Purely integer-location based indexing for selection by position.
.iloc is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.
Allowed inputs are:
- An integer, e.g. 5.
- A list or array of integers, e.g. [4, 3, 0].
- A slice object with ints, e.g. 1:7.
- A boolean array.
- A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference to the calling object, but would like to base your selection on some value.
.iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).
I don't remember why df.iloc is preferred over directly getitem'ing df ?
- Because `3` could be a key or an integer-location; to avoid ambiguity
- Because changing between df and df.loc and df[3:5] and df.iloc[3:5] is unnecessary cognitive burden: it's easier to determine that the code is intentionally accessing by integer-location from '.iloc' than from ':' (indicating slice notation)
Would this be the only way to access only item 4 from an odict of length greater than 4 with slice notation for dict views generated from selection by integer-location?
What does this do? Confusing to a beginner: