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.
Answers:
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.
loc is specially useful when the index is not numeric (e.g. a DatetimeIndex) because you can get rows with particular labels from the index:
df.loc['2010-05-04 07:00:00']
df.loc['2010-1-1 0:00:00':'2010-12-31 23:59:59 ','Price']
However [] is intended to get columns with particular names:
df['Price']
With [] you can also filter rows, but it is more elaborated:
df[df['Date'] < datetime.datetime(2010,1,1,7,0,0)]['Price']
There seems to be a difference between df.loc[] and df[] when you create dataframe with multiple columns.
You can refer to this question:
Is there a nice way to generate multiple columns using .loc?
Here, you can’t generate multiple columns using df.loc[:,['name1','name2']] but you can do by just using double bracket df[['name1','name2']]. (I wonder why they behave differently.)
If you’re confused which of these approaches is (at least) the recommended one for your use-case, take a look at this brief instructions from pandas tutorial:
-
When selecting subsets of data, square brackets [] are used.
-
Inside these brackets, you can use a single column/row label, a list
of column/row labels, a slice of labels, a conditional expression or
a colon.
-
Select specific rows and/or columns using loc when using the row and
column names
-
Select specific rows and/or columns using iloc when using the
positions in the table
-
You can assign new values to a selection based on loc/iloc.
I highlighted some of the points to make their use-case differences even more clear.
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.
In the following situations, they behave the same:
- Selecting a single column (
df['A']is the same asdf.loc[:, 'A']-> selects column A) - Selecting a list of columns (
df[['A', 'B', 'C']]is the same asdf.loc[:, ['A', 'B', 'C']]-> selects columns A, B and C) - Slicing by rows (
df[1:3]is the same asdf.iloc[1:3]-> selects rows 1 and 2. Note, however, if you slice rows withloc, instead ofiloc, 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.
loc is specially useful when the index is not numeric (e.g. a DatetimeIndex) because you can get rows with particular labels from the index:
df.loc['2010-05-04 07:00:00']
df.loc['2010-1-1 0:00:00':'2010-12-31 23:59:59 ','Price']
However [] is intended to get columns with particular names:
df['Price']
With [] you can also filter rows, but it is more elaborated:
df[df['Date'] < datetime.datetime(2010,1,1,7,0,0)]['Price']
There seems to be a difference between df.loc[] and df[] when you create dataframe with multiple columns.
You can refer to this question:
Is there a nice way to generate multiple columns using .loc?
Here, you can’t generate multiple columns using df.loc[:,['name1','name2']] but you can do by just using double bracket df[['name1','name2']]. (I wonder why they behave differently.)
If you’re confused which of these approaches is (at least) the recommended one for your use-case, take a look at this brief instructions from pandas tutorial:
-
When selecting subsets of data, square brackets
[]are used. -
Inside these brackets, you can use a single column/row label, a list
of column/row labels, a slice of labels, a conditional expression or
a colon. -
Select specific rows and/or columns using
locwhen using the row and
column names -
Select specific rows and/or columns using
ilocwhen using the
positions in the table -
You can assign new values to a selection based on
loc/iloc.
I highlighted some of the points to make their use-case differences even more clear.