Hello friends! Take this Python Data Analysis Quiz to quickly test your skills. This beginner-friendly quiz covers 20 key questions on Exploratory Data Analysis (EDA), data munging, Pandas, NumPy, and more.
Python is widely used for data analysis, and learning it opens up great career opportunities. Whether you’re just starting or refining your skills, this quiz will help you assess your knowledge and improve your understanding of Python for data analysis. Let’s begin!
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Python Data Analysis & ML Quiz – Answer Explanations
| # | Question | Correct Answer(s) | Reasoning |
|---|---|---|---|
| 1 | Why is Python widely used for data analysis? | Large community support, Easy to learn, Rich data libraries | Python is popular due to its beginner-friendly syntax, extensive libraries (Pandas, NumPy), and strong community support. |
| 2 | Why is Python 3.x preferred over Python 2.x? | More libraries available, Improved syntax & speed, Better Unicode support | Python 3.x introduced better memory management, faster execution, and native Unicode support, making it more efficient than Python 2.x. |
| 3 | Which of these are built-in Python data structures? | List, Tuple, Dictionary, Set | These are fundamental Python data types used for storing and organizing data. |
| 4 | Which library provides advanced random number functions? | NumPy | NumPy’s numpy.random module offers powerful random number generation for scientific computing. |
| 5 | Which library is best for handling structured tabular data? | Pandas | Pandas is designed for handling structured data, offering DataFrames for easy manipulation. |
| 6 | Which library is primarily used for machine learning? | Scikit-learn | Scikit-learn provides ML algorithms, data preprocessing, and model evaluation tools. |
| 7 | Which Python library is best for web scraping? | Scrapy | Scrapy is optimized for large-scale web scraping, while Requests and BeautifulSoup handle smaller tasks. |
| 8 | What is BeautifulSoup mainly used for? | Parsing HTML & XML, Extracting data from pages | BeautifulSoup is used to parse and extract structured data from HTML/XML, not for large-scale scraping. |
| 9 | Which statements are true about Pandas Series & DataFrames? | Series is a 1D labeled array, DataFrame is a 2D table, Both support labeled indexing | Pandas Series and DataFrames support indexing and labeled data handling, but Series can store mixed data types. |
| 10 | Which Pandas function is used to read a CSV file? | read_csv | read_csv() is the standard Pandas function for reading CSV files into a DataFrame. |
| 11 | Which of these is NOT a valid Pandas writer function? | to_text | Pandas does not have a to_text() function, but it supports writing to clipboard, Stata, and Msgpack. |
| 12 | How can you plot two Pandas DataFrame columns? | df.plot(style=['o','rx']), df.plot(x, y, style='o') | These methods allow visualization using Pandas’ built-in Matplotlib integration. |
| 13 | Does apply() in Pandas sometimes execute twice? | True | apply() can execute twice under debugging or when Pandas optimizes function execution. |
| 14 | Which Pandas function is used to apply a function to a DataFrame? | apply | The apply() method allows applying custom functions across rows or columns. |
| 15 | Which statement about Pandas acceleration is NOT correct? | Needed for handling missing data | Pandas acceleration improves performance but does not directly help in handling missing data. |
| 16 | What are key goals of Exploratory Data Analysis (EDA)? | Understand data distributions, Detect patterns & relationships, Identify missing values | EDA is used to explore data before applying statistical models, but it does not involve training ML models. |
| 17 | What is the default data type of a NumPy array? | float64 | NumPy defaults to float64 for numerical computations unless specified otherwise. |
| 18 | What will np.array([[1,2],[3,4]]).ndim, .shape, .dtype return? | 2, (2,2), int32 | The array has 2 dimensions, a shape of (2,2), and defaults to int32 when using integers. |
| 19 | What will dataset == 'paul' return if dataset = np.array(['paul', 'john', 'alice', 'paul'])? | [True, False, False, True] | NumPy performs element-wise comparison, returning a Boolean mask indicating where values match 'paul'. |
| 20 | What will dataset[dataset > 10] return if dataset is a NumPy array? | [Only elements >10] | NumPy filters elements greater than 10, returning only matching values. |
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