python interview questions for data science
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data science interviews often include python coding assessments to evaluate a candidate's programming skills and problem-solving abilities. in this tutorial, we'll cover some common python interview questions for data science positions, along with detailed explanations and code examples.
question: explain what a list comprehension is and provide an example.
answer:
a list comprehension is a concise way to create lists in python. it consists of an expression followed by a for loop inside square brackets.
example:
question: how do you select specific rows and columns from a pandas dataframe?
answer:
you can use the loc or iloc methods. loc is label-based, and iloc is integer-based.
example:
question: explain the difference between numpy arrays and python lists.
answer:
numpy arrays are more efficient for numerical operations than python lists. they have a fixed size and are homogeneous, meaning all elements must be of the same data type.
example:
question: how can you handle missing values in a pandas dataframe?
answer:
you can use methods like dropna() to remove missing values or fillna() to fill them with a specific value.
example:
question: explain the basic steps to train a machine learning model using scikit-learn.
answer:
example:
this tutorial covers some fundamental python interview questions for data science roles. practicing these examples will not only help you in interviews but also enhance your overall python programming skills in the context of data science. good luck!
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