What is the difference between using loc and using just square brackets to filter for columns in Pandas/Python?

Question

I've noticed three methods of selecting a column in a Pandas DataFrame:

First method of selecting a column using loc:

df_new = df.loc[:, 'col1']

Second method - seems simpler and faster:

df_new = df['col1']

Third method - most convenient:

df_new = df.col1

Is there a difference between these three methods? I don't think so, in which case I'd rather use the third method.

I'm mostly curious as to why there appear to be three methods for doing the same thing.

Or what about df.col1? All three of these are essentially equivalent for the very simple case of selecting a column. .loc will let you do much more than select a column. Possible duplicate of stackoverflow.com/questions/31593201/… — juanpa.arrivillaga
They do the same thing for simple slices. loc is more explicit, especially when your columns are numbers. — Gabriel A
Thanks @juanpa.arrivillaga. Good point re: df.col1, which is yet another method of column selection. I've actually looked at that other question before, several times. It's great for explaining loc and iloc. However, this question is about the other method: "df['col1']". I'm just confused as to why there are two (or three) equivalent ways of doing what appears to be the same thing. — Sean McCarthy
The big disadvantage of 3rd method is that it's ambiguous when your column name is identical to an existing pandas attribute or method. E.g. you name a column 'sum'. Then if you type df.sum, what happens? (spoiler alert, nothing useful, although df.sum() still works luckily) So 3rd way should be seen as a shortcut that is fine, but need to be careful with — JohnE
A decent explanation here stackoverflow.com/questions/38886080/… — Gaurav Taneja

ayhan ayhan · Accepted Answer · 2018-01-23T22:00:57

In the following situations, they behave the same:

Selecting a single column (df['A'] is the same as df.loc[:, 'A'] -> selects column A)
Selecting a list of columns (df[['A', 'B', 'C']] is the same as df.loc[:, ['A', 'B', 'C']] -> selects columns A, B and C)
Slicing by rows (df[1:3] is the same as df.iloc[1:3] -> selects rows 1 and 2. Note, however, if you slice rows with loc, instead of iloc, you'll get rows 1, 2 and 3 assuming you have a RangeIndex. See details here.)

However, [] does not work in the following situations:

You can select a single row with df.loc[row_label]
You can select a list of rows with df.loc[[row_label1, row_label2]]
You can slice columns with df.loc[:, 'A':'C']

These three cannot be done with []. More importantly, if your selection involves both rows and columns, then assignment becomes problematic.

df[1:3]['A'] = 5

This selects rows 1 and 2 then selects column 'A' of the returning object and assigns value 5 to it. The problem is, the returning object might be a copy so this may not change the actual DataFrame. This raises SettingWithCopyWarning. The correct way of making this assignment is:

df.loc[1:3, 'A'] = 5

With .loc, you are guaranteed to modify the original DataFrame. It also allows you to slice columns (df.loc[:, 'C':'F']), select a single row (df.loc[5]), and select a list of rows (df.loc[[1, 2, 5]]).

Also note that these two were not included in the API at the same time. .loc was added much later as a more powerful and explicit indexer. See unutbu's answer for more detail.

Note: Getting columns with [] vs . is a completely different topic. . is only there for convenience. It only allows accessing columns whose names are valid Python identifiers (i.e. they cannot contain spaces, they cannot be composed of numbers...). It cannot be used when the names conflict with Series/DataFrame methods. It also cannot be used for non-existing columns (i.e. the assignment df.a = 1 won't work if there is no column a). Other than that, . and [] are the same.

What is the difference between using loc and using just square brackets to filter for columns in Pandas/Python?

3 Answers