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} | 1 | "2012-12-12T09:05:45Z" | "2019-12-25T05:12:45Z" | null | CONTRIBUTOR | null | It would be really nice to allow for a weight argument to be passed to allow computatio of weighted statistics (mean, media, quantile etc) in pivot-table. Could be really convenient when you deal with weighted survey data.
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} | 3 | "2012-12-12T09:39:16Z" | "2013-09-28T19:36:29Z" | "2013-09-28T19:36:29Z" | NONE | null | In for exampe DataFrame.to_csv there is the option "float_format" with the following description:
```
float_format : string, default None
Format string for floating point numbers
```
The currently allowed format in Pandas follows the "old" Python style of '%.2f' % 1.234, so you have to write
float_format = '%.df'.
Being used to 2.7 and 3.x formatting style of Python, I expected that just ':.2f' or even '.2f' would be valid.
To keep up with the new formatting style, should these two last variants (or one of them) also be accepted in Pandas? If not, it should be specified in the documentation, perhaps with an example, that the '%'-style is required.
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https://api.github.com/repos/pandas-dev/pandas/issues/2503 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2503/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2503/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2503/events | https://github.com/pandas-dev/pandas/issues/2503 | 9,212,401 | MDU6SXNzdWU5MjEyNDAx | 2,503 | Ginput giving wrong date for datetimeindex plot | {
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} | 7 | "2012-12-12T10:59:05Z" | "2014-06-09T02:24:52Z" | "2014-06-09T02:24:52Z" | CONTRIBUTOR | null | I rewrote this [StackOverflow question](http://stackoverflow.com/questions/13832938/how-use-ginput-method-get-the-correct-points) to include an example. I think this looks like a bug (possibly in `num2date` or `date2num`)?
---
I read-in a file and plot it with pandas `DataFrame`. The index is DatetimeIndex, and then I use `ginput(1)` method to get one point, however the coordinate which I get is wrong.
The code is as follows:
```
import pandas as pd
from matplotlib.dates import num2date, date2num
ts = pd.date_range('2012-04-12,16:13:09', '2012-04-14,00:13:09', freq='H')
df = pd.DataFrame(index=ts)
df[0] = 20.6
```
I then plot and click on the graph using [ginput](http://glowingpython.blogspot.co.uk/2011/08/how-to-use-ginput.html):
```
df.plot()
t = pylab.ginput(n=1) #click somewhere near 13-APR-2012
```
However, the first item appears to be a float
```
In [8]: x = t[0][0] # ~ 370631.67741935479
In [9]: num2date(x)
Out[9]: datetime.datetime(1015, 10, 3, 16, 15, 29, 32253, tzinfo=<matplotlib.dates._UTC object at 0x104196550>)
# this is way out!
```
The docs suggest that it should be using these floats (from [`datetonum`](http://matplotlib.org/api/dates_api.html#matplotlib.dates.date2num)):
```
In [10]: dt = pd.to_datetime('13-4-2012', dayfirst=True)
In [11]: date2num(dt)
Out[11]: 734606.0
```
What is this float, and how can I convert it to a datetime?
_Note: If I remove one of the rows from the dataframe this works correctly:_
```
df1 = df.drop(ts[1], axis=0)
...
```
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} | 2 | "2012-12-12T11:36:20Z" | "2014-07-08T14:50:42Z" | "2012-12-13T14:50:39Z" | NONE | null | I'm not married to the "mode.X" option key, but be aware that the dynamic generated
docstrings for the options API favour a small number of top level keys with multiple keys
under them and so i consolidated `test.interactive` into `mode.sim_interactive`.
Some sane taxonomy needs to be established early on to keep things tidy,
Be an option-naming fascist I say.
http://martinfowler.com/bliki/TwoHardThings.html
related: #1919
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} | 0 | "2012-12-12T14:07:57Z" | "2012-12-13T14:52:22Z" | "2012-12-13T14:52:22Z" | NONE | null | rebased on top of #2504, merge that first.
see here: https://github.com/y-p/pandas/blob/prettify_release/RELEASE.rst
related #842.
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https://api.github.com/repos/pandas-dev/pandas/issues/2506 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2506/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2506/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2506/events | https://github.com/pandas-dev/pandas/issues/2506 | 9,218,395 | MDU6SXNzdWU5MjE4Mzk1 | 2,506 | read_csv fails when a column ends in a quote. | {
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} | 2 | "2012-12-12T14:59:59Z" | "2012-12-12T15:18:45Z" | "2012-12-12T15:18:45Z" | NONE | null | I use pandas to read csv files that I get from mysql. Using pandas 0.9 and 0.10 beta, the following files give unexpected results when using read_csv
> "A","B"
> "row_1","foo\""
> "row_2","bar"
output is
> row_1 foo\"\nrow_2" bar
When I add a space after the first quote after foo,
> "A","B"
> "row_1","foo\" "
> "row_2","bar"
we do get the correct number of rows back but output is still unexpected
> 0 row_1 foo\ "
> 1 row_2 bar
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} | 4 | "2012-12-12T16:01:17Z" | "2016-10-12T23:03:24Z" | "2012-12-14T05:22:26Z" | CONTRIBUTOR | null | getitem/setitem magic:
`pd.options['expand'] = True`
context manager for temporary options setting:
```
with pd.set_option('expand', False):
...
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2508 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2508/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2508/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2508/events | https://github.com/pandas-dev/pandas/pull/2508 | 9,222,273 | MDExOlB1bGxSZXF1ZXN0MzM0MDQ1Ng== | 2,508 | BUG: fix type spec bug in roll_window | {
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} | 11 | "2012-12-12T23:48:07Z" | "2012-12-13T03:58:52Z" | "2012-12-13T01:42:35Z" | NONE | null | The repo seems to be missing a file called pandas/index.c.
Build log, on a Debian GNU/Linux system:
$ python setup.py build
running build
running build_py
creating build/lib.linux-x86_64-2.7
creating build/lib.linux-x86_64-2.7/pandas
copying pandas/version.py -> build/lib.linux-x86_64-2.7/pandas
copying pandas/**init**.py -> build/lib.linux-x86_64-2.7/pandas
copying pandas/info.py -> build/lib.linux-x86_64-2.7/pandas
creating build/lib.linux-x86_64-2.7/pandas/compat
copying pandas/compat/**init**.py -> build/lib.linux-x86_64-2.7/pandas/compat
copying pandas/compat/scipy.py -> build/lib.linux-x86_64-2.7/pandas/compat
creating build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/config_init.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/sparse.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/format.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/frame.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/matrix.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/categorical.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/api.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/reshape.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/internals.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/panelnd.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/panel4d.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/strings.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/**init**.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/algorithms.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/indexing.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/datetools.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/nanops.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/groupby.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/config.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/panel.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/daterange.py -> build/lib.linux-x86_64-2.7/pandas/core
copying pandas/core/index.py -> build/lib.linux-x86_64-2.7/pandas/core
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copying pandas/core/series.py -> build/lib.linux-x86_64-2.7/pandas/core
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running build_ext
building 'pandas.index' extension
gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fPIC -I/usr/lib/pymodules/python2.7/numpy/core/include -Ipandas/src/klib -Ipandas/src -I/usr/include/python2.7 -c pandas/index.c -o build/temp.linux-x86_64-2.7/pandas/index.o
gcc: error: pandas/index.c: No such file or directory
gcc: fatal error: no input files
compilation terminated.
error: command 'gcc' failed with exit status 4
I can reproduce the problem on Windows, but I imagine you're happier with a bug report on Debian.
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"closed_issues": 276,
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} | 5 | "2012-12-13T00:32:46Z" | "2012-12-13T05:21:05Z" | "2012-12-13T03:57:51Z" | NONE | null | I tried installing today's release of 0.10.0.b1 in a virtualenv with:
```
pip install git+https://github.com/pydata/pandas.git
```
This fails after src/datetime:
```
i686-apple-darwin11-llvm-gcc-4.2: pandas/index.c: No such file or directory
i686-apple-darwin11-llvm-gcc-4.2: no input files
i686-apple-darwin11-llvm-gcc-4.2: pandas/index.c: No such file or directory
i686-apple-darwin11-llvm-gcc-4.2: no input files
lipo: can't figure out the architecture type of: /var/folders/cd/_zzqv0h90ms8k4s6skxvgt0w0000gp/T//cc7oTKEl.out
error: command 'llvm-gcc-4.2' failed with exit status 1
```
Should I be installing the new version of pandas in a different way?
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} | 1 | "2012-12-13T02:40:43Z" | "2018-01-12T11:09:03Z" | "2018-01-12T11:09:03Z" | NONE | null | I noticed in the documentation:
"
Specifying column data types
Starting with v0.10, you can indicate the data type for the whole DataFrame or individual columns:
"
This is indeed a great feature epically for numerical objects codes that would otherwise be parsed as integers with leading zeros removed.
From the example I was hoping I could specify reduced precision int or float for each column, but alas they are up-casted to the 64bit versions. :/ Except....
In [1]:
data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'
df = pd.read_csv(StringIO.StringIO(data), dtype={'a': object, 'b': np.float32, 'c': np.int16})
df.dtypes
Out[1]: <-- Upcasted to 64 bit
a object
b float64
c int64
The upcasting occurs even if I explicitly try to set a column to a 32bit float/int.
In [2]:
df['b'] = df['b'].astype(np.float32)
type(df['b'][0])
Out[2]: <-- Upcasted to 64 bit even with explicit column set.
numpy.float64
However, if I start with the object type, and then explicitly set the column to a float16/32 everything it seems to work.
In [3]:
data = 'a,b,c\nCat,2.3456789,3\nDog,5,6\nHat,8,9'
df = pd.read_csv(StringIO.StringIO(data), dtype={'a': object, 'b': object, 'c': np.int16})
df.dtypes
Out[3]:
a object
b object
c int64 <-- upcasting during read_csv
In [4]:
print type(df['b'][0])
df['b'][0]
<type 'str'> <-- here we have the string type object not parsed
Out[4]:
'2.3456789'
In [5]:
df['b'] = df['b'].astype(np.float16) <-- explicit casting and setting of Object to float16
In [6]:
print type(df['b'][0])
print df['b'][0]
print df.dtypes
<type 'numpy.float16'>
2.3457
a object
b float16 <-- Yay 16 bit!
c int64
<-- Correctly Cast Object into float32, with correct truncation of the data value.
Now my next question is does this have any possibly bad memory implications? When converting many Object columns to np.float32 or np.float16 does pandas properly take care to allow enough memory for the different width numbers? I'm assuming an entirely new column is created and the old object column is just freed from memory. This may be a good workaround for me since I often have several million low resolution real and int columns that are adding extra overhead for storing/reading/parsing/writing in 64bit wide format.
Thanks for any input,
-Gagi
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} | 1 | "2012-12-13T03:50:23Z" | "2012-12-17T15:21:50Z" | "2012-12-17T15:21:50Z" | MEMBER | null | I don't have much confidence that the GIL is released in the places that matter
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} | 3 | "2012-12-13T03:58:53Z" | "2016-10-12T23:03:24Z" | "2012-12-13T16:47:43Z" | CONTRIBUTOR | null | Not sure if this is an issue in my setup or the intended behavior, but str.split behavior with multi-character patterns changed between 0.9.0 and 0.9.1, most likely due to #2119. Here is the original behavior going back to 0.8.1 as best I can tell.
```
In [1]: pd.__version__
Out[1]: '0.9.1.dev-c252129'
In [2]: s = pd.Series(["D0->D2"])
In [3]: s.str.split("->")
Out[3]: 0 [D0, D2]
```
In 0.9.1:
```
In [1]: pd.__version__
Out[1]: '0.9.1'
In [2]: s = pd.Series(["D0->D2"])
In [3]: s.str.split("->")
Out[3]: 0 [D0->D2]
```
Setting n=0 restores the behavior, on my Python install at least.
```
In [5]: s.str.split("->", n=0)
Out[5]: 0 [D0, D2]
```
Reproducible in 0.10.0b1.
```
In [1]: pd.__version__
Out[1]: '0.10.0b1'
In [2]: s = pd.Series(["D0->D2"])
In [3]: s.str.split("->")
Out[3]: 0 [D0->D2]
```
Python 2.7.2+ (default, Jul 20 2012, 22:12:53)
[GCC 4.6.1] on linux2
Any thoughts on changing the default n for str.split back to 0?
Many thanks!
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gcc: error: pandas/index.c: No such file or directory
I'm currently in the project root dir. There is a pandas/index.pyx, but no pandas/index.c
The only place where index.c is currently mentioned is in pandas.egg-info/SOURCES.txt (I am not very familiar with the c build tools)
I am running ubuntu and have the project installed in a virtualenv. numpy.**version** = 1.6.2
I am running gcc-4.6
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 1 | "2012-12-13T05:25:41Z" | "2013-06-19T03:00:47Z" | "2013-06-19T03:00:47Z" | NONE | null | see #2076.
the recurring "not really a terminal" issue again (#2275).
will need to check for qtconsole, notebook and IDLE.
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} | 0 | "2012-12-13T08:15:27Z" | "2014-07-11T02:59:46Z" | "2012-12-13T14:50:39Z" | NONE | null | #2507 (partial)
best merge this soonish to save constant rebasing and conflicts.
rebased on top of #2504
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} | 6 | "2012-12-13T10:01:57Z" | "2012-12-14T02:29:47Z" | "2012-12-14T02:29:40Z" | CONTRIBUTOR | null | Hi, I'm repeatedly running into an issue where I want a specific ordering of columns and rows. Often that won't work. I usually have to get around the restriction by converting this to lists etc., which makes everything a little messier than using dictionaries for indexing.
I more or less figured out why. In DataFrame.from_dict my OrderedDict is used to populate a OrderedDict(dict) class.
Do you think it would be a good idea to change this to "OrderedDict"?
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} | 0 | "2012-12-13T10:43:30Z" | "2014-06-19T10:31:02Z" | "2012-12-13T22:59:36Z" | NONE | null | related #2517
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https://api.github.com/repos/pandas-dev/pandas/issues/2519 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2519/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2519/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2519/events | https://github.com/pandas-dev/pandas/pull/2519 | 9,254,648 | MDExOlB1bGxSZXF1ZXN0MzM1NjM4Nw== | 2,519 | Fixed DataFrame.any() docstring | {
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} | 4 | "2012-12-13T16:47:27Z" | "2012-12-13T17:33:34Z" | "2012-12-13T17:33:34Z" | NONE | null | ```
pandas\core\frame.pyc in to_html(self, buf, columns, col_space, colSpace, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, force_unicode, bold_rows, classes)
1537 header=header, index=index,
1538 bold_rows=bold_rows)
-> 1539 formatter.to_html(classes=classes)
1540
1541 if buf is None:
pandas\core\format.pyc in to_html(self, classes)
374 """
375 html_renderer = HTMLFormatter(self, classes=classes)
--> 376 html_renderer.write_result(self.buf)
377
378 def _get_formatted_column_labels(self):
pandas\core\format.pyc in write_result(self, buf)
549 indent += self.indent_delta
550 indent = self._write_header(indent)
--> 551 indent = self._write_body(indent)
552
553 self.write('</table>', indent)
pandas\core\format.pyc in _write_body(self, indent)
635 fmt_values = {}
636 for i in range(len(self.columns)):
--> 637 fmt_values[i] = self.fmt._format_col(i)
638
639 # write values
pandas\core\format.pyc in _format_col(self, i)
363 def _format_col(self, i):
364 col = self.columns[i]
--> 365 formatter = self.formatters.get(col)
366 return format_array(self.frame.icol(i).values, formatter,
367 float_format=self.float_format,
AttributeError: 'list' object has no attribute 'get'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2521 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2521/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2521/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2521/events | https://github.com/pandas-dev/pandas/issues/2521 | 9,259,593 | MDU6SXNzdWU5MjU5NTkz | 2,521 | More flexible binning/aggregation of time series data | {
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} | 3 | "2012-12-13T17:37:44Z" | "2014-05-28T22:53:39Z" | null | MEMBER | null | from mailing list
``````
I have TimeSeries that has observations at the second level:
mport pandas as pd
import numpy as np
rng = pd.date_range('2012-01-01', '2012-01-10', freq='T')
s = pd.TimeSeries(np.random.randn(12961)*100, index=rng)
I now want to resample the data to daily at 17:59:59, so that I have the sum of positions at 17:59:59 for all days in range.
If I didn't care about the time I could just do:
s1 = s.resample('D',how='sum')
Does anyone have a suggestion?```
``````
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} | [] | closed | false | null | [] | null | 1 | "2012-12-13T17:58:52Z" | "2013-07-13T19:57:54Z" | "2012-12-13T18:13:17Z" | CONTRIBUTOR | null | Hi there,
I was reading this useful and very good doc. I saw an 'argmax' instead of 'idxmax' in the 'Essential basic functionality' part.
See you.
Damien
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} | 2 | "2012-12-13T18:20:20Z" | "2012-12-14T03:19:46Z" | "2012-12-14T03:19:46Z" | NONE | null | from pandas import DataFrame
df = DataFrame({'US': [1, 2]}, index=[datetime(2012,1,1), datetime(2012,1,2)])
df.diff(1.)
This succeeded in 0.9.0. Now it raises:
pandas\core\frame.pyc in diff(self, periods)
3937 diffed : DataFrame
3938 """
-> 3939 new_blocks = [b.diff(periods) for b in self._data.blocks]
3940 new_data = BlockManager(new_blocks, [self.columns, self.index])
3941 return self._constructor(new_data)
pandas\core\internals.pyc in diff(self, n)
292
293 def diff(self, n):
--> 294 new_values = com.diff(self.values, n, axis=1)
295 return make_block(new_values, self.items, self.ref_items)
296
pandas\core\common.pyc in diff(arr, n, axis)
470 if arr.ndim == 2 and arr.dtype.name in _diff_special:
471 f = _diff_special[arr.dtype.name]
--> 472 f(arr, out_arr, n, axis)
473 else:
474 res_indexer = [slice(None)] \* arr.ndim
pandas\algos.pyd in pandas.algos.diff_2d_int64 (pandas\algos.c:14030)()
TypeError: 'float' object cannot be interpreted as an index
This might very well be a desired change - but it's something we'll have to work around. I'm just reporting it for completeness :-)
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} | 3 | "2012-12-13T19:58:00Z" | "2012-12-14T02:35:31Z" | "2012-12-14T02:35:31Z" | NONE | null | ``` python
from pandas import DataFrame
from datetime import datetime
df = DataFrame({'US': [1]}, index=[datetime(2012,1,1)])
df2 = DataFrame({}, columns=['EU'])
df.combine_first(df2)
```
In 0.9.0, this returns:
```
US
2012-01-01 1
```
In 0.10.0b1, this returns:
```
EU US
2012-01-01 NaN 1
```
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} | 2 | "2012-12-13T20:19:43Z" | "2012-12-14T01:09:17Z" | "2012-12-14T01:09:17Z" | NONE | null | Hello,
I get a segfault in pandas version `0.10.0.dev` when attempting to import a tab-delimited file with `pandas.read_table`.
Here is the code that produces the bug:
``` python
# note: tf is a string containing the path to the file
pd.read_table(tf, names=range(1, 129) + ['ref'], header=None)
```
I tried loading the data into a list and then passing it to the `DataFrame` constructor and was successful. Here's what I did:
``` python
with open(filename, 'rt') as f:
# there is only one line because this file was encoded using '\r' instead of '\n'
data = f.readlines()[0]
# remove the empty strings that occasionally pop up
data = [[s for s in line.split('\t') if s] for line in data.split('\r') if line]
pd.DataFrame(data, columns=range(1, 129) + ['ref']) # works
```
[Here](https://dl.dropbox.com/u/6160029/data.txt) is some example data that you may use.
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} | 2 | "2012-12-13T21:46:27Z" | "2012-12-13T22:21:57Z" | "2012-12-13T22:21:16Z" | NONE | null | Seeing this in a few cases. This is the simplest repro I can find:
``` python
from pandas import date_range, rolling_mean, Series
s = Series(index=date_range('1999-02-03','1999-04-05'))
s['1999-02-03'] = 0.00012456
s['1999-02-04'] = 0.0003
s['1999-04-05'] = 0
rolling_mean(s, 1)['1999-04-05']
```
Returns 0 in 0.9.0.
Returns -5E-20 in 0.10.0.
We found this issue because we do a sqrt on the result and expected 0s when the input was a 0.
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} | 7 | "2012-12-13T21:55:21Z" | "2014-06-26T11:13:06Z" | "2012-12-13T22:52:58Z" | CONTRIBUTOR | null | added benchmarcks to compare (100,000) rows:
- read/write store
- read/write store mixed
- read/write table
- read/write table wide (200 columns)
- read/write table mixed
- query wide/table
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} | 1 | "2012-12-13T21:57:31Z" | "2014-06-27T11:27:29Z" | "2012-12-13T22:47:43Z" | CONTRIBUTOR | null | also marked a few tests as slow
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} | 3 | "2012-12-14T04:10:10Z" | "2012-12-14T04:22:56Z" | "2012-12-14T04:22:56Z" | NONE | null | sorry about this change yet again, but this will
prevent implementing `pd.option.x.y = z` and tab-completion.
what's a good substitute - "disp"? "display"? back to "print_config"?
@wesm, @changhiskhan
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a,b,c
vs
a, b,c
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} | 4 | "2012-12-14T15:15:16Z" | "2012-12-23T21:44:50Z" | "2012-12-14T16:41:14Z" | CONTRIBUTOR | null | Hi. I've got a data set that I'm working with from [here](http://epp.eurostat.ec.europa.eu/NavTree_prod/everybody/BulkDownloadListing?dir=data&sort=1&sort=2&start=b), the `bop_its_det.tsv.gz` file. [Direct Link](http://epp.eurostat.ec.europa.eu/NavTree_prod/everybody/BulkDownloadListing?sort=1&file=data%2Fbop_its_det.tsv.gz). It's about 80 MB unzipped. I tried to create a csv of just the first 10 rows [here](https://www.dropbox.com/s/u1pz5gvbwwaupjn/test.csv) but I think I messed up some of the newlines. [Here's](https://gist.github.com/4286107) a gist of the plain text.
I want to say up front that I'm using pandas version `0.10.0.dev-16774b3`, which I think is from a couple days before the official 10.0 beta was released, so maybe this isn't an issue anymore.
Someone in the EU's stats department decided it would be a good idea to separate some column names by columns and others by tabs so the first line looks like
```
In [332]: f = open('BulkDownloadListing.tsv', 'r')
In [333]: f.readline()
Out[333]: 'currency,post,flow,partner,geo\\time\t2011 \t2010 \t2009 \t2008 \t2007 \t2006 \t2005 \t2004 \n'
```
read_csv can handle this fine with:
```
df = pd.read_csv('BulkDownloadListing.tsv', sep=',|s*\t', na_values=[':', ' :', ': '], nrows=10)
```
when I try with the compressed file `BulkDownloadListing.gz`:
```
df = pd.read_csv('BulkDownloadListing.gz', compression='gzip', sep=',|s*\t', na_values=[':', ' :', ': '], nrows=10)
```
I get
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-337-07c5e7dff532> in <module>()
----> 1 df = pd.read_csv('BulkDownloadListing.gz', compression='gzip', sep=',|s*\t', na_values=[':', ' :', ': '], nrows=10)
/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.10.0.dev_16774b3-py2.7-macosx-10.8-x86_64.egg/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
392 buffer_lines=buffer_lines)
393
--> 394 return _read(filepath_or_buffer, kwds)
395
396 parser_f.__name__ = name
/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.10.0.dev_16774b3-py2.7-macosx-10.8-x86_64.egg/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
204
205 if nrows is not None:
--> 206 return parser.read(nrows)
207 elif chunksize or iterator:
208 return parser
/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.10.0.dev_16774b3-py2.7-macosx-10.8-x86_64.egg/pandas/io/parsers.pyc in read(self, nrows)
622 # self._engine.set_error_bad_lines(False)
623
--> 624 ret = self._engine.read(nrows)
625
626 if self.options.get('as_recarray'):
/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.10.0.dev_16774b3-py2.7-macosx-10.8-x86_64.egg/pandas/io/parsers.pyc in read(self, rows)
1238 content = content[1:]
1239
-> 1240 alldata = self._rows_to_cols(content)
1241 data = self._exclude_implicit_index(alldata)
1242
/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.10.0.dev_16774b3-py2.7-macosx-10.8-x86_64.egg/pandas/io/parsers.pyc in _rows_to_cols(self, content)
1454 msg = ('Expected %d fields in line %d, saw %d' %
1455 (col_len, row_num + 1, zip_len))
-> 1456 raise ValueError(msg)
1457
1458 return zipped_content
ValueError: Expected 5 fields in line 3, saw 11
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2536 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2536/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2536/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2536/events | https://github.com/pandas-dev/pandas/pull/2536 | 9,291,379 | MDExOlB1bGxSZXF1ZXN0MzM3NTMzNQ== | 2,536 | BUG: pytables minor changes | {
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} | 1 | "2012-12-14T17:17:34Z" | "2014-06-17T10:09:37Z" | "2012-12-15T22:28:56Z" | CONTRIBUTOR | null | - ENH: moved Term to top-level panas namespace (like HDFStore)
- BUG: writing a Panel4D in cython (lib.pyx)
- BUG: interpreting axes paremeter to orient data
- BUG/ENH: allow appending of a different order of non_index_axes data
which then is ordered according to on-disk data
- DOC: explanation of how to orient data to provide optimal append/delete operations
- BUG/TST: more complete coverage of ndim and non-default axes ordering
- removed debugging memory calls
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} | 1 | "2012-12-14T18:01:02Z" | "2012-12-16T04:43:19Z" | "2012-12-16T04:43:19Z" | CONTRIBUTOR | null | http://nbviewer.ipython.org/4287202/
When using a regular groupby, custom agg functions will receive a Panel as expected. If you groupby with TimeGrouper, the agg function will receive a Series. The regular cython aggregates likes mean, sum work fine.
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} | 1 | "2012-12-14T19:31:37Z" | "2012-12-14T23:21:37Z" | "2012-12-14T23:21:36Z" | NONE | null | Hi all,
I am using date_range() and noticed that if the arguments start, end and period are all passed, the _period_ argument essentially overrides _end_.
For example, consider the following index created using start and end:
```
start = datetime(2011, 1, 1, 5, 3, 40)
end = datetime(2011, 1, 1, 8, 9, 40)
tmanual=date_range(start, end, freq='s')
>>>[2011-01-01 05:03:40, ..., 2011-01-01 08:09:40]
```
An alternative is to provide a start time, and then a number of periods.
```
tperiods=date_range(start, freq='s', periods=10)
>>> [2011-01-01 05:03:40, ..., 2011-01-01 05:03:49]
```
The weird part is when I pass all three arguments (start, end and periods). It actually defers to the output of tperiods.
```
tweird=date_range(start, end, freq='s', period=10)
>>>[2011-01-01 05:03:40, ..., 2011-01-01 05:03:49]
```
Thus, there is no difference between the following calls:
```
date_range(start, freq='s', periods=10)
date_range(start, end, freq='s', period=10)
```
IMO, I think this should at least yield an error or a flag. My preferred behavior would actually yield a DatetimeIndex that runs from start to end, sampling 10 times over the entire array of second-spaced data. One would be effectively defining a step size for sampling of length (start-end)/period, where start-end is an array spaced at second intervals.
What do you guys think? Here's an example of how the function could work:
```
def date_range(...):
........
if start and end and period:
tindex=date_range(start, end, freq=freq)
return tindex[0 :: period]
```
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} | 4 | "2012-12-15T00:17:30Z" | "2012-12-16T16:14:59Z" | "2012-12-15T23:12:48Z" | CONTRIBUTOR | null | Given the awesome work on read_csv() pending for 0.10, this may be a necessary change, however, for backwards compatibility, can the `names` parameter change to implicitly set `header` to None again? The new behavior of `names` is clearly documented, so feel free to reject as the new default.
Up to and including 0.9.1, specifying the names parameter implied the first row is data, not header.
```
In [1]: pd.__version__
Out[1]: '0.9.1'
In [2]: pd.read_csv("sample.csv", names=['c0', 'c1'])
Out[2]:
c0 c1
0 abcd efghi
1 jkl mnop
```
It came as a surprise that this no longer worked -- easily worked around though.
```
In [1]: pd.__version__
Out[1]: '0.10.0b1'
In [2]: pd.read_csv("sample.csv", names=['c0', 'c1'])
Out[2]:
c0 c1
0 jkl mnop
In [3]: pd.read_csv("sample.csv", names=['c0', 'c1'], header=None)
Out[3]:
c0 c1
0 abcd efghi
1 jkl mnop
```
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} | 1 | "2012-12-15T03:36:15Z" | "2014-06-27T11:49:27Z" | "2012-12-15T21:09:53Z" | CONTRIBUTOR | null | Another MSVC hack, this time to deal with the fact that signbit is not in math.h.
By using the _copysign, you can avoid potential issues with "negative zeros"
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} | 1 | "2012-12-15T18:51:44Z" | "2013-01-20T01:43:00Z" | "2013-01-20T01:43:00Z" | CONTRIBUTOR | null | I don't know if the piece of code to read/write into SQL database such as Sqlite is robust enough to write something about this (great) feature in the doc (i.e "io.rst" file).
I would like to write a tiny section about 'execute', 'tquery' & 'read_frame' methods with some examples.
Do you think that it's relevant?
Cheers,
Damien
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```
In [18]: grades.ix[8]
Out[18]:
Unnamed: 0 8
Name Johnny Student
hw1 50
hw2 50
hw3 50
hw4 50
exam 100
grade 100
Name: 8
```
Let's say I want to update Johnny's exam score to 95:
```
In [19]: grades.ix[8]["exam"]
Out[19]: 100
In [20]: grades.ix[8]["exam"] = 95
In [21]: grades.ix[8]["exam"]
Out[21]: 100
```
Its not clear why this should fail, but more importantly, it should not fail silently.
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} | 1 | "2012-12-15T21:07:25Z" | "2016-10-12T23:03:25Z" | "2012-12-28T18:47:23Z" | MEMBER | null | ``````
How can I change the label for a 'col1'-'col2'-plot of a DataFrame? pandas seems to set the label automatically, which then clashes with my label setting:
ch7[:21].plot('det','clat',label='ch7')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-122-0f4833abff5e> in <module>()
1 clf()
----> 2 ch7[:21].plot('det','clat',label='ch7')
/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas-0.10.0b1-py2.7-macosx-10.5-x86_64.egg/pandas/tools/plotting.pyc in plot_frame(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, **kwds)
1419 grid=grid, logy=logy, secondary_y=secondary_y,
1420 title=title, figsize=figsize, fontsize=fontsize,
-> 1421 **kwds)
1422
1423 plot_obj = klass(frame, kind=kind, subplots=subplots, rot=rot,
TypeError: plot_series() got multiple values for keyword argument 'label'
Version: '0.10.0b1'```
``````
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} | 1 | "2012-12-16T00:44:11Z" | "2013-01-23T16:10:46Z" | "2013-01-23T16:10:46Z" | CONTRIBUTOR | null | like numpy.sqeeze but returns the correct pandas object
add in core/generic?
anyone like squash better?
(prob a fair number of special cases where need to check, e.g. empty Series)...
so propose for 0.11
```
def squeeze(self):
""" squeeze length 1 dimensions """
return self.ix[tuple([ a[0] if len(a) == 1 else slice(None) for a in self.axes ])]
```
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} | 0 | "2012-12-16T00:50:02Z" | "2014-06-26T11:25:39Z" | "2012-12-16T17:34:32Z" | CONTRIBUTOR | null | ordering in returned data on an index axis if the selection (Term) has a lot of values
e.g. had > 61 fields that were specified in a Term (weird but true)
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} | 3 | "2012-12-16T11:15:58Z" | "2013-07-22T20:01:05Z" | "2013-07-22T20:01:05Z" | NONE | null | a vbench jobs was added to travis (ae80d24e) 3 weeks ago,
Today I did some more work there, added some code to make it easy
to collect all that data back into a comfy dataframe,
Although I think this was a cool hack (and although using pandas
and statsmodels to fit a model and get some confidence intervals on the results
would make it even cooler), It basically looks like the results fluctuate too much
to make this a useful metric. The repeatability is awful,
@changhiskhan mentioned you have a jenkins server stowed somewhere,
all the building blocks are already in place for jenkins to test commits on master
and give a reliable alert when a performance regression occurs, It might be a good
idea to set that up.
I'll leave the VBENCH build active in travis, It's a poignant dataset to use in some
future pandas tutorial ( timeseries, plotting, parsing, aligning, cleaning, etc')
You can play with the data yourself if you're interested:
``` python
In [1]: cd ~/src/pandas/vb_suite/
In [3]: from perf_HEAD import get_all_results_joined
In [4]: df=get_all_results_joined()
In [27]: df
Out[27]:
<class 'pandas.core.frame.DataFrame'>
Index: 140 entries, append_frame_single_homogenous to write_csv_standard
Columns: 187 entries, 771 to 968
dtypes: float64(187)
In [28]: df.index[:5]
Out[28]: Index([append_frame_single_homogenous, append_frame_single_mixed, concat_series_axis1, concat_small_frames, ctor_index_array_string], dtype=object)
```
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} | 0 | "2012-12-16T18:17:49Z" | "2012-12-17T15:15:50Z" | "2012-12-17T15:15:50Z" | CONTRIBUTOR | null | used to work in 0.9.1.
this is on current master
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: index = pd.PeriodIndex(['2011-1', '2011-2', '2011-3'], freq='M')
In [4]: frame = pd.DataFrame(np.random.randn(3,4),index=index)
In [5]: frame.to_string()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-b6219037419a> in <module>()
----> 1 frame.to_string()
/mnt/home/jreback/pandas/pandas/core/frame.pyc in to_string(self, buf, columns, col_space, colSpace, header, index, na_rep, formatters, float_format, sparsify, nanRep, index_names, justify, force_unicode, line_width)
1501 header=header, index=index,
1502 line_width=line_width)
-> 1503 formatter.to_string()
1504
1505 if buf is None:
/mnt/home/jreback/pandas/pandas/core/format.pyc in to_string(self, force_unicode)
295 text = info_line
296 else:
--> 297 strcols = self._to_str_columns()
298 if self.line_width is None:
299 text = adjoin(1, *strcols)
/mnt/home/jreback/pandas/pandas/core/format.pyc in _to_str_columns(self)
240
241 # may include levels names also
--> 242 str_index = self._get_formatted_index()
243 str_columns = self._get_formatted_column_labels()
244
/mnt/home/jreback/pandas/pandas/core/format.pyc in _get_formatted_index(self)
444 formatter=fmt)
445 else:
--> 446 fmt_index = [index.format(name=show_index_names, formatter=fmt)]
447
448 adjoined = adjoin(1, *fmt_index).split('\n')
TypeError: format() got an unexpected keyword argument 'formatter'
```
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requirements.txt:
nose==1.2.1
Cython==0.17.3
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- created during the PyData Workshop-Sprint 2012
- Hannah Chen, Henry Chow, Eric Cox, Robert Mauriello
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} | 1 | "2012-12-17T12:41:19Z" | "2012-12-17T15:19:33Z" | "2012-12-17T15:19:33Z" | NONE | null | When I slice a DataFrame with an index of datetimes that contains time zone info, the resulting DataFrame index is missing the timezone info.
> > > numpy.**version**
> > > '1.7.0b2'
> > > pandas.**version**
> > > '0.9.0'
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} | [] | closed | false | null | [] | null | 5 | "2012-12-17T17:10:09Z" | "2013-05-27T13:57:19Z" | "2012-12-17T17:43:43Z" | NONE | null | In the Time Series documentation (http://pandas.pydata.org/pandas-docs/stable/timeseries.html), DatetimeIndex is said to have the following functionality:
```
In [1118]: ts['2011']
Out[1118]:
2011-01-31 -1.281247
2011-02-28 -0.727707
2011-03-31 -0.121306
2011-04-29 -0.097883
2011-05-31 0.695775
2011-06-30 0.341734
2011-07-29 0.959726
2011-08-31 -1.110336
2011-09-30 -0.619976
2011-10-31 0.149748
2011-11-30 -0.732339
2011-12-30 0.687738
Freq: BM
In [1119]: ts['2011-6']
Out[1119]:
2011-06-30 0.341734
Freq: BM
```
I have tried it out in pandas 0.9.1 but it doesn't work.
What does work is `ts.ix['2011']` and `ts.ix['2011-6]`. I hope you correct this misleading mistake. There are a couple more examples of these on that page so please check it out.
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 24 | "2012-12-17T20:13:15Z" | "2014-06-21T02:43:58Z" | "2013-03-14T11:37:05Z" | CONTRIBUTOR | null | This is my attempt to fix the so far missing nanosecond support for pd.DatetimeIndex (see issue #1812).
The Python modification should be relatively easy to review, but unfortunately I had to do a bigger refactoring of period.c in order to support subsecond times.
Im still working on adding more tests and I will try to include the tests on C level as well (test_period.c, which can only be used standalone so far).
I'm looking forward to your remarks and I will be happy to modify the code to fit it into the project.
Cheers
Andreas
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https://api.github.com/repos/pandas-dev/pandas/issues/2556 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2556/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2556/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2556/events | https://github.com/pandas-dev/pandas/issues/2556 | 9,346,579 | MDU6SXNzdWU5MzQ2NTc5 | 2,556 | DataFrame sort not stable | {
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} | [] | closed | false | null | [] | null | 2 | "2012-12-17T20:28:57Z" | "2012-12-18T22:09:32Z" | "2012-12-18T22:09:32Z" | NONE | null | Passing a list of strings to the DataFrame.sort() method is stable, but passing a string by itself causes the sort to be unstable.
I'm defining sort stability to mean row order is maintained for duplicate values in the sorted column.
## Code to recreate
```
>>>import pandas as pd
>>>df = pd.DataFrame(list('ABCDABCDABCD'), columns=['x'])
>>>print df.sort(['x'])
x
0 A
4 A
8 A
1 B
5 B
9 B
2 C
6 C
10 C
3 D
7 D
11 D
>>>print df.sort('x')
x
0 A
4 A
8 A
1 B
5 B
9 B
6 C
10 C
2 C
3 D
11 D
7 D
```
The index is out of order for C and D in the 2nd case.
pandas version 0.10.0
Python version 2.7.2
Windows 7, 64-bit
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} | 4 | "2012-12-18T20:02:50Z" | "2013-09-16T18:59:14Z" | "2013-09-16T18:59:14Z" | NONE | null | To avoid the verbose syntax currently needed to select across many columns of a data frame, here's a suggestion. Inside of DataFrame's **getitem** function, make some special case logic to handle the case where a lambda function is passed in. If a lambda is passed in, then apply it to the dataframe itself and attempt to get the items based on the lambda's result.
Here's an example of what I mean. Suppose that I create a data frame named "dfrm" and it has columns A, B, C, D, and E. Then currently, the following syntax will work to sub-select across conditions on the A and B columns:
dfrm[(lambda x: (x.A < 0) & (x.B > 0))(dfrm)]
By adding the extra handling to **getitem**, you can remove the need for the the last set of parentheses where dfrm itself is passed as the argument to the lambda. **getitem** can check for a callable and just always pass itself to the callable, so that the syntax would look like this:
dfrm[(lambda x: (x.A < 0) & (x.B > 0))]
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} | 8 | "2012-12-18T23:06:59Z" | "2014-06-12T15:36:20Z" | "2012-12-28T15:07:48Z" | CONTRIBUTOR | null | - can construct searches on the actual columns of the data, by passing keyword `data_columns` to `append`
e.g. store.select('df_dc',[ Term('B>0'), Term('string=foo') ])
(where B and string are columns in the frame)
- added `nan_rep` for supporting string columns with nan's in them
- corectly determine dtypes of data columns that we cannot deal with (date,unicode), raise UnImplementedError
- added support for datetime64 in columns
- added method `unique` to fast retrieval of indexables or data columns
- added keyword `index=True` to automagically create indicies on all indexables and data columns!
- performance enhancements on string columns
- added `chunksize` parameter to append to allow write chunking, significantly lower memory usage on writes
- added `expectedrows` parameter to append to allow specification of the TOTAL expected rows in a table (to optimize performance)
- added `start/stop` parameters to select to allow limiting of the selection space
- added multi-index dataframe support (closes GH #1277 )
- added `append_to_multple`, `select_as_multiple` and `select_as_coordinates` methods to support multiple-table creation & selection
- removed compression kw from put, replaced with complib (to make more consistent across library)
- Term parsing a little more robust
- more tests & docs for data columns
- added whatsnew 0.10.1 docs
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https://api.github.com/repos/pandas-dev/pandas/issues/2562 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2562/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2562/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2562/events | https://github.com/pandas-dev/pandas/issues/2562 | 9,385,926 | MDU6SXNzdWU5Mzg1OTI2 | 2,562 | pytz/dateutil and matplotlib | {
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} | 5 | "2012-12-18T23:33:23Z" | "2014-01-25T01:07:34Z" | "2013-09-20T17:19:36Z" | CONTRIBUTOR | null | I just installed the python stack on a new system and replicated a problem I see often. I assume it comes from the fact that dateutil and pytz are installed by matplotlib and then pandas adds them to easy-install.pth because of the install_requires in setup.py. I see this message every time I start the interpreter due to my default imports. This gets quite old. Do others see this. Should I be doing something else? So far I just go in and delete the entries that pandas puts in easy-install.pth.
```
/usr/local/lib/python2.7/dist-packages/pytz/__init__.py:35: UserWarning: Module pytz was already imported from /usr/local/lib/python2.7/dist-packages/pytz/__init__.pyc, but /usr/local/lib/python2.7/dist-packages/pytz-2012h-py2.7.egg is being added to sys.path
from pkg_resources import resource_stream
/usr/local/lib/python2.7/dist-packages/pytz/__init__.py:35: UserWarning: Module dateutil was already imported from /usr/local/lib/python2.7/dist-packages/dateutil/__init__.pyc, but /usr/local/lib/python2.7/dist-packages/python_dateutil-2.1-py2.7.egg is being added to sys.path
from pkg_resources import resource_stream
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2563 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2563/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2563/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2563/events | https://github.com/pandas-dev/pandas/issues/2563 | 9,393,507 | MDU6SXNzdWU5MzkzNTA3 | 2,563 | DatetimeIndex.unique() returns datetime64 | {
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} | 0 | "2012-12-19T08:19:01Z" | "2012-12-28T14:10:16Z" | "2012-12-28T14:10:16Z" | CONTRIBUTOR | null | ```
>>> pd.DatetimeIndex(['2011-01-01', '2011-01-02']).unique()
array([1970-01-15 104:00:00, 1970-01-15 128:00:00], dtype=datetime64[ns])
```
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} | 5 | "2012-12-19T10:40:41Z" | "2012-12-29T21:55:09Z" | "2012-12-29T21:54:50Z" | NONE | null | Hi,
I have tried the following with
pandas-f014b01af7bd2d03266697e672c2d41daded3fca.zip
and
pandas-0.10.0.tar.gz
The unit tests on my Pandas build are failing with:
#
## FAIL: test_read_csv (pandas.io.tests.test_parsers.TestCParserHighMemory)
Traceback (most recent call last):
File "/home/woodri/build/out/lib/python2.7/site-packages/pandas-0.10.0-py2.7-linux-x86_64.egg/pandas/io/tests/test_parsers.py", line 109, in test_read_csv
assert(False), "read_csv should accept unicode objects as urls"
AssertionError: read_csv should accept unicode objects as urls
#
## FAIL: test_read_csv (pandas.io.tests.test_parsers.TestCParserLowMemory)
Traceback (most recent call last):
File "/home/woodri/build/out/lib/python2.7/site-packages/pandas-0.10.0-py2.7-linux-x86_64.egg/pandas/io/tests/test_parsers.py", line 109, in test_read_csv
assert(False), "read_csv should accept unicode objects as urls"
AssertionError: read_csv should accept unicode objects as urls
#
## FAIL: test_read_csv (pandas.io.tests.test_parsers.TestPythonParser)
Traceback (most recent call last):
File "/home/woodri/build/out/lib/python2.7/site-packages/pandas-0.10.0-py2.7-linux-x86_64.egg/pandas/io/tests/test_parsers.py", line 109, in test_read_csv
assert(False), "read_csv should accept unicode objects as urls"
AssertionError: read_csv should accept unicode objects as urls
---
Ran 2934 tests in 193.600s
FAILED (SKIP=44, failures=3)
I have compiled python, cython, numpy, etc. myself. Are there any dependencies or compile options that I am likely to be missing?
Please let me know if there is any other information I can provide to help identify the problem.
Thank you
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} | 1 | "2012-12-19T12:40:27Z" | "2014-06-25T01:09:44Z" | "2012-12-28T15:22:34Z" | CONTRIBUTOR | null | Trivial but convenient, as discussed in https://github.com/pydata/pandas/issues/2327
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} | 12 | "2012-12-19T17:12:35Z" | "2014-08-12T15:17:32Z" | "2013-09-21T13:36:53Z" | NONE | null | read_csv() converts the string '0.011277' into a np.float64 whose repr() is:
'0.011276999999999999' .
However, repr(np.float64('0.011277')) returns:
'0.011277000000000001'
Also, in Pandas v.0.9.0, read_csv() produced '0.011276999999999999', while in Pandas v.10.0 read_csv() produces '0.011277000000000001'.
This problem showed up when I truncated the number (with 1e-6 precision).
System setting:
Pandas v. 10.0,
Numpy v.1.7.0b2
Windows 7
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} | 1 | "2012-12-19T21:20:55Z" | "2012-12-28T16:41:12Z" | "2012-12-28T16:41:12Z" | NONE | null | With the master branch (f014b01af7bd2d03266697e672c2d41daded3fca), grouping over a PeriodIndex is breaking the format of the index:
```
In [100]: df = pandas.DataFrame({'x': [1, 2, 3], 'y': ['2012-12-18', '2012-12-19', '2013-01-04']})
In [101]: df['y'] = df.y.astype('datetime64[D]')
In [102]: df = df.set_index('y').to_period('M')
In [103]: df
Out[103]:
x
2012-12 1
2012-12 2
2013-01 3
In [104]: df.groupby(level=0).sum()
Out[104]:
x
515 3
516 3
```
I would expect to still see the dates in the index.
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} | 18 | "2012-12-19T23:12:36Z" | "2021-11-14T21:15:01Z" | null | NONE | null | How can I force a dtype on a column and ensure that any not-parseable data entry are filled as NaN? This is important in cases where there are unpredictable data entry errors in CSVs or database streams that cannot be mapped to missing values a priori.
Eg: Below I want column 'a' to be parsed as np.float but the erroneous 'Dog' entry causes an exception. Is there a way to tell read_csv() to force parsing a column 'a' as np.float and fill all non-parseable entries with NaN?
```
data = 'a,b,c\n1.1,2,3\nDog,5,6\n7.7,8,9.5'
df = pd.read_csv(StringIO.StringIO(data), dtype={'a': np.float})
df.dtypes
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-cd8b6f868aec> in <module>()
1 data = 'a,b,c\n1.1,2,3\nDog,5,6\n7.7,8,9.5'
----> 2 df = pd.read_csv(StringIO.StringIO(data), dtype={'a': np.float})
3 df.dtypes
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
389 buffer_lines=buffer_lines)
390
--> 391 return _read(filepath_or_buffer, kwds)
392
393 parser_f.__name__ = name
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in _read(filepath_or_buffer, kwds)
205 return parser
206
--> 207 return parser.read()
208
209 _parser_defaults = {
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
622 # self._engine.set_error_bad_lines(False)
623
--> 624 ret = self._engine.read(nrows)
625
626 if self.options.get('as_recarray'):
C:\Python27\lib\site-packages\pandas\io\parsers.pyc in read(self, nrows)
943
944 try:
--> 945 data = self._reader.read(nrows)
946 except StopIteration:
947 if nrows is None:
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader.read (pandas\src\parser.c:5785)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._read_low_memory (pandas\src\parser.c:6002)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._read_rows (pandas\src\parser.c:6870)()
C:\Python27\lib\site-packages\pandas\_parser.pyd in pandas._parser.TextReader._convert_column_data (pandas\src\parser.c:7919)()
AttributeError: 'NoneType' object has no attribute 'dtype'
```
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} | 2 | "2012-12-20T10:46:53Z" | "2016-10-12T23:03:25Z" | "2013-01-21T17:32:00Z" | NONE | null | If I plot an hourly time series with a different base (don't know if this is correct englisch I mean if it is not 12:00, 13:00, ... but something like 12:30, 13:30, ...) the values are plotted as full hours at the xaxis. If I convert to a resolution of '60min' it works.
``` python
In [250]: idx = pd.date_range('2012-12-20', periods=24, freq='H') + datetime.timedelta(minutes=30)
In [251]: idx
Out[251]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-12-20 00:30:00, ..., 2012-12-20 23:30:00]
Length: 24, Freq: H, Timezone: None
In [252]: df = pd.DataFrame(np.arange(24), index=idx)
In [253]: df.plot(style='r.')
Out[253]: <matplotlib.axes.AxesSubplot at 0xd34920d0>
In [254]: df2 = df.resample('60min', base=30, closed='right')
In [255]: df2.index
Out[255]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-12-20 00:30:00, ..., 2012-12-20 23:30:00]
Length: 24, Freq: 60T, Timezone: None
In [256]: df2.plot(style='r.')
Out[256]: <matplotlib.axes.AxesSubplot at 0xd43fde90>
```
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} | 2 | "2012-12-20T14:49:38Z" | "2012-12-28T15:27:33Z" | "2012-12-28T15:27:33Z" | CONTRIBUTOR | null | The kh_hash library includes the ability to delete elements, but this is not used anywhere in pandas. In the khash library, there are two flags bits for every entry: ''isempty' and 'isdel'. This change removes the support for kh_del from the khash library, allowing it to use half as many flag bits.
All tests pass. I ran test_perf ~90 times, and got the results below (baseline = parent commit, times and ratios ± stdev). There do seem to be a few (at the bottom) whose times increase slightly, which surprises me.
```
name head baseline ratio
series_value_counts_int64 1.8630 ± 0.0067 2.0982 ± 0.0131 0.8880 ± 0.0063
groupby_frame_singlekey_integer 1.6717 ± 0.0510 1.8728 ± 0.0508 0.8932 ± 0.0355
index_int64_intersection 24.6203 ± 0.3258 27.0208 ± 0.2654 0.9112 ± 0.0149
groupby_last 2.5098 ± 0.0265 2.7299 ± 0.0252 0.9195 ± 0.0129
stat_ops_level_series_sum 1.5520 ± 0.0257 1.6864 ± 0.0237 0.9205 ± 0.0200
groupby_first 2.5838 ± 0.0281 2.8024 ± 0.0269 0.9221 ± 0.0134
sparse_series_to_frame 120.5008 ± 0.7108 129.8587 ± 1.1512 0.9280 ± 0.0098
datetimeindex_add_offset 0.1599 ± 0.0009 0.1715 ± 0.0013 0.9325 ± 0.0088
stat_ops_level_series_sum_multiple 5.2357 ± 0.1633 5.5971 ± 0.1624 0.9362 ± 0.0390
stat_ops_level_frame_sum_multiple 6.1508 ± 0.0653 6.5037 ± 0.0922 0.9459 ± 0.0166
read_csv_comment2 20.9364 ± 0.7056 22.0784 ± 0.6586 0.9491 ± 0.0421
groupby_multi_size 24.8205 ± 0.2378 26.1333 ± 0.2586 0.9499 ± 0.0131
stat_ops_level_frame_sum 2.3653 ± 0.1711 2.5029 ± 0.1835 0.9500 ± 0.0966
timeseries_add_irregular 17.1444 ± 0.1979 17.9006 ± 0.2983 0.9580 ± 0.0188
groupby_frame_median 5.0956 ± 0.0642 5.3151 ± 0.0801 0.9589 ± 0.0184
match_strings 0.3116 ± 0.0042 0.3250 ± 0.0052 0.9590 ± 0.0200
timeseries_infer_freq 7.0249 ± 0.1438 7.3208 ± 0.0869 0.9597 ± 0.0232
dataframe_reindex 0.2581 ± 0.0017 0.2680 ± 0.0019 0.9633 ± 0.0092
reshape_pivot_time_series 158.4428 ± 1.6615 164.4806 ± 2.1991 0.9635 ± 0.0164
index_int64_union 67.5624 ± 0.5516 69.9954 ± 0.5946 0.9653 ± 0.0113
reindex_multiindex 0.8870 ± 0.0103 0.9166 ± 0.0103 0.9678 ± 0.0155
groupby_simple_compress_timing 30.2154 ± 0.2435 31.1854 ± 0.2564 0.9690 ± 0.0111
groupby_indices 6.2456 ± 0.0674 6.4342 ± 0.0803 0.9708 ± 0.0158
panel_from_dict_all_different_indexes 64.0143 ± 0.3919 65.8792 ± 0.4151 0.9717 ± 0.0085
join_dataframe_integer_key 1.3216 ± 0.0212 1.3559 ± 0.0206 0.9750 ± 0.0215
panel_from_dict_two_different_indexes 43.3036 ± 0.3092 44.2583 ± 0.3232 0.9785 ± 0.0100
join_dataframe_integer_2key 3.8902 ± 0.0218 3.9749 ± 0.0241 0.9787 ± 0.0081
reshape_unstack_simple 2.3005 ± 0.0093 2.3416 ± 0.0115 0.9825 ± 0.0062
groupby_multi_series_op 12.2459 ± 0.0667 12.4435 ± 0.0809 0.9842 ± 0.0083
groupby_multi_cython 13.6919 ± 0.0685 13.8917 ± 0.0937 0.9857 ± 0.0082
concat_series_axis1 50.1771 ± 0.7017 50.8870 ± 0.8207 0.9863 ± 0.0210
unstack_sparse_keyspace 0.9630 ± 0.0071 0.9743 ± 0.0086 0.9884 ± 0.0113
frame_fancy_lookup_all 21.7081 ± 0.2252 21.9861 ± 0.8485 0.9885 ± 0.0303
groupby_frame_apply_overhead 15.0398 ± 0.2534 15.2089 ± 0.3118 0.9893 ± 0.0260
frame_insert_500_columns 74.6911 ± 0.5674 75.3304 ± 0.7608 0.9916 ± 0.0124
sort_level_one 3.2917 ± 0.0885 3.3215 ± 0.1132 0.9921 ± 0.0415
timeseries_timestamp_tzinfo_cons 0.0133 ± 0.0002 0.0134 ± 0.0003 0.9922 ± 0.0253
groupby_multi_python 51.7335 ± 0.9452 52.1447 ± 1.1834 0.9926 ± 0.0286
stats_rank_average 31.3220 ± 1.3618 31.5993 ± 1.2585 0.9928 ± 0.0591
read_csv_standard 9.4535 ± 0.1348 9.5228 ± 0.1152 0.9929 ± 0.0185
frame_reindex_both_axes 0.2233 ± 0.0014 0.2248 ± 0.0018 0.9933 ± 0.0098
timeseries_large_lookup_value 0.0179 ± 0.0003 0.0180 ± 0.0004 0.9941 ± 0.0239
frame_reindex_both_axes_ix 0.2794 ± 0.0017 0.2809 ± 0.0020 0.9947 ± 0.0092
frame_iteritems 1.8838 ± 0.0227 1.8937 ± 0.0279 0.9950 ± 0.0188
series_align_irregular_string 60.9044 ± 0.7522 61.2000 ± 0.6323 0.9953 ± 0.0160
groupbym_frame_apply 60.5675 ± 0.9349 60.8692 ± 0.9463 0.9953 ± 0.0217
stats_rank2d_axis1_average 13.5227 ± 0.8773 13.6383 ± 0.8856 0.9957 ± 0.0909
frame_to_csv 277.9836 ± 3.7095 279.2427 ± 5.0754 0.9958 ± 0.0222
read_csv_thou_vb 26.4393 ± 0.2321 26.5419 ± 0.2718 0.9962 ± 0.0134
indexing_dataframe_boolean_rows 0.1765 ± 0.0011 0.1772 ± 0.0015 0.9965 ± 0.0102
frame_ctor_list_of_dict 67.3875 ± 0.8141 67.6344 ± 0.9001 0.9965 ± 0.0177
frame_get_numeric_data 0.0459 ± 0.0004 0.0461 ± 0.0007 0.9966 ± 0.0169
frame_iteritems_cached 0.0639 ± 0.0020 0.0642 ± 0.0024 0.9967 ± 0.0471
groupby_multi_different_functions 11.8200 ± 0.0624 11.8596 ± 0.0932 0.9967 ± 0.0093
append_frame_single_mixed 0.9737 ± 0.0846 0.9841 ± 0.0847 0.9967 ± 0.1209
timeseries_asof_single 0.0377 ± 0.0005 0.0378 ± 0.0006 0.9968 ± 0.0197
timeseries_slice_minutely 0.0414 ± 0.0005 0.0415 ± 0.0005 0.9969 ± 0.0164
groupby_apply_dict_return 34.7286 ± 0.3478 34.8450 ± 0.5679 0.9969 ± 0.0188
period_setitem 746.4594 ± 8.6212 748.9278 ± 11.0465 0.9969 ± 0.0185
concat_small_frames 10.6045 ± 0.0675 10.6383 ± 0.1020 0.9969 ± 0.0114
frame_constructor_ndarray 0.0334 ± 0.0003 0.0335 ± 0.0005 0.9971 ± 0.0182
write_csv_standard 267.4531 ± 3.9582 268.3362 ± 5.3285 0.9971 ± 0.0243
groupby_multi_different_numpy_functions 11.8358 ± 0.0759 11.8699 ± 0.0738 0.9972 ± 0.0089
reindex_daterange_pad 0.1449 ± 0.0010 0.1453 ± 0.0014 0.9972 ± 0.0120
indexing_panel_subset 0.4469 ± 0.0033 0.4481 ± 0.0053 0.9976 ± 0.0137
reindex_daterange_backfill 0.1494 ± 0.0010 0.1497 ± 0.0016 0.9977 ± 0.0121
frame_ctor_nested_dict_int64 103.3669 ± 1.0917 103.6315 ± 1.7836 0.9977 ± 0.0198
append_frame_single_homogenous 0.2324 ± 0.0022 0.2330 ± 0.0023 0.9977 ± 0.0136
timeseries_1min_5min_ohlc 0.4956 ± 0.0030 0.4966 ± 0.0039 0.9980 ± 0.0097
reindex_frame_level_align 0.6218 ± 0.0040 0.6230 ± 0.0056 0.9982 ± 0.0110
panel_from_dict_equiv_indexes 22.4439 ± 0.1723 22.4863 ± 0.2186 0.9982 ± 0.0123
series_align_int64_index 32.8477 ± 0.1784 32.9009 ± 0.1936 0.9984 ± 0.0080
panel_from_dict_same_index 22.4174 ± 0.1834 22.4507 ± 0.2267 0.9986 ± 0.0129
read_table_multiple_date 1852.1563 ± 19.4540 1855.0047 ± 27.9213 0.9987 ± 0.0180
timeseries_1min_5min_mean 0.4872 ± 0.0026 0.4878 ± 0.0033 0.9989 ± 0.0087
replace_fillna 1.4792 ± 0.1068 1.4877 ± 0.1059 0.9990 ± 0.0974
sparse_frame_constructor 4.6531 ± 0.0459 4.6582 ± 0.0524 0.9990 ± 0.0148
series_ctor_from_dict 2.5720 ± 0.0111 2.5746 ± 0.0150 0.9990 ± 0.0072
read_table_multiple_date_baseline 837.9644 ± 8.9147 838.9167 ± 12.1464 0.9991 ± 0.0177
frame_to_string_floats 47.9212 ± 0.5630 47.9764 ± 0.7628 0.9991 ± 0.0194
frame_reindex_axis0 1.0288 ± 0.0078 1.0298 ± 0.0119 0.9991 ± 0.0133
frame_reindex_columns 0.2020 ± 0.0016 0.2022 ± 0.0019 0.9991 ± 0.0121
frame_boolean_row_select 0.2075 ± 0.0009 0.2077 ± 0.0014 0.9991 ± 0.0077
frame_fillna_many_columns_pad 11.8545 ± 0.1323 11.8662 ± 0.1362 0.9991 ± 0.0158
series_align_left_monotonic 14.8503 ± 0.0912 14.8613 ± 0.1038 0.9993 ± 0.0093
melt_dataframe 1.0850 ± 0.0078 1.0858 ± 0.0077 0.9993 ± 0.0101
frame_fillna_inplace 11.6751 ± 0.2087 11.6855 ± 0.2025 0.9994 ± 0.0247
frame_drop_dup_na_inplace 2.0111 ± 0.0103 2.0122 ± 0.0096 0.9994 ± 0.0070
groupby_frame_cython_many_columns 2.4077 ± 0.0116 2.4091 ± 0.0141 0.9995 ± 0.0075
frame_ctor_nested_dict 67.9949 ± 0.4608 68.0334 ± 0.4236 0.9995 ± 0.0092
timeseries_asof_nan 7.1119 ± 0.0627 7.1157 ± 0.0601 0.9995 ± 0.0122
frame_reindex_axis1 2.4248 ± 0.0368 2.4263 ± 0.0341 0.9996 ± 0.0203
series_drop_duplicates_int 0.6140 ± 0.0023 0.6142 ± 0.0023 0.9997 ± 0.0053
timeseries_period_downsample_mean 9.3964 ± 0.0262 9.3991 ± 0.0291 0.9997 ± 0.0042
dti_reset_index 0.4529 ± 0.0091 0.4531 ± 0.0085 0.9998 ± 0.0275
indexing_dataframe_boolean_rows_object 0.4054 ± 0.0016 0.4055 ± 0.0022 0.9998 ± 0.0067
stats_rank_average_int 18.1329 ± 0.2115 18.1389 ± 0.2243 0.9998 ± 0.0170
stats_rolling_mean 1.3577 ± 0.0044 1.3578 ± 0.0072 1.0000 ± 0.0062
groupby_pivot_table 16.3420 ± 0.0934 16.3417 ± 0.1068 1.0001 ± 0.0086
sort_level_zero 3.3187 ± 0.1217 3.3238 ± 0.1456 1.0001 ± 0.0524
reindex_fillna_backfill 0.0707 ± 0.0006 0.0707 ± 0.0007 1.0002 ± 0.0125
reindex_fillna_pad 0.0742 ± 0.0019 0.0742 ± 0.0021 1.0003 ± 0.0385
frame_drop_dup_inplace 2.2140 ± 0.0116 2.2133 ± 0.0129 1.0004 ± 0.0078
frame_drop_duplicates 18.3091 ± 0.1064 18.3022 ± 0.1000 1.0004 ± 0.0080
series_drop_duplicates_string 0.4529 ± 0.0036 0.4528 ± 0.0036 1.0004 ± 0.0112
series_value_counts_strings 4.2207 ± 0.0162 4.2188 ± 0.0329 1.0005 ± 0.0085
datetimeindex_normalize 22.4277 ± 0.2073 22.4172 ± 0.1919 1.0005 ± 0.0126
timeseries_to_datetime_iso8601 3.8940 ± 0.0482 3.8915 ± 0.0367 1.0007 ± 0.0152
lib_fast_zip_fillna 13.4962 ± 0.0750 13.4855 ± 0.0651 1.0008 ± 0.0074
timeseries_timestamp_downsample_mean 5.8368 ± 0.0176 5.8310 ± 0.0183 1.0010 ± 0.0044
reshape_stack_simple 1.7456 ± 0.0143 1.7440 ± 0.0154 1.0010 ± 0.0120
frame_drop_duplicates_na 17.9970 ± 0.0999 17.9767 ± 0.1071 1.0012 ± 0.0082
stats_rank2d_axis0_average 21.7999 ± 0.0819 21.7708 ± 0.1766 1.0014 ± 0.0092
timeseries_sort_index 17.6179 ± 0.1460 17.5926 ± 0.1269 1.0015 ± 0.0110
lib_fast_zip 10.6688 ± 0.0536 10.6489 ± 0.0585 1.0019 ± 0.0075
timeseries_asof 7.5577 ± 0.0625 7.5386 ± 0.0509 1.0026 ± 0.0107
stat_ops_series_std 0.5937 ± 0.0111 0.5922 ± 0.0109 1.0030 ± 0.0263
index_datetime_intersection 11.0669 ± 0.0623 11.0133 ± 0.0645 1.0049 ± 0.0081
groupby_series_simple_cython 4.1735 ± 0.0477 4.1508 ± 0.0511 1.0056 ± 0.0166
index_datetime_union 11.1319 ± 0.0546 11.0699 ± 0.0639 1.0056 ± 0.0075
read_csv_vb 18.0190 ± 0.2602 17.8894 ± 0.2271 1.0074 ± 0.0194
frame_fancy_lookup 2.0631 ± 0.0375 2.0469 ± 0.0496 1.0085 ± 0.0304
reindex_frame_level_reindex 1.1200 ± 0.0074 1.1125 ± 0.0541 1.0111 ± 0.0907
replace_replacena 1.6274 ± 0.1358 1.6190 ± 0.1444 1.0128 ± 0.1209
frame_sort_index_by_columns 35.3555 ± 0.2725 34.7723 ± 0.1765 1.0168 ± 0.0094
merge_2intkey_sort 34.2841 ± 0.3041 33.6600 ± 0.2171 1.0186 ± 0.0112
join_dataframe_index_single_key_small 5.6301 ± 0.8275 5.5872 ± 0.7877 1.0278 ± 0.2101
join_dataframe_index_multi 17.5588 ± 0.1660 17.0397 ± 0.1173 1.0305 ± 0.0120
merge_2intkey_nosort 15.8161 ± 0.9392 15.0701 ± 0.9006 1.0533 ± 0.0887
join_dataframe_index_single_key_bigger 9.4991 ± 3.4921 9.4172 ± 3.2368 1.1438 ± 0.5936
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2573 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2573/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2573/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2573/events | https://github.com/pandas-dev/pandas/issues/2573 | 9,456,219 | MDU6SXNzdWU5NDU2MjE5 | 2,573 | TimeSeries indexing by string not working (for microseconds?) | {
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} | [] | closed | false | null | [] | null | 2 | "2012-12-21T05:08:37Z" | "2012-12-21T06:47:46Z" | "2012-12-21T06:47:46Z" | CONTRIBUTOR | null | I was trying out examples from the pandas book, stating that one can index a time series index by string expressions. I copied the exact string from the pandas Timestamp printout and tried to use it as an index but it fails with KeyError. Maybe due to microseconds?
see http://nbviewer.ipython.org/4350756/
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} | 0 | "2012-12-21T11:27:51Z" | "2012-12-28T15:42:03Z" | "2012-12-28T15:42:03Z" | CONTRIBUTOR | null | ``` python
import panda as pd
s = pd.Series(range(10))
s.sub(1)
# AssertionError: Other operand must be Series
df = pd.DataFrame({'test':s})
df.sub(1) # works
```
Am I misunderstanding or should scalars work with sub/mul/div/etc?
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} | 1 | "2012-12-21T13:41:03Z" | "2016-10-12T23:03:26Z" | "2013-01-20T02:42:08Z" | NONE | null | The following code should clarify the problem:
``` python
import pandas
import pytz
index = pandas.date_range(start='2012-12-24 16:00', end='2012-12-24 18:00', freq='H', tz='Europe/Berlin')
ts = pandas.TimeSeries(index=index, data=index.hour)
time_pandas = pandas.Timestamp('2012-12-24 17:00', tz='Europe/Berlin')
time_datetime = datetime.datetime(2012,12,24,17,00, tzinfo=pytz.timezone('Europe/Berlin'))
time_pandas == time_datetime
ts[time_pandas] == ts[time_datetime]
```
While the two time stamps are equal, using them as an index on the TimeSeries results in different values. The same is true for slicing.
When using a tz-aware datetime for indexing/slicing, pandas seems to remove the timezone and interprete the time stamp as UTC.
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https://api.github.com/repos/pandas-dev/pandas/issues/2576 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2576/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2576/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2576/events | https://github.com/pandas-dev/pandas/issues/2576 | 9,468,832 | MDU6SXNzdWU5NDY4ODMy | 2,576 | UnicodeDecodeError in DataFrame.__repr__() for non-ascii column names | {
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} | 4 | "2012-12-21T16:09:50Z" | "2013-12-04T00:40:22Z" | "2012-12-28T15:49:38Z" | CONTRIBUTOR | null | Here is the traceback using Python 2.7.3 and Pandas trunk.
```
/pandas/core/frame.pyc in __repr__(self)
678 Yields Bytestring in Py2, Unicode String in py3.
679 """
--> 680 return str(self)
681
682 def _repr_html_(self):
/pandas/core/frame.pyc in __str__(self)
634 if py3compat.PY3:
635 return self.__unicode__()
--> 636 return self.__bytes__()
637
638 def __bytes__(self):
/pandas/core/frame.pyc in __bytes__(self)
644 """
645 encoding = com.get_option("display.encoding")
--> 646 return self.__unicode__().encode(encoding , 'replace')
647
648 def __unicode__(self):
/pandas/core/frame.pyc in __unicode__(self)
655 buf = StringIO(u"")
656 if self._need_info_repr_():
--> 657 self.info(buf=buf, verbose=self._verbose_info)
658 else:
659 is_wide = self._need_wide_repr()
/pandas/core/frame.pyc in info(self, verbose, buf, max_cols)
1624 dtypes = ['%s(%d)' % k for k in sorted(counts.iteritems())]
1625 lines.append('dtypes: %s' % ', '.join(dtypes))
-> 1626 _put_lines(buf, lines)
1627
1628 @property
/pandas/core/format.pyc in _put_lines(buf, lines)
1443 def _put_lines(buf, lines):
1444 if any(isinstance(x, unicode) for x in lines):
-> 1445 lines = [unicode(x) for x in lines]
1446 buf.write('\n'.join(lines))
1447
UnicodeDecodeError: 'ascii' codec can't decode byte 0xd5 in position 8: ordinal not in range(128)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2577 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2577/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2577/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2577/events | https://github.com/pandas-dev/pandas/pull/2577 | 9,469,012 | MDExOlB1bGxSZXF1ZXN0MzQ2MTQ0Nw== | 2,577 | Update pandas/core/format.py | {
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} | 4 | "2012-12-21T16:16:49Z" | "2014-08-11T01:06:12Z" | "2012-12-28T15:49:57Z" | CONTRIBUTOR | null | Fixes #2576.
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https://api.github.com/repos/pandas-dev/pandas/issues/2578 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2578/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2578/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2578/events | https://github.com/pandas-dev/pandas/issues/2578 | 9,471,792 | MDU6SXNzdWU5NDcxNzky | 2,578 | BUG/ENH: should .ix allow partial setting? | {
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} | 0 | "2012-12-21T18:11:09Z" | "2014-09-04T18:11:29Z" | "2013-09-05T17:23:51Z" | CONTRIBUTOR | null | related to GH #2033
```
In [27]: df = pd.DataFrame(np.random.rand(4,2),index=pd.date_range('2001/1/12',periods=4),columns=['A','B'])
In [28]: df
Out[28]:
A B
2001-01-12 0.861201 0.530351
2001-01-13 0.434518 0.692022
2001-01-14 0.794237 0.912073
2001-01-15 0.421282 0.930005
In [29]: df.ix[:,'B'] = df.ix[:,'A'].copy()
In [30]: df.ix[:,'C'] = df.ix[:,'A'].copy()
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-30-2107107d0bd8> in <module>()
----> 1 df.ix[:,'C'] = df.ix[:,'A'].copy()
/usr/local/lib/python2.7/site-packages/pandas-0.10.0-py2.7-linux-x86_64.egg/pandas/core/index.pyc in get_loc(self, key)
714 loc : int if unique index, possibly slice or mask if not
715 """
--> 716 return self._engine.get_loc(key)
717
718 def get_value(self, series, key):
KeyError: 'C'
```
of course in this case `df['C'] = df.ix[:,'A'].copy()` accomplishes this
Panel example: have to transpose, setitem, then transpose back to accomplish this
```
In [46]: p = pd.Panel(np.random.rand(2,4,2),items=['Item1','Item2'],major_axis=pd.date_range('2001/1/12',periods=4),minor_axis=['A','B'])
In [47]: p
Out[47]:
<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 [48]: pt = p.transpose(2,1,0)
In [49]: pt
Out[49]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 2 (minor_axis)
Items axis: A to B
Major_axis axis: 2001-01-12 00:00:00 to 2001-01-15 00:00:00
Minor_axis axis: Item1 to Item2
In [50]: pt['C'] = p.ix[:,:,'B'].copy()
In [51]: pt.transpose(2,1,0)
Out[51]:
<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
```
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} | 26 | "2012-12-21T22:30:47Z" | "2017-11-15T16:26:20Z" | "2017-11-15T16:26:08Z" | CONTRIBUTOR | null | register it upon plotting?
http://stackoverflow.com/questions/13988111/importing-pandas-in-python-changes-how-matplotlib-handles-datetime-objects
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} | 7 | "2012-12-21T23:39:50Z" | "2013-01-20T14:58:46Z" | "2013-01-20T14:58:46Z" | NONE | null | I noticed some really weird behaviour of new HD5Store. I am using pandas 0.10.0 and pytables 2.4.0.
When I try to serialize my Panel structure and then remove it from the store, the memory is not reclaimed - the store appears to be empty while it is still using 90MB on my hard drive.
I am trying to find any repro steps using synthetic data but so far I am failing - It is working as I would expect (file becomes empty after removal of the panel). I can't give you the actual files/data but maybe any stats of the h5 file itself would be helpful.
What makes it even worse, when I repeat the same operation over and over again, h5 file grows to ~num_iterations \* 90MB in size while still appearing as empty. I am able to reproduce it every single time with my dataset.
My code:
``` python
# some_panel is a ~ 30 x 10000 x 30 structure with mostly floats:
store = pd.HDFStore(file_name)
store["panel"] = some_panel
del store["panel"]
store
```
Output:
```
<class 'pandas.io.pytables.HDFStore'>
File path: <my_file_name>
Empty
```
However, the file is 90M big. Here is the h5dump output of my file:
```
HDF5 "<my_file_name.h5>" {
GROUP "/" {
ATTRIBUTE "CLASS" {
DATATYPE H5T_STRING {
STRSIZE 5;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "GROUP"
}
}
ATTRIBUTE "PYTABLES_FORMAT_VERSION" {
DATATYPE H5T_STRING {
STRSIZE 3;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "2.0"
}
}
ATTRIBUTE "TITLE" {
DATATYPE H5T_STRING {
STRSIZE 1;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): ""
}
}
ATTRIBUTE "VERSION" {
DATATYPE H5T_STRING {
STRSIZE 3;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "1.0"
}
}
}
}
```
and h5debug output:
```
Reading signature at address 0 (rel)
File Super Block...
File name (as opened): <my_file_name>
File name (after resolving symlinks): <my_file_name>
File access flags 0x00000000
File open reference count: 1
Address of super block: 0 (abs)
Size of userblock: 0 bytes
Superblock version number: 0
Free list version number: 0
Root group symbol table entry version number: 0
Shared header version number: 0
Size of file offsets (haddr_t type): 8 bytes
Size of file lengths (hsize_t type): 8 bytes
Symbol table leaf node 1/2 rank: 4
Symbol table internal node 1/2 rank: 16
File status flags: 0x00
Superblock extension address: UNDEF (rel)
Shared object header message table address: UNDEF (rel)
Shared object header message version number: 0
Number of shared object header message indexes: 0
Address of driver information block: UNDEF (rel)
Root group symbol table entry:
Name offset into private heap: 0
Object header address: 96
Cache info type: Symbol Table
Cached entry information:
B-tree address: 136
Heap address: 680
```
and h5stat:
```
Filename: <my_file_name>
File information
# of unique groups: 1
# of unique datasets: 0
# of unique named datatypes: 0
# of unique links: 0
# of unique other: 0
Max. # of links to object: 1
Max. # of objects in group: 0
File space information for file metadata (in bytes):
Superblock extension: 0
User block: 0
Object headers: (total/unused)
Groups: 272/0
Datasets(exclude compact data): 0/0
Datatypes: 0/0
Groups:
B-tree/List: 544
Heap: 120
Attributes:
B-tree/List: 0
Heap: 0
Chunked datasets:
Index: 0
Datasets:
Heap: 0
Shared Messages:
Header: 0
B-tree/List: 0
Heap: 0
Small groups:
# of groups of size 0: 1
Total # of small groups: 1
Group bins:
# of groups of size 0: 1
Total # of groups: 1
Small # of attributes:
# of objects with 4 attributes: 1
Total # of objects with small # of attributes: 1
Attribute bins:
# of objects with 1 - 9 attributes: 1
Total # of objects with attributes: 1
Max. # of attributes to objects: 4
Summary of file space information:
File metadata: 936 bytes
Raw data: 0 bytes
Unaccounted space: 94094088 bytes
Total space: 94095024 bytes
```
Total space is showing actual space on the drive ~90MB. Let me know if I can provide any more information
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} | [] | closed | false | null | [] | null | 1 | "2012-12-22T05:05:07Z" | "2012-12-25T16:04:15Z" | "2012-12-25T16:04:11Z" | CONTRIBUTOR | null | Fixed a small typo in the docs
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https://api.github.com/repos/pandas-dev/pandas/issues/2582 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2582/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2582/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2582/events | https://github.com/pandas-dev/pandas/issues/2582 | 9,482,547 | MDU6SXNzdWU5NDgyNTQ3 | 2,582 | Cannot resample('M') after tz_convert | {
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In Pandas v.0.9.0:
``` python
dd=pd.date_range('2012-1-1 1:30', periods=3, freq='min').tz_localize('UTC').tz_convert('EST')
pd.Series([1,2,3],index=dd).resample('M')
```
Output:
```
Exception: Cannot compare tz-naive and tz-aware timestamps
```
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} | 6 | "2012-12-22T14:27:36Z" | "2013-09-28T19:34:23Z" | "2013-09-28T19:34:23Z" | MEMBER | null | http://stackoverflow.com/questions/13999850/how-to-specify-date-format-when-using-pandas-to-csv
http://stackoverflow.com/questions/15651527/how-to-stop-python-pandas-from-adding-000000-to-every-date
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} | 1 | "2012-12-22T18:04:37Z" | "2012-12-28T15:12:07Z" | "2012-12-28T15:12:07Z" | CONTRIBUTOR | null | Hi,
I was reading some docstring of some `Series` & `DataFrame` methods from IPython. I wondered what the parameter `index` of the method `Series.iteritems` -- and thus the `iterkv` method -- did. I think it's not used. Tests passed.
Cheers,
Damien G.
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} | 8 | "2012-12-22T20:39:56Z" | "2012-12-29T04:30:44Z" | "2012-12-28T18:44:30Z" | MEMBER | null | Bummer. This happened in IPython on git master, `display.line_width` set to 80
```
In [18]: df.columns
Out[18]: Index([Symbol, Description, MTM MTD, MTM YTD, Realized ST MTD, Realized ST YTD, Realized LT MTD, Realized LT YTD, Underlying, Kind], dtype=object)
In [19]: df
Out[19]:
Symbol Description MTM MTD MTM YTD Realized ST MTD Realized ST YTD Realized LT MTD Realized LT YTD Underlying Kind
0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
```
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} | 19 | "2012-12-22T23:57:52Z" | "2019-12-01T20:58:24Z" | null | CONTRIBUTOR | null | `date_format` keyword could take the format, dict of columns to format, or list of formats (and could then obviate the need for parse_dates)
Sometimes months and days get mixed up.
E.g.
test.csv:
a,b
27.03.2003 14:55:00.000,1
03.08.2003 15:20:00.000,2
read_csv("/home/john/Documents/test.csv",index_col=0, parse_dates=True)
b
a
2003-03-27 14:55:00 1
2003-03-08 15:20:00 2
There doesn't appear to be any continuity in the date parsing over the rows. As well as meaning things can easily get switched around, this makes date parsing VERY slow. Once you know the format, using the datetime constructor with string slicing as a parser makes read_csv 20x faster on my machine.
I think there needs to be some more parameters for specifying date formats. seeing as in the general case dates a string of dates can be ambiguous (see above).
A possible approach: Have a few default formats to choose from, as well as a more general format string approach. Obviously the defaults could use the datetime constructor with string slicing, which is very fast.
Perhaps have a dayfirst and yearfirst flag that gets passed to dateutil.parser.parse to solve ambiguities when using automatic parsing.
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} | 2 | "2012-12-23T05:58:27Z" | "2012-12-28T15:42:29Z" | "2012-12-28T15:42:29Z" | NONE | null | s = pd.Series([1,2,3])
s.add(3)
raise AssertionError: Other operand must be Series.
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} | 2 | "2012-12-23T06:38:47Z" | "2013-01-19T22:14:52Z" | "2013-01-19T22:14:50Z" | CONTRIBUTOR | null | Using ipython 0.13.1 and pandas 0.10.0, having both the 'color' and 'style' parameter to pandas.Series.plot() gives a value error in pylab. Not sure if I should report this here or in ipython?
``` python
from pandas import *
ts = Series(randn(1000), index=date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
# having color and style causes Value error crash
ts.plot(color='#000099', style='b-')
```
``` python
ValueError: to_rgba: Invalid rgba arg "['#000099']"
need more than 1 value to unpack
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2589 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2589/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2589/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2589/events | https://github.com/pandas-dev/pandas/pull/2589 | 9,488,998 | MDExOlB1bGxSZXF1ZXN0MzQ3MDU1Mw== | 2,589 | Add some default parameters to TextParser in _parse_options_data() | {
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} | 1 | "2012-12-23T09:33:22Z" | "2012-12-28T15:09:42Z" | "2012-12-28T15:09:42Z" | CONTRIBUTOR | null | The Options class can return some awkward dtypes:
``` python
In [1]: from pandas.io.data import *
In [2]: aapl = Options('aapl')
In [3]: a = aapl.get_put_data(1,2013)
In [4]: a.dtypes
Out[4]:
Strike object
Symbol object
Last float64
Chg float64
Bid object
Ask float64
Vol object
Open Int object
```
There are two causes for Strike/Bid/Vol/Open Int having `object` dtypes: not parsing the NaN value and not passing a thousands separator.
``` python
In [5]: a.Bid[:5]
Out[5]:
0 0.01
1 N/A
2 N/A
3 N/A
4 N/A
Name: Bid
In [6]: a['Open Int'][:5]
Out[6]:
0 8,744
1 2,381
2 1,117
3 4,586
4 1,535
Name: Open Int
```
I've checked with a couple non-US proxies and it seems that finance.yahoo.com (the hardcoded url) does not redirect to the local version (yahoo.com does redirect). Since we're getting the US site, we can pass `','` as the thousands separator and `'N/A'`as the NaN value.
These changes yield the following dtypes:
``` python
In [4]: a.dtypes
Out[4]:
Strike float64
Symbol object
Last float64
Chg float64
Bid float64
Ask float64
Vol int64
Open Int int64
In [5]: a[:5]
Out[5]:
Strike Symbol Last Chg Bid Ask Vol Open Int
0 135 AAPL130119P00135000 0.02 0.01 0.01 0.03 5 8744
1 140 AAPL130119P00140000 0.02 0.00 NaN 0.04 11 2381
2 145 AAPL130119P00145000 0.01 0.03 NaN 0.11 3 1117
3 150 AAPL130119P00150000 0.01 0.00 NaN 0.11 20 4586
4 155 AAPL130119P00155000 0.01 0.00 NaN 0.11 4 1535
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2590 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2590/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2590/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2590/events | https://github.com/pandas-dev/pandas/pull/2590 | 9,490,146 | MDExOlB1bGxSZXF1ZXN0MzQ3MDk0Nw== | 2,590 | Update force_unicode documentation | {
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} | 1 | "2012-12-23T13:10:29Z" | "2012-12-28T13:49:54Z" | "2012-12-28T13:49:54Z" | CONTRIBUTOR | null | Should resolve #2053.
For consistency, I've re-added the `force_unicode` parameter to `to_latex()` and have it now raise a deprecation warning. I've also updated the common docstring for the `to_*` methods to have a brief explanation as to why the parameter is deprecated.
There were a couple other missing deprecation warnings (`force_unicode` in `to_html()` and `colSpace` in `to_latex()`) that should now have consistent behavior with other methods.
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https://api.github.com/repos/pandas-dev/pandas/issues/2591 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2591/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2591/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2591/events | https://github.com/pandas-dev/pandas/issues/2591 | 9,491,283 | MDU6SXNzdWU5NDkxMjgz | 2,591 | Localize a column of Timestamps | {
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 1 | "2012-12-23T16:10:21Z" | "2013-09-20T22:49:51Z" | "2013-09-20T22:49:51Z" | CONTRIBUTOR | null | Migrated from [StackOverflow](http://stackoverflow.com/questions/14004545/how-to-convert-the-time-zone-of-the-values-of-a-pandas-series):
```
s = pd.Series(pd.date_range('2012-1-1 1:30', periods=3, freq='min', tz='EST'))
```
You can convert each item individually:
```
In [86]: s[0]
Out[86]: <Timestamp: 2012-01-01 06:30:00>
In [87]: s[0].tz_localize('UTC')
Out[87]: <Timestamp: 2012-01-01 06:30:00+0000 UTC, tz=UTC>
```
But when you try to apply across all columns (same with `convert_dtype=True`):
```
In [81]: s.apply(lambda x: x.tz_localize('UTC'), convert_dtype=False)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-81-0b4fa5c03650> in <module>()
----> 1 s.apply(lambda x: x.tz_localize('UTC'), convert_dtype=False)
/usr/lib/pymodules/python2.7/pandas/core/series.pyc in apply(self, func, convert_dtype, args, **kwds)
2244 return f(self)
2245
-> 2246 mapped = lib.map_infer(self.values, f, convert=convert_dtype)
2247 if isinstance(mapped[0], Series):
2248 from pandas.core.frame import DataFrame
/usr/lib/pymodules/python2.7/pandas/lib.so in pandas.lib.map_infer (pandas/lib.c:39084)()
<ipython-input-81-0b4fa5c03650> in <lambda>(x)
----> 1 s.apply(lambda x: x.tz_localize('UTC'), convert_dtype=False)
AttributeError: 'numpy.datetime64' object has no attribute 'tz_localize'
```
One workaround is forcing it to be a Timestamp:
```
In [91]: from pandas.lib import Timestamp
In [92]: s.apply(lambda x: Timestamp(x).tz_localize('UTC'))
Out[92]:
0 2012-01-01 06:30:00+00:00
1 2012-01-01 06:31:00+00:00
2 2012-01-01 06:32:00+00:00
```
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} | 5 | "2012-12-23T18:58:15Z" | "2014-06-14T16:53:15Z" | "2012-12-28T16:01:42Z" | CONTRIBUTOR | null | The World Bank's World Development Indicators (WDI) is a collection of nearly 8000 data series in country-year format. These data cover a wide range of topics (e.g. demography, economics), and they are freely available freely through the World Bank API.
http://data.worldbank.org/
This PR introduces a very simple io module to search and downloading data from the WDI. Usage example: http://nbviewer.ipython.org/4365089/
I don't know the best way to test features such as this one, so I may need a bit of help there.
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} | 4 | "2012-12-24T16:40:28Z" | "2012-12-28T14:48:30Z" | "2012-12-28T14:47:55Z" | NONE | null | The CParser doesn't appear to handle the compression flag in 0.10.0
```
vishnu@grsectoo ~/python/pandas $ cat compression.csv
"Time","A","B"
"12/24/2012 12:00:00","1.00","2.00"
vishnu@grsectoo ~/python/pandas $ zcat compression.csv.gz
"Time","A","B"
"12/24/2012 12:00:00","1.00","2.00",
vishnu@grsectoo ~/python/pandas $ bzcat compression.csv.bz2
"Time","A","B"
"12/24/2012 12:00:00","1.00","2.00"
```
I've shown my results with Python 3.2.3 and IPython 0.13.1 but it also manifests in Python 2.7.3
```
vishnu@grsectoo ~/python/pandas $ ipython
Python 3.2.3 (default, Dec 17 2012, 23:03:08)
Type "copyright", "credits" or "license" for more information.
IPython 0.13.1 -- 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 as pd
In [2]: import gzip
In [3]: import bz2
```
The raw csv file reads just fine:
```
In [4]: with open('compression.csv') as fh:
pd.read_csv(fh, sep=',', parse_dates=[0], index_col=[0])
...:
```
Attempting to read the compressed version, while passing compression='gzip' fails:
```
In [5]: with open('compression.csv.gz') as fh:
pd.read_csv(fh, sep=',', parse_dates=[0], index_col=[0], compression='gzip')
...:
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-5-3e61cc453d63> in <module>()
1 with open('compression.csv.gz') as fh:
----> 2 pd.read_csv(fh, sep=',', parse_dates=[0], index_col=[0], compression='gzip')
3
/usr/lib64/python3.2/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
390 buffer_lines=buffer_lines)
391
--> 392 return _read(filepath_or_buffer, kwds)
393
394 parser_f.__name__ = name
/usr/lib64/python3.2/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
199
200 # Create the parser.
--> 201 parser = TextFileReader(filepath_or_buffer, **kwds)
202
203 if nrows is not None:
/usr/lib64/python3.2/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
499 self.options['has_index_names'] = kwds['has_index_names']
500
--> 501 self._make_engine(self.engine)
502
503 def _get_options_with_defaults(self, engine):
/usr/lib64/python3.2/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
601 def _make_engine(self, engine='c'):
602 if engine == 'c':
--> 603 self._engine = CParserWrapper(self.f, **self.options)
604 else:
605 if engine == 'python':
/usr/lib64/python3.2/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
880 # #2442
881 kwds['allow_leading_cols'] = self.index_col is not False
--> 882 self._reader = _parser.TextReader(src, **kwds)
883
884 # XXX
/usr/lib64/python3.2/site-packages/pandas/_parser.cpython-32.so in pandas._parser.TextReader.__cinit__ (pandas/src/parser.c:3915)()
/usr/lib64/python3.2/site-packages/pandas/_parser.cpython-32.so in pandas._parser.TextReader._get_header (pandas/src/parser.c:4956)()
/usr/lib64/python3.2/site-packages/pandas/_parser.cpython-32.so in pandas._parser.TextReader._tokenize_rows (pandas/src/parser.c:6531)()
/usr/lib64/python3.2/site-packages/pandas/_parser.cpython-32.so in pandas._parser.raise_parser_error (pandas/src/parser.c:16903)()
CParserError: Error tokenizing data. C error: Calling read(nbytes) on source failed. Try engine='python'.
```
However if I pass a GzipFile handle instead, we are able to read the csv;
```
In [6]: with gzip.GzipFile('compression.csv.gz') as fh:
pd.read_csv(fh, sep=',', parse_dates=[0], index_col=[0], compression='gzip')
...:
```
Also, opening a bzip2 version while passing in compression='bz2' fails:
```
In [7]: with open('compression.csv.bz2') as fh:
pd.read_csv(fh, sep=',', parse_dates=[0], index_col=[0], compression='bz2')
...:
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-7-2736c68aa21d> in <module>()
1 with open('compression.csv.bz2') as fh:
----> 2 pd.read_csv(fh, sep=',', parse_dates=[0], index_col=[0], compression='bz2')
3
/usr/lib64/python3.2/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, nrows, iterator, chunksize, verbose, encoding, squeeze)
390 buffer_lines=buffer_lines)
391
--> 392 return _read(filepath_or_buffer, kwds)
393
394 parser_f.__name__ = name
/usr/lib64/python3.2/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
199
200 # Create the parser.
--> 201 parser = TextFileReader(filepath_or_buffer, **kwds)
202
203 if nrows is not None:
/usr/lib64/python3.2/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
499 self.options['has_index_names'] = kwds['has_index_names']
500
--> 501 self._make_engine(self.engine)
502
503 def _get_options_with_defaults(self, engine):
/usr/lib64/python3.2/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
601 def _make_engine(self, engine='c'):
602 if engine == 'c':
--> 603 self._engine = CParserWrapper(self.f, **self.options)
604 else:
605 if engine == 'python':
/usr/lib64/python3.2/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
880 # #2442
881 kwds['allow_leading_cols'] = self.index_col is not False
--> 882 self._reader = _parser.TextReader(src, **kwds)
883
884 # XXX
/usr/lib64/python3.2/site-packages/pandas/_parser.cpython-32.so in pandas._parser.TextReader.__cinit__ (pandas/src/parser.c:3915)()
/usr/lib64/python3.2/site-packages/pandas/_parser.cpython-32.so in pandas._parser.TextReader._get_header (pandas/src/parser.c:4956)()
/usr/lib64/python3.2/site-packages/pandas/_parser.cpython-32.so in pandas._parser.TextReader._tokenize_rows (pandas/src/parser.c:6531)()
/usr/lib64/python3.2/site-packages/pandas/_parser.cpython-32.so in pandas._parser.raise_parser_error (pandas/src/parser.c:16903)()
CParserError: Error tokenizing data. C error: Calling read(nbytes) on source failed. Try engine='python'.
```
Passing a BZ2File handle allows the parser to read the csv:
```
In [9]: with bz2.BZ2File('compression.csv.bz2') as fh:
pd.read_csv(fh, sep=',', parse_dates=[0], index_col=[0], compression='bz2')
...:
```
Version:
```
In [10]: pd.version.version
Out[10]: '0.10.0'
```
This issue also manifests in the current git master.
Thanks,
Vishnu
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```
It seems that the decimal format works ok for the decimal sign or for the thousands but not combined.
Reopen the issue?
Example
import pandas as pd
from StringIO import StringIO
data = """A;B;C
0;0,11;0,11
1.000;1000,11;1.000,11
20.000;20000,22;20.000,22
300.000;300000,33;300.000,33
4.000.000;4000000,44;4.000.000,44
5.000.000.000;5000000000,55;5.000.000.000,55"""
df = pd.read_csv(StringIO(data), sep=';', thousands='.', decimal =',')
print df.dtypes
print df
Results in
A int64
B float64
C object
A B C
0 0 1.100000e-01 0,11
1 1000 1.000110e+03 1.000,11
2 20000 2.000022e+04 20.000,22
3 300000 3.000003e+05 300.000,33
4 4000000 4.000000e+06 4.000.000,44
5 5000000000 5.000000e+09 5.000.000.000,55
```
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} | 3 | "2012-12-25T04:48:59Z" | "2014-06-19T17:13:25Z" | "2012-12-28T16:40:22Z" | CONTRIBUTOR | null | - construct series that have M8[ns] dtype even in the presence of NaN (rather than object)
see below.
- allow np.nan to be used in place of NaT in series construction (requires explicity dtype to be passed)
e.g. `Series(np.nan,index=range(5),dtype='M8[ns]')`
- bug fix in applymap (tseries/tests/test_timeseries/test_frame_apply_dont_convert_datetime64), because explicit
conversion of the series to M8ns, now has the function operating on datetime64 values and ufuncs dont' work for some reason,
explicity required conversion to Timestamp values make this work (same issue as in GH #2591)
```
In [1]: import numpy as np
In [2]: import pandas as pd
```
This currently works
```
In [12]: df = pd.DataFrame(np.random.randn(8,2),pd.date_range('20010102',periods=8),columns=['A','B'])
In [13]: df['timestamp'] = pd.Timestamp('20010103')
In [14]: x = df.convert_objects()
In [15]: x
Out[15]:
A B timestamp
2001-01-02 -0.228005 -1.376432 2001-01-03 00:00:00
2001-01-03 -0.555251 0.277356 2001-01-03 00:00:00
2001-01-04 -0.295289 0.534981 2001-01-03 00:00:00
2001-01-05 -0.332815 -1.436975 2001-01-03 00:00:00
2001-01-06 -1.848818 0.405773 2001-01-03 00:00:00
2001-01-07 -0.734903 -0.174140 2001-01-03 00:00:00
2001-01-08 -0.388864 -0.765329 2001-01-03 00:00:00
2001-01-09 -0.341015 0.170630 2001-01-03 00:00:00
In [16]: x.get_dtype_counts()
Out[16]:
datetime64[ns] 1
float64 2
In [21]: x.ix[3,'timestamp'] = iNaT
In [22]: x
Out[22]:
A B timestamp
2001-01-02 -0.228005 -1.376432 2001-01-03 00:00:00
2001-01-03 -0.555251 0.277356 2001-01-03 00:00:00
2001-01-04 -0.295289 0.534981 2001-01-03 00:00:00
2001-01-05 -0.332815 -1.436975 NaT
2001-01-06 -1.848818 0.405773 2001-01-03 00:00:00
2001-01-07 -0.734903 -0.174140 2001-01-03 00:00:00
2001-01-08 -0.388864 -0.765329 2001-01-03 00:00:00
2001-01-09 -0.341015 0.170630 2001-01-03 00:00:00
```
This leaves you in limbo, as you have basically a datatime64 column, but with a float nan in it (so it has to stay object)
```
In [3]: df = pd.DataFrame(np.random.randn(8,2),pd.date_range('20010102',periods=8),columns=['A','B'])
In [4]: df
Out[4]:
A B
2001-01-02 0.939393 -2.524448
2001-01-03 -1.059561 0.104651
2001-01-04 -0.842478 1.033888
2001-01-05 -1.009903 -0.334782
2001-01-06 -0.452043 -0.382408
2001-01-07 -0.058516 1.162884
2001-01-08 0.660251 0.688290
2001-01-09 0.069637 0.366915
In [5]: df['timestamp'] = pd.Timestamp('20010103')
In [6]: df
Out[6]:
A B timestamp
2001-01-02 0.939393 -2.524448 2001-01-03 00:00:00
2001-01-03 -1.059561 0.104651 2001-01-03 00:00:00
2001-01-04 -0.842478 1.033888 2001-01-03 00:00:00
2001-01-05 -1.009903 -0.334782 2001-01-03 00:00:00
2001-01-06 -0.452043 -0.382408 2001-01-03 00:00:00
2001-01-07 -0.058516 1.162884 2001-01-03 00:00:00
2001-01-08 0.660251 0.688290 2001-01-03 00:00:00
2001-01-09 0.069637 0.366915 2001-01-03 00:00:00
In [7]: df.get_dtype_counts()
Out[7]:
float64 2
object 1
In [9]: df.ix[3,'timestamp'] = np.nan
In [10]: df
Out[10]:
A B timestamp
2001-01-02 0.939393 -2.524448 2001-01-03 00:00:00
2001-01-03 -1.059561 0.104651 2001-01-03 00:00:00
2001-01-04 -0.842478 1.033888 2001-01-03 00:00:00
2001-01-05 -1.009903 -0.334782 NaN
2001-01-06 -0.452043 -0.382408 2001-01-03 00:00:00
2001-01-07 -0.058516 1.162884 2001-01-03 00:00:00
2001-01-08 0.660251 0.688290 2001-01-03 00:00:00
2001-01-09 0.069637 0.366915 2001-01-03 00:00:00
In [11]: df.convert_objects().get_dtype_counts()
Out[11]:
float64 2
object 1
```
This PR enables this syntax:
```
In [3]: df = pd.DataFrame(np.random.randn(8,2),pd.date_range('20010102',periods=8),columns=['A','B'])
In [4]: df['timestamp'] = pd.Timestamp('20010103')
# datetime64[ns] out of the box
In [5]: df.get_dtype_counts()
Out[5]:
datetime64[ns] 1
float64 2
# use the traditional nan, which is mapped to iNaT internally
In [6]: df.ix[2:4,'timestamp'] = np.nan
In [7]: df
Out[7]:
A B timestamp
2001-01-02 0.234354 -3.167626 2001-01-03 00:00:00
2001-01-03 0.141300 -0.548670 2001-01-03 00:00:00
2001-01-04 0.738884 -0.104497 NaT
2001-01-05 0.873046 1.193593 NaT
2001-01-06 1.174226 1.697421 2001-01-03 00:00:00
2001-01-07 -0.069534 -0.145611 2001-01-03 00:00:00
2001-01-08 -0.951426 -1.158884 2001-01-03 00:00:00
2001-01-09 -0.322041 -0.800181 2001-01-03 00:00:00
```
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} | 0 | "2012-12-25T18:59:22Z" | "2016-10-12T23:03:26Z" | "2013-01-20T02:40:45Z" | MEMBER | null | http://stackoverflow.com/questions/14025879/assertion-error-in-columns-in-dataframe-with-hierarchical-indexing
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} | [] | closed | false | null | [] | null | 4 | "2012-12-26T00:54:39Z" | "2013-12-04T00:40:19Z" | "2012-12-26T01:45:22Z" | NONE | null | I have two nearly identical DataFrames (same index structure and column names) with different names. One is called DF the other is df (only difference is case of the name). When updating values in columns of one DataFrame, the corresponding values in the other are also updated. I am guessing somewhere in the back end writing to DataFrames is not case sensitive where it ought to be.
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} | 0 | "2012-12-26T01:11:34Z" | "2014-02-13T23:12:31Z" | "2014-02-13T23:12:31Z" | MEMBER | null | http://stackoverflow.com/questions/13993524/pandas-slicing-along-multiindex-and-separate-indices
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} | 13 | "2012-12-26T04:47:38Z" | "2013-01-05T21:22:21Z" | "2013-01-05T21:22:21Z" | NONE | null | If `data.csv` contains the following (column B has a zero in the first row, and is empty in the second)
```
A,B
0,0
0,
```
... pandas 0.10.0 incorrectly reads it as:
```
In [7]: pd.read_csv('data.csv')
Out[9]:
A B
0 0 NaN
1 0 NaN
```
... whereas pandas 0.9.0 reads it right:
```
In [5]: pd.read_csv('data.csv')
Out[6]:
A B
0 0 0
1 0 NaN
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2600 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2600/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2600/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2600/events | https://github.com/pandas-dev/pandas/issues/2600 | 9,529,284 | MDU6SXNzdWU5NTI5Mjg0 | 2,600 | Buggy negative slicing with ix | {
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} | 6 | "2012-12-27T00:12:12Z" | "2016-04-08T13:02:12Z" | "2016-04-08T13:02:12Z" | MEMBER | null | http://stackoverflow.com/questions/14035817/slicing-pandas-dataframe-with-negative-index-with-ix-method
```
In [1]: df = pd.DataFrame(np.random.randn(10, 4))
In [2]: df
Out[2]:
0 1 2 3
0 -3.100926 -0.580586 -1.216032 0.425951
1 -0.264271 -1.091915 -0.602675 0.099971
2 -0.846290 1.363663 -0.382874 0.065783
3 -0.099879 -0.679027 -0.708940 0.138728
4 -0.302597 0.753350 -0.112674 -1.253316
5 -0.213237 -0.467802 0.037350 0.369167
6 0.754915 -0.569134 -0.297824 -0.600527
7 0.644742 0.038862 0.216869 0.294149
8 0.101684 0.784329 0.218221 0.965897
9 -1.482837 -1.325625 1.008795 -0.150439
In [3]: df.ix[-2:]
Out[3]:
0 1 2 3
0 -3.100926 -0.580586 -1.216032 0.425951
1 -0.264271 -1.091915 -0.602675 0.099971
2 -0.846290 1.363663 -0.382874 0.065783
3 -0.099879 -0.679027 -0.708940 0.138728
4 -0.302597 0.753350 -0.112674 -1.253316
5 -0.213237 -0.467802 0.037350 0.369167
6 0.754915 -0.569134 -0.297824 -0.600527
7 0.644742 0.038862 0.216869 0.294149
8 0.101684 0.784329 0.218221 0.965897
9 -1.482837 -1.325625 1.008795 -0.150439
```
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