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Remove unwanted rows

Quiz by Support - BusinessPromoted .com

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10 questions
Show answers
  • Q1
    You have a dataset with missing email addresses. How to handle these rows for future communication?
    Fill the missing values with an average email address.
    Remove the rows with missing values in the "email address" column.
    Manually enter a generic email address for each missing value.
    Leave the rows with missing values as they are.
    30s
  • Q2
    How to efficiently remove duplicate entries for customers?
    Utilize a function to identify and remove duplicate rows based on specific criteria.
    Delete all rows with identical data, regardless of whether they are true duplicates.
    Manually sort and delete the duplicate rows.
    Use a filter to identify the duplicates and then delete them one by one.
    30s
  • Q3
    What's the most appropriate action for negative sales values (likely errors)?
    Change all negative sales values to zero.
    Investigate the negative sales values to determine if they are genuine or errors. Depending on the outcome, remove or adjust the values.
    Leave the negative sales values as they are, as they might represent returns.
    Automatically delete all rows with negative sales figures.
    30s
  • Q4
    When filtering unwanted rows, it's important to:
    Delete any rows that seem suspicious or irrelevant.
    Remove as many rows as possible to ensure a cleaner dataset.
    Establish clear criteria for what constitutes an unwanted row before filtering.
    Focus solely on removing rows with missing values.
    30s
  • Q5
    What can happen if you remove too many rows from your data?
    The analysis results might not accurately represent the entire population.
    The remaining data will be more visually appealing.
    You'll have a cleaner and more concise dataset for future use.
    The data analysis will be more efficient.
    30s
  • Q6
    When removing unwanted rows, it's beneficial to:
    Work with a copy of the data to avoid accidentally modifying the original.
    Hide unwanted rows instead of removing them completely.
    Permanently delete unwanted rows without the option to recover them.
    Delete rows directly from the original data source.
    30s
  • Q7
    Which is NOT a common method for removing unwanted rows?
    Using filters based on specific criteria.
    Creating calculated columns to identify unwanted rows.
    Adding rows with additional information about the unwanted data.
    Sorting the data and manually deleting rows.
    30s
  • Q8
    After removing unwanted rows, it's a good practice to:
    Review the data again to ensure all unwanted rows have been removed.
    Assume the remaining data is perfect and requires no further cleaning.
    Proceed with data analysis without considering the impact of removed rows.
    Fill any remaining gaps in the data with estimated values.
    30s
  • Q9
    Data cleaning techniques like removing unwanted rows are most important for:
    Simplifying the data entry process for future data collection.
    Reducing the overall size and complexity of the data.
    Ensuring the accuracy and reliability of your data analysis.
    Creating visually appealing charts and graphs.
    30s
  • Q10
    When working with large datasets, the most efficient technique for removing unwanted rows is likely:
    Deleting entire columns containing unwanted data.
    Filtering based on visual inspection of the data.
    Manually reviewing and deleting rows one by one.
    Utilizing functions and filters based on specific criteria.
    30s

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