In pandas 0.12 the order you indexed a DataFrame didn't matter, which I think is the correct behaviour:
In [6]: df = pd.DataFrame({'A': 5*[np.zeros(3)], 'B':5*[np.ones(3)]})
In [7]: df
Out[7]:
A B
0 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
1 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
2 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
3 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
4 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
In [8]: df['A'].iloc[2]
Out[8]: array([ 0., 0., 0.])
In [9]: df.iloc[2]['A']
Out[9]: array([ 0., 0., 0.])
In [10]: pd.__version__
Out[10]: '0.12.0'
In [11]: assert type(df.ix[2, 'A']) == type(df['A'].iloc[2]) == type(df.iloc[2]['A'])
In [12]:
In pandas 0.13 if you index in a different order you can get a different type out which can be problematic for code expecting an array, especially because of the difference between array indexing and label indexing.
In [1]: df = pd.DataFrame({'A': 5*[np.zeros(3)], 'B':5*[np.ones(3)]})
In [2]: df
Out[2]:
A B
0 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
1 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
2 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
3 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
4 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
5 rows × 2 columns
In [3]: df['A'].iloc[2]
Out[3]: array([ 0., 0., 0.])
In [4]: df.iloc[2]['A']
Out[4]:
A 0
A 0
A 0
Name: 2, dtype: float64
In [5]: pd.__version__
Out[5]: '0.13.1'
In [6]: assert type(df.ix[2, 'A']) == type(df['A'].iloc[2]) == type(df.iloc[2]['A'])
Traceback (most recent call last):
File "<ipython-input-11-946e15564ee1>", line 1, in <module>
assert type(df.ix[2, 'A']) == type(df['A'].iloc[2]) == type(df.iloc[2]['A'])
AssertionError
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