How are iloc, ix and loc different?
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
DataFrame. How is it that all three of these work?
df.loc[:5] df.ix[:5] df.iloc[:5]
Can someone present three cases where the distinction in uses are clearer?
Note: in pandas version 0.20.0 and above,
ix is deprecated and the use of
iloc is encouraged instead. I have left the parts of this answer that describe
ix intact as a reference for users of earlier versions of pandas. Examples have been added below showing alternatives to
First, here's a recap of the three methods:
locgets rows (or columns) with particular labels from the index.
ilocgets rows (or columns) at particular positions in the index (so it only takes integers).
ixusually tries to behave like
locbut falls back to behaving like
ilocif a label is not present 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,
ixwill 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,
ixwill immediately use position-based indexing rather than label-based indexing. If however
ixis 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
We'll look at slicing with the integer value
In this case,
s.iloc[:3] returns us the first 3 rows (since it treats 3 as a position) and
s.loc[:3] returns us the first 8 rows (since it treats 3 as a label):
>>> s.iloc[:3] # slice the first three rows 49 NaN 48 NaN 47 NaN >>> s.loc[:3] # slice up to and including label 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 working on the position (and the index for
s 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
ix 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 different types True >>> s2.ix[:6] # now 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
As general advice, if you're only indexing using labels, or only indexing using integer positions, stick with
iloc to avoid unexpected results - try not use
Combining position-based and label-based indexing
Sometimes given a DataFrame, you will want to mix label and positional indexing methods for the rows and columns.
For example, consider the following DataFrame. How best to slice the rows up to and including 'c' and take the first four columns?
>>> df = pd.DataFrame(np.nan, index=list('abcde'), columns=['x','y','z', 8, 9]) >>> df x y z 8 9 a NaN NaN NaN NaN NaN b NaN NaN NaN NaN NaN c NaN NaN NaN NaN NaN d NaN NaN NaN NaN NaN e NaN NaN NaN NaN NaN
In earlier versions of pandas (before 0.20.0)
ix lets you do this quite neatly - we can slice the rows by label and the columns by position (note that for the columns,
ix will default to position-based slicing since
4 is not a column name):
>>> df.ix[:'c', :4] x y z 8 a NaN NaN NaN NaN b NaN NaN NaN NaN c NaN NaN NaN NaN
In later versions of pandas, we can achieve this result using
iloc and the help of another method:
>>> df.iloc[:df.index.get_loc('c') + 1, :4] x y z 8 a NaN NaN NaN NaN b NaN NaN NaN NaN c NaN NaN NaN NaN
get_loc() is an index method meaning "get the position of the label in this index". Note that since slicing with
iloc is exclusive of its endpoint, we must add 1 to this value if we want row 'c' as well.
There are further examples in pandas' documentation here.