Quiz: Mastering Handling Missing or Inconsistent Data in Datasets — Questions & Answers

This quiz contains 10 questions. Below is a complete reference of all questions, answer choices, and correct answers. You can use this section to review after taking the interactive quiz above.

1. Question 1: Imputation Basics

   Which method replaces missing numeric data with the most common value in a column?

   • Mode imputation
   • Median impuation
   • Mean imputation
   • Forward fill
   • Drop row
   Show correct answer

   Correct answer: Mode imputation

2. Question 2: Handling Categorical Missing Values

   If you have a missing value in a categorical feature, what is a commonly used placeholder to represent missing data?

   • 'Unknown'
   • 'Mean'
   • '333'
   • 'Nullify'
   • 'Random'
   Show correct answer

   Correct answer: 'Unknown'

3. Question 3: Recognizing Inconsistent Data

   You see the listed country values: 'USA', 'U.S.A.', 'United States', and 'usa'. What would best describe this scenario?

   • Inconsistent data formatting
   • Complete missing data
   • Properly encoded dataset
   • Noisy numeric data
   • Type conversion error
   Show correct answer

   Correct answer: Inconsistent data formatting

4. Question 4: Detecting Missing Data with Pandas

   Which Pandas function would you use to detect missing values in a DataFrame?

   • isnull()
   • missing()
   • fillna()
   • imputate()
   • dropdf()
   Show correct answer

   Correct answer: isnull()

5. Question 5: Dropping Rows or Columns

   In Pandas, which function is used to remove all rows with at least one missing value?

   • dropna()
   • removerows()
   • trmna()
   • isnotna()
   • clear()
   Show correct answer

   Correct answer: dropna()

6. Question 6: Forward Fill Usage

   What does the 'ffill' method do when handling missing data in a time series?

   • It fills missing values with the previous non-null value.
   • It replaces missing values with zeros.
   • It drops all remaining nulls at the end of the data.
   • It duplicates the next valid entry.
   • It fills missing values with random values.
   Show correct answer

   Correct answer: It fills missing values with the previous non-null value.

7. Question 7: Numeric Data Imputation

   If you want to minimize the effect of outliers when filling missing numeric values, which method should you use?

   • Median imputation
   • Mean imputation
   • Mode imputation
   • Random sample imputation
   • Zero imputation
   Show correct answer

   Correct answer: Median imputation

8. Question 8: Data Consistency

   Given the dataset with 'M', 'F', and 'Femail' as possible entries for gender, what is the correct way to make the data consistent?

   • Standardize entries like 'Femail' to 'F'
   • Leave all as is
   • Replace all 'M' and 'F' with 'Unknown'
   • Remove all rows with 'Femail'
   • Ignore inconsistencies
   Show correct answer

   Correct answer: Standardize entries like 'Femail' to 'F'

9. Question 9: Advanced Imputation

   You decide to use KNN imputation on missing values. What does KNN imputation primarily rely on?

   • Similarity to nearby data points
   • Random guessing
   • Filling with overall mean
   • Dropping rows with nulls
   • Reversing the data columns
   Show correct answer

   Correct answer: Similarity to nearby data points

10. Question 10: Identifying Incomplete Rows

    Which code snippet identifies rows in a Pandas DataFrame 'df' that contain any missing values?

    • df[df.isnull().any(axis=1)]
    • df[df.notnull().all(axis=0)]
    • df.fillna(df.mean())
    • df[df.empty()]
    • df.remove(nan=True)
    Show correct answer

    Correct answer: df[df.isnull().any(axis=1)]