Post Test ICT 18 Batch 4

1. Which of the following cannot be done with Pandas package in Python?

• Data manipulation and processing
• Data cleansing
• Creating DataFrame object
• Create SQL query

2. What is the main reason that makes this DataFrame is uncleansed (“dirty”)?

• long text or string
• inconsistent entry
• missing values
• duplicate values

3. What are the correct types of data these columns should have in Pandas?

• object, float, int
• object, int, object
• bool, int, bool
• bool, float, bool

4. What can happen if we don’t clean our data before processing it further?

• We can get misleading insights from data
• Wrong visualizations
• Poor performance modeling (machine learning)
• All of the above answers

5. What function that can be used to convert type of data in Pandas (e.g. Float to Int)?

• .replace
• .split
• .astype
• .conversion

6. What is the other advantage of cleaning data by correcting data types for each column in Pandas?

• Reduce memory usage
• Decrease missing values percentage
• Drop duplication
• Nothing is right

7. To be able to process long strings in Pandas, we can separate the string into several substrings. What function is used to do this?

• .split
• .separate
• .groupby
• .replace

8. What does “ %B” stand for in Datetime directive code?

• Year, full version
• Month name, full version
• Month as number (01-12)
• Nothing is right

9. Which of the following cannot be done when imputing missing values in Pandas?

• Forward Filling
• Backward Filling
• Fill with Mean/Median
• Fill with np.nan

10. What should we do if we find duplicate values in our data?

• Change the value in one or more columns
• Keep the data as it is
• Drop the duplicated rows
• Nothing is right