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} | 2 | "2012-12-27T20:13:30Z" | "2012-12-28T14:35:37Z" | "2012-12-28T14:35:37Z" | CONTRIBUTOR | null | While answering a question on SO I came across something which puzzled me:
```
>>> import pandas as pd
>>> pd.__version__
'0.10.0b1'
>>> repr(open('cusip.txt').read())
"'65248E10 11\\n55555E55 22\\n'"
>>> !cat cusip.txt
65248E10 11
55555E55 22
>>> df = pd.read_table("cusip.txt", header=None, sep=" ")
>>> df
0 1
0 6.524800e+14 11
1 5.555500e+59 22
>>> type(df[0][0])
<type 'numpy.float64'>
>>> df = pd.read_table("cusip.txt", header=None, sep=r"\s+")
>>> df
0 1
0 652480000000000 11
1 -9223372036854775808 22
>>> type(df[0][0])
<type 'numpy.int64'>
```
Changing the delimiter from `" "` to `r"\s+"` somehow triggered the interpretation of the first column as integers instead of floats.
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} | 1 | "2012-12-27T20:27:24Z" | "2012-12-28T14:26:57Z" | "2012-12-28T14:26:20Z" | NONE | null | I understand the benefit of Series.str methods which automatically handle NA, but the implementation seems really slow.
```
>>> s = pd.Series(['abcdefg', np.nan]*500000)
>>> timeit s.str[:5]
1 loops, best of 3: 2.55 s per loop
>>> timeit s.map(lambda row: row[:5], na_action='ignore')
1 loops, best of 3: 558 ms per loop
```
Looking in the code the difference seems to be that Series.map with na_action='ignore' uses some vectorized code to filter out the NA values while Series.str uses the _na_map function with a try/except for each item in the Series (non-vectorized).
Can I make a request to eliminate the _na_map in favor of something more like Series.map(na_action='ignore')?
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https://api.github.com/repos/pandas-dev/pandas/issues/2604 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2604/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2604/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2604/events | https://github.com/pandas-dev/pandas/issues/2604 | 9,548,742 | MDU6SXNzdWU5NTQ4NzQy | 2,604 | Indexes created from datetimes that are tz aware but tz=None (pytz.FixedOffset()) lose tz awareness | {
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} | 1 | "2012-12-28T00:03:16Z" | "2016-10-12T23:03:27Z" | "2012-12-28T18:42:39Z" | NONE | null | The index created from datetimes with a FixedOffset tz get converted to the correct UTC time but lose their timezone awareness so that further operations with tz aware datetimes fail.
pytz.FixedOffset() timezones have tzname() return None
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} | 3 | "2012-12-28T05:01:32Z" | "2012-12-28T16:43:45Z" | "2012-12-28T13:48:05Z" | NONE | null | The following code raises an AssertionError with pandas 0.10.0, but works fine in 0.9.1. The error still exists in the latest dev version here. The code comes from Wes' book, pages 33-36. The data files are from https://github.com/pydata/pydata-book
``` python
import pandas as pd
years = range(1880, 2011)
pieces = []
columns = ['name', 'sex', 'births']
for year in years:
path = 'ch02/names/yob%d.txt' % year
frame = pd.read_csv(path, names=columns)
frame['year'] = year
pieces.append(frame)
names = pd.concat(pieces, ignore_index=True)
def get_top1000(group):
return group.sort_index(by='births', ascending=False)[:1000]
top1000 = names.groupby(['year', 'sex']).apply(get_top1000)
```
The last line results in the following error trace:
```
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-8-569425503c6b> in <module>()
----> 1 top1000 = names.groupby(['year', 'sex']).apply(get_top1000)
/home/tlmaloney/vedev/ve-pydata-book/local/lib/python2.7/site-packages/pandas/core/groupby.pyc in apply(self, func, *args, **kwargs)
319 func = _intercept_function(func)
320 f = lambda g: func(g, *args, **kwargs)
--> 321 return self._python_apply_general(f)
322
323 def _python_apply_general(self, f):
/home/tlmaloney/vedev/ve-pydata-book/local/lib/python2.7/site-packages/pandas/core/groupby.pyc in _python_apply_general(self, f)
322
323 def _python_apply_general(self, f):
--> 324 keys, values, mutated = self.grouper.apply(f, self.obj, self.axis)
325
326 return self._wrap_applied_output(keys, values,
/home/tlmaloney/vedev/ve-pydata-book/local/lib/python2.7/site-packages/pandas/core/groupby.pyc in apply(self, f, data, axis, keep_internal)
583 if hasattr(splitter, 'fast_apply') and axis == 0:
584 try:
--> 585 values, mutated = splitter.fast_apply(f, group_keys)
586 return group_keys, values, mutated
587 except lib.InvalidApply:
/home/tlmaloney/vedev/ve-pydata-book/local/lib/python2.7/site-packages/pandas/core/groupby.pyc in fast_apply(self, f, names)
2125
2126 sdata = self._get_sorted_data()
-> 2127 results, mutated = lib.apply_frame_axis0(sdata, f, names, starts, ends)
2128
2129 return results, mutated
/home/tlmaloney/vedev/ve-pydata-book/local/lib/python2.7/site-packages/pandas/lib.so in pandas.lib.apply_frame_axis0 (pandas/lib.c:24934)()
/home/tlmaloney/vedev/ve-pydata-book/local/lib/python2.7/site-packages/pandas/core/frame.pyc in __setattr__(self, name, value)
2026 super(DataFrame, self).__setattr__(name, value)
2027 elif name in self.columns:
-> 2028 self[name] = value
2029 else:
2030 object.__setattr__(self, name, value)
/home/tlmaloney/vedev/ve-pydata-book/local/lib/python2.7/site-packages/pandas/core/frame.pyc in __setitem__(self, key, value)
2041 else:
2042 # set column
-> 2043 self._set_item(key, value)
2044
2045 def _boolean_set(self, key, value):
/home/tlmaloney/vedev/ve-pydata-book/local/lib/python2.7/site-packages/pandas/core/frame.pyc in _set_item(self, key, value)
2076 ensure homogeneity.
2077 """
-> 2078 value = self._sanitize_column(key, value)
2079 NDFrame._set_item(self, key, value)
2080
/home/tlmaloney/vedev/ve-pydata-book/local/lib/python2.7/site-packages/pandas/core/frame.pyc in _sanitize_column(self, key, value)
2110 else:
2111 if len(value) != len(self.index):
-> 2112 raise AssertionError('Length of values does not match '
2113 'length of index')
2114
AssertionError: Length of values does not match length of index
```
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} | 5 | "2012-12-28T16:51:51Z" | "2014-06-24T17:11:46Z" | "2013-01-06T04:33:36Z" | CONTRIBUTOR | null | - added copy method to allow file upgrades to new version
- refactor to add Storer class to represent all non-tables
- add shape field to display for non-tables
- close GH #2637
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} | 0 | "2012-12-28T20:10:10Z" | "2013-01-21T19:30:16Z" | "2013-01-21T19:30:16Z" | NONE | null | Running multiple threads each calling read_csv crashes on OS/X. I've seen two traps, which I'll put into an attachment. Sometimes it says "Fatal Python error: GC object already tracked\nAbort trap: 6"" and sometimes "Segmentation fault: 11".
I've put together a small example to reproduce it: https://gist.github.com/4401461
I've also added some of the OS/X crash report in case it isn't crashing on your install.
I'm using the built-in Python 2.7.2 on OS/X. Pandas 0.10.0 was built locally using pip install -U pandas. I don't know if this is new to 0.10.0 since I wasn't threading 0.9 yet.
This might be relevant for the GC already tracked: http://pyrit.wordpress.com/2010/02/18/385/
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} | 2 | "2012-12-28T21:40:27Z" | "2013-01-20T05:00:55Z" | "2013-01-20T05:00:55Z" | CONTRIBUTOR | null | Migrated from [StackOverflow](http://stackoverflow.com/questions/14075326/pandas-runtimeerror-in-tseries-convertor-when-plotting). The following gives an exception (when trying to plot a DataFrame with date indices). I'm using '0.10.0b1'.
```
In [1]: df = DataFrame(randn(3,1),index=[date(2012,10,1),date(2012,9,1),date(2012,8,1)], columns=['test'])
#the same bug is present using datetime rather than date
In [2]: df
Out[2]:
test
2012-10-01 -0.315065
2012-09-01 0.100678
2012-08-01 -0.230353
In [4]: df.plot()
#error
```
Here is the traceback:
```
Exception in Tkinter callback
Traceback (most recent call last):
File "/usr/lib/python2.7/lib-tk/Tkinter.py", line 1413, in __call__
return self.func(*args)
File "/usr/lib/python2.7/lib-tk/Tkinter.py", line 498, in callit
func(*args)
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_tkagg.py", line 254, in idle_draw
self.draw()
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_tkagg.py", line 239, in draw
FigureCanvasAgg.draw(self)
File "/usr/lib/pymodules/python2.7/matplotlib/backends/backend_agg.py", line 421, in draw
self.figure.draw(self.renderer)
File "/usr/lib/pymodules/python2.7/matplotlib/artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/figure.py", line 898, in draw
func(*args)
File "/usr/lib/pymodules/python2.7/matplotlib/artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 1997, in draw
a.draw(renderer)
File "/usr/lib/pymodules/python2.7/matplotlib/artist.py", line 55, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/axis.py", line 1041, in draw
ticks_to_draw = self._update_ticks(renderer)
File "/usr/lib/pymodules/python2.7/matplotlib/axis.py", line 931, in _update_ticks
tick_tups = [ t for t in self.iter_ticks()]
File "/usr/lib/pymodules/python2.7/matplotlib/axis.py", line 878, in iter_ticks
majorLocs = self.major.locator()
File "/usr/lib/pymodules/python2.7/matplotlib/dates.py", line 750, in __call__
return self._locator()
File "/usr/lib/pymodules/python2.7/pandas/tseries/converter.py", line 317, in __call__
(estimate, dmin, dmax, self.MAXTICKS * 2))
RuntimeError: MillisecondLocator estimated to generate 5270400 ticks from 2012-08-01 00:00:00+00:00 to 2012-10-01 00:00:00+00:00: exceeds Locator.MAXTICKS* 2 (2000)
```
Note that it plots ok, using `df.sort().plot()`.
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https://api.github.com/repos/pandas-dev/pandas/issues/2610 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2610/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2610/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2610/events | https://github.com/pandas-dev/pandas/issues/2610 | 9,573,237 | MDU6SXNzdWU5NTczMjM3 | 2,610 | describe() fails with stacktrace for an empty DataFrame | {
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} | 1 | "2012-12-29T13:09:34Z" | "2013-01-02T17:21:00Z" | "2013-01-02T17:21:00Z" | CONTRIBUTOR | null | This is from a first try to select all rows where an ID is in another dataset. The code does not work as intended (see last line), but I think that the `describe()` call shouldn't fail.
[column names changed in output; output from last call shortened]
```
In [47]: all
Out[47]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 974757 entries, 0 to 974756
Data columns:
eid 974757 non-null values
number 974757 non-null values
a 972510 non-null values
b 974757 non-null values
c 929268 non-null values
d 922700 non-null values
e 974757 non-null values
dtypes: int64(1), object(6)
In [48]: subset = all[all["eid"].isin(other["eid"])]
In [49]: subset
Out[49]:
Int64Index([], dtype=int64)
Empty DataFrame
In [50]: subset.describe()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-50-ff735ef04a17> in <module>()
----> 1 business_authors.describe()
C:\portabel\Python27\lib\site-packages\pandas\core\frame.pyc in describe(self, percentile_width)
4539 series = self[column]
4540 destat.append([series.count(), series.mean(), series.std(),
-> 4541 series.min(), series.quantile(lb), series.median(),
4542 series.quantile(ub), series.max()])
4543
C:\portabel\Python27\lib\site-packages\pandas\core\series.pyc in min(self, axis, out, skipna, level)
1320 if level is not None:
1321 return self._agg_by_level('min', level=level, skipna=skipna)
-> 1322 return nanops.nanmin(self.values, skipna=skipna)
1323
1324 @Substitution(name='maximum', shortname='max',
C:\portabel\Python27\lib\site-packages\pandas\core\nanops.pyc in f(values, axis, skipna, **kwds)
46 result = alt(values, axis=axis, skipna=skipna, **kwds)
47 except Exception:
---> 48 result = alt(values, axis=axis, skipna=skipna, **kwds)
49
50 return result
C:\portabel\Python27\lib\site-packages\pandas\core\nanops.pyc in _nanmin(values, axis, skipna)
179 or values.size == 0):
180 result = values.sum(axis)
--> 181 result.fill(np.nan)
182 else:
183 result = values.min(axis)
ValueError: cannot convert float NaN to integer
In[51]: all["eid"].isin(other["eid"])
Out[51]:
0 False
1 False
2 False
3 False
4 False
...
974752 False
974753 False
974754 False
974755 False
974756 False
Name: eid, Length: 974757
```
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} | 2 | "2012-12-29T14:18:04Z" | "2012-12-30T08:55:52Z" | "2012-12-30T08:55:52Z" | NONE | null | the csv file structure below:
(header:) Chinese string , Chinese string, Chinese string ,Chines string
(row1 :) 新浪行业, 生物制药 , swzy, 0
while using
data=pd.read_csv(filepath,encoding='utf-8')
the read_csv can parse the file content, but when use print data, the Chines string represents like "???" which makes it impossible to use the column names to index.
by the way , while using the code below , it will show the correct Chinese string:
print open("data.csv").read() although open(“data.csv").read() will not display the human-readable Chinese text .
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} | 9 | "2012-12-29T14:50:05Z" | "2015-10-03T14:41:03Z" | "2015-10-03T14:41:03Z" | MEMBER | null | ```
In [17]: open('/home/wesm/tmp/foo.csv', 'rb').read()
Out[17]: '\xe6\xb8\xac\xe8\xa9\xa6\xe4\xb8\x80,\xe6\xb8\xac\xe8\xa9\xa6\xe4\xb8\x89\r\[email protected],\xe6\xb8\xac\xe8\xa9\xa6\xe4\xb8\x80\r\[email protected],\xe6\xb8\xac\xe8\xa9\xa6\xe4\xba\x8c\r\[email protected],\xe6\xb8\xac\xe8\xa9\xa6\xe4\xb8\x89\r\n'
In [18]: read_csv('/home/wesm/tmp/foo.csv', encoding='utf-8')
Out[18]:
測試一 測試三
0 [email protected] 測試一
1 [email protected] 測試二
2 [email protected] 測試三
In [24]: df
Out[24]:
測試一 測試三
0 [email protected] 測試一
1 [email protected] 測試二
2 [email protected] 測試三
In [25]: df.columns[0]
Out[25]: u'\u6e2c\u8a66\u4e00'
In [26]: df.columns[1]
Out[26]: u'\u6e2c\u8a66\u4e09'
```
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class ExcelFile(object):
"""
Class for parsing tabular excel sheets into DataFrame objects.
Uses xlrd for parsing .xls files or openpyxl for .xlsx files.
See ExcelFile.parse for more documentation
Parameters
----------
path : string or file-like object
Path to xls file
kind : {'xls', 'xlsx', None}, default None
"""
def __init__(self, path_or_buf):
```
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} | 1 | "2012-12-29T21:42:30Z" | "2015-01-26T00:39:04Z" | "2015-01-26T00:39:04Z" | MEMBER | null | re: http://stackoverflow.com/questions/14085517/pandas-sorting-pivot-table-or-grouping-dataframe
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} | 4 | "2012-12-29T23:32:29Z" | "2012-12-30T23:01:23Z" | "2012-12-30T16:11:04Z" | NONE | null | I know that there are na issues for indices but I have never encountered this behavior before (pandas master):
(fully functional code depending on the requests and xlrd library and a berlin open data set)
```
import pandas as pd
import requests
the_data = requests.get('http://www.berlin.de/imperia/md/content/senatsverwaltungen/finanzen/haushalt/ansatzn2013.xls?download.html',
stream=True)
xl_file = pd.io.parsers.ExcelFile(the_data.raw) # only works with xlrd installed
df = xl_file.parse('Ansatz 2013')
original_data = df.copy()
df.columns = ['bereich', 'einzelplan', 'kapitel', 'titelart', 'titel', 'titelbezeichnung', 'funktion', 'betrag_tausend']
df['bereich_id'] = df.bereich.str.slice(start=1, stop=3).astype(int)
df['bereich'] = df.bereich.str.slice(start=5)
df['einzelplan_id'] = df.einzelplan.str.slice(start=1, stop=3).astype(int)
df['einzelplan'] = df.einzelplan.str.slice(start=5)
df['kapitel_id'] = df.kapitel.str.slice(start=1, stop=5).astype(int)
df['kapitel'] = df.kapitel.str.slice(start=7)
df['funktion_id'] = df.funktion.str.slice(start=1, stop=4).astype(float) # one missing value
df['funktion'] = df.funktion.str.slice(start=6)
df['betrag'] = df.betrag_tausend * 1000
del df['betrag_tausend']
df = df[['bereich', 'bereich_id', 'einzelplan', 'einzelplan_id',
'kapitel', 'kapitel_id', 'funktion', 'funktion_id',
'titel', 'titelbezeichnung', 'titelart', 'betrag']]
df.set_index(['bereich', 'bereich_id', 'einzelplan', 'einzelplan_id',
'kapitel', 'kapitel_id', 'funktion', 'funktion_id',
'titel', 'titelbezeichnung', 'titelart'], inplace=True)
```
now calling
```
df.groupby(level=['bereich']).sum()
```
works nicely whereas
```
df.groupby(level=['funktion_id']).sum()
```
results in
```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-3-d8991eeaebb9> in <module>()
----> 1 df.groupby(level=['funktion_id']).sum()
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1.dev_dcd9df7-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in groupby(self, by, axis, level, as_index, sort, group_keys)
132 from pandas.core.groupby import groupby
133 return groupby(self, by, axis=axis, level=level, as_index=as_index,
--> 134 sort=sort, group_keys=group_keys)
135
136 def asfreq(self, freq, method=None, how=None, normalize=False):
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1.dev_dcd9df7-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in groupby(obj, by, **kwds)
498 raise TypeError('invalid type: %s' % type(obj))
499
--> 500 return klass(obj, by, **kwds)
501
502
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1.dev_dcd9df7-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys)
189 if grouper is None:
190 grouper, exclusions = _get_grouper(obj, keys, axis=axis,
--> 191 level=level, sort=sort)
192
193 self.grouper = grouper
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1.dev_dcd9df7-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in _get_grouper(obj, key, axis, level, sort)
1244 name = gpr
1245 gpr = obj[gpr]
-> 1246 ping = Grouping(group_axis, gpr, name=name, level=level, sort=sort)
1247 groupings.append(ping)
1248
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1.dev_dcd9df7-py2.7-linux-x86_64.egg/pandas/core/groupby.pyc in __init__(self, index, grouper, name, level, sort)
1115 self._labels = labels
1116 self._group_index = level_index
-> 1117 self.grouper = level_index.take(labels)
1118 else:
1119 if isinstance(self.grouper, (list, tuple)):
/usr/local/lib/python2.7/dist-packages/pandas-0.10.1.dev_dcd9df7-py2.7-linux-x86_64.egg/pandas/core/index.pyc in take(self, indexer, axis)
415 """
416 indexer = com._ensure_platform_int(indexer)
--> 417 taken = self.view(np.ndarray).take(indexer)
418 return self._constructor(taken, name=self.name)
419
IndexError: index 138 is out of bounds for axis 0 with size 138
```
and
```
df.groupby(level=['funktion']).sum()
```
segfaults
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} | 2 | "2012-12-30T21:04:22Z" | "2016-10-12T23:03:27Z" | "2013-01-20T01:46:13Z" | CONTRIBUTOR | null | Object representations are not escaped for HTML when giving HTML repr to IPython Notebook,
for instance if the items themselves are types, whose reprs include `<`, and are interpreted as tags.
[an example notebook](http://nbviewer.ipython.org/4415176)
Originally from [this SO question](http://stackoverflow.com/questions/14092798).
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} | 3 | "2012-12-31T01:02:31Z" | "2013-03-28T05:54:41Z" | "2013-03-28T05:54:41Z" | NONE | null | I recently updated Pandas to 0.10.0 under Python 2.7.3. I have encountered problems with the read_table function. My program read in the following file:
> ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/monthly.land_ocean.90S.90N.df_1901-2000mean.dat
Here's the head of the file:
<pre>
1880 1 -0.0760
1880 2 -0.2099
1880 3 -0.2170
1880 4 -0.1180
1880 5 -0.1680
1880 6 -0.2055
1880 7 -0.1863
1880 8 -0.1128
1880 9 -0.1192
1880 10 -0.1951
</pre>
The code to load the data:
<pre>
import pandas as pd
noaa_file = "monthly.land_ocean.90S.90N.df_1901-2000mean.dat"
noaa = pd.read_table(noaa_file, header=None, sep=r'\s*', parse_dates=[[0,1]], index_col=0, squeeze=True, na_values='-999.0000').to_period(freq='M')
</pre>
This throws an exception:
<pre>AttributeError: 'Series' object has no attribute 'to_period'</pre>
I invoke to_period because the dates that were parsed were appearing as YYYY-MM-DD. The data is monthly and there is no need for a day component. I dropped the to_period method and the error disappeared. But I noticed something strange about the index:
<pre> 2
0_1
1880 1 -0.0760
1880 2 -0.2099
1880 3 -0.2170
1880 4 -0.1180
1880 5 -0.1680
</pre>
The index is a pandas.core.index.Index object. Under the previous version of the library, the index was a pandas.tseries.period.PeriodIndex object. It looks like the dates aren't be parsed at all. If I drop the to_period method and follow-up with
<pre>noaa_temp = pd.Series(noaa_temp.values, pd.PeriodIndex(noaa_temp.index, freq='M'))</pre>
then I get exactly what I need and what the original one-liner at the top produced under the previous version. The only way I can accomplish this in "one" line is to define a parser:
<pre>
from datetime import datetime
parse = lambda x: datetime.strptime(x, '%Y %m')
noaa = pd.read_table(noaa_file, header=None, delim_whitespace=True, parse_dates=[[0,1]], index_col=0, squeeze=True, na_values='-999.0000', date_parser=parse).to_period(freq='M')
</pre>
Now everything works. I think this is a bug in the date parser.
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} | 2 | "2012-12-31T10:28:29Z" | "2013-01-20T18:56:44Z" | "2013-01-20T18:56:34Z" | CONTRIBUTOR | null | The docs at http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html do not explain how the dtype parameter is to be used.
Using google, it seems to be a dict with keys=columnnames, but what are the possible values in that dict?.
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} | 5 | "2012-12-31T12:16:57Z" | "2014-07-11T12:00:15Z" | "2013-03-06T20:22:32Z" | NONE | null | closes #2618.
when parsing partial dates, will fill in missing day as first of the month, rather then now().day
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} | 1 | "2012-12-31T22:47:32Z" | "2013-01-20T02:45:41Z" | "2013-01-20T02:45:41Z" | NONE | null | ``` python
def test_index_tz(self):
ind = pd.date_range("2012-12-01", periods=10, tz="utc")
ind = ind.drop(ind[-1])
self.assertIsNone(ind.tz)
```
That seems counter-intuitive.
Freq information is also dropped, but that is IMHO less important
This is on pandas 0.10
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} | 9 | "2013-01-01T13:08:53Z" | "2014-08-09T13:55:55Z" | "2013-12-18T20:05:10Z" | NONE | null | There already appears to be an excellent Python interface for the World Bank API called [wbdata](https://github.com/OliverSherouse/wbdata) written by Oliver Sherouse. Currently outputs data into Pandas DataFrame, but would be nice to have country-year Panels instead.
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} | 4 | "2013-01-01T19:07:33Z" | "2013-03-22T21:48:21Z" | "2013-03-22T21:48:21Z" | CONTRIBUTOR | null | ``` python
rows = []
rows.append([pd.datetime(2010, 1, 1), 1])
rows.append([pd.datetime(2010, 1, 2), 'hi']) # test col upconverts to obj
df2_obj = pd.DataFrame.from_records(rows, columns=['date', 'test'])
print df2_obj.date.dtype # object
```
If the `'hi'` is changed to an `int` then `df2_obj.date.dtype` is equal to `M8[ns]`
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} | 2 | "2013-01-01T19:18:45Z" | "2013-12-04T00:40:13Z" | "2013-01-20T19:51:00Z" | CONTRIBUTOR | null | ``` python
rows = []
rows.append([pd.datetime(2010, 1, 1), 1])
rows.append([pd.datetime(2010, 1, 2), 'hi']) # test col upconverts to obj
df2_obj = pd.DataFrame.from_records(rows, columns=['date', 'test'])
ind = pd.date_range(start="2000/1/1", freq="D", periods=10)
df1 = pd.DataFrame({'date': ind, 'test':range(10)})
cdf = pd.concat([df1, df2_obj])
cdf
```
The rows where date was a np.datetime are converted to ints, while the object dates are left as datetimes.
I'm not sure if this is a bug. I would expect the np.datetime column to upconvert into Timestamp objects?
I ran into this from a test failure. Thing is, it's a fairly old test and it used to pass. I checked it against 0.9.1 and 0.10.0 which failed as well. So I'm not sure if I was on a previous commit that worked differently. it's weird.
Either way, just wanted to get a check on what the expected behavior is.
EDIT: http://nbviewer.ipython.org/4428511/
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https://api.github.com/repos/pandas-dev/pandas/issues/2625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2625/events | https://github.com/pandas-dev/pandas/issues/2625 | 9,620,125 | MDU6SXNzdWU5NjIwMTI1 | 2,625 | quantile throws error if not convertible to float | {
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} | 2 | "2013-01-02T16:38:13Z" | "2013-01-05T14:18:01Z" | "2013-01-05T14:18:01Z" | CONTRIBUTOR | null | If we try and `quantile` a DataFrame with string entries which are not convertible, there is a ValueError. Should this behave like mean (and ignore these entries)? (taken from [this StackOverflow question](http://stackoverflow.com/questions/14125428/how-to-apply-quantile-to-pandas-groupby-object)).
```
In [1]: df = DataFrame({'col1':['A','A','B','B'], 'col2':[1,2,3,4]})
In [2]: df
Out[2]:
col1 col2
0 A 1
1 A 2
2 B 3
3 B 4
In [3]: g = df.groupby('col1')
In [4]: g.mean()
Out[4]:
col2
col1
A 1.5
B 3.5
In [5]: g.quantile()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/andy/<ipython-input-70-8b0757805794> in <module>()
----> 1 g.quantile()
/usr/lib/pymodules/python2.7/pandas/core/groupby.pyc in wrapper(*args, **kwargs)
258 return self.apply(curried_with_axis)
259 except Exception:
--> 260 return self.apply(curried)
261
262 return wrapper
/usr/lib/pymodules/python2.7/pandas/core/groupby.pyc in apply(self, func, *args, **kwargs)
319 func = _intercept_function(func)
320 f = lambda g: func(g, *args, **kwargs)
--> 321 return self._python_apply_general(f)
322
323 def _python_apply_general(self, f):
/usr/lib/pymodules/python2.7/pandas/core/groupby.pyc in _python_apply_general(self, f)
322
323 def _python_apply_general(self, f):
--> 324 keys, values, mutated = self.grouper.apply(f, self.obj, self.axis)
325
326 return self._wrap_applied_output(keys, values,
/usr/lib/pymodules/python2.7/pandas/core/groupby.pyc in apply(self, f, data, axis, keep_internal)
594 # group might be modified
595 group_axes = _get_axes(group)
--> 596 res = f(group)
597 if not _is_indexed_like(res, group_axes):
598 mutated = True
/usr/lib/pymodules/python2.7/pandas/core/groupby.pyc in <lambda>(g)
318 """
319 func = _intercept_function(func)
--> 320 f = lambda g: func(g, *args, **kwargs)
321 return self._python_apply_general(f)
322
/usr/lib/pymodules/python2.7/pandas/core/groupby.pyc in curried(x)
253
254 def curried(x):
--> 255 return f(x, *args, **kwargs)
256
257 try:
/usr/lib/pymodules/python2.7/pandas/core/frame.pyc in quantile(self, q, axis)
4946 return _quantile(arr, per)
4947
-> 4948 return self.apply(f, axis=axis)
4949
4950 def clip(self, upper=None, lower=None):
/usr/lib/pymodules/python2.7/pandas/core/frame.pyc in apply(self, func, axis, broadcast, raw, args, **kwds)
4079 return self._apply_raw(f, axis)
4080 else:
-> 4081 return self._apply_standard(f, axis)
4082 else:
4083 return self._apply_broadcast(f, axis)
/usr/lib/pymodules/python2.7/pandas/core/frame.pyc in _apply_standard(self, func, axis, ignore_failures)
4154 # no k defined yet
4155 pass
-> 4156 raise e
4157
4158 if len(results) > 0 and _is_sequence(results[0]):
ValueError: ('could not convert string to float: A', u'occurred at index col1')
```
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} | 2 | "2013-01-02T19:18:52Z" | "2013-01-20T04:33:14Z" | "2013-01-20T04:33:14Z" | NONE | null | Best illustrated by example:
In [957]: s0 = pd.Series(["2010", np.NaN])
In [958]: s1 = pd.Series([np.NaN, "2011"])
In [959]: s0.combine_first(s1)
Out[959]:
0 2010
1 2011
In [960]: s0 = pd.to_datetime(pd.Series(["2010", np.NaN]))
In [961]: s1 = pd.to_datetime(pd.Series([np.NaN, "2011"]))
In [962]: s0.combine_first(s1)
Out[962]:
0 2221-02-23 04:49:47.750490112
1 2051-05-17 05:40:40.331386880
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} | 9 | "2013-01-02T20:47:31Z" | "2013-01-20T18:53:58Z" | "2013-01-20T18:51:53Z" | CONTRIBUTOR | null | ``` python
import pandas as pd
s = pd.Series(pd.date_range(start="2000/1/1", freq="D", periods=10))
s.apply(lambda x: x.date())
# AttributeError: 'numpy.datetime64' object has no attribute 'date'
```
This is an error from some unit tests. Did something major change with how datetimes are handled? I recall that np.datetime64 was autoboxed into Timestamps.
I'll update after poking around.
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} | 3 | "2013-01-02T23:40:49Z" | "2018-12-19T12:02:48Z" | "2018-12-19T12:02:47Z" | NONE | null | The output from .groups on a grouped DataFrame should be handled similarly to a large DataFrame, where beyond a certain length, the data is displayed in a summary form rather than output in its entirety.
This truncation should occur both at the group level (large number of groups) and per-group (large amount of data points in a group).
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In [11]: d0 = pd.to_datetime(pd.Series(["12/1/2011"]))
In [12]: d1 = pd.to_datetime(pd.Series(["12/3/2011"]))
In [13]: d1[0] - d0[0]
Out[13]: datetime.timedelta(2)
In [14]: (d1 - d0)[0]
Out[14]: 2000 days, 0:00:00
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} | 2 | "2013-01-03T20:24:17Z" | "2014-06-25T19:18:04Z" | "2013-01-20T02:33:06Z" | CONTRIBUTOR | null | converts to i8 then back to timedelta64[ns] if both datetime64
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} | 4 | "2013-01-03T22:26:01Z" | "2019-10-05T18:49:17Z" | "2013-01-20T00:10:56Z" | CONTRIBUTOR | null | Reading in a csv file with an integer column which has empty cells will cast that column to float (which in the end will resulted in problems with merging this dataframe on that column with a dataframe where the corresponding column is int).
It would be nice if a warning could be printed when such conversation (maybe only when an explicit dtype={"col":np.int64} setting is passed to read_csv) takes place and optional let me specify that such rows should be droped (isn't there a NA value for int columns...?)
```
data = """YEAR, DOY, a
2001,106380451,10
2001,,11
2001,106380451,67"""
import numpy as np
f = pandas.read_csv(StringIO(data), sep=",", dtype={'DOY': np.int64})
f.dtypes
```
```
YEAR int64
DOY float64
a int64
```
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} | 2 | "2013-01-03T22:39:00Z" | "2014-06-12T14:19:52Z" | "2013-01-05T20:31:20Z" | CONTRIBUTOR | null | Used [autopep8](https://github.com/hhatto/autopep8) to make all of pandas py files more PEP8 compliant (Excluding 230 `E501 lines too long` (over 80 chars), and 6 changes which had to be reverted to fix test failures - in second commit). See statistics in my comment.
I iterated over every py file in the package using [autopep8](https://github.com/hhatto/autopep8), inspected the output of [pep8](https://github.com/jcrocholl/pep8) to drill-down and consider any compliance issues found which may change the meaning of the code (and considered those individually).
I haven't included a unittest for PEP compliance using pep8.
For me this passes all the same tests as the master branch.
_Since this affects such a large set of the code it may not be reasonable to pull, however it is certainly a proof of concept._
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} | 4 | "2013-01-04T01:28:46Z" | "2019-12-19T14:29:50Z" | "2013-01-19T23:55:20Z" | NONE | null | What it says on the tin. from_records() fails with a KeyError if passed an empty list. If the list is empty, an empty DataFrame should be created, conforming to the other arguments (columns, etc).
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https://api.github.com/repos/pandas-dev/pandas/issues/2634 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2634/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2634/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2634/events | https://github.com/pandas-dev/pandas/issues/2634 | 9,663,263 | MDU6SXNzdWU5NjYzMjYz | 2,634 | Sums not treating NaN as zero. | {
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```
In [136]: dfa
Out[136]:
clicks
ad
327 3
In [137]: dfc
Out[137]:
clicks
ad
326 1
In [138]: dfa + dfc
Out[138]:
clicks
ad
326 NaN
327 NaN
#Expected
In [138]: dfa + dfc
Out[138]:
clicks
ad
326 1
327 3
```
Per docs, NaN should be treated as 0 unless all values are NaN.
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https://api.github.com/repos/pandas-dev/pandas/issues/2635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2635/events | https://github.com/pandas-dev/pandas/pull/2635 | 9,663,547 | MDExOlB1bGxSZXF1ZXN0MzU0MzYwOQ== | 2,635 | Try to fix BUG #2599 | {
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} | 3 | "2013-01-04T03:26:59Z" | "2014-06-27T22:59:58Z" | "2013-01-05T21:21:44Z" | CONTRIBUTOR | null | Not sure if this is a good way to go about it, but it seems to work.
Might be good to get rid of all the INT64_MAX/MIN stuff in parser.pyx and replace with np.iinfo(np.int64).max/min instead.
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} | 8 | "2013-01-04T08:09:45Z" | "2015-10-12T15:50:29Z" | "2013-07-29T05:15:16Z" | CONTRIBUTOR | null | Writing csv files with gzip compression is very useful as it both minimized the disk size taken by large data files, and can be easily read by _R_, often faster than the same csv file without compression.
Reading compressed csv files in _R_ is done without any additional input by the user, and is also already supported in _Pandas_
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} | 1 | "2013-01-05T16:21:28Z" | "2013-04-02T12:46:28Z" | "2013-04-02T12:46:28Z" | CONTRIBUTOR | null | it would be nice if one could use `df.describe()` with a boolean column (resulting in output like casting the column to int).
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https://api.github.com/repos/pandas-dev/pandas/issues/2642 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2642/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2642/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2642/events | https://github.com/pandas-dev/pandas/issues/2642 | 9,707,196 | MDU6SXNzdWU5NzA3MTk2 | 2,642 | pandas developers should put a "thank us" link on the webpage | {
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} | 1 | "2013-01-05T16:33:22Z" | "2014-05-28T22:56:48Z" | "2014-05-28T22:56:48Z" | CONTRIBUTOR | null | I really would like to send you a thank you card or a little present via amazon whishlists (or buy you a beer when you are in Germany)!
I'm using pandas to analyze data for my PhD thesis and I was dreading to use a database and having to learn SQL but up to now pandas handled everything I've thrown at it with ease (1.5GB csv file, 2.5GB data overall, merging and grouping in a few seconds).
Thanks!
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https://api.github.com/repos/pandas-dev/pandas/issues/2643 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2643/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2643/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2643/events | https://github.com/pandas-dev/pandas/issues/2643 | 9,707,470 | MDU6SXNzdWU5NzA3NDcw | 2,643 | Improve pivot_table for more complex aggregation function specs | {
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} | 0 | "2013-01-05T17:02:58Z" | "2013-01-20T01:31:00Z" | "2013-01-05T19:52:41Z" | MEMBER | null | Currently hampered by multiple unstacking not being implemented for existing hierarchical indexes. This was just a function of my running out of time when I was last working on this
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} | 36 | "2013-01-05T20:56:27Z" | "2014-04-25T12:13:04Z" | "2014-04-23T21:29:28Z" | CONTRIBUTOR | null | At least by searching google, I found no references for the DataFrameGroupBy. The docs at http://pandas.pydata.org/pandas-docs/dev/groupby.html speak about a "GroupBy object"...
What are all the functions of that object? Using IPython, I get inline help, but this informations seems to be missing in the online docs.
I'm also missing the "combine" step in the split-apply-combine doc: how can values be added to the original dataframe? I only found something about transform, which returns a complete dataframe. grouped.apply() returns a single Series. Did I miss something or is the combine step really just a "merge the new dataframe/series with the old one".
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} | 14 | "2013-01-05T21:24:27Z" | "2013-01-05T22:48:23Z" | "2013-01-05T22:43:06Z" | MEMBER | null | ```
C:\code\pandas>c:\python27\scripts\nosetests.exe pandas\io\tests\test_pytables.py
.S......SSS...........F.F...S..S...E....F..E..E.....E........E........E....
======================================================================
ERROR: test_put (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\code\pandas\pandas\io\tests\test_pytables.py", line 179, in test_put
ValueError, self.store.put, 'f', df[10:], append=True)
File "c:\python27\lib\unittest\case.py", line 471, in assertRaises
callableObj(*args, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 447, in put
self._write_to_group(key, value, table=table, append=append, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 664, in _write_to_group
wrapper(value)
File "C:\code\pandas\pandas\io\pytables.py", line 652, in <lambda>
complib=complib, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 808, in _write_frame_table
t.write(axes=axes, obj=df, append=append, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 2145, in write
min_itemsize=min_itemsize, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 1896, in create_axes
self.validate(existing_table)
File "C:\code\pandas\pandas\io\pytables.py", line 1532, in validate
% (c, getattr(self, c, None), getattr(other, c, None)))
Exception: invalid combinate of [non_index_axes] on appending data [[(1, ['A', 'B', 'C', 'D'])]] vs current table [[(1, ['A', 'B', 'C', 'D', 'string', 'string2'])]]
======================================================================
ERROR: test_reopen_handle (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\code\pandas\pandas\io\tests\test_pytables.py", line 142, in test_reopen_handle
self.store.open('w', warn=False)
File "C:\code\pandas\pandas\io\pytables.py", line 291, in open
self.handle = _tables().openFile(self.path, self.mode)
File "c:\python27\lib\site-packages\tables\file.py", line 230, in openFile
return File(filename, mode, title, rootUEP, filters, **kwargs)
File "c:\python27\lib\site-packages\tables\file.py", line 495, in __init__
self._g_new(filename, mode, **params)
File "hdf5Extension.pyx", line 317, in tables.hdf5Extension.File._g_new (tables\hdf5Extension.c:3039)
HDF5ExtError: HDF5 error back trace
File "..\..\hdf5-1.8.8\src\H5F.c", line 1440, in H5Fcreate
unable to create file
File "..\..\hdf5-1.8.8\src\H5F.c", line 1243, in H5F_open
unable to truncate a file which is already open
End of HDF5 error back trace
Unable to open/create file '__test__.h5'
======================================================================
ERROR: test_select_as_multiple (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\code\pandas\pandas\io\tests\test_pytables.py", line 1592, in test_select_as_multiple
self.store.append('df2', df2)
File "C:\code\pandas\pandas\io\pytables.py", line 512, in append
self._write_to_group(key, value, table=True, append=True, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 664, in _write_to_group
wrapper(value)
File "C:\code\pandas\pandas\io\pytables.py", line 652, in <lambda>
complib=complib, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 808, in _write_frame_table
t.write(axes=axes, obj=df, append=append, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 2145, in write
min_itemsize=min_itemsize, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 1891, in create_axes
raise Exception("cannot find the correct atom type -> [dtype->%s] %s" % (b.dtype.name, str(detail)))
Exception: cannot find the correct atom type -> [dtype->object] list index out of range
======================================================================
ERROR: test_start_stop (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\code\pandas\pandas\io\tests\test_pytables.py", line 1631, in test_start_stop
self.store.append('df', df)
File "C:\code\pandas\pandas\io\pytables.py", line 512, in append
self._write_to_group(key, value, table=True, append=True, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 664, in _write_to_group
wrapper(value)
File "C:\code\pandas\pandas\io\pytables.py", line 652, in <lambda>
complib=complib, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 808, in _write_frame_table
t.write(axes=axes, obj=df, append=append, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 2145, in write
min_itemsize=min_itemsize, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 1896, in create_axes
self.validate(existing_table)
File "C:\code\pandas\pandas\io\pytables.py", line 1532, in validate
% (c, getattr(self, c, None), getattr(other, c, None)))
Exception: invalid combinate of [non_index_axes] on appending data [[(1, ['A', 'B'])]] vs current table [[(1, ['A', 'B', 'C', 'D'])]]
======================================================================
ERROR: test_table_values_dtypes_roundtrip (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\code\pandas\pandas\io\tests\test_pytables.py", line 787, in test_table_values_dtypes_roundtrip
self.store.append('df1', df1)
File "C:\code\pandas\pandas\io\pytables.py", line 512, in append
self._write_to_group(key, value, table=True, append=True, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 664, in _write_to_group
wrapper(value)
File "C:\code\pandas\pandas\io\pytables.py", line 652, in <lambda>
complib=complib, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 808, in _write_frame_table
t.write(axes=axes, obj=df, append=append, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 2145, in write
min_itemsize=min_itemsize, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 1896, in create_axes
self.validate(existing_table)
File "C:\code\pandas\pandas\io\pytables.py", line 1532, in validate
% (c, getattr(self, c, None), getattr(other, c, None)))
Exception: invalid combinate of [non_index_axes] on appending data [[(1, ['a'])]] vs current table
[[(1, ['A', 'B', 'C', 'D'])]]
======================================================================
ERROR: test_unique (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\code\pandas\pandas\io\tests\test_pytables.py", line 1509, in test_unique
self.store.append('df2', df2, data_columns=['string'])
File "C:\code\pandas\pandas\io\pytables.py", line 512, in append
self._write_to_group(key, value, table=True, append=True, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 664, in _write_to_group
wrapper(value)
File "C:\code\pandas\pandas\io\pytables.py", line 652, in <lambda>
complib=complib, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 808, in _write_frame_table
t.write(axes=axes, obj=df, append=append, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 2145, in write
min_itemsize=min_itemsize, **kwargs)
File "C:\code\pandas\pandas\io\pytables.py", line 1891, in create_axes
raise Exception("cannot find the correct atom type -> [dtype->%s] %s" % (b.dtype.name, str(detail)))
Exception: cannot find the correct atom type -> [dtype->object] list index out of range
======================================================================
FAIL: test_get (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\code\pandas\pandas\io\tests\test_pytables.py", line 160, in test_get
self.assertRaises(KeyError, self.store.get, 'b')
AssertionError: KeyError not raised
======================================================================
FAIL: test_keys (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\code\pandas\pandas\io\tests\test_pytables.py", line 67, in test_keys
self.assertEquals(len(self.store), 5)
AssertionError: 24 != 5
======================================================================
FAIL: test_remove (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\code\pandas\pandas\io\tests\test_pytables.py", line 868, in test_remove
self.assertEquals(len(self.store), 1)
AssertionError: 27 != 1
----------------------------------------------------------------------
Ran 75 tests in 12.297s
FAILED (SKIP=6, errors=6, failures=3)
C:\code\pandas>
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2646 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2646/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2646/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2646/events | https://github.com/pandas-dev/pandas/issues/2646 | 9,712,660 | MDU6SXNzdWU5NzEyNjYw | 2,646 | multi-index indexing for 3-level index behaving mysteriously. | {
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} | 9 | "2013-01-06T03:41:10Z" | "2014-11-21T23:20:24Z" | "2014-11-21T23:20:24Z" | CONTRIBUTOR | null | This seems to be solvable my using df.sortlevel, but Wesley encouraged me to submit it for further investigation.
I wrote a pretty detailed investigation here:
http://nbviewer.ipython.org/4465051/
with the gist
https://gist.github.com/4465051
In short words:
2-level indexing for a 3-level multi-index fails for me, when the size of one of the first 2 levels is larger than 9 and _not_ using sortlevel.
In examples:
df.ix['c1','det4'] works as long as the sizes of the multi-index are up to (9,9,x) with x anything.
But if the levels size is (10,9,x) I am forced to use sortlevel(0) to make it work. (see notebook).
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https://api.github.com/repos/pandas-dev/pandas/issues/2647 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2647/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2647/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2647/events | https://github.com/pandas-dev/pandas/issues/2647 | 9,714,023 | MDU6SXNzdWU5NzE0MDIz | 2,647 | reset_index does not allow to specify level | {
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} | [] | closed | false | null | [] | null | 2 | "2013-01-06T07:56:23Z" | "2013-01-06T19:14:21Z" | "2013-01-06T18:37:25Z" | CONTRIBUTOR | null | df has a 3-level multi-index, the 3rd level (level=2) is a time-index.
newseries has the time-index only:
```
newseries.head()
time
2009-07-20 00:00:00.045000 312.591132
2009-07-20 00:00:00.173000 312.589225
2009-07-20 00:00:00.301000 312.587320
2009-07-20 00:00:00.429000 312.585419
2009-07-20 00:00:00.557000 312.583521
```
I now want to broadcast the newseries to the index of df, which looks like this:
```
df.index[0]
(1.0, 1.0, <Timestamp: 2009-07-20 00:00:00.045000>)
```
using reset_index with the level parameter:
```
df['bb2temp_interp'] = newseries.reset_index(df.index, level=2)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-31-cb55de3c4846> in <module>()
----> 1 df['bb2temp_interp'] = newseries.reset_index(df.index,level=0)
TypeError: reset_index() got multiple values for keyword argument 'level'
```
It looks like the level is pre-occupied with a default level setting from the method call?
According to this Stackoverflow issue answer from Chang She, this should work?
http://stackoverflow.com/questions/12461470/how-to-broadcast-on-a-single-index-in-hierarchically-indexed-dataframe
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} | 4 | "2013-01-06T19:21:30Z" | "2013-02-04T12:00:22Z" | "2013-02-04T12:00:22Z" | CONTRIBUTOR | null | swaplevel also can take index level names instead of integer levels, but this is not mentioned in it's doc string.
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} | 1 | "2013-01-06T21:34:29Z" | "2013-01-20T20:15:38Z" | "2013-01-20T20:15:38Z" | CONTRIBUTOR | null | I've seen this error lot's of times in the last few days and usually it usuall took me a long time to get it working (and mostly I had no idea why it worked when I did something differently :-( ). The below error is actually from https://github.com/statsmodels/statsmodels/pull/582/files
Would it be possibleto let the merge method catch this error and let it produce a more informative error message which says what has to be done differently? Or the original Exception say what index (value) is the problem?
```
C:\portabel\Python27\lib\site-packages\pandas\core\frame.pyc in merge(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy)
4362 left_on=left_on, right_on=right_on,
4363 left_index=left_index, right_index=right_index, sort=sort,
-> 4364 suffixes=suffixes, copy=copy)
4365
4366 #----------------------------------------------------------------------
C:\portabel\Python27\lib\site-packages\pandas\tools\merge.pyc in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy)
34 right_index=right_index, sort=sort, suffixes=suffixes,
35 copy=copy)
---> 36 return op.get_result()
37 if __debug__: merge.__doc__ = _merge_doc % '\nleft : DataFrame'
38
C:\portabel\Python27\lib\site-packages\pandas\tools\merge.pyc in get_result(self)
185
186 # this is a bit kludgy
--> 187 ldata, rdata = self._get_merge_data()
188
189 # TODO: more efficiently handle group keys to avoid extra
C:\portabel\Python27\lib\site-packages\pandas\tools\merge.pyc in _get_merge_data(self)
276 lsuf, rsuf = self.suffixes
277 ldata, rdata = ldata._maybe_rename_join(rdata, lsuf, rsuf,
--> 278 copydata=False)
279 return ldata, rdata
280
C:\portabel\Python27\lib\site-packages\pandas\core\internals.pyc in _maybe_rename_join(self, other, lsuffix, rsuffix, copydata)
1174
1175 def _maybe_rename_join(self, other, lsuffix, rsuffix, copydata=True):
-> 1176 to_rename = self.items.intersection(other.items)
1177 if len(to_rename) > 0:
1178 if not lsuffix and not rsuffix:
C:\portabel\Python27\lib\site-packages\pandas\core\index.pyc in intersection(self, other)
653 this = self.astype('O')
654 other = other.astype('O')
--> 655 return this.intersection(other)
656
657 if self.is_monotonic and other.is_monotonic:
C:\portabel\Python27\lib\site-packages\pandas\core\index.pyc in intersection(self, other)
662 pass
663
--> 664 indexer = self.get_indexer(other.values)
665 indexer = indexer.take((indexer != -1).nonzero()[0])
666 return self.take(indexer)
C:\portabel\Python27\lib\site-packages\pandas\core\index.pyc in get_indexer(self, target, method, limit)
789 this = self.astype(object)
790 target = target.astype(object)
--> 791 return this.get_indexer(target, method=method, limit=limit)
792
793 if not self.is_unique:
C:\portabel\Python27\lib\site-packages\pandas\core\index.pyc in get_indexer(self, target, method, limit)
792
793 if not self.is_unique:
--> 794 raise Exception('Reindexing only valid with uniquely valued Index '
795 'objects')
796
Exception: Reindexing only valid with uniquely valued Index objects
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2650 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2650/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2650/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2650/events | https://github.com/pandas-dev/pandas/issues/2650 | 9,721,001 | MDU6SXNzdWU5NzIxMDAx | 2,650 | DOC: can't find data file in compatibility section of HDF5 docs | {
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} | 3 | "2013-01-06T22:16:02Z" | "2013-01-18T19:53:50Z" | "2013-01-18T19:53:50Z" | CONTRIBUTOR | null | see this section
http://pandas.pydata.org/pandas-docs/dev/io.html#compatibility
this file legacy_0.10.h5 is in
pandas/doc/sources/_static
on website it's not finding it (find ok when I build docs locally)
any thoughts?
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https://api.github.com/repos/pandas-dev/pandas/issues/2651 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2651/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2651/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2651/events | https://github.com/pandas-dev/pandas/issues/2651 | 9,724,341 | MDU6SXNzdWU5NzI0MzQx | 2,651 | Regression: pandas 0.10 fails to read an excel file that pandas 0.9 can read | {
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} | 7 | "2013-01-07T03:28:41Z" | "2013-01-24T05:41:58Z" | "2013-01-24T05:41:58Z" | CONTRIBUTOR | null | I have an excel file that reads fine in pandas 0.9, fails to read in pandas 0.10
```
fname = sys.argv[1]
xlsx = pd.ExcelFile(fname)
sheet = xlsx.sheet_names[0]
# this works in pandas 0.9, fails in pandas 0.10.0
df = xlsx.parse(sheet, index_col=[1, 2])
```
Triggers this error:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/vagrant/.virtualenvs/foo/local/lib/python2.7/site-packages/IPython/utils/py3compat.pyc in execfile(fname, *where)
176 else:
177 filename = fname
--> 178 __builtin__.execfile(filename, *where)
/tmp/X/pandas_error_test.py in <module>()
15
16 if __name__ == '__main__':
---> 17 main()
/tmp/X/pandas_error_test.py in main(files)
11
12 # this works in pandas 0.9, fails in pandas 0.10.0
---> 13 df = xlsx.parse(sheet, index_col=[1, 2])
14
15
/home/vagrant/.virtualenvs/foo/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in parse(self, sheetname, header, skiprows, skip_footer, index_col, parse_cols, parse_dates, date_parser, na_values, thousands, chunksize, **kwds)
1866 thousands=thousands,
1867 chunksize=chunksize,
-> 1868 skip_footer=skip_footer)
1869
1870 def _should_parse(self, i, parse_cols):
/home/vagrant/.virtualenvs/foo/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _parse_xlsx(self, sheetname, header, skiprows, skip_footer, index_col, has_index_names, parse_cols, parse_dates, date_parser, na_values, thousands, chunksize)
1934 chunksize=chunksize)
1935
-> 1936 return parser.read()
1937
1938 def _parse_xls(self, sheetname, header=0, skiprows=None,
/home/vagrant/.virtualenvs/foo/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in read(self, nrows)
622 # self._engine.set_error_bad_lines(False)
623
--> 624 ret = self._engine.read(nrows)
625
626 if self.options.get('as_recarray'):
/home/vagrant/.virtualenvs/foo/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in read(self, rows)
1245 content = content[1:]
1246
-> 1247 alldata = self._rows_to_cols(content)
1248 data = self._exclude_implicit_index(alldata)
1249
/home/vagrant/.virtualenvs/foo/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _rows_to_cols(self, content)
1461 msg = ('Expected %d fields in line %d, saw %d' %
1462 (col_len, row_num + 1, zip_len))
-> 1463 raise ValueError(msg)
1464
1465 return zipped_content
ValueError: Expected 10 fields in line 2, saw 11
```
I suspect a similar error happened when building the docs on the pandas site. See:
http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#v0-10-0-december-17-2012
And search for "expected", and you'll see a similar error is being triggered. See screenshot for details.
I haven't attached the file, as it contains a client's data.
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https://api.github.com/repos/pandas-dev/pandas/issues/2652 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2652/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2652/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2652/events | https://github.com/pandas-dev/pandas/pull/2652 | 9,733,314 | MDExOlB1bGxSZXF1ZXN0MzU2ODUzOA== | 2,652 | Writing doc about I/O SQL features. See #2541 | {
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} | 2 | "2013-01-07T13:10:10Z" | "2014-07-05T04:31:15Z" | "2013-01-20T01:42:54Z" | CONTRIBUTOR | null | Hi there,
I wrote some contents about I/O SQL pandas features:
- some examples about the `read_frame` function with SQLite ;
- list other functions such as `tquery` (without example) ;
- write a tiny note about `write_frame` function.
I'm not sure about the title of this section. Any proposal is welcome ! Don't hesitate to comment the content (typo, misunderstanding, ...)
## Cheers
Damien G.
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} | 1 | "2013-01-07T13:11:39Z" | "2014-07-07T14:09:03Z" | "2013-01-19T23:41:30Z" | CONTRIBUTOR | null | Hi,
Just add docstring to a function in the `io.sql` module.
##
Damien G.
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} | 7 | "2013-01-07T17:39:54Z" | "2017-10-06T19:57:35Z" | "2013-01-21T19:07:46Z" | NONE | null | Starting point:
http://pandas.pydata.org/pandas-docs/stable/io.html#index-columns-and-trailing-delimiters
If there is one more column of data than there are colum names, usecols exhibits some (at least for me) unintuitive behavior:
``` python
>>> data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'
>>> pd.read_csv(StringIO(data))
a b c
4 apple bat 5.7
8 orange cow 10.0
>>> pd.read_csv(StringIO(data), usecols=['a', 'b'])
a b
0 4 apple
1 8 orange
>>>
```
I was expecting it to be equal to
``` python
>>> pd.read_csv(StringIO(data))[['a', 'b']]
a b
4 apple bat
8 orange cow
```
I am not sure if my expectation is unfounded, though, and that this behavior is indeed intentional?
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] | closed | false | null | [] | null | 6 | "2013-01-08T08:15:50Z" | "2013-12-01T10:54:18Z" | "2013-11-30T12:08:45Z" | CONTRIBUTOR | null | Summary:
Input:
``` python
df.D.ix['c1','d1']
t1 0
t2 0
t3 1
t4 1
t5 1
Name: D
```
Operation:
``` python
grouped = df.groupby('D')
for i,j in grouped:
print 'D:',i
print 'Actual index[2]:',j.index[0][2]
print 'First element of levels[2]:',j.index.levels[2][0]
```
Output:
``` python
D: 0.0
Actual index[2]: t1
First element of levels[2]: t1
D: 1.0
Actual index[2]: t3
First element of levels[2]: t1
```
Details:
http://nbviewer.ipython.org/4482106/
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} | 8 | "2013-01-08T15:39:44Z" | "2019-01-26T15:19:17Z" | "2013-09-21T12:38:36Z" | NONE | null | It seems that the apply method of a group invokes the function passed as parameter once for each group, as expectes. However, the function is invoked twice for the first group. Enclose is a short code reproducing this behavior. Is it a normal behavior or am I doing something wrong here.
I run the code with pandas 0.10.0 on. Windows 7, 32 bit version.
The code reproducing this behavior:
import pandas as pd
tdf = pd.DataFrame( { 'cat' : [1,1,1,2,2,1,1,2], 'B' : range(8)})
category = tdf['cat']
pGroups = tdf.groupby(by=category)
def getLen(df) :
print 'len ',len(df)
return len(df)
plen = pGroups.apply(getLen)
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} | 4 | "2013-01-08T16:10:59Z" | "2018-10-20T13:53:15Z" | "2018-10-19T22:01:32Z" | NONE | null | I was a little surprised when clip would not take other pandas objects as parameters. I hacked a little something below, but for readability I thought I might suggest that this feature be added.
```
df1 = DataFrame(ones((2, 2)))
data = ones((2, 2))
data[0, 0] = 0
data[1, 1] = 2
df2 = DataFrame(data)
print df2.clip(1.5, 0.5)
df3 = df2.copy()
df3[df2 > df1 * 1.5] = (df1 * 1.5)[df2 > df1 * 1.5]
df3[df2 < df1 * .5] = (df1 * .5)[df2 < df1 * .5]
print df3
```
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} | 4 | "2013-01-08T20:54:08Z" | "2013-01-10T23:37:49Z" | "2013-01-10T23:37:49Z" | NONE | null | I have a DataFrame with a DateTime index that I have read from a CSV file:
```
In [131]: d=pd.read_csv('data.csv', parse_dates=[0], index_col=0)
```
The index values consist of ISO-8601-formatted timestamps of the form "2012-12-28T09:00:54.273000-0500", which Pandas seems to handle perfectly:
```
In [132]: d.index
Out[132]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-12-28 09:00:54.272000, ..., 2012-12-28 09:59:46.518000]
Length: 1310, Freq: None, Timezone: tzoffset(None, -18000)
```
But if I try to access data using stringified times, I get this error (I've left the last 2 stack fames for brevity):
```
In [133]: d['2012-12-28 09:30:00-05:00':]
---------------------------------------------------------------------------
DateParseError Traceback (most recent call last)
<ipython-input-133-4564ed4dbf71> in <module>()
----> 1 d['2012-12-28 09:30:00-05:00':]
<...elided...>
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in _get_string_slice(self, key)
1126 freq = getattr(self, 'freqstr',
1127 getattr(self, 'inferred_freq', None))
-> 1128 asdt, parsed, reso = parse_time_string(key, freq)
1129 key = asdt
1130 loc = self._partial_date_slice(reso, parsed)
/usr/local/lib/python2.7/dist-packages/pandas/tseries/tools.pyc in parse_time_string(arg, freq, dayfirst, yearfirst)
234 yearfirst=yearfirst)
235 except Exception, e:
--> 236 raise DateParseError(e)
237
238 if parsed is None:
DateParseError: global name 'tz' is not defined
```
If I specify the timestamp without the GMT offset, I must specify it in GMT (e.g. 9:30am my time is "2012-12-28 14:30:00") which is awkward. I can use localized datetime objects instead of strings, but this is also a bit of a pain.
Is there a fix for this "tz" error so I can use plain ol' strings? Thanks!
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} | 15 | "2013-01-08T21:33:27Z" | "2021-10-07T08:55:09Z" | "2013-03-28T05:09:16Z" | MEMBER | null | A mystery to be debugged soon:
``` python
import pandas as pd
import numpy as np
arr = np.random.randn(100000, 5)
def leak():
for i in xrange(10000):
df = pd.DataFrame(arr.copy())
result = df.xs(1000)
# result = df.ix[5000]
if __name__ == '__main__':
leak()
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2660 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2660/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2660/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2660/events | https://github.com/pandas-dev/pandas/issues/2660 | 9,784,208 | MDU6SXNzdWU5Nzg0MjA4 | 2,660 | dataframe merge does not work correctly when dataframe sizes are bigger | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2663 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2663/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2663/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2663/events | https://github.com/pandas-dev/pandas/issues/2663 | 9,788,870 | MDU6SXNzdWU5Nzg4ODcw | 2,663 | ExcelWriter not in docs | {
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} | 3 | "2013-01-09T00:18:10Z" | "2013-03-17T01:41:53Z" | "2013-03-17T01:41:53Z" | CONTRIBUTOR | null | [Searching for "ExcelWriter"](http://pandas.pydata.org/pandas-docs/stable/search.html?q=excelwriter&check_keywords=yes&area=default) on the documentation doesn't find anything about [`pandas.io.parser.ExcelWriter`](https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L2101).
Is this intentional or is it not being scraped correctly?
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https://api.github.com/repos/pandas-dev/pandas/issues/2664 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2664/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2664/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2664/events | https://github.com/pandas-dev/pandas/issues/2664 | 9,798,124 | MDU6SXNzdWU5Nzk4MTI0 | 2,664 | MultiIndex generates IndexErrors for valid indices in large DataFrames | {
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} | [] | closed | false | null | [] | null | 2 | "2013-01-09T09:29:14Z" | "2013-01-09T10:02:12Z" | "2013-01-09T10:02:12Z" | CONTRIBUTOR | null | Example (can provide the relevant file via email):
``` python
In [134]: len(data)
Out[134]: 59693
In [135]: data.index.names
Out[135]: ['chr', 'name', 'start', 'end']
In [136]: data.head()
Out[136]:
coverage covered total percentage
chr name start end
chr1 ARID1A 27022884 27024041 10000 216 1157 0.186690
27024041 10000 48 1157 0.041487
27024041 10000 9 1157 0.007779
27024041 10000 31 1157 0.026793
27024041 10000 16 1157 0.013829
In [141]: idx = ('chr1', 'ARID1A', 27022884, 27024041)
# get_value works
In [143]: data.get_value(idx, "total").head()
Out[143]:
chr name start end
chr1 ARID1A 27022884 27024041 1157
27024041 1157
27024041 1157
27024041 1157
27024041 1157
Name: total
# Indexing doesn't
In [144]: data.ix[idx]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-144-96cd6bbdcd31> in <module>()
----> 1 data.ix[idx]
/usr/lib64/python2.7/site-packages/core/indexing.pyc in __getitem__(self, key)
30 pass
31
---> 32 return self._getitem_tuple(key)
33 else:
34 return self._getitem_axis(key, axis=0)
/usr/lib64/python2.7/site-packages/pandas/indexing.pyc in _getitem_tuple(self, tup)
202 def _getitem_tuple(self, tup):
203 try:
--> 204 return self._getitem_lowerdim(tup)
205 except IndexingError:
206 pass
/usr/lib64/python2.7/site-packages/core/indexing.pyc in _getitem_lowerdim(self, tup)
320 new_key, = new_key
321
--> 322 return section.ix[new_key]
323
324 raise IndexingError('not applicable')
/usr/lib64/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key)
30 pass
31
---> 32 return self._getitem_tuple(key)
33 else:
34 return self._getitem_axis(key, axis=0)
/usr/lib64/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
219 continue
220
--> 221 retval = retval.ix._getitem_axis(key, axis=i)
222
223 return retval
/usr/lib64/python2.7/site-packages/pandas/indexing.pyc in _getitem_axis(self, key, axis)
356 if com.is_integer(key) and not _is_integer_index(labels):
357 return self._get_loc(key, axis=axis)
--> 358 return self._get_label(lab, axis=axis)
359
360 def _getitem_iterable(self, key, axis=0):
/usr/lib64/python2.7/site-packages/pandas/core/indexing.pyc in _get_label(self, label, axis)
44 return self.obj.xs(label, axis=axis, copy=False)
45 except Exception:
---> 46 return self.obj.xs(label, axis=axis, copy=True)
47
48 def _get_loc(self, key, axis=0):
/usr/lib64/python2.7/site-packages/pandas/core/frame.pyc in xs(self, key, axis, level, copy)
2278
2279 if axis == 1:
-> 2280 data = self[key]
2281 if copy:
2282 data = data.copy()
/usr/lib64/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
1962 raise ValueError('Cannot index using non-boolean DataFrame')
1963 else:
-> 1964 return self._get_item_cache(key)
1965
1966 def _getitem_array(self, key):
/usr/lib64/python2.7/site-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
532 return cache[item]
533 except Exception:
--> 534 values = self._data.get(item)
535 res = self._box_item_values(item, values)
536 cache[item] = res
/usr/lib64/python2.7/site-packages/pandas/core/internals.pyc in get(self, item)
880
881 def get(self, item):
--> 882 _, block = self._find_block(item)
883 return block.get(item)
884
/usr/lib64/python2.7/site-packages/pandas/core/internals.pyc in _find_block(self, item)
1011
1012 def _find_block(self, item):
-> 1013 self._check_have(item)
1014 for i, block in enumerate(self.blocks):
1015 if item in block:
/usr/lib64/python2.7/site-packages/pandas/core/internals.pyc in _check_have(self, item)
1018 def _check_have(self, item):
1019 if item not in self.items:
-> 1020 raise KeyError('no item named %s' % com.pprint_thing(item))
1021
1022 def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
KeyError: u'no item named ARID1A'
```
The data from this test file was generated by
``` python
data = pandas.read_table(
"test_file", names=["chr", "start", "end", "name", "coverage",
"covered", "total", "percentage"],
header=None, index_col=["chr", "name", "start", "end"])
```
Why "large"? Because If I use a smaller number of rows, indexing works:
``` python
In [171]: data.ix[1:100].ix[idx].head()
Out[171]:
coverage covered total percentage
chr name start end
chr1 ARID1A 27022884 27024041 10000 48 1157 0.041487
27024041 10000 9 1157 0.007779
27024041 10000 31 1157 0.026793
27024041 10000 16 1157 0.013829
27024041 10000 13 1157 0.011236
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2665 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2665/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2665/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2665/events | https://github.com/pandas-dev/pandas/issues/2665 | 9,799,777 | MDU6SXNzdWU5Nzk5Nzc3 | 2,665 | Incorrect resampling from hourly to monthly values | {
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} | 9 | "2013-01-09T10:37:45Z" | "2020-03-31T03:23:39Z" | null | NONE | null | EDIT: Modern reproducible example:
```
In [1]: import pandas as pd
In [2]: dates = pd.date_range('3/20/2000', '5/20/2000', freq='1h')
In [4]: ts = pd.Series(range(len(dates)), index=dates)
In [5]: def start(x):
...: return x.index[0]
...:
...: def end(x):
...: return x.index[-1]
...:
In [6]: ts.resample('1M', closed='right', label='right').apply(start)
Out[6]:
2000-03-31 2000-03-20
2000-04-30 2000-04-01
2000-05-31 2000-05-01
Freq: M, dtype: datetime64[ns]
In [7]: ts.resample('1M', closed='right', label='right').apply(end)
Out[7]:
2000-03-31 2000-03-31 23:00:00
2000-04-30 2000-04-30 23:00:00
2000-05-31 2000-05-20 00:00:00
Freq: M, dtype: datetime64[ns]
In [8]: ts.resample('1M', closed='left', label='right').apply(start)
Out[8]:
2000-03-31 2000-03-20
2000-04-30 2000-03-31
2000-05-31 2000-04-30
Freq: M, dtype: datetime64[ns]
In [9]: ts.resample('1M', closed='left', label='right').apply(end)
Out[9]:
2000-03-31 2000-03-30 23:00:00
2000-04-30 2000-04-29 23:00:00
2000-05-31 2000-05-20 00:00:00
Freq: M, dtype: datetime64[ns]
```
Expected behavior: https://github.com/pandas-dev/pandas/issues/2665#issuecomment-29155578
There seems to be something wrong when resampling hourly values to monthly values. The 'closed=' argument does not do what it should. I am using pandas 0.10.0.
```
import pandas as pd
from pandas import TimeSeries
import numpy as np
# Timeseries with hourly values
dates = pd.date_range('3/20/2000', '5/20/2000', freq='1h')
ts = TimeSeries(np.arange(len(dates)), index=dates)
# I want monthly values equal to the summed values of the hours in that month
# With "closed=left" I want the hourly value (1 value) at midnight to be
# included in the month after that very midnight
data_monthly_1 = ts.resample('1M', how='sum', closed='left', label='right')
sum_april1 = data_monthly_1.ix[1]
# If we check this, the summed values per month do not match :
data_hourly_april2 = ts[ts.index.month == 4]
sum_april2 = sum(data_hourly_april2)
# Instead, "closed=left" above resulted in an inclusion of the entire last day of the
# previous month (25 values !) (and exclusion of the last day of the month)
# This is also strange as I am nowhere concerned with using 'days'.
data_hourly_april3 = ts[ ((ts.index.month == 3) & (ts.index.day == 31)) |
((ts.index.month == 4) & (ts.index.day < 30)) ]
sum_april3 = sum(data_hourly_april3)
# PS: "closed=right" results in the answer I would expect for "closed = left":
data_monthly_4 = ts.resample('1M', how='sum', closed='right', label='right')
sum_april4 = data_monthly_4.ix[1]
```
When resampling these hourly to daily values, there was no problem.
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windows 7 32bit english
python 2.7.3
when I run nose test:
```
nosetests pandas
```
issue:
FAIL: test_to_string_repr_unicode (pandas.tests.test_format.TestDataFrameFormatting)
Traceback (most recent call last):
File "D:\Python27\lib\site-packages\pandas\tests\test_format.py", line 141, in
test_to_string_repr_unicode
self.assert_(len(line) == line_len)
AssertionError: False is not true
how to do?
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} | 6 | "2013-01-09T14:13:55Z" | "2016-08-01T10:45:57Z" | "2016-08-01T10:18:33Z" | CONTRIBUTOR | null | Test case:
``` python
In [12]: test = pandas.DataFrame({"A": [1,2,3], "B": [1e-10,4.3434323e-8,3]})
In [13]: test.to_csv("test.txt", sep="\t", line_terminator="\n---\n")
In [14]: cat test.txt
A B
---
0 1 "1e-10"
---
1 2 "4.3434322999999997e-08"
---
2 3 3.0
---
```
Notice the quotes around the first two items in "B".
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} | 2 | "2013-01-09T15:25:55Z" | "2013-01-20T01:29:48Z" | "2013-01-20T01:29:42Z" | CONTRIBUTOR | null | This is a strange example from [this StackOverflow question](http://stackoverflow.com/questions/14237749/pandas-read-csv-strange-behavior-for-empty-default-values):
```
Date,Currenncy,Symbol,Type,Units,UnitPrice,Cost,Tax
2012-03-14,USD,AAPL,BUY,1000
2012-05-12,USD,SBUX,SELL,500
```
```
In [1]: pd.read_csv('ttt.csv', index_col=0, parse_dates=True)
Out[1]:
Currenncy Symbol Type Units UnitPrice Cost Tax
Date
2012-03-14 USD AAPL BUY 1000 2012-05-12 012-05-12 12-05-12
2012-02-05 USD SBUX SELL 500 NaN NaN NaN
```
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} | 11 | "2013-01-09T16:44:02Z" | "2014-05-15T15:55:43Z" | "2014-01-29T13:22:01Z" | NONE | null | Please could you add this functionality on top of the already existing kde functionality.
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https://api.github.com/repos/pandas-dev/pandas/issues/2670 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2670/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2670/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2670/events | https://github.com/pandas-dev/pandas/pull/2670 | 9,822,144 | MDExOlB1bGxSZXF1ZXN0MzYxMDk2Mg== | 2,670 | add support for microsecond periods (GH2145) | {
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} | 8 | "2013-01-09T21:38:40Z" | "2014-08-10T13:31:25Z" | "2013-04-01T02:04:24Z" | MEMBER | null | (Continues #2145)
Alrighty. This should be fine. There may be a few formatting changes that I rolled back through the git diff / apply patch, but all not-skipped tests pass so I figured it wasn't a big deal. Here's what I did to fix the problem, which was continually merging with upstream:
``` shell
function get_my_commits {
local curdir="$(pwd)"
cd "$1"
# %n adds a newline in addition to the file name
# sed removes blank lines here
# sort -u sorts and removes duplicates lines
git log --name-only --pretty=%n --author=Phillip 0eeff..master | sort -u | sed '/^$/d'
}
old_pandas=/path/to/evil/pandas/merge/of/death
new_pandas=/shiny/new/checkout/of/pandas
get_my_commits $old_pandas | while read file; do
# some boolean tests to filter out setup.py and missing files between versions
diff -pu "$old_pandas/$file" "$new_pandas/$file" | patch -p0 "$new_pandas/$file"
done
```
Needless to say I won't be doing any more merging of upstream into my branch. Thanks to those who helped me out here.
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} | 3 | "2013-01-09T22:23:21Z" | "2013-01-20T03:04:17Z" | "2013-01-20T03:04:17Z" | NONE | null | nosetests generated the following error:
> #
> ## ERROR: test using ndim tables in new ways
>
> Traceback (most recent call last):
> File "C:\Python27\lib\site-packages\pandas\io\tests\test_pytables.py", line 34
> 6, in test_ndim_indexables
> tm.assert_panel4d_equal(self.store['p4d'], p4d)
> File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 215, in **get
> item**
> return self.get(key)
> File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 337, in get
> return self._read_group(group)
> File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 850, in _read
> _group
> v = handler(group, where, **kwargs)
> File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 646, in _read
> _ndim_table
> return t.read(where)
> File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 1605, in read
>
> obj = obj.transpose(*tuple(Series(self.data_orientation).argsort()))
> File "C:\Python27\lib\site-packages\pandas\core\panel.py", line 1153, in trans
> pose
> axes = [self._get_axis_number(kwargs[a]) for a in self._AXIS_ORDERS]
> File "C:\Python27\lib\site-packages\pandas\core\generic.py", line 45, in _get_
> axis_number
> return cls._AXIS_NUMBERS[axis]
> KeyError: 1
---
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} | 2 | "2013-01-09T23:33:25Z" | "2013-01-20T02:25:18Z" | "2013-01-20T01:47:26Z" | NONE | null | I cannot figure out how to attach files to issues, so let's try this:
Create a file text.txt with tabs as delimiters:
key1 key2 x1 x2 x3
1 a 0 100
2 b 100 0
3 c 25 75
4 d 35 65
5 e 15 85
Notice that all data lines have 4 columns while the header has 5 columns.
Load this file into a DataFrame:
```
import pandas as pd
test = pd.read_table('test.txt')
```
Observe the result:
```
> test
key1 key2 x1 x2 x3
0 1 a 0 100 2
1 NaN b 100 0 NaN
2 3 c 25 75 NaN
3 4 d 35 65 NaN
4 5 e 15 85 NaN
```
As you can see, the last three data lines are loaded correctly, i.e. the missing column of data has NaN. However, the first two data lines are mangled; key1 value of the second line was loaded into x3 value of the first line. This was not the case in version 0.9.0.
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} | 1 | "2013-01-10T05:38:14Z" | "2014-07-07T20:35:40Z" | "2013-01-10T14:44:53Z" | CONTRIBUTOR | null | Fixed a minor typo.
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} | 1 | "2013-01-10T10:51:18Z" | "2014-07-05T04:29:14Z" | "2013-01-10T14:44:19Z" | CONTRIBUTOR | null | This seems to be the way the RegEx was meant
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} | 4 | "2013-01-10T12:37:24Z" | "2014-06-17T13:14:00Z" | "2013-01-20T03:04:37Z" | CONTRIBUTOR | null | - shape attribute on GenericStorer returns tuple correctly now (not string)
- data_orientation was potentitally returning non-ints in a tuple (fixed downstream), fixes GH #2671, though fixed downstream anyhow
- fix for contains with a non-natural name (GH #2694 )
- added ability to read generic PyTables flavor tables
- added PerformanceWarning to trigger on putting of non-endemic types (only on Storers)
- correctly store float32 dtypes (when not-mixed with float64, otherwise converted)
- can close GH #1985 as well
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It would be really appreciated if someone could upload a build, for 0.10.1.dev.
Or is there a reason why this is not supported?
Thanks,
Chris.
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} | 0 | "2013-01-10T14:48:53Z" | "2014-06-30T00:12:03Z" | "2013-01-10T23:24:19Z" | NONE | null | closes #2666
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} | 1 | "2013-01-10T14:52:36Z" | "2018-11-16T13:55:54Z" | "2013-01-10T19:49:19Z" | NONE | null | Code assumes utf-8 in some places, and issues can slip through because
most devs have utf-8 boxes, so it's not tested.
example: #2666
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} | 5 | "2013-01-10T16:25:39Z" | "2018-12-13T07:09:40Z" | "2018-12-13T07:09:40Z" | CONTRIBUTOR | null | When the data value is a string that represents an URL, have DataFrame.to_html() create hyperlinks out of them in the resulting html file.
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} | 0 | "2013-01-10T17:50:54Z" | "2014-01-09T08:00:54Z" | "2013-01-20T04:00:36Z" | NONE | null | Note the output skips the weekday 12/23/2011.
```
dt = datetime(2012, 1, 1)
for i in range(1,10):
print i, (dt - i * BDay()).strftime('%m/%d/%Y')
```
1 12/30/2011
2 12/29/2011
3 12/28/2011
4 12/27/2011
5 12/26/2011
6 12/22/2011
7 12/21/2011
8 12/20/2011
9 12/19/2011
however when the start date is onoffset it works fine
```
dt = datetime(2011, 12, 30)
for i in range(1, 10):
print i, (dt - i * BDay()).strftime('%m/%d/%Y')
```
1 12/29/2011
2 12/28/2011
3 12/27/2011
4 12/26/2011
5 12/23/2011
6 12/22/2011
7 12/21/2011
8 12/20/2011
9 12/19/2011
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} | 0 | "2013-01-10T19:08:55Z" | "2013-02-10T22:33:48Z" | "2013-02-10T22:33:48Z" | MEMBER | null | Currently `df.ix[time_obj]` is aliased internally to `df.at_time(time_obj)`. The same could be done for slice syntax and the `between_time` function, and that would be nice
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} | 2 | "2013-01-10T19:31:05Z" | "2014-05-28T22:52:09Z" | "2014-05-28T22:52:09Z" | MEMBER | null | Currently TimeSeries.asof will do this for an array of datetimes-- it shouldn't be too difficult to sugar this to generate the appropriate dates from a `datetime.time` object.
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} | 3 | "2013-01-10T23:37:28Z" | "2016-10-12T23:03:28Z" | "2013-01-20T02:26:23Z" | NONE | null | Moved original posted by @Bklyn in #2658 to here
I find I need to call tz_convert() on my DataFrame before I can plot it. By default the index tz is a "tzoffset" I get an exception when plottng.
``` python
AttributeError: 'module' object has no attribute 'tz'
d=pd.read_csv('vix.dlmp.csv', parse_dates=[0], index_col=0)
d.index.tz
Out[23]: tzoffset(None, -18000)
d['2012-12-28 09:30:15-05:00':]
Out[24]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 981 entries, 2012-12-28 09:30:16.311000-05:00 to 2012-12-28 09:59:46.518000-05:00
Data columns:
Und 981 non-null values
BidPrice 981 non-null values
AskPrice 981 non-null values
TradePrice 981 non-null values
BidSize 981 non-null values
AskSize 981 non-null values
TradeSize 981 non-null values
T0 981 non-null values
T1 981 non-null values
T2 981 non-null values
T3 981 non-null values
T4 981 non-null values
T5 981 non-null values
dtypes: float64(9), int64(3), object(1)
d['2012-12-28 09:30:15-05:00':].plot(y=['BidPrice', 'AskPrice', 'TradePrice', 'T0', 'T1'],style=['g', 'b', 'r', 'k--', 'g--'],drawstyle='steps',figsize=(16,9))

---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-25-d7985a840c41> in <module>()
----> 1 d['2012-12-28 09:30:15-05:00':].plot(y=['BidPrice', 'AskPrice', 'TradePrice', 'T0', 'T1'],style=['g', 'b', 'r', 'k--', 'g--'],drawstyle='steps',figsize=(16,9))
/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.pyc in plot_frame(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, **kwds)
1419 grid=grid, logy=logy, secondary_y=secondary_y,
1420 title=title, figsize=figsize, fontsize=fontsize,
-> 1421 **kwds)
1422
1423 plot_obj = klass(frame, kind=kind, subplots=subplots, rot=rot,
/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logy, secondary_y, **kwds)
1504 secondary_y=secondary_y, **kwds)
1505
-> 1506 plot_obj.generate()
1507 plot_obj.draw()
1508
/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.pyc in generate(self)
731 self._compute_plot_data()
732 self._setup_subplots()
--> 733 self._make_plot()
734 self._post_plot_logic()
735 self._adorn_subplots()
/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.pyc in _make_plot(self)
1063 lines = []
1064 labels = []
-> 1065 x = self._get_xticks(convert_period=True)
1066
1067 plotf = self._get_plot_function()
/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.pyc in _get_xticks(self, convert_period)
878 values for xaxis, several actions are already taken by plt.
879 """
--> 880 x = index._mpl_repr()
881 else:
882 self._need_to_set_index = True
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in _mpl_repr(self)
474 def _mpl_repr(self):
475 # how to represent ourselves to matplotlib
--> 476 return tslib.ints_to_pydatetime(self.asi8, self.tz)
477
478 def __repr__(self):
/usr/local/lib/python2.7/dist-packages/pandas/tslib.so in pandas.tslib.ints_to_pydatetime (pandas/tslib.c:2998)()
AttributeError: 'tzoffset' object has no attribute '_transition_info'
In [26]: d=d.tz_convert('US/Eastern')
In [27]: d['2012-12-28 09:30:15-05:00':].plot(y=['BidPrice', 'AskPrice', 'TradePrice', 'T0', 'T1'],style=['g', 'b', 'r', 'k--', 'g--'],drawstyle='steps',figsize=(16,9))
Out[27]: <matplotlib.axes.AxesSubplot at 0x4c25a50>
```
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} | 0 | "2013-01-11T11:41:40Z" | "2013-12-04T00:40:11Z" | "2013-01-21T16:54:33Z" | NONE | null | see http://stackoverflow.com/questions/14265539/when-does-pandas-pandas-pydata-org-throw-a-memory-error-on-df-sortlevelk/14267566#14267566
Unfortunately I can't provide the data set.
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} | 20 | "2013-01-11T14:55:38Z" | "2014-06-30T19:26:24Z" | "2014-06-30T19:26:24Z" | MEMBER | null | http://stackoverflow.com/questions/14276661/python-pandas-read-file-skipping-commented
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} | 1 | "2013-01-11T16:44:55Z" | "2014-06-19T13:19:40Z" | "2013-01-20T03:28:34Z" | CONTRIBUTOR | null | - fixed assignment with a boolean indexer and a non-scalar as the value (see below)
- 2nd commit fixed the alignment issue; that is if the value is not a full series, align first (using where machinery)
- add neg and inv methods
- _check_bool_indexer now coerces to bool if needed (otherwise weird effects when doing neg/inv on boolean series)
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: x = pd.Series(index=np.arange(10))
In [4]: y = pd.Series(np.random.rand(10),index=np.arange(10))
In [5]: y
Out[5]:
0 0.197830
1 0.134425
2 0.766618
3 0.057765
4 0.790927
5 0.153636
6 0.585305
7 0.906787
8 0.647155
9 0.494154
In [6]: x[y>0.5] = y
In [7]: x
Out[7]:
0 NaN
1 NaN
2 0.197830
3 NaN
4 0.134425
5 NaN
6 0.766618
7 0.057765
8 0.790927
9 NaN
```
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} | 0 | "2013-01-13T16:21:34Z" | "2013-01-20T05:12:54Z" | "2013-01-20T05:12:54Z" | NONE | null | It's pretty easy to authenticate yourself with the wrong credentials for GA - lots of us have different accounts and id's. In the pandas utility, if you get yourself authenticated, but then replace your secrets file with another one, you will keep getting a 401 error forever after saying your credentials are messed up. The fix is apparently to delete the analytics.dat file in the pandas.io directory so you can use your new client secrets.json file. A function to do this might be nicer (and require less hunting).
Chang asked me to file this.
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} | 1 | "2013-01-13T23:40:38Z" | "2014-07-07T14:09:13Z" | "2013-01-19T23:25:05Z" | CONTRIBUTOR | null | Fix small typo in merge documentation: change `suffixes=('.x', '.y')` to `suffixes=('_x', '_y')` to match current implementation.
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} | 2 | "2013-01-14T00:59:48Z" | "2013-01-20T18:54:26Z" | "2013-01-20T18:54:26Z" | NONE | null | Hi,
This may or may not be a bug. I'm unsure of which functionality you were intending.
I have a timestamp created by
```
rng = pd.date_range(start_date, periods = 100, freq='2H')
A = DataFrame(rng) # We'll leave it as a generic index for now, not important.
```
I then have a function which is applied to that dataframe via A.apply(f)
```
def f(x):
return (x.hour, x.day, x.month)
```
if I apply this functionality to the raw dataframe via .apply, this will result in a numpy.datetime64 error.
However, if I apply it to individual points.
E.g. f(A.ix[10]), then the correct functionality results.
I have determined that this is due to a difference between the types of the two
e.g. A.dtype = dtype('datetime64[n]s')
type(A.ix[10]) = pandas.tslib.Timestamp
Thus to get around the issue above you may simply add the following functionality to the function
def f(x):
x = pd.Timestamp(x)
return (x.hour, x.month, x.year)
I am filing this as I'm unsure of what you're intended functionality is with this matter
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} | 1 | "2013-01-14T09:32:28Z" | "2013-01-20T00:12:22Z" | "2013-01-19T23:39:12Z" | CONTRIBUTOR | null | Hi Wes, Not sure if this is real, but opening to follow up on a comment in http://stackoverflow.com/questions/14199168/combinatorial-explosion-when-merging-dataframes-in-pandas. Here's a fabricated example to reproduce `MergeError: Combinatorial explosion! (boom)` in tools/merge.py:
```
df1 = pd.DataFrame(np.random.randn(1000, 7),
columns=list('ABCDEF') + ['G1'])
df2 = pd.DataFrame(np.random.randn(1000, 7),
columns=list('ABCDEF') + ['G2'])
df1.merge(df2) # Boom
```
The value of group_sizes at the time of exception is:
```
[2000, 2000, 2000, 2000, 2000, 2000]
```
The exception does not occur if the common columns are set as the index, though.
```
df1 = df1.set_index(list('ABCDEF'))
df2 = df2.set_index(list('ABCDEF'))
df1.merge(df2, left_index=True, right_index=True, how='outer') # OK
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2691 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2691/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2691/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2691/events | https://github.com/pandas-dev/pandas/issues/2691 | 9,943,125 | MDU6SXNzdWU5OTQzMTI1 | 2,691 | TimeSeries indexing with not-specific-enough indices only works for strings | {
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} | 2 | "2013-01-14T14:24:18Z" | "2018-07-06T21:53:24Z" | "2018-07-06T21:53:17Z" | CONTRIBUTOR | null | For indexing a sub-series of some TimeSeries, one can use indices that don't completely specify the moment. For example, in a Series
```
s = Series(randn(1000), index=pd.date_range(start='2001-01-23 00:00', periods=1000, freq='H'))
```
I can say s['2001-01-23'] to get a Series for all the values for that day. This, however doesn't work with datetime-like objects. E.g. for a datetime object
```
day = pd.Timestamp('2001-03-05').date()
```
for indexing the Series with `s[day]` you get a single element such as
```
-1.0028671116269237
```
but for a string representation of the object `s[str(day)]` you get a whole Series such as
```
2001-03-05 00:00:00 -1.002867
2001-03-05 01:00:00 0.416343
2001-03-05 02:00:00 -0.012255
...
2001-03-05 13:00:00 -1.140122
2001-03-05 14:00:00 0.742483
2001-03-05 15:00:00 -0.297421
Freq: H
```
This seems inconsistent and being able to work with objects more abstract than strings would be much nicer.
## Expected behaviour
Strings aren't always very convenient, e.g. for iterating over a TimeSeries by days. It would be nice to be able to use some sort of datetime-like objects to do not-specific-enough indexing and get sub-series - be it Datetimes,Timestamps or Timespans. This would allow us to treat indices as something we can do arithmetic with, adding offsets to move freely etc.
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} | 1 | "2013-01-14T18:52:07Z" | "2013-12-04T00:40:09Z" | "2013-01-19T23:23:56Z" | NONE | null | While upgrading pandas from 0.7.2 to 0.9.1 we have bumped into slowness of certain groupby().sum() operations. Here is a simple example:
N=10000
aa=DataFrame({'ii':range(N),'bb':[True for x in range(N)]})
timeit aa.sum() # fast
timeit aa.groupby('bb').sum() #fast
timeit aa.groupby('ii').sum() # very slow (~ 1000 times slower)
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when slicing rows using df.ix[], it returns more rows than it should
In [2]: df=DataFrame({'a':range(5),'b':range(5)})
In [3]: df.ix[1:3,:] # should return rows 1,2
Out[3]:
a b
1 1 1
2 2 2
3 3 3 # this last row should not here
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} | 6 | "2013-01-14T22:13:51Z" | "2014-06-12T07:28:04Z" | "2013-03-14T02:29:17Z" | NONE | null | ... a dataframe and attempts to overload all operators and promote dataframe methods to object methods in hopes that users can subclass this effectively as if it were a Dataframe itself.
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} | 2 | "2013-01-14T23:27:22Z" | "2014-05-28T23:18:29Z" | "2014-05-28T23:18:29Z" | NONE | null | Problem:
A data set is distributed over multiple csv files generated by different instances. Some of these instances log the 'header' line multiple times (when restarted). Currently I'm cleaning out these spurious header lines and doing pd.concat.
But this results in the dtype of columns being object as columns from files with spurious header lines fail the lib.maybe_convert_numeric in the parser. Hence, although pd.concat is being given clean data, the concatenated data ends up having columns with object dtype
Possible Solution:
Call parser._convert_types or equivalent from pd.concat for object type columns
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} | 8 | "2013-01-15T02:34:18Z" | "2017-11-06T18:18:29Z" | "2017-11-06T18:18:29Z" | CONTRIBUTOR | null | I've found a problem in importing a csv file with numbers that loses the precision of all the numbers in a column.
If any of the entries in a column are in scientific format, all of the entries are converted to that and loose precision. Only happens if a number in the column is 12 digits or more (either representation - so either 1E+11 or 123456789012). Much larger numbers in a column with no scientific representation entries don't trigger the problem.
The mixed type is an error in my data, but thought I'd report the problem in pandas in case it effects legitimate data.
Happens in both 10.0 and 0.10.1.dev-6e2b6ea on OSX with numpy 1.6.2.
csv file:
``` csv
id, text
135217135789158401, 'testing lost precision from csv'
1352171357E+5, 'any item scientific format loses the precision on all other entries'
```
``` python
test = pandas.DataFrame.from_csv('test.csv')
print test.index[0] == 135217135789158401
print test.index[1] == 1352171357E+5
```
Example of large number - column A is effected, column C isn't.
``` csv
id, A, B, C
1, 99999999999, 'a', 99999999999
2, 123456789012345, 'b', 123456789012345
3, 1234E+0, 'c', 1234
```
This may be related to Issue #2069
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} | 0 | "2013-01-15T15:31:18Z" | "2013-01-19T23:45:48Z" | "2013-01-19T23:45:48Z" | MEMBER | null | ```
In [7]: df.date
Out[7]:
0 2007-10-29 00:00:00
1 2007-10-30 00:00:00
2 2007-10-31 00:00:00
3 2007-11-01 00:00:00
4 2007-11-02 00:00:00
5 2007-11-05 00:00:00
6 2007-11-06 00:00:00
7 2007-11-07 00:00:00
8 2007-11-08 00:00:00
9 2007-11-09 00:00:00
10 2007-11-12 00:00:00
11 2007-11-13 00:00:00
12 2007-11-14 00:00:00
13 2007-11-15 00:00:00
14 2007-11-16 00:00:00
...
985 2011-09-26 00:00:00
986 2011-09-27 00:00:00
987 2011-09-28 00:00:00
988 2011-09-29 00:00:00
989 2011-09-30 00:00:00
990 2011-10-03 00:00:00
991 2011-10-04 00:00:00
992 2011-10-05 00:00:00
993 2011-10-06 00:00:00
994 2011-10-07 00:00:00
995 2011-10-10 00:00:00
996 2011-10-11 00:00:00
997 2011-10-12 00:00:00
998 2011-10-13 00:00:00
999 2011-10-14 00:00:00
Name: date, Length: 1000
In [8]: pd.to_datetime(df.date)
Out[8]:
0 1970-01-18 08:00:00
1 1970-01-19 08:00:00
2 1970-01-20 08:00:00
3 1970-01-21 08:00:00
4 1970-01-22 08:00:00
5 1970-01-14 16:00:00
6 1970-01-15 16:00:00
7 1970-01-16 16:00:00
8 1970-01-17 16:00:00
9 1970-01-18 16:00:00
10 1970-01-21 16:00:00
11 1970-01-22 16:00:00
12 1970-01-23 16:00:00
13 1970-01-14 00:00:00
14 1970-01-15 00:00:00
...
985 1970-01-24 08:00:00
986 1970-01-25 08:00:00
987 1970-01-26 08:00:00
988 1970-01-16 16:00:00
989 1970-01-17 16:00:00
990 1970-01-20 16:00:00
991 1970-01-21 16:00:00
992 1970-01-22 16:00:00
993 1970-01-23 16:00:00
994 1970-01-24 16:00:00
995 1970-01-17 00:00:00
996 1970-01-18 00:00:00
997 1970-01-19 00:00:00
998 1970-01-20 00:00:00
999 1970-01-21 00:00:00
Name: date, Length: 1000
```
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} | 2 | "2013-01-15T17:24:16Z" | "2013-01-20T01:41:36Z" | "2013-01-20T01:41:22Z" | NONE | null | While upgrading pandas from 0.7.2 to 0.9.1 we have found that groupby().max() operation now removes non-numeric columns. This broke our code in several places. Workaround is to use groupby().aggregate(np.max).
Here is an example demonstrating the problem:
aa=DataFrame({'nn':[11,11,22,22],'ii':[1,2,3,4],'ss':4*['mama']})
aa.groupby('nn').max()
output on pandas 0.7.2
ii nn ss
nn
11 2 11 mama
22 4 22 mama
output on pandas 0.9.1
ii
nn
11 2
22 4
As you see, object column 'ss' is dropped in new version !!!
This was very un-intuitive.
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