Python Essentials for Coding Interviews: Do You Know Them All?

“Laziness is a programmers main virtue.” — Larry Wall

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Python Built-in Functions

The Python interpreter comes with numerous built-in functions that not only save you time but are also optimized for performance. Using these functions can be faster than writing equivalent code manually.

For example, the built-in sum() function is more efficient than calculating the sum using for loops. I encountered a situation during an interview where switching my for loops to the sum() function allowed previously timed-out cases to pass.

Another useful function is pow(base, exp, mod=None), which calculates the power of a base to an exponent. It can also take an optional modulus argument, making it more efficient than performing the modulus operation separately, like pow(base, exp) % mod.

The min() and max() functions are also invaluable. They can take an iterable (such as a list) or multiple arguments and return the smallest or largest value, respectively. I frequently use these functions in dynamic programming problems that require numeric answers.

Here are some other notable built-in functions:

• abs(x): Returns the absolute value of a number.
• filter(function, iterable): Creates an iterator from the elements of iterable for which function returns true.
• float() and int(): Convert a number or a string representing a number to a floating-point number or an integer, respectively.
• map(function, iterable): Returns an iterator that applies function to all elements in iterable.
• zip(*iterables): Produces tuples by iterating through multiple iterables simultaneously, taking one item from each. Remember that you can also use this function to unzip!

examples of some of these built-in functions

For a complete list of built-in functions, visit the official documentation.

Collections

The collections module offers specialized container datatypes that can be useful alternatives to standard types like list, set, dict, and tuple.

The Counter class is a subclass of dict that counts hashable objects. It's particularly useful for tasks like counting the occurrences of elements in a list, dictionary, or string, which is common in coding challenges. Using Counter instead of a traditional for loop is cleaner, faster, and more efficient for long iterables often seen in edge cases.

Counter also has helpful methods, such as most_common(), which returns elements in order of frequency. You can even specify an integer to get the top N most frequent elements.

Another useful class is defaultdict, a subclass of dict that allows you to set a default value for dictionary entries upon first encountering them.

example of Counter and defaultdict being used to count characters in a string

itertools

The itertools module is incredibly powerful and often under-appreciated. Its functions save time, are more memory efficient, and are less error-prone than manually writing out the equivalent code.

Some of my favorites include:

• count(start=0, step=1): Returns an iterator that generates evenly spaced values starting from start.
• cycle(iterable): Cycles through an iterable indefinitely.
• accumulate(iterable): Returns an iterator with the accumulated sums.
• batched(iterable, n): Returns tuples of size n, batching the data in the iterable. This is useful for handling large data loads in ETL processes or machine learning workflows.
• filterfalse(function, iterable): Similar to filter(), but retains elements for which the function returns false.
• permutations(iterable, r=None) and combinations(iterable, r): Yield permutations and combinations of the elements in an iterable. While useful, consider if a more efficient algorithm is needed when dealing with large data sets.

Examples of some of the itertools functions

Explore the full capabilities of itertools in the official documentation.

What else do you think is important to know? Let me know in the comments! Let’s connect! Links on my bio :)

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