ENH/API: GH2578, allow ix and friends to partially set when the key is not contained in the object

[jreback]

closes #2578

On a series, this is kind of like an inplace append operation (note the dtype change - on purpose)

In [6]: s = Series([1,2,3])

In [7]: s[4] = 5.
In [3]: s
Out[3]: 
0    1
1    2
2    3
4    5
dtype: float64

This is now allowed: (previously would KeyError), you first have to create the column C, then set it

In [1]: df = DataFrame(np.arange(6).reshape(3,2),columns=['A','B'])

In [3]: df
Out[3]: 
   A  B
0  0  1
1  2  3
2  4  5

In [4]: df.ix[:,'C'] = df.ix[:,'A']

In [5]: df
Out[5]: 
   A  B  C
0  0  1  0
1  2  3  2
2  4  5  4

This would previously raise an IndexError

In [4]: df.loc[3] = 5

In [5]: df
Out[5]: 
   A  B  C
0  0  1  0
1  2  3  2
2  4  5  4
3  5  5  5

and on Panel:

In [6]: p = Panel(np.arange(16).reshape(2,4,2),
   items=['Item1','Item2'],
   major_axis=pd.date_range('2001/1/12',periods=4),
   minor_axis=['A','B'],dtype='float64')

In [7]: p
Out[7]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 2 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2001-01-12 00:00:00 to 2001-01-15 00:00:00
Minor_axis axis: A to B

In [10]: p.loc[:,:,'C'] = Series([30,32],index=p_orig.items)

In [11]: p
Out[11]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 3 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2001-01-12 00:00:00 to 2001-01-15 00:00:00
Minor_axis axis: A to C

In [13]: p.loc[:,:,'C']
Out[13]: 
            Item1  Item2
2001-01-12     30     32
2001-01-13     30     32
2001-01-14     30     32
2001-01-15     30     32

[jreback]

@jtratner @hayd @cpcloud @wesm thoughts when you have a chance

[jtratner]

I guess I wonder what should happen in these kinds of cases cases - any magic?

df.loc[5] = 3
s[6] = 5

[hayd]

looks good.

[jreback]

@jtratner

these are straightforward

In [1]: s = Series([1,2,3])

In [2]: s[6] = 5

In [3]: s
Out[3]: 
0    1
1    2
2    3
6    5
dtype: int64

In [4]: df = DataFrame(np.arange(6).reshape(3,2),columns=['A','B'])

In [5]: df.loc[5] = 3

In [6]: df
Out[6]: 
   A  B
0  0  1
1  2  3
2  4  5
5  3  3

I have disabled both iloc/iat from being able to enlarge, as would require filling in the intermediary
index with something; which potentially is ambiguous.

In [7]: df.iloc[5] = 3
IndexError: iloc cannot enlarge its target object

[jreback]

@wesm take a look? this is non-trivial but you were already going it bith set_value in any event, this just generalizes (and with series refactor this is able to be done in-place, which makes pretty natural)

[jreback]

any comments before merging?

[jtratner]

@jreback is this supposed to return None?

[jreback]

no...they will just raise if needed