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} | 9 | 2014-12-04T21:09:36Z | 2016-02-29T09:46:03Z | 2016-02-27T15:09:05Z | NONE | null | ```
df = pd.read_excel(r'\\path_to_Excel_file',
'Sheet1', parse_cols=['MODEL_YEAR'])
returns:
---------------------------------------------------------------------------
StopIteration Traceback (most recent call last)
<ipython-input-29-336e4fd9eea1> in <module>()
4
5 df = pd.read_excel(r'\\path_to_Excel_file',
----> 6 'Sheet1', parse_cols=["MODEL_YEAR"])
D:\Miniconda3\envs\notebook\lib\site-packages\pandas\io\excel.py in read_excel(io, sheetname, **kwds)
125 engine = kwds.pop('engine', None)
126
--> 127 return ExcelFile(io, engine=engine).parse(sheetname=sheetname, **kwds)
128
129
D:\Miniconda3\envs\notebook\lib\site-packages\pandas\io\excel.py in parse(self, sheetname, header, skiprows, skip_footer, index_col, parse_cols, parse_dates, date_parser, na_values, thousands, chunksize, convert_float, has_index_names, **kwds)
236 skip_footer=skip_footer,
237 convert_float=convert_float,
--> 238 **kwds)
239
240 def _should_parse(self, i, parse_cols):
D:\Miniconda3\envs\notebook\lib\site-packages\pandas\io\excel.py in _parse_excel(self, sheetname, header, skiprows, skip_footer, index_col, has_index_names, parse_cols, parse_dates, date_parser, na_values, thousands, chunksize, convert_float, **kwds)
355 skip_footer=skip_footer,
356 chunksize=chunksize,
--> 357 **kwds)
358
359 return parser.read()
D:\Miniconda3\envs\notebook\lib\site-packages\pandas\io\parsers.py in TextParser(*args, **kwds)
1285 """
1286 kwds['engine'] = 'python'
-> 1287 return TextFileReader(*args, **kwds)
1288
1289
D:\Miniconda3\envs\notebook\lib\site-packages\pandas\io\parsers.py in __init__(self, f, engine, **kwds)
555 self.options['has_index_names'] = kwds['has_index_names']
556
--> 557 self._make_engine(self.engine)
558
559 def _get_options_with_defaults(self, engine):
D:\Miniconda3\envs\notebook\lib\site-packages\pandas\io\parsers.py in _make_engine(self, engine)
698 elif engine == 'python-fwf':
699 klass = FixedWidthFieldParser
--> 700 self._engine = klass(self.f, **self.options)
701
702 def _failover_to_python(self):
D:\Miniconda3\envs\notebook\lib\site-packages\pandas\io\parsers.py in __init__(self, f, **kwds)
1392 # infer column indices from self.usecols if is is specified.
1393 self._col_indices = None
-> 1394 self.columns, self.num_original_columns = self._infer_columns()
1395
1396 # Now self.columns has the set of columns that we will process.
D:\Miniconda3\envs\notebook\lib\site-packages\pandas\io\parsers.py in _infer_columns(self)
1607 columns = []
1608 for level, hr in enumerate(header):
-> 1609 line = self._buffered_line()
1610
1611 while self.line_pos <= hr:
D:\Miniconda3\envs\notebook\lib\site-packages\pandas\io\parsers.py in _buffered_line(self)
1734 return self.buf[0]
1735 else:
-> 1736 return self._next_line()
1737
1738 def _empty(self, line):
D:\Miniconda3\envs\notebook\lib\site-packages\pandas\io\parsers.py in _next_line(self)
1758 break
1759 except IndexError:
-> 1760 raise StopIteration
1761 else:
1762 while self.pos in self.skiprows:
StopIteration:
```
Documentation says the following:
parse_cols : int or list, default None
- If None then parse all columns
- If int then indicates last column to be parsed
- If list of ints then indicates list of column numbers to be parsed
- If string then indicates comma separated list of column names and column ranges (e.g. “A:E” or “A,C,E:F”)
Also did:
```
df = pd.read_excel(r'\\path_to_Excel_file',
'Sheet1', parse_cols="MODEL_YEAR")
```
But still got the same error.
```
Using Windows 7 with Miniconda3
conda list output:
beautiful-soup 4.3.2 py34_0
beautifulsoup4 4.3.2 <pip>
brewer2mpl 1.4.1 <pip>
dateutil 2.1 py34_2
ggplot 0.6.5 <pip>
ipython 2.3.1 py34_0
jdcal 1.0 py34_0
jinja2 2.7.3 py34_1
lxml 3.4.0 py34_0
markupsafe 0.23 py34_0
matplotlib 1.4.2 np19py34_0
numexpr 2.3.1 np19py34_0
numpy 1.9.1 py34_0
openpyxl 2.0.2 py34_0
pandas 0.15.1 np19py34_0
patsy 0.3.0 np19py34_0
pip 1.5.6 py34_0
pycrypto 2.6.1 py34_2
pygments 2.0.1 py34_0
pyodbc 3.0.7 py34_0
pyparsing 2.0.1 py34_0
pyqt 4.10.4 py34_0
pyreadline 2.0 py34_0
python 3.4.2 0
python-dateutil 2.1 <pip>
pytz 2014.9 py34_0
pywin32 219 py34_0
pyzmq 14.4.1 py34_0
requests 2.4.3 py34_0
scipy 0.14.0 np19py34_0
seaborn 0.5.0 <pip>
setuptools 7.0 py34_0
six 1.8.0 py34_0
statsmodels 0.6.0 np19py34_0
tornado 4.0.2 py34_0
xlrd 0.9.3 py34_0
```
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} | 9 | 2014-12-04T21:51:47Z | 2015-07-29T14:05:50Z | 2015-07-29T14:05:50Z | NONE | null | SO : http://stackoverflow.com/questions/27295220/matplotlib-simple-case-memory-leak-with-pandas
Seems to be a leak memory bug with pandas.plot. It works well with plt.plot(df.index, df.test) (see below)
Simple case here :
```
import sys
import gc
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pandas as pd
pdindex = pd.date_range(start='01/01/2013', freq='15min', end='01/01/2019')
df = pd.DataFrame({'test':np.random.normal(0,1,len(pdindex))}, index=pdindex)
def memplot_plot(df, i):
df.test.plot()
plt.title('graph' + str(i))
plt.savefig(str(i) + '.png', dpi=144)
plt.close()
for i in range(1, 100):
print '*******************************'
print 'i : ' + str(i)
print len( gc.get_objects())
print sys.getsizeof(gc.get_objects())
memplot_plot(df, i)
gc.collect()
```
And the output is (memory error as of i=6):
```
*******************************
i : 1
74682
325680
*******************************
i : 2
290627
1190248
*******************************
i : 3
506420
2145012
*******************************
i : 4
721993
3054204
*******************************
i : 5
937566
3865524
*******************************
i : 6
1153139
4892352
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Anaconda\lib\site- packages\spyderlib\widgets\externalshell\sitecustomize.py", line 580, in runfile
execfile(filename, namespace)
File "C:/PERSO/script_backtesting.py", line 124, in <module>
memplot_plot(df, i)
File "C:/PERSO/script_backtesting.py", line 107, in memplot_plot
plt.savefig(str(i) + '.png', dpi=144)
File "C:\Anaconda\lib\site-packages\matplotlib\pyplot.py", line 576, in savefig
res = fig.savefig(*args, **kwargs)
File "C:\Anaconda\lib\site-packages\matplotlib\figure.py", line 1470, in savefig
self.canvas.print_figure(*args, **kwargs)
File "C:\Anaconda\lib\site-packages\matplotlib\backend_bases.py", line 2192, in print_figure
**kwargs)
File "C:\Anaconda\lib\site-packages\matplotlib\backends\backend_agg.py", line 513, in print_png
FigureCanvasAgg.draw(self)
File "C:\Anaconda\lib\site-packages\matplotlib\backends\backend_agg.py", line 461, in draw
self.figure.draw(self.renderer)
File "C:\Anaconda\lib\site-packages\matplotlib\artist.py", line 59, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File "C:\Anaconda\lib\site-packages\matplotlib\figure.py", line 1079, in draw
func(*args)
File "C:\Anaconda\lib\site-packages\matplotlib\artist.py", line 59, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File "C:\Anaconda\lib\site-packages\matplotlib\axes\_base.py", line 2092, in draw
a.draw(renderer)
File "C:\Anaconda\lib\site-packages\matplotlib\artist.py", line 59, in draw_wrapper
draw(artist, renderer, *args, **kwargs)
File "C:\Anaconda\lib\site-packages\matplotlib\axis.py", line 1103, in draw
ticks_to_draw = self._update_ticks(renderer)
File "C:\Anaconda\lib\site-packages\matplotlib\axis.py", line 957, in _update_ticks
tick_tups = [t for t in self.iter_ticks()]
File "C:\Anaconda\lib\site-packages\matplotlib\axis.py", line 903, in iter_ticks
self.major.formatter.set_locs(majorLocs)
File "C:\Anaconda\lib\site-packages\pandas\tseries\converter.py", line 982, in set_locs
self._set_default_format(vmin, vmax)
File "C:\Anaconda\lib\site-packages\pandas\tseries\converter.py", line 966, in _set_default_format
format = np.compress(info['maj'], info)
File "C:\Anaconda\lib\site-packages\numpy\core\fromnumeric.py", line 1563, in compress
return compress(condition, axis, out)
MemoryError
```
Graph of the memory for the computer from launch the script to breack and to kill the console.
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} | 12 | 2014-12-04T22:29:27Z | 2014-12-07T21:13:31Z | 2014-12-07T15:15:07Z | CONTRIBUTOR | null | This addresses the issue raised in #8994.
Here is the behavior before this PR:
``` python
data = pd.DataFrame({'x':[1, 2, 3]})
data.y = 2
data['y'] = [2, 4, 6]
data.y = 5
print(data.y)
#2
print(data['y'].values)
# [5, 5, 5]
```
Here is the behavior after this PR:
``` python
data = pd.DataFrame({'x':[1, 2, 3]})
data.y = 2
data['y'] = [2, 4, 6]
data.y = 5
print(data.y)
#5
print(data['y'].values)
# [2, 4, 6]
```
The fix boils down to the fact that when `data.y` is called, Python first calls `data.__getattribute__('y')` before calling `data.__getattr__('y')`, but when `data.y = 5` is called, Python goes directly to `data.__setattr__('y', 5)` without any prior search for defined attributes.
I don't think there are any unintended side-effects to this change, but I'd appreciate some other folks thinking through whether this is the right solution. Thanks!
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} | 9 | 2014-12-04T23:13:52Z | 2016-07-11T01:37:09Z | 2016-07-11T01:37:08Z | MEMBER | null | xref #12877 (test on Series & DataFrame)
When having a 'NaT' in a datetime series and comparing this to a datetime series, the comparison with NaT returns True (and shouldn't comparisons with NA not always return False?) Comparing it with a scalar does return False:
```
In [54]: s = pd.Series([pd.Timestamp('2012-01-01'), pd.Timestamp('NaT')])
In [55]: s2 = pd.Series([pd.Timestamp('2013-01-01'), pd.Timestamp('2014-01-01')])
In [57]: s
Out[57]:
0 2012-01-01
1 NaT
dtype: datetime64[ns]
In [58]: s2
Out[58]:
0 2013-01-01
1 2014-01-01
dtype: datetime64[ns]
In [59]: s < s2
Out[59]:
0 True
1 True
dtype: bool
In [60]: s < pd.Timestamp('2013-01-01')
Out[60]:
0 True
1 False
dtype: bool
```
Original from http://stackoverflow.com/questions/27305324/pandas-comparison-with-variable-number-of-columns/27305661#27305661
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} | 4 | 2014-12-04T23:41:06Z | 2021-04-11T06:00:29Z | null | MEMBER | null | When trying to compare a dataframe to a column/series (I know, in the following case not useful due to the alignement of the series with the columns of the dataframe and not the rows, but it is something typical users will try), I get the correct results if there are strings in the dataframe and series, but a TypeError when the dataframe contains datetime values:
```
In [1]: from io import StringIO
In [2]: s = """id date birth_date_1 birth_date_2
...: 1 2000-01-01 2000-01-03 2000-01-05
...: 1 2000-01-07 2000-01-03 2000-01-05
...: 2 2000-01-02 2000-01-10 2000-01-01
...: 2 2000-01-05 2000-01-10 2000-01-01"""
In [3]: df = pd.read_csv(StringIO(s), sep='\s+')
In [5]: df[['birth_date_1','birth_date_2']] > df['date']
Out[5]:
0 1 2 3 birth_date_1 birth_date_2
0 False False False False True True
1 False False False False True True
2 False False False False True True
3 False False False False True True
In [7]: df = pd.read_csv(StringIO(s), sep='\s+', parse_dates=[1,2,3])
In [8]: df[['birth_date_1','birth_date_2']] > df['date']
...
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\internals.pyc in handle_error()
954 if raise_on_error:
955 raise TypeError('Could not operate %s with block values
%s'
--> 956 % (repr(other), str(detail)))
957 else:
958 # return the values
TypeError: Could not operate array(['2000-01-01T01:00:00.000000000+0100',
'2000-01-07T01:00:00.000000000+0100',
'2000-01-02T01:00:00.000000000+0100',
'2000-01-05T01:00:00.000000000+0100'], dtype='datetime64[ns]') with block
values invalid type promotion
```
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} | 2 | 2014-12-05T00:24:42Z | 2014-12-05T06:11:47Z | 2014-12-05T02:11:13Z | CONTRIBUTOR | null | @jorisvandenbossche : Fixes code in example, and makes final averaging by len(x) stylistically similar to previous adds.
New pull request replacing my earlier one, which contained some other erroneous commits.
closes #8944
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} | 0 | 2014-12-05T01:58:13Z | 2014-12-05T02:34:01Z | 2014-12-05T02:34:01Z | CONTRIBUTOR | null | closes #8795
closes #8866
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| null | 2 | 2014-12-05T03:01:25Z | 2015-01-25T18:01:04Z | 2015-01-25T18:01:04Z | NONE | null | When saving DataFrame to MySQL, Pandas will map Python float (by default double precision) to MySQL FLOAT (by default single precision). I couldn't find a way to either specify the use of MySQL DOUBLE, or MySQL DECIMAL.
When saving some financial data this will cause loss of precision. For example, 2-year treasury future price quotes are multiples of 1/4/32 = 0.0078125, and there are usually 3 digits before the decimal point. A real-world example is 109.8828125 (TUZ4 settlement price on 12/04/2014). Saving a DataFrame with this number to MySQL results in the rounded 109.883. The error itself is not huge (2.4% of a tick), but still undesirable, especially considering using MySQL DOUBLE or DECIMAL can easily improve the precision.
Currently a workaround is to modify pandas/io/sql.py to map Python float to MySQL FLOAT(53), which forces the use of DOUBLE. There are two places to change, one inside method definition SQLTable._sqlalchemy_type(self, col), the other inside dictionary definition _SQL_TYPES. This work around will waste storage when only single precision is needed. It will be better if some option is provided to utilize MySQL DOUBLE and DECIMAL only when needed.
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} | 3 | 2014-12-05T04:59:56Z | 2015-03-05T23:28:23Z | 2015-03-05T23:28:23Z | NONE | null | I ran into this problem retrieving option data for the SPX settlement index:
```
>>> options_object = web.Options('^spxpm', 'yahoo')
>>> options_object.get_call_data( expiry=datetime.date(2014, 12, 20) )
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/pollyp/anaconda/lib/python2.7/site-packages/pandas/io/data.py", line 785, in get_call_data
return self._get_data_in_date_range(expiry, call=True, put=False)
File "/Users/pollyp/anaconda/lib/python2.7/site-packages/pandas/io/data.py", line 1098, in _get_data_in_date_range
frame = self._get_option_data(expiry=expiry_date, name=name)
File "/Users/pollyp/anaconda/lib/python2.7/site-packages/pandas/io/data.py", line 717, in _get_option_data
frames = self._get_option_frames_from_yahoo(expiry)
File "/Users/pollyp/anaconda/lib/python2.7/site-packages/pandas/io/data.py", line 655, in _get_option_frames_from_yahoo
option_frames = self._option_frames_from_url(url)
File "/Users/pollyp/anaconda/lib/python2.7/site-packages/pandas/io/data.py", line 684, in _option_frames_from_url
self.underlying_price, self.quote_time = self._get_underlying_price(url)
File "/Users/pollyp/anaconda/lib/python2.7/site-packages/pandas/io/data.py", line 696, in _get_underlying_price
.getchildren()[0].text)
ValueError: invalid literal for float(): 2,071.92
```
Here's my show_versions output:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.7
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: 0.9999-dev
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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} | 5 | 2014-12-05T11:13:14Z | 2014-12-09T11:16:05Z | 2014-12-05T13:20:41Z | NONE | null | ``` python
import pandas as pd
a = pd.TimedeltaIndex(range(3),unit='S')
print (a[1])
```
gives output
```
11574 days 01:46:40
```
I would expect the same output as from
``` python
import pandas as pd
import numpy as np
npa = np.array(range(3),dtype='timedelta64[s]')
a = pd.TimedeltaIndex(npa)
print(a[1])
```
giving output
```
0 days 00:00:01
```
This is pandas 0.15.1 as installed by conda update pandas from a fresh python 3.4 install of anaconda.
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: nb_NO
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.1
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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} | 9 | 2014-12-05T14:05:11Z | 2014-12-11T10:28:32Z | 2014-12-11T01:34:25Z | NONE | null | Hi have the following snippet:
```
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plot_df = pd.DataFrame(
np.random.rand(21, 2),
index=pd.bdate_range(datetime(2000, 1, 1), datetime(2000, 1, 31)),
columns=['a', 'b'])
fig = plt.figure()
ax = fig.add_subplot(111)
ax = plot_df.plot(ax=ax)
plt.axhline(y=0)
```
This gives an exception in Pandas 0.15.2, **not** in Pandas 0.14.1.
Trace:
```
Traceback (most recent call last):
File "test_p15.py", line 14, in <module>
plt.axhline(y=0)
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/matplotlib/pyplot.py", line 2501, in axhline
ret = ax.axhline(y=y, xmin=xmin, xmax=xmax, **kwargs)
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/matplotlib/axes/_axes.py", line 729, in axhline
self.add_line(l)
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/matplotlib/axes/_base.py", line 1486, in add_line
self._update_line_limits(line)
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/matplotlib/axes/_base.py", line 1497, in _update_line_limits
path = line.get_path()
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/matplotlib/lines.py", line 871, in get_path
self.recache()
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/matplotlib/lines.py", line 568, in recache
xconv = self.convert_xunits(self._xorig)
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/matplotlib/artist.py", line 163, in convert_xunits
return ax.xaxis.convert_units(x)
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/matplotlib/axis.py", line 1448, in convert_units
ret = self.converter.convert(x, self.units, self)
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/pandas/tseries/converter.py", line 121, in convert
return PeriodIndex(values, freq=axis.freq).values
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/pandas/tseries/period.py", line 641, in __new__
ordinal, freq = cls._from_arraylike(data, freq, tz)
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/pandas/tseries/period.py", line 691, in _from_arraylike
data = _get_ordinals(data, freq)
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/pandas/tseries/period.py", line 507, in _get_ordinals
return lib.map_infer(data, f)
File "pandas/src/inference.pyx", line 1020, in pandas.lib.map_infer (pandas/lib.c:56502)
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/pandas/tseries/period.py", line 503, in <lambda>
f = lambda x: Period(x, freq=freq).ordinal
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/pandas/tseries/period.py", line 126, in __init__
dt, _, reso = parse_time_string(value, freq)
File "/Users/dcoevord/anaconda/envs/p15/lib/python2.7/site-packages/pandas/tseries/tools.py", line 460, in parse_time_string
raise DateParseError(e)
pandas.tseries.tools.DateParseError: day is out of range for month
```
Version info:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 14.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.1
nose: None
Cython: None
numpy: 1.9.1
scipy: 0.14.0
statsmodels: None
IPython: 2.3.1
sphinx: None
patsy: None
dateutil: 1.5
pytz: 2014.9
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 3 | 2014-12-05T14:23:10Z | 2014-12-05T14:37:09Z | 2014-12-05T14:30:23Z | NONE | null | Dropping rows is removing the values from a multiindex dataframe but not removing the key values from the multiindex. I don't think this is intended behavior. This is pandas 15.1
``` python
In [33]: s = pd.Series(np.random.randn(8), index=arrays)
In [34]: df = pd.DataFrame(np.random.randn(8, 4), index=arrays)
In [35]: df
Out[35]:
0 1 2 3
bar one -0.236317 0.105585 -0.611251 0.016047
two 0.296703 -2.415282 -0.595308 0.388648
baz one 1.388280 0.261497 0.717756 0.407535
two 0.379969 -0.592787 1.093628 0.082563
foo one 0.100581 -0.186784 -0.150018 -0.032548
two 2.009603 -0.516111 0.801641 0.693063
qux one -0.194570 0.952694 0.913118 0.299275
two 1.716092 0.465544 1.453519 -0.679434
In [36]: df.index
Out[36]:
MultiIndex(levels=[[u'bar', u'baz', u'foo', u'qux'], [u'one', u'two']],
labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]])
In [37]: df.drop('two', level=1, axis=0,inplace=True)
In [38]: df
Out[38]:
0 1 2 3
bar one -0.236317 0.105585 -0.611251 0.016047
baz one 1.388280 0.261497 0.717756 0.407535
foo one 0.100581 -0.186784 -0.150018 -0.032548
qux one -0.194570 0.952694 0.913118 0.299275
In [39]: df.index
Out[39]:
MultiIndex(levels=[[u'bar', u'baz', u'foo', u'qux'], [u'one', u'two']],
labels=[[0, 1, 2, 3], [0, 0, 0, 0]])
```
I noticed this because when I then try to create a panel from from the dataframe I get:
``` python
In [40]: panel = df.to_panel()
In [41]: panel
Out[41]:
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 4 (major_axis) x 2 (minor_axis)
Items axis: 0 to 3
Major_axis axis: bar to qux
Minor_axis axis: one to two
```
I also noticed this same behavior when I used query on a multiindex dataframe and then tried to create a panel from the result. Is there a way to get around having to drop extra array from the panel?
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} | 7 | 2014-12-05T19:57:58Z | 2015-01-08T02:01:29Z | 2015-01-08T01:59:16Z | CONTRIBUTOR | null | closes #8888
This does not implement immerrr's suggestion of implementing `__setitem__`, as all `StringMethod` methods return a new `Series` (or `DataFrame`) rather than mutating.
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} | 2 | 2014-12-05T20:38:36Z | 2015-05-05T08:23:02Z | 2014-12-06T15:17:00Z | CONTRIBUTOR | null | closes #8996
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} | 10 | 2014-12-05T21:36:18Z | 2015-02-05T11:37:52Z | 2015-02-05T11:37:52Z | NONE | null | It seems like `pd.Series([x]) | pd.Series([y])` with `x, y` integers returns `pd.Series([x | y]).astype(bool)`. This is a reasonable semantic, but `pd.Series([x]) & pd.Series([y])` seems to return `pd.Series([x & y % 2]) == 1`, which is a lot weirder. Is there a justification for this? I couldn't find one in the documentation (`&` is hard to search!), so it may be a bug.
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| null | 3 | 2014-12-05T22:19:10Z | 2014-12-18T18:58:15Z | 2014-12-18T18:58:15Z | NONE | null | Hello, I'm using pandas together with IPython (btw, thanks for your library!). At this moment I met a problem of processing big files - there is not enough memory on the server, and the swap is disabled (can't change this).
As long as I am writing library for data processing / machine learning, I eager to create some pipeline which will involve pandas (as most popular and simple way to operate data) and different classifiers (in particular `sklearn`, but more advanced classifiers also will be available, which will train on large files and predict large files as well). Large files processing also needed to be resolved.
Finally I came to the following concept, which I called `PandasFlow`. Streams are very useful abstraction, this is chunk version of stream. Each operation returns a **lazily-evaluated sequence of DataFrames** (say, ~10000 rows each). Some simplest examples:
```
# nothing done really
PF.from_csv(filename, features)
# this will return pandas.DataFrame with two columns
PF.from_csv(filename, features).where('width * height > 10').select_columns(width='width', volume='width * height * length').to_pandas()
# this will save with one additional column
PF.from_hdf(filename, path, features).where(lambda x: x.width * x.height > 10).add_columns(prediction=lambda x:classifier.predict(x)).to_csv(new_filename)
```
So, I believe this is
1. not hard to implement
2. easy to use
3. NB: you can prepare a query and pass to classifier - if classifier uses pandas (as `sklearn`) - it will convert this to pandas. If it trains on `csv` - it will save your query to `csv` file. Some other format not a problem as well. No need to think about final format that will be used
If there is something like that already available? If not, any suggestions/links are appreciated.
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} | 3 | 2014-12-06T01:01:08Z | 2014-12-07T22:27:24Z | 2014-12-07T22:27:17Z | CONTRIBUTOR | null | Fix for #8452
Index column was added when index=False.
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} | 3 | 2014-12-06T15:51:22Z | 2014-12-07T00:29:18Z | 2014-12-07T00:18:23Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/4088
on branch:
```
>>> df
vals
first second third
mid 3rd 992 1.96
562 12.06
1st 73 -6.46
818 -15.75
658 5.90
btm 2nd 915 9.75
474 -1.47
905 -6.03
1st 717 8.01
909 -21.12
3rd 616 11.91
675 1.06
579 -4.01
top 1st 241 1.79
363 1.71
3rd 677 13.38
238 -16.77
407 17.19
2nd 728 -21.55
36 8.09
>>> df.reindex(['top', 'mid', 'btm'], level='first')
vals
first second third
top 1st 241 1.79
363 1.71
3rd 677 13.38
238 -16.77
407 17.19
2nd 728 -21.55
36 8.09
mid 3rd 992 1.96
562 12.06
1st 73 -6.46
818 -15.75
658 5.90
btm 2nd 915 9.75
474 -1.47
905 -6.03
1st 717 8.01
909 -21.12
3rd 616 11.91
675 1.06
579 -4.01
>>> df.reindex(['1st', '2nd', '3rd'], level='second')
vals
first second third
mid 1st 73 -6.46
818 -15.75
658 5.90
3rd 992 1.96
562 12.06
btm 1st 717 8.01
909 -21.12
2nd 915 9.75
474 -1.47
905 -6.03
3rd 616 11.91
675 1.06
579 -4.01
top 1st 241 1.79
363 1.71
2nd 728 -21.55
36 8.09
3rd 677 13.38
238 -16.77
407 17.19
>>> df.reindex(['top', 'btm'], level='first').reindex(['1st', '2nd'], level='second')
vals
first second third
top 1st 241 1.79
363 1.71
2nd 728 -21.55
36 8.09
btm 1st 717 8.01
909 -21.12
2nd 915 9.75
474 -1.47
905 -6.03
```
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} | 2 | 2014-12-06T16:11:36Z | 2017-02-27T21:47:19Z | 2017-02-27T21:47:15Z | CONTRIBUTOR | null | currently much of the sorting algorithms are in groupby.py (e.g. _index_from_factorized, _lexsort_indexer), the whole section under 'misc utilities'.
I think it makes sense to move these to algorithms.py. (OR maybe to sorting.py). Likewise might make sense to pull out 'sorting' type cython algos and to add a section / extension (or at least group the code a bit more).
xref #9019
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} | 1 | 2014-12-06T16:28:33Z | 2014-12-07T22:26:13Z | 2014-12-07T22:26:13Z | CONTRIBUTOR | null | cc @dbew
xref #8639
appears that python-dateutil 2.3 (just release) is breaking everything on windows (linux/mac) seem to be ok
I think that the naming convention changed
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} | 3 | 2014-12-06T18:27:51Z | 2014-12-22T14:55:29Z | 2014-12-07T21:01:06Z | CONTRIBUTOR | null | closes #9000
I opted to not change np_datetime_strings but instead short-circuit in Timestamp. I don't have a strong preference one way or another.
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} | 76 | 2014-12-06T19:11:36Z | 2017-01-01T16:32:21Z | 2016-01-20T14:14:44Z | CONTRIBUTOR | null | closes #8851
closes #9399
closes #9406
closes #9533
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In [6]: df = DataFrame({'A' : [pd.Timedelta('1 day 1 sec')],'B' : [5], 'C' : [pd.Timestamp('20130101') ]})
In [7]: df
Out[7]:
A B C
0 1 days 00:00:01 5 2013-01-01
In [8]: df.dtypes
Out[8]:
A timedelta64[ns]
B int64
C datetime64[ns]
dtype: object
In [9]: df[['A']].to_json()
Out[9]: '{"A":{"0":86401000}}'
In [10]: df.to_json(default_handler=lambda x: x.asm8)
Out[10]: '{"A":{"0":86401000000000},"B":{"0":5},"C":{"0":1356998400000}}'
In [11]: df.to_json()
OverflowError: Maximum recursion level reached
```
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| null | 1 | 2014-12-06T22:47:34Z | 2014-12-07T12:33:36Z | 2014-12-07T12:33:36Z | CONTRIBUTOR | null | fixes #8985
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} | 4 | 2014-12-07T01:11:21Z | 2018-10-30T12:29:32Z | 2018-10-30T12:29:32Z | CONTRIBUTOR | null | on master:
```
>>> from pandas.core.base import FrozenNDArray
>>> a = FrozenNDArray(1)
>>> a
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.4/site-packages/pandas-0.15.1_192_g7bd1b24-py3.4-linux-x86_64.egg/pandas/core/base.py", line 65, in __repr__
return str(self)
File "/usr/lib/python3.4/site-packages/pandas-0.15.1_192_g7bd1b24-py3.4-linux-x86_64.egg/pandas/core/base.py", line 44, in __str__
return self.__unicode__()
File "/usr/lib/python3.4/site-packages/pandas-0.15.1_192_g7bd1b24-py3.4-linux-x86_64.egg/pandas/core/base.py", line 268, in __unicode__
...
...
RuntimeError: maximum recursion depth exceeded while calling a Python object
```
keeps going back and forth between `__str__` and `__unicode__`. a workaround would be to change `self` on [this line](https://github.com/pydata/pandas/blob/e51eb9eca56f31055309d83c592cbefa4a14e42c/pandas/core/base.py#L267) to `self if self.ndim else self.item()`.
I ran into this bug while taking `.max` on the array:
```
>>> xs
FrozenNDArray([1, 2, 3], dtype='int64')
>>> xs.max()
RuntimeError: maximum recursion depth exceeded while calling a Python object
```
it is kind of unusual to me that here `.max` returns a zero dimensional array, whereas numpy returns a scalar:
```
>>> xs
FrozenNDArray([1, 2, 3], dtype='int64')
>>> type(xs.max())
<class 'pandas.core.base.FrozenNDArray'>
>>> type(np.array([1, 2, 3]).max())
<class 'numpy.int64'>
```
in particular, `np.isscalar` returns differently for the two:
```
>>> np.isscalar(np.array([1, 2, 3]).max())
True
>>> np.isscalar(FrozenNDArray([1, 2, 3]).max())
False
```
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} | 5 | 2014-12-07T02:37:01Z | 2016-11-26T02:19:06Z | 2016-11-25T22:47:50Z | CONTRIBUTOR | null | Anaconda now ships with lxml 3.4.0:
- http://docs.continuum.io/anaconda/pkg-docs.html
http://pandas.pydata.org/pandas-docs/stable/gotchas.html#html-table-parsing
> Issues with using Anaconda
>
> Anaconda ships with lxml version 3.2.0; the following workaround for Anaconda was successfully used to deal with the versioning issues surrounding lxml and BeautifulSoup4.
Is this still a issue, now that Anaconda ships with lxml 3.4.0? (e.g. is there a test for this)
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} | 7 | 2014-12-07T15:20:52Z | 2021-04-11T06:01:01Z | null | CONTRIBUTOR | null | xref #8994
xref #5904
xref #8572
This might be a bit controversial, but the issues raised in #8994 and #5904
point to some continued confusion w.r.t. attribute setting 'being' column setting
so if we now have
```
df = DataFrame({'A' : [1,2,3], 'B' : 5 })
df.C = 5
```
is an attribute set
it _could_ be a column set
e.g. de-facto `df['C'] = 5`
If someone actually wants an attribute to 'stick' around. (meaning they would have to intercept the `__finalize__` methods and actually deal with them properly, then I think it is reasonable to also have them add to the `_internal_names` as well (see #8572)
So basically would try to set a column (unless its an internal name).
(note that `getattr` is de-facto already equivalent to `__getitem__`, e.g. `df.B === df['B']`)
(don't mind my JS equivalence notion :)
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| null | 1 | 2014-12-07T19:15:15Z | 2014-12-07T19:28:16Z | 2014-12-07T19:28:05Z | NONE | null | Hi,
When slicing a Series/DataFrame which has a multilevel index which is not sorted on the first level, selecting on the first level with .ix or .loc returns an empty dataframe instead of the true values.
``` python
In [524]: s = pd.Series(range(30),index=pd.MultiIndex.from_arrays([["A"]*10+["C"]*10+["B"]*10,range(30)]))
In [526]: x = s.iloc[::3]
In [527]: x
Out[527]:
A 0 0
3 3
6 6
9 9
C 12 12
15 15
18 18
B 21 21
24 24
27 27
dtype: int64
In [528]: x.ix["B"]
Out[528]: Series([], dtype: int64)
```
It works if "C" and "B" are exchanged in In [524].
## INSTALLED VERSIONS
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-70-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.14.0
nose: 1.3.3
Cython: 0.20.2
numpy: 1.8.1
scipy: 0.14.0
statsmodels: None
IPython: 2.1.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 1.8.6
xlrd: None
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: 0.6.2.None
psycopg2: None
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} | 16 | 2014-12-07T20:51:49Z | 2014-12-08T14:46:53Z | 2014-12-07T22:26:13Z | CONTRIBUTOR | null | fixes #9021
but going to reopen #8639
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https://api.github.com/repos/pandas-dev/pandas/issues/9037 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9037/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9037/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9037/events | https://github.com/pandas-dev/pandas/issues/9037 | 51,231,210 | MDU6SXNzdWU1MTIzMTIxMA== | 9,037 | PERF: json should process data column-by-column (and not use .values) | {
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} | 3 | 2014-12-07T20:56:17Z | 2014-12-24T15:53:33Z | 2014-12-24T15:53:33Z | CONTRIBUTOR | null | xref #9027
So `to_json` first converts all the a DataFrame by using `.values`. This converts everything to object dtype (if its a mixed frame) and is pretty expensive perf wise.
If you do this column by column you get excellent perf (and tiny bit more complexity in the code).
cc @Komnomnomnom
@cpcloud
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} | 1 | 2014-12-07T21:21:55Z | 2014-12-08T11:59:49Z | 2014-12-08T11:59:49Z | MEMBER | null | Some doc changes related to #8926 and #8973
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https://api.github.com/repos/pandas-dev/pandas/issues/9039 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9039/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9039/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9039/events | https://github.com/pandas-dev/pandas/issues/9039 | 51,265,525 | MDU6SXNzdWU1MTI2NTUyNQ== | 9,039 | append & data_columns=True | {
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| null | 5 | 2014-12-08T07:34:55Z | 2014-12-11T11:34:49Z | 2014-12-11T11:34:49Z | NONE | null | Hello
i create a new store and i am trying to append it.
all is right when i did :
store.append("table", dataframe)
but when i did the whole thing with data_columns=dataframe.columns or data_columns=True
i can write the first append and then nothing ....
no more data input
tore.append("table", dataframe,data_columns=dataframe.columns )
any help ?
sorry for the delay i was in week end.
CODE
dt = '405B'
dataall = np.fromfile(fname, dtype=dt)
dataall = np.delete(dataall, 0, axis=0)
data = dataall
datas = pd.DataFrame(data)
DU = datetime.timedelta(minutes=1)
DT = datetime.datetime(an, mois, jour, data[0,0], data[0,1], data[0,2], data[0,3])
DF = DT + DU
path = "/stream2"
datas["datetime"]=pd.to_datetime(d[0] + "T" +datas[0].astype(str)+":"+datas[1].astype(str)+":"+datas[2].astype(str)+"."+datas[3].astype(str), format='%d/%m/%yT%H:%M:%S.%f')
ind = datas["datetime"]
polarite = np.empty((data.shape[0],1),dtype ="U")
polarite[::2,0]="D"
polarite[1::2,0]="G"
DATA = data[:,coldeb:colfin]
L = []
for j in np.arange(0,400):
z = str(j)
z = "f"+z
L.append(z)
dt = np.dtype((str, 4))
Z = np.fromiter(L, dt)
table = pd.DataFrame(DATA,index=ind,columns=Z)
table["polarite"] = polarite
table["status"]=data[:,-1]
store.append(path,
table,format='table',
data_columns=True,
append=True )
INSTALLED VERSIONS
commit: None
python: 3.4.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-40-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: None
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
DF.INFOS()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 57469 entries, 2013-02-02 08:05:06 to 2013-02-02 16:03:59.610000
Columns: 402 entries, f0 to status
dtypes: object(1), uint8(401)
memory usage: 22.9 MB
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https://api.github.com/repos/pandas-dev/pandas/issues/9040 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9040/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9040/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9040/events | https://github.com/pandas-dev/pandas/pull/9040 | 51,265,725 | MDExOlB1bGxSZXF1ZXN0MjU2NDk4Njg= | 9,040 | Return from to_timedelta is forced to dtype timedelta64[ns]. | {
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} | 18 | 2014-12-08T07:38:37Z | 2014-12-12T14:37:30Z | 2014-12-09T21:23:19Z | NONE | null | This is a revised change, fixing the issue I experienced (issue #9011). The proper change seems to be setting the dtype to `'timedelta64[ns]'`, as was already done for the other cases in the `to_timedelta` routine.
I have also added more tests, and moved them to the `test_timedeltas` function (next to the other tests of the functionality of the constructor method). I have not looked too much at #8886, it is difficult for me to understand the issue.
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} | 17 | 2014-12-08T08:17:20Z | 2015-01-25T18:01:21Z | 2015-01-25T18:01:04Z | CONTRIBUTOR | null | Closes #9009 . @jorisvandenbossche -- do you think this should test/use single precision ? We could test for float32 perhaps? My own feeling is that this is the safer default.
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} | 5 | 2014-12-08T11:22:11Z | 2021-04-11T06:01:14Z | null | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/27354281/query-term-is-not-valid-condition-none/27356827#27356827)
```
In [1]: df = pd.DataFrame.from_dict({'A':[1,2],'B':[100,200], 'C':[42,11]})
In [2]: df_a = df.set_index('A')
In [3]: df_a
Out[3]:
B C
A
1 100 42
2 200 11
In [4]: store = pd.HDFStore('foo.h5','w')
In [5]: store.put('bar', df_a, format='table', data_columns=True)
In [7]: store.select('bar','index==1')
```
```
store.select('bar','A==1)
```
should work as well (obviously only for a named index)
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} | 3 | 2014-12-08T14:44:13Z | 2015-05-09T16:05:40Z | 2015-05-09T16:05:40Z | NONE | null | Here's a simple example:
``` python
import pandas as pd
import numpy as np
x = pd.Series(range(5))
x.iloc[1] = np.nan
x.fillna(0) # works
x.fillna('foo') # works
x.fillna('0') # crash
```
in the function `_putmask_smart()`, two numpy arrays are compared to determine if they have compatible dtypes. However if one array contains quoted numbers, then the test does NOT resolve as element wise compare, but rather as a "normal" python comparison. This means the result is `False`, rather than an array, and the attempt to get `False.all()` obviously fails.
Not sure exactly why the arrays don't compare as expected -- perhaps there are other criteria that would trigger it -- but an easy fix is to add `AttributeError` to the try/except clause as a "valid" error.
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} | 1 | 2014-12-08T15:29:40Z | 2014-12-09T00:51:22Z | 2014-12-08T16:32:56Z | MEMBER | null | some fixes for different int sizes on windows
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| null | 6 | 2014-12-08T15:45:38Z | 2019-12-12T02:09:15Z | 2019-12-12T02:09:15Z | NONE | null | Using pandas 0.15.1
I have a csv file which unfortunately I cannot share (sanitizing it resolved the error) that gives me trouble when reading using the default (c) engine for parsing files. I switched to the python version which also failed but gave me the hint that it might be a problem with "universal-newline".
If I pass a file buffer instead that was opened with the universal-newline option then all works as expected.
This is the error I receive:
```
*** error: can't allocate region
*** set a breakpoint in malloc_error_break to debug
python(732,0x7fff7bc25300) malloc: *** mach_vm_map(size=18446744071562067968) failed (error code=3)
```
I've narrowed it down to the following (commented) interactions:
```
filePath = '/Volumes/Macintosh HD/data/FPGA Compile Cloud/aug14-nov14.csv'
fUniv = open(filePath, 'U')
f = open(filePath)
d = pd.read_csv(filePath,parse_dates=False, engine='c') # kernel restarts or malloc region error continuously and must be manually killed
d = pd.read_csv(f,parse_dates=False, engine='c') # kernel restarts or malloc region error continuously and must be manually killed
d = pd.read_csv(f,parse_dates=False, engine='python') # gives universal-newline error when passing file handle or filePath
d = pd.read_csv(fUniv,parse_dates=False, engine='c') # reads the dataframe correctly
```
This is my installed software:
anyreadline (0.1.1)
apptools (4.2.0)
argcomplete (0.6.7)
astropy (0.3.2)
atom (0.3.7)
awake (1.0)
backports.ssl-match-hostname (3.4.0.2)
beautifulsoup4 (4.3.1)
binstar (0.5.3)
biopython (1.62)
bitarray (0.8.1)
blaze (0.5)
blz (0.6.2)
bokeh (0.4.4)
boto (2.28.0)
brewer2mpl (1.4)
casuarius (1.1)
cdecimal (2.3)
chaco (4.4.1)
colorama (0.2.5)
conda (3.5.5)
configobj (5.0.5)
cubes (0.10.2)
Cython (0.20.1)
DataShape (0.2)
dill (0.2b1)
distribute (0.6.45)
Django (1.5.4)
docutils (0.11)
enable (4.3.0)
enaml (0.9.1)
envisage (4.3.0)
Flask (0.9)
Flask-KVSession (0.3.2)
future (0.12.1)
gevent (1.0.1)
gevent-websocket (0.9.3)
gevent-zeromq (0.2.2)
ggplot (0.5.8)
gnureadline (6.3.3)
google-api-python-client (1.1)
greenlet (0.4.2)
grin (1.2.1)
guppy (0.1.10)
h5py (2.3.0)
httplib2 (0.8)
icalendar (3.5)
ipdb (0.8)
ipython (2.3.1)
iso8601 (0.1.10)
itsdangerous (0.24)
jdcal (1.0)
Jinja2 (2.7.3)
keyring (3.2)
kiwisolver (0.1.2)
line-profiler (1.0b3)
llvmmath (0.1)
llvmpy (0.12.6)
lxml (3.3.5)
MarkupSafe (0.23)
matplotlib (1.3.1)
matplotlylib (0.1.0)
mayavi (4.3.0)
MDP (3.3)
memory-profiler (0.31)
meta (development)
mock (1.0.1)
multipledispatch (0.4.3)
netCDF4 (1.0.6)
networkx (1.8.1)
nltk (2.0.4)
nose (1.3.3)
numba (0.13.2)
numexpr (2.3.1)
numpy (1.9.1)
numpydoc (0.4)
openpyxl (1.8.5)
pandas (0.15.1)
patsy (0.2.1)
pep8 (1.5.6)
PIL (1.1.7)
pip (1.5.2)
plotly (1.4.0)
ply (3.4)
prettytable (0.7.2)
progressbar (2.2)
psutil (2.1.1)
py (1.4.20)
PyAudio (0.2.7)
pycosat (0.6.1)
pycparser (2.10)
pycrypto (2.6.1)
pycurl (7.19.3.1)
pydot (1.0.2)
pyface (4.4.0)
pyflakes (0.8.1)
Pygments (1.6)
pykit (0.1)
pylev (1.2.0)
pypandoc (0.8.1)
pyparsing (1.5.7)
PySAL (1.6.0)
pysam (0.6)
pytest (2.5.2)
python-dateutil (2.3)
python-gflags (2.0)
pytz (2014.10)
pyxDamerauLevenshtein (1.1)
PyYAML (3.11)
pyzmq (14.3.1)
qgrid (0.1.1)
redis (2.9.1)
requests (2.3.0)
rope (0.9.4)
runipy (0.1.1)
scikit-image (0.10.1)
scikit-learn (0.15.2)
scipy (0.14.0)
selenium (2.44.0)
setuptools (0.6c11)
simplekv (0.9.2)
six (1.8.0)
sklearn-compiledtrees (1.2)
Sphinx (1.2.2)
spyder (2.3.0rc)
SQLAlchemy (0.9.4)
statsmodels (0.5.0)
sympy (0.7.5)
tables (3.1.1)
tornado (3.2.1)
traits (4.4.0)
traitsui (4.4.0)
ujson (1.33)
VTK (5.10.1)
websocket-client (0.12.0)
Werkzeug (0.9.6)
wsgiref (0.1.2)
xlrd (0.9.3)
XlsxWriter (0.5.5)
xlwt (0.7.5)
yhat (0.7.5)
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} | 1 | 2014-12-08T15:51:33Z | 2020-10-24T02:49:09Z | 2020-10-24T02:49:09Z | NONE | null | In the example below I would expect that 'np.nan' would be replaced by 'aaah'.
```
In [3]: df = pd.DataFrame({'a': np.arange(3), 'b': [0.1, np.nan, 0.2]})
In [4]: df
Out[4]:
a b
0 0 0.1
1 1 NaN
2 2 0.2
In [5]: print(df.to_latex(formatters={'b': lambda x: '{:0.4f}'.format(x)}, na_rep='aaah'))
\begin{tabular}{lrr}
\toprule
{} & a & b \\
\midrule
0 & 0 & 0.1000 \\
1 & 1 & nan \\
2 & 2 & 0.2000 \\
\bottomrule
\end{tabular}
```
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} | 0 | 2014-12-09T01:21:31Z | 2014-12-10T11:11:03Z | 2014-12-10T11:11:03Z | CONTRIBUTOR | null | xref #8639
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} | 6 | 2014-12-09T12:12:51Z | 2020-03-31T04:31:46Z | null | NONE | null | Creating a timedelta with fractional hours gives an unexpected large round-off error.
```
>>> to_timedelta(1.0/3, unit='h')
Timedelta('0 days 00:19:59.998800')
```
This is indicates a precision around 2x10^-7 ( 1/(1000_60_60) ), which is less than a single precision float.
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} | 4 | 2014-12-09T14:19:34Z | 2015-02-10T14:52:04Z | 2015-02-10T14:52:04Z | NONE | null | Problem:
data.sum(level=...) for multi-index table produce different result (lots of NAs) than groupby
in certain cases. It's also much slower than groupby. Seems that the new version
produced a cross join of the keys and produce NAs for pair of keys with no data, which makes
the result bigger and significantly slower.
data.groupby(level=...).sum(). This happens in the following example:
Code:
``` python
import pandas as pd
print "-------------- pandas version: ", pd.__version__
max_num_of_syms = 4000
list_of_df = []
for i,a in enumerate(pd.Series(range(100)).astype(str)):
#Each 'A' has difference number of 'B' entries in order to produce the problem
num_of_syms = int(i*max_num_of_syms/100.0)# if i<3 else max_num_of_syms
#print num_of_syms
d = pd.DataFrame({'A': [a]*num_of_syms , 'B': pd.Series(range(num_of_syms)).astype(str), 'C':1})
list_of_df.append(d)
data = pd.concat(list_of_df).set_index(['A','B'])
%time a= data.sum(level=['A','B'])
print a.shape
#This is a lot faster
%time a= data.reset_index().groupby(['A','B']).sum()
print a.shape
```
-------------- pandas version: 0.15.1.dev
CPU times: user 876 ms, sys: 17 ms, total: 893 ms
Wall time: 894 ms
(392040, 1)
CPU times: user 109 ms, sys: 0 ns, total: 109 ms
Wall time: 108 ms
(198000, 1)
-------------- pandas version: 0.14.1
CPU times: user 94 ms, sys: 0 ns, total: 94 ms
Wall time: 94.2 ms
(198000, 1)
CPU times: user 120 ms, sys: 0 ns, total: 120 ms
Wall time: 120 ms (198000, 1)
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} | 16 | 2014-12-10T12:38:24Z | 2014-12-11T01:34:14Z | 2014-12-11T01:34:14Z | MEMBER | null | For example coming up when [0, 1] is passed by axhline
Closes #9012
test and whatsnew are coming
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| null | 9 | 2014-12-10T16:27:04Z | 2014-12-11T16:35:25Z | 2014-12-10T22:11:21Z | NONE | null | ``` python
keys = [('a', 'b', 'c'), ('d', 'e', 'f', 'g', 'h'), ('i', 'j', 'k', 'l')]
pd.MultiIndex.from_tuples(keys).lexsort_depth
Out[106]: 5
```
work correctly while:
``` python
keys = [('spot_es', 'spot', 'es'),
('prod_de_wind_ecen', 'prod', 'de', 'wind', 'ecen'),
('avu_fr_maxe-1', 'avu', 'fr', 'maxe-1')]
pd.MultiIndex.from_tuples(keys).lexsort_depth
Out[108]: 0
```
does not
``` python
pd.__version__
Out[128]: '0.15.1'
```
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} | 12 | 2014-12-10T19:47:53Z | 2017-01-28T15:02:37Z | 2016-02-15T20:28:58Z | CONTRIBUTOR | null | I didn't realize this was possible, and didn't see it in the docs.
```
df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'a', 'b'], 'C': [3, 4, 5]})
df.groupby('B').agg({'A': {'mean1': 'mean', 'med1': 'median'}, 'C': {'mean2': 'mean', 'med2': 'median'}})
```
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| null | 1 | 2014-12-11T10:03:00Z | 2017-11-24T15:18:30Z | null | MEMBER | null | If you do a time series plot:
```
s = pd.Series(np.random.randn(100), index=pd.date_range('1970-01-02', periods=100, freq='T'))
fig, ax = plt.subplots()
s.plot(ax=ax)
```
and then want to add a line through matplotlib using the index or datetime64 values, this does not work (it plots the data at a totally wrong place outside of sight in this case):
```
ax.plot(s.index, s.values, color='g')
# or
ax.plot(s.index.values, s.values, color='r')
```
This is because `PeriodConverter` cannot deal with datetime64 values (`Period`/`PeriodIndex` also cannot deal with them), while when plotting this through the pandas plot interface, the datetime64 values will first be converted to periods.
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} | 1 | 2014-12-11T10:10:25Z | 2015-04-14T16:14:49Z | 2015-04-14T16:14:49Z | MEMBER | null | I didn't directly find something about this, but is there a reason why the `Period(Index)` constructor does not take datetime64 values? As it does accept `datetime`:
```
In [15]: t = pd.Timestamp('2012-01-01')
In [16]: t.to_pydatetime()
Out[16]: datetime.datetime(2012, 1, 1, 0, 0)
In [17]: t.asm8
Out[17]: numpy.datetime64('2012-01-01T01:00:00.000000000+0100')
In [19]: pd.Period(t, freq='D')
Out[19]: Period('2012-01-01', 'D')
In [20]: pd.Period(t.to_pydatetime(), freq='D')
Out[20]: Period('2012-01-01', 'D')
In [21]: pd.Period(t.asm8, freq='D')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-21-f08079765cf6> in <module>()
----> 1 pd.Period(t.asm8, freq='D')
C:\Anaconda\lib\site-packages\pandas\tseries\period.pyc in __init__(self, value,
freq, ordinal, year, month, quarter, day, hour, minute, second)
141 else:
142 msg = "Value must be Period, string, integer, or datetime"
--> 143 raise ValueError(msg)
144
145 base, mult = _gfc(freq)
ValueError: Value must be Period, string, integer, or datetime
```
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} | 4 | 2014-12-11T10:29:35Z | 2014-12-12T15:25:07Z | 2014-12-12T15:25:07Z | MEMBER | null | @jreback The tests I started related to PR #9050
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} | 6 | 2014-12-11T10:50:54Z | 2017-06-01T19:59:44Z | 2017-06-01T19:59:44Z | MEMBER | null | See the docstring of PeriodIndex: https://github.com/pydata/pandas/blob/master/pandas/tseries/period.py#L557, it says:
```
A value of 1 is the period containing the Gregorian proleptic datetime Jan 1, 0001 00:00:00.
This ordinal representation is from the scikits.timeseries project.
For instance,
# construct period for day 1/1/1 and get the first second
i = Period(year=1,month=1,day=1,freq='D').asfreq('S', 'S')
i.ordinal
===> 1
```
but that is not really the truth:
```
In [24]: pd.Period('1970-01-01', freq='D')
Out[24]: Period('1970-01-01', 'D')
In [25]: pd.Period('1970-01-01', freq='D').ordinal
Out[25]: 0
In [34]: pd.Period(year=1,month=1,day=1,freq='D').ordinal
Out[34]: -719162
```
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} | 1 | 2014-12-11T11:36:40Z | 2015-06-07T22:28:37Z | 2015-06-07T22:28:37Z | CONTRIBUTOR | null | ```
In [39]: N = 1000
In [40]: df = DataFrame(np.random.randint(0,8,size=N*400).astype('uint8').reshape(N,-1),index=pd.date_range('20130206',periods=N,freq='ms'))
In [41]: df.to_hdf('test.h5','df',mode='w',format='table')
In [42]: df.to_hdf('test.h5','df',mode='w',format='table',data_columns=True)
AttributeError: 'numpy.int64' object has no attribute 'startswith'
```
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} | 0 | 2014-12-11T13:32:05Z | 2014-12-11T13:41:59Z | 2014-12-11T13:41:59Z | MEMBER | null | @jreback some more clean-up of whatsnew
- some small fixes so now everything builds ok, ready for release
I also reworded a little bit the intro of the minor release (I thought was a bit more appropriate for minor release vs major release)
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} | 3 | 2014-12-11T18:00:54Z | 2015-06-02T19:25:19Z | 2015-05-15T20:26:36Z | CONTRIBUTOR | null | tried to build on my debian box and got flooded... for now just a quick report and I would appreciate any help/advise
full build log http://www.onerussian.com/tmp/pandas_0.15.2-1_amd64.build
```
ERROR: test moving from daylight savings to standard time
ERROR: test moving from standard to daylight savings
ERROR: test_timestamp_tz_convert (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
ERROR: test_utc_with_system_utc (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
ERROR: test_with_tz (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
ERROR: test_tz_convert_roundtrip (pandas.tseries.tests.test_timezones.TestTimeZones)
ERROR: test_tz_convert_roundtrip (pandas.tseries.tests.test_tslib.TestTimestamp)
FAIL: test_append_with_timezones_dateutil (pandas.io.tests.test_pytables.TestHDFStore)
FAIL: test_period_resample_with_local_timezone_dateutil (pandas.tseries.tests.test_timeseries.TestTimeSeries)
FAIL: test_ambiguous_flags (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_ambiguous_infer (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_ambiguous_nat (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_datetimeindex_tz_nat (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_frame_no_datetime64_dtype (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_index_with_timezone_repr (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_infer_tz (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_localize_utc_conversion (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_localize_utc_conversion_explicit (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_timedelta_push_over_dst_boundary (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_timedelta_push_over_dst_boundary_explicit (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_tz_localize_dti (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_utc_box_timestamp_and_localize (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_with_tz_ambiguous_times (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
FAIL: test_tz_localize_roundtrip (pandas.tseries.tests.test_timezones.TestTimeZones)
FAIL: test_constructor (pandas.tseries.tests.test_tslib.TestTimestamp)
FAIL: test_repr (pandas.tseries.tests.test_tslib.TestTimestamp)
FAIL: test_tz_localize_roundtrip (pandas.tseries.tests.test_tslib.TestTimestamp)
FAIL: test_getitem_setitem_datetime_tz_dateutil (pandas.tests.test_series.TestSeries)
FAILED (SKIP=252, errors=7, failures=21)
```
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| null | 6 | 2014-12-12T00:59:14Z | 2014-12-14T01:41:50Z | 2014-12-14T01:31:39Z | NONE | null | There is no module named openpyxl.style on the Python3 version of openpyxl. There is, however a openpyxl.styles module in the namespace of openpyxl. But a simply name change update on line 577 of pandas/io/excel.py isn't the quick fix as there are other differences causing errors.
My offending code:
`diff_output = diff_panel.apply(report_diff, axis=0)`
`def has_change(row):`
`if "--->" in row.to_string():`
`return "Y"`
`else:`
`return "N"`
`diff_output['has_change'] = diff_output.apply(has_change, axis=1)`
-->`diff_output[(diff_output.has_change == 'Y')].to_excel('my-diff.xlsx')`
## Stacktrace tail
.
.
.
/usr/lib/python3/dist-packages/pandas/io/excel.py in _convert_to_style(cls, style_dict)
575 """
576
--> 577 from openpyxl.style import Style
578 xls_style = Style()
579 for key, value in style_dict.items():
ImportError: No module named 'openpyxl.style'
Name: openpyxl
Version: 2.1.3
Location: /usr/local/lib/python3.4/dist-packages
Requires: jdcal
Name: jdcal
Version: 1.0
Location: /usr/local/lib/python3.4/dist-packages
Requires:
Python 3.4.0 (default, Apr 11 2014, 13:05:11)
Type "copyright", "credits" or "license" for more information.
IPython 1.2.1 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
`In [1]: import pandas`
`In [2]: pandas.__version__`
`Out[2]: '0.13.1'`
## Modified 577
(-)`from openpyxl.style import Style`
(+)`from openpyxl.styles import Styles`
## New Stacktrace
---
TypeError Traceback (most recent call last)
<ipython-input-10-a608c8865a0a> in <module>()
----> `1 diff_output[(diff_output.has_change == 'Y')].to_excel('my-diff.xlsx')`
/usr/lib/python3/dist-packages/pandas/core/frame.py in to_excel(self, excel_writer, sheet_name, na_rep, float_format, cols, header, index, index_label, startrow, startcol, engine, merge_cells)
1202 formatted_cells = formatter.get_formatted_cells()
1203 excel_writer.write_cells(formatted_cells, sheet_name,
-> 1204 startrow=startrow, startcol=startcol)
1205 if need_save:
1206 excel_writer.save()
/usr/lib/python3/dist-packages/pandas/io/excel.py in write_cells(self, cells, sheet_name, startrow, startcol)
528 style = None
529 if cell.style:
--> 530 style = self._convert_to_style(cell.style)
531 for field in style.__fields__:
532 xcell.style.**setattr**(field,
/usr/lib/python3/dist-packages/pandas/io/excel.py in _convert_to_style(cls, style_dict)
583 .__setattr__('border_style', nv))
584 else:
--> 585 xls_style.**getattribute**(key).**setattr**(nk, nv)
586
587 return xls_style
/usr/local/lib/python3.4/dist-packages/openpyxl/styles/hashable.py in **setattr**(self, _args, *_kwargs)
51 getattr(self, name) is not None
52 and not isinstance(getattr(self, name), Descriptor)):
---> 53 raise TypeError('cannot set %s attribute' % name)
54 return object.**setattr**(self, _args, *_kwargs)
55
TypeError: cannot set bold attribute
It appears to me that there seems to be sufficient differences in openpyxl between Python2 and Python3. Something the pandas devs might want to be aware of.
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} | 8 | 2014-12-12T02:10:36Z | 2015-01-10T20:21:07Z | 2014-12-22T13:08:53Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/7466
on branch:
```
>>> df
a b c
0 R1 C1 10
1 R2 C2 15
2 NaN C3 17
3 R4 C4 20
>>> df.pivot('a', 'b', 'c').fillna('.')
b C1 C2 C3 C4
a
NaN . . 17 .
R1 10 . . .
R2 . 15 . .
R4 . . . 20
>>> df.pivot('b', 'a', 'c').fillna('.')
a NaN R1 R2 R4
b
C1 . 10 . .
C2 . . 15 .
C3 17 . . .
C4 . . . 20
```
the `pivot` function requires the `unstack` function to work with `nan`s in the index:
```
>>> df
4th 5th
1st 2nd 3rd
d y 67 d.y.67 67.y.d
39 d.y.39 39.y.d
w 53 d.w.53 53.w.d
NaN w 72 .w.72 72.w.
57 .w.57 57.w.
NaN 80 . .80 80. .
31 . .31 31. .
18 . .18 18. .
a z 11 a.z.11 11.z.a
30 a.z.30 30.z.a
c z 59 c.z.59 59.z.c
50 c.z.50 50.z.c
NaN 62 c. .62 62. .c
e NaN 59 e. .59 59. .e
76 e. .76 76. .e
b x 52 b.x.52 52.x.b
14 b.x.14 14.x.b
53 b.x.53 53.x.b
NaN 60 b. .60 60. .b
51 b. .51 51. .b
```
the `4th` and `5th` column are so that it is easy to verify the frame:
```
>>> df.unstack(level=1).fillna('-')
4th 5th
2nd NaN w x y z NaN w x y z
1st 3rd
NaN 18 . .18 - - - - 18. . - - - -
31 . .31 - - - - 31. . - - - -
57 - .w.57 - - - - 57.w. - - -
72 - .w.72 - - - - 72.w. - - -
80 . .80 - - - - 80. . - - - -
a 11 - - - - a.z.11 - - - - 11.z.a
30 - - - - a.z.30 - - - - 30.z.a
b 14 - - b.x.14 - - - - 14.x.b - -
51 b. .51 - - - - 51. .b - - - -
52 - - b.x.52 - - - - 52.x.b - -
53 - - b.x.53 - - - - 53.x.b - -
60 b. .60 - - - - 60. .b - - - -
c 50 - - - - c.z.50 - - - - 50.z.c
59 - - - - c.z.59 - - - - 59.z.c
62 c. .62 - - - - 62. .c - - - -
d 39 - - - d.y.39 - - - - 39.y.d -
53 - d.w.53 - - - - 53.w.d - - -
67 - - - d.y.67 - - - - 67.y.d -
e 59 e. .59 - - - - 59. .e - - - -
76 e. .76 - - - - 76. .e - - - -
```
`nan` values are kept out of levels and are handled by labels:
```
>>> df.unstack(level=1).index
MultiIndex(levels=[['a', 'b', 'c', 'd', 'e'], [11, 14, 18, 30, 31, 39, 50, 51, 52, 53, 57, 59, 60, 62, 67, 72, 76, 80]],
labels=[[-1, -1, -1, -1, -1, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4], [2, 4, 10, 15, 17, 0, 3, 1, 7, 8, 9, 12, 6, 11, 13, 5, 9, 14, 11, 16]],
names=['1st', '3rd'])
>>> df.unstack(level=1).columns
MultiIndex(levels=[['4th', '5th'], ['w', 'x', 'y', 'z']],
labels=[[0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [-1, 0, 1, 2, 3, -1, 0, 1, 2, 3]],
names=[None, '2nd'])
```
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| null | 3 | 2014-12-12T02:20:57Z | 2015-01-07T02:42:03Z | 2015-01-07T02:42:03Z | NONE | null | Hi,
I have noticed that importing/exporting data from/to Stata seems to increase file size a lot (sometimes more than double). I thought it mostly was caused by export to Stata, where string variables would be initially set to a length of 256 or something like that (usually after exporting I have to run the compress command in Stata and save the file again in order to save space). But now I noticed that importing data in Stata format and then exporting it to CSV causes similar increases in file size, even after dropping variables from the original file (so one would expect the file to be smaller). Any ideas of what is going on?
An example of a stata file with the exported csv file and script can be found [here](https://www.dropbox.com/s/xdimn6k40t7fmf4/Export_filesize_issue.zip?dl=0).
I ran the following commands in a script (included in the zip file):
```
#!/usr/bin/env python
# coding: utf-8
from __future__ import division
import sys, os, time
# Math, data
import numpy as np
import pandas as pd
# Print filesize original
print('Original filesize= '+str(os.path.getsize('./cn59_mat.dta'))) #Original filesize= 2883761
# Load data
df=pd.read_stata('cn59_mat.dta')
# Drop one variable
df.drop('index', axis=1, inplace=True)
# Export
df.to_csv('cn59.csv', encoding='utf-8', index=False)
# Print filesize original
print('New filesize= '+str(os.path.getsize('./cn59.csv'))) #New filesize= 4984671
```
So as you can see it doubled in size. I am using Python 2.7.6 with Pandas 0.15.1 on OS X 10.9.
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| null | 2 | 2014-12-12T12:08:29Z | 2014-12-12T12:52:39Z | 2014-12-12T12:52:39Z | NONE | null | The meaning of the `before` and `after` parameters of `truncate()` are swapped.
The code returns a `slice(before, after)` so `before` actually is the lower-bound and everything _after_ it is returned; and vice versa for `after`.
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} | 6 | 2014-12-12T14:28:38Z | 2015-01-18T20:53:40Z | 2015-01-18T20:53:40Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/27444949/build-a-pandas-pd-tseries-offsets-from-timedelta)
This works, but I think `td.to_offset()` makes sense
```
In [1]: td = Timedelta('1h')
In [2]: td
Out[2]: Timedelta('0 days 01:00:00')
In [7]: pd.DateOffset(hours=td.hours)
Out[7]: <DateOffset: kwds={'hours': 1}>
```
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} | 3 | 2014-12-12T14:55:48Z | 2014-12-12T15:54:51Z | 2014-12-12T15:54:26Z | NONE | null | xref #3025
To reproduce:
``` python
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3],
'B': ['20140101', nan, '20140101'],
'C': [nan, nan, nan]})
df2 = pd.DataFrame({'A': [1, 2],
'B': ['20140104', '20140105'],
})
df.B = pd.to_datetime(df.B, coerce=True)
df2.B = pd.to_datetime(df2.B, coerce=True)
df.set_index('A', inplace=True)
df2.set_index('A', inplace=True)
#df.update(df2[['B']]) # Works fine
df.update(df2[['B']], overwrite=False) # Fails with 'invalid type promotion' error.
```
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} | 1 | 2014-12-12T16:57:43Z | 2014-12-24T19:11:56Z | 2014-12-24T19:11:56Z | CONTRIBUTOR | null | Right now AFAIK there isn't any ~ expansion. Seems like nice, low-hanging fruit.
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| null | 6 | 2014-12-12T17:19:49Z | 2021-04-16T04:25:29Z | null | CONTRIBUTOR | null | In csv files that have odd spacing
```
name, amount
Alice,100
Bob,200
```
Loaded dataframes can sometimes have column names with leading or trailing whitespace. Should these column names be stripped by default?
``` Python
In [4]: pd.read_csv('foo.csv').columns
Out[4]: Index([u'name', u' amount'], dtype='object')
```
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} | 0 | 2014-12-12T18:01:49Z | 2015-04-10T13:50:14Z | 2015-04-10T13:50:14Z | CONTRIBUTOR | null | currently
`Series.str.strip()` works
but
`Index(Series(Index.values).str.strip())` would be necessary for an index
should be very straightforward to move the `.str` accessor to `IndexOpsMixin` (from Series)
to support this (would need to prevent Index sub-classes from using it though as they don't support string ops.
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} | 5 | 2014-12-13T01:07:55Z | 2017-07-08T02:32:50Z | 2017-07-08T02:32:47Z | CONTRIBUTOR | null | In the case of the index being a DateTimeIndex, I think there's a nice easy default case for plotting a categorical column: Let each category assume one value on the y-axis (user-providable, or range(len(categories)) by default) and plot a symbol ('*' by default maybe) at every point in time the categorical value has an entry. The y-axis tick labels should be the categorical values of course.
This can provide nice overviews in astronomy or other remote sensing sciences for example to indicate what kind of observation happened when and in what sequence. Or which of some set of lab experiments was executed when.
What do you guys think?
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} | 59 | 2014-12-13T11:45:25Z | 2019-02-24T07:00:20Z | 2015-09-01T18:52:24Z | CONTRIBUTOR | null | This work should eventually lead to a reader and writer for ods spreadsheets (LibreOffice / OpenOffice Open Document Format). I am doing this work in the master of my clone of pandas. From the wiki I understood it is fine for me to commit to my master and rebase with upstream from time to time. Please let me know if I should follow another git workflow (this is my first PR).
This is what I have so far:
- It seems to make sense to implement an OdsFile and OdsWriter in pandas/io/excel.py
- I created ods test files identical to the other Excel test files.
- Basic reading works, but does not handle all corner cases correctly yet
- Tests and WritingOds are not implemented yet
I am not sure how to approach the following issues:
- For the tests, it would make sense to also use pandas/io/tests/test_excel.py (there would be a lot of code duplication otherwise). However, this would require some kind of a more elaborate test skipping mechanism than currently implemented. One could have ods dependencies installed, but not the Excel ones and vice versa.
- When considering pandas could support more spreadsheet formats, does it make sense to rename pandas/io/excel.py to spreadsheet.py, and correspondingly, read_excel to read_spreadsheet? I guess this is not easy since it a lot of stuff out there already counts on the excel naming scheme.
For the record, there is one message on the [mailing list](https://groups.google.com/forum/?fromgroups#!topic/pydata/Zv-BXJNsP2U), and this PR will fix issue #2311.
ods support depends on [ezodf](https://github.com/T0ha/ezodf), and issue #2311 mentions some of the alternatives.
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} | 1 | 2014-12-13T19:28:52Z | 2015-01-05T23:50:41Z | 2015-01-05T23:50:41Z | CONTRIBUTOR | null | Please, expose the interval flag of the Yahoo CSV API. Right now is hardcoded to have daily interval.
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} | 12 | 2014-12-13T19:30:10Z | 2015-01-05T23:50:25Z | 2015-01-05T23:49:33Z | CONTRIBUTOR | null | PR for the issue #9071
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| null | 2 | 2014-12-13T21:38:33Z | 2019-10-06T01:08:57Z | 2019-10-06T01:08:57Z | CONTRIBUTOR | null | xref: https://github.com/pydata/pandas/pull/8676#issuecomment-66888559
**memory cost**:
with `int16` labels:
```
$ python -m memory_profiler mem-profile.py
Filename: mem-profile.py
Line # Mem usage Increment Line Contents
================================================
4 80.156 MiB 0.000 MiB @profile
5 def ix(obj):
6 87.809 MiB 7.652 MiB obj.ix[999]
```
with `int64` labels:
```
$ python -m memory_profiler mem-profile.py
Filename: mem-profile.py
Line # Mem usage Increment Line Contents
================================================
4 79.387 MiB 0.000 MiB @profile
5 def ix(obj):
6 79.387 MiB 0.000 MiB obj.ix[999]
```
**performance cost**:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_xs_mi_ix | 8.5303 | 0.6206 | 13.7452 |
series_xs_mi_ix | 8.0659 | 0.5600 | 14.4023 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [c11e75c] : PERF: set multiindex labels with coerced dtype (GH8456)
Base [6bbb39e] : Merge pull request #8675 from pydata/setitem
```
the `mem-profile.py` used for memory profiling:
```
@profile
def ix(obj):
obj.ix[999]
if __name__ == '__main__':
import numpy as np
from pandas import MultiIndex, Series
mi = MultiIndex.from_tuples([(x,y) for x in range(1000) for y in range(1000)])
ts = Series(np.random.randn(1000000), index=mi)
ix(ts)
```
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} | 1 | 2014-12-14T06:05:46Z | 2014-12-14T22:14:09Z | 2014-12-14T21:50:02Z | CONTRIBUTOR | null | The power of an 'e'.
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} | 1 | 2014-12-14T19:51:04Z | 2014-12-18T11:40:22Z | 2014-12-18T11:40:22Z | NONE | null | The following code prints False when run with Pandas 0.15.0, but True with 0.15.2; I'm using Python 2.7.6 on Ubuntu 14.04.1 in both cases. Is this change in behavior expected? (I was using has_duplicates to detect duplicate tuples in the index.)
```
import pandas as pd
t = [(u'x', u'out', u'z', 5, u'y', u'in', u'z', 169),
(u'x', u'out', u'z', 7, u'y', u'in', u'z', 119),
(u'x', u'out', u'z', 9, u'y', u'in', u'z', 135),
(u'x', u'out', u'z', 13, u'y', u'in', u'z', 145),
(u'x', u'out', u'z', 14, u'y', u'in', u'z', 158),
(u'x', u'out', u'z', 16, u'y', u'in', u'z', 122),
(u'x', u'out', u'z', 17, u'y', u'in', u'z', 160),
(u'x', u'out', u'z', 18, u'y', u'in', u'z', 180),
(u'x', u'out', u'z', 20, u'y', u'in', u'z', 143),
(u'x', u'out', u'z', 21, u'y', u'in', u'z', 128),
(u'x', u'out', u'z', 22, u'y', u'in', u'z', 129),
(u'x', u'out', u'z', 25, u'y', u'in', u'z', 111),
(u'x', u'out', u'z', 28, u'y', u'in', u'z', 114),
(u'x', u'out', u'z', 29, u'y', u'in', u'z', 121),
(u'x', u'out', u'z', 31, u'y', u'in', u'z', 126),
(u'x', u'out', u'z', 32, u'y', u'in', u'z', 155),
(u'x', u'out', u'z', 33, u'y', u'in', u'z', 123),
(u'x', u'out', u'z', 12, u'y', u'in', u'z', 144)]
idx = pd.MultiIndex.from_tuples(t)
print idx.has_duplicates
```
Reference on StackOverflow [here](http://stackoverflow.com/questions/27473093/understanding-the-pandas-multiindex-has-duplicates-property).
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} | 28 | 2014-12-14T21:58:32Z | 2015-03-04T21:13:49Z | 2015-03-03T01:04:51Z | CONTRIBUTOR | null | xref #4343
closes #8048
This passes locally run nosetests but is failing on Travis. I think the latest Travis CI changes might have caused failures. I was able to get master to pass on Travis last week but now it is failing.
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} | 6 | 2014-12-14T23:55:41Z | 2014-12-18T11:39:07Z | 2014-12-18T11:39:07Z | CONTRIBUTOR | null | closes #9075
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} | 7 | 2014-12-15T08:11:15Z | 2014-12-18T02:06:30Z | 2014-12-17T22:14:26Z | CONTRIBUTOR | null | Let me know if this needs any work.
closes #8943
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} | 3 | 2014-12-15T13:03:45Z | 2014-12-20T20:31:45Z | 2014-12-20T20:31:45Z | CONTRIBUTOR | null | Hi,
The latest version of pandas 0.15.2 can no longer read a file that was no problem before.
The file has the following format.
```
SMOSMANIA SMOSMANIA Narbonne 43.15000 2.95670 112.00 0.05 0.05 ThetaProbe-ML2X
2007/01/01 01:00 0.2140 U M
2007/01/01 02:00 0.2140 U M
2007/01/01 03:00 0.2140 U M
```
The file can be found [here](https://raw.githubusercontent.com/TUW-GEO/pytesmo/master/tests/test_ismn/test_data/format_header_values/SMOSMANIA/SMOSMANIA_SMOSMANIA_Narbonne_sm_0.050000_0.050000_ThetaProbe-ML2X_20070101_20070131.stm)
Since the first line is not directly related to the number of columns below I use skiprows=1 and specify the names explicitly.
```
fname='https://raw.githubusercontent.com/TUW-GEO/pytesmo/master/tests/test_ismn/test_data/format_header_values/SMOSMANIA/SMOSMANIA_SMOSMANIA_Narbonne_sm_0.050000_0.050000_ThetaProbe-ML2X_20070101_20070131.stm'
pd.read_csv(fname, skiprows=1, delim_whitespace=True, names=['date', 'time', 'variable','flag','orig_flag'])
```
Please compare the two code sample below. The first using pandas 0.15.2, the second one 0.15.1
### 0.15.2
```
In [1]: import pandas as pd
In [2]: from pandas.util.print_versions import show_versions
In [3]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.6.0
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
In [4]: pd.read_csv('https://raw.githubusercontent.com/TUW-GEO/pytesmo/master/tests/test_ismn/test_data/format_header_values/SMOSMANIA/SMOSMANIA_SMOSMANIA_Narbonne_sm_0.050000_0.050000_ThetaProbe-ML2X_20070101_20070131.stm', skiprows=1, delim_whitespace=True, names=['date', 'time', 'variable','flag','orig_flag'])
Out[4]:
Empty DataFrame
Columns: [date, time, variable, flag, orig_flag]
Index: []
```
### 0.15.1
```
In [1]: import pandas as pd
In [2]: from pandas.util.print_versions import show_versions
In [3]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.1
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.6.0
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.9
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
In [4]: pd.read_csv('https://raw.githubusercontent.com/TUW-GEO/pytesmo/master/tests/test_ismn/test_data/format_header_values/SMOSMANIA/SMOSMANIA_SMOSMANIA_Narbonne_sm_0.050000_0.050000_ThetaProbe-ML2X_20070101_20070131.stm', skiprows=1, delim_whitespace=True, names=['date', 'time', 'variable','flag','orig_flag'])
Out[4]:
date time variable flag orig_flag
0 2007/01/01 01:00 0.2140 U M
1 2007/01/01 02:00 0.2140 U M
2 2007/01/01 03:00 0.2140 U M
3 2007/01/01 04:00 0.2140 U M
4 2007/01/01 05:00 0.2140 U M
5 2007/01/01 06:00 0.2140 U M
6 2007/01/01 07:00 0.2135 U M
7 2007/01/01 08:00 0.2135 U M
8 2007/01/01 09:00 0.2135 U M
9 2007/01/01 10:00 0.2140 U M
10 2007/01/01 11:00 0.2140 U M
11 2007/01/01 12:00 0.2145 U M
12 2007/01/01 13:00 0.2149 U M
13 2007/01/01 14:00 0.2149 U M
14 2007/01/01 15:00 0.2149 U M
15 2007/01/01 16:00 0.2145 U M
16 2007/01/01 17:00 0.2135 U M
17 2007/01/01 18:00 0.2130 U M
18 2007/01/01 19:00 0.2130 U M
19 2007/01/01 20:00 0.2126 U M
20 2007/01/01 21:00 0.2121 U M
21 2007/01/01 22:00 0.2121 U NaN
22 2007/01/01 23:00 0.2116 U M
23 2007/01/02 00:00 0.2116 U M
24 2007/01/02 01:00 0.2112 U M
25 2007/01/02 02:00 0.2107 U M
26 2007/01/02 03:00 0.2107 U M
27 2007/01/02 04:00 0.2102 U M
28 2007/01/02 05:00 0.2098 U M
29 2007/01/02 06:00 0.2098 U M
.. ... ... ... ... ...
711 2007/01/30 18:00 0.1538 U M
712 2007/01/30 19:00 0.1534 U M
713 2007/01/30 20:00 0.1534 U M
714 2007/01/30 21:00 0.1534 U M
715 2007/01/30 22:00 0.1534 U M
716 2007/01/30 23:00 0.1534 U M
717 2007/01/31 00:00 0.1534 U M
718 2007/01/31 01:00 0.1531 U M
719 2007/01/31 02:00 0.1531 U M
720 2007/01/31 03:00 0.1527 U M
721 2007/01/31 04:00 0.1527 U M
722 2007/01/31 05:00 0.1524 U M
723 2007/01/31 06:00 0.1524 U M
724 2007/01/31 07:00 0.1524 U M
725 2007/01/31 08:00 0.1521 U M
726 2007/01/31 09:00 0.1521 U M
727 2007/01/31 10:00 0.1521 U M
728 2007/01/31 11:00 0.1524 U M
729 2007/01/31 12:00 0.1527 U M
730 2007/01/31 13:00 0.1534 U M
731 2007/01/31 14:00 0.1541 U M
732 2007/01/31 15:00 0.1545 U M
733 2007/01/31 16:00 0.1541 U M
734 2007/01/31 17:00 0.1538 U M
735 2007/01/31 18:00 0.1534 U M
736 2007/01/31 19:00 0.1531 U M
737 2007/01/31 20:00 0.1527 U M
738 2007/01/31 21:00 0.1527 U M
739 2007/01/31 22:00 0.1524 U M
740 2007/01/31 23:00 0.1524 U M
[741 rows x 5 columns]
```
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} | 5 | 2014-12-15T14:13:23Z | 2018-07-06T20:21:06Z | 2017-07-10T21:06:29Z | NONE | null | I've encountered a problem when using .loc and .iloc to assign values to a 1D slice of a Panel when the axis in the direction of the slice has a MulitIndex. Here is a simple example to demonstrate:
```
import pandas as pd
import numpy as np
pn = pd.Panel(np.zeros((3, 3, 3)))
pn.loc[0, :, 0] = [1, 2, 3]
pn.major_axis = pd.MultiIndex.from_tuples(zip([1, 2, 3], list('abc')))
pn.loc[0, :, 0] = [1, 2, 3]
```
Running this code gives an IndexError at the last line with the message 'tuple index out of range'.
Note how the error is only raised after the major axis has been set with the MulitIndex.
Using .iloc or .ix instead produces the same error. A similar procedure using either of the other two axes produces no error. Pandas version is 0.15.1
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| null | 3 | 2014-12-15T14:28:43Z | 2014-12-23T18:28:36Z | 2014-12-23T18:28:36Z | NONE | null | Now rolling functions like _rolling_mean_ give NaN's if the window size is outside the input series. I would like to have an extra option that the window size shrinkes if the window is outside the input series. Or to put it different: use an expanding mean for the first values and the rolling mean for all the intermediate values. Below an example of such a code and my workaround.
``` python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#%% create data
df = pd.DataFrame()
data = np.cumsum(np.random.random(10)-0.5)
df['data'] = data
#%% do the calculations
# now:
window_size = 5
df['rolling_mean'] = pd.rolling_mean(df['data'], window=window_size)
# or:
df['expanding']= pd.expanding_mean(df['data'],)
# what I wont
df['whatIwant'] = df['rolling_mean'].fillna(df['expanding'])
#%% create picture
fig, ax = plt.subplots()
ax.plot(df.index, df['data'], lw=5, label='data')
ax.plot(df.index, df['rolling_mean'], lw=5, label='rolling mean')
ax.plot(df.index, df['expanding'], lw=5, label='expanding')
ax.plot(df.index, df['whatIwant'], lw=2, label='what I want')
ax.legend()
```
This workaround works quite good, and is sort of clear. But if _rolling_mean_ is used in combination with _center=True_ this becomes more complicated. This workaround is also not very usefull when some data is missing. I think pandas can be improved by adding this option into the rolling_statistics functions.
Disclaimer: This is the first issue ever that I add.
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} | 4 | 2014-12-15T15:08:08Z | 2017-02-02T19:38:14Z | 2015-01-10T17:33:40Z | CONTRIBUTOR | null | Wes' old [blog post](http://wesmckinney.com/blog/update-on-upcoming-pandas-v0-10-new-file-parser-other-performance-wins/) indicates that you can read the malformed FEC data by passing `index_col=False`, the docstring for read_csv seems to also say this. It doesn't look like this works anymore?
```
[9]: cat test.csv
cmte_id,cand_id,cand_nm,contbr_nm,contbr_city,contbr_st,contbr_zip,contbr_employer,contbr_occupation,contb_receipt_amt,contb_receipt_dt,receipt_desc,memo_cd,memo_text,form_tp,file_num,tran_id,election_tp
C00410118,"P20002978","Bachmann, Michele","HARVEY, WILLIAM","MOBILE","AL","366010290","RETIRED","RETIRED",250,20-JUN-11,"","","","SA17A","736166","A1FDABC23D2D545A1B83","P2012",
C00410118,"P20002978","Bachmann, Michele","HARVEY, WILLIAM","MOBILE","AL","366010290","RETIRED","RETIRED",50,23-JUN-11,"","","","SA17A","736166","A899B9B0E223743EFA63","P2012",
[10]: cols = ['cand_nm', 'contbr_st', 'contbr_employer', 'contb_receipt_amt',
'contbr_occupation', 'contb_receipt_amt', 'contb_receipt_dt']
# raises an error
pd.read_csv("test.csv", usecols=cols, index_col=False)
# gets "incorrect" columns
pd.read_csv("test.csv", usecols=cols)
```
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} | 13 | 2014-12-15T15:29:25Z | 2018-05-19T05:43:47Z | 2014-12-24T10:36:06Z | NONE | null | I'm trying to use DataFrame().to_sql to input a time aware dataframe series. Here is an example of my code.
```
times = ['201412120154', '201412110254']
df = pd.DataFrame()
df['time'] = pd.to_datetime(times, utc=True)
df.time.to_sql('test', engine, dtype={'time': sqlalchemy.TIMESTAMP(timezone=True)})
```
The error I recieve is:
```
TypeError: issubclass() arg 1 must be a class
```
The following code works but obviously results in a postgresql column that is not timezone aware.
```
times = ['201412120154', '201412110254']
df = pd.DataFrame()
df['time'] = pd.to_datetime(times, utc=True)
df.time.to_sql('test', engine, dtype={'time': sqlalchemy.TIMESTAMP})
```
I'm using python 2.7, pandas 0.15.2, postsgresql 9.3 and SQLAlchemy 0.9.7. This same issue also occurs with sqlalchemy.DATETIME(timezone=True) vs sqlalchemy.DATETIME
Full Traceback:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-161-ec79a553e6d0> in <module>()
----> 1 df.time.to_sql('test', w.engine, dtype={'time': sqlalchemy.TIMESTAMP(timezone=True)})
/home/stew/.pyenv/versions/p276/lib/python2.7/site-packages/pandas/core/generic.pyc in to_sql(self, name, con, flavor, schema, if_exists, index, index_label, chunksize, dtype)
964 self, name, con, flavor=flavor, schema=schema, if_exists=if_exists,
965 index=index, index_label=index_label, chunksize=chunksize,
--> 966 dtype=dtype)
967
968 def to_pickle(self, path):
/home/stew/.pyenv/versions/p276/lib/python2.7/site-packages/pandas/io/sql.pyc in to_sql(frame, name, con, flavor, schema, if_exists, index, index_label, chunksize, dtype)
536 pandas_sql.to_sql(frame, name, if_exists=if_exists, index=index,
537 index_label=index_label, schema=schema,
--> 538 chunksize=chunksize, dtype=dtype)
539
540
/home/stew/.pyenv/versions/p276/lib/python2.7/site-packages/pandas/io/sql.pyc in to_sql(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype)
1162 import sqlalchemy.sql.type_api as type_api
1163 for col, my_type in dtype.items():
-> 1164 if not issubclass(my_type, type_api.TypeEngine):
1165 raise ValueError('The type of %s is not a SQLAlchemy '
1166 'type ' % col)
TypeError: issubclass() arg 1 must be a class
```
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} | 2 | 2014-12-15T16:21:49Z | 2021-04-12T04:38:51Z | null | MEMBER | null | `pd.io.sql.get_schema` can be used to see the schema 'create table' statement. This function should support including the index (which is actually the default for `to_sql`)
```
In [31]: df = pd.DataFrame({'col':[1,2,3]}, index=pd.date_range('2012-01-01', periods=3))
In [33]: print pd.io.sql.get_schema(df, 'test_schema', con=engine_postgres)
CREATE TABLE test_schema (
col BIGINT
)
In [34]: print pd.io.sql.get_schema(df.reset_index(), 'test_schema', con=engine_postgres)
CREATE TABLE test_schema (
index TIMESTAMP WITHOUT TIME ZONE,
col BIGINT
)
```
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} | 0 | 2014-12-15T16:44:17Z | 2014-12-20T22:30:13Z | 2014-12-20T22:30:13Z | MEMBER | null | When you have datetime data with timezone info attached, the data is stored as `Timestamp` objects in a `object` dtyped column, instead of in a ´datetime64´ column (as this cannot hold the timezone info).
But, this causes `to_sql` to gives this the `TEXT` type:
With a postgresql engine:
```
In [48]: times = ['201412120154', '201412110254']
....: df = pd.DataFrame()
....: df['time'] = pd.to_datetime(times, utc=True)
In [49]: df
Out[49]:
time
0 2014-12-12 01:54:00+00:00
1 2014-12-11 02:54:00+00:00
In [50]: df.dtypes
Out[50]:
time object
dtype: object
In [51]: df.time.values
Out[51]:
array([Timestamp('2014-12-12 01:54:00+0000', tz='UTC'),
Timestamp('2014-12-11 02:54:00+0000', tz='UTC')], dtype=object)
In [53]: print pd.io.sql.get_schema(df, 'test', con=engine_postgres)
CREATE TABLE test (
time TEXT
)
```
This is because:
```
In [54]: pd.lib.infer_dtype(df['time'])
Out[54]:
'datetime'
```
instead of `datetime64`, and this case is not handled at the moment.
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} | 2 | 2014-12-15T16:49:41Z | 2018-11-08T14:55:50Z | 2018-11-08T14:55:50Z | MEMBER | null | This currently works a bit for datetime values in the index (not in the columns, reason for this is that is then are object columns: #9085).
I say 'a bit', as it does give a 'timestamp with time zone' type (example for postgresql), but it is the time localized in your local timezone (I suppose because this is the way numpy works on input/output of datetimes).
In any case, also the code here (https://github.com/pydata/pandas/blob/v0.15.2/pandas/io/sql.py#L916) is not working (as columns never have a `tzinfo` attribute), and there are also no tests for this.
So in practice, timezones are not yet supported.
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} | 2 | 2014-12-15T16:58:53Z | 2019-12-30T18:51:39Z | 2019-12-30T18:51:39Z | MEMBER | null | Noticed when looking at #9083 that we used an sqlalchemy feature in #8926 that is not supported in at least sqlalchemy
Concrete, it is about `import sqlalchemy.sql.type_api as type_api` (https://github.com/pydata/pandas/blob/v0.15.2/pandas/io/sql.py#L1162). I cannot import this 0.8.2 (and in any case, this should better be `sqlalchemy.types.TypeEngine` instead of `type_api.TypeEngine`)
We should check if we test older slqalchemy versions (0.8.x series, or even 0.7.x series?), or why this has not been caught by the tests.
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} | 3 | 2014-12-15T20:32:57Z | 2015-01-19T13:21:47Z | 2015-01-19T13:21:47Z | NONE | null | When using `df.plot(kind='bar')` pandas adds a dotted line to the x-axis (`kind='barh'`, line is added to y-axis). Why is the dotted line added ([line in the code](https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L1899))?
This line is undocumented and there is no way to remove it without a hack. It'd be nice to have it documented (if it is necessary) or removed.
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} | 7 | 2014-12-15T21:24:10Z | 2018-11-16T19:36:06Z | 2018-11-16T17:24:13Z | NONE | null | A user may expect that the end_time of a Period minus the start_time of a Period returns its duration. Unfortunately, the end_time of a Period is inclusive, consequently, subtracting start from end yields an incorrect period duration:
```
jan = Period('2014-01', freq='M')
(jan.end_time - jan.start_time).days # returns 30, not 31
```
This can be manually rectified by the user:
```
jan = Period('2014-01', freq='M')
(Timestamp(jan.end_time.value + 1) - jan.start_time).days # correctly returns 31
```
However, this is not intuitive.
The end_time should either be exclusive or Period should provide a duration property.
Although a strong argument can be made that the definitions of duration, start_time, and end_time should all be consistent, changing the end_time definition to exclusive is a significant modification of long-standing behavior.
Consequently, I propose the addition of a duration property that is logically inconsistent with subtracting start_time from end_time.
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} | 5 | 2014-12-16T00:37:56Z | 2015-01-02T16:29:36Z | 2015-01-02T16:29:36Z | NONE | null | closes #8979
Let me know if this makes sense
Problem mentioned in issue 8979 too
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} | 3 | 2014-12-16T02:03:07Z | 2018-07-06T20:21:06Z | 2017-07-10T21:06:38Z | NONE | null | Panel.describe() docs for v15.2 [here](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Panel.describe.html)
**Environment:**
fedora 20
pandas 15.2
Python 2.7.5
IPython 2.3.0
**To duplicate:**
```
$ pip install --upgrade pandas
In [8]: thresh = pd.Panel({'1': pd.DataFrame(pd.np.arange(2,3)), '2': pd.DataFrame(pd.np.arange(1,2))})
In [9]: thresh
Out[9]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 1 (major_axis) x 1 (minor_axis)
Items axis: 1 to 2
Major_axis axis: 0 to 0
Minor_axis axis: 0 to 0
In [10]: thresh['1'].describe()
Out[10]:
0
count 1
mean 2
std NaN
min 2
25% 2
50% 2
75% 2
max 2
In [11]: thresh.describe()
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
/usr/src/projects/predict/predict/webapp/dashboard/models.pyc in <module>()
----> 1 thresh.describe()
/usr/local/share/.virtualenvs/predict/lib/python2.7/site-packages/pandas/core/generic.pyc in describe(self, percentile_width, percentiles, include, exclude)
3748 if self.ndim >= 3:
3749 msg = "describe is not implemented on on Panel or PanelND objects."
-> 3750 raise NotImplementedError(msg)
3751
3752 if percentile_width is not None and percentiles is not None:
NotImplementedError: describe is not implemented on on Panel or PanelND objects.
In [12]: pd.__version__
Out[12]: '0.15.2'
In [13]: type(thresh)
Out[13]: pandas.core.panel.Panel
```
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} | 6 | 2014-12-16T02:38:35Z | 2017-07-02T17:39:17Z | 2017-07-02T17:38:59Z | NONE | null | Hi,
I have recently come across an unexpexted behaviour of the dataframe "shift" function.
When column titles are duplicated (or present more often), a column-specific shift using index-based positioning (by "iloc") does not only shift the specified columns, but all columns sharing the same name. From the behaviour I guess that internally, the column names and not the indices are used, causing all identically named columns to shift.
This behaviour did not occur (at least to my knowledge ) with v. 0.13, but is present in both 0.15.1 and 0.15.2.
Please find attached a small code snippet which demonstrates the behaviour, and the system information.
Thanks a lot for any help
Kay
#### Begin example code
``` python
from pandas import *
import pandas as pd
data = DataFrame(np.random.randn(20, 5)) # Generating a random data frame
data2 = data.copy()
data3 = data.copy()
shifting_matrix = [1,2,3,4,5] # shifting matrix - we want to shift the columns by these values
#Shifting the columns: Expected behaviour. Column 1 is shifter by 1, Column 2 by 2 and so on
for position, movement in enumerate(shifting_matrix):
print position
print movement
data.iloc[:, position] = data.iloc[:,position].shift(movement)
## Changing the titles of the dataframe
titles = [1,1,1,1,1]
data2.columns = titles
# Shifting the columns again. Since we are using index based positions, the shifting behaviour should be the same as above
for position, movement in enumerate(shifting_matrix):
print position
print movement
data2.iloc[:, position] = data2.iloc[:,position].shift(movement)
titles2 = [1,1,2,2,1] # Making mixed column titles, duplicating
data3.columns = titles2
# Shifting the columns again. Since we are using index based positions, the shifting behaviour should be the same as above
for position, movement in enumerate(shifting_matrix):
print position
print movement
data3.iloc[:, position] = data3.iloc[:,position].shift(movement)
print data
print data2
print data3
```
#### End example code
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: nb_NO
pandas: 0.15.2
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
dateutil: 2.2
pytz: 2014.3
bottleneck: 0.8.0
tables: None
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: 2.1.0
xlrd: None
xlwt: 0.7.5
xlsxwriter: 0.5.8
lxml: 3.3.5
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 9 | 2014-12-16T08:15:59Z | 2020-06-26T00:37:09Z | 2020-06-26T00:37:09Z | CONTRIBUTOR | null | The current [plot()](http://pandas.pydata.org/pandas-docs/version/0.15.1/generated/pandas.DataFrame.plot.html#pandas.DataFrame.plot) method for Dataframe cannot set the x/y label in it. [Result](http://stackoverflow.com/questions/21487329/add-x-and-y-labels-to-a-pandas-plot?lq=1) is more lines of code for just doing this.
As someone commented in that post:
> Is there a particular reason why x and y labels can't be added as arguments to pd.plot()? Given the additional concision of pd.plot() over plt.plot() it seems it would make sense to make it even more succinct instead of having to call ax.set_ylabel()
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} | 14 | 2014-12-16T12:58:35Z | 2016-09-13T21:34:01Z | 2015-03-11T10:21:41Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/27502406/convert-pandas-datetimeindex-to-fractional-day-of-year) and previous discussions
currently you cannot subtract 2 datetimeindexes ('-' is actually a set op). This is the ONLY valid numerical type of operation (e.g. you cannot add dti).
We current support pretty much the fully gamut of obvious ops, with `dti-dti` being the exception.
full list of tests are here: https://github.com/pydata/pandas/blob/master/pandas/tseries/tests/test_base.py#L434
I will propose
`dti.sub(dti)` to rectify this
thoughts
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} | 12 | 2014-12-16T23:41:59Z | 2015-03-11T10:21:41Z | 2015-03-11T10:21:41Z | MEMBER | null | In 0.15.0, the `-` index set operation raises a warning as is explained in the whatsnew docs: http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#deprecations
```
In [1]: pd.__version__
Out[1]: '0.15.0'
In [2]: idx = pd.Index([1,2])
In [3]: idx
Out[3]: Int64Index([1, 2], dtype='int64')
In [4]: idx - idx
C:\Anaconda\envs\pandas0150\lib\site-packages\pandas\core\index.py:1162: FutureW
arning: using '-' to provide set differences with Indexes is deprecated, use .di
fference()
"use .difference()",FutureWarning)
Out[4]: Index([], dtype='object')
In [6]: idx = pd.date_range('2012-01-01', periods=2)
In [7]: idx - idx
Out[7]: Index([], dtype='object')
In [8]: idx = pd.Index([1.1,2])
In [10]: idx
Out[10]: Float64Index([1.1, 2.0], dtype='float64')
In [11]: idx - idx
Out[11]: Index([], dtype='object')
In [12]: idx = pd.Index(['a', 'b'])
In [13]: idx - idx
Out[13]: Index([], dtype='object')
```
But in 0.15.1:
```
In [1]: pd.__version__
Out[1]: '0.15.1'
In [2]: idx = pd.Index([1,2])
In [3]: idx
Out[3]: Int64Index([1, 2], dtype='int64')
In [4]: idx - idx
Out[4]: Int64Index([0, 0], dtype='int64')
In [5]: idx = pd.date_range('2012-01-01', periods=2)
In [6]: idx - idx
Out[6]: Index([], dtype='object')
In [7]: idx = pd.Index([1.1,2])
In [8]: idx - idx
Out[8]: Float64Index([0.0, 0.0], dtype='float64')
In [9]: idx = pd.Index(['a', 'b'])
In [10]: idx - idx
pandas\core\index.py:1172: FutureWarning: using '-' to provide set differences w
ith Indexes is deprecated, use .difference()
"use .difference()",FutureWarning)
Out[10]: Index([], dtype='object')
```
So
- for int/float index, it is a numeric operation and not set -> api break
- for datetime it is set operation, but does not raise a warning
- only for object (`Index` object itself) it raises the warning
And the same on master (and 0.15.2)
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} | 10 | 2014-12-17T09:26:06Z | 2020-07-06T11:30:42Z | 2015-01-31T16:23:42Z | NONE | null | On a groupby with a composed key if the product of all possible values is bigger than 2^63 we get a `ValueError "negative dimensions are not allowed"` when we call `len(grouped_data)`.
A simple version to reproduce it:
``` python
values = range(55109)
data = pd.DataFrame.from_dict({'a': values, 'b': values, 'c': values, 'd': values})
grouped = data.groupby(['a', 'b', 'c', 'd'])
len(grouped)
```
A side effect of this error is that if there are NaN values as possible keys it won't ignore them, it will replace the NaN values with some other values present in the index.
Here there is a complete IPython notebook example to reproduce it:
http://nbviewer.ipython.org/gist/jordeu/cd86fc99f5f89451cf93
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} | 5 | 2014-12-17T09:47:00Z | 2019-12-02T07:14:11Z | 2019-12-01T21:19:03Z | MEMBER | null | http://www.appveyor.com/docs/services-databases
We could add tests for SQL Server to test_sql.py, as AppVeyor supports this.
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} | 10 | 2014-12-17T10:24:44Z | 2019-10-21T19:52:14Z | null | NONE | null | This code shows 3 situations.
``` python
import pandas as pd
from io import StringIO
import random
import sys
def fun(s,n,u):
random.seed(s)
names = [str(e) for e in range(1, n)]
data = ','.join([str(e) for e in names])
usecols = random.sample(names, u)
index_col = random.choice(usecols)
print('usecols', usecols)
print('index_col', index_col)
try:
df = pd.read_csv(StringIO(data), names=names, usecols=usecols, index_col=index_col, header=None)
print(df)
except:
print(sys.exc_info())
df = pd.read_csv(StringIO(data), names=names, usecols=usecols, header=None)
df.set_index(index_col, inplace=True)
print(df)
print('--------------------------------------------------')
fun(123, 10, 4) # exception
fun(123, 10, 5) # works
fun(123, 20, 4) # works BUT index name and value are not proper
```
here are the results
fun(123, 10, 4), an exception occurs but when `index_col` is ommitted and later `set_index` is used then it works ok.
``` python
usecols ['1', '5', '9', '4']
index_col 9
(<class 'IndexError'>, IndexError('list index out of range',), <traceback object at 0x7f129001a348>)
1 4 5
9
9 1 4 5
--------------------------------------------------
```
fun(123, 10, 5), this worked ok.
``` python
usecols ['1', '5', '9', '4', '3']
index_col 1
3 4 5 9
1
1 3 4 5 9
--------------------------------------------------
```
fun(123, 20, 4), this worked ok but it picked up the wrong value for the index
``` python
usecols ['2', '9', '3', '14']
index_col 3
2 3 14
3
9 2 3 14
--------------------------------------------------
```
`pandas.__version__` is '0.15.2'
64 bit archlinux
$ python --version
Python 3.4.2
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} | 2 | 2014-12-17T14:05:44Z | 2014-12-20T20:05:52Z | 2014-12-20T20:05:52Z | NONE | null | I just noticed that `DataFrame` constructor ignores the `copy=True` argument if `dtype` is set. In the code snippet below, the `orig` dataframe should stay unmodified after any modification of `new1` and `new2`. Instead, the columns of `new2` (or at least the first one as shown in the snippet) are references to the same data, as highlighted by the modification shown on statement 13 and onwards.
``` python
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-25-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21.1
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.4.3
IPython: 2.3.1
sphinx: 1.1.2
patsy: 0.3.0
dateutil: 2.3
pytz: 2014.10
bottleneck: 0.6.0
tables: None
numexpr: 2.4
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.7.4
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
In [4]: orig_data = {
...: 'col1': [1.],
...: 'col2': [2.],
...: 'col3': [3.],}
In [5]: orig = pd.DataFrame(orig_data)
In [6]: new1 = pd.DataFrame(orig, copy=True)
In [7]: new2 = pd.DataFrame(orig, dtype=float, copy=True)
In [8]: new1
Out[8]:
col1 col2 col3
0 1 2 3
In [9]: new2
Out[9]:
col1 col2 col3
0 1 2 3
In [10]: new1['col1'] = 100.
In [11]: new1
Out[11]:
col1 col2 col3
0 100 2 3
In [12]: orig
Out[12]:
col1 col2 col3
0 1 2 3
In [13]: new2['col1'] = 200.
In [14]: new2
Out[14]:
col1 col2 col3
0 200 2 3
In [15]: orig
Out[15]:
col1 col2 col3
0 200 2 3
In [16]:
```
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"url": "https://api.github.com/users/jreback"
},
"description": "after 0.15 of course!",
"due_on": "2015-03-22T07:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/25",
"id": 569113,
"labels_url": "https://api.github.com/repos/pandas-dev/pandas/milestones/25/labels",
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} | 2 | 2014-12-17T18:26:03Z | 2014-12-19T21:03:11Z | 2014-12-19T21:03:11Z | NONE | null | When iterating over a Series of datetime objects that have timezone info on them, the returned value has the timezone info but also alters the time according to that timezone info, as seen in example below:
`time_points` is the index of a dataframe with values:
```
[2014-12-01 03:32:39.987000-08:00, ..., 2014-12-01 04:12:34.987000-08:00]
Length: 4, Freq: None, Timezone: tzoffset(None, -28800)
2014-12-01 03:32:39.987000-08:00
2014-12-01 03:52:38.987000-08:00
2014-12-01 04:02:36.987000-08:00
2014-12-01 04:12:34.987000-08:00
```
However, for the following command:
```
for time in time_points:
print time
```
the output is:
```
2014-12-01 11:32:39.987000-08:00
2014-12-01 11:52:38.987000-08:00
2014-12-01 12:02:36.987000-08:00
2014-12-01 12:12:34.987000-08:00
```
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"description": "after 0.15 of course!",
"due_on": "2015-03-22T07:00:00Z",
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"updated_at": "2017-08-24T09:17:49Z",
"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/25"
} | 3 | 2014-12-18T01:10:15Z | 2014-12-21T13:52:03Z | 2014-12-18T11:40:22Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9075
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