How would you handle large DataFrames that do not fit into memory using Pandas?
- Reducing the precision of data
- Reshaping the DataFrame
- Splitting the DataFrame into smaller chunks
- Using the Dask library
When dealing with large DataFrames that do not fit into memory, you can use the Dask library, which allows for distributed computing and can handle larger-than-memory datasets.
How would you handle missing data for a numerical feature in a dataset before training a machine learning model?
- Ignore missing data, it won't affect the model
- Remove the rows with missing data
- Replace missing values with a random value
- Replace missing values with the mean of the feature
Handling missing data is crucial. Replacing missing values with the mean of the feature is a common practice as it retains data and doesn't introduce bias, especially in numerical features. Removing rows or using random values can lead to loss of information or noise.
How does a metaclass differ from a class in Python?
- A class can be instantiated multiple times.
- A metaclass can be instantiated multiple times.
- A metaclass defines the structure of a class, while a class defines the structure of an instance.
- A metaclass is an instance of a class.
In Python, a metaclass is a class for classes. It defines the structure and behavior of classes, while a regular class defines the structure of instances created from it. A metaclass is used to customize class creation and behavior.
How is a generator function different from a normal function in Python?
- A generator function is a built-in Python function
- A generator function is defined using the generator keyword
- A generator function returns multiple values simultaneously
- A generator function yields values lazily one at a time
A generator function differs from a normal function in that it uses the yield keyword to yield values lazily one at a time, allowing it to generate values on-the-fly without consuming excessive memory.
How would you analyze the reference count of an object in Python to debug memory issues?
- Reference count analysis is not relevant for debugging memory issues in Python.
- Use the gc module to manually increment and decrement the reference count.
- Utilize the sys.getrefcount() function to inspect the reference count.
- Write custom code to track object references in your application.
You can use the sys.getrefcount() function to inspect the reference count of an object in Python. It's a built-in way to gather information about an object's reference count. Options 1 and 4 are not recommended practices, and Option 3 is incorrect since reference count analysis is indeed relevant for debugging memory issues.
How can you integrate a Python back-end with a Single Page Application (SPA) framework like Angular or React?
- Create RESTful APIs
- Embed Python code in SPA components
- Use SOAP protocols
- Utilize Django templates
To integrate a Python back-end with an SPA framework like Angular or React, you should create RESTful APIs. This allows the front-end to communicate with the back-end through standardized HTTP requests, enabling data retrieval and manipulation.
How can you invoke the method of a superclass from a subclass?
- By calling the superclass method directly
- By importing the superclass module
- By using the extends keyword
- Using the super() function
In Python, you invoke the method of a superclass from a subclass using the super() function. This allows you to access and call methods from the superclass within the subclass.
How can you detect a cycle in a linked list?
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- Floyd's Tortoise and Hare Algorithm
- Linear Search
You can detect a cycle in a linked list using Floyd's Tortoise and Hare Algorithm. This algorithm uses two pointers moving at different speeds to traverse the list. If there's a cycle, the two pointers will eventually meet. It's an efficient O(n) algorithm for cycle detection.
How can you dynamically create a new type (class) at runtime in Python?
- Using closures
- Using decorators
- Using list comprehensions
- Using metaclasses
You can dynamically create a new type (class) at runtime in Python by using metaclasses. Metaclasses allow you to define the behavior of classes themselves. Decorators are used to modify the behavior of functions or methods, not to create classes. Closures and list comprehensions are not directly related to class creation.
How can you ensure that user-uploaded files in a web application are securely handled when integrating Python back-end with front-end technologies?
- Allow direct access to uploaded files to improve performance.
- Implement proper file validation, authentication, and authorization.
- Store user-uploaded files in a publicly accessible directory.
- Use third-party services to handle file uploads.
To ensure the secure handling of user-uploaded files, you should implement proper file validation to check file types and content, authentication to ensure that only authorized users can upload files, and authorization to control who can access the files. Storing files in a publicly accessible directory can be a security risk.