Basic mistakes encounter during removing duplicate values

In data analysis, handling duplicate values is a crucial responsibility, and newbies frequently commit certain typical mistakes. Here are some examples of errors made while using Pandas to handle duplicate values, along with some sample code.

1. Not identifying duplicate values:

Learners might not be able to spot duplicate values in the dataset, which could result in inaccurate analysis or biased findings.

In this case, failing to spot the duplicate value “foo” in column “B” could result in inaccurate analysis and deceptive findings.

2. Incorrectly removing duplicates without considering relevant columns:

Learners could wrongly eliminate duplicates without considering pertinent columns, which could result in the accidental loss of important data.

In this example, the second occurrence of “foo” is accidentally lost when duplicates are removed without taking the relevant column “B” into account.

3. Not handling duplicates in a specific column:

Duplicates in a particular column may not be handled properly by beginners, which might result in biased analysis or inaccurate results.

Due to the fact that only the duplicate occurrence is chosen in this example, failing to handle duplicates in column “B” can lead to biased analysis.