 if nrows is None: pandas\parser.pyx in pandas.parser.TextReader.read (pandas\parser.c:7426)() pandas\parser.pyx in pandas.parser.TextReader._read_rows (pandas\parser.c:8377)() pandas\parser.pyx in pandas.parser.raise_parser_error (pandas\parser.c:20728)() CParserError: Error tokenizing data. C error: Buffer overflow caught - possible malformed input file. ``` This happens on pandas 0.16.0, on anaconda python 2.7.8. On an older version - 0.14.1. it works correctly. Note: When I use `engine='python'`, the txt file is loaded normaly.
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DataFrame.where does not respect axis parameter for n x n frames
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1
2015-03-26T15:06:50Z
2015-08-18T11:20:29Z
2015-08-18T11:20:29Z
CONTRIBUTOR
null
``` In [3]: df = pd.DataFrame(np.random.randn(5, 5)) In [4]: s = pd.Series(range(5)) In [5]: df.where(df.isnull(), s, axis=0) Out[5]: 0 1 2 3 4 0 0 1 2 3 4 1 0 1 2 3 4 2 0 1 2 3 4 3 0 1 2 3 4 4 0 1 2 3 4 In [6]: df.where(df.isnull(), s, axis=1) Out[6]: 0 1 2 3 4 0 0 1 2 3 4 1 0 1 2 3 4 2 0 1 2 3 4 3 0 1 2 3 4 4 0 1 2 3 4 ``` The right axis _is_ used when aligning indices, but it's not used when broadcasting the aligned Series. You also get the right result if the DataFrame is not square, because core.internals.where will transpose if necessary to the get the dimensions to fit.
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plotting: passing in ax is removing the wrong x labels
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6
2015-03-26T17:52:47Z
2015-09-01T14:14:18Z
2015-04-02T14:30:11Z
CONTRIBUTOR
null
The following code will show the x labels on the right side, but actually it should show the labels at the bottom (or better: should show all!) ``` %matplotlib inline import matplotlib.pyplot as plt import matplotlib as mpl import matplotlib.gridspec as gridspec import pandas as pd gs = mpl.gridspec.GridSpec(2, 2) ax_tl = plt.subplot(gs[0,0]) ax_ll = plt.subplot(gs[1,0]) ax_tr = plt.subplot(gs[0,1]) ax_lr = plt.subplot(gs[1,1]) axes = [ax_tl, ax_ll, ax_tr, ax_lr] df = pd.DataFrame({"a":[1,2,3,4,5,6], "b":[1,2,3,4,5,6]}) for ax in axes: df.plot(x="a", y="b", title="title", ax=ax) gs.tight_layout(plt.gcf()) ``` IMO: in `MPLPlot.__init__()` ``` def __init__(..., sharex=None,...): [...] if sharex is None: if not ax is None: # if we get a ax, the users should know what he does... sharex=False else: sharex = True [...] ```
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9,738
Raise warning when automatically converting datatypes
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12
2015-03-27T02:25:52Z
2021-04-18T06:05:37Z
2021-04-18T06:05:37Z
CONTRIBUTOR
null
A frequent bug I end up fixing in my own code when working with integer data results from my data auto converting to float, usually on a merge, reindex, unstack, or pivot. ``` In [6]: df = pd.DataFrame(np.random.randn(5, 5), columns=list('abcde'), dtype=int) In [7]: df.dtypes Out[7]: a int64 b int64 c int64 d int64 e int64 dtype: object In [8]: df.reindex(range(3)).dtypes Out[8]: a int64 b int64 c int64 d int64 e int64 dtype: object In [9]: df.reindex(range(6)).dtypes Out[9]: a float64 b float64 c float64 d float64 e float64 dtype: object ``` As far as I know, there is no way to know whether this has happened without doing additional checks after an operation.... or fixing my code, but that would be much easier if I could catch a warning, since i am often performing a long series of operations and figuring out which one converted everything to float is time consuming. I would also like to suggest converting to object over converting to float if nans are being produced. At least types of existing data would be preserved. (I realize reindex is not the ideal choice since it has fill_value as an argument)
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9,739
Yahoo Datareader RemoteDataError when end date added in Python 3
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2015-03-27T09:04:37Z
2015-04-07T09:06:10Z
2015-03-27T14:53:02Z
NONE
null
See stackoverflow description: http://stackoverflow.com/questions/29296045/pandas-yahoo-datareader-remotedataerror-when-end-date-added?noredirect=1#comment46788752_29296045
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Plotting: don't change visibility of xaxis labels and ticklabels if passing in a axis
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8
2015-03-27T13:50:33Z
2015-03-31T11:54:12Z
2015-03-31T11:54:03Z
CONTRIBUTOR
null
This does two things: - fix for changing the wrong xlabels/xticklabels if a gridspec is used and the ax is passed in (not sure about using subplot without gridspec) - changes the default behaviour for sharex if an axis is passed in: before the visibility changes were applied per default, now they are only applied if explicitly requested via `sharex=True` Closes: #9737
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ENH: support CategoricalIndex (GH7629)
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58
2015-03-27T22:01:57Z
2015-04-20T14:35:36Z
2015-04-20T11:19:57Z
CONTRIBUTOR
null
closes #7629 xref #8613 xref #8074 - [x] docs / whatsnew - [x] ~~auto-create a `CategoricalIndex` when grouping by a `Categorical` (this doesn't ATM)~~ - [x] adding a value not in the Index, e.g. `df2.loc['d'] = 5` should do what? (currently will coerce to an `Index`) - [x] `pd.concat([df2,df])` should STILL have a `CategoricalIndex` (yep)? - [x] implement `min/max` - [x] fix groupby on cat column - [x] add `Categorical` wrapper methods - [x] make repr evalable / fix - [x] contains should be on values not categories A `CategoricalIndex` is essentially a drop-in replacement for `Index`, that works nicely for non-unique values. It uses a `Categorical` to represent itself. The behavior is very similar to using a duplicated Index (for say indexing). Groupby works naturally (and returns another `CategoricalIndex`). The only real departure is that `.sort_index()` works like you would expected (which is a good thing:). Clearly this will provide idempotency for `set/reset` index w.r.t. Categoricals, and thus memory savings by its representation. This doesn't change the API at all. IOW, this is not turned on by default, you have to either use `set/reset`, assign an index, or pass a `Categorical` to `Index`. ``` In [1]: df = DataFrame({'A' : np.arange(6,dtype='int64'), ...: 'B' : Series(list('aabbca')).astype('category',categories=list('cab')) }) In [2]: df Out[2]: A B 0 0 a 1 1 a 2 2 b 3 3 b 4 4 c 5 5 a In [3]: df.dtypes Out[3]: A int64 B category dtype: object In [5]: df.B.cat.categories Out[5]: Index([u'c', u'a', u'b'], dtype='object') In [6]: df2 = df.set_index('B') In [7]: df2 Out[7]: A B a 0 a 1 b 2 b 3 c 4 a 5 In [8]: df2.index Out[8]: CategoricalIndex([u'a', u'a', u'b', u'b', u'c', u'a'], dtype='category') In [9]: df2.index.categories Out[9]: Index([u'c', u'a', u'b'], dtype='object') In [10]: df2.index.codes Out[10]: array([1, 1, 2, 2, 0, 1], dtype=int8) In [11]: df2.loc['a'] Out[11]: A B a 0 a 1 a 5 In [12]: df2.loc['a'].index Out[12]: CategoricalIndex([u'a', u'a', u'a'], dtype='category') In [13]: df2.loc['a'].index.categories Out[13]: Index([u'c', u'a', u'b'], dtype='object') In [14]: df2.sort_index() Out[14]: A B c 4 a 0 a 1 a 5 b 2 b 3 In [15]: df2.groupby(level=0).sum() Out[15]: A B a 6 b 5 c 4 In [16]: df2.groupby(level=0).sum().index Out[16]: CategoricalIndex([u'a', u'b', u'c'], dtype='category') ```
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BUG: Bring pandas up to date with pandas-datareader
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2
2015-03-28T03:30:41Z
2015-04-07T09:05:49Z
2015-03-29T17:10:43Z
CONTRIBUTOR
null
Two bug fixes from pandas-datareader. pydata/pandas-datareader#25 pydata/pandas-datareader#24
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BUG: where gives incorrect results when upcasting (GH 9731)
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3
2015-03-28T14:08:51Z
2015-06-10T13:41:24Z
2015-04-02T21:25:39Z
CONTRIBUTOR
null
closes #9731 The main issue is when the destination and source arrays have different lengths, `np.putmask` doesn't behave like `arr[mask] = values`: "Sets `a.flat[n] = values[n]` for each n where `mask.flat[n]==True`" We have to use `np.place` instead. A secondary issue is that `np.place` doesn't automatically convert an integer to a `datetime64` like `np.putmask` does (I created a numpy issue for this), so we need an additional check for that case. The rest of the commit is just cleaning up `_maybe_upcast_putmask`, which had some parameters that were never used, and a confusing docstring.
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BUG: utils.testing.get_locales()
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6
2015-03-28T17:42:50Z
2015-04-09T15:13:42Z
2015-04-09T15:13:42Z
CONTRIBUTOR
null
Line 334: raw_locales = [] deletes the data received from locale_getter, so the return from this function is always a blank list. ``` python try: raw_locales = locale_getter() except: return None try: # raw_locales is "\n" seperated list of locales # it may contain non-decodable parts, so split # extract what we can and then rejoin. raw_locales = [] for x in raw_locales: try: raw_locales.append(str(x, encoding=pd.options.display.encoding)) except: pass except TypeError: pass if prefix is None: return _valid_locales(raw_locales, normalize) found = re.compile('%s.*' % prefix).findall('\n'.join(raw_locales)) return _valid_locales(found, normalize) ``` https://github.com/pydata/pandas/blob/8d2818e32d0bbb50e183ccb5724c391e4f604670/pandas/util/testing.py#L334
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BUG: DataFrame.equals should not care about block order (GH #9330)
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13
2015-03-28T22:43:43Z
2015-04-05T23:10:10Z
2015-04-05T23:10:10Z
CONTRIBUTOR
null
closes #9330 and another version of the same problem [here on SO](http://stackoverflow.com/questions/29311659/how-can-pandas-dataframes-appear-identical-but-fail-equals) by canonicalizing the block order during an `equals` comparison. Tested at the block manager level and above it at the frame level.
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65,023,484
MDU6SXNzdWU2NTAyMzQ4NA==
9,746
fill_value kwarg for unstack
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3
2015-03-29T04:41:40Z
2016-01-30T19:27:56Z
2016-01-30T19:27:56Z
CONTRIBUTOR
null
Currently: ``` python In [2]: df = pd.DataFrame({'x':['a', 'a', 'b'], 'y':['j', 'k', 'j'], 'z':[0, 1, 2]}) In [3]: df.set_index(['x', 'y']).unstack() Out[3]: z y j k x a 0 1 b 2 NaN ``` If I want to fill with -1, i need to `fillna` and then `astype` back to `int`. Ideally: ``` python In [3]: df.set_index(['x', 'y']).unstack(fill_value=-1) Out[3]: z y j k x a 0 1 b 2 -1 ```
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Upstream Style.copy constructor regression breaks _Openpyxl2Writer
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1
2015-03-29T21:49:59Z
2015-09-21T15:35:33Z
2015-09-11T14:52:55Z
CONTRIBUTOR
null
Starting with openpyxl 2.2.0, various tests in `pandas.io.tests.test_excel` fail with tracebacks similar to this one. ``` ====================================================================== ERROR: test_basics_with_nan (pandas.io.tests.test_excel.Openpyxl2Tests) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.7/site-packages/pandas/io/tests/test_excel.py", line 1229, in wrapped orig_method(self, *args, **kwargs) File "/usr/local/lib/python2.7/site-packages/pandas/io/tests/test_excel.py", line 641, in test_basics_with_nan self.frame.to_excel(path, 'test1') File "/usr/local/lib/python2.7/site-packages/pandas/core/frame.py", line 1267, in to_excel startrow=startrow, startcol=startcol) File "/usr/local/lib/python2.7/site-packages/pandas/io/excel.py", line 778, in write_cells xcell.style = xcell.style.copy(**style_kwargs) File "/usr/local/lib/python2.7/site-packages/openpyxl/styles/styleable.py", line 107, in style protection=self.protection File "/usr/local/lib/python2.7/site-packages/openpyxl/styles/__init__.py", line 42, in __init__ self._font = font File "/usr/local/lib/python2.7/site-packages/openpyxl/descriptors/base.py", line 35, in __set__ raise TypeError('expected ' + str(self.expected_type)) TypeError: expected <class 'openpyxl.styles.fonts.Font'> ``` `pandas.io.excel._Openpyxl2Writer.write_cells` uses `openpyxl.styles.Style.copy` to merge newly specified style data with style data on existing cells. Sadly, the obvious workaround&mdash;iterate over the fields of the `Style` object and use them to build an updated `Style` object&mdash;won't work either without additional coddling, because the fields don't reliably return the native object that the `Style` constructor demands. ``` python >>> from openpyxl.styles import Style >>> s0 = Style() >>> s1 = Style(fill=s0.fill) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/obrienjw/venv/py27-openpyxl220/lib/python2.7/site-packages/openpyxl-2.2.0-py2.7.egg/openpyxl/styles/__init__.py", line 43, in __init__ self._fill = fill File "/Users/obrienjw/venv/py27-openpyxl220/lib/python2.7/site-packages/openpyxl-2.2.0-py2.7.egg/openpyxl/descriptors/base.py", line 35, in __set__ raise TypeError('expected ' + str(self.expected_type)) TypeError: expected <class 'openpyxl.styles.fills.Fill'> ``` (See upstream issue: 444 [copy() method on Style object raises TypeError](https://bitbucket.org/openpyxl/openpyxl/issue/444/copy-method-on-style-object-raises))
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9,748
ENH: support CategoricalIndex Followups
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3
2015-03-29T23:34:21Z
2019-10-21T20:06:20Z
null
CONTRIBUTOR
null
followup to #9741 These are all commented in `pandas/core/test_frame/test_categorical_indexing_index` - [ ] support slice semantics - [x] fix index name issue on selection for non-existant label that IS in the categories - [x] fix index name issue on assignment for non-existan label that IS in the categories - [ ] duplicated returns Boolean array? / test - [ ] in repr, use `max_categories/max_seq_len` to display a truncated view These last 2 were 'solved' by putting the following in `core/internals/reindex_indexer` but this caused some issues as the `MultiIndex._shallow_copy` is really a view semantic (so that needs to be fixed as well) ``` # make sure that we propogate the correct meta data for this axis # we need to do this because we are not doing a take / reindex # which would do this automatically new_axis = self.axes[axis]._shallow_copy(new_axis) ``` ``` In [1]: In [4]: df = DataFrame({'A' : np.arange(6,dtype='int64'), ...: ...: 'B' : Series(list('aabbca')).astype('category',categories=list('cabe')) }).set_index('B') In [2]: df Out[2]: A B a 0 a 1 b 2 b 3 c 4 a 5 In [3]: df.loc['e'] = 20 In [4]: df Out[4]: A a 0 a 1 b 2 b 3 c 4 a 5 e 20 ```
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9,749
Pandas io data.py market data feed source in question
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1
2015-03-30T00:57:25Z
2016-07-04T11:21:41Z
2016-07-04T11:21:38Z
NONE
null
I was trying to fix a stock data feed error in data.py for python 2.7 I used line 124 to look into the problem. ``` # for codes see: http://www.gummy-stuff.org/[redacted]-data.htm ``` Is another reliable source available?
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65,193,712
MDU6SXNzdWU2NTE5MzcxMg==
9,750
Mode() not compatible with fillna()
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9
2015-03-30T11:49:16Z
2018-10-21T00:57:42Z
2018-10-21T00:57:42Z
CONTRIBUTOR
null
I made an toy dataframe: df = pandas.DataFrame([[1, 1, 1],[2, 1, 1],[2, 1, 1],[numpy.nan, numpy.nan, numpy.nan]], columns=["a","b","c"]) I try different methods to fill missing values. These work as expected: df.fillna(df.mean()) df.fillna(df.median()) But this doesnt work: df.fillna(df.mode()) Inspecting the output from df.mode() I see it has different format than df.mean() and df.median(). As I user I would expect the same behavior for these functions, and be able to fill missing values as described. Using Pandas 0.15.2
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9,751
DataFrame.from_dict converts single-tuple index to plain Index when orient='index'
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3
2015-03-31T00:30:34Z
2021-04-18T06:10:33Z
null
NONE
null
I have a dictionary of Series that I'm converting to a DataFrame via `DataFrame.from_dict`, and the Series have index values that are tuples, as in these examples: ``` python In [39]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x',), ('y',), ('z',)])}, orient='index') Out[39]: x y z a 1 2 3 In [40]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x', 1), ('y', 2), ('z', 3)])}, orient='index') Out[40]: x y z 1 2 3 a 1 2 3 ``` Note that in the first example the columns are a plain `Index`, but in the second example it's converted to `MultiIndex`. This causes a problem for me later when I index into the DataFrame columns and expect to get tuples out (with `MultiIndex` the values are represented as tuples). When `orient='columns'` the tuples are not touched: ``` python In [49]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x',), ('y',), ('z',)])}, orient='columns') Out[49]: a (x,) 1 (y,) 2 (z,) 3 In [50]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x', 1), ('y', 2), ('z', 3)])}, orient='columns') Out[50]: a (x, 1) 1 (y, 2) 2 (z, 3) 3 ``` I can get what I'm after by using `orient='columns'` and then doing the transpose: ``` python In [51]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x',), ('y',), ('z',)])}, orient='columns').transpose() Out[51]: (x,) (y,) (z,) a 1 2 3 In [52]: pd.DataFrame.from_dict({'a': pd.Series([1, 2, 3], index=[('x', 1), ('y', 2), ('z', 3)])}, orient='columns').transpose() Out[52]: (x, 1) (y, 2) (z, 3) a 1 2 3 ``` But I was caught unawares by single-item tuples being treated differently than multi-item tuples, and the difference in behavior between index/columns orientation.
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9,752
BUG: first_valid_index() empty Series and DataFrame inconsistencies
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4
2015-03-31T00:35:32Z
2019-12-12T21:05:08Z
2019-12-12T21:05:08Z
CONTRIBUTOR
null
I came across some inconsistencies in how `first_valid_index()` works on empty `Series` and `DataFrames` (and `last_valid_index()`). ``` python >>> print(pd.DataFrame().first_valid_index()) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/frame.py", line 3332, in first_valid_index return self.index[self.count(1) > 0][0] File "/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/index.py", line 915, in __getitem__ return getitem(key) IndexError: index 0 is out of bounds for axis 0 with size 0 >>> print(pd.Series().first_valid_index()) None >>> print(pd.SparseDataFrame([]).first_valid_index()) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/frame.py", line 3332, in first_valid_index return self.index[self.count(1) > 0][0] File "/home/will/.virtualenvs/dev3/lib/python3.4/site-packages/pandas/core/ops.py", line 919, in f res = self._combine_const(other, func, raise_on_error=False) TypeError: _combine_const() got an unexpected keyword argument 'raise_on_error' >>> print(pd.SparseSeries([]).first_valid_index()) None ``` Is there a reason for this different behavior? Note also that the `DataFrame`/`SparseDataFrame` exceptions are different. Here's the [`DataFrame`](https://github.com/pydata/pandas/blob/671c4b3c52a02ee3736752b2645d08762c0fedaf/pandas/core/frame.py#L3421) and [`Series`](https://github.com/pydata/pandas/blob/a4772028fef7f660cf6d94129a1a9a6f3852e441/pandas/core/series.py#L2375) methods. The `Series` method has a `if len(self) == 0` check. I can make a PR if you think one of these should be changed and tell me which way you want to go. For what it's worth, I came across this issue trying to use this function in a list comprehension to get a list of minimum times from a handful of `Series, DataFrames, and SparseDataFrames` that may or may not be empty. (Although it just occurred to me that I could, of course, put an if empty check in my list comprehension.)
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9,753
Performance degradation appending rows to sqlite
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2015-03-31T01:43:02Z
2019-12-13T20:38:40Z
2019-12-13T20:38:40Z
NONE
null
Hey folks -- I'm writing a large CSV (100M rows) to a sqlite database through Pandas in 10k chunks. ``` disk_engine = create_engine('sqlite:///100M.db') chunksize = 10000 index_start = 0 for df in pd.read_csv('100M_rows.csv', chunksize=chunksize, iterator=True, encoding='utf-8'): df.index += index_start df.to_sql('data', disk_engine, if_exists='append') index_start = df.index[-1] + 1 ``` I'm seeing non-linear writing speeds after writing 1M rows: ![](https://plot.ly/~chris/7407.png) Is there a more efficient way I should be writing these appends? Many thanks!
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9,754
Start combining various development documentation into one place.
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13
2015-03-31T02:23:20Z
2015-05-05T18:03:08Z
2015-04-03T00:41:09Z
CONTRIBUTOR
null
Closes #6232 In working with the various "Contributing" documents recently it was clear that we needed to combine them. This is a stab at doing so. I took the various components from pydata.org, CONTRIBUTING.md, various wiki pages, and the already included Documentation documents and created this file. I think it's a good start.
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65,392,950
MDU6SXNzdWU2NTM5Mjk1MA==
9,755
read_csv with names, usecols and parse_dates
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2
2015-03-31T08:01:57Z
2016-04-06T19:17:26Z
2016-04-06T19:17:26Z
NONE
null
xref #12203 The arrays passed to the date_parser function is different when `names` and `use_cols` are specified to limit the number of parsed columns. When running the example code below, the date_parser function receives two arguments, one array with '20140101' strings, and one array with integers. The default `date_parser` fails to process this input. When assigning an empty list to `DROPPED_COLUMNS` (so that all columns are parsed), the second array contains strings instead of integers, and the datetimes are parsed correctly. The problem doesn't occur with `engine='python'`. I haven't tested the influence of the `header` and `index_cols` options. Python script: ``` from __future__ import print_function, division import pandas as pd CSV = '2014.csv' DROPPED_COLUMNS = ['NCDC', 'I', 'QCP'] # DROPPED_COLUMNS = [] with open(CSV) as csv: csv.readline() column_names = csv.readline().split() used_columns = [i for i, column_name in enumerate(column_names) if column_name not in DROPPED_COLUMNS] used_col_names = [column_name for i, column_name in enumerate(column_names) if i in used_columns] parse_dates = [[i for i, column_name in enumerate(used_col_names) if column_name in ('Date', 'HrMn')]] print(parse_dates) data = pd.read_csv(csv, header=None, names=used_col_names, index_col=False, engine='python', parse_dates=parse_dates, usecols=used_columns) print(data) ``` Contents of 2014.csv: ``` Identification TEMP USAF NCDC Date HrMn I Type QCP Temp Q 062693,99999,20140101,0025,4,FM-15, , 7.0,1, 062693,99999,20140101,0055,4,FM-15, , 6.0,1, 062693,99999,20140101,0125,4,FM-15, , 6.0,1, 062693,99999,20140101,0155,4,FM-15, , 6.0,1, 062693,99999,20140101,0225,4,FM-15, , 6.0,1, 062693,99999,20140101,0255,4,FM-15, , 6.0,1, 062693,99999,20140101,0325,4,FM-15, , 6.0,1, 062693,99999,20140101,0355,4,FM-15, , 6.0,1, ```
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9,756
Resample BM adds extra index point
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6
2015-03-31T08:15:01Z
2015-05-04T19:09:49Z
2015-05-04T19:09:49Z
CONTRIBUTOR
null
``` In [1]: index = pd.DatetimeIndex(start='20150101', end='20150331', freq='B') In [2]: df = pd.DataFrame(index=index, data=len(index)*[0]) In [3]: df.resample('BM', how='last') Out[3]: 0 2015-01-30 0 2015-02-27 0 2015-03-31 0 2015-04-30 NaN ``` This is both in 0.14.1 and 0.15.2
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65,415,237
MDU6SXNzdWU2NTQxNTIzNw==
9,757
Cannot convert a datetimelike to 'str' with astype
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8
2015-03-31T10:09:05Z
2015-04-11T13:05:32Z
2015-04-11T13:05:32Z
NONE
null
I find this surprising: ``` import pandas as pd pd.Series([pd.Timestamp('2010-01-04 00:00:00')]).astype(str) TypeError: cannot astype a datetimelike from [datetime64[ns]] to [|S0] ``` str(pd.Timestamp) works though, so why astype shouldn't? Also the type representation "|S0" is not entirely clear to me.
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9,758
BUG: allow conversion of Timestamp and Timedelta to string in astype
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7
2015-03-31T11:47:51Z
2015-09-19T00:38:25Z
2015-04-10T06:20:57Z
CONTRIBUTOR
null
Fixes GH #9757
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MDU6SXNzdWU2NTQzNDM0Ng==
9,759
FloatArrayFormatter._format_strings cannot handle float arrays containing NaNs
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7
2015-03-31T11:55:39Z
2015-04-08T12:22:22Z
2015-04-04T18:34:13Z
CONTRIBUTOR
null
Get `FloatingPointError` ([stack trace](http://pastebin.com/m1fZH4ie)) if call `to_html` method of a DataFrame that contains some NaN values. To reproduce: ``` python import pandas as pd import numpy as np pd.DataFrame(data=[np.nan, 3, 4]).to_html() ``` The problem seems to be that `FloatArrayFormatter._format_strings` assumes that the array it is trying to format as a string contains no `np.nan` values when checking to see if the array contains any very large or very small values. Version info: ``` commit: None python: 3.4.3.final.0 OS: Linux machine: x86_64 pandas: 0.16.0 Cython: 0.22 numpy: 1.9.2 ```
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65,450,274
MDU6SXNzdWU2NTQ1MDI3NA==
9,760
USMemorialDay defined incorrectly.
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3
2015-03-31T13:27:19Z
2015-08-18T11:09:30Z
2015-08-18T11:09:30Z
NONE
null
Memorial day is incorrectly defined. ``` >>> from pandas.tseries.holiday import AbstractHolidayCalendar, USMemorialDay >>> class MemorialDayCalendar(AbstractHolidayCalendar): rules=[USMemorialDay] >>> MemorialDayCalendar().holidays('2021','2022') <class 'pandas.tseries.index.DatetimeIndex'> [2021-05-24] Length: 1, Freq: None, Timezone: None ``` The [actual date](http://www.calendar-12.com/holidays/memorial_day/2021) of Memorial day in 2021 is 5/31. Memorial day should be defined as: `Holiday('Memorial Day' , month=5 , day=25, offset=DateOffset(weekday=MO(1)))`
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65,452,978
MDExOlB1bGxSZXF1ZXN0MzIzMjI4Mzc=
9,761
**BUG**: GH9759 - allow FloatArrayFormatter to format arrays with NaNs
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2
2015-03-31T13:38:50Z
2015-04-04T18:34:32Z
2015-04-04T18:34:20Z
CONTRIBUTOR
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9,762
API: DataFrame constructor for subclassing Series
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1
2015-03-31T13:58:19Z
2015-04-18T23:18:33Z
2015-04-18T23:18:33Z
MEMBER
null
`Series.to_frame` uses `pandas.DataFrame` statically as frame constructor. It is nice if `Series` has overridable property to specify frame-like constructor, like `DataFrame` has `_constructor_sliced`. How about `Series._constructor_frame`?
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9,763
BUG: incorrectly defined Memoridal Day holiday
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7
2015-03-31T16:45:07Z
2015-08-12T15:14:02Z
2015-08-12T15:14:02Z
NONE
null
closes #9760 Memorial Day was incorrectly defined. It is the last Monday in May, so the earliest it could be is the 25th (not the 24th). This change fixes the problem. ``` >>> from pandas.tseries.holiday import AbstractHolidayCalendar, USMemorialDay >>> class MemorialDayCalendar(AbstractHolidayCalendar):rules=[USMemorialDay] >>> MemorialDayCalendar().holidays('2021','2022') ``` Will now return Timestamp('2021-05-31 00:00:00') instead of Timestamp('2021-05-24 00:00:00')
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65,498,174
MDU6SXNzdWU2NTQ5ODE3NA==
9,764
Displaying DataFrame with values from range [1e-7, 5e-7] shows 0 in some situations.
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2
2015-03-31T16:53:40Z
2015-04-06T00:18:30Z
2015-04-06T00:18:30Z
CONTRIBUTOR
null
Hi, For illustration purpose, I'll assume the following dataset: ``` python In [1]: d=pd.DataFrame({'col1':[9.999e-8, 1e-7, 1.0001e-7, 2e-7, 4.999e-7, 5e-7, 5.0001e-7, 6e-7]}) In [2]: d Out[2]: col1 0 9.999000e-08 1 1.000000e-07 2 1.000100e-07 3 2.000000e-07 4 4.999000e-07 5 5.000000e-07 6 5.000100e-07 7 6.000000e-07 ``` I've noticed the following behavior (in pandas 0.16 and 0.15.2): 1) When values from range [1e-7, 5e-7] are displayed along with values that are less than 1e-7, the output is OK: ``` python In [3]: d[0:6] Out[3]: col1 0 9.999000e-08 1 1.000000e-07 2 1.000100e-07 3 2.000000e-07 4 4.999000e-07 5 5.000000e-07 ``` 2) When values exclusively from that range are displayed, the output is 0: ``` python In [4]: d[1:6] Out[4]: col1 1 0 2 0 3 0 4 0 5 0 ``` 3) When values from that range are displayed along with values greater than 5e-7, the output for them is 0.000000: ``` python In [5]: d[1:8] Out[5]: col1 1 0.000000 2 0.000000 3 0.000000 4 0.000000 5 0.000000 6 0.000001 7 0.000001 ``` I'm assuming it's a rounding / output formatting error. Also: ``` python In [6]: pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.4.2.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-431.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.16.0 nose: 1.3.4 Cython: 0.21.2 numpy: 1.9.2 scipy: 0.14.0 statsmodels: 0.6.1 IPython: 2.3.1 sphinx: None patsy: 0.3.0 dateutil: 2.4.1 pytz: 2015.2 bottleneck: None tables: None numexpr: None matplotlib: 1.4.2 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None ```
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MDU6SXNzdWU2NTUzMjI0NA==
9,765
concat erroneously sets series to NaN
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1
2015-03-31T19:51:46Z
2016-04-25T13:42:20Z
2016-04-25T13:42:20Z
NONE
null
# problem `concat` fails when: 1. Using a SparseDataFrame 2. AND that SparseDataFrame has a column of all `0.0` and a `fill_value` of `0.0` If that SparseDataFrame doesn't have an all `0.0` column, it works beautifully. ``` python import unittest import pandas as pd from pandas.util.testing import assert_frame_equal import sys class TestSparseConcat(unittest.TestCase): def setUp(self): self.orig = {"A": [1.0, 0.0, 1.0, 0.0]} self.to_add = { "B": [0.0, 0.0, 0.0, 0.0], "C": [0.0, 0.0, 1.0, 0.0] } merged = self.orig.copy() merged.update(self.to_add) self.expected = pd.DataFrame(data=merged) def test_concat_df_to_df(self): A = pd.DataFrame(data=self.orig) B = pd.DataFrame(data=self.to_add) C = pd.concat([A, B], axis=1) assert_frame_equal(C, self.expected) # this works def test_concat_sparse_to_df(self): A = pd.DataFrame(data=self.orig) B = pd.DataFrame(data=self.to_add).to_sparse(fill_value=0.0) C = pd.concat([A, B], axis=1) sys.stderr.write("\nExpected:\n{}\nGot:\n{}\n".format(self.expected, C)) assert_frame_equal(C, self.expected) # this DOESN'T work if __name__ == "__main__": unittest.main() ``` # output ``` . Expected: A B C 0 1 0 0 1 0 0 0 2 1 0 1 3 0 0 0 Got: A B C 0 1 NaN 0 1 0 NaN 0 2 1 NaN 1 3 0 NaN 0 F ====================================================================== FAIL: test_concat_sparse_to_df (__main__.TestSparseConcat) ---------------------------------------------------------------------- Traceback (most recent call last): File "example.py", line 28, in test_concat_sparse_to_df assert_frame_equal(C, self.expected) File "/home/vagrant/.virtualenvs/ai-modeling/lib/python2.7/site-packages/pandas/util/testing.py", line 748, in assert_frame_equal check_exact=check_exact) File "/home/vagrant/.virtualenvs/ai-modeling/lib/python2.7/site-packages/pandas/util/testing.py", line 692, in assert_series_equal assert_almost_equal(left.values, right.values, check_less_precise) File "das/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2758) File "das/src/testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1843) File "das/src/testing.pyx", line 102, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2010) AssertionError: First object is null, second isn't: nan != 0.0 ---------------------------------------------------------------------- Ran 2 tests in 0.024s FAILED (failures=1) ``` # metadata ``` INSTALLED VERSIONS ------------------ commit: None python: 2.7.6.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-300.3.1.el6uek.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.16.0 nose: 1.3.3 Cython: None numpy: 1.9.2 scipy: 0.15.1 statsmodels: None IPython: 3.0.0 sphinx: 1.2 patsy: None dateutil: 2.2 pytz: 2014.4 bottleneck: None tables: None numexpr: None matplotlib: 1.2.1 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: 4.3.2 html5lib: None httplib2: None apiclient: None sqlalchemy: None pymysql: None psycopg2: None ```
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9,766
ENH: Add StringMethods.capitalize and swapcase
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1
2015-03-31T20:51:31Z
2015-04-02T13:58:00Z
2015-04-01T19:17:09Z
MEMBER
null
Derived from #9111.
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9,767
False negative on SettingWithCopyWarning
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5
2015-03-31T22:56:10Z
2020-10-23T22:07:24Z
null
CONTRIBUTOR
null
This is pandas 0.15.2 ``` In [1]: from pandas import * In [2]: df = DataFrame({"A" : [1,2,3,4,5], "B": [3.1, 4, 5, 6, 7]}) In [3]: df[["A", "B"]]["A"] = 5 # no warning - bug?! In [4]: df Out[4]: A B 0 1 3.1 1 2 4.0 2 3 5.0 3 4 6.0 4 5 7.0 In [5]: df[["A"]]["A"] = 5 C:\Anaconda\Scripts\ipython-script.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__': ```
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65,637,712
MDU6SXNzdWU2NTYzNzcxMg==
9,768
Invalid comparison of object Series objects containing None
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2015-04-01T08:59:55Z
2015-04-12T13:30:23Z
2015-04-12T13:30:23Z
NONE
null
In pandas 0.15.2: ``` from pandas import Series a = Series(['A', None]) b = Series(['A', None]) a == b ``` gives ``` 0 True 1 False dtype: bool ``` while `a[1] == b[1]` gives `True`. This is a) inconsistent and b) the behaviour of `a == b` is wrong (None is equal to None, so `a==b` should return `[True, True]`.
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9,769
Added documentation for mode()
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2015-04-01T11:20:03Z
2015-04-02T18:25:54Z
2015-04-02T18:25:44Z
CONTRIBUTOR
null
This relates to issue #9750
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9,770
ENH: Add option in read_csv to infer compression type from filename
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2015-04-01T12:11:53Z
2015-04-18T03:41:12Z
2015-04-18T02:53:14Z
CONTRIBUTOR
null
Ideally, I would love for this to be the default, but that wouldn't be backwards-compatible in the case where the filename ends in '.gz' or '.bz2' and you want to treat it as uncompressed. That seems like it would be very rare, though.
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9,771
Website links to incorrect doc versions
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2015-04-01T13:41:02Z
2015-05-27T13:08:30Z
2015-05-27T13:07:49Z
NONE
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http://pandas.pydata.org/pandas-docs/dev/index.html PDF linnk returns a 0.14.x version of the document, other PDF links return 404. It all depends on how you get to the link. I suggest checking all the documentation links on the website to verify they are linking to the appropriate version of the documentation.
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9,772
Groupby built by columns : cannot use .head() or .apply()
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7
2015-04-01T13:50:32Z
2020-11-13T04:43:10Z
2020-11-13T04:43:10Z
CONTRIBUTOR
null
``` python import numpy as np import pandas as pd df = pd.DataFrame({i:pd.Series(np.random.normal(size=10), index=range(10)) for i in range(11)}) df_g = df.groupby(['a']*6+['b']*5, axis=1) ``` This, if I well understood, should build a groupby object grouping columns, and so give the possibility to later aggregate them. And indeed : ``` python df_g.sum() ``` works well. But ``` python df_g.head() ``` Throws an error: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/jonas/Code/pandas/pandas/core/groupby.py", line 986, in head in_head = self._cumcount_array() < n File "/home/jonas/Code/pandas/pandas/core/groupby.py", line 1044, in _cumcount_array cumcounts[indices] = values IndexError: index 10 is out of bounds for axis 1 with size 10 ``` and ``` python df_g.apply(lambda x : x.sum()) ``` from which I expected the same result as the first example, gives this table : ``` a b 0 -0.381070 NaN 1 -1.214075 NaN 2 -1.496252 NaN 3 3.392565 NaN 4 -0.782376 NaN 5 1.306043 NaN 6 NaN -1.772334 7 NaN 4.125280 8 NaN 1.992329 9 NaN 4.283854 10 NaN -4.791092 ``` I didn't really get what's happening, I don't exclude a misunderstanding or an error from myself. ``` pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 2.7.8.final.0 python-bits: 64 OS: Linux OS-release: 3.13.0-46-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: fr_FR.UTF-8 pandas: 0.16.0-28-gcb8c130 nose: 1.3.4 Cython: 0.20.2 numpy: 1.9.2 scipy: 0.14.0 statsmodels: None IPython: 3.0.0-dev sphinx: 1.2.2 patsy: None dateutil: 2.4.1 pytz: 2015.2 bottleneck: None tables: 3.1.1 numexpr: 2.4 matplotlib: 1.4.3 openpyxl: 1.7.0 xlrd: 0.9.2 xlwt: 0.7.5 xlsxwriter: None lxml: None bs4: None html5lib: 0.999 httplib2: None apiclient: None sqlalchemy: 0.9.7 pymysql: None psycopg2: 2.5.3 (dt dec mx pq3 ext) ```
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ENH: Add StringMethods.partition and rpartition
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16
2015-04-01T13:58:57Z
2015-05-07T17:18:42Z
2015-05-07T11:08:58Z
MEMBER
null
Derived from #9111.
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MDU6SXNzdWU2NTY5NDg4OQ==
9,774
pd.concat() fails with two large Series with datetime
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4
2015-04-01T14:08:43Z
2016-07-11T13:28:23Z
2016-07-11T13:28:20Z
CONTRIBUTOR
null
I have two multi-index dataframes `a_df` and `b_df`. When I try to concat them, the operation fails, but it works if `head` them first to a small size. More specifically, note the following: ``` > pd.concat([a, b], join='outer', axis=1, verify_integrity=True).dropna() Empty DataFrame Columns: [budget, default_bid] Index: [] ``` versus the following: ``` > pd.concat([a.head(100), b.head(100)], join='outer', axis=1, verify_integrity=True).dropna() budget default_bid flight_uid created_at 0F0092Ntoi 2015-03-28 306115.26 6.00 0F0099v8iI 2015-03-28 10984.27 41.00 0F01MfxxSI 2015-03-28 3000.00 0.90 0F01SZnTfs 2015-03-28 3000.00 1.60 0F01ddgkRa 2015-03-28 1414.71 2.52 0F01ee81fL 2015-03-28 0.00 6.00 0F01f2HpD3 2015-03-28 425.00 40.00 0F01f4aW8n 2015-03-28 1575.76 1.25 0F01o6T23a 2015-03-28 0.00 9.00 0F02BZeTr6 2015-03-28 50893.42 1.50 ``` Here are the first few entries for `a_df` and `b_df`: ``` > a_df.head(10) flight_uid created_at 0F0092Ntoi 2015-03-28 306115.26 0F0099v8iI 2015-03-28 10984.27 0F01MfxxSI 2015-03-28 3000.00 0F01SZnTfs 2015-03-28 3000.00 0F01ddgkRa 2015-03-28 1414.71 0F01ee81fL 2015-03-28 0.00 0F01f2HpD3 2015-03-28 425.00 0F01f4aW8n 2015-03-28 1575.76 0F01o6T23a 2015-03-28 0.00 0F02BZeTr6 2015-03-28 50893.42 Name: budget, dtype: float64 ``` ``` > b_df.head(10) flight_uid created_at 0F0092Ntoi 2015-03-28 6.00 0F0099v8iI 2015-03-28 41.00 0F01MfxxSI 2015-03-28 0.90 0F01SZnTfs 2015-03-28 1.60 0F01ddgkRa 2015-03-28 2.52 0F01ee81fL 2015-03-28 6.00 0F01f2HpD3 2015-03-28 40.00 0F01f4aW8n 2015-03-28 1.25 0F01o6T23a 2015-03-28 9.00 0F02BZeTr6 2015-03-28 1.50 Name: default_bid, dtype: float64 ``` Here are the types for both: ``` > a_df.reset_index().dtypes flight_uid object created_at object budget float64 dtype: object ``` More specifically: ``` > a_df.reset_index()['created_at'][0] datetime.date(2015, 3, 28) ``` This is my configuration: ``` INSTALLED VERSIONS ------------------ commit: None python: 3.4.3.final.0 python-bits: 64 OS: Darwin OS-release: 14.1.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.16.0 nose: 1.3.4 Cython: 0.22 numpy: 1.9.2 scipy: 0.15.1 statsmodels: 0.6.1 IPython: 3.0.0 sphinx: 1.2.3 patsy: 0.3.0 dateutil: 2.4.1 pytz: 2015.2 bottleneck: None tables: 3.1.1 numexpr: 2.3.1 matplotlib: 1.4.3 openpyxl: 1.8.5 xlrd: 0.9.3 xlwt: None xlsxwriter: 0.5.7 lxml: 3.4.0 bs4: 4.3.2 html5lib: None httplib2: None apiclient: None sqlalchemy: 0.9.8 pymysql: None psycopg2: 2.6 (dt dec pq3 ext lo64) ``` Happy to post the data anywhere if that helps. In case it helps, it seems to work if I pre-convert the field `'created_at'` (in the index) to `pd.DatetimeIndex()`, i.e., doing the following with both `a_df` and `b_df`: ``` python a_df = a_df.reset_index() a_df['created_at'] = pd.DatetimeIndex(a_df['created_at']) a_df.set_index(['flight_uid', 'created_at']) ```
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KeyError when key is present
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2
2015-04-01T14:22:32Z
2015-04-01T15:22:04Z
2015-04-01T15:20:56Z
NONE
null
I got a KeyError when running my program with pandas 0.15.2 on python 3.4. I have a TimeSeries created from a dp.read_sql_query using a sqlalchemy engine for a sqlite database. I use this TimeSeries as a storage for data to work on. In each iteration I work on some data and errase it from the series with drop inplace. It has been working without hassle for some time now, but somehow today it threw a KeyError. By postmortem debugging I verified that the key was indeed present, so I do not know how I could avoid this crash from happening again. Here is the traceback: File "<ipython-input-35-6945f8f1d7ab>", line 1, in <module> runfile('C:/Users/u5en/Programação/Projeto Extração/Extração.py', wdir='C:/Users/u5en/Programação/Projeto Extração') File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 580, in runfile execfile(filename, namespace) File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 48, in execfile exec(compile(open(filename, 'rb').read(), filename, 'exec'), namespace) File "C:/Users/u5en/Programação/Projeto Extração/Extração.py", line 448, in <module> main() File "C:/Users/u5en/Programação/Projeto Extração/Extração.py", line 438, in main executor(sap, tarefa, banco ) File "C:/Users/u5en/Programação/Projeto Extração/Extração.py", line 357, in executor extrai_frac(sap, tabela, preencher_sap, linha_a_iterar, leiaute, produto) File "C:/Users/u5en/Programação/Projeto Extração/Extração.py", line 284, in extrai_frac registra_log(produto, tabela, leiaute, seq, dados[ind_entrada[0]], File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py", line 1780, in **getitem** return self._getitem_column(key) File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py", line 1787, in _getitem_column return self._get_item_cache(key) File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1068, in _get_item_cache values = self._data.get(item) File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\internals.py", line 2849, in get loc = self.items.get_loc(item) File "C:\Users\u5en\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\index.py", line 1402, in get_loc return self._engine.get_loc(_values_from_object(key)) File "pandas\index.pyx", line 134, in pandas.index.IndexEngine.get_loc (pandas\index.c:3807) File "pandas\index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas\index.c:3687) File "pandas\hashtable.pyx", line 696, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12310) File "pandas\hashtable.pyx", line 704, in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12261) KeyError: 6 In [36]: debug > c:\users\u5en\appdata\local\continuum\anaconda3\lib\site-packages\pandas\core\frame.py(1780)**getitem**() > 1779 else: > -> 1780 return self._getitem_column(key) > 1781 ipdb> key 6 ipdb> u > c:\users\u5en\programação\projeto extração\extração.py(284)extrai_frac() > 283 if tam_bloco==1: #Desiste da entrada única > --> 284 registra_log(produto, tabela, leiaute, seq, dados[ind_entrada[0]], > 285 tam_bloco, inicio_dados, fim_dados, "x") ipdb> ind_entrada[0] 6 ipdb> dados GUID 6 4E1DCC24F58173FAE10000000A19FF81 7 B07D0F53BB47EF79E10000000A1D00F6 8 5081D6378BE20D30E10000000A1D001D 9 4D4A2852F17736BEE10000000A19FF83 10 A6BF5F5365BB183EE10000000A1D2028 11 9175355128FCEC72E10000000A1D2028 12 DDA3DE53533C041DE10000000A1D2028 13 142207533F63D632E10000000A1D2028 14 50741D7B4FE61F18E10000000A1D0022 15 08485E5381EB4B3EE10000000A1D00F8 16 F4657B5249D9ED18E10000000A1D00F8 17 D0A95451844A9D19E10000000A1D00F8 18 4FD60BFC2B7A1159E10000000A1D0026 19 50920B577EF70BE2E10000000A1D0026 20 765E59539656A375E10000000A1D00F6 21 16FA7B538A6A3A7DE10000000A1D2028 22 E5330753B6589922E10000000A1D2028 23 1748E852010AA076E10000000A1D2028 24 4DD1A2D327FB51F3E10000000A19FF83 25 5085A682BF56192BE10000000A1D0022 26 4E1D0227DB213A10E10000000A19FF82 27 1805E953691ECA44E10000000A1D00F8 28 101E6E52598EE224E10000000A1D00F8 29 DE221954306A8713E10000000A1D00F8 30 4E6EAC19A5F6699EE10000000A19FF81 31 51317752E9E2956EE10000000A1D00F6 32 EEB3D551DEA7923EE10000000A1D00F8 33 20D366516BFE074FE10000000A1D00F8 34 CFFB63534A6A3F12E10000000A1D2028 35 99AC1A50E6C0A128E20030C60D1E02BC ... ... 1212 48A25A511DA62E47E10000000A1D00F8 1213 97B754526E0B863CE10000000A1D00F6 1214 E20EFD543AF42D4BE10000000A1D211B 1215 CA250554AEF2857FE10000000A1D00F6 1216 6249EC54DF70A2CBE10000000A1D211B 1217 BD5A73541E886A70E10000000A1D00F6 1218 CB3173544288AC4EE10000000A1D00F8 1219 C96FAC5406FADA26E10000000A1D00F8 1220 7F749754BB617815E10000000A1D211B 1221 E70FEB54E2CBCC70E10000000A1D00F8 1222 EA460854938F9350E10000000A1D2028 1223 EA6E4F54EF75025AE10000000A1D00F8 1224 E3BB98543622BF62E10000000A1D2028 1225 E3DA035508382825E10000000A1D00F6 1226 96BE97545F8ADB4EE10000000A1D00F6 1227 15407352C1CD7A33E10000000A1D2028 1228 EE55BF51D50E2E23E10000000A1D00F6 1229 ECBFAD53CB8DC10DE10000000A1D00F6 1230 F120BC5464E73829E10000000A1D2028 1231 EF2E0E556FDDB927E10000000A1D211B 1232 C4AEAA50BF24A371E2000D3560A76C25 1233 34A84A54A762386DE10000000A1D00F6 1234 F2DF19544FC51832E10000000A1D00F6 1235 DAE89750A2E7A12CE2009DD40A5B4CB2 1236 722CAD53CE9B961FE10000000A1D00F6 1237 EA0EBC5069E94D74E2005420EF347364 1238 F922745456997A40E10000000A1D00F8 1239 E4124A510D49371DE10000000A1D00F8 1240 BFB753526DF6F81BE10000000A1D2028 1241 A0CFDA53E983EA44E10000000A1D2028 [1236 rows x 1 columns] ipdb> dados[ind_entrada[0]] **\* KeyError: 6 ipdb> dados[6] **\* KeyError: 6 ipdb> dados[ind_entrada] **\* IndexError: indices are out-of-bounds ipdb> entrada.index Int64Index([6], dtype='int64') ipdb> dados[entrada.index.values] **\* IndexError: indices are out-of-bounds ipdb> dados[dados.index.values[0]] **\* KeyError: 6 ipdb>
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BUG: DataFrame doesn't hold _metadata when sliced by slice instance
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2
2015-04-01T14:31:30Z
2015-04-04T18:32:40Z
2015-04-04T18:32:40Z
MEMBER
null
``` import pandas as pd class TestDataFrame(pd.DataFrame): _metadata = ['testattr'] @property def _constructor(self): return TestDataFrame df = TestDataFrame({0: [1, 2, 3], 1: [1, 2, 3]}) df.testattr = 'XXX' df.testattr # XXX # Using list is OK df.iloc[[0, 1], :].testattr # XXX # Using slice is NG df.iloc[0:1, :].testattr # AttributeError: 'TestDataFrame' object has no attribute 'testattr' ```
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65,704,870
MDU6SXNzdWU2NTcwNDg3MA==
9,777
Predictable order for columns in assign through sorting
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7
2015-04-01T14:53:48Z
2016-09-09T21:22:15Z
2015-04-07T22:36:59Z
CONTRIBUTOR
null
The order of columns coming out of `.assign` is hard to predict ``` Python In [1]: import pandas as pd In [2]: df = pd.DataFrame({'a': [1], 'b': [2]}) In [3]: df Out[3]: a b 0 1 2 In [4]: df.assign(d=df.a*10, a=df.b*10, e=df.a*20) Out[4]: a b e d 0 20 2 20 10 ``` Would it be reasonable to keep all existing keys in their current location and then add the remaining keys in sorted order at the end?
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65,708,727
MDU6SXNzdWU2NTcwODcyNw==
9,778
Wrong number of row in to_latex output for MultiIndex Dataframe
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4
2015-04-01T15:11:18Z
2015-07-23T11:01:52Z
2015-04-16T06:15:12Z
NONE
null
If I try to run this code: ``` python pd.DataFrame.from_dict({ ('c1', 0): pd.Series({x: x for x in range(4)}), ('c1', 1): pd.Series({x: x+4 for x in range(4)}), ('c2', 0): pd.Series({x: x for x in range(4)}), ('c2', 1): pd.Series({x: x+4 for x in range(4)}), ('c3', 0): pd.Series({x: x for x in range(4)}), }).T.to_latex() ``` The result is 2 rows instead of 5 in the latex output. If I use a csv it works. I'm running pandas 0.16.0 under python 2.7 on Mac OS X 10.10
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65,714,847
MDU6SXNzdWU2NTcxNDg0Nw==
9,779
secondary_y plot shows in separate figure
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11
2015-04-01T15:43:04Z
2015-04-09T15:21:34Z
2015-04-05T13:52:22Z
NONE
null
Hi, don't know if this is my Python/some module version.. but when I try to do the following, I end up with two figures popping up, instead of the expected one: ``` df[[0, 1]].plot(kind='bar', stacked=True, ylim=(0, 100)) df[[2]].plot(secondary_y=True, color='orange', linewidth=2.0) ``` The data in both plots looks right, the left/right y axes are correct etc.. but they're not on the same figure :( Also if this is of any help, if I do `fig = plt.figure()` before running the commands above, I end up with another completely empty figure shown to me first.. Sorry if this is something stupid and/or not caused by pandas..
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MDU6SXNzdWU2NTcyNjQ2NA==
9,780
pd.merge() doesn't merge int and str column dtypes but no warning or error
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15
2015-04-01T16:36:12Z
2018-03-19T21:45:09Z
2017-12-10T15:41:37Z
NONE
null
When merging an int dtype with a str dtype the join does not work: ``` python >>> import pandas as pd >>> df1 = pd.DataFrame({"A":[0]}) >>> df2 = pd.DataFrame({"A":["0"]}) >>> pd.merge(df1, df2, on=["A"]) Empty DataFrame Columns: [A] Index: [] ``` I think it would be better to get a warning that the join is performed on incompatible column dtypes. This is my pandas version: ``` >>> pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 2.7.9.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: 0.15.2 nose: 1.3.4 Cython: 0.21 numpy: 1.9.2 scipy: 0.15.1 statsmodels: 0.6.1 IPython: 2.2.0 sphinx: 1.2.3 patsy: 0.3.0 dateutil: 2.4.1 pytz: 2014.9 bottleneck: None tables: 3.1.1 numexpr: 2.3.1 matplotlib: 1.4.3 openpyxl: 1.8.5 xlrd: 0.9.3 xlwt: 0.7.5 xlsxwriter: 0.5.7 lxml: 3.4.0 bs4: 4.3.2 html5lib: None httplib2: None apiclient: None rpy2: 2.5.6 sqlalchemy: 0.9.7 pymysql: None psycopg2: None ``` Thanks for all your work on pandas!
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MDU6SXNzdWU2NTc2ODQ5Nw==
9,781
bug in dataframe.to_json causing segfault with 0.16.0
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6
2015-04-01T20:06:37Z
2015-04-03T22:42:19Z
2015-04-03T22:42:19Z
NONE
null
calling to_json on an apparently empty dataframe may segfault. using the same dataframe with 0.15.2 works. A git bisect says that commit a67bef49e0b8e89e785c08011e8331e9eb7e54b8 is when the problem first appeared. segfault: Program terminated with signal SIGSEGV, Segmentation fault. #0 0x00007fd42aae8735 in PdBlock_iterBegin (_obj=0x7fd423d22410, tc=0x7fff1bf57310) at pandas/src/ujson/python/objToJSON.c:1057 (gdb) list 1052 // init a dedicated context for this column 1053 NpyArr_iterBegin(obj, tc); 1054 npyarr = GET_TC(tc)->npyarr; 1055 1056 // set the dataptr to our desired column and initialise 1057 npyarr->dataptr += npyarr->stride \* idx; 1058 NpyArr_iterNext(obj, tc); 1059 GET_TC(tc)->itemValue = NULL; 1060 ((PyObjectEncoder*) tc->encoder)->npyCtxtPassthru = NULL; 1061 Following is the test code and ascii-pickle file that reproduces the behavior: ``` import pickle p = pickle.load(open("p.pkl", "rb")) p.to_json("/tmp/foo.json", date_format='iso', orient='records') ``` below is the pickled dataframe that causes the problem. (I've been unable to construct another dataframe that exhibits this behavior) ``` ccopy_reg _reconstructor p0 (cpandas.core.frame DataFrame p1 c__builtin__ object p2 Ntp3 Rp4 g0 (cpandas.core.internals BlockManager p5 g2 Ntp6 Rp7 ((lp8 cpandas.core.index _new_Index p9 (cpandas.core.index Index p10 (dp11 S'data' p12 cnumpy.core.multiarray _reconstruct p13 (cnumpy ndarray p14 (I0 tp15 S'b' p16 tp17 Rp18 (I1 (I5 tp19 cnumpy dtype p20 (S'O8' p21 I0 I1 tp22 Rp23 (I3 S'|' p24 NNNI-1 I-1 I63 tp25 bI00 (lp26 S'symbol' p27 aS'side' p28 aS'shares' p29 aS'entry_price' p30 aS'entry_time' p31 atp32 bsS'name' p33 Nstp34 Rp35 ag9 (cpandas.core.index Int64Index p36 (dp37 g12 g13 (g14 (I0 tp38 g16 tp39 Rp40 (I1 (I0 tp41 g20 (S'i8' p42 I0 I1 tp43 Rp44 (I3 S'<' p45 NNNI-1 I-1 I0 tp46 bI00 S'' p47 tp48 bsg33 Nstp49 Rp50 a(lp51 g13 (g14 (I0 tp52 g16 tp53 Rp54 (I1 (I3 I0 tp55 g44 I00 g47 tp56 bag13 (g14 (I0 tp57 g16 tp58 Rp59 (I1 (I2 I0 tp60 g23 I00 (lp61 tp62 ba(lp63 g9 (g10 (dp64 g12 g13 (g14 (I0 tp65 g16 tp66 Rp67 (I1 (I3 tp68 g23 I00 (lp69 g28 ag29 ag31 atp70 bsg33 Nstp71 Rp72 ag9 (g10 (dp73 g12 g13 (g14 (I0 tp74 g16 tp75 Rp76 (I1 (I2 tp77 g23 I00 (lp78 g27 ag30 atp79 bsg33 Nstp80 Rp81 a(dp82 S'0.14.1' p83 (dp84 S'axes' p85 g8 sS'blocks' p86 (lp87 (dp88 S'mgr_locs' p89 g13 (g14 (I0 tp90 g16 tp91 Rp92 (I1 (I3 tp93 g44 I00 S'\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00' p94 tp95 bsS'values' p96 g54 sa(dp97 g89 c__builtin__ slice p98 (I0 I6 I3 tp99 Rp100 sg96 g59 sasstp101 bb. ```
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65,782,709
MDU6SXNzdWU2NTc4MjcwOQ==
9,782
Boolean negation gives wrong output when dtype is not boolean
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4
2015-04-01T21:28:00Z
2015-04-01T21:57:19Z
2015-04-01T21:57:19Z
NONE
null
When a Series or DataFrame has boolean elements, but the dtype is not bool, the elementwise negation operator (~) performs bitwise complement instead of negation. I got bit by this surprising behavior and it took awhile to track down and understand: ``` In [1]: import pandas as pd In [2]: s = pd.Series([None,True]) In [3]: s.iloc[0] = False In [4]: s Out[4]: 0 False 1 True dtype: object In [5]: ~s Out[5]: 0 -1 1 -2 dtype: object ``` whereas one would normally expect this sort of behavior: ``` In [6]: t = pd.Series([False,True]) In [7]: t Out[7]: 0 False 1 True dtype: bool In [8]: ~t Out[8]: 0 True 1 False dtype: bool ``` Here's my version information: ``` % ipython Python 2.7.9 |Anaconda 2.2.0 (x86_64)| (default, Dec 15 2014, 10:37:34) Type "copyright", "credits" or "license" for more information. IPython 3.0.0 -- An enhanced Interactive Python. In [12]: pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 2.7.9.final.0 python-bits: 64 OS: Darwin OS-release: 14.1.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.15.2 nose: 1.3.4 Cython: 0.22 numpy: 1.9.2 ```
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MDExOlB1bGxSZXF1ZXN0MzI0NzQ4OTE=
9,783
Fix zlib and blosc imports in to_msgpack
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21
2015-04-02T00:52:27Z
2015-04-13T16:01:48Z
2015-04-13T16:01:48Z
CONTRIBUTOR
null
6717aa06dcaa1950ffb46fef454f5df9404209bd removed zlib and blosc from the global namespace. ``` from pandas import read_csv table = read_csv('aadhaar_data.csv') table.to_msgpack('d.msg') # NameError: global name 'blosc' is not defined table.to_msgpack('d-blosc.msg', compress='blosc') # NameError: global name 'zlib' is not defined table.to_msgpack('d-zlib.msg', compress='zlib') ``` This pull request restores zlib and blosc compression in to_msgpack via local imports.
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65,818,583
MDU6SXNzdWU2NTgxODU4Mw==
9,784
DataFrame.to_string truncates long strings
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23
2015-04-02T02:12:17Z
2019-10-21T08:25:45Z
2019-09-16T02:33:40Z
CONTRIBUTOR
null
I am calling to_string() without any parameters and it beautifully fixed-formatted my dataframe apart from my very wide filename column, that is being truncated with "...". How can I avoid that? ``` text FILENAME OBS_ID XUV 0 'mvn_iuv_l1a_IPH3-cycle00007-mode040-muvdark_2... 40 MUV 1 'mvn_iuv_l1a_IPH2-cycle00047-mode050-muvdark_2... 50 MUV 2 'mvn_iuv_l1a_apoapse-orbit00127-mode2001-muvda... 2001 MUV 3 'mvn_iuv_l1a_APP1-orbit00087-mode1031-fuvdark_... 1031 FUV 4 'mvn_iuv_l1a_IPH2-cycle00005-mode060-fuvdark_2... 60 FUV ``` I tried calling it like this, but to no avail (same output): ``` python with open('test_summary_out.txt','w') as f: f.write(summarydf.head().to_string(formatters={'filename':lambda x: "{:100}".format(x)})) ``` Version: 0.16 with Python 3.4
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9,785
Error when comparing equality of indexes
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2
2015-04-02T07:15:43Z
2015-04-14T13:41:01Z
2015-04-14T13:41:01Z
CONTRIBUTOR
null
In 0.16.0 comparing MultiIndices using `==` raises an exception. ``` python from StringIO import StringIO import pandas as pd # create a series using a multiindex df = pd.read_csv(StringIO('a,b,c\n1,2,3'), index_col=[0, 1]) series = df['c'] series.index == series.index # File "/Users/dan/venvs/pandas-0.16.0/lib/python2.7/site-packages/pandas/core/index.py", line 54, in wrapper # func = getattr(self._data.view(np.ndarray), opname) # AttributeError: 'NoneType' object has no attribute 'view' print pd.__version__ #0.16.0 ``` Another one I've come across is that there is an `AttributeError` when comparing indexes for which the underlying numpy arrays cannot be broadcast together. The error message itself is very obscure. I gather that `.equals()` can be used for what I'm trying to do in these examples, but still the errors when using `==` are surprising. ``` >>> pd.Index(['a', 'b']) == pd.Index(['a', 'b', 'c']) --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-2-44325051df92> in <module>() ----> 1 pd.Index(['a', 'b']) == pd.Index(['a', 'b', 'c']) /Users/dan/venvs/pandas-0.16.0/lib/python2.7/site-packages/pandas/core/index.pyc in wrapper(self, other) 57 # technically we could support bool dtyped Index 58 # for now just return the indexing array directly ---> 59 if is_bool_dtype(result): 60 return result 61 try: /Users/dan/venvs/pandas-0.16.0/lib/python2.7/site-packages/pandas/core/common.py in is_bool_dtype(arr_or_dtype) 2510 2511 def is_bool_dtype(arr_or_dtype): -> 2512 tipo = _get_dtype_type(arr_or_dtype) 2513 return issubclass(tipo, np.bool_) 2514 /Users/dan/venvs/pandas-0.16.0/lib/python2.7/site-packages/pandas/core/common.py in _get_dtype_type(arr_or_dtype) 2439 elif isinstance(arr_or_dtype, CategoricalDtype): 2440 return CategoricalDtypeType -> 2441 return arr_or_dtype.dtype.type 2442 2443 AttributeError: 'NotImplementedType' object has no attribute 'dtype' ```
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MDU6SXNzdWU2NTg4OTQyMg==
9,786
Timedeltaindex as columns + groupby fails
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3
2015-04-02T09:35:10Z
2015-04-02T12:37:45Z
2015-04-02T12:01:46Z
CONTRIBUTOR
null
``` python import numpy as np import pandas as pd df = pd.DataFrame(np.random.normal(size=(10,15))) df.columns = pd.timedelta_range(start='0s',periods=15,freq='1s') df.groupby(['a']*5+['b']*5) ``` fails (throwing ValueError), whereas ``` python df.transpose().groupby(['a']*5+['b']*5,axis=1) ``` doesn't. ``` INSTALLED VERSIONS ------------------ commit: None python: 2.7.8.final.0 python-bits: 64 OS: Linux OS-release: 3.13.0-46-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: fr_FR.UTF-8 pandas: 0.16.0-28-gcb8c130 nose: 1.3.4 Cython: 0.20.2 numpy: 1.9.2 scipy: 0.14.0 statsmodels: None IPython: 3.0.0-dev sphinx: 1.2.2 patsy: None dateutil: 2.4.1 pytz: 2015.2 bottleneck: None tables: 3.1.1 numexpr: 2.4 matplotlib: 1.4.3 openpyxl: 1.7.0 xlrd: 0.9.2 xlwt: 0.7.5 xlsxwriter: None lxml: None bs4: None html5lib: 0.999 httplib2: None apiclient: None sqlalchemy: 0.9.7 pymysql: None psycopg2: 2.5.3 (dt dec mx pq3 ext) ```
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9,787
Dividing Series of Decimal objects by Decimal raises AttributeError
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2
2015-04-02T11:02:27Z
2015-04-14T13:28:04Z
2015-04-14T13:28:04Z
NONE
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This worked fine with pandas 0.15: ``` python In [24]: import pandas as pd In [25]: from decimal import Decimal In [26]: s = pd.Series([Decimal(10)] * 10) In [27]: s / Decimal(5) Out[27]: 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 2 dtype: object ``` When I use 0.16.0, it raises: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-29-f6e1a63d5a28> in <module>() ----> 1 s / Decimal(5) /home/jck/anaconda/lib/python2.7/site-packages/pandas/core/ops.pyc in wrapper(left, right, name) 530 if hasattr(lvalues, 'values'): 531 lvalues = lvalues.values --> 532 return left._constructor(wrap_results(na_op(lvalues, rvalues)), 533 index=left.index, name=left.name, 534 dtype=dtype) /home/jck/anaconda/lib/python2.7/site-packages/pandas/core/ops.pyc in na_op(x, y) 483 result, changed = com._maybe_upcast_putmask(result, ~mask, np.nan) 484 --> 485 result = com._fill_zeros(result, x, y, name, fill_zeros) 486 return result 487 /home/jck/anaconda/lib/python2.7/site-packages/pandas/core/common.pyc in _fill_zeros(result, x, y, name, fill) 1407 y = np.array(y) 1408 -> 1409 if is_integer_dtype(y): 1410 1411 if (y == 0).any(): /home/jck/anaconda/lib/python2.7/site-packages/pandas/core/common.pyc in is_integer_dtype(arr_or_dtype) 2448 2449 def is_integer_dtype(arr_or_dtype): -> 2450 tipo = _get_dtype_type(arr_or_dtype) 2451 return (issubclass(tipo, np.integer) and 2452 not issubclass(tipo, (np.datetime64, np.timedelta64))) /home/jck/anaconda/lib/python2.7/site-packages/pandas/core/common.pyc in _get_dtype_type(arr_or_dtype) 2439 elif isinstance(arr_or_dtype, CategoricalDtype): 2440 return CategoricalDtypeType -> 2441 return arr_or_dtype.dtype.type 2442 2443 AttributeError: 'Decimal' object has no attribute 'dtype' ``` ``` INSTALLED VERSIONS ------------------ commit: None python: 2.7.9.final.0 python-bits: 64 OS: Linux OS-release: 3.13.0-24-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 pandas: 0.16.0 nose: 1.3.4 Cython: 0.21 numpy: 1.9.2 scipy: 0.14.0 statsmodels: 0.5.0 IPython: 2.2.0 sphinx: 1.2.3 patsy: 0.3.0 dateutil: 2.4.2 pytz: 2015.2 bottleneck: None tables: 3.1.1 numexpr: 2.3.1 matplotlib: 1.4.0 openpyxl: 1.8.5 xlrd: 0.9.3 xlwt: 0.7.5 xlsxwriter: 0.5.7 lxml: 3.4.0 bs4: 4.3.2 html5lib: None httplib2: None apiclient: None sqlalchemy: 0.9.7 pymysql: None psycopg2: None ```
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groupby() changes values of pandas.Timestamp (pandas 0.15.2)
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2015-04-02T11:49:45Z
2015-04-02T12:00:07Z
2015-04-02T11:59:47Z
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``` import datetime as dt; import pandas as pd d2 = dt.datetime(2015, 4, 2, 12, 23, 12, 567000) df = pd.DataFrame([['Bar', d2]], columns=['name', 'date']) df2 = df.sort('date').groupby('name').last().reset_index() ``` `df2` is name date 0 Bar 2015-04-02 12:23:12.567000064 i.e. 64 nanoseconds were added to the timestamp. The amounts added seem to be always the powers of 2, perhaps it is caused by some internal conversion to double?
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pandas.Grouper documentation thin
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6
2015-04-02T15:44:39Z
2015-08-20T20:21:45Z
2015-08-20T13:24:26Z
NONE
null
The documentation of http://pandas.pydata.org/pandas-docs/dev/generated/pandas.Grouper.html is quite thin, when it comes to _additional kwargs to control time-like groupers (when freq is passed)_: - convention is not described at all - default values are not described for any of the additional kwargs
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65,956,662
MDU6SXNzdWU2NTk1NjY2Mg==
9,790
ERR: better error reporting for passing list to level grouper on non-MultiIndex
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3
2015-04-02T15:53:03Z
2020-01-03T23:23:44Z
2020-01-03T23:23:44Z
NONE
null
`groupy(level=[0])` doesn't work on a unique index. I guess it is semantics whether one allows not only level=0, but also level=[0], but if not, the error message should be different: ``` df = pd.DataFrame(np.arange(0,9).reshape(3,3)) df.groupby(level=[0]) ValueError: level > 0 only valid with MultiIndex ```
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65,979,247
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9,791
Issue when installing Pandas 0.16 on Windows using Conda
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4
2015-04-02T18:00:12Z
2015-04-02T20:33:05Z
2015-04-02T20:33:05Z
NONE
null
Hi, I tried installing pandas .16 using conda in windows but am getting the following issue. I tried reinstalling using an admin console but it doesn't work. I've downgraded to 0.15.2 which works again but could use help. This is my first time posting so if this is not the appropriate channel please let me know. Thanks in advance C:\Users\kumarra>ipython Python 3.4.1 |Continuum Analytics, Inc.| (default, Jun 11 2014, 17:27:11) [MSC v .1600 64 bit (AMD64)] Type "copyright", "credits" or "license" for more information. ``` IPython 3.0.0 -- An enhanced Interactive Python. Anaconda is brought to you by Continuum Analytics. Please check out: http://continuum.io/thanks and https://binstar.org ? -> Introduction and overview of IPython's features. %quickref -> Quick reference. help -> Python's own help system. object? -> Details about 'object', use 'object??' for extra details. In [1]: import pandas --------------------------------------------------------------------------- TypeError Traceback (most recent call last) pandas\tslib.pyx in pandas.tslib.Timedelta.__new__ (pandas\tslib.c:29661)() TypeError: 'nanoseconds' is an invalid keyword argument for this function During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) <ipython-input-1-d6ac987968b6> in <module>() ----> 1 import pandas C:\Miniconda3\lib\site-packages\pandas\__init__.py in <module>() 45 46 # let init-time option registration happen ---> 47 import pandas.core.config_init 48 49 from pandas.core.api import * C:\Miniconda3\lib\site-packages\pandas\core\config_init.py in <module>() 15 is_instance_factory, is_one_of_factory, 16 get_default_val) ---> 17 from pandas.core.format import detect_console_encoding 18 19 C:\Miniconda3\lib\site-packages\pandas\core\format.py in <module>() 22 import csv 23 ---> 24 from pandas.tseries.period import PeriodIndex, DatetimeIndex 25 26 docstring_to_string = """ C:\Miniconda3\lib\site-packages\pandas\tseries\period.py in <module>() 6 from pandas.core.base import PandasObject 7 ----> 8 import pandas.tseries.frequencies as frequencies 9 from pandas.tseries.frequencies import get_freq_code as _gfc 10 from pandas.tseries.index import DatetimeIndex, Int64Index, Index C:\Miniconda3\lib\site-packages\pandas\tseries\frequencies.py in <module>() 7 8 from pandas.core.algorithms import unique ----> 9 from pandas.tseries.offsets import DateOffset 10 from pandas.util.decorators import cache_readonly 11 import pandas.tseries.offsets as offsets C:\Miniconda3\lib\site-packages\pandas\tseries\offsets.py in <module>() 2150 2151 -> 2152 class Nano(Tick): 2153 _inc = Timedelta(nanoseconds=1) 2154 _prefix = 'N' C:\Miniconda3\lib\site-packages\pandas\tseries\offsets.py in Nano() 2151 2152 class Nano(Tick): -> 2153 _inc = Timedelta(nanoseconds=1) 2154 _prefix = 'N' 2155 pandas\tslib.pyx in pandas.tslib.Timedelta.__new__ (pandas\tslib.c:29707)() ValueError: cannot construct a TimeDelta from the passed arguments, allowed keyw ords are [days, seconds, microseconds, milliseconds, minutes, hours, weeks] ```
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65,987,721
MDU6SXNzdWU2NTk4NzcyMQ==
9,792
Pandas DataFrame.plot() does not default to matplotlib rcparams grid setting
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5
2015-04-02T18:44:22Z
2015-06-02T19:25:19Z
2015-05-27T02:48:47Z
NONE
null
When using df.plot() to creat a simple line chart from a dataframe, the expected behavior is that, if no 'grid' keyword is passed, the plot will default to the matplotlib 'axes.grid' rcParam setting to determine whether to show a grid or not. However (in 0.16), the grid is always being shown, regardless of the matplotlib setting, unless I explicitly pass grid=False. In general, there are several plot types where grid=True is forced, which basically breaks the consistent styling that you get from matplotlib rcparams or another package like Seaborn. I can understand forcing a default grid=False for the type of plots where a grid doesn't make sense, but forcing grid=True, overriding the configured defaults, for things like boxplots seems a bit counter intuitive.
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9,793
BUG: DataFrame._slice doesnt retain metadata
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4
2015-04-02T21:04:00Z
2015-04-04T21:28:12Z
2015-04-04T18:32:40Z
MEMBER
null
Closes #9776.
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9,794
ERR: raise on mutli-index columns as its not implemented in to_excel
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11
2015-04-02T21:56:56Z
2016-02-11T19:10:53Z
2015-04-17T12:35:41Z
NONE
null
Try to the following code, and you will find column labels mismatched with the data in the Excel file : `cols = pd.MultiIndex.from_tuples([('site',''),('2014','height'),('2014','weight')])` `df = pd.DataFrame(np.random.randn(10,3), columns=cols)` `df.to_excel('test.xlsx', index=False)` I am using pandas v0.16.0.
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MDU6SXNzdWU2NjAzMzEwOA==
9,795
Unexpected in-place changes when saving a DataFrame to Stata with write_index=False
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1
2015-04-02T23:39:12Z
2015-04-07T10:28:15Z
2015-04-07T10:28:15Z
NONE
null
After executing `df.to_stata()` with `write_index=False`, all `NaNs` in `df` were automatically replaced by `8.988466e+307`. Please see the following code: `df = pd.DataFrame(np.random.randn(5,4), columns=list('abcd'))` `df.ix[2, 'a':'c'] = np.nan` `print df` `df.to_stata('test.dta', write_index=False)` `print df` I am using `pandas v0.16.0`. Thanks.
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DOC: add doc/source/contributing.rst as the CONTRIBUTING.md
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2
2015-04-03T02:04:49Z
2015-04-03T11:07:53Z
2015-04-03T11:07:53Z
CONTRIBUTOR
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xref #9754 I just did `pandoc doc/source/contributing.rst -t markdown > CONTRIBUTING.md` to generate the new CONTRIBUTING file.
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DOC: regenerate CONTRIBUTING.md
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11
2015-04-03T04:54:57Z
2015-04-03T19:58:47Z
2015-04-03T17:18:52Z
MEMBER
null
This is an alternative to #9796. I simply did a couple of changes to the restructuredtext source so it generates cleaner markdown. The main thing was making the categories not quite so nested. (Was that [heavy nesting](http://pandas-docs.github.io/pandas-docs-travis/contributing.html#committing-your-code) intentional?) I think pandoc was running out of header styles or something like that. cc @rockg @jreback @jorisvandenbossche
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read_csv() interprets index_col="True" as "1"
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2
2015-04-03T07:38:16Z
2015-04-11T00:52:18Z
2015-04-09T15:03:48Z
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epi.txt ``` OBS ID PERIOD TMT Y0 AGE COUNT 1 1 1 0 11 31 5 2 1 2 0 11 31 3 3 1 3 0 11 31 3 4 1 4 0 11 31 3 5 2 1 0 11 30 3 6 2 2 0 11 30 5 7 2 3 0 11 30 3 8 2 4 0 11 30 3 9 3 1 0 6 25 2 10 3 2 0 6 25 4 ``` ``` epi = pandas.read_csv("./epi.datn.txt", sep=" ", index_col=True) print(epi) ``` ``` OBS PERIOD TMT Y0 AGE COUNT ID 1 1 1 0 11 31 5 1 2 2 0 11 31 3 1 3 3 0 11 31 3 1 4 4 0 11 31 3 2 5 1 0 11 30 3 2 6 2 0 11 30 5 2 7 3 0 11 30 3 2 8 4 0 11 30 3 3 9 1 0 6 25 2 3 10 2 0 6 25 4 ``` I had some code that used `False` to disable reading indexes from the data, and was expecting `True` to be the opposite. Instead it read the **second** column, because True == 1 in python. I think that `True` should be explicitly handled, because this behaviour is counterintuitive given that `False` is meaningful. `False` is not the same as `index_col=0`, after all.
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2015-04-03T12:22:51Z
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Closes #9795 (Stata writer changes input frame)
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2015-04-03T12:44:36Z
2015-11-12T23:44:34Z
2015-04-07T10:28:47Z
CONTRIBUTOR
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closes #9795 Just needed to move the data frame copy earlier in the execution path.
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