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} | 1 | 2013-08-18T16:19:58Z | 2013-09-20T22:34:04Z | 2013-09-20T22:34:04Z | NONE | null | ```
In [10]: ix = [-352.737091, 183.575577]
...: df=pd.DataFrame([0,1],index=ix)
...: df.to_csv("/tmp/1.csv")
...: df2=pd.DataFrame.from_csv("/tmp/1.csv",parse_dates=True)
...: print df
...: print df2
0
-352.737091 0
183.575577 1
0
2105-11-21 22:43:41.128654 0
1936-11-21 22:43:41.128654 1
```
Moved from https://github.com/pydata/pandas/issues/3171.
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} | 13 | 2013-08-18T18:03:34Z | 2014-06-13T17:28:42Z | 2013-09-19T12:24:50Z | CONTRIBUTOR | null | Fixes #4596
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} | [] | closed | false | null | [] | null | 4 | 2013-08-18T18:49:43Z | 2016-08-05T20:41:05Z | 2013-08-18T21:28:05Z | NONE | null | I usually run my python code in an interpreter. There running import pandas works fine and I have been able to use pandas functions regulary.
However, I am not able to use pandas from cmd line: for instance the below crashes python after half a second (it crashes when exiting):
> python -c "import pandas"
This application has requested the Runtime to terminate it in an unusual way.
Please contact the application's support team for more information.
I can import other modules fine this way. Any idea how to solve this? Pandas is the only one with this behavior.
My config is: Win32 / Python 2.7.5 / pandas 0.12.0 / GCC 4.7.2 (I also tried 0.13.0 dev and experience the same issue)
See stackoverflow question for more info: http://stackoverflow.com/questions/18293838/python-c-import-pandas-is-crashing-python-exe-on-exit
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} | 4 | 2013-08-18T22:45:56Z | 2013-08-21T12:44:43Z | 2013-08-21T12:44:43Z | CONTRIBUTOR | null | When I try to reindex a Series object I get a really weird result.
``` python
>>> s = pd.Series([1,2,3,4,5], index=['a', 'b', 'c', 'd', 'e'])
>>> s.reindex(['a', 'g', 'c', 'f'], method='ffill')
a 1
g 5
c NaN
f 5
dtype: float64
```
According to the documentation I should have got something like:
``` python
a 1
g 1
c 3
f 3
dtype: float64
```
So either the documentation is not valid, or reindex is not working correctly or I haven't understood how this beast works?
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} | 12 | 2013-08-19T14:53:41Z | 2013-08-21T23:46:11Z | 2013-08-21T23:46:11Z | CONTRIBUTOR | null | If I try to rename a MultiIndexed DataFrame, everything works correctly:
``` python
In [3]: pd.DataFrame([11,21,31],
index=pd.MultiIndex.from_tuples([("A",x) for x in ["a","B","c"]])).rename(str.lower)
Out[3]:
0
a a 11
b 21
c 31
```
But if I do the same thing with the Series, it doesn't work:
``` python
In [4]: pd.Series([11,21,31],
index=pd.MultiIndex.from_tuples([("A",x) for x in ["a","B","c"]])).rename(str.lower)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-aa25b537c1df> in <module>()
----> 1 pd.Series([11,21,31],index=pd.MultiIndex.from_tuples([("A",x) for x in ["a","B","c"]])).rename(str.lower)
/home/viktor/pandas/pandas/core/series.py in rename(self, mapper, inplace)
3187 result = self if inplace else self.copy()
3188 result.index = Index([mapper_f(x)
-> 3189 for x in self.index], name=self.index.name)
3190
3191 if not inplace:
TypeError: descriptor 'lower' requires a 'str' object but received a 'tuple'
```
This behavior is observed in 0.12 and in the master branch.
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} | 1 | 2013-08-19T17:34:41Z | 2014-03-31T14:37:16Z | 2014-03-31T14:36:58Z | NONE | null | Error when reading a pickled pd.NaT value.
Example
``` python
In [1]: import pandas as pd
In [2]: test_val = pd.NaT
In [3]: from cPickle import loads, dumps
In [4]: loads(dumps(test_val))
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-12-8e4233b9b57a> in <module>()
----> 1 loads(dumps(pd.NaT))
TypeError: ('__new__() takes exactly one argument (2 given)', <class 'pandas.tslib.NaTType'>, ('\x00\x01\xff\xff\x00\x00\x00\x00\x00\x00',))
```
For the more common usage case
`loads(dumps(pd.to_datetime(['2013-01-01', pd.NaT, '2014-01-06'])))`
works as expected.
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} | 5 | 2013-08-19T20:05:44Z | 2020-05-26T13:07:03Z | 2020-05-26T13:07:03Z | CONTRIBUTOR | null | Add clarification in docs about business `DateOffset` (for example BMonthEnd and BQuarterEnd).
How are these `DateOffset`s defined?
The docstring is not particularly helpful:
> DateOffset increments between business EOM dates
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} | 2 | 2013-08-19T21:13:21Z | 2013-09-27T21:29:12Z | 2013-09-27T21:29:12Z | MEMBER | null | ```
In [164]: df = DataFrame(randn(10, 1))
In [165]: concat([df[0], df], axis=1)
```
throws an `AttributeError`. It should just say something about incompatible types in `concat`.
I wonder if it's worth allowing mixed types where it makes sense. E.g., `concat`-ing a `Series` and a `DataFrame`. It might be more consistent with the fact that `Series` is now an `NDFrame`.
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} | 4 | 2013-08-19T21:56:10Z | 2013-08-24T00:11:41Z | 2013-08-24T00:11:41Z | CONTRIBUTOR | null | I believe that many of the `DateOffset`s that extend `CacheableOffset` do so incorrectly.
Looking at `pandas.tseries.offsets`, there are some subclasses of `DateOffset` are declared as `class Foo(DateOffset, CacheableOffset)` and some as `class Foo(CacheableOffset, DateOffset)`.
All that `CacheableOffset` does is to set the class field `_cacheable = True`. `DateOffset` sets `_cacheable = False`. Therefore, depending on the order of the parent classes, in some cases `_cacheable` will be `False` and some cases `True`
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} | 51 | 2013-08-19T22:16:17Z | 2014-06-18T12:22:45Z | 2013-08-21T12:44:43Z | CONTRIBUTOR | null | closes #4604, #3317, #4618
Incorrect results
```
In [1]: s = pd.Series([1,2,3,4,5], index=['a', 'b', 'c', 'd', 'e'])
In [2]: s.reindex(['a', 'g', 'c', 'f'], method='ffill')
Out[2]:
a 1
g 5
c NaN
f 5
dtype: float64
```
Fixed (and happens to automatically cast as well back to the original dtype)
(this was broken in 0.12)
```
In [2]: s.reindex(['a', 'g', 'c', 'f'], method='ffill')
Out[2]:
a 1
g 1
c 3
f 3
dtype: int64
```
This does NOT automatically downcast here as this is a major
performance bottleneck if we did (this is the same result as in 0.12)
```
In [3]: >>> s.reindex(['a', 'g', 'c', 'f']).ffill()
Out[3]:
a 1
g 1
c 3
f 3
dtype: float64
```
You can force downcasting by passing 'infer' if you want
(this option was not available in 0.12 for Series)
```
In [4]: >>> s.reindex(['a', 'g', 'c', 'f']).ffill(downcast='infer')
Out[4]:
a 1
g 1
c 3
f 3
dtype: int64
```
TST: GH4604, reindexing with a method of 'ffill' gives incorrect results
BUG/CLN: (GH4604) Refactor Series.reindex to core/generic.py allow method= in reindexing
on a Series to work
API/CLN: GH4604 Infer and downcast dtype if appropriate on ffill/bfill
this is for consistency when doing: df.reindex().ffill() and df.reindex(method='ffill')
CLN: allow backfill/pad/interpolate to operate on integers (by float conversion)
provide downcasting back to original dtype where needed core.internals.interpolate
ENH: provide core.index.identical method to compare values and attributes similar to .equals
API: changed back to pre-GH3482 where a reindex with no args will by default copy
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} | 1 | 2013-08-19T23:21:29Z | 2013-08-20T00:24:39Z | 2013-08-20T00:24:31Z | MEMBER | null | {
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} | 6 | 2013-08-20T02:48:56Z | 2014-02-14T13:11:12Z | 2014-02-14T13:11:12Z | NONE | null | I'm not sure if this functionality exists but it is boiler plate which I need to use quite often.
Typically I need to work with very large datasets, e.g. 10mil rows or more.
On these datasets performing a calculations such as taking a date in a specific string format can be sped up incredibly by assessing what needs to be done first.
As such I find myself writing the following boiler plate code for a lot of functions.
```
def map_strptime(series, format=None):
mapping = {x: datetime.strptime(x, format) for x in set(series)}
return series.map(mapping)
```
Now comparing this implementation v.s a more naive implementation just using the standard apply function, e.g.
```
def nonmap_strptime(x, format=None):
return datetime.strptime(x, format)
```
Usage:
```
df["Date"] = map_strptime(df["Trading Date"], format="%Y-%m-%d")
# vs.
df["Date"] = df["Trading Date"].apply(nonmap_strptime, format="%Y-%m-%d")
```
Assessing these two implementations for performance I get the following.
Note, my Dataset has 10 million rows across 365 days, e.g. a lot of repeated calculations.
```
# Mapped version
>>>> %timeit df["Date"] = map_strptime(df["Trading Date"], format="%Y-%m-%d")
>>>> 900 ms
# Nonmapped version
>>>> %timeit df["Date"] = df["Trading Date"].apply(nonmap_strptime, format="%Y-%m-%d")
>>>> (Still hasn't finished in the time it has taken me to write this issue, 10min plus)
```
As you can see for some use cases, e.g. when a calculation is being repeated often a simple set - map type wrapper around the operation could improve the performance substantially.
However, if a large number of non-duplicates exists then this would obviously slow the operation down quite substantially as first the calculation would be performed, then the mapping in two steps rather than one.
To get around this the number of unique items and non-unique items could be computed. If a large difference between them exists then a shift to the mapping implementation could impart large performance gains.
I'm not sure as to the best implementation, but something along the lines of:
```
df.memoapply(*args, **kargs):
if len(set(df[cols]) >> len(df[cols]):
# use memo version
else:
# Use standard versions.
```
This works best for one to one type operations, I'm unsure how well it would work for one to many or for many to one type applications.
Could possible be extended by using the inbuilt merge operations of Pandas and using a multi indexed series for the result?
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} | 15 | 2013-08-20T03:15:21Z | 2014-06-12T13:19:05Z | 2013-08-27T13:39:59Z | CONTRIBUTOR | null | This is a prelude to the larger refactoring of arithmetic into core/ops,
etc. It doesn't change any external behavior (and I'm relatively confident that
the test suite covers all the branches).
The complexity of the time arithmetic code was making it hard to tweak,
so I decided to change it up. It's not that complicated, but wanted to
run it by you all in an easy-to-read diff before I change its files.
Specifically it:
1. Moves Series time arithmetic out of `_arith_method` into a encapsulated `_TimeOp` class.
2. Consolidates the previously repeated type conversions into one.
3. Separates validations from type conversions, which makes the function
easier to follow and write.
4. Changes all obscure character codes to clear ones.
5. Removes the unnecessary `set()` calls in convert_to_array.
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https://api.github.com/repos/pandas-dev/pandas/issues/4614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4614/events | https://github.com/pandas-dev/pandas/pull/4614 | 18,276,933 | MDExOlB1bGxSZXF1ZXN0NzcxNjMyNw== | 4,614 | Fixes usage of CacheableOffset | {
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} | 34 | 2013-08-20T04:07:57Z | 2014-06-21T13:19:40Z | 2013-08-24T00:11:41Z | CONTRIBUTOR | null | `CacheableOffset` should be first parent class when sub-classing both `CacheableOffset` and `DateOffset`
Closes #4609
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} | 9 | 2013-08-20T06:08:16Z | 2021-04-11T03:05:37Z | 2021-04-11T03:05:37Z | MEMBER | null | related: #4585
```
In [5]: randn(10, 2) == Series(randn(2))
Out[5]:
array([[False, False],
[False, False],
[False, False],
[False, False],
[False, False],
[False, False],
[False, False],
[False, False],
[False, False],
[False, False]], dtype=bool)
```
but
```
In [6]: randn(10, 2) <= Series(randn(2))
```
throws an `Exception`.
got a PR in the works
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} | 2 | 2013-08-20T15:35:22Z | 2021-04-11T03:06:25Z | 2021-04-11T03:06:25Z | CONTRIBUTOR | null | I've thrown in a few functions which I think are similar (but I've seen a few times asked about). e.g. this [SO question](http://stackoverflow.com/questions/18327624/find-elements-index-in-pandas-series).
User wants to get the index where a criteria is true:
```
import pandas as pd
myseries = pd.Series([1,4,0,7,5], index=[0,1,2,3,4])
print myseries.find(7) # should output 3
```
I guess the usual way is to use:
```
s[msk].index
s[msk].index[0]
```
Or as @jreback suggests (in this simple example):
```
pd.index(s).get_loc(7)
```
Perhaps there should be some methods to grab these out efficiently (i.e. short-circuit in cython)... maybe it makes more sense to have a crit function rather than a mask.
cc @cpcloud
_also similar to idxmax and idxmin._
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} | 15 | 2013-08-20T16:49:05Z | 2014-05-29T07:57:59Z | 2014-05-29T07:57:59Z | NONE | null | I posted this question <a href="http://stackoverflow.com/questions/18180763/set-difference-for-pandas/18187648?noredirect=1">set difference for pandas</a> in SO but the answer I received produces some loss of precision.
The basic idea is that if you have two dataframes df1, df2, you should be able to get the rows in df1 that are not included in df2. That is, the set difference of df1 and df2.
I think this would be a good feature request in case it's not possible to solve this easily using Panda's current capabilities
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} | 2 | 2013-08-21T04:09:09Z | 2013-08-21T12:45:22Z | 2013-08-21T12:45:22Z | NONE | null | ``` python
import pandas as pd
# '0.11.0'
print pd.__version__
df = pd.DataFrame(index=range(0,5), columns=['mybool'])
df.mybool = False
store = pd.HDFStore('shiftedbool.h5')
# this works
store.append('noshiftedbool', df)
# now add a shifted bool, backfilled
df['shiftedbool'] = df.mybool.shift(1).fillna(method='bfill')
print df
# causes exception:
store.append('shiftedbool', df)
```
This can be worked around; for now I'm using 0.0 and 1.0. But this seems like an issue that could be improved.
My first issue submitted; I hope it's up to scratch
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} | 1 | 2013-08-21T04:54:02Z | 2013-08-21T18:22:24Z | 2013-08-21T18:22:24Z | CONTRIBUTOR | null | Hitting a seg fault on a specific usage of .ix/.loc with release 0.12.0 (and current master). Any help is greatly appreciated. Let me know if I can provide any more info.
Here's an example produced with pandas 0.12.0 and numpy 1.7.1.
```
>>> df = pd.DataFrame({"A": [0, 1, 2]})
>>> print df.ix[[0,8,0]] # unexpected results
A
0 0.000000e+00
8 NaN
0 4.677219e+12
>>> df = pd.DataFrame({"A": list('abc')})
>>> print df.ix[[0,8,0]]
Segmentation fault (core dumped)
```
(.loc in place of .ix fails in the same way)
For comparison, here's the behavior with 0.11.0:
```
>>> df = pd.DataFrame({"A": [0, 1, 2]})
>>> print df.ix[[0,8,0]]
A
0 0
8 NaN
0 0
>>> df = pd.DataFrame({"A": list('abc')})
>>> print df.ix[[0,8,0]]
A
0 a
8 NaN
0 a
```
System info
```
i686 GNU/Linux
```
Python info
```
Python 2.7.3 (default, Apr 10 2013, 05:09:49)
[GCC 4.7.2] on linux2
```
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} | 6 | 2013-08-21T15:33:38Z | 2013-11-01T18:34:35Z | 2013-11-01T18:34:35Z | CONTRIBUTOR | null | Example:
```
In [1]: data = pandas.DataFrame(
{'pid' : [1,1,1,2,2,3,3,3],
'tag' : [23,45,62,24,45,34,25,62],
}, index=[0] * 8)
In [2]: g = data.groupby('tag')
In [3]: g.filter(lambda x: len(x) > 1)
Exception: Reindexing only valid with uniquely valued Index objects
```
cc #3680
_(I had thought there was also a similar behaviour with transform, but that seems ok.)_
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} | 9 | 2013-08-21T15:38:17Z | 2013-10-15T00:55:05Z | 2013-10-15T00:55:05Z | CONTRIBUTOR | null | At the moment filter reorders wrt the groups.
Example:
```
In [1]: data = pd.DataFrame(
{'pid' : [1,1,1,2,2,3,3,3],
'tag' : [23,45,62,24,45,34,25,62],
})
In [2]: g = data.groupby('tag')
In [3]: g.filter(lambda x: len(x) > 1)
Out[3]:
pid tag
1 1 45
4 2 45
2 1 62
7 3 62
```
If there is a way to efficiently keep the order that would be ideal I think, failing that sort back afterwards (but being wary of sorting with dupe/unordered indexes).
cc #3680
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} | 0 | 2013-08-21T16:00:40Z | 2014-06-23T06:54:56Z | 2013-08-21T18:22:24Z | CONTRIBUTOR | null | closes #4619
```
In [7]: df = DataFrame({"A": [0, 1, 2]})
In [8]: df
Out[8]:
A
0 0
1 1
2 2
In [9]: df.ix[[0,8,0]]
Out[9]:
A
0 0
8 NaN
0 0
In [10]: df = DataFrame({"A": list('abc')})
In [12]: df
Out[12]:
A
0 a
1 b
2 c
In [11]: df.ix[[0,8,0]]
Out[11]:
A
0 a
8 NaN
0 a
```
and this beauty here!
```
In [13]: df = DataFrame({'test': [5,7,9,11]}, index=['A','A','B','C'])
In [14]: df
Out[14]:
test
A 5
A 7
B 9
C 11
In [15]: df.ix[['A','A','E']]
Out[15]:
test
A 5
A 7
A 5
A 7
E NaN
```
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} | 5 | 2013-08-21T20:31:20Z | 2016-08-03T10:15:47Z | 2016-08-03T10:15:47Z | CONTRIBUTOR | null | related #4505
It seems that commenting on the first line is a little buggy (or perhaps not well-defined):
```
In [11]: s1 = '# notes\na,b,c\n# more notes\n1,2,3'
In [12]: s2 = 'a,b,c\n# more notes\n1,2,3'
In [13]: pd.read_csv(StringIO(s1), comment='#')
Out[13]:
Unnamed: 0
a b c
NaN NaN NaN
1 2 3
In [14]: pd.read_csv(StringIO(s2), comment='#')
Out[14]:
a b c
0 NaN NaN NaN
1 1 2 3
```
If you ignore the header:
```
In [15]: pd.read_csv(StringIO(s1), comment='#', header=None)
CParserError: Error tokenizing data. C error: Expected 1 fields in line 2, saw 3
```
related #3001 and from [this SO question](http://stackoverflow.com/questions/18366797/pandas-read-csv-how-to-skip-comment-lines).
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} | 4 | 2013-08-21T20:40:34Z | 2014-06-16T23:53:39Z | 2013-08-21T23:15:01Z | CONTRIBUTOR | null | closes #4312
setitem in a frame that can change dtype
setitem_with_indexer now uses the Block.setitem machinery
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} | 0 | 2013-08-21T21:24:48Z | 2013-09-20T18:05:33Z | 2013-08-31T14:09:35Z | CONTRIBUTOR | null | maybe `dropna='` as default (current behavior), and allow values: `None`,`all`,False, where `all` is equivalent of `None` (current behavior), maybe settable by a `set_option` as well
related: https://github.com/pydata/pandas/pull/3013
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} | 0 | 2013-08-21T21:57:28Z | 2013-08-26T15:21:56Z | 2013-08-26T15:21:56Z | CONTRIBUTOR | null | I don't have time to debug right now, and maybe my expectations are just off, but it looks like `read_stata` doesn't respect the encoding keyword. I'm also not sure it's needed. AFAIK, Stata doesn't (and likely won't) support unicode. It always uses latin-1, so we can always use the latin-1 encoding for strings (maybe not desirable though).
https://www.dropbox.com/s/hq42trq4327ker8/encoding_issue.dta
```
dta = pd.read_stata("./encoding_issue.dta")
dta.head()
dta = pd.read_stata("./encoding_issue.dta", encoding="latin-1")
dta.head()
dta = pd.read_stata("./encoding_issue.dta")
dta.kreis1849.str.decode("latin-1")
```
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closes #4605
TST: add tests/test_generic.py
API/CLN: Refactor rename methods to core/generic.py; fixes Series.rename for (GH4605), and adds rename
with the same signature for `Panel`
API/CLN: Refactor Series.copy to core/generic.py; removes the order argument from Series.copy (inconsistent)
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} | 3 | 2013-08-21T23:00:22Z | 2013-09-17T03:24:43Z | 2013-09-01T01:21:37Z | CONTRIBUTOR | null | Change up `MultiIndex.rename` to be an alias for `MultiIndex.set_names`.
- `Index.rename` : takes single object
- `MultiIndex.set_names`, `Index.set_names` - same type signature: take list of names [Index.set_names can only take iterable of length 1)
- `MultiIndex.rename` - synonym for `set_names`
So if you want to do something generically, you should use `set_names` (just like you would with other attributes). If you expect a non-MI index, then you could use `rename` with a string, etc.
cc @jreback
I personally think that this behavior is how it should work for Index. Everyone okay with this? I'd prefer not to do type sniffing if at all possible.
``` python
ind = Index(range(10))
ind.rename(["apple"], inplace=True)
print(ind.name) # ['apple']
print(ind.names) # [['apple']]
ind.set_names('apple') # Fails with error that it's too long
```
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} | 6 | 2013-08-21T23:01:57Z | 2013-08-24T00:15:29Z | 2013-08-24T00:15:29Z | CONTRIBUTOR | null | Not sure if this will cause weirdness but haveing the arithmetic operations able to accept an index (when appropriate type), e.g. a datecolumn should be able to interact with a DatetimeIndex, but prob should prohibit an int column from interacting with a IntIndex?
e.g. in below `df['date']-df.index` should work
This actually DOES work now! (and I take it back, does make some sense)
should `df[1]-df.reset_index().index` work????
http://stackoverflow.com/questions/18368442/how-to-directly-use-pandas-date-time-index-in-calculations/18368793?noredirect=1#comment26971689_18368793
```
In [1]: df = DataFrame(randn(5,2),index=date_range('20130101',periods=5))
In [3]: df['date'] = Timestamp('20130102')
In [4]: df
Out[4]:
0 1 date
2013-01-01 2.406932 -0.313473 2013-01-02 00:00:00
2013-01-02 0.034162 -0.708450 2013-01-02 00:00:00
2013-01-03 -1.585031 0.587243 2013-01-02 00:00:00
2013-01-04 1.454818 1.089208 2013-01-02 00:00:00
2013-01-05 -0.778016 -0.994242 2013-01-02 00:00:00
In [5]: df['td'] = df['date']-df.index.to_series()
In [6]: df
Out[6]:
0 1 date td
2013-01-01 2.406932 -0.313473 2013-01-02 00:00:00 1 days, 00:00:00
2013-01-02 0.034162 -0.708450 2013-01-02 00:00:00 00:00:00
2013-01-03 -1.585031 0.587243 2013-01-02 00:00:00 -1 days, 00:00:00
2013-01-04 1.454818 1.089208 2013-01-02 00:00:00 -2 days, 00:00:00
2013-01-05 -0.778016 -0.994242 2013-01-02 00:00:00 -3 days, 00:00:00
```
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} | 8 | 2013-08-21T23:19:55Z | 2013-10-08T14:51:18Z | 2013-10-08T14:51:18Z | NONE | null | All of the examples in the docs for [Groupby.Filter](http://pandas.pydata.org/pandas-docs/dev/groupby.html#filtration) showcase examples in which the GroupBy collapses into a series, and is evaluated in a single dimension.
I'd like to suggest adding (or amending the third example) to include a case in which the initial dataframe has > 2 columns, and thereby the GroupBy retains two dimensions.
An additional example might look like:
```
In [86]: df3 = DataFrame({'A': np.arange(8), 'B': list('aabbbbcc'), 'C': np.arange(8)})
In [86]: df3.groupby('B').filter(lambda x: len(x['C']) > 2)
A B C
2 2 b 2
3 3 b 3
4 4 b 4
5 5 b 5
```
I suggest this because I struggled with the amusing errors generated by not specifying a column for most of the afternoon.
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} | 45 | 2013-08-21T23:44:51Z | 2013-10-08T16:27:17Z | 2013-09-25T17:12:44Z | CONTRIBUTOR | null | the merge conflict chaos was too hard ;-(
This is a continuation of:
https://github.com/pydata/pandas/pull/4404
Because:
- we hit aparently a GitHub bug
- the branches diverged too much.
I shall fix:
- in the excel.py there is a fix enabling reading xlsx files with both
datemodes: (see #4332)
- in the parsers.py there is the fix for readinh the header even if there
are additional rows (to be skipped) between a header and the data (see: #4340)
- a few doc improvements
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} | 2 | 2013-08-22T01:32:19Z | 2014-07-30T21:47:46Z | 2014-01-01T03:41:02Z | CONTRIBUTOR | null | Date converters used for testing the resolution of #4332
See also: #4631
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} | 22 | 2013-08-22T01:38:33Z | 2014-04-25T17:06:59Z | 2013-08-31T17:51:58Z | MEMBER | null | Awkward side effect of the new and improved `Series` probably, I typed something wrong and was shocked to not get the usual `numpy` `ValueError` barf:
```
In [57]: a, b = Series(rand(10) > 0.5), Series(rand(10) > 0.5)
In [58]: a
Out[58]:
0 True
1 True
2 False
3 False
4 True
5 False
6 False
7 True
8 True
9 True
dtype: bool
In [59]: b
Out[59]:
0 True
1 True
2 True
3 True
4 False
5 True
6 False
7 True
8 True
9 True
dtype: bool
In [60]: a and b # what?! no way!!
Out[60]:
0 True
1 True
2 True
3 True
4 False
5 True
6 False
7 True
8 True
9 True
dtype: bool
In [61]: a & b # whew
Out[61]:
0 True
1 True
2 False
3 False
4 False
5 False
6 False
7 True
8 True
9 True
dtype: bool
```
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} | 3 | 2013-08-22T01:55:08Z | 2013-09-28T19:31:48Z | 2013-09-28T19:31:48Z | CONTRIBUTOR | null | This is the second part of the fix.
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} | 7 | 2013-08-22T03:48:28Z | 2013-09-20T17:24:17Z | 2013-09-20T17:24:17Z | NONE | null | I have been encountering this error when reading a large csv file and writing to HDFStore table. Initially this error would occur if I would read a large chunk from csv for e.g. > 200000 rows on the sencond or third chunk. Hence I resorted to reading a smaller chunk of 100000 and appending to table and program seemed to run fine upto the 178th chunk thus indicating that there isn't any problem with the data in the first few 100,000's of rows that would cause the exception to be raised.
Please refer to http://stackoverflow.com/questions/18343726/pandas-hdfstore-append-data-columns-after-append-operation for the code I'm running.
Traceback (most recent call last):
File "D:\AIMS\csvtohdf.py", line 99, in <module>
f.append('df', chunk) #, dtype=dt, data_columns = ['MONTH', 'COD', 'FLNLOCCOD', 'ISO', 'OR', 'DES', 'MAIN', 'SHARE', 'TYPE'])
File "D:\Python27\lib\site-packages\pandas\io\pytables.py", line 658, in append
self._write_to_group(key, value, table=True, append=True, *_kwargs)
File "D:\Python27\lib\site-packages\pandas\io\pytables.py", line 923, in _write_to_group
s.write(obj = value, append=append, complib=complib, *_kwargs)
File "D:\Python27\lib\site-packages\pandas\io\pytables.py", line 3012, in write
self.write_data(chunksize)
File "D:\Python27\lib\site-packages\pandas\io\pytables.py", line 3056, in write_data
values=[v[start_i:end_i] for v in values])
File "D:\Python27\lib\site-packages\pandas\io\pytables.py", line 3079, in write_data_chunk
raise Exception("tables cannot write this data -> %s" % str(detail))
Exception: tables cannot write this data ->
I am using Windows 7 with a 32-bit version of both windows and python.
## INSTALLED VERSIONS
Python: 2.7.3.final.0
Cython: 0.19.1
Numpy: 1.7.1
Scipy: Not installed
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 1.5
pytz: Not installed
bottleneck: Not installed
PyTables: 3.0.0
numexpr: 2.1
matplotlib: Not installed
openpyxl: 1.5.7
xlrd: 0.7.1
xlwt: Not installed
sqlalchemy: 0.6.6
lxml: Not installed
bs4: 4.1.3
html5lib: Not installed
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} | 4 | 2013-08-22T10:12:16Z | 2014-06-05T01:13:47Z | 2014-06-05T01:13:47Z | MEMBER | null | When I construct a boxplot with pandas, e.g. like this:
``` python
df = pd.DataFrame({'A':[1,3,2,5,4,7,6], 'B':[1,1,1,1,2,2,2]})
fig, ax = plt.subplots()
df.boxplot('A', 'B', ax=ax)
```
I cannnot change the plot anymore via `ax`, e.g. `ax.set_ylim(0,20)` or `ax.set_ylabel("Something")` has no effect on the figure.
Complete example: http://nbviewer.ipython.org/5868420/bug_pandas-boxplot-set-ylim.ipynb
I tested it on the dev version of pandas (and I know it worked previously, but cannot check which version exactly anymore).
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} | 5 | 2013-08-22T12:18:49Z | 2015-04-13T19:05:36Z | 2014-02-18T22:42:42Z | CONTRIBUTOR | null | Create a `DateOffset` that represents the a weekday in last week of the month, such as "the last Tuesday of each month"
Currently there is a `WeekOfMonth` `DateOffset` but that class only takes `week : {0, 1, 2, 3, ...}`. What we actually want is `week=-1`
I will submit a PR.
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} | 3 | 2013-08-22T12:23:31Z | 2013-09-27T03:50:38Z | 2013-09-27T03:50:38Z | CONTRIBUTOR | null | Currently the signature for constructing most `DateOffset` is `(n, **kwds)`.
Optional ags are then read using `get(key)` from the `kwds` and required args using `[key]`. This is fine, but it leaves what was extracted from `kwds` still in `kwds` leading to a redundant `str` or `repr`:
```
Week(weekday=0)
<1 Week: kwds={'weekday': 0}, weekday=0>
```
I would recommend using `pop` instead of `get` and using `del` after reading required values.
Thoughts?
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} | 0 | 2013-08-22T12:28:40Z | 2014-07-14T11:47:18Z | 2013-08-23T01:43:33Z | CONTRIBUTOR | null | ```
In [1]: df2 = DataFrame(randn(3000,1),columns=['A'])
In [2]: df3 = DataFrame(randn(3000,1))
In [3]: def h():
...: for i in xrange(10000):
...: df2['A']
...:
In [4]: def j():
...: for i in xrange(10000):
...: df3[0]
...:
```
0.12
```
In [5]: %timeit h()
%time10 loops, best of 3: 39.8 ms per loop
In [6]: %timeit j()
10 loops, best of 3: 31.8 ms per loop
```
with PR
```
In [8]: %timeit h()
10 loops, best of 3: 25.5 ms per loop
In [9]: %timeit j()
10 loops, best of 3: 27.4 ms per loop
```
and the vbench
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_getitem_single_column | 25.5514 | 43.3776 | 0.5890 |
frame_getitem_single_column2 | 27.3627 | 32.4320 | 0.8437 |
```
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} | 6 | 2013-08-22T13:37:09Z | 2015-03-03T01:25:30Z | 2015-03-03T01:25:30Z | CONTRIBUTOR | null | Does Pandas have its own `tslib.monthrange` rather than using `calendar.monthrange` for performance reasons?
If so, I will toss some comments on it indicating such.
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} | 5 | 2013-08-22T15:23:10Z | 2014-07-16T08:24:21Z | 2013-08-24T00:15:30Z | CONTRIBUTOR | null | closes #4629
```
In [1]: s = Series(date_range('20130101',periods=5),index=date_range('20130102',periods=5))
In [2]: s
Out[2]:
2013-01-02 2013-01-01 00:00:00
2013-01-03 2013-01-02 00:00:00
2013-01-04 2013-01-03 00:00:00
2013-01-05 2013-01-04 00:00:00
2013-01-06 2013-01-05 00:00:00
Freq: D, dtype: datetime64[ns]
In [3]: s-s.index
Out[3]:
2013-01-02 -1 days, 00:00:00
2013-01-03 -1 days, 00:00:00
2013-01-04 -1 days, 00:00:00
2013-01-05 -1 days, 00:00:00
2013-01-06 -1 days, 00:00:00
Freq: D, dtype: timedelta64[ns]
```
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} | 13 | 2013-08-22T22:05:30Z | 2014-06-13T00:06:16Z | 2013-08-26T12:43:52Z | CONTRIBUTOR | null | - API: the `fmt` keyword now replaces the table keyword; allowed values are `s|t`
the same defaults as prior < 0.13.0 remain, e.g. `put` implies 's' (Storer) format
and `append` imples 't' (Table) format
closes #4584 as well
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} | 37 | 2013-08-23T00:05:27Z | 2013-11-29T08:32:04Z | 2013-11-14T22:04:00Z | CONTRIBUTOR | null | I'm not sure what a good word for this is (count is taken, order means sort)!
But it's quite an often used thing to create a column which enumerates the items in each group / counts their occurrences.
You can hack it:
```
In [1]: df = pd.DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]])
In [2]: g = df.groupby(0)
In [3]: g.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
Out[3]:
0 0
1 0
2 1
3 2
4 1
dtype: int64
In [5]: df['order'] = _
In [6]: df
Out[6]:
0 1 order
0 1 2 0
1 2 3 0
2 1 4 1
3 1 5 2
4 2 6 1
```
_I've seen this in a few SO questions, here's just [one](http://stackoverflow.com/questions/18392283/pandas-return-first-item-in-dataframe-grouped-by-user)._
cc @cpcloud (and I've seen @jreback answer a question with this)
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} | 1 | 2013-08-23T00:06:53Z | 2013-08-23T00:09:33Z | 2013-08-23T00:09:33Z | CONTRIBUTOR | null | if passing a `dtype= { 'column_that_is_a_timedelta' : lambda x: the_td64_parser_of_doom(x) }`
this could provide a specific dtype parser for a column
alternatively, maybe provide a `parse_timedelta` option?
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} | 1 | 2013-08-23T00:16:52Z | 2013-08-27T00:20:23Z | 2013-08-27T00:20:23Z | MEMBER | null | `as_index` doesn't seem to work for non reductive operations
**Note:** I've only tested with `head`
```
In [7]: df = pd.DataFrame({'item_id': ['b', 'b', 'a', 'c', 'a', 'b'], 'user_id': [1,2,1,1,3,1], 'time': ra
nge(6)})
In [8]: df.groupby('user_id', as_index=False).sum()
Out[8]:
user_id time
0 1 10
1 2 1
2 3 4
In [9]: df.groupby('user_id', as_index=False).head()
Out[9]:
item_id time user_id
user_id
1 0 b 0 1
2 a 2 1
3 c 3 1
5 b 5 1
2 1 b 1 2
3 4 a 4 3
```
from [this SO question](http://stackoverflow.com/q/18392283/564538)
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} | 11 | 2013-08-23T01:32:16Z | 2014-06-22T10:18:48Z | 2013-08-27T00:20:23Z | MEMBER | null | closes #4648
closes #3417
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} | 2 | 2013-08-23T01:41:53Z | 2014-09-06T20:40:40Z | 2014-09-06T20:40:40Z | CONTRIBUTOR | null | similar to `parse_dates`
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} | 1 | 2013-08-23T01:48:08Z | 2013-11-02T20:58:35Z | 2013-11-02T20:58:35Z | CONTRIBUTOR | null | related #4236
maybe should print as:
`Series([], name: foo, dtype: int64)` ?
```
In [9]: s = Series([], dtype=np.int64, name='foo')
In [10]: s
Out[10]: Series([], dtype: int64)
```
```
In [7]: s2 = Series([1,2],name='foo')
In [8]: s2
Out[8]:
0 1
1 2
Name: foo, dtype: int64
```
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} | 5 | 2013-08-23T02:34:07Z | 2013-08-23T12:58:09Z | 2013-08-23T05:10:29Z | CONTRIBUTOR | null | ```
import pandas as pd
import numpy as np
s = pd.Series(range(10))
wh = np.where(s, s > 5, 0)
```
In 12.0 this returns a pd.Series. In master it returns an ndarray.
Is the old behavior not possible now that Series doesn't subclass ndarray?
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``` python
import pandas as pd
frame = pd.DataFrame(np.arange(6*6).reshape((6,6)),\
index=[['a', 'a', 'b', 'b', 'c', 'c'], [1, 2, 1, 2, 1, 2]],\
columns=[['a', 'a', 'b', 'b', 'c', 'c'], [1, 2, 1, 2,1,2]])
print frame['a']
print frame['a':'b']
print frame[['a','b']]
```
gives as output:
```
1 2
```
a 1 0 1
2 6 7
b 1 12 13
2 18 19
c 1 24 25
2 30 31
a b c
1 2 1 2 1 2
a 1 0 1 2 3 4 5
2 6 7 8 9 10 11
b 1 12 13 14 15 16 17
2 18 19 20 21 22 23
a b
1 2 1 2
a 1 0 1 2 3
2 6 7 8 9
b 1 12 13 14 15
2 18 19 20 21
c 1 24 25 26 27
2 30 31 32 33
If I understand correctly how this should work. frame[...] should be selecting from the columns. However, as can be seen, the result of the second print statement selects rows from the dataframe.
Thus print frame['a':'b'] does not give the correct output.
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} | [] | closed | false | null | [] | null | 1 | 2013-08-23T09:07:32Z | 2013-12-04T00:40:35Z | 2013-08-23T19:55:17Z | CONTRIBUTOR | null | Short git revision hashes are generated by reducing the length of the long hash to 7 characters which working most of the time, but not in all cases.
Examples:
```
0958d5f7fff105a593108aeab81413551e5e63c2 -> 0958d5f (7 characters)
8625aaeefdc89549b9a1017efcee4abb68840b06 -> 8625aaee (8 characters)
```
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} | [] | closed | false | null | [] | null | 3 | 2013-08-23T09:51:33Z | 2013-08-25T09:20:48Z | 2013-08-23T11:52:36Z | NONE | null | I can't give an example as I only get the error in some cases and I can't really reproduce it.
However I get this error from the `_repr_footer` method of `TimeSeries`. By comparing this method with `_repr_footer` of `Series` I saw that Series uses `com.pprint_thing` to get `namestr`, while TimeSeries uses `str`.
Changing the line
``` python
namestr = u"Name: %s, " % str(
```
in `TimeSeries._repr_footer` to
``` python
namestr = u"Name: %s, " % com.pprint_thing(
```
resolves the issue.
Not sure, if this relevant any more, as Series inherits from NDFrame now.
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} | 13 | 2013-08-23T12:42:55Z | 2014-06-12T13:11:07Z | 2013-08-31T17:51:58Z | CONTRIBUTOR | null | closes #4633
this is a revert to #1073/#1069
now a call to `__nonzero__` raises `ValueError` ALWAYS
The following is the behavior
```
In [17]: s = Series(randn(4))
In [18]: df = DataFrame(randn(10,2))
In [19]: s_empty = Series()
In [20]: df_empty = DataFrame()
In [5]: bool(s)
Out[5]: ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
In [6]: bool(s_empty)
Out[6]: ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
In [7]: bool(df)
Out[7]: ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
In [8]: bool(df_empty)
Out[8]: ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
And prevents these fun ones (same for Series/Panel)
```
In [4]: df1 = DataFrame(np.ones((4,4)))
In [5]: df2 = DataFrame(np.zeros((4,4)))
In [6]: df1 and df2
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
In [7]: df1 or df2
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
In [8]: not df1
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
In [9]: def f():
...: if df1:
...: print("this is cool")
...:
In [10]: f()
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
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} | 0 | 2013-08-23T13:00:50Z | 2014-06-18T16:11:49Z | 2013-08-23T13:03:32Z | CONTRIBUTOR | null | Fixes #4235.
This just adds a release note to #4236: Boolean indexing on an empty series loses index names.
Actually that commit really just adds tests for this, since they now already pass in master. It also, fixes behaviour of `infer_dtype` with empty arrays.
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} | 13 | 2013-08-23T13:39:07Z | 2014-06-20T13:45:34Z | 2013-09-29T22:07:33Z | CONTRIBUTOR | null | closes #4554
I just fixed the call to ndarray.reshape with order as a positional argument, which is wrong. Please note that I had to remove a test.
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} | 1 | 2013-08-23T17:04:36Z | 2013-09-20T17:11:34Z | 2013-09-20T17:11:34Z | NONE | null | Been trying to use ExcelWriter and getting the error:
writer = pd.ExcelWriter(filename)
" File "C:\Anaconda\lib\site-packages\pandas\io\excel.py", line 350, in **init**
from openpyxl.workbook import Workbook
ImportError: No module named openpyxl.workbook"
I'm using Python 2.7 and updated Pandas to 0.12.0
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} | 18 | 2013-08-23T20:01:27Z | 2014-06-25T22:52:46Z | 2013-09-21T23:29:27Z | CONTRIBUTOR | null | Added initial Stata 13 .dta file format support. Newly added string features ("strls") and writing are not supported. Fixes #4291.
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} | 0 | 2013-08-23T21:39:42Z | 2021-04-11T03:07:37Z | null | CONTRIBUTOR | null | http://stackoverflow.com/questions/18411110/python-pandas-timeseries-resample-giving-unexpected-results/18411516?noredirect=1#comment27046758_18411516
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} | 0 | 2013-08-23T22:02:42Z | 2014-06-27T09:10:17Z | 2013-08-24T00:12:34Z | CONTRIBUTOR | null | closes #4330
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} | 2 | 2013-08-24T11:51:03Z | 2013-08-25T00:56:27Z | 2013-08-25T00:56:27Z | CONTRIBUTOR | null | The following code (or with any other frequency instead of ms):
```
t1 = pd.Series(pd.date_range('1/1/2011', periods=10, freq='H'))
t2 = pd.Series(pd.date_range('1/1/2011 01:02:03', periods=10, freq='H'))
x = t2-t1
x.astype('m8[ms]')
```
works well in pandas-0.11.0 (numpy-1.7.1), however raises an error in pandas-0.12.0:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/vladimir/.virtual_envs/system/lib/python2.7/site-packages/pandas/core/series.py", line 934, in astype
values = com._astype_nansafe(self.values, dtype)
File "/Users/vladimir/.virtual_envs/system/lib/python2.7/site-packages/pandas/core/common.py", line 1653, in _astype_nansafe
raise TypeError("cannot astype a timedelta from [%s] to [%s]" % (arr.dtype,dtype))
TypeError: cannot astype a timedelta from [timedelta64[ns]] to [timedelta64[ms]]
```
At the same time astype for a single element (which is numpy.timedelta64)
```
x[1].astype('m8[ms]')
```
obviously works well.
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} | 6 | 2013-08-24T18:55:24Z | 2021-06-24T04:43:41Z | 2021-06-24T04:43:40Z | MEMBER | null | related: https://github.com/pydata/pandas/issues/3513
[related SO question](http://stackoverflow.com/questions/18419657/dataframe-merge-in-pandas)
would probably have to write our own nan-handling dot since multiply-and-sum is inefficient for large frames.
cc @hayd
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} | 3 | 2013-08-25T00:04:15Z | 2014-07-03T14:52:51Z | 2013-08-26T15:18:22Z | CONTRIBUTOR | null | ``` python
s = pd.Series(range(10)).astype(float)
s[s > 8] = None
s
```
```
/Users/datacaliber/Dropbox/Projects/trading/python/externals/pandas/pandas/core/internals.py in create_block(v, m, n, item, reshape)
596 # change the dtype
597 if nv is None:
--> 598 dtype, _ = com._maybe_promote(n.dtype)
599 nv = v.astype(dtype)
600 try:
AttributeError: 'NoneType' object has no attribute 'dtype'
```
This is on latest master.
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} | 20 | 2013-08-25T05:14:47Z | 2021-04-11T03:09:18Z | 2021-04-11T03:09:17Z | MEMBER | null | - [ ] date formatting #2583, PR #4313, #6797
- [ ] string spacing (justification?) #4158
- [ ] float format #2502, #2069
- [ ] int format, #6502
- [ ] timedelta fromat #6783
[SO question](http://stackoverflow.com/questions/18425400/printing-to-csv-file-with-automatic-width)
something like
``` python
df.to_csv(format='%10.4f', sep=' ')
```
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```
frame = pd.DataFrame({
'home' : ['CHI', 'ATL', 'SEA', 'DET', 'STL','HOU' ,'CHI','CHI'],
'away' : ['DET', 'CHI', 'HOU', 'TOR', 'DAL', 'STL', 'MIA', 'SEA'],
'awayPTS' : [88, 75, 105, 99, 110, 85, 95, 100],
'homePTS' : [92, 88, 95, 97, 100, 74, 98, 110],
})
frame.columns=[['away', 'away', 'home', 'home'], ['team', 'PTS', 'team', 'PTS']]
In [11]: frame.T.unstack(0).T
Out[11]:
PTS team
0 away 88 DET
home 92 CHI
1 away 75 CHI
home 88 ATL
2 away 105 HOU
home 95 SEA
3 away 99 TOR
home 97 DET
4 away 110 DAL
home 100 STL
5 away 85 STL
home 74 HOU
6 away 95 MIA
home 98 CHI
7 away 100 SEA
home 110 CHI
```
Annoying thing is that this will change the dtypes (because the transpose has mixed dtype).
```
In [12]: frame.T.unstack(0).T.dtypes
Out[12]:
PTS object
team object
dtype: object
```
_see this [SO answer](http://stackoverflow.com/a/18429210/1240268)_
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} | 2 | 2013-08-25T19:42:22Z | 2014-06-24T02:01:04Z | 2013-08-27T00:20:23Z | CONTRIBUTOR | null | closes #4648
closes #3417
closes #4649 (pr)
```
In [1]: df = pd.DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]], index=list('abcde'))
In [2]: g = df.groupby(0, as_index=False)
In [3]: g.apply(lambda x: x)
Out[3]:
0 1
a 1 2
b 2 3
c 1 4
d 1 5
e 2 6
In [4]: g.head(2)
Out[4]:
0 1
a 1 2
c 1 4
b 2 3
e 2 6
```
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} | 4 | 2013-08-25T22:36:44Z | 2018-03-12T02:20:33Z | 2013-09-28T19:27:13Z | CONTRIBUTOR | null | The display output of a DataFrame may truncate floats, for example:
``` python
from pandas import DataFrame
import numpy as np
dat_dtype = {
'names' : ('column_one', 'col_two'),
'formats' : ('i', 'd')}
dat = np.zeros(4, dat_dtype)
dat['column_one'] = 10**(np.arange(4))
dat['col_two'] = 10**(-np.arange(4, dtype='d') - 4)
print(DataFrame(dat))
```
shows
```
column_one col_two
0 1 0.000100
1 10 0.000010
2 100 0.000001
3 1000 0.000000
```
The last number in `col_two` has been truncated from `0.0000001` to `0.000000`. I consider this formatting deceptive and unacceptable.
---
As a side note, here is tabular output for the same data from different programs.
Using [prettytable](https://code.google.com/p/prettytable/wiki/Tutorial):
``` python
from prettytable import PrettyTable
x = PrettyTable(dat.dtype.names)
for row in dat:
x.add_row(row)
x.align = 'r'
print(x)
```
showing
```
+------------+---------+
| column_one | col_two |
+------------+---------+
| 1 | 0.0001 |
| 10 | 1e-05 |
| 100 | 1e-06 |
| 1000 | 1e-07 |
+------------+---------+
```
PostgreSQL is similar:
```
postgres=# select * from dat;
column_one | col_two
------------+---------
1 | 0.0001
10 | 1e-005
100 | 1e-006
1000 | 1e-007
(4 rows)
```
R formats the whole column using scientific exponential formatting:
```
> dat
> dat <- data.frame(column_one=10**(0:4), col_two=10**(0:-4 - 4))
column_one col_two
1 1 1e-04
2 10 1e-05
3 100 1e-06
4 1000 1e-07
5 10000 1e-08
```
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} | 3 | 2013-08-26T02:56:51Z | 2013-09-28T19:39:59Z | 2013-09-28T19:39:59Z | CONTRIBUTOR | null | Create method `get_effective_cell` that takes `sheet`, `cellx`, `rowx` and returns the Excel `cell` at that location taking in to account merged cells.
I will submit PR
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} | 39 | 2013-08-26T13:59:28Z | 2019-12-28T05:07:47Z | null | CONTRIBUTOR | null | Create a class / method for number parsing. This would be a generalization of the code that remove the thousands separators from numbers (see: https://github.com/cancan101/pandas/blob/1703ef44cd6b98e17c785c9120e29bbeefdefd1c/pandas/io/parsers.py#L1502).
Other items include:
1. Alternative decimal point
2. Using parenthesis for negative numbers
3. Stripping currency character
I can submit PR for this. Do people have other suggestions for this?
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} | 5 | 2013-08-26T19:37:21Z | 2013-09-26T01:03:38Z | 2013-09-26T01:03:38Z | CONTRIBUTOR | null | When reading a csv with a date column, the date is sometimes parsed as a number:
```
In [1]: s = '06.02.2013;13:00;1.000,215;0,215;0,185;0,205;0,00'
In [2]: pd.read_csv(StringIO(s), sep=';', header=None, parse_dates={'Dates': [0, 1]}, index_col=0, decimal=',', thousands='.')
Out[2]:
2 3 4 5 6
Dates
6022013 13:00 1.000,215 0.215 0.185 0.205 0
```
Here `06.02.2013` is read as a number `0602013` before the date is parsed (which fails)... I think [dates are sometimes written this way on the continent](http://en.wikipedia.org/wiki/Date_and_time_notation_in_Europe) (along with . thousands).
This was found in #4322 (but that issue was more about . being ignored), I guess another test case would be with `-`:
```
In [3]: s = '06-02-2013;13:00;1.000,215;0,215;0,185;0,205;0,00'
In [4]: pd.read_csv(StringIO(s), sep=';', header=None, parse_dates={'Dates': [0, 1]}, decimal=',', thousands='-')
Out[4]:
Dates 2 3 4 5 6
0 6022013 13:00 1.000,215 0.215 0.185 0.205 0
```
@jreback suggests:
> but it should ignore dates columns entirely (for thousands parsing...)
cc #4598 @guyrt
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} | 52 | 2013-08-26T21:52:25Z | 2015-09-09T12:06:09Z | 2015-09-09T12:06:09Z | CONTRIBUTOR | null | related #4468
Add keyword argument to ExcelFile parser to take an integer / list for the rows to interpret as header rows. If more than one, interpret as hierarchical columns / MultiIndex.
Presumably this would also allow you to round-trip Data Frame with hierarchical columns.
Basically, if you have something spanning two columns, just converts to two cells with the data in the original cell, ending up just like what you need for csv reader.
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} | 9 | 2013-08-26T22:22:14Z | 2014-06-12T12:36:30Z | 2014-02-16T22:09:22Z | CONTRIBUTOR | null | Simple changes to add PostgreSQL syntax to pandas.io.sql.get_schema. Added data type cases for get_sqltype() and a columns "flavor" for get_schema().
The logic of `get_schema` seems simple enough at the moment (though it still doesn't support Oracle). Should it be replaced by SQLAlchemy integration?
ref: https://github.com/pydata/pandas/issues/4163
old pr: https://github.com/pydata/pandas/pull/4574
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} | 2 | 2013-08-26T22:33:32Z | 2013-09-28T19:25:53Z | 2013-09-28T19:25:53Z | NONE | null | dupe of #3866
The same item occurs in one column of dtype object with different types. This can cause problems in later operations that rely on the same item having the same type for all its occurrences, for example, groupby.
In the example below, nothing in the file data.csv is quoted. I have been trying to reproduce this with a simulated dataset but have not been able so far to do so. However, this problem does not exist if I only read a subset of the 3.5 million rows of data.csv.
The problem also doesn't exist when the 'python' engine is used instead of the 'c' engine, see below.
```
In [204]: df = pd.read_csv('data.csv', index_col=0)
In [205]: df
Out[205]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 3774283 entries, 0 to 3774282
Columns: 4 entries, sym to name
dtypes: int64(1), object(3)
In [206]: df.dtypes
Out[206]:
sym object
id int64
pid object
name object
dtype: object
In [207]: df[df.pid == 135].shape # <-- number 135
Out[207]: (311, 4)
In [208]: df[df.pid == '135'].shape # <-- string '135'
Out[208]: (74, 4)
In [209]: for n, g in grouped:
if n in ('135', 135):
print(n, g.shape)
.....:
(135, (311, 4))
('135', (74, 4))
In [210]: min(df[df.pid.isin([135, '135'])].index)
Out[210]: 1966006
# get 4000 lines around the area of the 385 entries with pid=135
In [211]: !head -n 1968006 data.csv | tail -n4000 > small.csv
In [212]: df = pd.read_csv('small.csv', index_col=0, names=['sym', 'id', 'pid', 'name'])
In [213]: df[df.pid == 135].shape
Out[213]: (385, 4)
In [214]: df[df.pid == '135'].shape
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-214-070d5748621c> in <module>()
----> 1 df[df.pid == '135'].shape
/usr/local/lib/python2.7/site-packages/pandas/core/series.pyc in wrapper(self, other)
238 if np.isscalar(res):
239 raise TypeError('Could not compare %s type with Series'
--> 240 % type(other))
241 return Series(na_op(values, other),
242 index=self.index, name=self.name)
TypeError: Could not compare <type 'str'> type with Series
In [215]: df.dtypes
Out[215]:
sym object
id int64
pid int64
name object
dtype: object
In [216]: df = pd.read_csv('data.csv', index_col=0, engine='python')
In [217]: df[df.pid == 135].shape
Out[217]: (0, 4)
In [218]: df[df.pid == '135'].shape
Out[218]: (385, 4)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/4682 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/4682/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/4682/comments | https://api.github.com/repos/pandas-dev/pandas/issues/4682/events | https://github.com/pandas-dev/pandas/issues/4682 | 18,577,971 | MDU6SXNzdWUxODU3Nzk3MQ== | 4,682 | ENH: HTML - parse multiple rows in header as MultiIndex columns | {
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} | [] | closed | false | null | [] | null | 1 | 2013-08-26T22:44:57Z | 2013-08-26T22:51:42Z | 2013-08-26T22:46:14Z | CONTRIBUTOR | null | Add keyword argument to HTML parser to take an integer / list for the rows to interpret as header rows. If more than one, interpret as hierarchical columns / MultiIndex.
Presumably this would also allow you to round-trip Data Frame with hierarchical columns.
I don't think this already exists.
related #4468
related #4679
related #4673
See for example: http://www.sec.gov/Archives/edgar/data/47217/000104746913006802/a2215416z10-q.htm#CCSCI
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} | 7 | 2013-08-26T23:29:29Z | 2021-04-11T03:11:54Z | 2021-04-11T03:11:54Z | CONTRIBUTOR | null | See the table here: http://www.sec.gov/Archives/edgar/data/47217/000104746913006802/a2215416z10-q.htm#CCSE
the trailing ")" and the leading "$" are in different columns (aka td's / cells) from the number..
There should be an option to merge all column under a given heading (#4679).
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} | 6 | 2013-08-27T01:48:45Z | 2014-09-05T14:39:46Z | 2013-09-08T19:46:53Z | CONTRIBUTOR | null | closes #3371
- ENH: GH3371 support timedelta fillna
- BUG: add support for datetime64 ffill/bfill
- INT: add TimeDeltaBlock support in internals
```
In [3]: s = Series([Timestamp('20130101'),Timestamp('20130101'),
Timestamp('20130102'),Timestamp('20130103 9:01:01')])
In [4]: td = s.diff()
In [5]: td
Out[5]:
0 NaT
1 00:00:00
2 1 days, 00:00:00
3 1 days, 09:01:01
dtype: timedelta64[ns]
In [6]: td.fillna(0)
Out[6]:
0 00:00:00
1 00:00:00
2 1 days, 00:00:00
3 1 days, 09:01:01
dtype: timedelta64[ns]
In [7]: from datetime import timedelta
In [8]: td.fillna(timedelta(days=3,seconds=5))
Out[8]:
0 3 days, 00:00:05
1 00:00:00
2 1 days, 00:00:00
3 1 days, 09:01:01
dtype: timedelta64[ns]
```
ffill
```
In [9]: td[2] = np.nan
In [10]: td
Out[10]:
0 NaT
1 00:00:00
2 NaT
3 1 days, 09:01:01
dtype: timedelta64[ns]
In [11]: td.ffill()
Out[11]:
0 NaT
1 00:00:00
2 00:00:00
3 1 days, 09:01:01
dtype: timedelta64[ns]
```
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} | 9 | 2013-08-27T04:57:44Z | 2013-09-28T17:26:33Z | 2013-09-20T14:37:39Z | CONTRIBUTOR | null | How about circumventing annoying match objects by providing an additional, somewhat redundant string method?
```
s.str.extract(pattern)
```
as a shortcut for
```
s.str.match(pattern).str.get(0)
```
If the pattern contains multiple groups, a DataFrame should be returned. Thoughts? Maybe @hayd would be into this one.
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} | 1 | 2013-08-27T06:06:56Z | 2013-09-10T19:54:07Z | 2013-09-10T19:54:07Z | CONTRIBUTOR | null | Hitting an exception with the following code on 0.12.0 (and current master). Copied 0.11.0 behavior for reference, but feel free to reject if assignment to non-contiguous columns of a MultiIndex using a partial label is illegal/unsupported.
```
import numpy as np
import pandas as pd
df = pd.DataFrame(
np.ones((1, 3)),
columns=pd.MultiIndex.from_tuples(
[('A', '1'), ('B', '1'), ('A', '2')]
),
dtype=object
)
print 'Before:'
print df.dtypes
df['A'] = df['A'].astype(float)
print 'After:'
print df.dtypes
```
0.11.0 output:
```
Before:
A 1 object
B 1 object
A 2 object
dtype: object
After:
A 1 float64
B 1 object
A 2 float64
dtype: object
```
0.12.0 output:
```
Before:
A 1 object
B 1 object
A 2 object
dtype: object
Traceback (most recent call last):
File "repro.py", line 15, in <module>
df['A'] = df['A'].astype(float)
File "/home/gmd/ENV/pandas-master/lib/python2.7/site-packages/pandas-0.12.0_270_ge3c71f2-py2.7-linux-i686.egg/pandas/core/frame.py", line 1929, in __setitem__
self._set_item(key, value)
File "/home/gmd/ENV/pandas-master/lib/python2.7/site-packages/pandas-0.12.0_270_ge3c71f2-py2.7-linux-i686.egg/pandas/core/frame.py", line 1977, in _set_item
NDFrame._set_item(self, key, value)
File "/home/gmd/ENV/pandas-master/lib/python2.7/site-packages/pandas-0.12.0_270_ge3c71f2-py2.7-linux-i686.egg/pandas/core/generic.py", line 798, in _set_item
self._data.set(key, value)
File "/home/gmd/ENV/pandas-master/lib/python2.7/site-packages/pandas-0.12.0_270_ge3c71f2-py2.7-linux-i686.egg/pandas/core/internals.py", line 2448, in set
self._reset_ref_locs()
File "/home/gmd/ENV/pandas-master/lib/python2.7/site-packages/pandas-0.12.0_270_ge3c71f2-py2.7-linux-i686.egg/pandas/core/internals.py", line 1644, in _reset_ref_locs
self._rebuild_ref_locs()
File "/home/gmd/ENV/pandas-master/lib/python2.7/site-packages/pandas-0.12.0_270_ge3c71f2-py2.7-linux-i686.egg/pandas/core/internals.py", line 1652, in _rebuild_ref_locs
if self._ref_locs is not None:
AttributeError: _ref_locs
```
Thanks!
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} | 2 | 2013-08-27T09:53:05Z | 2016-10-12T23:04:09Z | 2013-09-28T19:55:36Z | NONE | null | The following code
```
import numpy as np
import pandas as pd
hor= [str(k) for k in range(3)]
columns=[ 'A','B','C', 'D' ]
ind1=np.repeat(hor, len(columns))
ind2=np.tile(columns,4)
df=pd.DataFrame(index=[ind1,ind2],columns=columns)
print(df)
print(df.xs('A',level=1))
```
generates an error when trying to trying to slice the dataframe. The slicing syntax is correct, but the issue appears because the dataframe is invalid:
len(ind1)= 12 and len(ind2)=16.
I think an error should be generated upon creation of the dataframe.
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} | 7 | 2013-08-27T15:05:21Z | 2014-06-04T12:46:42Z | 2014-06-04T12:46:42Z | CONTRIBUTOR | null | This enhancement allows Pandas to use `dateutil` timezones as well as the default `pytz` ones. The `Timestamp`'s would be inter-operable.
It has required a bit of coding in `tslib.pyx` to discriminate between `pytz` and `dateutil` tzinfo objects and load the appropriate data into the cache.
I'll create PR for this.
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} | 24 | 2013-08-27T16:31:45Z | 2014-06-18T23:18:32Z | 2014-02-16T12:08:19Z | CONTRIBUTOR | null | This adds `dateutil` time zone support as a compliment to `pytz`. The tests are designed to demonstrate that the same result is obtained using either library except where the libraries themselves differ (at the onset of DST).
There is a fair amount of tricky coding here in `tslib.pyx` as it is accessing private members of the time zone object, cacheing them and performing calculations on those cached values.
This should close #4688.
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} | 0 | 2013-08-27T17:11:33Z | 2014-06-14T14:30:36Z | 2014-06-14T14:30:36Z | NONE | null | pandas' intersection method on DatetimeIndex should preserve the timezone. I had some cases where it works, and some where it doesn't. Example that does not work:
```
In [75]: a = pd.date_range('2012-01-01', '2012-01-20', freq='D', tz='CET')
In [76]: b = pd.date_range('2012-01-01', '2012-01-19', freq='D', tz='CET')
In [77]: a.intersection(b)
Out[77]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-12-31 23:00:00, ..., 2012-01-18 23:00:00]
Length: 19, Freq: D, Timezone: None
In [78]: pd.__version__
Out[78]: '0.12.0'
```
I would expect Timezone: CET in the output of line [77], and the times to be correctly displayed in CET.
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} | 4 | 2013-08-27T17:13:45Z | 2013-09-28T19:26:14Z | 2013-09-28T19:26:14Z | CONTRIBUTOR | null | Large tsv files with columns that are mixed integers and strings are not consistently converted to the same data type. Some of the integers are interpreted as integers and identical entries are interpreted as strings. Transition seems to happen at power of 2 indices indicating something is being done in chunks but not for all chunks.
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'a':['1']*100000 + ['X']*100000 + ['1']*100000, 'b':['b']*300000})
In [3]: df.to_csv('test', sep='\t', index=False, na_rep='NA')
In [4]: df2 = pd.read_csv('test', sep='\t')
In [5]: print df2['a'].unique()
['1' 'X' 1]
In [6]: for a in df2['a'][262140:262150]:
...: print repr(a)
...:
'1'
'1'
'1'
'1'
1
1
1
1
1
1
```
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] | closed | false | null | [] | null | 2 | 2013-08-27T17:43:59Z | 2013-08-27T17:56:10Z | 2013-08-27T17:56:10Z | CONTRIBUTOR | null | Poster has an example which tricks read_csv into thinking column a is a int, but then throws it lots of strings (and it then infers 1s as strings).
```
import pandas as pd
df = pd.DataFrame({'a':['1']*100000 + ['X']*100000 + ['1']*100000, 'b':['b']*300000})
df.to_csv('test', sep='\t', index=False, na_rep='NA')
df2 = pd.read_csv('test', sep='\t')
print df2['a'].unique()
```
http://stackoverflow.com/questions/18471859/pandas-read-csv-dtype-inference-issue
I think this is rather an edge case tbh. :)
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} | 1 | 2013-08-27T18:30:20Z | 2013-08-28T16:49:33Z | 2013-08-28T16:49:33Z | NONE | null | I'm using Python 2.7.5 (64-bit)
Pandas 0.12.0
on Windows 8
I'm trying to access the boolean value 'containsSampledData' per Google documentation here:
https://developers.google.com/analytics/devguides/reporting/core/v3/reference#sampling
so I run:
```
from pandas.io.ga import GAnalytics
gAnalytics = GAnalytics()
z = gAnalytics.create_query(‘web id’, ‘pageviewsPerVisit’, ’2013-08-12′,’2013-08-14′, ‘date, 'segment id'’).execute().get(‘containsSampledData’)
```
but the segment causes this error:
“Invalid value ‘ga:1234′ for segment.”
running this also causes the same error:
```
gAnalytics.create_query('web id', 'pageviews', '2013-08-12','2013-08-14', 'date', 'segment id').execute().get('rows', [])
```
When I take the segment out, I get the expected results that match the Google Analytics web portal. The reason I would like to access segments is so I can test a case where I get an expected value of 'true' returned from 'containsSampledData' since segment pretty much always uses sampled data.
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It seems that str[1] is significantly slower than `.apply(lambda x: x[1])`
See this So answer http://stackoverflow.com/a/18473330/1240268
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> pd.io.parsers.ExcelFile.parse(xl)
where xl is of type pd.ExcelFile()
I get the message: _TypeError_: unbound method parse() must be called with ExcelFile instance as first argument (got ExcelFile instance instead)
and the message should read: unbound method parse() must be called with _ExcelSheet name_ as first argument
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} | 19 | 2013-08-27T23:52:04Z | 2014-06-13T04:34:46Z | 2013-09-03T11:08:14Z | CONTRIBUTOR | null | Hi,
Using HDFStore.append_to_multiple, if an entire row written to any one table consists entirely of np.nan, the row is not written to the table, but is written to the other tables. The following code reproduces and fixes the issue.
I would prefer that append_to_multiple maintain synchronized rows across tables, and to my knowledge, the best way to do that is drop that row from the other tables. We would probably need a fix that looks something like this PR.
I'm not sure if this is the best way to do this, and would love some feedback!
Thanks,
Alex
The Fix:
```
# setup
d = {'table1': ['col1', 'col2'], 'table2': ['col3', 'col4']}
value = pandas.DataFrame([[1,2,3,4], [5,6,None, None]], columns=['col1', 'col2', 'col3', 'col4'])
# fix (as in PR)
dfs = (value[cols] for cols in d.values())
valid_index = reduce(
lambda index1, index2: index2.intersection(index1),
[df.dropna(how='all').index for df in dfs])
value = value.ix[valid_index]
```
To reproduce the error:
```
In [1]: from pandas import DataFrame, HDFStore
In [2]: store = HDFStore('tmp1.h5')
In [3]: df = DataFrame([[1,2], [3,None]], columns=['a', 'b'])
In [4]: store.append_to_multiple({'table1': ['a'], 'table2': ['b']}, df, 'table1')
In [5]: store
Out[5]:
<class 'pandas.io.pytables.HDFStore'>
File path: tmp1.h5
/table1 frame_table (typ->appendable,nrows->2,ncols->1,indexers->[index],dc->[a])
/table2 frame_table (typ->appendable,nrows->1,ncols->1,indexers->[index])
store.select_as_multiple(['table1', 'table2'])
In [6]: store.select_as_multiple(['table1', 'table2'])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-1a2861fcaa34> in <module>()
----> 1 store.select_as_multiple(['table1', 'table2'])
/Users/alexgaudio/.virtualenvs/ds/lib/python2.7/site-packages/pandas/io/pytables.pyc in select_as_multiple(self, keys, where, selector, columns, start, stop, iterator, chunksize, auto_close, **kwargs)
540 nrows = t.nrows
541 elif t.nrows != nrows:
--> 542 raise ValueError("all tables must have exactly the same nrows!")
543
544 # select coordinates from the selector table
ValueError: all tables must have exactly the same nrows!
```
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} | 2 | 2013-08-28T10:33:28Z | 2013-10-04T12:50:04Z | 2013-10-04T12:50:04Z | CONTRIBUTOR | null | (I love Pandas! Great work! One small issue...)
The docstring for the `Period` constructor reads:
```
freq : str, default None
e.g., 'B' for businessday, ('T', 5) or '5T' for 5 minutes
```
i.e. the docstring says that `freq` can be multiples of a period code (e.g. 5 minutes).
But if we try to use `mult != 1` then we get an exception:
``` Python
In [43]: pd.Period('2013-01-01', freq='5T')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-43-770fd8956b5c> in <module>()
----> 1 pd.Period('2013-01-01', freq='5T')
/usr/local/lib/python2.7/dist-packages/pandas/tseries/period.pyc in __init__(self, value, freq, ordinal, year, month, quarter, day, hour, minute, second)
120 base, mult = _gfc(freq)
121 if mult != 1:
--> 122 raise ValueError('Only mult == 1 supported')
123
124 if self.ordinal is None:
ValueError: Only mult == 1 supported
```
This same error is given if we provide `freq` as a tuple like `freq=('T', 5)`
So either the code can't handle `mult != 1` (in which case the docstring is wrong). Or, if the docstring is _correct_ (the code _can_ handle a `mult != 1`) then the exception is wrong.
(For my own work, I would love to be able to use arbitrary-length `Period` instances. Here's a related StackOverflow question that I have just asked: [Irregular, non-contiguous Periods in Pandas](http://stackoverflow.com/questions/18486106/irregular-non-contiguous-periods-in-pandas))
(I'm using Pandas 0.12)
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} | 0 | 2013-08-28T12:40:23Z | 2013-10-03T02:26:05Z | 2013-10-03T02:26:05Z | MEMBER | null | see https://github.com/pydata/pandas/issues/4679#issuecomment-23410759
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