AI Quiz 2: NumPy & Pandas Data Cleaning

AI Quiz 2: 15 NumPy & Pandas Data Cleaning Questions

This quiz tests your knowledge of data cleaning with NumPy and Pandas, focusing on handling missing values, vectorized operations, and DataFrame manipulation. You'll be tested on data preprocessing techniques essential for data science.

Perfect for Python developers looking to improve their data science skills or assess their knowledge of data cleaning techniques.

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1. Why do we need to clean data?

A. To make the dataset bigger B. To make better decisions and get accurate results C. To make the data look pretty D. To confuse the computer

2. Which of the following is an example of wrong data?

A. Missing a student's score B. Recording a person's age as 200 years old C. Entering a person's name twice D. Dates in different formats

3. Duplicate rows in a DataFrame can cause misleading results.

True False

4. Fill in the blank: Replace missing values (NaN) with 0 in a NumPy array.

import numpy as np

arr = np.array([1, 2, np.nan, 4])

arr[np.isnan(arr)] = ____

A. None B. 0 C. np.nan D. "missing"

5. What does vectorized math mean in NumPy? Give one example.

A. Performing operations on individual elements one by one B. Performing operations on entire arrays at once, like arr + 5 C. Converting arrays to vectors D. Creating mathematical graphs

6. What does dtype=str do when creating a NumPy array?

A. Forces all elements to be stored as strings B. Converts all strings to numbers C. Removes missing values automatically D. Makes the array faster

7. What will the following code output?

import numpy as np

arr = np.array([1, 2, 3, 4])

print(arr + 5)

A. [1, 2, 3, 4, 5] B. [6, 7, 8, 9] C. [5, 5, 5, 5] D. Error

8. What does np.empty_like(original) create?

A. A copy of the original array with the same data B. A new array with the same shape and dtype, but uninitialized C. A DataFrame D. A fully empty DataFrame

9. What is a DataFrame in Pandas?

A. A Python list B. A NumPy array C. A table with rows and columns D. A type of string

10. Fill in the missing code: Create a DataFrame from a dictionary.

import pandas as pd

data = {

    "Name": ["Alice", "Bob"],

    "Age": [12, 15]

}

df = pd.____(data)

A. DataFrame B. create C. dict_to_df D. from_dict

11. Explain what this line of code does: df["Score"] = df["Score"].fillna(df["Score"].mean())

A. Calculates the mean of all scores B. Replaces missing values in the Score column with the average score C. Removes all missing values D. Creates a new Score column

12. How can we replace missing author names in a DataFrame with "Unknown"?

A. df['Author'].dropna() B. df['Author'].fillna("Unknown", inplace=True) C. df['Author'].replace("Unknown") D. df['Author'] = "Unknown"

13. Pandas automatically ignores NaN values when calculating the mean.

True False

14. Which of the following best describes the advantage of Pandas over NumPy for data cleaning?

A. Pandas can automatically handle tables with mixed data types. B. Pandas is always faster than NumPy. C. Pandas only works with numbers. D. Pandas doesn't support missing values.

15. You scraped this dataset:

Name	Score
Alice	95
Bob	None
Charlie	88
David	None

Write Pandas code to replace missing scores with the average score.

A. df['Score'].fillna(df['Score'].mean()) B. df['Score'].replace(None, df['Score'].average()) C. df['Score'].dropna() D. df['Score'] = df['Score'].mean()

AI Quiz 2: NumPy & Pandas Data Cleaning

AI Quiz 2: 15 NumPy & Pandas Data Cleaning Questions

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