Ace your data scientist interview with these top ten Python interview questions and answers-
Python document strings, also known as docstrings, explain what the function does. These are enclosed in triple quotation marks. Additionally, there are two ways to access it: either by using the doc attribute or by placing a period (.) next to the function name and pressing tab. It is a method for relating Python modules, functions, classes, and methods with their respective documentation.
Pychecker and Pylint. Pylint checks whether a module complies with all coding standards. Pychecker is a static analysis utility that helps to identify bugs in the course code.
Pickle is a module in Python that takes any Python object as input and converts it to a string representation before using the dump function to dump it into a file. Pickling is the name of this process. Unpickling is the method of obtaining Python objects from a pickled file.
The generator function is in charge of streamlining the iterator creation process. A decorator adds, removes, or modifies characteristics to modify pre-existing functions or their output.
Vectors: It can only assign the values 0, 1,... (n-1) to the index positions.
Series: There is only one column in it. For each and every data series, it can assign unique values to the custom index positions.
Lambda functions are nameless or anonymous functions. Since they are not defined using the def keyword, these functions are known as anonymous functions. The return keyword is not necessary. The lambda function can yield a single value in the form of an expression with a single statement and any number of parameters. No commands or multiple expressions are allowed in these functions.
The functions that carry out mathematical calculations on every element of an n-dimensional array are known as universal functions. Example: The functions np.sqrt() and np.exp() calculate the square root and exponential of each array element, respectively.
When your model fits the training dataset too well, it scores poorly on the test dataset, which is known as overfitting a dataset. The model may have picked up on the dataset's noise, which is one of the main causes of overfitting.
When a dataset is underfitted, your model is not correctly fitting the training dataset. It typically happens when you start looking for alternatives instead of finetuning a model's parameters.
If a problem arises while the code is running, exception handling will help you keep your code from crashing. Try and except are two keywords that can be used in Python to perform exception handling.
Try attempts to run the associated code.
The except statement, which is used after the try block, catches all the individual errors that might occur when the codes under the try block are executed.
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