Selecting a row of pandas series/dataframe by integer index

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

You can think DataFrame as a dict of Series. df[key] try to select the column index by key and returns a Series object.

However slicing inside of [] slices the rows, because it's a very common operation.

You can read the document for detail:

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

To index-based access to the pandas table, one can also consider numpy.as_array option to convert the table to Numpy array as

np_df = df.as_matrix()

and then

np_df[i] 

would work.

You can take a look at the source code .

DataFrame has a private function _slice() to slice the DataFrame, and it allows the parameter axis to determine which axis to slice. The __getitem__() for DataFrame doesn't set the axis while invoking _slice(). So the _slice() slice it by default axis 0.

You can take a simple experiment, that might help you:

print df._slice(slice(0, 2))
print df._slice(slice(0, 2), 0)
print df._slice(slice(0, 2), 1)