Python Data Cleansing is the process of identifying and correcting inaccurate, incomplete, or inconsistent data to ensure quality and usability. Using Python libraries like pandas, data cleansing helps prepare real-world datasets for analysis, machine learning, or reporting.
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Which Python library is most commonly used for data cleansing?
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Question 3 of 15
3. Question
Which method in pandas checks for missing values?
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Question 4 of 15
4. Question
What does df.dropna() do in pandas?
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Question 5 of 15
5. Question
Which method replaces missing values with a default in pandas?
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Question 6 of 15
6. Question
What does df.duplicated() return?
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Question 7 of 15
7. Question
How do you remove duplicate rows from a DataFrame?
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Question 8 of 15
8. Question
Which method can be used to standardize text data (e.g., lowercase)?
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Question 9 of 15
9. Question
What does df[‘col’].astype(int) do?
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Question 10 of 15
10. Question
Which of the following handles inconsistent formatting best?
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Question 11 of 15
11. Question
How do you detect outliers using pandas?
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Question 12 of 15
12. Question
What does inplace=True do in pandas operations?
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Question 13 of 15
13. Question
What is the best way to handle mixed-type columns?
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Question 14 of 15
14. Question
What kind of values should be removed during data cleansing?
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Question 15 of 15
15. Question
Which method fills missing values using forward fill?
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Summary
Python data cleansing is a crucial step in data preprocessing that involves fixing missing, inconsistent, or duplicated values. Using tools like pandas, developers ensure clean, structured, and reliable data for downstream tasks like analysis or modeling.
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