Documentation for .iloc is misleadingly incomplete

[cswarth]

The documentation for .loc at http://pandas.pydata.org/pandas-docs/version/0.15.1/indexing.html#different-choices-for-indexing should mention that .iloc also takes a boolean array instead of insisting that it is "strictly integer position based".

.iloc is strictly integer position based (from 0 to length-1 of the axis), will raise IndexError if an indexer is requested and it is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with python/numpy slice semantics). Allowed inputs are:

An integer e.g. 5
A list or array of integers [4, 3, 0]
A slice object with ints 1:7

Unlike .loc immediately above, no mention is made of "A boolean array"
This is incorrect as later in the same document an example uses .iloc and isin to retrieve elements from a Series. isin returns a list of booleans (well, strictly it is a numpy.ndarray, but converting to a list works as well)

[jreback]

yep that was an oversight iloc can take a boolean array

want to do a pr to add a doc example and update?

[cswarth]

Sure, I'll give it a shot.

[TomAugspurger]

Is .iloc ever necessary to index with a boolean? In the example @cswarth mentions from the isin docs, s_mi.iloc[s_mi.index.isin(['a', 'c', 'e'], level=1)] should be the same as s_mi[s_mi.index.isin(['a', 'c', 'e'], level=1)], i.e. without the .iloc, I think.

[jorisvandenbossche]

Yes, for the example here, the iloc is not needed / not the logical choice of indexer. So that example could be adapted.

But in general, I think the use case where this can come in handy is if you want to index multiple axes with mixed positional/boolean indexing. Eg if you want to combine iloc on the columns with boolean on the rows:

df.iloc[df.index.isin(...), 0]

[cswarth]

While adding a brief mention of boolean indexers to .iloc I realized I didn't really know how they worked in all cases. For example, what happens when a boolean indexer has a different shape than the axis it is indexing? What happens if the index is too short? Or too long?

Numpy is explicit, "Boolean arrays must be of the same shape as the initial dimensions of the array being indexed", but the examples below suggests that is not true. I actually don't know what the hell numpy is doing in this case - it looks totally unexpected.

pandas.Series behaves rationally, in my opinion. But is this silent 'leftmost-favored' behavior explicitly documented? If not, should it be?

x=np.arange(100,1,-1)
print(x[[True, False,  True,  True, True]])
[ 99 100  99  99  99]
-c:2: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index

from pandas import *
s_mi = Series(np.arange(100,1,-1))
print(s_mi.iloc[[True, False,  True,  True, True]])
0    100
2     98
3     97
4     96
dtype: int64

[cswarth]

NVM the numpy example, I realize my example was bogus b/c it passed an array as the index. This example works as documented.

import numpy as np
x=np.arange(100,1,-1)
print(x[True, False,  True,  True, True])

IndexError: too many indices for array

[immerrr]

On Tue, Dec 2, 2014 at 9:11 PM, cswarth notifications@github.com wrote:

Numpy is explicit
http://docs.scipy.org/doc/numpy/user/basics.indexing.html#boolean-or-mask-index-arrays,
"Boolean arrays must be of the same shape as the initial dimensions of the
array being indexed", but the examples below suggests that is not true. I
actually don't know what the hell numpy is doing in this case - it looks
totally unexpected.

Numpy only allows ndarrays of bool as masks, your snippet used a list of
bool. This is a common source of confusion, e.g.
http://stackoverflow.com/questions/17779468/numpy-indexing-with-a-one-dimensional-boolean-array
.

[jorisvandenbossche]

I rather find the pandas behaviour a bit strange here. Shouldn't the array not have the same length as the series with boolean indexing?

[jreback]

@jorisvandenbossche what do you find strange? the boolean array must be equal to the length of the axis