Pandas Indexing Basics

What’s the difference between .loc, .iloc, and .ix?

Data access attributes

The primary means of doing Series/DataFrame selection (read: data access) are the attributes loc, iloc, and ix. All of these provide dictionary-style access to the items in a Series or rows in a DataFrame. It’s not easy to deduce from their names how these attributes differ from each other though, so let’s take a closer look at each.


According to the pandas documentation this is a “Purely label-location based indexer for selection by label”. Basically, use loc for indexes with meaningul label values. In this example our index labels are strings:

In [1]: import numpy as np

In [2]: import pandas as pd

In [3]: s = pd.Series([1, 2, 3], pd.Index(["whitefish", "perch", "trout"]))

In [4]: s.loc["perch"]
Out[4]: 2

A more common scenario however is to import data with a date column that we want to use as the index. Below we’ll manually construct a Series whose index is a bunch of strings that we want to parse as dates. So we’ll first use the pandas.to_datetime function to convert these to dates in a fast, vectorized way, and then we can use the loc attribute to look up items in the series by date:

In [5]: import datetime

In [6]: s = pd.Series([1, 2, 3], pd.Index(["2016-01-01", "2016-02-01", "2016-03-01"]))

In [7]: s.index
Out[7]: Index(['2016-01-01', '2016-02-01', '2016-03-01'], dtype='object')

In [8]: s.index = pd.to_datetime(s.index)

In [9]: s.loc[datetime.datetime(2016, 3, 1)]
Out[9]: 3

You can even do slices!

In [10]: s.loc[datetime.datetime(2016, 2, 1):]
2016-02-01    2
2016-03-01    3
dtype: int64

Trying to access s.loc[0] throws a TypeError though, because there is no element in the index whose value is 0. If we want to use position-based lookups, we need to use a different attribute: iloc:


iloc performs integer-based access to the index, purely by position: that is, if you think of the of the Series or DataFrame as a list of values or rows, iloc does normal 0-based list access.

In [11]: s
2016-01-01    1
2016-02-01    2
2016-03-01    3
dtype: int64

In [12]: s.iloc[0]
Out[12]: 1

In [13]: df = pd.DataFrame({"Superior": {"whitefish": 4, "perch": 0, "trout": 2},
                            "Erie":     {"whitefish": 0, "perch": 3, "trout": 1}})

In [14]: df
           Erie  Superior
perch         3         0
trout         1         2
whitefish     0         4

In [15]: df.iloc[2]
Erie        0
Superior    4
Name: whitefish, dtype: int64

iloc of course only accepts integers, although it’ll happily accept negatives or slices:

In [18]: df.iloc[-2]
Erie        1
Superior    2
Name: trout, dtype: int64

In [19]: df.iloc[:-1]
       Erie  Superior
perch     3         0
trout     1         2


ix supports both label (IE, index label value) and positional access: it looks first for a label and falls back to positional access if no label with the given value is found. It is a handy shortcut, especially if you want to do things like access your index by label but your columns by position:

In [20]: df = pd.DataFrame(np.random.randint(10, size=12).reshape(3,4),
                  index=['perch', 'trout', 'whitefish'],
                  columns=['male', 'female', 'adult', 'juvenile'])
           male  female  adult  juvenile
perch         2       3      6         2
trout         0       3      1         4
whitefish     7       3      1         0

In [21]: df.ix['perch', 2:]
adult       9
juvenile    8
Name: perch, dtype: int32

ix is safe to use for all cases except when your series or dataframe has a “sparse” integer index – IE an integer index where values may be missing. In that case ix only does label-based selection and if the index does not contain the desired value a KeyError will be raised. In this case, to avoid ambiguity, either use .loc (if you explicitly want a KeyError in this case) or .iloc (if you don’t).

Dictionary-style access

Series and DataFrame objects also support standard python dictionary style access, which works exactly the same as ix:

In [22]: s
1    0
2    1
3    2
4    3
5    4
dtype: int32

In [23]: s[1]
Out[23]: 0

In [24]: s[0]


Here are the basic rules of thumb for data selection in pandas: