Unmask Your Inner Data Cleaning Hero! (Final)

H. Different formats representing same value (i.e. NULL vs Blank)

Question no. 5

Q5. You are given the following dataset (look at the monitor) on eggs. How many possible data errors can you spot? (select all that apply)

B.	Group them manually into buckets, even if it takes forever. 

A.	Leave it. Variety is the spice of life, right?

C.	Keep one version, replace the rest with “Others” and call it a day. 

D.	Standardise the spelling so they are all the same. 

Q4. You discover that your “City” column has entries like [New York], [newyork], [NYC] and [Nw York]. How do you handle it? 

D.	Rename all columns to [Col 1], [Col 2], etc., and merge away. 

B.	Copy-paste the data under each other and hope for the best. 

C.	Check that join columns match in name and format first—because mismatched data is how nightmares begin. 

A.	Do a trial run on a small slice of data before the real merge—because surprises belong at birthday parties, not in joins. 

Q3. You need to merge two datasets. What do you do? 

D.	Close the file immediately and pretend your Wi-Fi stopped working. 

A.	Dive in and start making charts. The patterns will reveal themselves eventually. 

C.	Look around for a README or data dictionary like it is buried treasure—because you refuse to be cursed by mystery data. 

B.	Scroll through the data and look for your name. 

Q1. You need to analyse data from a spreadsheet titled "MDDI_all_final_v4.xlsx" which has some column names. Your first move?

B.	Delete every row with blanks—sure, you will lose some data, but at least it is clean…ish. 

A.	Investigate why it is missing—because mysteries belong in novels, not spreadsheets. 

D.	Pretend you did not see anything. It is basically missing right? 

C.	Fill the gaps with zeros, random numbers, or what you ate yesterday for lunch. Problem solved, right? 

Q2. Uh-oh, you find missing values in your data. What is your move? 