When it comes to selecting rows and columns of a pandas DataFrame, .loc and .at are two commonly used functions.
Here is the subtle difference between the two functions:
- .loc can take multiple rows and columns as input arguments
- .at can only take one row and one column as input arguments
The following examples show how to use each function in practice with the following pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'], 'points': [18, 22, 19, 14, 14, 11, 20, 28], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #view DataFrame print(df) team points assists rebounds 0 A 18 5 11 1 B 22 7 8 2 C 19 7 10 3 D 14 9 6 4 E 14 12 6 5 F 11 9 5 6 G 20 9 9 7 H 28 4 12
Example 1: How to Use loc in Pandas
The following code shows how to use .loc to access the value in the DataFrame located at index position 0 of the points column:
#select value located at index position 0 of the points column
df.loc[0, 'points']
18
This returns a value of 18.
And the following code shows how to use .loc to access rows between index values 0 and 4 along with the columns points and assists:
#select rows between index values 0 and 4 and columns 'points' and 'assists'
df.loc[0:4, ['points', 'assists']]
points assists
0 18 5
1 22 7
2 19 7
3 14 9
4 14 12
Whether we’d like to access one single value or a group of rows and columns, the .loc function can do both.
Example 2: How to Use at in Pandas
The following code shows how to use .at to access the value in the DataFrame located at index position 0 of the points column:
#select value located at index position 0 of the points column
df.at[0, 'points']
18
This returns a value of 18.
However, suppose we try to use at to access rows between index values 0 and 4 along with the columns points and assists:
#try to select rows between index values 0 and 4 and columns 'points' and 'assists'
df.at[0:4, ['points', 'assists']]
TypeError: unhashable type: 'list'
We receive an error because the at function is unable to take multiple rows or multiple columns as input arguments.
Conclusion
When you’d like to access just one value in a pandas DataFrame, both the loc and at functions will work fine.
However, when you’d like to access a group of rows and columns, only the loc function is able to do so.
Related: Pandas loc vs. iloc: What’s the Difference?
Additional Resources
The following tutorials explain how to perform other common operations in pandas:
How to Select Rows by Multiple Conditions Using Pandas loc
How to Select Rows Based on Column Values in Pandas
How to Select Rows by Index in Pandas
