Selecting a row of pandas series/dataframe by integer index

Question

I am curious as to why df[2] is not supported, while df.ix[2] and df[2:3] both work.

In [26]: df.ix[2]
Out[26]: 
A    1.027680
B    1.514210
C   -1.466963
D   -0.162339
Name: 2000-01-03 00:00:00

In [27]: df[2:3]
Out[27]: 
                  A        B         C         D
2000-01-03  1.02768  1.51421 -1.466963 -0.162339

I would expect df[2] to work the same way as df[2:3] to be consistent with Python indexing convention. Is there a design reason for not supporting indexing row by single integer?

Answer 1

echoing @HYRY, see the new docs in 0.11

http://pandas.pydata.org/pandas-docs/stable/indexing.html

Here we have new operators, .iloc to explicity support only integer indexing, and .loc to explicity support only label indexing

e.g. imagine this scenario

In [1]: df = pd.DataFrame(np.random.rand(5,2),index=range(0,10,2),columns=list('AB'))

In [2]: df
Out[2]: 
          A         B
0  1.068932 -0.794307
2 -0.470056  1.192211
4 -0.284561  0.756029
6  1.037563 -0.267820
8 -0.538478 -0.800654

In [5]: df.iloc[[2]]
Out[5]: 
          A         B
4 -0.284561  0.756029

In [6]: df.loc[[2]]
Out[6]: 
          A         B
2 -0.470056  1.192211

[] slices the rows (by label location) only