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https://api.github.com/repos/pandas-dev/pandas/issues/1801
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MDU6SXNzdWU2Mzc4MTkz
1,801
Confusing formatting after groupby
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5
"2012-08-22T14:15:27Z"
"2014-05-29T08:18:35Z"
"2014-05-29T08:18:35Z"
NONE
null
``` >>> import pandas as pd >>> import numpy as np >>> df = pd.DataFrame([(3,np.datetime64('2012-07-03')),(3,np.datetime64('2012-07-04'))], columns = ['a', 'date']) >>> print df a date 0 3 2012-07-03 1 3 2012-07-04 >>> print df.groupby('a').first() date a 3 2012-07-03 00:00:00 ``` this looks as if I had a DataFrame with indices 'a' and 3 and a column date with values '' and a datetime.
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https://api.github.com/repos/pandas-dev/pandas/issues/1802
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6,379,431
MDU6SXNzdWU2Mzc5NDMx
1,802
DatetimeIndex values returns the wrong values
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3
"2012-08-22T14:59:57Z"
"2012-08-22T21:03:50Z"
"2012-08-22T21:03:50Z"
NONE
null
When I try to use the values method for a DatetimeIndex, I get the wrong values. import pandas as pd index = pd.date_range('2000-1-1',periods=24,freq='MS') print 'Version:', pd.**version** print '\nIndex:',index print '\nValues:',index.values print '\nReal Values:',[ i for i in index] Version: 0.8.2.dev-c99d9cd Index: <class 'pandas.tseries.index.DatetimeIndex'> [2000-01-01 00:00:00, ..., 2001-12-01 00:00:00] Length: 24, Freq: MS, Timezone: None Values: [1970-01-11 184:00:00 1970-01-11 160:00:00 1970-01-12 152:00:00 1970-01-12 128:00:00 1970-01-12 80:00:00 1970-01-12 56:00:00 1970-01-12 08:00:00 1970-01-12 240:00:00 1970-01-12 216:00:00 1970-01-12 168:00:00 1970-01-12 144:00:00 1970-01-12 96:00:00 1970-01-12 72:00:00 1970-01-12 48:00:00 1970-01-12 208:00:00 1970-01-12 184:00:00 1970-01-12 136:00:00 1970-01-12 112:00:00 1970-01-12 64:00:00 1970-01-12 40:00:00 1970-01-12 16:00:00 1970-01-12 224:00:00 1970-01-12 200:00:00 1970-01-12 152:00:00] Real Values: [<Timestamp: 2000-01-01 00:00:00>, <Timestamp: 2000-02-01 00:00:00>, <Timestamp: 2000-03-01 00:00:00>, <Timestamp: 2000-04-01 00:00:00>, <Timestamp: 2000-05-01 00:00:00>, <Timestamp: 2000-06-01 00:00:00>, <Timestamp: 2000-07-01 00:00:00>, <Timestamp: 2000-08-01 00:00:00>, <Timestamp: 2000-09-01 00:00:00>,<Timestamp: 2000-10-01 00:00:00>, <Timestamp: 2000-11-01 00:00:00>, <Timestamp:2000-12-01 00:00:00>, <Timestamp: 2001-01-01 00:00:00>, <Timestamp: 2001-02-01 00:00:00>, <Timestamp: 2001-03-01 00:00:00>, <Timestamp: 2001-04-01 00:00:00>, <Timestamp: 2001-05-01 00:00:00>, <Timestamp: 2001-06-01 00:00:00>, <Timestamp: 2001-07-01 00:00:00>, <Timestamp: 2001-08-01 00:00:00>, <Timestamp: 2001-09-01 00:00:00>, <Timestamp: 2001-10-01 00:00:00>, <Timestamp: 2001-11-01 00:00:00>, <Timestamp: 2001-12-01 00:00:00>]
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https://api.github.com/repos/pandas-dev/pandas/issues/1803
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6,379,768
MDU6SXNzdWU2Mzc5NzY4
1,803
Can't assign to subsets of MultiIndex columns DataFrames using partial labels
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0
"2012-08-22T15:12:03Z"
"2012-12-02T02:13:09Z"
"2012-12-02T02:13:09Z"
NONE
null
Assigning a value (scalar or Series) to the subset of a dataframe with MultiIndex columns addressed by a partial label doesn't work. ``` df = pandas.DataFrame(index=[1, 3, 5], columns=pandas.MultiIndex.from_tuples([('A', '1'), ('A', '2'), ('B', '1')])) df['A'] = 0.0 # Doesn't work df['A'] = df['B', '1'] # Works, but adds a column instead of updating the two existing ones df2 = df.T.copy() df2.ix['A'] = 0.0 # Works df2.ix['A'] = df2['B', '1'] # Works ``` The same thing in the rows dimension works as expected.
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https://api.github.com/repos/pandas-dev/pandas/issues/1804
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6,383,399
MDExOlB1bGxSZXF1ZXN0MjEyOTA2Nw==
1,804
Fix tostring unicode
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1
"2012-08-22T17:24:27Z"
"2014-07-17T13:49:12Z"
"2012-09-09T01:18:30Z"
CONTRIBUTOR
null
I'm not sure if this is perfect (or where else things like this might come up), but this fixes the failing test I added and the rest of the test suite passes. The issue arises when you give a string containing non-ascii characters. You have to decode to unicode (which I force to be utf-8...). I don't know the Python 3 issues, if any (it's in a Python 2 protected block of code). See issue #1620.
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MDU6SXNzdWU2NDEzNzE0
1,805
Include .c files in repo.
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"2012-08-23T17:01:24Z"
"2012-09-08T02:29:14Z"
"2012-09-08T02:28:47Z"
CONTRIBUTOR
null
`pip install git+https://github.com/pydata/pandas.git` does currently not work because the .pyx files are not being cythonized (not sure why). An easy fix is to include .c files in the git repo which should make it easier for people to deploy. I used a simple try: import cython in setup.py that cythonizes if cython is installed and uses the .c files otherwise: https://github.com/hddm-devs/hddm/blob/develop/setup.py#L4
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6,416,112
MDExOlB1bGxSZXF1ZXN0MjE0NTU0Mg==
1,806
undo dumb setuptools bug clobbering .pyx sources back to .c
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1
"2012-08-23T18:41:37Z"
"2014-08-16T08:35:40Z"
"2012-09-08T02:29:05Z"
CONTRIBUTOR
null
setuptools (not distribute) will replace '.pyx' extensions with '.c' if _pyrex_ is not importable. This checks if that happened, and reverses it if so. closes #1805
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1,807
Sort broken after unique -> Series with object array
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4
"2012-08-23T20:25:06Z"
"2012-09-04T02:17:27Z"
"2012-09-04T02:16:37Z"
CONTRIBUTOR
null
I have no idea why this fails. ``` pandas.Series(pandas.Series(["a","c","b"]).unique()).sort() ``` but this doesn't ``` pandas.Series(np.array(["a", "c", "b"], dtype=object)).sort() ```
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Handling of lambda functions in list for resample
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0
"2012-08-25T11:51:17Z"
"2012-09-09T18:25:19Z"
"2012-09-09T18:25:19Z"
MEMBER
null
DataFrame bad column name reported by user
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groupby().last() drops columns
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5
"2012-08-25T19:06:50Z"
"2017-09-07T19:25:59Z"
"2012-09-20T03:12:48Z"
NONE
null
DataFrame.groupby().last() gives incorrect result in the following case (pandas version: 0.8.1): ``` df1 = DataFrame({"A": [1, 1], "B": [1, 1], "C": ["x", "y"]}) print df1.groupby("A").last() # B # A #1 1 ``` The integer column B is handled correctly, but the string column C is dropped. The result is correct if column B is not present: ``` df2 = DataFrame({"A": [1, 1], "C": ["x", "y"]}) print df2.groupby("A").last() # C # A #1 y ```
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Index.asof returns different types
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1
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"2012-09-18T02:46:30Z"
"2012-09-18T02:46:30Z"
CONTRIBUTOR
null
depending on whether the date exists or not: In [42]: idx.asof(datetime(2008, 5, 2)) Out[42]: datetime.datetime(2008, 5, 2, 0, 0) In [43]: idx.asof(datetime(2008, 5, 3)) Out[43]: <Timestamp: 2008-05-02 00:00:00>
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1
"2012-08-25T22:17:03Z"
"2014-05-29T17:38:38Z"
"2014-05-29T17:38:38Z"
NONE
null
I wrote a function to aggregate data by time, for instance to get average profiles over a day or week if you have a yearly timeseries. It's nothing highly complicated, most of the functionality is already in pandas. But as I could not figure out how to do it with the current API in a single line, I thought this function may be useful to add. The function and some code to demonstrate it can be found here: https://www.dropbox.com/s/wffzg11djtrdz23/aggregate_ts.py Sorry I couldn't do this in the more professional way with a pull request. I hope this helps. (Wes, you'll remember we discussed this very briefly on Euroscipy)
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MDU6SXNzdWU2NDU3NDY2
1,812
availability of nanosecond periods in DatetimeIndex
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2
"2012-08-26T09:08:20Z"
"2013-09-30T19:43:55Z"
"2013-09-30T19:43:55Z"
CONTRIBUTOR
null
Hi there, when trying to create a DatetimeIndex for data sampled in the nanosecond range ``` python pd.DatetimeIndex(start=pd.Timestamp('2012-12-01'), periods=10, freq=pd.offsets.Nano(2800)) ``` there appears an error due to an uninitialized timestamp. Inside the pandas code python datetime objects seem to be used for time representation of "slower" than nanosecond times, whereas the nanosecond period seems to appear as a simple int objects which cannot be handled by the internal arithmetics at the moment. Is there a plan to switch to nanosecond aware time objects internally? As I would like to use this great library for fast data as well I can also offer to help working on that. Best regards Andreas
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1,813
Allow duplicate keys in set_index()
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"2012-08-26T11:08:30Z"
"2012-09-03T23:35:07Z"
"2012-09-03T23:35:07Z"
NONE
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The set_index() method for a DataFrame checks for indices with duplicate keys, even though Pandas (version 0.7.0) is now supposed to allow duplicate keys. For example: ``` from pandas import * x = [0, 0] print DataFrame(dict(a=x, b=x), index=x) print DataFrame(dict(a=x, b=x), index=x).set_index('a') ``` fails with ``` /usr/lib/pymodules/python2.7/pandas/core/frame.pyc in set_index(self, col_or_cols, drop, inplace, verify_integrity) 1925 if verify_integrity and not index._verify_integrity(): 1926 duplicates = index.get_duplicates() -> 1927 raise Exception('Index has duplicate keys: %s' % duplicates) 1928 1929 # clear up memory usage Exception: Index has duplicate keys: [0] ```
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bool dataframe: df.shift fill rows with True instead of NaN like series.shift
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0
"2012-08-26T15:27:20Z"
"2012-09-10T20:27:30Z"
"2012-09-10T20:27:30Z"
CONTRIBUTOR
null
``` import pandas as pd df = pd.DataFrame({'high':[True, False], 'low':[False, False]}) df.shift(1) # high low #0 True True #1 True False ``` series.shift works as expected.
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6,465,425
MDU6SXNzdWU2NDY1NDI1
1,815
concat() drops index when used on series with a PeriodIndex
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1
"2012-08-27T02:57:38Z"
"2012-09-10T23:06:39Z"
"2012-09-10T23:06:27Z"
NONE
null
New to this, sorry if I'm just missing something... ``` python import numpy as np import pandas as pd d1 = pd.date_range('12/31/1990', '12/31/1999', freq='A-DEC') d2 = pd.date_range('12/31/2000', '12/31/2009', freq='A-DEC') s1 = pd.Series(np.random.randn(10), d1) s2 = pd.Series(np.random.randn(10), d2) # works pd.concat([s1,s2]) s1 = s1.to_period() s2 = s2.to_period() # drops index pd.concat([s1,s2]) # workaround pd.concat([s1.to_timestamp(), s2.to_timestamp()]).to_period('A-DEC') ```
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6
"2012-08-27T10:18:50Z"
"2015-06-22T18:55:08Z"
"2012-09-10T16:19:16Z"
CONTRIBUTOR
null
from [stackoverflow](http://stackoverflow.com/questions/12138126/difference-between-parse-date-0-and-parse-date-true-in-pandas-read-csv) ``` python import pandas as pd from StringIO import StringIO data = "date,c1\n2012-07-31 02:00,1.1\n2012-07-31 02:15,2.2\n2012-07-31 02:30,3.3\n" df1 = pd.read_csv(StringIO(data),parse_dates=True).set_index(('date')) df2 = pd.read_csv(StringIO(data),parse_dates=[0] ).set_index(('date')) print "df1:\n{index}".format(index=df1.index) print "df2:\n{index}".format(index=df2.index) ``` df1 has a index of dtype object.
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6,473,041
MDU6SXNzdWU2NDczMDQx
1,817
Can't select columns in dataframe anymore
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2
"2012-08-27T13:03:40Z"
"2012-08-28T07:52:03Z"
"2012-08-28T07:52:03Z"
NONE
null
Since I updated my pandas lib, I can't do this anymore ``` db = pandas.HDFStore(path) df = db.get('x') df = df[['a', 'b', 'c']] ``` It throw me Exception('Reindexing only valid with uniquely valued Index objects',) Mrknacky
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6,482,198
MDU6SXNzdWU2NDgyMTk4
1,818
Raise more helpfull exception on s.plot, df.plot ... without numerical data
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2
"2012-08-27T19:24:22Z"
"2013-12-04T00:57:58Z"
"2013-05-21T21:01:11Z"
CONTRIBUTOR
null
``` python In [23]: df1 = pandas.DataFrame(list('abcd')) In [24]: df1.plot() --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) ... AttributeError: 'numpy.ndarray' object has no attribute 'find' ```
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MDU6SXNzdWU2NTAwMzY4
1,819
MySQLdb driver returns tuple instead of list and Pandas isn't happy about it
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"2012-08-28T14:35:35Z"
"2012-08-29T12:21:05Z"
"2012-08-28T20:13:10Z"
NONE
null
The MySQLdb driver returns rows as tuples instead of lists and Pandas isn't happy about it: > > > sql.read_frame("select token,status FROM purchases WHERE purchases.created_at > '2012-08-26 00:00:00' ", conn) > > > Traceback (most recent call last): > > > File "<stdin>", line 1, in <module> > > > File "C:\Python27\lib\site-packages\pandas\io\sql.py", line 140, in read_frame > > > coerce_float=coerce_float) > > > File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 714, in from_records > > > coerce_float=coerce_float) > > > File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 4078, in _to_sdict > > > return _list_to_sdict(data, columns, coerce_float=coerce_float) > > > File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 4091, in _list_to_sdict > > > content = list(lib.to_object_array_tuples(data).T) > > > File "inference.pyx", line 511, in pandas._tseries.to_object_array_tuples (pandas\src\tseries.c:79688) > > > TypeError: Argument 'rows' has incorrect type (expected list, got tuple) I confirmed that Listifying the rows fixes the issue: ``` cursor, conn = connex.get_connex() cursor.execute(""" SELECT token,status FROM purchases WHERE purchases.created_at > '2012-08-26 00:00:00' """) rows = cursor.fetchall() lrows = [] for row in rows: lrows.append(list(row)) colnames = tuple([desc[0] for desc in cursor.description]) df = DataFrame(lrows,columns=colnames) # this works ``` Also, calling the DataFrame constructor with the Tuple rows raises an error that isn't very informative: `>>> df = DataFrame(rows,columns=colnames)` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 337, in **init** raise PandasError('DataFrame constructor not properly called!') pandas.core.common.PandasError: DataFrame constructor not properly called!
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6,503,483
MDU6SXNzdWU2NTAzNDgz
1,820
Unstacking boolean DataFrame fills 'True' for missing values
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1
"2012-08-28T16:25:00Z"
"2012-09-10T19:59:55Z"
"2012-09-10T19:59:55Z"
NONE
null
Is this a bug? If this is intentional, it is pretty dangerous to fill missing values with `True`. ``` >>> df = pd.DataFrame([False, False], index=pd.MultiIndex.from_arrays([['a', 'b'], ['c', 'l']]), columns=['col']) >>> df col a c False b l False >>> df.unstack() col c l a False True b True False ```
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1,821
DatetimeIndex, df.ix[date] and df.ix[[date]] failure
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1
"2012-08-28T20:28:40Z"
"2012-09-18T21:09:31Z"
"2012-09-18T21:09:23Z"
CONTRIBUTOR
null
from [stackoverflow](http://stackoverflow.com/questions/11991627/selecting-a-subset-of-a-pandas-dataframe-indexed-by-datetimeindex-with-a-list-of) The original frame can not be shared, but following code reproduces the issue. ``` python import pandas import numpy as np import datetime # create large list of non periodic datetime dates = [] sec = datetime.timedelta(seconds=1) half_sec = sec / 2 d = datetime.datetime(2011, 12, 5, 20, 30) n = 100000 for i in range(n): dates.append(d) dates.append(d + sec) dates.append(d + sec + half_sec) dates.append(d + sec + sec + half_sec) d += 3 * sec # duplicate some values in the list duplicate_positions = np.random.randint(0, len(dates) - 1, 20) for p in duplicate_positions: dates[p + 1] = dates[p] print "duplicated values at %s" % str(duplicate_positions) df = pandas.DataFrame(np.random.randn(len(dates), 4), index=dates, columns=list('ABCD')) pos = n * 3 timestamp = df.index[pos] print "timestamp = df.index[%d] = %s" % (pos, str(timestamp)) print "timestamp in index? %s" % str(timestamp in df.index) print "df.ix[timestamp] = \n%s" % str(df.ix[timestamp]) print "df.ix[[timestamp]] = \n%s" % str(df.ix[[timestamp]]) ``` running this script outputs: ``` duplicated values at [143779 365504 338898 192427 63827 333772 366805 378282 375136 77760 59843 215934 173395 185449 310734 184004 48594 221298 348967 86615] timestamp = df.index[300000] = 2011-12-08 11:00:00 timestamp in index? True df.ix[timestamp] = A 0.959639 B 0.651874 C 1.059355 D -1.296393 Name: 2011-12-08 11:00:00 df.ix[[timestamp]] = Empty DataFrame Columns: array([A, B, C, D], dtype=object) Index: <class 'pandas.tseries.index.DatetimeIndex'> Length: 0, Freq: None, Timezone: None ``` for n = 1000000 it gets worse ``` python duplicated values at [ 686452 3862593 3747433 63099 2422495 2191536 486238 1632442 2373460 3266942 3127937 2538658 1405505 739509 1519644 2817907 1005119 755410 1784244 86211] timestamp = df.index[3000000] = 2011-12-31 21:30:00 timestamp in index? False --------------------------------------------------------------------------- TypeError Traceback (most recent call last) ... TypeError: Cannot compare Timestamp with 1326116999500000000 ```
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MDU6SXNzdWU2NTM3MjAz
1,822
Long variable names table formatting in ols
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2
"2012-08-29T20:03:30Z"
"2016-02-23T00:57:55Z"
"2016-02-23T00:57:55Z"
MEMBER
null
``` Running pd.ols from pandas 0.8.1, I get misaligned output when some of my variable names are long. For example: [...] -----------------------Summary of Estimated Coefficients------------------------ Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5% -------------------------------------------------------------------------------- provision_alll_ratio 0.8377 0.0030 281.62 0.0000 0.8319 0.8436 lead_1_provision_alll_ratio 0.0322 0.0033 9.70 0.0000 0.0257 0.0387 lead_2_provision_alll_ratio 0.0332 0.0033 10.02 0.0000 0.0267 0.0397 lead_3_provision_alll_ratio 0.0245 0.0033 7.39 0.0000 0.0180 0.0310 lead_4_provision_alll_ratio 0.0119 0.0030 4.00 0.0001 0.0061 0.0177 -------------------------------------------------------------------------------- intercept -0.0005 0.0000 -28.57 0.0000 -0.0005 -0.0004 ---------------------------------End of Summary--------------------------------- Is there a way to make the first output field long enough to fit the longest variable name? Thanks, Richard Stanton ```
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1,823
Panel truncate failure
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0
"2012-08-29T21:13:23Z"
"2012-09-09T03:07:58Z"
"2012-09-09T03:07:58Z"
MEMBER
null
user reported this error (may be for all panels) ``` panel.truncate(before=None, after=None, axis=’items’).fillna(value=0.0) will fail with an AssertionError The failure path is pretty narrow. If ‘before’ or ‘after’ is binding, it won’t fail. If the axis is ‘major’ or ‘minor’ it won’t fail. If fillna method is ‘pad’, it won’t fail. So, it seems to be just this pretty narrow set of parameter values that causes a problem (i.e. reindexing the items of a Panel that results in no change to the Index). ```
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1,824
HDFStore format of very long DataFrames
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1
"2012-08-29T22:37:44Z"
"2012-09-09T01:04:55Z"
"2012-09-09T01:04:55Z"
MEMBER
null
http://stackoverflow.com/questions/12053003/pandas-pytables-warnings-and-slow-performance
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Test datetime64 conversion in from_records error
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"2012-08-30T00:02:04Z"
"2012-09-10T22:36:39Z"
"2012-09-10T22:36:39Z"
MEMBER
null
http://stackoverflow.com/questions/11945897/python-pandas-0-81-dataframe-from-records-convert-django-datetime-to-datetime
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1,826
Better error message in Panel.__setitem__ for TypeError
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0
"2012-08-30T01:14:01Z"
"2012-09-09T03:40:45Z"
"2012-09-09T03:40:45Z"
MEMBER
null
`else` case never handled; can least to unboundlocalerror
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1,827
WEB DOC: 'Not Interpolated' and 'Interpolated' plots on http://pandas.pydata.org/pandas-docs/stable/missing_data.html
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1
"2012-08-30T18:01:58Z"
"2012-08-30T21:04:44Z"
"2012-08-30T21:04:44Z"
CONTRIBUTOR
null
The 'Not Interpolated' and 'Interpolated' plots on http://pandas.pydata.org/pandas-docs/stable/missing_data.html appear identical. Suspect that the 'Not Interpolated' should have gaps?
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1,828
to_excel() fails if unicode sheet name is used
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5
"2012-08-30T18:44:07Z"
"2016-10-12T23:03:10Z"
"2012-09-13T01:50:47Z"
CONTRIBUTOR
null
Like the subject says, the check in DataFrame.to_excel() checks for an instance of str, rather than basestring, hence unicode names skip the if check and are treated like files, which causes an exception later on in the code. Changing isistance() to check for basestring rather than str fixes the issue.
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Add args/kwds arguments to Series.apply(), like for DataFrame.apply()
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"2012-08-30T20:10:48Z"
"2012-09-08T02:48:26Z"
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Period Indexing Bug
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1
"2012-08-31T05:32:59Z"
"2012-09-10T12:49:00Z"
"2012-09-10T12:49:00Z"
CONTRIBUTOR
null
From mailing list: In [82]: df Out[82]: data 1971 2 1972 3 1973 4 In [83]: df.ix[Period(1971)] Out[83]: data 3 Name: 1972
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set_index drops data in the presence of duplicates when inplace=True and verify_integrity=True
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0
"2012-08-31T15:52:45Z"
"2012-09-10T17:48:43Z"
"2012-09-10T17:48:43Z"
CONTRIBUTOR
null
When calling set_index on an index with duplicates, the verify_integrity=True option correctly identifies the duplicates but this check appears to take place after the original columns have already been dropped when inplace=True is also passed. This results in data being lost. I believe it would be better if the original DataFrame object was only modified in the case that the set_index operation is successful. Code to reproduce the problem: ``` python In [189]: df = DataFrame({'one':[1, 1, 2], 'two':[1,2,3]}) In [190]: df Out[190]: one two 0 1 1 1 1 2 2 2 3 In [191]: df.set_index(['one'], inplace=True, verify_integrity=True) --------------------------------------------------------------------------- Exception Traceback (most recent call last) /mnt/hgfs/fastdata/<ipython-input-191-e1c0e8c92f6c> in <module>() ----> 1 df.set_index(['one'], inplace=True, verify_integrity=True) /home/tobias/code/envs/mac/local/lib/python2.7/site-packages/pandas/core/frame.pyc in set_index(self, keys, drop, append, inplace, verify_integrity) 2328 if verify_integrity and not index.is_unique: 2329 duplicates = index.get_duplicates() -> 2330 raise Exception('Index has duplicate keys: %s' % duplicates) 2331 2332 # clear up memory usage Exception: Index has duplicate keys: [1] In [192]: df Out[192]: two 0 1 1 2 2 3 In [202]: print sys.version 2.7.3 (default, Aug 1 2012, 05:14:39) [GCC 4.6.3] In [203]: print pd.version.version 0.8.1 In [204]: ```
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print exception when importing pandas.lib
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1
"2012-09-02T16:03:16Z"
"2014-07-22T20:27:46Z"
"2012-09-08T02:42:04Z"
NONE
null
I was trying to install pandas and kept getting the error: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Library/Python/2.7/site-packages/pandas/**init**.py", line 16, in <module> raise ImportError('C extensions not built: if you installed already ' ImportError: C extensions not built: if you installed already verify that you are not importing from the source directory I couldn't figure out what was happening until I made this change and saw from the printed out exception that I didn't have the dateutils package installed.
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1,833
Bug in DataFrame.duplicated() when dealing with datetime64
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1
"2012-09-03T11:07:26Z"
"2013-12-04T00:47:26Z"
"2012-09-09T01:33:37Z"
CONTRIBUTOR
null
The following looks like a bug to me as DataFrame.duplicated() gives different results on what should be identical inputs. To me it looks like the problem is with the datetime64 values because if you look at the output of `dates.values` it's clear that the last 4 values are duplicates. Please see the code below that reproduces the problem: ``` python In [701]: dates = date_range('2010-07-01', end='2010-08-05') In [702]: tst = DataFrame({'symbol': 'AAA', 'date': dates}) In [703]: tst.tail() Out[703]: date symbol 31 2010-08-01 00:00:00 AAA 32 2010-08-02 00:00:00 AAA 33 2010-08-03 00:00:00 AAA 34 2010-08-04 00:00:00 AAA 35 2010-08-05 00:00:00 AAA In [704]: tst.duplicated().tail() Out[704]: 31 False 32 False 33 False 34 False 35 False In [705]: tst.duplicated(['date', 'symbol']).tail() Out[705]: 31 False 32 True 33 True 34 True 35 True In [706]: dates.values Out[706]: array([1970-01-15 40:00:00, 1970-01-15 64:00:00, 1970-01-15 88:00:00, 1970-01-15 112:00:00, 1970-01-15 136:00:00, 1970-01-15 160:00:00, 1970-01-15 184:00:00, 1970-01-15 208:00:00, 1970-01-15 232:00:00, 1970-01-15 00:00:00, 1970-01-15 24:00:00, 1970-01-15 48:00:00, 1970-01-15 72:00:00, 1970-01-15 96:00:00, 1970-01-15 120:00:00, 1970-01-15 144:00:00, 1970-01-15 168:00:00, 1970-01-15 192:00:00, 1970-01-15 216:00:00, 1970-01-15 240:00:00, 1970-01-15 08:00:00, 1970-01-15 32:00:00, 1970-01-15 56:00:00, 1970-01-15 80:00:00, 1970-01-15 104:00:00, 1970-01-15 128:00:00, 1970-01-15 152:00:00, 1970-01-15 176:00:00, 1970-01-15 200:00:00, 1970-01-15 224:00:00, 1970-01-15 248:00:00, 1970-01-15 16:00:00, 1970-01-15 40:00:00, 1970-01-15 64:00:00, 1970-01-15 88:00:00, 1970-01-15 112:00:00], dtype=datetime64[ns]) In [707]: dates Out[707]: <class 'pandas.tseries.index.DatetimeIndex'> [2010-07-01 00:00:00, ..., 2010-08-05 00:00:00] Length: 36, Freq: D, Timezone: None In [708]: dates.dtype Out[708]: dtype('datetime64[ns]') In [709]: dates.values.dtype Out[709]: dtype('datetime64[ns]') In [710]: sys.version Out[710]: '2.7.3 (default, Aug 1 2012, 05:14:39) \n[GCC 4.6.3]' In [711]: np.version.version Out[711]: '1.6.1' In [713]: pd.version.version Out[713]: '0.8.1' In [714]: ``` I remember reading somewhere that there are problems with datetime64 in numpy 1.6 but I don't understand what coercions are taking place behind the scenes. Also, if someone could please explain to me why the dates in `dates.values` above are wrong and how to avoid this, I would appreciate it.
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1,834
fixed row/col orientation for 2D arrays
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5
"2012-09-03T18:55:21Z"
"2014-06-13T02:22:47Z"
"2012-09-09T01:03:04Z"
CONTRIBUTOR
null
Pytables uses the opposite convention to pandas for row/col. Using the numpy transpose .T corrects for this without copying the data and keeping the orientation of 1D arrays intact.
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6,621,496
MDU6SXNzdWU2NjIxNDk2
1,835
read_csv fails when both converts and index_col are used on the same column
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1
"2012-09-03T19:39:24Z"
"2016-10-12T23:03:10Z"
"2012-09-10T22:00:10Z"
CONTRIBUTOR
null
``` python In [473]: d = """\ A;B 1;2 3;4 """ In [474]: pandas.read_csv(StringIO(d), sep=';', converters={'A': lambda x: x}, index_col='A') --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-474-6bc08702ea91> in <module>() ... KeyError: 'A' In [475]: pandas.read_csv(StringIO(d), sep=';', converters={'A': lambda x: x}).set_index('A') Out[475]: B A 1 2 3 4 ```
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6,628,710
MDU6SXNzdWU2NjI4NzEw
1,836
Define column-wise na/default value for merge operations.
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2
"2012-09-04T08:21:41Z"
"2016-12-28T11:24:36Z"
"2016-12-28T11:24:36Z"
NONE
null
I find myself often using fillna on a lot of columns after a merge operation and having to cast columns back to their original dtype. Pseudo code now data_types = df.dtypes df_new = merge(df, df_other) for col in former_int64_columns: df_new[col] = df_new[col].fillna(0).astype(np.int64) in some cases I may want a different default value, also for other column types than int64. An idea would be something like a keyword argument "column_default_values = { 'a': 0, 'b': -1, ...}".
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1,837
Tiny str() fix, pandas/stats/ols.py
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1
"2012-09-04T13:43:34Z"
"2014-06-14T08:07:32Z"
"2012-09-08T02:40:35Z"
CONTRIBUTOR
null
Fix for unexpected behaviour (TypeError: not all arguments converted during string formatting) if c is a tuple, as it can be when having collapsed the multiindex columns like df_with_tuple_columns = df_with_multiindex.columns.tolist()
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6,636,196
MDU6SXNzdWU2NjM2MTk2
1,838
Adding an offset to a timezone-aware DatetimeIndex shifts the times
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0
"2012-09-04T14:34:49Z"
"2016-10-12T23:03:10Z"
"2012-09-19T02:18:53Z"
NONE
null
For example: > > > dr = pandas.date_range('03/06/2012 00:00', periods=25, freq='W-FRI', tz='US/Eastern') > > > <class 'pandas.tseries.index.DatetimeIndex'> > > > [2012-03-09 00:00:00, ..., 2012-08-24 00:00:00] > > > Length: 25, Freq: W-FRI, Timezone: US/Eastern > > > > > > dr + pandas.datetools.BDay() > > > <class 'pandas.tseries.index.DatetimeIndex'> > > > [2012-03-12 05:00:00, ..., 2012-08-27 04:00:00] > > > Length: 25, Freq: None, Timezone: US/Eastern Contrast with a timezone-naive DatetimeIndex: > > > dr = pandas.date_range('03/06/2012 00:00', periods=25, freq='W-FRI') > > > <class 'pandas.tseries.index.DatetimeIndex'> > > > [2012-03-09 00:00:00, ..., 2012-08-24 00:00:00] > > > Length: 25, Freq: W-FRI, Timezone: None > > > > > > dr + pandas.datetools.BDay() > > > <class 'pandas.tseries.index.DatetimeIndex'> > > > [2012-03-12 00:00:00, ..., 2012-08-27 00:00:00] > > > Length: 25, Freq: W-MON, Timezone: None
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Weekly anchored frequency offsets don't behave as expected
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"2012-09-04T14:38:57Z"
"2016-10-12T23:03:10Z"
"2012-11-28T18:39:21Z"
NONE
null
For example: > > > period_index = pandas.period_range('2012/6/3', periods=10, freq='W-FRI') > > > <class 'pandas.tseries.period.PeriodIndex'> > > > freq: W-FRI > > > [02-Jun-2012/08-Jun-2012, ..., 04-Aug-2012/10-Aug-2012] > > > length: 10 Although the frequency is "W-FRI", the start dates listed (02-Jun-2012, ..., 04-Aug-2012) are Saturdays. Converting the frequency of the PeriodIndex to daily using "start", you get Saturdays: > > > period_index.asfreq(freq='D', how='start') > > > <class 'pandas.tseries.period.PeriodIndex'> > > > freq: D > > > [02-Jun-2012, ..., 04-Aug-2012] > > > length: 10 And if you convert the PeriodIndex to a DatetimeIndex using to_timestamp(), you get a frequency that is explicitly "W-SAT": > > > period_index.to_timestamp(freq='D', how='s') > > > <class 'pandas.tseries.index.DatetimeIndex'> > > > [2012-06-02 00:00:00, ..., 2012-08-04 00:00:00] > > > Length: 10, Freq: W-SAT, Timezone: None
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MDU6SXNzdWU2NjQyNDQ3
1,840
pandas.rolling_std() gives nans where it probably shouldn't
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4
"2012-09-04T18:35:53Z"
"2012-09-07T18:47:49Z"
"2012-09-07T18:47:41Z"
CONTRIBUTOR
null
The output differs from bottleneck, which also gives nans, but fewer. The Factor version never outputs nans and otherwise matches the pandas version. https://gist.github.com/3624548
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6,647,058
MDU6SXNzdWU2NjQ3MDU4
1,841
ewma docs don't explain time_rule
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0
"2012-09-04T21:42:29Z"
"2012-09-08T02:36:27Z"
"2012-09-08T02:36:27Z"
CONTRIBUTOR
null
Not much else to say--should add an explanation in `_ewm_doc`. ``` def ewma(arg, com=None, span=None, min_periods=0, freq=None, time_rule=None, ```
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df.plot(x=..., y=...) fails for mixed-integer column names
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6
"2012-09-05T08:00:07Z"
"2012-09-10T19:52:55Z"
"2012-09-10T19:52:55Z"
CONTRIBUTOR
null
``` python In [187]: df = pandas.DataFrame(np.random.randn(100,3),columns=['A', 'B', 0]) In [188]: df.head() Out[188]: A B 0 0 0.699932 -1.090587 1.014325 1 -0.148856 -0.994177 -1.107741 2 1.384710 0.107488 1.108757 3 -0.795969 -1.297598 -0.268348 4 -0.247695 -0.627048 0.010415 In [189]: df.plot(x='A', y=0) --------------------------------------------------------------------------- KeyError Traceback (most recent call last) ... KeyError: 'no item named A' In [190]: ```
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6,655,302
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1,843
ExcelFile(...).parse(skiprows=...) fails for rows at the end of the sheet
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1
"2012-09-05T08:30:11Z"
"2012-09-25T18:40:55Z"
"2012-09-18T15:10:45Z"
CONTRIBUTOR
null
from [mailinglist](https://groups.google.com/forum/?fromgroups=#!searchin/pydata/richard$20stanton/pydata/z1BNtywmc80/qzgG2pUrsk8J) (tried it myself on master also) I've been using the ExcelFile function to read in a dataset that has some lines of text above and below the numbers I'm interested in. The option skiprows=[0,1,3,4,5,6,7] works fine to ignore the text above the data (apart from line 2, which contains the labels). However, if I add values 151, 152, etc., to try to ignore the text lines after the end of the data, this seems to have no effect. Is this behavior deliberate?* I can get rid of those extra lines at the end by using dropna(), but this seems to have the effect of making the first series of type object rather than float, which is (a little) inconvenient.
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API design discussion
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5
"2012-09-05T16:51:32Z"
"2015-01-17T18:00:08Z"
"2015-01-17T18:00:08Z"
NONE
null
kk=DataFrame({'a':[1,0,3,5],'b':[2,4,6,8]}) kk['c']=kk.b/kk.a > > > kk > > > a b c > > > 0 1 2 2 > > > 1 0 4 0 > > > 2 3 6 2 > > > 3 5 8 1 Two issues: (1) I don't think kk[1,'c']=0 is a suitable answer (2) I don't think kk[3,'c']=1 is a suitable answer, it should be 1.6 while kk['c']=1.0*kk.b/kk.a give an OK answer > > > kk > > > a b c > > > 0 1 2 2.0 > > > 1 0 4 NaN > > > 2 3 6 2.0 > > > 3 5 8 1.6 So pls treat 'int/int' to 'float/float', for current 'int/int' treatment will cause difficult checking error
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1,845
Update pandas/__init__.py
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4
"2012-09-06T06:17:59Z"
"2014-06-14T01:51:07Z"
"2012-09-08T02:20:08Z"
NONE
null
Removed try/except around import pandas.lib The try/except hid a dateutil import error.
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MDU6SXNzdWU2NjgwMzg5
1,846
A bug of Pandas DataFrame.plot or xticks
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4
"2012-09-06T06:18:48Z"
"2017-08-02T10:44:23Z"
"2012-09-06T06:58:53Z"
NONE
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I am using Pandas to develop a financial report analysis tool . And I find a bug related to xticks setting. In general, I define a multilevel dataframe 'df' , draw a bar plot, and try to reset the default xtick value, and find the xtick mismatch with the bar, the position shift to left side . Pls check the attach the code. from FinReporter_Engine_v1 import * df = Get_Ratio('600809',["mg_r","ttl3_exp_r","net_pft_r"],sta_date="20110101",end_date="20201231") print df ''' df is the multileve indexed dataframe ''' ''' mg_r ttl3_exp_r net_pft_r STK_ID RPT_Date 600809 20110331 0.757328 0.232423 0.270287 20110630 0.756847 0.228334 0.227518 20110930 0.761260 0.244068 0.198542 20111231 0.759992 0.274064 0.173913 20120331 0.769676 0.240521 0.222462 ''' DataFrame.plot(df,kind="bar",stacked=False, use_index=False) ''' reset the xtick for I hate default ugly multilevel index lable ''' plt.xticks(range(len(df)),df.index.get_level_values("RPT_Date")) plt.show()
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DatetimeIndex gives strange answer when trying to find intervals
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4
"2012-09-06T12:55:36Z"
"2014-02-18T22:19:10Z"
"2014-02-18T22:19:10Z"
CONTRIBUTOR
null
The following is strange to me: ``` >>> import datetime >>> import pandas >>> dates = [datetime.datetime(2012, 5, 1), datetime.datetime(2012, 5, 2), datetime.datetime(2012, 5, 3)] >>> index = pandas.DatetimeIndex(dates) >>> index[1:] - index[:-1] <class 'pandas.tseries.index.DatetimeIndex'> [2012-05-03 00:00:00] Length: 1, Freq: None, Timezone: None ``` I would expect something that would represents the 2 intervals. I thought at first a PeriodIndex, but that will not work in this case. Probably though should return a DateOffset? Maybe? Note if using the datetime representation from above: ``` >>> import numpy as np >>> npdates = np.array(dates) >>> npdates[1:] - npdates[:-1] array([1 day, 0:00:00, 1 day, 0:00:00], dtype=object) ``` Kindest regards, Tim
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lib.infer_dtype_list(MultiIndex) crashes python
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1
"2012-09-06T14:47:53Z"
"2014-03-05T15:18:23Z"
"2012-09-09T03:17:31Z"
CONTRIBUTOR
null
If lib.infer_dtype_list is called with a MultiIndex, it crashes python (on OS X 10.7 at least) This crash occurs when writing a dataframe with a multiindex to a pytables table (_write_table)
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1,849
merge or join fails if the left input dataframe contains a column of float32
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1
"2012-09-06T20:47:11Z"
"2016-10-12T23:03:11Z"
"2012-09-10T22:22:29Z"
NONE
null
A merge or join involving a left DataFrame that has single precision floats will fail. Seems to be OK if the right dataframe has float32; these get cast to float64 (which they probably shouldn't be) ``` python import numpy as np import pandas a = np.random.randint(0, 5, 100) df = pandas.DataFrame({'a': a}) s = pandas.DataFrame( pandas.Series(np.random.random(5).astype('f'), name='md') ) df.merge(s, left_on='a', right_index=True) # this is OK df['b'] = np.random.randint(0, 5, 100) df.merge(s, left_on='a', right_index=True) # this is OK df['c'] = np.random.random(100).astype('Float64') df.merge(s, left_on='a', right_index=True) # this is OK df['d'] = np.random.random(100).astype('Float32') print df.dtypes df.merge(s, left_on='a', right_index=True) # this fails ```
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6,702,258
MDU6SXNzdWU2NzAyMjU4
1,850
pandas.rolling_apply: "Out of bounds on buffer access"
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1
"2012-09-06T23:02:13Z"
"2012-09-08T02:34:55Z"
"2012-09-08T02:34:47Z"
NONE
null
Looks like `pandas.rolling_apply` doesn't like a window larger than the length of the array: ``` In [3]: import pandas In [4]: import numpy as np In [5]: a = np.array(range(4)) In [6]: pandas.rolling_sum(a, 10) Out[6]: array([ nan, nan, nan, nan]) In [7]: pandas.rolling_apply(a, 10, np.sum) --------------------------------------------------------------------------- IndexError Traceback (most recent call last) <ipython-input-7-1ff0414da8bd> in <module>() ----> 1 pandas.rolling_apply(a, 10, np.sum) /Library/Python/2.7/site-packages/pandas-0.8.1-py2.7-macosx-10.8-intel.egg/pandas/stats/moments.pyc in rolling_apply(arg, window, func, min_periods, freq, time_rule) 465 return lib.roll_generic(arg, window, minp, func) 466 return _rolling_moment(arg, window, call_cython, min_periods, --> 467 freq=freq, time_rule=time_rule) /Library/Python/2.7/site-packages/pandas-0.8.1-py2.7-macosx-10.8-intel.egg/pandas/stats/moments.pyc in _rolling_moment(arg, window, func, minp, axis, freq, time_rule, **kwargs) 227 return_hook, values = _process_data_structure(arg) 228 # actually calculate the moment. Faster way to do this? --> 229 result = np.apply_along_axis(calc, axis, values) 230 231 return return_hook(result) /Library/Python/2.7/site-packages/numpy-1.6.2-py2.7-macosx-10.8-intel.egg/numpy/lib/shape_base.pyc in apply_along_axis(func1d, axis, arr, *args) 78 outshape = asarray(arr.shape).take(indlist) 79 i.put(indlist, ind) ---> 80 res = func1d(arr[tuple(i.tolist())],*args) 81 # if res is a number, then we have a smaller output array 82 if isscalar(res): /Library/Python/2.7/site-packages/pandas-0.8.1-py2.7-macosx-10.8-intel.egg/pandas/stats/moments.pyc in <lambda>(x) 224 """ 225 arg = _conv_timerule(arg, freq, time_rule) --> 226 calc = lambda x: func(x, window, minp=minp, **kwargs) 227 return_hook, values = _process_data_structure(arg) 228 # actually calculate the moment. Faster way to do this? /Library/Python/2.7/site-packages/pandas-0.8.1-py2.7-macosx-10.8-intel.egg/pandas/stats/moments.pyc in call_cython(arg, window, minp) 463 def call_cython(arg, window, minp): 464 minp = _use_window(minp, window) --> 465 return lib.roll_generic(arg, window, minp, func) 466 return _rolling_moment(arg, window, call_cython, min_periods, 467 freq=freq, time_rule=time_rule) /Library/Python/2.7/site-packages/pandas-0.8.1-py2.7-macosx-10.8-intel.egg/pandas/lib.so in pandas.lib.roll_generic (pandas/src/tseries.c:74581)() IndexError: Out of bounds on buffer access (axis 0) ```
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MDU6SXNzdWU2NzEyNzc2
1,851
python-dateutils < 2 requirement for python 2. Is it necessary?
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1
"2012-09-07T12:24:09Z"
"2012-09-08T02:13:31Z"
"2012-09-08T02:13:31Z"
NONE
null
The most recent version (2.1) supports both 2.x and 3.x. Pandas works for me using version 2.1: ``` (VENV)[~/ng] $ nosetests pandas ... ... ---------------------------------------------------------------------- Ran 2228 tests in 112.553s OK (SKIP=45) (VENV)[~/ng] $ python -c "import dateutil; print dateutil.__version__" 2.1 ``` I suggest change setup.py.
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6,713,640
MDU6SXNzdWU2NzEzNjQw
1,852
Test long string formatting in to_string and to_html
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12
"2012-09-07T13:11:08Z"
"2018-07-06T22:02:41Z"
"2018-07-06T22:02:05Z"
NONE
null
Start with a data frame -- large-ish number of rows ``` df = DataFrame({'a':['a1']*100+['a2']*100, 'b':['b']*200}) dfg = df.groupby(['a']) dfg.aggregate(len) ## this gives right answer, every entry is of length 100 dfg.aggregate(lambda x: ', '.join(x.values)) ## this is wrong, the output string is somehow truncated? ``` It works fine if the string is not very long, eg start with df = DataFrame({'a':['a1']_10+['a2']_10, 'b':['b']*20)
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6,714,268
MDU6SXNzdWU2NzE0MjY4
1,853
Add ability to estimate full covariance matrices a la ewmcov/rolling_cov
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1
"2012-09-07T13:41:04Z"
"2014-03-28T14:47:49Z"
"2014-03-28T14:47:49Z"
MEMBER
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MDU6SXNzdWU2NzE0NTA5
1,854
indicate __repr__ truncation by ...
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0
"2012-09-07T13:53:20Z"
"2012-09-15T17:20:59Z"
"2012-09-15T17:20:41Z"
CONTRIBUTOR
null
When a column is wider than 50 chars (controllable by `pandas.setprintoptions(max_colwidth=...)`) it is truncated in the `__repr__`, indicate this by adding `...` at the end of the truncated strings. see also #1852
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MDU6SXNzdWU2NzIzMzc2
1,855
Problems converting a large file to a panel using read_csv
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1
"2012-09-07T20:19:52Z"
"2013-09-21T13:51:39Z"
"2013-09-21T13:51:39Z"
MEMBER
null
Bug? x-reference from SO http://stackoverflow.com/questions/12085393/problems-converting-a-large-file-to-a-panel-using-read-csv-into-python-pandas
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Enable DataFrame to accept scalar constructor values like Series
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"2012-09-07T20:27:49Z"
"2012-11-19T01:43:20Z"
"2012-11-19T01:43:20Z"
MEMBER
null
e.g. ``` df = DataFrame(val,index=[1,2,3],columns=['a','b','c']) ```
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6,723,984
MDU6SXNzdWU2NzIzOTg0
1,857
Period.start_time returns the same value as Period.end_time
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"2012-09-07T20:46:20Z"
"2016-10-12T23:03:11Z"
"2012-09-10T21:57:01Z"
NONE
null
If you create a Period object with a non-Day frequency, the start_time property incorrectly returns the end_time value. ex: ``` python In [1]: from pandas import * In [2]: w = period_range('2012-8-20', '2012-9-10', freq='W-MON') In [3]: w[0] Out[3]: Period('14-Aug-2012/20-Aug-2012', 'W-MON') In [4]: w[0].start_time Out[4]: <Timestamp: 2012-08-20 00:00:00> In [5]: w[0].end_time Out[5]: <Timestamp: 2012-08-20 00:00:00> In [6]: w[0].freq Out[6]: 'W-MON' ``` The expected behavior would return a `<Timestamp: 2012-08-14 00:00:00>` object for the Period.start_time property.
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1,858
Explicit column dtype specification in read_* functions
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e.g. columns with values like `01001` are getting converted to int example from mailing list: ``` df = read_csv('test_data.csv') df.head() oid did mode ox oy dx dy 0 1001 1001 01 272311.659358 176751.822655 272675 176375 1 1001 1001 01 272311.659358 176751.822655 272375 176375 2 1001 1001 01 272311.659358 176751.822655 272125 176675 3 1001 1001 06 272311.659358 176751.822655 272675 177125 4 1001 1001 06 272311.659358 176751.822655 272675 176375 df.oid = df.oid.apply(lambda x: str(x).zfill(5)) df.head() oid did mode ox oy dx dy 0 01001 1001 01 272311.659358 176751.822655 272675 176375 1 01001 1001 01 272311.659358 176751.822655 272375 176375 2 01001 1001 01 272311.659358 176751.822655 272125 176675 3 01001 1001 06 272311.659358 176751.822655 272675 177125 4 01001 1001 06 272311.659358 176751.822655 272675 176375 ```
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MDU6SXNzdWU2NzI1ODgy
1,859
Series.str.split doesn't work with no arguments
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"2012-09-07T22:23:57Z"
"2012-09-08T02:02:01Z"
"2012-09-08T02:02:01Z"
MEMBER
null
Inconsistent with builtin string API
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6,727,621
MDExOlB1bGxSZXF1ZXN0MjI4NDA4Ng==
1,860
FDPanel, a 4 dimensional pandas container
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3
"2012-09-08T00:29:40Z"
"2014-07-07T00:24:37Z"
"2012-09-18T17:41:34Z"
CONTRIBUTOR
null
a FDPanel is like a Panel object, but provides 4 dimensions: labels, items, major_axis, minor_axis instead of using a dict of Panels to hold data, the FDPanel provides a convenient represenation in pandas space with named dimensions to allow easy axis swapping and slicing see examples/4D Example 2.pdf for creation and usage ## motivation io/pytables currently provides a nice representation of a table to store a Panel object directly (assume item x major (dates) x minor) FDPanel allows many named tables to represent a collection of these tables in a single hdf5 file ## testing tests/test_fdpanel.py provides a similar methodology to test_panel.py FDPanel is completely self contained (in core/fdpanel.py), with the exception of a single change that was needed in core/indexing.py (regarding multi indexing) all tests that are not skipped pass (some were skipped due to deprecation of some panel like terms that are somewhat confusing in a 4D representation, e.g. minor_xs,major_xs); arithmetic operations not really tested much - almost always reduce the dimensionaliity (to panel/frame/series) before these types of operations some code in panel.py could be changed to avoid some method duplication in fdpanel.py (e.g. _prep_ndarray, hard coding of some axes) this was based originally on 0.7.3 (but currently working on 0.9.dev)
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MDU6SXNzdWU2NzMxMTAy
1,861
deprecated from pandas.stats import misc fails in 0.8.1
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0
"2012-09-08T13:04:34Z"
"2012-09-09T01:22:33Z"
"2012-09-09T01:22:33Z"
CONTRIBUTOR
null
I believe this is a deprecated import (I used it to access bucket_series) exists currently in master 0.9.dev as well Python 2.7.3 (default, Jun 21 2012, 07:50:29) > > > import pandas > > > print pandas.**version** > > > 0.8.1 > > > from pandas.stats import misc > > > Traceback (most recent call last): > > > File "<stdin>", line 1, in <module> > > > File "/usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linux-x86_64.egg/pandas/stats/misc.py", line 7, in <module> > > > from pandas.tools.tile import quantileTS > > > ImportError: cannot import name quantileTS
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Pytables support for FDPanel
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1
"2012-09-08T17:10:15Z"
"2014-06-19T10:31:27Z"
"2012-11-24T01:47:03Z"
CONTRIBUTOR
null
add pytables branch to support FDPanel this is on top of my previous fdpanel branch supporting put/append/select of FDPanel to hdf5 tables (and makes selection a bit more generic, as store.select() now works). FYI - the included FDPanel definitions (eg fdpanel.py) are v1 - so merge FDPanel (v2) first Then cherry pick this merge (eg only need pytables.py and tests and included examples)
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OLS issues interacting with MultiIndex
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1
"2012-09-08T22:47:44Z"
"2015-01-26T00:34:27Z"
"2015-01-26T00:34:27Z"
MEMBER
null
x-linking: http://stackoverflow.com/questions/11586068/collapse-a-pandas-multiindex-or-run-ols-regression-on-a-multiindexed-dataframe
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MDU6SXNzdWU2NzM4NTQ1
1,864
API for rearranging data within a hierarchical level
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5
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"2014-04-17T19:10:36Z"
"2014-04-17T19:10:36Z"
MEMBER
null
x-reference for someday http://stackoverflow.com/questions/11194610/how-can-i-reorder-multi-indexed-dataframe-columns-at-a-specific-level
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1,865
Add pivot (reshape) option to handle duplicate keys (e.g. take first or last obs)
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3
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"2017-01-05T18:14:01Z"
"2017-01-05T18:14:01Z"
MEMBER
null
http://stackoverflow.com/questions/11232275/pandas-pivot-warning-about-repeated-entries-on-index
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6,739,082
MDU6SXNzdWU2NzM5MDgy
1,866
Doc build bug
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0
"2012-09-09T00:40:21Z"
"2012-09-10T02:39:13Z"
"2012-09-10T02:39:13Z"
MEMBER
null
@changhiskhan there's some bug showing up in the docs but not in the main test suite. Can you have a look? ``` building [html]: targets for 22 source files that are out of date updating environment: 218 added, 0 changed, 0 removed --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-767-cc6f0e841b62> in <module>() 1 df = read_csv('tmp.csv', header=None, parse_dates=date_spec, ----> 2 date_parser=conv.parse_date_time) /home/wesm/code/pandas/pandas/io/parsers.pyc in read_csv(filepath_or_buffer, sep, dialect, header, index_col, names, skiprows, na_values, keep_default_na, thousands, comment, parse_dates, keep_date_col, dayfirst, date_parser, nrows, iterator, chunksize, skip_footer, converters, verbose, delimiter, encoding, squeeze) 239 kwds['delimiter'] = sep 240 --> 241 return _read(TextParser, filepath_or_buffer, kwds) 242 243 @Appender(_read_table_doc) /home/wesm/code/pandas/pandas/io/parsers.pyc in _read(cls, filepath_or_buffer, kwds) 192 return parser 193 --> 194 return parser.get_chunk() 195 196 @Appender(_read_csv_doc) /home/wesm/code/pandas/pandas/io/parsers.pyc in get_chunk(self, rows) 812 columns = list(self.columns) 813 if self.parse_dates is not None: --> 814 data, columns = self._process_date_conversion(data) 815 816 df = DataFrame(data=data, columns=columns, index=index) /home/wesm/code/pandas/pandas/io/parsers.pyc in _process_date_conversion(self, data_dict) 989 990 _, col, old_names = _try_convert_dates( --> 991 self._conv_date, colspec, data_dict, self.orig_columns) 992 993 new_data[new_name] = col /home/wesm/code/pandas/pandas/io/parsers.pyc in _try_convert_dates(parser, colspec, data_dict, columns) 1120 to_parse = [data_dict[c] for c in colspec if c in data_dict] 1121 try: -> 1122 new_col = parser(*to_parse) 1123 except DateConversionError: 1124 new_col = parser(_concat_date_cols(to_parse)) /home/wesm/code/pandas/pandas/io/parsers.pyc in _conv_date(self, *date_cols) 951 return lib.try_parse_dates(_concat_date_cols(date_cols), 952 parser=self.date_parser, --> 953 dayfirst=self.dayfirst) 954 955 def _process_date_conversion(self, data_dict): /home/wesm/code/pandas/pandas/lib.so in pandas.lib.try_parse_dates (pandas/src/tseries.c:98928)() /home/wesm/code/pandas/pandas/lib.so in pandas.lib.try_parse_dates (pandas/src/tseries.c:98870)() TypeError: parse_date_time() takes exactly 2 arguments (1 given) reading sources... [100%] whatsnew /home/wesm/code/pandas/doc/source/faq.rst:74: ERROR: Unknown interpreted text role "issue". source/v0.8.0.txt:151: WARNING: Inline literal start-st ```
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6,739,132
MDExOlB1bGxSZXF1ZXN0MjI4NjY4Nw==
1,867
Add expanding moment functions to docs.
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1
"2012-09-09T00:50:48Z"
"2014-06-16T20:12:53Z"
"2012-09-09T01:07:37Z"
CONTRIBUTOR
null
Per @wesm's request on #1785. I wasn't able to get the latex to build, but I believe that was due to a bug that's since been fixed.
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6,739,675
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1,868
Use on_demand option in xlrd if available
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MEMBER
null
xref: http://stackoverflow.com/questions/12250024/how-to-obtain-sheet-names-from-xls-files-without-loading-the-whole-file-in-pytho
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MDU6SXNzdWU2NzM5Njgw
1,869
Add POSIX timestamp conversion function
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"2013-09-20T16:51:47Z"
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null
http://stackoverflow.com/questions/12251483/idiomatic-way-to-parse-posix-timestamps-in-pandas
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ordered_merge improvements
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"2016-06-17T00:09:32Z"
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null
http://stackoverflow.com/questions/12322289/kdb-like-asof-join-for-timeseries-data-in-pandas/12336039#12336039
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1,871
Improve between_time to be able to select time ranges that cross midnight
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0
"2012-09-09T19:23:39Z"
"2012-09-24T21:46:18Z"
"2012-09-24T21:46:18Z"
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null
inspired by: http://stackoverflow.com/questions/12041519/how-to-get-the-mean-of-a-specific-span-of-time
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1,872
pandas <-> R binary interface
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MEMBER
null
http://stackoverflow.com/questions/12278347/how-can-i-efficiently-save-a-python-pandas-dataframe-in-hdf5-and-open-it-as-a-da
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MDU6SXNzdWU2NzQ5OTEx
1,873
Add inplace option to sortlevel / sort functions on DataFrame
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"2012-09-09T21:09:22Z"
"2012-11-19T00:36:52Z"
"2012-11-19T00:36:52Z"
MEMBER
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MDU6SXNzdWU2NzQ5OTMy
1,874
Improve memory usage for large heterogeneous DataFrame in sortlevel
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1
"2012-09-09T21:12:18Z"
"2018-07-06T21:58:09Z"
"2018-07-06T21:58:05Z"
MEMBER
null
http://stackoverflow.com/questions/11987270/getting-memoryerror-from-dataframe-sortlevel-in-pandas/12343023#12343023
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6,750,405
MDExOlB1bGxSZXF1ZXN0MjI4ODc4OA==
1,875
BUG: converters and index_col on same column
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0
"2012-09-09T22:05:26Z"
"2014-07-09T16:39:27Z"
"2012-09-10T21:59:46Z"
CONTRIBUTOR
null
Also minor refactor of parsers At some point we need to get rid of all the methods with side effects so the logic is clear. #1835
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6,751,521
MDU6SXNzdWU2NzUxNTIx
1,876
Could the html documentation be provided for download
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5
"2012-09-10T00:50:23Z"
"2016-10-12T23:03:11Z"
"2014-01-20T23:59:21Z"
NONE
null
I like html documentation for offline browsing. I wonder if pandas could do the same as django or the official python documentation to provide a html tarball for download. Thank you. BTW, I have tried building the pandas html documentation from a git checkout and didn't succeed.
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6,751,545
MDU6SXNzdWU2NzUxNTQ1
1,877
Expected behavior for asi8 property?
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"2012-09-10T00:54:25Z"
"2012-09-13T02:07:44Z"
"2012-09-11T02:41:48Z"
NONE
null
Does asi8 always return int64 timestamps in UTC? I have 2 dataframes : df with index in UTC and df2 with index in US/Eastern. The timezones are correctly interpreted but when I convert them to int64, I was a little surprised to find that both are identical. Is that intentional? Thanks. In [137]: df.index Out[137]: <class 'pandas.tseries.index.DatetimeIndex'> [2010-09-01 14:30:00.228000, ..., 2010-09-01 18:14:59.909000] Length: 20604, Freq: None, Timezone: UTC In [138]: df2.index Out[138]: <class 'pandas.tseries.index.DatetimeIndex'> [2010-09-01 10:30:00.228000, ..., 2010-09-01 14:14:59.909000] Length: 20604, Freq: None, Timezone: US/Eastern In [139]: df.index.asi8 == df2.index.asi8 Out[139]: array([ True, True, True, ..., True, True, True], dtype=bool) In [140]: np.all(df.index.asi8 == df2.index.asi8) Out[140]: True
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6,751,942
MDU6SXNzdWU2NzUxOTQy
1,878
Apply Across Non-Unique Indices.
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0
"2012-09-10T01:52:52Z"
"2016-10-12T23:03:11Z"
"2012-09-18T20:21:41Z"
CONTRIBUTOR
null
Applying a function across a data-frame does not seem to work for non-unique indices. ``` df = pandas.DataFrame([[1,1,1], [2,2,2], [3,3,3]], index=['a','a','c']) df.apply(lambda s: s[0], axis=1) a 2 a 2 c 3 ``` It seems to be just returning the value for the first instance of the index.
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6,752,096
MDExOlB1bGxSZXF1ZXN0MjI4OTM1Mg==
1,879
BUG: start_time on Period fix
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0
"2012-09-10T02:08:28Z"
"2013-12-04T00:43:04Z"
"2012-09-10T21:57:09Z"
CONTRIBUTOR
null
For A-W frequencies, get the starting DAY For H-S frequencies, get the starting SEC For B, D, and S, start == end #1857
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1,880
gcc crashed when building pandas
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24
"2012-09-10T12:38:54Z"
"2015-12-19T11:03:26Z"
"2012-09-11T05:29:26Z"
NONE
null
I experienced a gcc crash when building the latest pandas. This was on Ubuntu 12.04 gcc version 4.6, Cython 0.17. On Ubuntu 11.10 gcc version 4.6.1, Cython 0.14 I did not have the issue.
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1,881
Backwards incompatibility in io/pytables with existing data prior to 0.8 causes data corruption
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1
"2012-09-10T13:27:05Z"
"2012-09-17T18:03:53Z"
"2012-09-17T18:03:39Z"
CONTRIBUTOR
null
trying to append to a table that was created before 0.8 (e.g. before the kind of datetime64 existed) causes corrupted data (essentially the index is converted to datetime64, but since its in nanoseconds, existing datetimes are not evaluated correctly). easiest to convert existing data (read in and just write it out), so this patch is simply an exception which is raised if incompatble kinds are detected. ``` --- pandas/io/pytables.py | 6 ++++++ 1 files changed, 6 insertions(+), 0 deletions(-) diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 0f386c7..e3b84fb 100755 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -825,6 +825,12 @@ class HDFStore(object): # the table must already exist table = getattr(group, 'table', None) + # check for backwards incompatibility + if append: + existing_kind = table._v_attrs.index_kind + if existing_kind != index_kind: + raise Exception("incompatible kind in index [%s - %s]" % (existing_kind,index_kind)) + # add kinds table._v_attrs.index_kind = index_kind table._v_attrs.columns_kind = cols_kind -- 1.7.2.5 ```
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BUG: mixed float64 float32 merge failure
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1
"2012-09-10T16:15:45Z"
"2013-12-04T00:43:03Z"
"2012-09-10T21:53:14Z"
CONTRIBUTOR
null
Now maps FloatBlock type to list of blocks in each join unit. Also checks for out type in merge blocks, otherwise if float32 block appears first then merge fails #1849
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Series or DataFrame asof() fails when the index is a PeriodIndex and the value is a datetime or Timestamp
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NONE
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For example: > > > pi = pandas.period_range('2012/09/10', periods=10, freq='D') > > > s = pandas.TimeSeries(data=range(len(pi)), index=pi) > > > dt = pandas.Timestamp('2012/09/13') > > > > > > s[dt] > > > 3 > > > > s.asof(dt) > > TypeError: Cannot compare Timestamp with 25-May-2012
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6,769,949
MDU6SXNzdWU2NzY5OTQ5
1,884
pandas.rolling_std() first value is nan
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5
"2012-09-10T18:34:27Z"
"2012-09-13T01:23:16Z"
"2012-09-13T01:22:46Z"
CONTRIBUTOR
null
The window is 3, but we want a std at `min_periods=1`. The one-period standard deviation is trivially 0. ``` In [28]: pandas.rolling_std(np.array([1,2,3,4,5], dtype='double'), 3, min_periods=1) Out[28]: array([ nan, 0.70710678, 1. , 1. , 1. ]) ``` The pathological case: ``` In [29]: pandas.rolling_std(np.array([1,2,3,4,5], dtype='double'), 1, min_periods=1) Out[29]: array([ nan, nan, nan, nan, nan]) ``` Maybe it's because pandas is taking the unbiased std for `N-1` where `N = 1`, so it's dividing by zero?
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1,885
df.set_index() fails for MultiIndex columns
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1
"2012-09-10T19:43:48Z"
"2012-09-20T16:49:01Z"
"2012-09-18T02:17:42Z"
CONTRIBUTOR
null
``` python In [20]: columns = pandas.MultiIndex.from_tuples([('foo', 1), ('foo', 2), ('bar', 1)]) In [21]: df = pandas.DataFrame(np.random.randint(0, 10, (3,3)), columns=columns) In [22]: df Out[22]: foo bar 1 2 1 0 0 5 0 1 5 6 6 2 5 1 6 In [23]: df.set_index(df.columns[0]) --------------------------------------------------------------------------- KeyError Traceback (most recent call last) ```
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6,773,580
MDU6SXNzdWU2NzczNTgw
1,886
pytables - optimized delete operation on a table
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1
"2012-09-10T20:27:32Z"
"2012-09-10T21:00:09Z"
"2012-09-10T20:59:39Z"
CONTRIBUTOR
null
following code allows consecutive delete operations to use native tables methods and consequently is much more effecient that individual row deletes. existing tests still pass. #### replace HDFStore._delete_from_table with the following ``` def _delete_from_table(self, group, where = None): table = getattr(group, 'table') # create the selection s = hdf.Selection(table,where,table._v_attrs.index_kind) s.select_coords() # delete the rows in reverse order l = list(s.values) ln = len(l) if ln: # if we can do a consecutive removal - do it! if l[0]+ln-1 == l[-1]: table.removeRows(start = l[0], stop = l[-1]+1) # one by one else: l.reverse() for c in l: table.removeRows(c) self.handle.flush() return ln ```
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6,773,972
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Series repr issue (numpy.int64)
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1
"2012-09-10T20:42:21Z"
"2012-09-10T22:09:42Z"
"2012-09-10T22:09:37Z"
CONTRIBUTOR
null
``` python In [18]: df = pandas.DataFrame(np.random.randn(4,4)) In [19]: s = df.ix[3] In [20]: type(s.name) Out[20]: numpy.int64 In [21]: s Out[21]: --------------------------------------------------------------------------- TypeError Traceback (most recent call last) ... TypeError: 'numpy.int64' object is not iterable ```
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panel ix change in 0.8?
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1
"2012-09-10T21:22:38Z"
"2012-09-17T20:23:33Z"
"2012-09-17T20:23:06Z"
CONTRIBUTOR
null
seems p.ix[:,[-1],:] throws an exception (from take) p.ix[:,-1:,:] is the workaround maybe add to the docs? ipython session follows: 9/10/12 panel test ix change ``` 1/2 In [7]: In [2]: Out[2]: <class 'pandas.core.panel.Panel'> Dimensions: 2 (items) x 8 (major) x 4 (minor) Items: Item1 to Item2 Major axis: 2000-01-01 00:00:00 to 2000-01-08 00:00:00 Minor axis: A to D In [6]: \--------------------------------------------------------------------------- Exception Traceback (most recent call last) /mnt/home/jreback/<ipython-input-6-1531bef93beb> in <module>() 1 # this worked prior to 0.8 ----> 2 p.ix[:,[-1],:] /usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linuxx86_ 64.egg/pandas/core/indexing.pyc in __getitem__(self, key) 31 pass 32 ---> 33 return self._getitem_tuple(key) 34 else: 35 return self._getitem_axis(key, axis=0) /usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linuxx86_ 64.egg/pandas/core/indexing.pyc in _getitem_tuple(self, tup) 137 continue 138 --> 139 retval = retval.ix._getitem_axis(key, axis=i) 140 141 return retval /usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linuxx86_ 64.egg/pandas/core/indexing.pyc in _getitem_axis(self, key, axis) 247 raise ValueError('Cannot index with multidimensional key') 248 --> 249 return self._getitem_iterable(key, axis=axis) 250 elif axis == 0: 251 is_int_index = _is_integer_index(labels) /usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linuxx86_ 64.egg/pandas/core/indexing.pyc in _getitem_iterable(self, key, axis) 295 296 if _is_integer_dtype(keyarr) and not _is_integer_index(labels): --> 297 return self.obj.take(keyarr, axis=axis) 298 299 # this is not the most robust, but... /usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linuxx86_ 64.egg/pandas/core/generic.pyc in take(self, indices, axis) 857 new_data = self._data.reindex_axis(new_items, axis=0) 858 else: import pandas index = pandas.date_range('1/1/2000', periods=8,) p = pandas.Panel(np.random.randn(2, len(index), 4), items=['Item1', 'Item2'], major_axis p \# this worked prior to 0.8 p.ix[:,[-1],:] 9/10/12 panel test ix change 2/2 --> 859 new_data = self._data.take(indices, axis=axis) 860 return self._constructor(new_data) 861 /usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linuxx86_ 64.egg/pandas/core/internals.pyc in take(self, indexer, axis) 1075 n = len(self.axes[axis]) 1076 if ((indexer == -1) | (indexer >= n)).any(): -> 1077 raise Exception('Indices must be nonzero and less than ' 1078 'the axis length') 1079 Exception: Indices must be nonzero and less than the axis length In [5]: Out[5]: <class 'pandas.core.panel.Panel'> Dimensions: 2 (items) x 1 (major) x 4 (minor) Items: Item1 to Item2 Major axis: 2000-01-08 00:00:00 to 2000-01-08 00:00:00 Minor axis: A to D In [ ]: \# this works ok p.ix[:,-1:,:] ```
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6,779,178
MDU6SXNzdWU2Nzc5MTc4
1,889
Add a DataFrame.show() method pls!
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14
"2012-09-11T01:38:17Z"
"2016-04-09T23:09:19Z"
"2016-04-09T23:09:18Z"
NONE
null
'print df' will give something like below if the dataframe 'df' is big to fit into the screen : <class 'pandas.core.frame.DataFrame'> MultiIndex: 41955 entries, (u'000002', u'20061231') to (u'603366', u'20120630') Columns: 147 entries, STK_ID to EPS dtypes: float64(135), object(12) But most of the time, I want to have a glimpse of the data , which help to know what happened to the dataframe. Can Pandas developers add a 'show()' method to DataFrame object to display part of the data inside ? Namely, show the four corner (up_left, up_right, down_left, down_right) data, and use '...' to represent the omitted part ? somewhat like : ``` STK_ID RPT_Date STK_Name .. OprCF_PS EPS ``` STK_ID RPT_Date 000002 20061231 000002 20061231 万科A .. -0.692 0.526 20070331 000002 20070331 万科A .. -0.741 0.140 20070630 000002 20070630 万科A .. -0.454 0.254 ............... ............. ............... ........... ... .......... ..... 20071231 000002 20071231 万科A .. -1.519 0.705 20080331 000002 20080331 万科A .. -0.207 0.105
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6,788,535
MDU6SXNzdWU2Nzg4NTM1
1,890
`TimeSeries.plot` ignores `color` kwarg
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6
"2012-09-11T11:49:09Z"
"2012-10-31T20:21:18Z"
"2012-10-31T20:21:18Z"
CONTRIBUTOR
null
I know this looks like a duplicate of #1636, but even with pandas 0.8.1, I don't get the expected plotting behaviour: ``` In [1]: from numpy import arange In [2]: import matplotlib.pyplot as plt In [3]: import pandas In [4]: pandas.__version__ Out[4]: '0.8.1' In [5]: plt.figure() Out[5]: <matplotlib.figure.Figure at 0x33afd50> In [6]: plt.plot(arange(12), arange(12), color='green') # this line is green Out[6]: [<matplotlib.lines.Line2D at 0x337c150>] In [7]: pandas.Series(arange(12) + 1).plot(color='green') # this line is blue Out[7]: <matplotlib.axes.AxesSubplot at 0x33c80d0> ```
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6,790,367
MDU6SXNzdWU2NzkwMzY3
1,891
read_csv utf-8 edge case with U+2028 (line separator)
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2
"2012-09-11T13:17:59Z"
"2012-09-13T02:30:43Z"
"2012-09-13T02:30:11Z"
NONE
null
I'm using Python 2.7 but this may be an issue for Python 3 as well. ``` In [6]: pd.io.parsers.read_csv('pandasproblem.csv',encoding="utf-8").shape Out[6]: (2, 2) In [7]: pd.io.parsers.read_csv('pandasproblem.csv').shape Out[7]: (1, 2) ``` Note the different shape. Contents of pandasproblem.csv: ``` Id,StringCol 7117434,Is it possible to modify drop plot code so that the output graph is displayed in iphone simulator, Is it possible to modify drop plot code so that the output graph is 
displayed in iphone simulator.Now we are adding the CSV file externally. I want to Call the File through the code.. ``` So I guess the line separator U+2028 (between "is" and "displayed") gets interpreted as '\n' which it surely should not be interpreted as.
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6,794,437
MDU6SXNzdWU2Nzk0NDM3
1,892
limit keyword for interpolate
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5
"2012-09-11T16:35:02Z"
"2017-05-23T11:10:34Z"
"2013-10-09T20:30:41Z"
CONTRIBUTOR
null
need to also define whether limit is relative to most recent non-na or immediate next non-na based on SO discussion in http://stackoverflow.com/questions/12372899/bug-in-pandas-series-dataframe-fillna-limit
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Inplace returns self?
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52
"2012-09-11T16:36:54Z"
"2013-03-31T20:37:46Z"
"2013-03-31T20:37:46Z"
CONTRIBUTOR
null
Is there any reason that whenever I'm doing inplace=True I always get self returned? Obviously not a huge deal, but this is kind of wart-y IMO. I wouldn't expect inplace to return anything.
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Sort by column but keep index
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"2012-09-11T16:53:24Z"
"2012-09-26T14:06:22Z"
"2012-09-26T14:06:22Z"
CONTRIBUTOR
null
I find myself using DataFrames a lot for obvious reasons but not using the indexing magics all the time. I can get by fine with an integer index a la numpy. This leads to situations where I have to do things like this quite often ``` python df = df.drop(drop_idx) df.index = pandas.Index(range(len(df))) ``` or ``` python df.sort(columns='col1', inplace=True) df.index = pandas.Index(range(len(df))) ``` Might be nice to have some sort of keyword in these situations to either reset the index or keep the index unless there's a better way. I always read the docs for reset_index too to see that I'm not missing this functionality there. Don't have time to look into this now but just thought I'd float the idea.
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DatetimeIndex min failure
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"2012-09-11T16:57:31Z"
"2012-09-13T02:12:23Z"
"2012-09-13T02:12:23Z"
CONTRIBUTOR
null
``` In [14]: rng = pd.date_range('2012-8-1', freq='T', periods=100) In [15]: m = rng.min() In [16]: m Out[16]: --------------------------------------------------------------------------- TypeError Traceback (most recent call last) ... c:\code\pandas\pandas\tseries\index.py in __repr__(self) 472 473 summary = str(self.__class__) --> 474 if len(self) == 1: 475 first = _format_datetime64(values[0], tz=self.tz) 476 summary += '\n[%s]' % first TypeError: len() of unsized object In [17]: type(m) Out[17]: pandas.tseries.index.DatetimeIndex In [20]: rng.asobject.min() Out[20]: Index(<Timestamp: 2012-08-01 00:00:00>, dtype=object) ```
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Mixed type DataFrame.diff converts to object
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"2012-09-11T17:17:03Z"
"2012-09-13T01:47:19Z"
"2012-09-13T01:47:19Z"
CONTRIBUTOR
null
``` In [68]: df = DataFrame(np.random.randn(5, 3)) In [69]: df Out[69]: 0 1 2 0 -1.102224 0.395433 -0.112327 1 -0.025758 2.810779 -0.823856 2 1.100939 0.741825 1.016203 3 0.452166 -1.914473 1.439475 4 0.030840 -0.690666 -0.346612 In [70]: df.diff().dtypes Out[70]: 0 float64 1 float64 2 float64 In [71]: df['A'] = np.array([1, 2, 3, 4, 5], dtype=object) In [72]: df.dtypes Out[72]: 0 float64 1 float64 2 float64 A object In [73]: df.diff().dtypes Out[73]: 0 object 1 object 2 object A object ```
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pandas.rolling_min/max fail if window size is larger than input array
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"2012-09-11T19:23:24Z"
"2012-09-12T14:53:40Z"
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CONTRIBUTOR
null
This is a regression since July 12. ``` import pandas import numpy as np pandas.rolling_max(np.arange(3), 100, min_periods=1) pandas.rolling_min(np.arange(3), 100, min_periods=1) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-4-782732058ee9> in <module>() ----> 1 pandas.rolling_max(np.arange(3), 100, min_periods=1) /Library/Python/2.7/site-packages/pandas-0.9.0.dev_1e1c922-py2.7-macosx-10.8-intel.egg/pandas/stats/moments.pyc in f(arg, window, min_periods, freq, time_rule, **kwargs) 436 return func(arg, window, minp, **kwds) 437 return _rolling_moment(arg, window, call_cython, min_periods, --> 438 freq=freq, time_rule=time_rule, **kwargs) 439 440 return f /Library/Python/2.7/site-packages/pandas-0.9.0.dev_1e1c922-py2.7-macosx-10.8-intel.egg/pandas/stats/moments.pyc in _rolling_moment(arg, window, func, minp, axis, freq, time_rule, **kwargs) 252 return_hook, values = _process_data_structure(arg) 253 # actually calculate the moment. Faster way to do this? --> 254 result = np.apply_along_axis(calc, axis, values) 255 256 return return_hook(result) /Library/Python/2.7/site-packages/numpy-1.6.2-py2.7-macosx-10.8-intel.egg/numpy/lib/shape_base.pyc in apply_along_axis(func1d, axis, arr, *args) 78 outshape = asarray(arr.shape).take(indlist) 79 i.put(indlist, ind) ---> 80 res = func1d(arr[tuple(i.tolist())],*args) 81 # if res is a number, then we have a smaller output array 82 if isscalar(res): /Library/Python/2.7/site-packages/pandas-0.9.0.dev_1e1c922-py2.7-macosx-10.8-intel.egg/pandas/stats/moments.pyc in <lambda>(x) 249 """ 250 arg = _conv_timerule(arg, freq, time_rule) --> 251 calc = lambda x: func(x, window, minp=minp, **kwargs) 252 return_hook, values = _process_data_structure(arg) 253 # actually calculate the moment. Faster way to do this? /Library/Python/2.7/site-packages/pandas-0.9.0.dev_1e1c922-py2.7-macosx-10.8-intel.egg/pandas/stats/moments.pyc in call_cython(arg, window, minp, **kwds) 434 def call_cython(arg, window, minp, **kwds): 435 minp = check_minp(minp, window) --> 436 return func(arg, window, minp, **kwds) 437 return _rolling_moment(arg, window, call_cython, min_periods, 438 freq=freq, time_rule=time_rule, **kwargs) /Library/Python/2.7/site-packages/pandas-0.9.0.dev_1e1c922-py2.7-macosx-10.8-intel.egg/pandas/lib.so in pandas.lib.roll_max2 (pandas/src/tseries.c:78735)() ValueError: Invalid window size 100 for len 3 array ```
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6,801,252
MDU6SXNzdWU2ODAxMjUy
1,898
algorithms.unique kills python interpreter
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5
"2012-09-11T20:40:50Z"
"2012-09-15T18:49:38Z"
"2012-09-15T18:49:38Z"
CONTRIBUTOR
null
32-bit Python 2.7.3 in 64-bit Windows 7 pandas master, numpy 1.6.1 Python 2.7.3 |EPD 7.3-2 (32-bit)| (default, Apr 12 2012, 14:30:37) [MSC v.1500 32 bit (Intel)] Type "copyright", "credits" or "license" for more information. IPython 0.14.dev -- An enhanced Interactive Python. ? -> 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]: lst = ['A', 'B', 'C', 'D', 'E'] In [2]: import pandas.core.algorithms as algos In [3]: algos.unique(lst) Out[3]: array([A, B, C, D, E], dtype=object) In [4]: len(algos.unique(lst)) # --> triggers "Python has stopped working..." Oddly enough this actually works: In [3]: rs = algos.unique(lst) In [4]: len(rs) Out[4]: 5
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MDExOlB1bGxSZXF1ZXN0MjMxMDg1OQ==
1,899
BUG: fix rolling_max/min for small inputs and large windows. Add a check...
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1
"2012-09-11T21:00:52Z"
"2014-06-25T07:13:13Z"
"2012-09-12T14:53:09Z"
CONTRIBUTOR
null
... that the min_period <= window size. Fixes #1897.
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6,806,493
MDU6SXNzdWU2ODA2NDkz
1,900
pandas.ewma doesn't handle empty inputs correctly
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1
"2012-09-12T00:38:07Z"
"2012-09-17T19:14:36Z"
"2012-09-17T19:14:26Z"
CONTRIBUTOR
null
``` In [34]: pandas.ewma(np.array([]), 3) --------------------------------------------------------------------------- IndexError Traceback (most recent call last) <ipython-input-34-dd4e4b00c97b> in <module>() ----> 1 pandas.ewma(np.array([]), 3) /usr/lib/python2.7/site-packages/pandas-0.9.0.dev_c32cc6e-py2.7-linux-x86_64.egg/pandas/stats/moments.pyc in ewma(arg, com, span, min_periods, freq, time_rule, adjust) 307 308 return_hook, values = _process_data_structure(arg) --> 309 output = np.apply_along_axis(_ewma, 0, values) 310 return return_hook(output) 311 /usr/lib/python2.7/site-packages/numpy/lib/shape_base.pyc in apply_along_axis(func1d, axis, arr, *args) 78 outshape = asarray(arr.shape).take(indlist) 79 i.put(indlist, ind) ---> 80 res = func1d(arr[tuple(i.tolist())],*args) 81 # if res is a number, then we have a smaller output array 82 if isscalar(res): /usr/lib/python2.7/site-packages/pandas-0.9.0.dev_c32cc6e-py2.7-linux-x86_64.egg/pandas/stats/moments.pyc in _ewma(v) 301 302 def _ewma(v): --> 303 result = lib.ewma(v, com, int(adjust)) 304 first_index = _first_valid_index(v) 305 result[first_index : first_index + min_periods] = NaN /usr/lib/python2.7/site-packages/pandas-0.9.0.dev_c32cc6e-py2.7-linux-x86_64.egg/pandas/lib.so in pandas.lib.ewma (pandas/src/tseries.c:74301)() IndexError: Out of bounds on buffer access (axis 0) ``` I think it should output an empty array instead, like `rolling_sum`. ``` In [43]: pandas.rolling_sum(np.array([]), 3) Out[43]: array([], dtype=float64) ```
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