AZhao - 1 year ago 216

Python Question

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`

`df.loc[:5]`

df.ix[:5]

df.iloc[:5]

Can someone present three cases where the distinction in uses are clearer?

Answer Source

First, a recap:

`loc`

works on*labels*in the index.`iloc`

works on the*positions*in the index (so it only takes integers).`ix`

usually tries to behave like`loc`

but falls back to behaving like`iloc`

if 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
```

Then `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
```

Notice `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 `6`

)?

Here `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 `iloc`

.

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 `loc`

:

```
>>> 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 `loc`

or `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
```

Using `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
```