Can someone explain how these three methods of slicing are different?
I've seen the docs,
and I've seen these answers, but I still find myself unable to explain how the three are different. To me, they seem interchangeable in large part, because they are at the lower levels of slicing.
For example, say we want to get the first five rows of a
First, a recap:
locworks on labels in the index.
ilocworks on the positions in the index (so it only takes integers).
ixusually tries to behave like
locbut falls back to behaving like
ilocif the label is not in the index.
It's important to note some subtleties that can make
ix slightly tricky to use:
if the index is of integer type,
ix will only use label-based indexing and not fall back to position-based indexing. If the label is not in the index, an error is raised.
if the index does not contain only integers, then given an integer,
ix will immediately use position-based indexing rather than label-based indexing. If however
ix is given another type (e.g. a string), it can use label-based indexing.
To illustrate the differences between the three methods, consider the following Series:
>>> s = pd.Series(np.nan, index=[49,48,47,46,45, 1, 2, 3, 4, 5]) >>> s 49 NaN 48 NaN 47 NaN 46 NaN 45 NaN 1 NaN 2 NaN 3 NaN 4 NaN 5 NaN
s.iloc[:3] returns the first 3 rows (since it looks at the position) and
s.loc[:3] returns the first 8 rows (since it looks at the labels):
>>> s.iloc[:3] 49 NaN 48 NaN 47 NaN >>> s.loc[:3] 49 NaN 48 NaN 47 NaN 46 NaN 45 NaN 1 NaN 2 NaN 3 NaN >>> s.ix[:3] # the integer is in the index so s.ix[:3] works like loc 49 NaN 48 NaN 47 NaN 46 NaN 45 NaN 1 NaN 2 NaN 3 NaN
s.ix[:3] returns the same Series as
s.loc[:3] since it looks for the label first rather than going by position (and the index is of integer type).
What if we try with an integer label that isn't in the index (say
s.iloc[:6] returns the first 6 rows of the Series as expected. However,
s.loc[:6] raises a KeyError since
6 is not in the index.
>>> s.iloc[:6] 49 NaN 48 NaN 47 NaN 46 NaN 45 NaN 1 NaN >>> s.loc[:6] KeyError: 6 >>> s.ix[:6] KeyError: 6
As per the subtleties noted above,
s.ix[:6] now raises a KeyError because it tries to work like
loc but can't find a
6 in the index. Because our index is of integer type it doesn't fall back to behaving like
If, however, our index was of mixed type, given an integer
ix would behave like
iloc immediately instead of raising a KeyError:
>>> s2 = pd.Series(np.nan, index=['a','b','c','d','e', 1, 2, 3, 4, 5]) >>> s2.index.is_mixed() # index is mix of types True >>> s2.ix[:6] # behaves like iloc given integer a NaN b NaN c NaN d NaN e NaN 1 NaN
Keep in mind that
ix can still accept non-integers and behave like
>>> s2.ix[:'c'] # behaves like loc given non-integer a NaN b NaN c NaN
General advice: if you're only indexing using labels, or only indexing using integer positions, stick with
iloc to avoid unexpected results.
If however you have a DataFrame and you want to mix label and positional index types,
ix lets you do this:
>>> df = pd.DataFrame(np.arange(25).reshape(5,5), index=list('abcde'), columns=['x','y','z', 8, 9]) >>> df x y z 8 9 a 0 1 2 3 4 b 5 6 7 8 9 c 10 11 12 13 14 d 15 16 17 18 19 e 20 21 22 23 24
ix, we can slice the rows by label and the columns by position (note that for the columns,
ix default to position-based slicing since the label
4 is not a column name):
>>> df.ix[:'c', :4] x y z 8 a 0 1 2 3 b 5 6 7 8 c 10 11 12 13