Common mistakes to avoid when using loc function in Python

There are some typical mistakes people frequently make when implementing Pandas’ loc function. The following examples and code samples demonstrate these issues.

1. Using integer-based indexing instead of label-based indexing:

The loc function is primarily used for label-based indexing, which means it expects labels for rows and columns instead of integer indices. Using integer indices with loc can lead to errors. Here’s an example:

2. Modifying data directly using `loc`:

In the below example, the code attempts to modify the Age column for the row where the name is .Bob using .loc. However, directly modifying data with .loc in this manner can lead to a SettingWithCopyWarning or unexpected behavior. To correct this mistake and avoid the warning, you should use .loc with a separate assignment statement to modify the data.

3. Specifying incorrect row and column labels:

In the below example, the code attempts to use .loc with row label 5 and column label FirstName to select a specific value. However, .loc expects the labels to match the index and column labels of the DataFrame. To correct this mistake, you should use the appropriate row and column labels based on the DataFrame’s index and columns.