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1. .append turns the index into numbers
> > > s1=s[:4]
> > > s2=s[4:]
> > > s1
> > > bar one 0.229167564165
> > > two -1.12529475186
> > > baz one 1.03310069555
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> > > options
> > > ForwardPrice Strike CallPut Premium
> > > 105169 57.2024993896 15.03 15.03 C 2.206
> > > -41.0596008301 15.03 15.03 P 2.231
> > > ...
> > >
> > > options.deleve... | {
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I was using hackish solutions to select rows where a particular column is empty/non-empty, because I didn't know about isnull and notnull. I think these should be exposed as methods, particularly of `Series` (i.e. columns), so they're readily discoverable via tab completion.
Example use:
```
sele... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/206/events | https://github.com/pandas-dev/pandas/pull/206 | 1,854,259 | MDExOlB1bGxSZXF1ZXN0Mzg4MTE2 | 206 | Use stdlib csv module to write csv | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2011-10-08T23:47:24Z | 2016-10-12T23:02:36Z | 2012-05-21T18:12:22Z | MEMBER | null | On larger datasets methods like
- sort_index
- rename
(add methods to list)
etc. would benefit from an inplace option, e.g.:
``` python
df.sort_index(by=col, inplace=True)
```
and this would be more memory efficient
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I've also slipped in a couple of other small tweaks - fixing a bug with writing series CSV, and adding trove classifiers to indicate Python 3 compatibility.
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If a column name in a dataframe is a valid Python name and doesn't conflict with any DataFrame attributes, this lets you type `df.MyColumn` than `df['MyColumn']`. That's much handier for interactive use, especially because IPython will tab complete `df.M... | {
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``` python
a.join(b, on=['foo', 'bar'])
```
where 'foo', and 'bar' are columns in a and b is indexed by a MultiIndex with 2 levels
I am already working on this...just here so I don't forget
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One way to solve this could be, if we try to index a series to multiple dimensions, simply return a plain numpy array of ... | {
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1. Should the default be to use an index column? My d... | {
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I also think the autosummary API documentation isn't particularly good - it doesn't seem to handle classes at all. I'll see if I can copy what IPython uses (example output is [here](http:/... | {
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``` Python
>>> import pandas
>>> df = pandas.DataFrame(range(5))
>>> df
0
0 0
1 1
2 2
3 3
4 4
>>> view_repr = repr(df)
>>> view_to_string = df.to_string()
0
0 0
1 1
2 2
3 3
4 4
>>> view_repr == view_to_string
False
```
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I'm not entirely happy about how we check whether IPython is imported, but it's the best option I could see, and I don't think we should unconditionally import IPython and dependent modules just because it's installed.
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https://api.github.com/repos/pandas-dev/pandas/issues/235 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/235/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/235/comments | https://api.github.com/repos/pandas-dev/pandas/issues/235/events | https://github.com/pandas-dev/pandas/issues/235 | 1,909,609 | MDU6SXNzdWUxOTA5NjA5 | 235 | Index class ignores dtype parameter | {
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Pandas 0.4.3
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``` python
df = DataFrame({'d' : [1.,1.,1.,2.,2.,2.],
'c' : np.tile(['a','b','c'], 2),
'v' : np.arange(1., 7.)})
# in IPython
In [34]: df
Out[34]:
c d v
0 a 1 1
1 b 1 2
2 c 1 3
3 a 2 4
4 b ... | {
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import pandas as ps
x = ps.DataFrame()
x.join(ps.DataFrame([3], index=[0], columns=['A']), how='outer')
x.join(ps.DataFrame([3], index=[0], columns=['A']))
Pandas 0.4.1
> > > import pandas ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/241 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/241/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/241/comments | https://api.github.com/repos/pandas-dev/pandas/issues/241/events | https://github.com/pandas-dev/pandas/pull/241 | 1,922,618 | MDExOlB1bGxSZXF1ZXN0NDA2NTUx | 241 | .describe() for series of objects | {
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I don't know if `series.dtype == object` is the best way to check for non-numeric data, but it appears to work.
At present, I've used fields 'count', 'unique', 'top' (i.e. most common) and 'freq' (i.e. count of the most common). These seem like obvious things for a summary.
I also noticed a problem doing `... | {
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The delimiter parameter is by default ",", hence there are no side effects with existing code.
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```
df.join(df2, on=['key'])
```
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In [1]: df = DataFrame(np.random.randint(0, 5, 10), dtype=float)
In [2]: df
Out[2]:
0
0 1
1 4
2 0
3 0
4 1
5 1
6 2
7 4
8 3
9 3
In [3]: df[0] = df[0].astype('i8')
In [4]: df
Out[4]:
0
0 1
1 4
2 0
3 0
4 1
5 1
6 2
7 4
8 3
9 3
In [5]: df.dtypes
Out[5]:
0 float64
Name: None, Length:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/255 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/255/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/255/comments | https://api.github.com/repos/pandas-dev/pandas/issues/255/events | https://github.com/pandas-dev/pandas/issues/255 | 1,994,533 | MDU6SXNzdWUxOTk0NTMz | 255 | Use Cython fused types | {
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https://api.github.com/repos/pandas-dev/pandas/issues/259 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/259/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/259/comments | https://api.github.com/repos/pandas-dev/pandas/issues/259/events | https://github.com/pandas-dev/pandas/pull/259 | 1,998,676 | MDExOlB1bGxSZXF1ZXN0NDE2Njgx | 259 | Counter class for Python < 2.7 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/262/events | https://github.com/pandas-dev/pandas/issues/262 | 2,005,758 | MDU6SXNzdWUyMDA1NzU4 | 262 | Index names lost when calling sort_index | {
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MultiIndex names get lost in the returned dataframe after sort_index is used.
Here is an example:
In [1]: import pandas
In [2]: pandas.__version__
Out[2]: '0.4.3'
In [3]: index = pandas.MultiIndex.from_tuples([('x','y','z'), ('a','b','c'), ('p','q','r')], names=['one','two','three'])
In [4]: df = pandas.DataFra... | {
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```
In [120]: pa.MultiIndex([],[])
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
C:\Users\dfortunov\<ipython-input-120-ca5695a01949> in <module>()
----> 1 pa.MultiIndex([... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/265 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/265/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/265/comments | https://api.github.com/repos/pandas-dev/pandas/issues/265/events | https://github.com/pandas-dev/pandas/issues/265 | 2,007,596 | MDU6SXNzdWUyMDA3NTk2 | 265 | Rename Series.histogram -> value_counts and sort descending | {
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```
Series([(1,2)])
```
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https://api.github.com/repos/pandas-dev/pandas/issues/272 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/272/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/272/comments | https://api.github.com/repos/pandas-dev/pandas/issues/272/events | https://github.com/pandas-dev/pandas/issues/272 | 2,018,610 | MDU6SXNzdWUyMDE4NjEw | 272 | Change Series.clip to work with numpy.clip | {
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```
I always find Series.clip() a bit confusing because the order of
arguments are reversed compared with Numpy's clip() method:
Series.clip(self, upper=None, lower=None)
numpy.clip(a, a_min, a_max, out=None)
Also, the following doesn't work well:
x = Series(data=np.random.uniform(size=10))
np.clip(x, ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/284 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/284/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/284/comments | https://api.github.com/repos/pandas-dev/pandas/issues/284/events | https://github.com/pandas-dev/pandas/issues/284 | 2,033,436 | MDU6SXNzdWUyMDMzNDM2 | 284 | Issues with mod_wsgi? | {
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I keep getting a 'module' object has no attribute 'core' when using with mod_wsgi. Please see http://stackoverflow.com/questions/7766400/pandas-django-mod-wsgi-virtualenv for the full error description.
Evan
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https://api.github.com/repos/pandas-dev/pandas/issues/285 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/285/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/285/comments | https://api.github.com/repos/pandas-dev/pandas/issues/285/events | https://github.com/pandas-dev/pandas/issues/285 | 2,037,201 | MDU6SXNzdWUyMDM3MjAx | 285 | Investigate erroneous int conversion in MultiIndex | {
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>> I was just using this feature and noticed a curious behaviour -- if you have dates in the ISO8601 YYYYMMDD format then importing from csv with a MultiIndex turns them into ints, whereas importing with a regular index parses them to dates.
>>
>> Looking at pandas/io/parsers.py (https://github.com/wesm/pandas/com... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/286 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/286/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/286/comments | https://api.github.com/repos/pandas-dev/pandas/issues/286/events | https://github.com/pandas-dev/pandas/pull/286 | 2,044,131 | MDExOlB1bGxSZXF1ZXN0NDMwODc3 | 286 | ENH: Adding argmin, argmax to Series and DataFrame. | {
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Pull request to let you know that i added argmin and argmax methods to Series and DataFrame.
I also included unit tests. It`s not that much work/code, but anyway an interesting enhancement i think.
some examples:
In [23]: s
Out[23]:
a 0.80015881463
b 0.983432892599
c 0.965122593504
d 0.8191082544... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/287 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/287/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/287/comments | https://api.github.com/repos/pandas-dev/pandas/issues/287/events | https://github.com/pandas-dev/pandas/pull/287 | 2,045,033 | MDExOlB1bGxSZXF1ZXN0NDMxMTg5 | 287 | ENH: Adding DataFrame.boxplot() | {
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Some syntactic sugar ;-)
Thinking about adding DataFrameGroupBy.boxplot(), now this call is transfered to DataFrame.boxplot().
But i would prefer the behavior to be different. When groupby is used i`m interested to see in a single plot ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/288 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/288/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/288/comments | https://api.github.com/repos/pandas-dev/pandas/issues/288/events | https://github.com/pandas-dev/pandas/issues/288 | 2,045,370 | MDU6SXNzdWUyMDQ1Mzcw | 288 | R-like range() function | {
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DataFrame's min() and max() functions operate only on single columns. The way to do it currently is
``` python
minimum, maximum = (min(df.min()), max(df.max())
```
It's not ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/290 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/290/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/290/comments | https://api.github.com/repos/pandas-dev/pandas/issues/290/events | https://github.com/pandas-dev/pandas/issues/290 | 2,052,174 | MDU6SXNzdWUyMDUyMTc0 | 290 | DataFrame.to_csv throws exception | {
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import cStringIO as StringIO
import pandas
f1 = StringIO.StringIO('a,1.0\nb,2.0')
df = pandas.DataFrame.from_csv(f1,header=None)
newdf = pandas.DataFrame({'t': df[df.columns[0]]})
newdf.to_csv('/tmp/test.csv')
The last line gives me an exception lik... | {
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Out[0]: '0.5.0'
In [94]: df = pandas.DataFrame({'A': [(1, 2)]})
---
AssertionError Traceback (most recent call last)
< .. snip ... >
The latter fails, however the following works fine:
In [95]: s = pandas.Series([(1, 2)]) ---> this was fixed in issue ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/294 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/294/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/294/comments | https://api.github.com/repos/pandas-dev/pandas/issues/294/events | https://github.com/pandas-dev/pandas/issues/294 | 2,057,371 | MDU6SXNzdWUyMDU3Mzcx | 294 | Series.__setitem__ ValueError | {
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Two examples below. First one has no problem, second on shows issue.
pandas 0.5.0
python 2.7
# example1 - ok
``` python
In [15]: s = pandas.Series([1, 2.2352345, (3, 4), 'hello world'], index=['a', 'b', 'c', 'd'])
In [16]: s
Out[16]:
a 1
b 2.235234... | {
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```
def dist(x):
return len(np.unique(x))
```
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