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} | 5 | 2013-08-07T14:25:50Z | 2016-10-12T23:04:05Z | 2013-10-01T00:04:14Z | MEMBER | null | Would be nice to have tab completion for groupby methods that are "lazily determined" (not sure if that is the correct terminology)
see related issue #4500
see [this SO question](http://stackoverflow.com/questions/18104704/counting-unique-values-in-a-pandas-grouped-object) for an example (also linked to in #4500 )
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https://api.github.com/repos/pandas-dev/pandas/issues/4502 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4502/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4502/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4502/events | https://github.com/pandas-dev/pandas/pull/4502 | 17,754,969 | MDExOlB1bGxSZXF1ZXN0NzQ0OTM3NA== | 4,502 | ENH add bins argument to value_counts | {
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} | 39 | 2013-08-07T15:34:27Z | 2014-07-07T06:28:32Z | 2013-08-27T00:07:49Z | CONTRIBUTOR | null | fixes #3945
Also, adds arguments present in top-level function to the Series
method (sort and ascending).
Note: this only works with numeric data (it raises a TypeError if there was a TypeError problem in cut, however perhaps cut should raise a more descirptive error...)
```
In [1]: s = pd.Series(np.random.randn(100))
In [2]: s.value_counts(bins=4)
Out[2]:
0.017354 44
-1.154431 35
1.189140 14
-2.330904 7
dtype: int64
In [3]: s.value_counts(bins=4, normalize=True) # also works with sort and ascending arguments.
Out[3]:
0.017354 0.44
-1.154431 0.35
1.189140 0.14
-2.330904 0.07
dtype: float64
In [11]: s = pd.Series(list('abc'))
In [12]: s.value_counts(bins=1)
TypeError: bins argument only works with numeric data.
```
_Note: Have a test failing on python3 (topically) a sorting one_.
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} | 1 | 2013-08-07T15:54:39Z | 2014-06-14T06:24:45Z | 2013-08-22T15:27:30Z | CONTRIBUTOR | null | related to #4491
provides an assert_memory_is_clean to test a function for leaking memory
provides a test that the current series does not leak memory
and properly cleans up
tests that an index generally populates the engine mapping (but not always
e.g. asof)
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} | 5 | 2013-08-07T16:19:48Z | 2013-08-12T13:23:49Z | 2013-08-12T13:23:49Z | NONE | null | read_hdf() called get_store() with default open mode, which is 'a', and so requires write permissions to read a file. Please change to 'r'.
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} | 27 | 2013-08-07T18:44:45Z | 2014-12-28T02:30:19Z | 2014-06-16T16:49:55Z | NONE | null | closes #2685
I have added the ability for both the C and Python CSV parsers to ignore commented lines (i.e., lines beginning with a comment character). Currently the C parser preserves commented lines as empty lines (all NaN), while the Python parser ignores them all together.
In addition, I fixed a small related problem with the CSV format sniffer in the Python parser.
I plan to finish up this work by ignoring empty lines as per #4466.
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} | 3 | 2013-08-07T18:48:57Z | 2013-08-09T14:05:00Z | 2013-08-09T14:05:00Z | NONE | null | From the documentation example
http://pandas.pydata.org/pandas-docs/stable/basics.html#descriptive-statistics
For the DataFrame df in the example:
```
In [39]: df
one three two
a -0.701368 NaN -0.087103
b 0.109333 -0.354359 0.637674
c -0.231617 -0.148387 -0.002666
d NaN -0.167407 0.104044
```
the example for cumsum() seems to expose a potential bug when the last element is NaN (d,one). Notice that the output from cumsum() have nan for (d,one) entry. This should be same as (c,one) value.
```
In [48]: df.cumsum()
one three two
a -0.701368 NaN -0.087103
b -0.592035 -0.354359 0.550570
c -0.823652 -0.502746 0.547904
d NaN -0.670153 0.651948
```
Am I missing something?
Thanks,
R
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} | 0 | 2013-08-07T19:18:42Z | 2014-06-25T04:37:54Z | 2013-08-07T20:20:41Z | CONTRIBUTOR | null | - ER: #3777, raise a `NotImplementedError` for `Panel` -> `Panel` setting with alignment (issue will still be open though)
- BUG: closes #3830, panel assignment using loc with a transpose frame did not work
- BUG: closes #4016 fix panel slicing issue that was returning an object that should not have be a reduction in ndim
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} | [] | closed | false | null | [] | null | 5 | 2013-08-07T19:47:54Z | 2013-08-07T23:37:22Z | 2013-08-07T23:37:22Z | NONE | null | This is with pandas 0.12.0.dev-be25266:
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'a':[1,2,3],'b':[0,1,2]})
In [3]: df.ix[[0,2,],'b'] = [100,-100] # Works as intended
In [4]: print df
a b
0 1 100
1 2 1
2 3 -100
In [6]: df = pd.DataFrame({'a':range(4)})
In [7]: df['b']=pd.np.nan
In [8]: df.ix[[1,3],'b'] = [100,-100] # Both rows get assigned 100
In [9]: print df
a b
0 0 NaN
1 1 100
2 2 NaN
3 3 100
In [10]: df['b'].ix[[1,3]] = [100,-100] # Works as intended
In [11]: print df
a b
0 0 NaN
1 1 100
2 2 NaN
3 3 -100
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} | 0 | 2013-08-07T23:20:07Z | 2014-07-16T08:22:35Z | 2013-08-07T23:35:30Z | CONTRIBUTOR | null | closes #4508 (is ok in master though), this are just tests
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} | 3 | 2013-08-08T00:08:15Z | 2014-03-13T01:24:49Z | 2014-03-13T01:24:49Z | MEMBER | null | [SO question](http://stackoverflow.com/questions/18115741/writing-percentages-using-pandas-to-excel)
this is possible with `to_csv`, i think this should be possible with `to_excel` though I'm not sure
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} | 28 | 2013-08-08T02:49:36Z | 2013-10-22T20:37:52Z | 2013-10-22T20:37:52Z | CONTRIBUTOR | null | See: http://en.wikipedia.org/wiki/4%E2%80%934%E2%80%935_calendar about the referenced calendars.
Certain companies, for example Green Mountain Coffee Roasters, Inc. (GMCR) use this calendar. See for example: http://files.shareholder.com/downloads/GMCR/1456137416x0x436353/9E2D04D9-79DE-4C08-A7C6-7B161E23E586/gmcr_2010_annual_report_lo.pdf
where it states:
> "The Company’s fiscal year ends on the last Saturday in September."
Here is some more information on this calendar: http://www.nrf.com/modules.php?name=Pages&sp_id=391
Walmart releases its sales numbers on this calendar: http://stock.walmart.com/financial-reporting/comparable-store-sales
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} | 0 | 2013-08-08T03:03:05Z | 2021-04-11T02:58:34Z | null | CONTRIBUTOR | null | See for example: http://www.datazoa.com/publish/export.asp?hash=vg6p5mBl1v&uid=dzadmin&a=exportcsv, http://www.datazoa.com/publish/export.asp?hash=yjPceG6fHL&uid=dzadmin&a=exportcsv or the http://pastebin.com/S2NqhFq0.
In all cases, it is important to set `skipfooter` in order to correctly parse the CSV.
I current set `skipfooter` in the above files using:
```
data_raw = pd.io.parsers.read_csv(text,skiprows=range(1,13+1))
skipfooter = data_raw["Series title"].index[-1]-data_raw[data_raw["Series title"].isnull()].index[0]+1
ret = pd.io.parsers.read_csv(text,skiprows=range(1,13+1),skipfooter=skipfooter,parse_dates=[0],index_col=0)
```
The issue is that the value of skipfooter is not static, but rather varies based on how much meta data the provider is placing at the end of the file.
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} | 2 | 2013-08-08T12:02:53Z | 2014-07-14T08:39:02Z | 2013-08-12T13:23:49Z | CONTRIBUTOR | null | closes #4504
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} | 7 | 2013-08-08T15:48:55Z | 2019-10-21T19:34:16Z | null | CONTRIBUTOR | null | As per discussion on [this SO question](http://stackoverflow.com/questions/18127160/pandas-fillna-output-still-has-nan-values) is NotImplementedError.
Solution/workaround is to transpose do transpose? This is used elsewhere in DataFrame.fillna method. just raise if inplace?
cc @cpcloud
```
In [9]: df = pd.DataFrame([[np.nan, np.nan], [np.nan, 4], [5, 6]], columns=list('AB'))
In [10]: df
Out[10]:
A B
0 NaN NaN
1 NaN 4
2 5 6
In [11]: df.mean(0)
Out[11]:
A 5
B 5
dtype: float64
In [12]: df.fillna(df.mean())
Out[12]:
A B
0 5 5
1 5 4
2 5 6
In [13]: df.mean(1)
Out[13]:
0 NaN
1 4.0
2 5.5
dtype: float64
In [14]: df.fillna(df.mean(1), axis=1)
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-14-aecc493431e2> in <module>()
----> 1 df.fillna(df.mean(1), axis=1)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/frame.pyc in fillna(self, value, method, axis, inplace, limit, downcast)
3452 if isinstance(value, (dict, Series)):
3453 if axis == 1:
-> 3454 raise NotImplementedError('Currently only can fill '
3455 'with dict/Series column '
3456 'by column')
NotImplementedError: Currently only can fill with dict/Series column by column
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4515 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4515/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4515/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4515/events | https://github.com/pandas-dev/pandas/pull/4515 | 17,824,191 | MDExOlB1bGxSZXF1ZXN0NzQ4NTQ1OA== | 4,515 | ENH/API: GH2578, allow ix and friends to partially set when the key is not contained in the object | {
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} | 6 | 2013-08-08T18:52:15Z | 2014-06-12T09:17:05Z | 2013-09-05T17:23:51Z | CONTRIBUTOR | null | closes #2578
On a series, this is kind of like an inplace append operation (note the dtype change - on purpose)
```
In [6]: s = Series([1,2,3])
In [7]: s[4] = 5.
In [3]: s
Out[3]:
0 1
1 2
2 3
4 5
dtype: float64
```
This is now allowed: (previously would `KeyError`), you first have to create the column `C`, then set it
```
In [1]: df = DataFrame(np.arange(6).reshape(3,2),columns=['A','B'])
In [3]: df
Out[3]:
A B
0 0 1
1 2 3
2 4 5
In [4]: df.ix[:,'C'] = df.ix[:,'A']
In [5]: df
Out[5]:
A B C
0 0 1 0
1 2 3 2
2 4 5 4
```
This would previously raise an `IndexError`
```
In [4]: df.loc[3] = 5
In [5]: df
Out[5]:
A B C
0 0 1 0
1 2 3 2
2 4 5 4
3 5 5 5
```
and on Panel:
```
In [6]: p = Panel(np.arange(16).reshape(2,4,2),
items=['Item1','Item2'],
major_axis=pd.date_range('2001/1/12',periods=4),
minor_axis=['A','B'],dtype='float64')
In [7]: p
Out[7]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 2 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2001-01-12 00:00:00 to 2001-01-15 00:00:00
Minor_axis axis: A to B
In [10]: p.loc[:,:,'C'] = Series([30,32],index=p_orig.items)
In [11]: p
Out[11]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 3 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2001-01-12 00:00:00 to 2001-01-15 00:00:00
Minor_axis axis: A to C
In [13]: p.loc[:,:,'C']
Out[13]:
Item1 Item2
2001-01-12 30 32
2001-01-13 30 32
2001-01-14 30 32
2001-01-15 30 32
```
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} | 13 | 2013-08-08T20:34:29Z | 2013-08-17T15:39:38Z | 2013-08-09T14:36:39Z | NONE | null | I've run into a data-dependent bug with MultiIndex. Attached are a test script and a data file generation script:
- sales-data.py loads CSV data from a CSV data file with read_csv and then displays a multiindexed subset.
- sales-gen.sh is an AWK script that generates CSV test files. By default, it creates a 600MB file, which causes the failure. To make a smaller test file, which succeeds, just use "head -1000".
My problem is that sales-data.py breaks with large CSV files but works with smaller CSV files. When it fails it displays the following error message:
```
'MultiIndex lexsort depth 0, key was length 4'
```
I know that MultiIndexes must be pre-sorted. So sales-gen.sh makes sure that the first four columns in the auto-generated CSV files are ordered. BTW, I'm not sure why lexsort is getting called.
# sales-data.py
```
#!/usr/bin/env python
import numpy as np
import pandas as pd
from memory_profiler import profile
pd.set_option('display.height', 400)
pd.set_option('display.width', 400)
pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', 30)
pd.set_option('display.line_width', 200)
# @profile
def load_data():
try:
df = pd.read_csv(
'./sales-large.csv',
# './sales-small.csv',
header = None,
na_values = ['NULL'],
names = [
'salesperson',
'customer',
'invoice_date',
'ship_date',
'product',
'quantity',
'price',
],
index_col = [
'salesperson',
'customer',
'invoice_date',
'ship_date',
],
parse_dates = [
'invoice_date',
'ship_date',
],
)
print(df.loc[(
'A00000', # salesperson
'A', # customer
pd.datetime(2011,3,1,0,0,0), # invoice_date
pd.datetime(2011,3,6,0,0,0), # ship_date
)])
except Exception as e:
print(e)
if __name__== '__main__':
load_data()
```
# sales-gen.sh
```
#!/usr/bin/env bash
# 'salesperson',
# 'customer',
# 'invoice_date',
# 'ship_date',
# 'product',
# 'quantity',
# 'price',
awk "BEGIN { \
first = 65; \
last = 91; \
s_char = first; \
c_char = first; \
\
n_sa = 100; \
n_cu = 100; \
n_dt = 10; \
n_pr = 100; \
\
for (i = 0; i < n_sa; i++ s_char++) { \
if (s_char == last) { \
s_char = first; \
} \
c_char = first; \
for (j = 0; j < n_cu; j++ c_char++) { \
if (c_char == last) { \
c_char = first; \
} \
for (k = 1; k <= n_dt; k++) { \
for (l = 0; l < n_pr; l++) { \
printf(\"%c%05d,%c,2011-03-%02d 00:00:00,2011-03-%02d 00:00:00,%d,%d,%f\n\", \
s_char, i, c_char, k, k+5, l, 1 + int(rand() * 10), rand()) \
} \
} \
} \
} \
}"
```
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} | 29 | 2013-08-09T03:38:15Z | 2019-02-13T08:03:13Z | 2019-02-13T08:03:13Z | CONTRIBUTOR | null | Here's an example with just 65,000 elements (it's _much_ worse with 250,000) so I can compare xls and xlsx:
from pandas import *
df = DataFrame({'col1' : [0.0] \* 65000,
'col2' : 1,
'col3' : 2})
%timeit df.to_csv('sample.csv')
10 loops, best of 3: 109 ms per loop
%timeit df.to_excel('sample.xls')
1 loops, best of 3: 8.35 s per loop
%timeit df.to_excel('sample.xlsx')
1 loops, best of 3: 1min 31s per loop
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} | 1 | 2013-08-09T04:12:37Z | 2014-07-02T21:24:28Z | 2013-08-09T14:26:17Z | CONTRIBUTOR | null | Google Finance is already a supported data source in the source code of DataReader, but it is not reflected in the docstring.
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} | 1 | 2013-08-09T08:31:41Z | 2013-08-15T17:23:58Z | 2013-08-15T17:23:58Z | CONTRIBUTOR | null | ``` python
>>> import pandas as pd
>>> pd.tslib.get_period_field(11, 1, 1)
Exception ValueError: ValueError('Unrecognized code: 11',) in 'pandas.tslib._get_accessor_func' ignored
Segmentation fault
```
I'll push a fix.
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} | 8 | 2013-08-09T08:43:30Z | 2014-06-13T09:16:39Z | 2013-08-14T11:02:44Z | CONTRIBUTOR | null | closes #4519
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} | 0 | 2013-08-09T13:14:10Z | 2013-10-23T23:55:58Z | 2013-08-13T22:19:43Z | CONTRIBUTOR | null | I belive numpy < 1.7 is very problematic with this, but 1.7 is ok
http://stackoverflow.com/questions/18142144/dividing-a-series-containing-datetime-by-a-series-containing-an-integer-in-panda
```
import pandas as pd
from pandas import Series
startdate = Series(pd.date_range('2013-01-01', '2013-01-03'))
enddate = Series(pd.date_range('2013-03-01', '2013-03-03'))
s1 = enddate - startdate
s2 = Series([2, 3, 4])
event_freq = Series(s1.values / s2)
Here are the Series:
>>> s1
0 59 days, 00:00:00
1 59 days, 00:00:00
2 59 days, 00:00:00
dtype: timedelta64[ns]
>>> s2
0 2
1 3
2 4
dtype: int64
>>> event_freq
0 29 days, 12:00:00
1 19 days, 16:00:00
2 14 days, 18:00:00
dtype: timedelta64[ns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4522 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4522/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4522/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4522/events | https://github.com/pandas-dev/pandas/pull/4522 | 17,863,233 | MDExOlB1bGxSZXF1ZXN0NzUwNDcwNw== | 4,522 | BUG: GH4516 Fixed issue with sorting a duplicate multi-index that has multiple dtypes | {
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} | 0 | 2013-08-09T13:42:27Z | 2020-11-06T03:13:44Z | 2013-08-09T14:36:39Z | CONTRIBUTOR | null | closes #4516
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https://api.github.com/repos/pandas-dev/pandas/issues/4523 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4523/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4523/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4523/events | https://github.com/pandas-dev/pandas/issues/4523 | 17,865,868 | MDU6SXNzdWUxNzg2NTg2OA== | 4,523 | 0.12.0 to_hdf() error on a multi-index DataFrame | {
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} | [] | closed | false | null | [] | null | 2 | 2013-08-09T14:37:18Z | 2013-08-09T15:08:47Z | 2013-08-09T15:08:47Z | NONE | null | I have the following DataFrame:
```
portfolio_data
```
> <class 'pandas.core.frame.DataFrame'>
> MultiIndex: 45846 entries, (2013-06-28, 000312) to (2013-07-01, Y8565N10)
> Data columns (total 12 columns):
> market_cap 45843 non-null values
> alpha 45778 non-null values
> gics_code 45843 non-null values
> investable 45846 non-null values
> issuer_country 45789 non-null values
> msci_country 32667 non-null values
> return_daily 44373 non-null values
> universe 45846 non-null values
> weight_benchmark 45846 non-null values
> weight_portfolio 45846 non-null values
> weight_active 45846 non-null values
> country 45846 non-null values
> dtypes: bool(2), float64(7), object(3)
```
portfolio_data.index[0]
```
> (datetime.date(2013, 6, 28), '000312')
I get the following error while
```
portfolio_data.to_hdf(OUTPUT_FILE_NAME_PORTFOLIO, OUTPUT_NAME_PORTFOLIO, mode='w', table=True)
```
> ---
>
> TypeError Traceback (most recent call last)
> C:\Python27\lib\site-packages\IPython\utils\py3compat.pyc in execfile(fname, glob, loc)
> 169 else:
> 170 filename = fname
> --> 171 exec compile(scripttext, filename, 'exec') in glob, loc
> 172 else:
> 173 def execfile(fname, *where):
>
> M:\Projects\PortfolioAnalytics\Code\Python\attribution\TopContributors.py in <module>()
> 487 if logger.isEnabledFor(logging.DEBUG):
> 488 logger.debug('Storing data for the portfolio into {} ...'.format(OUTPUT_FILE_NAME_PORTFOLIO))
> --> 489 portfolio_data.to_hdf(OUTPUT_FILE_NAME_PORTFOLIO, OUTPUT_NAME_PORTFOLIO, mode='w', table=True)
> 490
> 491 logger.info('Finished {}.'.format(MODULE_NAME))
>
> C:\Python27\lib\site-packages\pandas\core\generic.pyc in to_hdf(self, path_or_buf, key, *_kwargs)
> 519 """ activate the HDFStore """
> 520 from pandas.io import pytables
> --> 521 return pytables.to_hdf(path_or_buf, key, self, *_kwargs)
> 522
> 523 def to_clipboard(self):
>
> C:\Python27\lib\site-packages\pandas\io\pytables.pyc in to_hdf(path_or_buf, key, value, mode, complevel, complib, append, **kwargs)
> 192 if isinstance(path_or_buf, basestring):
> 193 with get_store(path_or_buf, mode=mode, complevel=complevel, complib=complib) as store:
> --> 194 f(store)
> 195 else:
> 196 f(path_or_buf)
>
> C:\Python27\lib\site-packages\pandas\io\pytables.pyc in <lambda>(store)
> 188 f = lambda store: store.append(key, value, *_kwargs)
> 189 else:
> --> 190 f = lambda store: store.put(key, value, *_kwargs)
> 191
> 192 if isinstance(path_or_buf, basestring):
>
> C:\Python27\lib\site-packages\pandas\io\pytables.pyc in put(self, key, value, table, append, *_kwargs)
> 583 encoding : default None, provide an encoding for strings
> 584 """
> --> 585 self._write_to_group(key, value, table=table, append=append, *_kwargs)
> 586
> 587 def remove(self, key, where=None, start=None, stop=None):
>
> C:\Python27\lib\site-packages\pandas\io\pytables.pyc in _write_to_group(self, key, value, index, table, append, complib, encoding, *_kwargs)
> 921 raise ValueError('Compression not supported on non-table')
> 922
> --> 923 s.write(obj = value, append=append, complib=complib, *_kwargs)
> 924 if s.is_table and index:
> 925 s.create_index(columns = index)
>
> C:\Python27\lib\site-packages\pandas\io\pytables.pyc in write(self, obj, data_columns, *_kwargs)
> 3251 data_columns.insert(0, n)
> 3252 self.levels = obj.index.names
> -> 3253 return super(AppendableMultiFrameTable, self).write(obj=obj.reset_index(), data_columns=data_columns, *_kwargs)
> 3254
> 3255 def read(self, columns=None, **kwargs):
>
> C:\Python27\lib\site-packages\pandas\io\pytables.pyc in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, *_kwargs)
> 2983 self.create_axes(axes=axes, obj=obj, validate=append,
> 2984 min_itemsize=min_itemsize,
> -> 2985 *_kwargs)
> 2986
> 2987 if not self.is_exists:
>
> C:\Python27\lib\site-packages\pandas\io\pytables.pyc in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs)
> 2715 self.values_axes.append(col)
> 2716 except (NotImplementedError, ValueError, TypeError), e:
> -> 2717 raise e
> 2718 except (Exception), detail:
> 2719 raise Exception("cannot find the correct atom type -> [dtype->%s,items->%s] %s" % (b.dtype.name, b.items, str(detail)))
>
> TypeError: [date] is not implemented as a table column
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https://api.github.com/repos/pandas-dev/pandas/issues/4524 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4524/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4524/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4524/events | https://github.com/pandas-dev/pandas/issues/4524 | 17,868,875 | MDU6SXNzdWUxNzg2ODg3NQ== | 4,524 | DOC: need SQL -> pandas tips | {
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} | 12 | 2013-08-09T15:33:14Z | 2013-12-10T12:51:32Z | 2013-12-10T12:51:32Z | CONTRIBUTOR | null | of course #4164 makes this syntax easier anyhow
Here's an example of the types of things we need (obviously not all apply here)
http://www.pytables.org/moin/HintsForSQLUsers
we have this in the docs for join/merge, but maybe useful for:
- groupby
- boolean indexing to replace select
e.g.
```
select * from mytables where A='bar' and B>to_date('20130101')
```
replaced by:
`df[(df.A=='bar') & (df.B>Timestamp('20130101'))`
```
select count(*) from mytables
```
replaced by
`len(df)`
etc
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} | 0 | 2013-08-09T18:52:50Z | 2014-03-26T20:50:44Z | 2014-03-26T20:50:44Z | MEMBER | null | [SO question](http://stackoverflow.com/questions/18151775/create-yoy-graph-with-matplotlib/18153525#18153525)
if you do it with pandas like this:
```
In [60]: s.plot(kind='bar', align='center')
Out[60]: <matplotlib.axes.AxesSubplot at 0xc1f3810>
In [61]: ax = gca()
In [62]: ax2 = twinx(ax)
In [63]: s.plot(ax=ax2)
Out[63]: <matplotlib.axes.AxesSubplot at 0xd1ffc50>
```
it overwrites the bar plot
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} | [] | closed | false | null | [] | null | 3 | 2013-08-09T19:21:04Z | 2013-08-09T20:02:02Z | 2013-08-09T19:54:11Z | NONE | null | It appears that when adding a column, a _DataFrame_ disregards its _dtype_ if it has a different _Index_. For example, **C1** happens to have the same index as **df**, so all is well:
```
In [19]:
df = pd.DataFrame({'C1':range(3)})
In [20]:
df['C2'] = pd.Series(range(3), dtype='uint32')
df.dtypes
Out [20]:
C1 int64
C2 uint32
dtype: object
```
But **C2** has a different _Index_, and its _dtype_ magically mutes:
```
In [21]:
df['C3'] = pd.Series(range(3), index=range(3,6), dtype='uint32')
df.dtypes
Out [21]:
C1 int64
C2 uint32
C3 float64
dtype: object
```
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} | 3 | 2013-08-09T19:59:50Z | 2013-08-09T20:19:21Z | 2013-08-09T20:19:21Z | NONE | null | Trying to filter out groups with less than 1 row with the new groupby().filter() in 0.12.0:
``` python
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
....: 'foo', 'bar', 'foo', 'foo'],
....: 'B' : ['one', 'one', 'two', 'three',
....: 'two', 'two', 'one', 'three'],
....: 'C' : np.random.randn(8),
....: 'D' : np.random.randn(8)})
grp = df.groupby(['A', 'B'])
grp.apply(lambda x: len(x)>1) # Returns correct bool series
A B
bar one False
three False
two False
foo one True
three False
two True
dtype: bool
grp.filter(lambda x: len(x)>1)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-140-4dccfb561597> in <module>()
----> 1 grp.filter(lambda x: len(x)>1)
C:\Python27\lib\site-packages\pandas\core\groupby.pyc in filter(self, func, dropna, *args, **kwargs)
2092 res = path(group)
2093
-> 2094 if res:
2095 indexers.append(self.obj.index.get_indexer(group.index))
2096
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
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} | 6 | 2013-08-09T21:05:54Z | 2014-02-18T19:46:30Z | 2014-02-18T19:46:30Z | NONE | null | Trying to print a data frame as plain, strict tsv (i.e., no quoting and no escaping, because I know none the fields will contain tabs), I wanted to use the "quoting" option, which is documented in pandas and is passed through to csv, as well as the "quotechar" option, not documented in pandas but also a csv option. But it doesn't work:
``` python
In [1]: import sys, csv
In [2]: from pandas import DataFrame
In [3]: data = {'col1': ['contents of col1 row1', 'contents " of col1 row2'], 'col2': ['contents of col2 row1', 'contents " of col2 row2'] }
In [4]: df = DataFrame(data)
In [5]: df.to_csv(sys.stdout, sep='\t', quoting=csv.QUOTE_NONE, quotechar=None)
col1 col2
0 contents of col1 row1 contents of col2 row1
---------------------------------------------------------------------------
Error Traceback (most recent call last)
<ipython-input-5-a30d32266fb4> in <module>()
----> 1 df.to_csv(sys.stdout, sep='\t', quoting=csv.QUOTE_NONE, quotechar=None)
/home/brechea/.local/lib/python2.6/site-packages/pandas-0.12.0-py2.6-linux-x86_64.egg/pandas/core/frame.pyc in to_csv(self, path_or_buf, sep, na_rep, float_format, cols, header, index, index_label, mode, nanRep, encoding, quoting, line_terminator, chunksize, tupleize_cols, **kwds)
1409 tupleize_cols=tupleize_cols,
1410 )
-> 1411 formatter.save()
1412
1413 def to_excel(self, excel_writer, sheet_name='sheet1', na_rep='',
/home/brechea/.local/lib/python2.6/site-packages/pandas-0.12.0-py2.6-linux-x86_64.egg/pandas/core/format.pyc in save(self)
974
975 else:
--> 976 self._save()
977
978
/home/brechea/.local/lib/python2.6/site-packages/pandas-0.12.0-py2.6-linux-x86_64.egg/pandas/core/format.pyc in _save(self)
1080 break
1081
-> 1082 self._save_chunk(start_i, end_i)
1083
1084 def _save_chunk(self, start_i, end_i):
/home/brechea/.local/lib/python2.6/site-packages/pandas-0.12.0-py2.6-linux-x86_64.egg/pandas/core/format.pyc in _save_chunk(self, start_i, end_i)
1098 ix = data_index.to_native_types(slicer=slicer, na_rep=self.na_rep, float_format=self.float_format)
1099
-> 1100 lib.write_csv_rows(self.data, ix, self.nlevels, self.cols, self.writer)
1101
1102 # from collections import namedtuple
/home/brechea/.local/lib/python2.6/site-packages/pandas-0.12.0-py2.6-linux-x86_64.egg/pandas/lib.so in pandas.lib.write_csv_rows (pandas/lib.c:13871)()
Error: need to escape, but no escapechar set
```
Adding the parameter
quotechar=kwds.get("quotechar")
to the
formatter = fmt.CSVFormatter(...
call in to_csv(), and doing corresponding changes to format.CSVFormatter()'s **init**() and save(), produces the expected output:
``` python
In [1]: import sys, csv
In [2]: from pandas import DataFrame
In [3]: data = {'col1': ['contents of col1 row1', 'contents " of col1 row2'], 'col2': ['contents of col2 row1', 'contents " of col2 row2'] }
In [4]: df = DataFrame(data)
In [5]: df.to_csv(sys.stdout, sep='\t', quoting=csv.QUOTE_NONE, quotechar=None)
col1 col2
0 contents of col1 row1 contents of col2 row1
1 contents " of col1 row2 contents " of col2 row2
```
i.e., unescaped, unquoted tsv.
More generally, there could be many reasons to want more control of the underlying csv writer, so a generic mechanism (as opposed to adding each param one by one) might be called for (e.g., allowign for a csv dialect object or at least a dictionary holding dialect attributes).
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} | 33 | 2013-08-10T01:52:44Z | 2014-07-06T19:09:26Z | 2014-07-06T19:09:26Z | CONTRIBUTOR | null | `profile_id` is ultimately what's needed to make a call to the reporting api. If it is specified in the parameter list it seems wasteful to call the management api 3 times.
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Changes install.rst to explicitly reference that pandas supports 2.6,
2.7, and 3.2+. (and removes line that Python 3 support is less
well-tested). Also adds 2.6, 2.7, 3.2, and 3.3 in classifiers in
setup.py
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] | closed | false | null | [] | null | 7 | 2013-08-10T02:33:36Z | 2013-08-13T02:52:02Z | 2013-08-10T12:08:46Z | NONE | null | Pandas 0.12.0 fails to print a column change in this specific example:
``` python
Python 2.7.3 (default, Apr 10 2013, 05:13:16)
[GCC 4.7.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> import pandas
>>> length = 5
>>> xx = pandas.DataFrame({'a':np.random.randn(length),'b':np.random.randn(length),'c':np.random.randn(length)})
>>> xx['d']=xx['a']
>>> np.may_share_memory(xx['d'],xx['a'])
False
>>> print xx
a b c d
0 0.370188 0.437854 -0.020475 0.370188
1 1.839874 0.539278 0.954254 1.839874
2 0.183890 -0.924539 -0.943913 0.183890
3 -2.047403 1.213499 -0.742084 -2.047403
4 -1.003271 0.839525 0.154962 -1.003271
>>> xx['d'][1] = 42
>>> print xx
a b c d
0 0.370188 0.437854 -0.020475 0.370188
1 1.839874 0.539278 0.954254 1.839874
2 0.183890 -0.924539 -0.943913 0.183890
3 -2.047403 1.213499 -0.742084 -2.047403
4 -1.003271 0.839525 0.154962 -1.003271
>>> quit()
```
Cell D1 should be 42 in the last print statement.
After poking at this a bit, I discovered the following additional required conditions:
1) You must enter this in the Python REPL (as in typing "python" at the command line and entering them manually). If you put the commands in a file and run the file, it will suddenly do what's expected.
2) The np.may_share_memory() call must be there. If you omit it, it will suddenly do what's expected.
3) A friend of mine attempted to reproduce this bug on Pandas 0.11.0, but he couldn't get it to work. Then again, he may have messed up conditions 1 and/or 2 -- he tried it before I realized the previous two conditions.
I am using Pandas '0.12.0' installed from pip on Linux Mint 14. Here is the (relevant) output from print_versions.py:
```
INSTALLED VERSIONS
------------------
Python: 2.7.3.final.0
OS: Linux 3.5.0-17-generic #28-Ubuntu SMP Tue Oct 9 19:31:23 UTC 2012 x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
Cython: Not installed
Numpy: 1.6.2
Scipy: 0.10.1
matplotlib: 1.1.1
lxml: 2.3.5
```
I have no idea where the root cause of this bug is -- it may not necessarily be pandas's fault. I just hope someone else can reproduce it...
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} | 0 | 2013-08-10T13:09:31Z | 2014-07-17T22:05:20Z | 2013-08-13T22:19:43Z | CONTRIBUTOR | null | Related #4134 and:
http://stackoverflow.com/questions/18159675/python-pandas-change-duplicate-timestamp-to-unique/18162121#18162121
```
In [81]: df = DataFrame(dict(time = [Timestamp('20130101 9:01'),Timestamp('20130101 9:02')]))
```
This is buggy
```
In [82]: df.time + np.timedelta64(1,'ms')
Out[82]:
0 2013-01-01 09:01:00.000000001
1 2013-01-01 09:02:00.000000001
Name: time, dtype: datetime64[ns]
In [84]: df.time + np.timedelta64(1,'s')
Out[84]:
0 2013-01-01 09:01:00.000000001
1 2013-01-01 09:02:00.000000001
Name: time, dtype: datetime64[ns]
```
Doesn't work
```
In [83]: df.time + pd.offsets.Milli(5)
ValueError: cannot operate on a series with out a rhs of a series/ndarray of type datetime64[ns] or a timedelta
```
But this is ok
```
In [86]: df.time.apply(lambda x: x + pd.offsets.Milli(5))
Out[86]:
0 2013-01-01 09:01:00.005000
1 2013-01-01 09:02:00.005000
Name: time, dtype: datetime64[ns]
```
This is ok
```
In [85]: df.time + timedelta(seconds=1)
Out[85]:
0 2013-01-01 09:01:01
1 2013-01-01 09:02:01
Name: time, dtype: datetime64[ns]
```
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} | 1 | 2013-08-10T13:56:01Z | 2014-09-13T22:59:30Z | 2014-09-13T22:59:30Z | CONTRIBUTOR | null | ```
In [10]: df = DataFrame(dict(time = [Timestamp('20130101 9:01'),Timestamp('20130101 9:02')],value=[1.0,2.0]))
In [11]: df
Out[11]:
time value
0 2013-01-01 09:01:00 1
1 2013-01-01 09:02:00 2
```
works on series
```
In [12]: df.time.diff()
Out[12]:
0 NaT
1 00:01:00
Name: time, dtype: timedelta64[ns]
```
Broken with a datelike dtype inclded
```
In [13]: df.diff()
ValueError: cannot include dtype 'm' in a buffer
```
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} | 17 | 2013-08-10T14:40:02Z | 2014-06-16T17:04:14Z | 2013-08-13T22:19:43Z | CONTRIBUTOR | null | closes #4532
closes #4134
closes #4135
closes #4521
Timedeltas can be converted to other ‘frequencies’ by dividing by another timedelta.
```
In [210]: td = Series(date_range('20130101',periods=4))-Series(date_range('20121201',periods=4))
In [211]: td[2] += np.timedelta64(timedelta(minutes=5,seconds=3))
In [212]: td[3] = np.nan
In [213]: td
0 31 days, 00:00:00
1 31 days, 00:00:00
2 31 days, 00:05:03
3 NaT
dtype: timedelta64[ns]
```
to days
```
In [214]: td / np.timedelta64(1,'D')
0 31.000000
1 31.000000
2 31.003507
3 NaN
dtype: float64
```
to seconds
```
In [215]: td / np.timedelta64(1,'s')
0 2678400
1 2678400
2 2678703
3 NaN
dtype: float64
```
Dividing or multiplying a timedelta64[ns] Series by an integer or integer Series yields a float64 dtyped Series.
```
In [216]: td * -1
0 -31 days, 00:00:00
1 -31 days, 00:00:00
2 -31 days, 00:05:03
3 NaT
dtype: timedelta64[ns]
In [217]: td * Series([1,2,3,4])
0 31 days, 00:00:00
1 62 days, 00:00:00
2 93 days, 00:15:09
3 NaT
dtype: timedelta64[ns]
```
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} | 0 | 2013-08-10T16:28:42Z | 2018-07-06T22:36:12Z | 2018-07-06T22:36:12Z | CONTRIBUTOR | null | For 0.14 or later, after #4039 is merged this becomes a todo item for the future.
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https://api.github.com/repos/pandas-dev/pandas/issues/4536 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4536/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4536/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4536/events | https://github.com/pandas-dev/pandas/issues/4536 | 17,901,531 | MDU6SXNzdWUxNzkwMTUzMQ== | 4,536 | DataFrame produces NaNs | {
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``` python
In [1]: import urllib2
In [2]: open("N_06_area.txt", "w").write(urllib2.urlopen("ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/Jun/N_06_area.txt").read())
In [3]: from pandas import read_table, DataFrame
In [4]: data = read_table("data/N_06_area.txt", skipfooter=16, sep="\s+")
In [5]: avg = data[2:32].extent.mean()
In [6]: anomaly = (data.extent - avg) / avg
In [7]: DataFrame({"anomaly": anomaly}).head()
Out[7]:
anomaly
0 0.058843
1 0.035295
2 0.057161
3 0.067254
4 0.039500
In [8]: DataFrame({"anomaly": anomaly}, index=data.year).head()
Out[8]:
anomaly
year
1979 NaN
1980 NaN
1981 NaN
1982 NaN
1983 NaN
In [9]: import pandas
In [10]: pandas.__version__
Out[10]: '0.11.0'
```
As you can see, [7] is correct, but when I try to assign years as the index in [8], I get NaN.
I hit this problem in this notebook: [Sea Ice Index.ipynb](http://nbviewer.ipython.org/urls/raw.github.com/certik/climate/4056bcc8318ab81a1fe6029f1597a35798f17cce/Sea%2520Ice%2520Index.ipynb)
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} | 17 | 2013-08-11T02:11:03Z | 2013-08-31T00:43:05Z | 2013-08-31T00:43:05Z | CONTRIBUTOR | null | I can't tell if this is expected or not (taken from part of `test_where_datetime` in tests/test_frame). The change in #3311 (which the test case came from) didn't specify what should happen with non-datetime comparisons. Right now they return True, which doesn't really make sense. They should probably return False instead.
``` python
In [16]: df = DataFrame(dict(A = date_range('20130102',periods=5),
B = date_range('20130104',periods=5),
C = np.random.randn(5)))
In [17]: stamp = datetime(2013,1,3)
In [18]: df
Out[18]:
A B C
0 2013-01-02 00:00:00 2013-01-04 00:00:00 -0.321163
1 2013-01-03 00:00:00 2013-01-05 00:00:00 1.219840
2 2013-01-04 00:00:00 2013-01-06 00:00:00 -1.048629
3 2013-01-05 00:00:00 2013-01-07 00:00:00 -0.861459
4 2013-01-06 00:00:00 2013-01-08 00:00:00 1.480088
In [19]: df > stamp
Out[19]:
A B C
0 False True True
1 False True True
2 True True True
3 True True True
4 True True True
In [20]: df < stamp
Out[20]:
A B C
0 True False True
1 False False True
2 False False True
3 False False True
4 False False True
In [21]: df == stamp
Out[21]:
A B C
0 False False False
1 True False False
2 False False False
3 False False False
4 False False False
In [22]: df != stamp
Out[22]:
A B C
0 True True True
1 False True True
2 True True True
3 True True True
4 True True True
In [23: pd.__version__
Out[23]: '0.12.0-154-g7fd6b20'
```
Column C probably should be False for every case.
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} | 3 | 2013-08-11T14:46:56Z | 2013-09-28T17:26:33Z | 2013-09-28T17:26:33Z | MEMBER | null | [SO question](http://stackoverflow.com/questions/18171079/using-fill-between-with-a-pandas-data-series/18173133#18173133)
if instead you don't use `.values` then a `KeyError` is thrown
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} | 22 | 2013-08-11T17:08:11Z | 2013-10-14T06:37:31Z | 2013-10-14T06:37:31Z | MEMBER | null | minor issue, but would be nice for consistency
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_table.html#pandas.io.parsers.read_table
shows it as `pandas.io.parsers.read_table` but it should be used as `pandas.read_table` and that's how it should appear
this could be a simple sphinx reference label change
FWIW this would probably invalidate a lot of links on SO
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Is it possible include any easy access to HDF4 files?
They are still in use specially for those data distributed by NASA satellite MODIS.
Cheers,
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} | 2 | 2013-08-12T01:22:06Z | 2014-02-25T17:56:39Z | 2014-02-25T17:56:39Z | CONTRIBUTOR | null | The test function is very useful for trials of code.
Please, make it freuqency aware. Currently, it just works with daily data.
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} | 20 | 2013-08-12T13:39:22Z | 2019-02-21T07:12:41Z | 2013-09-22T23:14:58Z | CONTRIBUTOR | null | related/closes #4517
Hi,
I am the author of the [XlsxWriter](https://github.com/jmcnamara/XlsxWriter) module, a Python module for writing Excel XLSX files.
I am interested in submitting a PR to add this as a option when writing Excel xlsx files via `to_excel()`.
It has a similar interface to the xlwt module but creates xlsx files. For large data files it is ~10x faster than OpenPyXL while maintaining low and constant memory usage. I occasionally see the current xlsx writer's speed and memory usage mentioned as a bug/limitation so it could be useful to have a fallback module option in those cases.
XlsxWriter also supports features such as charts and auto-filters that could be exposed at a later stage. See https://xlsxwriter.readthedocs.org/ for more details.
It uses standard libraries only and has an extensive test suite for Python 2.6, 2.7, 3.1, 3.2 and 3.3.
If you are interested I can work on a prototype and send a PR for you to evaluate. I have read CONTRIBUTING.md and I think I should be able to meet the criteria.
Regards,
John.
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} | 4 | 2013-08-12T17:23:32Z | 2016-07-20T07:44:57Z | 2016-07-19T21:06:38Z | NONE | null | It would work similar to drop_duplicates (same interface), but would only remove consecutive duplicates (like Unix's uniq does). So to in order to remove all duplicates with this function, the DataFrame would need to be sorted by the same criteria. This function can be useful when order is important and non-consecutive duplicates convey useful information.
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} | 2 | 2013-08-12T20:16:47Z | 2013-10-24T12:57:37Z | 2013-10-01T01:21:47Z | CONTRIBUTOR | null | I'm really sorry if this is a duplicate issue of #2997, but it seems to me that this is a little bit different. Here the `.ix` when getting a value interprets the integer as label, and when setting a value it interprets it as a positional index.
``` python
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame(np.arange(16).reshape((4, 4)),
columns=['a', 'b', 8, 'c'],
index=['e', 7, 'f', 'g'])
>>> df
a b 8 c
e 0 1 2 3
7 4 5 6 7
f 8 9 10 11
g 12 13 14 15
>>> df.ix['e', 8]
2
>>> df.ix['e', 8] = 42
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/indexing.py", line 90, in __setitem__
self._setitem_with_indexer(indexer, value)
File "pandas/core/indexing.py", line 190, in _setitem_with_indexer
values[indexer] = value
IndexError: index 8 is out of bounds for axis 1 with size 4
>>> df.loc['e', 8] = 42
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/indexing.py", line 90, in __setitem__
self._setitem_with_indexer(indexer, value)
File "pandas/core/indexing.py", line 190, in _setitem_with_indexer
values[indexer] = value
IndexError: index 8 is out of bounds for axis 1 with size 4
```
I see this issue with the 0.12 version and also with the latest git checkout.
_Update: @jreback noticed that the `.loc` is not working either._
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} | 3 | 2013-08-12T22:44:50Z | 2013-09-26T21:27:34Z | 2013-09-26T21:27:34Z | MEMBER | null | maybe would be nice to have `df.plot(kind='candle')` or `df.plot(kind='ohlc')`? kind of a pain to do all the date/time stuff manually
see [this SO question](http://stackoverflow.com/q/18195231/564538) for an example
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```
import pandas
df = pandas.DataFrame({'a':[7.3,5.1,3.0,5.5,6.4],'b':[5,4,5,1,6]})
df['a'] = df.apply(lambda x: 10.5, axis=1)
df.iloc[0]['a'] = 99.9
print df.iloc[0]['a']
```
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} | 23 | 2013-08-13T08:32:19Z | 2014-05-22T17:28:50Z | 2014-03-07T00:04:40Z | NONE | null | Running the follow code generates an error using pandas 0.11 (with winpython 2.7.5.2 64 bit):
import pandas as pd
ts1=pd.date_range('1/1/2000',periods=1,freq='Q')[0]
ts1-1-1
Error:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "tslib.pyx", line 566, in pandas.tslib._Timestamp.__sub__ (pandas\tslib.c:10775)
File "tslib.pyx", line 550, in pandas.tslib._Timestamp.__add__ (pandas\tslib.c:10477)
ValueError: Cannot add integral value to Timestamp without offset.
Reason:
ts1.freq has value:
1 QuarterEnd: startingMonth=12, kwds={'startingMonth': 12}, offset=3 MonthEnds
but
(ts1-1).freq has no value
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} | 4 | 2013-08-13T12:46:32Z | 2016-10-12T23:04:06Z | 2013-09-09T14:52:24Z | CONTRIBUTOR | null | Create a Series with non-unique index:
``` python
>>> import pandas as pd
>>> pd.__version__
'0.12.0'
>>> s1 = pd.Series(range(3))
>>> s2 = pd.Series(range(3))
>>> comb = pd.concat([s1,s2])
>>> comb
0 0
1 1
2 2
0 0
1 1
2 2
dtype: int64
```
Assign value by boolean mask:
``` python
>>> comb[comb<1] = 5
>>> comb
0 5
1 1
2 2
0 5
1 1
2 2
dtype: int64
```
This has worked. Now _add_ a value by boolean mask:
``` python
>>> comb[comb<2] += 10
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "***/pandas/core/series.py", line 852, in __setitem__
self.where(~key,value,inplace=True)
File "***/pandas/core/series.py", line 749, in where
other = other.reindex(ser.index)
File "***/pandas/core/series.py", line 2646, in reindex
return self._reindex_with_indexers(new_index, indexer, copy=copy, fill_value=fill_value)
File "***/pandas/core/series.py", line 2650, in _reindex_with_indexers
return Series(new_values, index=index, name=self.name)
File "***/pandas/core/series.py", line 492, in __new__
subarr.index = index
File "properties.pyx", line 74, in pandas.lib.SeriesIndex.__set__ (pandas/lib.c:29541)
AssertionError: Index length did not match values
```
Is this expected behavior? If it is, I am sorry, because this was not clear to me from the docs and I am just wondering why simple assignment via `=` works and special assignment via `+=` does not...
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} | 16 | 2013-08-13T12:49:27Z | 2014-06-19T20:26:24Z | 2013-08-21T13:42:36Z | CONTRIBUTOR | null | When "raise <exception instance>" is used inside a try/except block, the
original stacktrace (containing the code path that raised the original
exception). This makes debugging difficult since all we know is that an error
occured when `frame._apply_standard` attempted to call the provided function.
Preserve the original stacktrace by using "raise" instead of "raise <exception
instance>". This facilitates debugginb by allowing us to see where (in the
provided callable) the exception occured.
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} | 4 | 2013-08-13T12:55:03Z | 2013-09-09T02:03:14Z | 2013-09-09T02:03:14Z | CONTRIBUTOR | null | Create a Series with non-unique index:
``` python
>>> import pandas as pd
>>> pd.__version__
'0.12.0'
>>> s1 = pd.Series(range(3))
>>> s2 = pd.Series(range(3))
>>> comb = pd.concat([s1,s2])
>>> comb
0 0
1 1
2 2
0 0
1 1
2 2
dtype: int64
```
As of https://github.com/pydata/pandas/issues/4548 I cannot use `comb[comb<2] =+ 10`, so I tried working with `where` (according to http://pandas.pydata.org/pandas-docs/stable/indexing.html#where-and-masking, stating "To guarantee that selection output has the same shape as the original data, you can use the where method in Series and DataFrame"). But already calling `where` on `comb` is problematic:
``` python
>>> comb.where(comb < 2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/projects/bioinfp_apps/Python-2.7.3/lib/python2.7/site-packages/pandas/core/series.py", line 745, in where
ser._set_with(~cond, other)
File "/projects/bioinfp_apps/Python-2.7.3/lib/python2.7/site-packages/pandas/core/series.py", line 886, in _set_with
self._set_values(key, value)
File "/projects/bioinfp_apps/Python-2.7.3/lib/python2.7/site-packages/pandas/core/series.py", line 904, in _set_values
values[key] = _index.convert_scalar(values, value)
File "index.pyx", line 547, in pandas.index.convert_scalar (pandas/index.c:9752)
File "index.pyx", line 560, in pandas.index.convert_scalar (pandas/index.c:9639)
ValueError: Cannot assign nan to integer series
>>>
```
Is this expected?
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} | 5 | 2013-08-13T13:42:01Z | 2014-04-29T12:22:10Z | 2014-02-18T01:35:30Z | NONE | null | related #4056, #5202, #5502, #5519, #6027
I have a DataFrame of length 390,080, with int64 indices.
`df.index.size` gives the correct answer of 390,080, but `df.index.max` results in a huge traceback that ends with:
```
/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/common.pyc in pprint_thing(thing, _nest_lvl, escape_chars, default_escapes, quote_strings)
2051 hasattr(thing, 'next'):
2052 return unicode(thing)
-> 2053 elif (isinstance(thing, dict) and
2054 _nest_lvl < get_option("display.pprint_nest_depth")):
2055 result = _pprint_dict(thing, _nest_lvl,quote_strings=True)
RuntimeError: maximum recursion depth exceeded in __instancecheck__
```
Using pandas 0.12.
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} | 2 | 2013-08-13T13:44:42Z | 2013-09-27T20:13:44Z | 2013-09-27T20:13:44Z | CONTRIBUTOR | null | Hello,
I suggest to use np.random.randin where possible in the docstrings / examples.
With values imited like:
```
np.random.randint(1, 10, 4000)
```
...the reader would have it easy to re-calculate results (sum, max, min, mean, etc.) while reading the examples.
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} | 20 | 2013-08-13T15:22:38Z | 2014-03-10T20:01:30Z | 2014-03-10T20:01:30Z | CONTRIBUTOR | null | ```
In [6]: Timestamp('20130101',offset='D')
Out[6]: Timestamp('2013-01-01 00:00:00', tz=None)
In [7]: Timestamp('20130101',offset='D').freq
Out[7]: 'D'
```
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} | 5 | 2013-08-13T15:54:09Z | 2013-10-24T11:52:14Z | 2013-09-29T22:07:31Z | CONTRIBUTOR | null | This may be seen like a weird function call but it is actually triggered when plotting with development versions of matplotlib and the case is explicitly (and wrongly as it seems) by pandas:
```
>>> import pandas as pd
>>> x = pd.Series(range(5))
>>> x.reshape(x.shape)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/pymodules/python2.7/pandas/core/series.py", line 981, in reshape
return ndarray.reshape(self, newshape, order)
TypeError: an integer is required
```
It is easily fixed just replacing the offending line by this one:
`return ndarray.reshape(self, newshape, order=order)`
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} | 5 | 2013-08-13T17:02:23Z | 2013-08-16T19:29:56Z | 2013-08-16T19:29:56Z | CONTRIBUTOR | null | Using Pandas 0.12.0
It's hard to think of why a DataFrame would allow a `regex=True` replace method, and a Series wouldn't. This must just be an oversight, right?
```
>>> test = pd.DataFrame(['hello','goodbye','thankyou'],columns=['to_replace'])
>>> test
to_replace
0 hello
1 goodbye
2 thankyou
>>> test.replace("he", "je")
to_replace
0 hello
1 goodbye
2 thankyou
>>> test.replace("he", "je", regex=True)
to_replace
0 jello
1 goodbye
2 thankyou
>>> test['to_replace'].replace("he", "je", regex=True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: replace() got an unexpected keyword argument 'regex'
```
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} | 4 | 2013-08-13T18:14:00Z | 2014-01-24T15:14:48Z | 2014-01-24T15:12:14Z | CONTRIBUTOR | null | related #3281
Create a read_pdf method in IO tools for reading tables from PDF documents. Many data sets are released in PDF form.
For example:
- Walmart's Historical Unit Count and Square Footage: http://az204679.vo.msecnd.net/media/documents/unit-counts-q1-fy14_130131488115936836.pdf
- Las Vegas Visitor Statistics: http://www.lvcva.com/includes/content/images/media/docs/ES-YTD20128.pdf
- Coffee export data: http://www.ico.org/historical/2010-19/PDF/EXPCALY.pdf
There are a number of standalone tools, projects for this:
- http://ieg.ifs.tuwien.ac.at/projects/pdf2table/ (an academic project /paper; Java)
- https://pypi.python.org/pypi/pdfquery (Python lib)
- https://pypi.python.org/pypi/pdftable (Python lib)
- http://www.unixuser.org/~euske/python/pdfminer/ (Python lib)
- https://github.com/ashima/pdf-table-extract (Python lib)
There are also a number of site / projects to convert PDF to HTML:
- https://github.com/coolwanglu/pdf2htmlEX/wiki (open source)
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} | 11 | 2013-08-13T21:04:02Z | 2017-11-24T10:30:13Z | 2017-11-24T10:30:05Z | NONE | null | The corr() method attached to DataFrames is a great way to get a matrix of correlation coefficients. But for the Kendall and Spearman options, the p-value is discarded:
https://github.com/pydata/pandas/blob/8ac0e11b59b65f0ac898dca2beebde5f87836649/pandas/core/nanops.py#L510-L517
For a function called corr() this would certainly be expected behavior, but the problem remains that there is no other method (as far as I've been able to find) to get those significance values without going back to scipy and doing it myself.
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} | 9 | 2013-08-13T21:31:57Z | 2014-03-13T22:19:16Z | 2014-03-13T22:19:16Z | CONTRIBUTOR | null | The following code exporting a dataframe with a timeseries index with np.datetime64 type
``` python
import numpy as np
import pandas as pd
timestamps = pd.date_range(np.datetime64('2013-01-01 11:00:00.123456'), periods=5, freq='123U')
dataframe = pd.DataFrame(np.random.randn(len(timestamps)), index=timestamps)
dataframe.to_stata('out')
```
throws a
```
ValueError: Data type datetime64[ns] not currently understood. Please report an error to the developers.
```
It would be very nice if np.datetime64 (and thus pd.Timestamp as well) would be supported here.
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} | 9 | 2013-08-14T00:03:36Z | 2013-08-20T09:15:25Z | 2013-08-19T23:21:57Z | MEMBER | null | see [this SO question](http://stackoverflow.com/q/18219156/564538)
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} | 18 | 2013-08-14T01:17:18Z | 2014-06-19T16:42:59Z | 2013-08-15T02:42:02Z | CONTRIBUTOR | null | Resolves #3765, #4331, #4334, #4051.
This goes on top of @jreback's Series refactoring (#3482). If you want to review, I suggest looking [at the PR on top of jreback/series2](https://github.com/jreback/pandas/pull/6), so you only see the diffs specific to this PR.
This does a few things:
1. Normalizes the arithmetic signature so that all objects have the full-range of special methods, inplace methods and `__r*__` methods. Similarly, most objects get the full-range of flex methods (varies based on the object.
2. Refactors all arithmetic methods to `core/ops` and adds methods to add both special (e.g., `__add__`) and flex (e.g. `add()`) to PandasObjects.
3. Slight refactoring of the arithmetic methods themselves to abstract the common `na_op` idiom into a single function.
4. Uses `numexpr` throughout for most operations (except for the sparse objects)
5. Makes it easier to create new `PandasObjects` that support all arithmetic methods - just need to define 2-5 functions.
6. Establishes a standard type signature for arithmetic methods [documented in core/ops], specifically:
- arithmetic method: `f(op, name, str_rep, default_axis=None, fill_zeros=None, **eval_kwargs)`
- comp/bool method: `f(op, name, str_rep)`
This is a straight continuation of #4051, but since it's different enough (especially with moving the operations to core/ops and no longer using them as classmethods), I'm doing it in a separate PR.
I'm open to changing the naming of the arithmetic functions in core/ops. I named them as they are to make it easier to move them into `core/ops`
Sidenote: I don't know whether numexpr plays well with sparse objects (or if that's even an issue). It's trivial to disable numexpr on Sparse* objects, just need to know what you want to do.
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} | 0 | 2013-08-14T07:49:06Z | 2013-08-15T11:50:48Z | 2013-08-15T11:50:48Z | NONE | null | Since I updated pandas from version 0.11 to 0.12, read_clipboard fails:
```
import pandas as pd
df = pd.read_clipboard()
```
Results in:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-6dead334eb54> in <module>()
----> 1 df = pd.read_clipboard()
C:\Python33\lib\site-packages\pandas\io\clipboard.py in read_clipboard(**kwargs)
16 from pandas.io.parsers import read_table
17 text = clipboard_get()
---> 18 return read_table(StringIO(text), **kwargs)
19
20
TypeError: initial_value must be str or None, not bytes
```
What I did was:
- Open a csv file in Excel 2010
- Copy a range of cells, including headers
- Perform read_clipboard in iPython Qt console as described in above code block
After downgrading to 0.11, this procedure worked fine again.
OS: Win7, 64bit
Python: v3.3.2 32-bit
Pandas: 0.12.0
Numpy: 1.7.1
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} | 4 | 2013-08-14T11:09:00Z | 2014-07-02T22:36:41Z | 2013-08-15T17:23:31Z | CONTRIBUTOR | null | Fixes the following problem (issues #4519, #4520):
``` python
>>> import pandas as pd
>>> pd.tslib.get_period_field(11, 1, 1)
Exception ValueError: ValueError('Unrecognized code: 11',) in 'pandas.tslib._get_accessor_func' ignored
Segmentation fault
```
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} | 2 | 2013-08-14T13:29:10Z | 2013-09-22T20:14:55Z | 2013-09-22T20:14:55Z | CONTRIBUTOR | null | The `kind` parameter on `pandas.io.excel.read_excel` is totally undocumented:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.excel.read_excel.html#pandas.io.excel.read_excel
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} | 5 | 2013-08-14T13:42:16Z | 2015-01-06T14:43:46Z | 2013-09-26T00:49:44Z | CONTRIBUTOR | null | This pretty much boils down to this line: https://github.com/pydata/pandas/blob/master/pandas/tseries/index.py#L987
You can easily reproduce the error using the following code:
```
r = pd.date_range("2013-01-01", "2013-02-01")
r2 = r + pd.DateOffset(minutes=15)
r | r2 # Results in the index r having "2013-01-31 00:15" appended
```
This happens because the check on the given line does not take into account, that two indices with the same offset do not necessarily have to be aligned. If they are the `_fast_union`-method can be used, if not it should fall back to the `Index` implementation.
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} | 0 | 2013-08-14T14:14:11Z | 2014-04-29T23:41:57Z | 2014-04-29T23:41:57Z | CONTRIBUTOR | null | ```
In [110]: x = Timestamp('20130101')
```
Last day of month
```
In [111]: pd.tslib.monthrange(x.year,x.month)[1]==x.day
Out[111]: False
```
First day of month
```
In [112]: x.day==1
Out[112]: True
```
First day of year
```
In [113]: x.day==1 & x.month==1
Out[113]: True
```
http://stackoverflow.com/questions/18233107/pandas-convert-datetime-to-end-of-month/18233876?noredirect=1#comment26732170_18233876
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https://api.github.com/repos/pandas-dev/pandas/issues/4566 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4566/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4566/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4566/events | https://github.com/pandas-dev/pandas/pull/4566 | 18,054,665 | MDExOlB1bGxSZXF1ZXN0NzYwMjU2Mg== | 4,566 | Fixes check in _can_fast_union. | {
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} | 1 | 2013-08-14T14:19:06Z | 2014-07-18T15:00:07Z | 2013-08-15T07:55:02Z | CONTRIBUTOR | null | Fixes #4564.
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} | 11 | 2013-08-14T14:27:59Z | 2021-07-14T01:41:40Z | 2020-09-19T20:49:31Z | CONTRIBUTOR | null | I think an implementation of a general recurrent relation might be interesting. These
are not able to evaluated in numpy/numexpr directly
see here:
http://stackoverflow.com/questions/9984373/calculate-two-coupled-equation-with-numpy
http://stackoverflow.com/questions/21336794/python-recursive-vectorization-with-timeseries/21338665
http://stackoverflow.com/questions/23996260/python-pandas-using-the-previous-value-in-dataframe/23997030#23997030
```
R[i<=k] = start(i)
R[i] = R[i-k]*a(i) + b(i)
```
in the case where a(i) and b(i) are constants then this can be completely cythonized
if a and/or b are passed functions then the shell can still be cythonized (with the function called at each iteration)
Example
```
values = df['input']
In [62]: result = np.empty(len(values))
In [65]: result[0] = values[0]
In [66]: for i in xrange(len(result)):
....: if i > 0:
....: result[i] = result[i-1]*.8
....:
In [67]: result
Out[67]: array([ 5. , 4. , 3.2 , 2.56 , 2.048 , 1.6384])
```
I propose that pandas have a `recurrence` wrapper (that can then call a pure constant cython version and a cython version that takes functions)
Probably just for a 1-dim array atm
In theory this can also be done by a `rolling_apply` that has memory (or I guess that is sort of the same thing)
related #4546
https://groups.google.com/forum/#!topic/pydata/0MCWhwurOWs
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} | 2 | 2013-08-14T15:07:39Z | 2021-04-11T03:00:47Z | 2021-04-11T03:00:46Z | CONTRIBUTOR | null | Would prob need to be done in cython for perf
```
def cross_apply(func, is_symmetric=True, **kwargs):
l = len(df.columns)
results = np.zeros((l,l))
if is_symmetric:
for i in range(l):
for j in range(i + 1):
results[i,j] = results[j,i] = func(ac,bc, **kwargs)
else:
for i, ac in enumerate(df):
for j, bc in enumerate(df):
if is_symmetric:
results[j,i] = func(ac,bc, **kwargs)
return DataFrame(results,index=df.columns,columns=df.columns)
```
http://stackoverflow.com/questions/18234440/pairwise-correlation-of-pandas-dataframe-columns-with-custom-function
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} | 3 | 2013-08-14T15:19:07Z | 2014-06-08T01:07:51Z | 2014-06-08T01:07:51Z | NONE | null | I tried several ways of assigning values in a mixed type dataframe and it seems that one of them is not working. I am not sure if it is supposed to do so:
from pandas import DataFrame
df = DataFrame({'A':[:'aaa','bbb','ccc'],'B':[1,2,3]})
# this works, value is changed
df.ix[0,'A'] = 11
# this works, value is changed
df['A'][0] = 111
# this does not work, value is not changed
df.ix[0]['A'] = 1111
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} | 2 | 2013-08-14T15:27:16Z | 2013-08-14T16:03:04Z | 2013-08-14T16:03:04Z | CONTRIBUTOR | null | BAD
`df.ix[0]['A'] = 5`
GOOD
`df.ix[0,'A'] = 5`
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} | 10 | 2013-08-14T20:16:11Z | 2014-06-23T13:06:27Z | 2013-08-15T11:50:48Z | CONTRIBUTOR | null | closes #4561
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http://permalink.gmane.org/gmane.comp.python.pydata/2164
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} | 0 | 2013-08-14T22:45:08Z | 2014-07-02T21:24:33Z | 2013-08-14T23:54:39Z | CONTRIBUTOR | null | Guess this got reverted/messed up during rebasing, but needs to be
changed - urlparse is no longer imported in io/common (in favor of py2/3
compatible methods).
I added a dumb test case so it at least gets called going forward.
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} | 4 | 2013-08-15T01:14:41Z | 2014-06-24T17:22:47Z | 2013-08-26T21:35:47Z | CONTRIBUTOR | null | Simple changes to add PostgreSQL syntax to `pandas.io.sql.get_schema`. Added data type cases for `get_sqltype()` and a columns "flavor" for `get_schema()`.
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} | 5 | 2013-08-15T02:00:14Z | 2013-08-17T14:10:00Z | 2013-08-17T14:10:00Z | CONTRIBUTOR | null | cc @Komnomnomnom
skipping test via: b5ff81d34d74efdf88b8151ff58ed16ff7c02c67
fails about 1/3 times (and only seems on py3)
https://travis-ci.org/pydata/pandas/jobs/10223886
don't use now, pick a known value (that you can then compare too exactly)
```
def test_datetime_units(self):
from pandas.lib import Timestamp
val = datetime.datetime.now()
stamp = Timestamp(val)
```
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} | 16 | 2013-08-15T06:27:50Z | 2016-07-16T22:41:18Z | 2013-08-26T12:45:17Z | CONTRIBUTOR | null | It seems that when comparing DataFrame to list or tuple of values (lenght of DataFrame columns), the resulting boolean DataFrame is incorrect.
`````` python
>>> import pandas
>>> print pandas.__version__
0.12.0
>>> import numpy as np
>>> print np.__version__
1.6.1
>>> df=pandas.DataFrame([ [-1, 0], [1, 2] ])
>>> df > (0, 1)
0 1
0 False False
1 False True
>>> df > [0, 1]
0 1
0 False False
1 False True
``'
Comparison with numpy array works as expected:
```python
>>> df > np.array([0, 1])
0 1
0 False False
1 True True
``'
Note that the comparison behaved correctly at least in pandas 0.9.0:
```python
>>> import pandas
>>> print pandas.__version__
0.9.0
>>> df=pandas.DataFrame([ [-1, 0], [1, 2] ])
>>> df > (0, 1)
0 1
0 False False
1 True True
>>> df > [0, 1]
0 1
0 False False
1 True True
``'
``````
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```
/usr/local/lib/python2.7/dist-packages/numpy/core/scalarmath.so
/usr/local/lib/python2.7/dist-packages/numpy/linalg/lapack_lite.so
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0-py2.7-linux-x86_64.egg/pandas/lib.so
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0-py2.7-linux-x86_64.egg/pandas/algos.so
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0-py2.7-linux-x86_64.egg/pandas/index.so
/usr/local/lib/python2.7/dist-packages/numpy/core/multiarray.so
/usr/local/lib/python2.7/dist-packages/statsmodels-0.5.0-py2.7-linux-x86_64.egg/statsmodels/nonparametric/_smoothers_lowess.so
/usr/local/lib/python2.7/dist-packages/numpy/fft/fftpack_lite.so
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0-py2.7-linux-x86_64.egg/pandas/hashtable.so
/usr/local/lib/python2.7/dist-packages/numpy/core/umath.so
/usr/local/lib/python2.7/dist-packages/numpy/lib/_compiled_base.so
/usr/local/lib/python2.7/dist-packages/numpy/random/mtrand.so
/usr/local/lib/python2.7/dist-packages/statsmodels-0.5.0-py2.7-linux-x86_64.egg/statsmodels/tsa/kalmanf/kalman_loglike.so
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0-py2.7-linux-x86_64.egg/pandas/tslib.so
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0-py2.7-linux-x86_64.egg/pandas/_sparse.so
/usr/local/lib/python2.7/dist-packages/pandas-0.11.0-py2.7-linux-x86_64.egg/pandas/_parser.so
/usr/local/lib/python2.7/dist-packages/statsmodels-0.5.0-py2.7-linux-x86_64.egg/statsmodels/nonparametric/linbin.so
```
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border gives the user the possibility to style the table, using CSS Classes.
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} | 3 | 2013-08-16T12:30:24Z | 2016-08-25T10:20:52Z | 2016-08-25T10:20:52Z | NONE | null | Consider the following:
``` python
In [3]: df = pd.DataFrame(np.arange(5), columns=["foo"])
In [4]: df["foo"].iloc[:-1] != df["foo"].iloc[1:]
Out[4]:
0 True
1 True
2 True
3 True
dtype: bool
In [5]: df["foo"].iloc[1:] != df["foo"].iloc[:-1]
Out[5]:
1 True
2 True
3 True
4 True
dtype: bool
```
Obviously, the **ne** operator uses positional indexing when comparing the values and applies the index of the first argument to the result. I don't think this is the expected behaviour.
Again, the semantics are different when performing addition:
``` python
In [6]: df["foo"].iloc[:-1] + df["foo"].iloc[1:]
Out[6]:
0 NaN
1 2
2 4
3 6
4 NaN
dtype: float64
In [7]: df["foo"].values[:-1] + df["foo"].values[1:]
Out[7]: array([1, 3, 5, 7])
```
Here, everything works as expected.
I also tried the dataframe ne method:
``` python
In [8]: df.iloc[:-1].ne(df.iloc[1:])
Out[8]:
foo
0 True
1 False
2 False
3 False
4 True
```
This works as expected, and differently from Series comparisons. The respective method doesn't exist for Series objects, so I couldn't test that.
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} | 1 | 2013-08-16T14:15:17Z | 2015-06-12T21:49:51Z | 2015-06-12T21:49:51Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/18274973/using-compression-with-pandas-and-hd5-hdfstore/18275402#18275402
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https://api.github.com/repos/pandas-dev/pandas/issues/4583 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4583/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4583/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4583/events | https://github.com/pandas-dev/pandas/issues/4583 | 18,164,426 | MDU6SXNzdWUxODE2NDQyNg== | 4,583 | 0.12.0 concat() error | {
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} | [] | closed | false | null | [] | null | 8 | 2013-08-16T15:58:56Z | 2013-09-18T13:40:57Z | 2013-08-16T16:25:14Z | NONE | null | I run the following code on a list of DataFrames
```
columns = None
for portfolio_data in portfolio_data_list:
print(portfolio_data.shape)
if columns is None:
columns = portfolio_data.columns
else:
print(all(portfolio_data.columns == columns))
portfolio_data = pd.concat(portfolio_data_list)
```
and get the following
> (15494, 12)
> (15534, 12)
> True
> (15584, 12)
> True
> (15557, 12)
> True
> (15555, 12)
> True
> (15555, 12)
> True
> (15600, 12)
> True
> Traceback (most recent call last):
> File "M:/Projects/PortfolioAnalytics/Code/Python/attribution/TopContributors.py", line 1028, in <module>
> portfolio_data = pd.concat(portfolio_data_list)
> File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 878, in concat
> verify_integrity=verify_integrity)
> File "C:\Python27\lib\site-packages\pandas\tools\merge.py", line 929, in **init**
> obj.consolidate(inplace=True)
> File "C:\Python27\lib\site-packages\pandas\core\generic.py", line 770, in consolidate
> self._consolidate_inplace()
> File "C:\Python27\lib\site-packages\pandas\core\generic.py", line 752, in _consolidate_inplace
> self._data = self._protect_consolidate(f)
> File "C:\Python27\lib\site-packages\pandas\core\generic.py", line 790, in _protect_consolidate
> result = f()
> File "C:\Python27\lib\site-packages\pandas\core\generic.py", line 751, in <lambda>
> f = lambda: self._data.consolidate()
> File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 1638, in consolidate
> return BlockManager(new_blocks, self.axes)
> File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 1001, in __init__
> self._verify_integrity()
> File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 1245, in _verify_integrity
> tot_items))
> AssertionError: Number of manager items must equal union of block items
> # manager items: 12, # tot_items: 11
>
> Process finished with exit code 1
What's going wrong?
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} | 5 | 2013-08-16T16:53:24Z | 2013-08-26T12:43:52Z | 2013-08-26T12:43:52Z | NONE | null | `to_hdf(....., append=True,table=True)` raising, should accept this
Is there a way to use DataFrame.to_hdf() so that data can be appened the data? Somehow to_hdf(filename, table_name, mode='a', table=True) overwrites the data while to_hdf(filename, table_name, table=True, append=True) gives the following error:
> Traceback (most recent call last):
> File "M:/Projects/PortfolioAnalytics/Code/Python/attribution/TopContributors.py", line 1035, in <module>
> portfolio_data.to_hdf(filename, OUTPUT_NAME_PORTFOLIO, table=True, append=Settings.TC_OUTPUT_APPEND)
> File "C:\Python27\lib\site-packages\pandas\core\generic.py", line 521, in to_hdf
> return pytables.to_hdf(path_or_buf, key, self, *_kwargs)
> File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 194, in to_hdf
> f(store)
> File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 188, in <lambda>
> f = lambda store: store.append(key, value, *_kwargs)
> File "C:\Python27\lib\site-packages\pandas\io\pytables.py", line 658, in append
> self._write_to_group(key, value, table=True, append=True, **kwargs)
> TypeError: _write_to_group() got multiple values for keyword argument 'table'
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} | 0 | 2013-08-16T20:58:01Z | 2014-07-16T08:23:30Z | 2013-08-26T12:45:17Z | CONTRIBUTOR | null | closes #4576
```
In [1]: df = DataFrame(np.arange(6).reshape((3,2)))
In [2]: b = np.array([2, 2])
In [3]: b_r = np.atleast_2d([2,2])
In [4]: b_c = b_r.T
In [5]: df>b
Out[5]:
0 1
0 False False
1 False True
2 True True
In [6]: df.values>b
Out[6]:
array([[False, False],
[False, True],
[ True, True]], dtype=bool)
In [7]: df>b_r
Out[7]:
0 1
0 False False
1 False True
2 True True
In [8]: df.values>b_r
Out[8]:
array([[False, False],
[False, True],
[ True, True]], dtype=bool)
In [9]: df>b_c
ValueError: cannot broadcast shape [(3, 2)] with block values [(2, 1)]
In [10]: df.values>b_c
ValueError: operands could not be broadcast together with shapes (3,2) (2,1)
In [11]: df == b
Out[11]:
0 1
0 False False
1 True False
2 False False
In [12]: df.values == b
Out[12]:
array([[False, False],
[ True, False],
[False, False]], dtype=bool)
In [13]: df==b_r
Out[13]:
0 1
0 False False
1 True False
2 False False
In [14]: df.values==b_r
Out[14]:
array([[False, False],
[ True, False],
[False, False]], dtype=bool)
```
Numpy does weird things like this (bottom example), but we will raise
I believe it is actually a NotImplemented Type which is interpreted as False (but still very weird)
```
In [15]: df==b_c
ValueError: cannot broadcast shape [(3, 2)] with block values [(2, 1)]
In [16]: df.values==b_c
Out[16]: False
```
with list/tuple
```
In [2]: df>(2,2)
ValueError: operands could not be broadcast together with shapes (2,3) (2)
In [3]: df>(2,2,2)
Out[3]:
0 1
0 False False
1 False True
2 True True
In [4]: df>[2,2,2]
Out[4]:
0 1
0 False False
1 False True
2 True True
In [5]: df==[2,2,2]
Out[5]:
0 1
0 False False
1 True False
2 False False
```
This will work with a frame with non integer labels too
```
In [6]: df = DataFrame(np.arange(6).reshape((3,2)),columns=list('AB'),index=list('abc'))
In [7]: df==[2,2,2]
Out[7]:
A B
a False False
b True False
c False False
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4586 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4586/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4586/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4586/events | https://github.com/pandas-dev/pandas/pull/4586 | 18,179,729 | MDExOlB1bGxSZXF1ZXN0NzY3MTQ3Mg== | 4,586 | BUG: fix erroneous call to prrint_thing -> pprint_thing | {
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``` python
>>> import pandas
>>> df = pandas.DataFrame({'a':[1,2,3,4,5],'b':[2,4,6,8,10]})
>>> x = df['a'].values
>>> x is df['a'].values
False
```
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} | 36 | 2013-08-16T23:30:10Z | 2019-06-24T13:39:14Z | 2018-05-01T20:06:11Z | NONE | null | When concat'ing DataFrames, the column names get alphanumerically sorted *if* there are any differences between them. If they're identical across DataFrames, they don't get sorted.
This sort is undocumented and unwanted. Certainly the default behavior should be no-sort. EDIT: the standard order as in SQL would be: columns from df1 (same order as in df1), columns (uniquely) from df2 (less the common columns) (same order as in df2). Example:
``` python
df4a = DataFrame(columns=['C','B','D','A'], data=np.random.randn(3,4))
df4b = DataFrame(columns=['C','B','D','A'], data=np.random.randn(3,4))
df5 = DataFrame(columns=['C','B','E','D','A'], data=np.random.randn(3,5))
print "Cols unsorted:", concat([df4a,df4b])
# Cols unsorted: C B D A
print "Cols sorted", concat([df4a,df5])
# Cols sorted A B C D E
``'
```
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} | 0 | 2013-08-16T23:44:01Z | 2013-08-26T12:36:36Z | 2013-08-26T12:36:36Z | CONTRIBUTOR | null | I will make PR
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closes #4589
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} | 6 | 2013-08-17T00:11:47Z | 2021-10-17T18:27:06Z | null | CONTRIBUTOR | null | The documentation is unclear as to what the date passed to a `Period` means: whether it is the start date or the end date of the period. Convention is that fiscal quarters are usually stated by the date the quarter ends, whereas weeks are many times stated by the day week starts. This is especially unclear given that the default `freq='W'` is `W-SUN` which means the "week ending on Sunday" whereas one convention is to show weeks starting on Sunday.
I would recommend clearing this up in the documentation.
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As you surmised @jreback it looks like unnecessary float arithmetic was causing the test failure. I fixed this up and in the interest of consistency removed the dependence on `datetime.now()`
Example
``` python
In [8]: val = 1376774232215504000
In [9]: val / 1000
Out[9]: 1376774232215504
In [10]: val / 1e3
Out[10]: 1376774232215503.8
In [11]: val // 1e3
Out[11]: 1376774232215503.0
In [12]: val / 1000.0
Out[12]: 1376774232215503.8
In [13]: val / 10**3
Out[13]: 1376774232215504
```
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} | 2 | 2013-08-17T13:52:57Z | 2014-06-22T10:10:15Z | 2013-08-21T02:01:54Z | CONTRIBUTOR | null | With its current handling ujson ends up encoding labels twice, which can cause problems if they contain escapable characters:
``` python
In [16]: df = DataFrame([['a', 'b'], ['c', 'd']], index=['index " 1', 'index / 2'], columns=['a \\ b', 'y / z'])
In [17]: df
Out[17]:
a \ b y / z
index " 1 a b
index / 2 c d
In [18]: json = df.to_json()
In [19]: json
Out[19]: '{"a \\\\\\\\ b":{"index \\\\\\" 1":"a","index \\\\\\/ 2":"c"},"y \\\\\\/ z":{"index \\\\\\" 1":"b","index \\\\\\/ 2":"d"}}'
In [20]: pd.read_json(json)
Out[20]:
a \\ b y \/ z
index \" 1 a b
index \/ 2 c d
```
This PR fixes this behaviour so labels are only encoded a single time.
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} | 12 | 2013-08-17T19:18:50Z | 2014-01-24T19:42:23Z | 2014-01-24T14:48:49Z | CONTRIBUTOR | null | Currently the `flavor` argument to `read_html` must either be `None` or a string or a container of strings. This make is difficult for the user to write his own instance of `_HtmlFrameParser` and to use that when parsing. I suggest also allowing the user to pass in a subclass of `_HtmlFrameParser` in place of a string.
Implementation wise, this should be easy. `_parser_dispatch` would be modified to for this case. Likewise `_validate_parser_flavor` would need to allow this.
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} | 19 | 2013-08-18T00:07:44Z | 2018-02-03T10:09:14Z | 2013-08-18T12:36:22Z | NONE | null | This is not a bug; maybe it is more of a philosophical issue.
When I download data from Yahoo, mimicking the code on p. 139 of the book (python for data analysis) it creates a data frame. The index for this data frame is a time series index - timestamps provided by Yahoo.
If I save this DataFrame to disk using to_csv and then read it back using read_csv the resulting data frame now has a 'normal' range type index [0,1,2...n] and a new column labelled 'Date' which now contains the dates.
This means that code written to analyze this data needs to be different depending on whether it has been saved or not. I can deal with it as is, but I think this is not the best design; DataFrames should be invariant under saving.
Just my $ 0.02.
Respectfully,
Greg St. George
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} | 1 | 2013-08-18T00:58:17Z | 2016-10-12T23:04:07Z | 2013-09-19T12:24:50Z | CONTRIBUTOR | null | This code appears broken:
```
def _check_thousands(self, lines):
if self.thousands is None:
return lines
nonnum = re.compile('[^-^0-9^%s^.]+' % self.thousands)
ret = []
for l in lines:
rl = []
for x in l:
if (not isinstance(x, compat.string_types) or
self.thousands not in x or
nonnum.search(x.strip())):
rl.append(x)
else:
rl.append(x.replace(',', ''))
ret.append(rl)
return ret
```
It looks like the `thousands` argument to the class is used to check if the value is "non numeric" but then a hard coded comma is used when actually performing the cleaning.
In addition to fixing this, I would recommend factoring out this method so that it can be used elsewhere.
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} | 1 | 2013-08-18T06:41:44Z | 2013-09-06T22:15:48Z | 2013-09-06T22:15:48Z | MEMBER | null | If any of the other pandas developers would like to give a quick rundown of their workflow when hacking on pandas that would be greatly appreciated. I'll do the same. Once the dust settles, I'll collect the workflows and create a new wiki page with this information.
FYI, I'm trying to highlight `git` usage here, so if there are any tricks you have up your sleeves don't hesitate to show them!
cc @nehalecky
My pandas workflow usually goes something like this:
1. Always use `virtualenvwrapper`. Period. Then I do `workon pandas`.
2. Depending on how much time I have I'll try to prioritize based on some combination of which issues are high-priority and those for which I see an easy fix and those that I think would be fun to work on.
3. Once I've selected an issue I'll start hacking on it. I usually _don't_ use any local branches unless I'm implementing something new and there's a bunch of things for that feature that can be worked on separately.
4. I use `git stash` **all the time**. In fact so much so that I have aliases for every `git stash` subcommand. Often times I'll get interrupted and I'll need to stash something so I do. I can then come back to it. If you end up accumulating a crap ton of stashes (which I do) then you can inspect them with `git stash list` followed by `git stash -p stash@{0}` or whatever stash number you want to see the diff of. I'll also occasionally prune the stash via visual inspection and/or my own memory of what's been merged recently or by comparison of stashes to `upstream/master`.
5. Two things that I use a lot are the command `git push -u origin $(git rev-parse --abbrev-ref HEAD)`. This pushes a remote branch with the same name as the one that I made locally to the `origin` remote (my fork). I have `git rev-parse --abbrev-ref HEAD` aliased to `gcb` which I remember as "git current branch". The other command I use is `hub pull-request -b pydata:master` which _automatically_ submits a PR. I can essentially push the remote and the PR in a single step on the command line without having to use the mouse. Pretty neat.
6. `git reflog` is great for those times when you accidentally type `git reset --hard` and you need to recover something.
7. Finally I'll rebase using `git rebase upstream/master` to pull in the latest from the `upstream` remote (which I have set to `http://github.com/pydata/pandas.git`)
8. Throughout the dev process I'm constantly doing `git fetch && git rebase upstream/master`, although recently I've toned that down a bit and I've started to just do it when I need to.
9. I also use `git log` all the time. Since the standard `git log` is a bit too verbose I use [scm_breeze](https://github.com/ndbroadbent/scm_breeze) and it has a gorgeous standard `git log` (as long as you've set `git config --global color.ui auto`!)
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} | 23 | 2013-08-18T15:13:06Z | 2014-06-22T08:32:53Z | 2013-08-23T17:51:35Z | CONTRIBUTOR | null | Closes issue #4322
Adds support for the thousands character in csv parser for floats.
Previously, the thousands separator character did not pass into the core floating point number parsing algorithm:
https://github.com/pydata/pandas/blob/master/pandas/src/parser/tokenizer.c#L1861
I added an argument to this function and provided a test. Now, in a file like this:
```
A|B|C
1|2,334.01|5
10|13|10
```
Column B would import as float type.
Also related to issue #2594
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} | 6 | 2013-08-18T15:59:22Z | 2020-05-27T17:14:56Z | null | MEMBER | null | See [this SO question](http://stackoverflow.com/q/18295630/564538)
The docstring should indicate _what_ is not being copied unnecessarily (`Index`es, `ndarray`s, etc.)
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https://api.github.com/repos/pandas-dev/pandas/issues/4600 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4600/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4600/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4600/events | https://github.com/pandas-dev/pandas/issues/4600 | 18,209,632 | MDU6SXNzdWUxODIwOTYzMg== | 4,600 | Adding a column to a DataFrame always creates a copy of a Series | {
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} | 15 | 2013-08-18T16:19:17Z | 2018-11-01T14:29:32Z | 2013-08-19T14:23:31Z | CONTRIBUTOR | null | I don't know if this is a valid behaviour, but it seems to me like a bug?
``` python
>>> s = pd.Series([1,2,3])
>>> df = pd.DataFrame(s)
>>> df.index is s.index
True
>>> df.iloc[0, 0] = 33
>>> df
0
0 33
1 2
2 3
>>> s
0 33
1 2
2 3
dtype: int64
```
So far so good.
But if I do:
``` python
>>> s = pd.Series([1,2,3])
>>> df = pd.DataFrame(index=s.index)
>>> df[0] = s
>>> df.index is s.index
True
>>> df.iloc[0, 0] = 33
>>> df
0
0 33
1 2
2 3
>>> s
0 1
1 2
2 3
dtype: int64
```
Basically there is no way to add a column to the DataFrame without creating a copy of the data. This seems like a suboptimal behaviour since the operation:
``` python
df['c'] = df['a'] + df['b']
```
First create a Series object in the memory, and then create a copy of it that get's assigned to the DataFrame column `c`.
I also understand why this can be a desired behaviour, so maybe this issue could be reformulated into a question: Is there a way to add a column to a DataFrame without creating a copy of the data.
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