You are assigned a task to implement a decorator that logs the arguments and return value every time a function is called. How would you implement this logging decorator?
- Define a decorator function that wraps the original function, prints arguments, calls the function, prints return value, and returns the result.
- Modify the function itself to log arguments and return values.
- Use a built-in Python module like logging to log function calls automatically.
- Use a third-party library like Flask to create a logging decorator.
To implement a logging decorator, create a decorator function that wraps the original function, logs the arguments, calls the function, logs the return value, and returns the result. This is a common use case for decorators.
You are asked to implement lazy evaluation for a sequence of data in your project. Which Python concept will you use to accomplish this task?
- Decorators
- Generator Functions
- List Comprehensions
- Map Function
Generator functions in Python allow for lazy evaluation of sequences. They produce values one at a time and only when requested, making them suitable for handling large or infinite sequences of data efficiently.
You are asked to design an algorithm to reverse the words in a string ('hello world' becomes 'world hello'). Which approach would allow you to do this in-place, without using additional memory?
- A) Using a stack
- B) Using an array
- C) Using a linked list
- D) Using a queue
To reverse words in a string in-place, you can use a stack data structure. You push individual words onto the stack while iterating through the string and then pop them off to reconstruct the reversed string. This approach doesn't require additional memory. The other options do not naturally support an in-place reversal of words.
You are asked to create a plot comparing the distribution of a variable across different categories, highlighting the median and interquartile range. Which Seaborn plot would you choose?
- Box Plot
- Line Plot
- Swarm Plot
- Violin Plot
To compare the distribution of a variable across categories while highlighting the median and interquartile range, a Violin Plot in Seaborn is a suitable choice. It combines a box plot with a kernel density estimation to provide a richer visualization of the data distribution.
You are asked to create a new column in a DataFrame that is the sum of two other columns. How would you create this new column in Pandas?
- df.create_column('new_column', df.column1 + df.column2)
- df.new_column = df.column1 + df.column2
- df['new_column'] = df['column1'] + df['column2']
- df['new_column'] = df['column1'].add(df['column2'])
To create a new column in a Pandas DataFrame that is the sum of two existing columns, you would use the syntax df['new_column'] = df['column1'] + df['column2']. This operation will perform element-wise addition and create the new column.
Which type of tree would you use to implement an ordered map?
- AVL Tree
- Binary Search Tree (BST)
- Heap
- Red-Black Tree
To implement an ordered map, you would typically use a Binary Search Tree (BST). A BST ensures that elements are stored in sorted order, making it efficient for operations like search, insert, and delete in O(log n) time.
Which status code indicates that a request was successful in HTTP?
- 200 OK
- 401 Unauthorized
- 404 Not Found
- 500 Internal Server Error
The HTTP status code 200 OK indicates that a request was successful. It is used to signal that the request has been successfully received, understood, and accepted by the server. Other codes (404, 500, 401) indicate various error conditions.
Which sorting algorithm is best suited for large datasets?
- Bubble Sort
- Insertion Sort
- Quick Sort
- Selection Sort
Quick Sort is typically the best choice for sorting large datasets due to its average-case time complexity of O(n log n). Bubble Sort, Insertion Sort, and Selection Sort have worse time complexities and are less efficient for large datasets.
Which Seaborn function would you use to visualize a bivariate distribution of two variables?
- sns.barplot()
- sns.distplot()
- sns.jointplot()
- sns.plot()
To visualize a bivariate distribution of two variables in Seaborn, you should use the sns.jointplot() function. It creates a scatter plot with marginal histograms and can also display a regression line or a kernel density estimate.
Which Python web framework uses the “Don’t repeat yourself” principle?
- Django
- Flask
- Pyramid
- Tornado
Django is a Python web framework that follows the "Don't repeat yourself" (DRY) principle. DRY encourages developers to avoid duplicating code by providing reusable components and an organized structure.
Which Python tool would you use to visualize an application’s call stack and identify performance bottlenecks?
- cProfile
- Gunicorn
- Pyflame
- Pygraphviz
Pyflame is a tool for profiling Python applications. It helps visualize the call stack and identify performance bottlenecks. cProfile (Option 1) is a built-in profiler, but it doesn't offer visualization. Gunicorn (Option 3) is a web server. Pygraphviz (Option 4) is for graph visualization, not profiling.
Which Python module would you use for measuring the performance of small code snippets?
- benchmark
- datetime
- profiling
- timeit
You would use the timeit module to measure the performance of small code snippets in Python. It provides a simple way to time small bits of Python code and is a useful tool for optimizing code.