Key Python Interview Questions Involving DataFrame and zip() Explained

Python interviews often bring surprises. Candidates are expected to demonstrate practical skills rather than just theory. Two concepts that often come up are working with DataFrame and using the built-in zip() function. Both test how well someone can write clean, logical code that solves real problems. DataFrames are key when working with structured data, while zip() is a quiet helper for combining and iterating through sequences. Understanding how to answer questions about them with clarity can leave a strong impression. Below are thoughtful examples of interview questions and how to approach them confidently.

Top Python Coding Interview Questions on DataFrame and zip

How Do You Create a Simple DataFrame in Pandas from a Dictionary?

This question checks if you’re familiar with the pandas library and how to create tabular data. A DataFrame is essentially a table with rows and columns. You can create one by passing a dictionary to pd.DataFrame(). For example:

import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
print(df)

This will display a two-row DataFrame with columns “Name” and “Age”. Interviewers may follow up by asking how to specify an index or how to add more columns dynamically.

How Do You Select Specific Rows and Columns From a DataFrame?

You may be asked to extract specific parts of a DataFrame. The .loc[] and .iloc[] accessors are commonly used here. .loc[] is label-based while .iloc[] is integer-position based.

For example:

df.loc[0, 'Name']
df.iloc[1, 0]

These fetch a specific cell value. To select an entire column as a Series, you can use df['Name'], or as a DataFrame: df[['Name']]. Being able to explain the difference between Series and DataFrame output here helps you stand out.

How Can You Filter Rows in a DataFrame Based on a Condition?

Filtering is a key task. A simple way is to use a boolean mask:

df[df['Age'] > 25]

This returns rows where the Age column is greater than 25. Interviewers might extend this by asking you to combine multiple conditions with & or | (and remember to wrap conditions in parentheses).

How Do You Merge Two DataFrames?

DataFrames often need to be joined just like SQL tables. The merge() function is the most common way. Example:

df1 = pd.DataFrame({'ID': [1, 2], 'Name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'ID': [1, 2], 'Salary': [50000, 60000]})
merged = pd.merge(df1, df2, on='ID')
print(merged)

This combines the two tables using the ID column as a key. You can also specify how='left' or how='outer' to control the type of join.

How Do You Handle Missing Data in a DataFrame?

You could be asked how to clean or impute missing values. Pandas provides several methods:

• df.dropna() to remove rows with missing values.
• df.fillna(0) to replace missing values with zero or any specified value.
• df.isnull() to identify missing values.

You might even be asked how to replace missing values with the mean or median of the column, which shows you understand statistics and data cleaning.

What Does The zip() Function Do in Python?

The zip() function combines two or more iterables into an iterator of tuples, pairing elements together. A common question is simply: “What is the output of zip()?” For example:

list1 = [1, 2, 3]
list2 = ['a', 'b', 'c']
result = list(zip(list1, list2))
print(result)

Output: [(1, 'a'), (2, 'b'), (3, 'c')].

You might also be asked what happens if the lists are unequal in length — the result only contains pairs up to the shortest iterable.

How Do You Unzip a Zipped Object?

Once you’ve zipped lists, how do you get them back into separate lists? The interviewer may ask for an example:

zipped = zip([1, 2, 3], ['a', 'b', 'c'])
x, y = zip(*zipped)
print(x, y)

This uses the unpacking operator * to unzip.

How Would You Use zip() To Create a Dictionary?

Another question you may face is how to use zip() with dict(). This comes up when you have separate lists of keys and values:

keys = ['Name', 'Age']
values = ['Alice', 25]
my_dict = dict(zip(keys, values))
print(my_dict)

Output: {'Name': 'Alice', 'Age': 25}. This tests your ability to use Python’s built-in functions for clean, one-liner solutions.

Can You Use zip() With More Than Two Lists?

Yes, zip() can take any number of iterables and pairs of corresponding elements into tuples. For example:

list1 = [1, 2]
list2 = ['a', 'b']
list3 = [True, False]
result = list(zip(list1, list2, list3))
print(result)

Output: [(1, 'a', True), (2, 'b', False)]. Interviewers may test how well you understand zip()’s behavior when lists are unequal and how to handle it using itertools’ zip_longest().

How Do You Combine a DataFrame and zip() In A Task?

A more advanced question may ask you to create a DataFrame from zipped data. You could show how to create columns from zipped lists:

names = ['Alice', 'Bob']
ages = [25, 30]
df = pd.DataFrame(list(zip(names, ages)), columns=['Name', 'Age'])
print(df)

This creates a clean DataFrame directly from zipped data, demonstrating knowledge of both topics together.

How Can You Apply a Function to Each Row or Column in a DataFrame?

Interviewers may ask how to manipulate data inside a DataFrame by applying custom functions. The apply() method allows you to run a function across rows or columns. For example, to add a new column based on existing ones, you could write:

def age_group(row):
    if row['Age'] < 30:
        return 'Young'
    else:
        return 'Adult'

df['Group'] = df.apply(age_group, axis=1)
print(df)

Here, axis=1 applies the function row-wise, giving you control over data transformation without looping manually. This shows you understand pandas beyond simple selection and filtering, revealing your ability to customize data processing.

Conclusion

When preparing for interviews, practicing Python coding interview questions on DataFrame and zip() can help you feel more confident. These two concepts often appear because they reveal how well someone can work with data structures and write simple, elegant solutions. DataFrames show your skill in managing structured data, while zip() highlights your ability to combine sequences efficiently. Being able to explain what the code does and why you chose that approach leaves a lasting impression. Take the time to practice different variations of these questions so you’re ready for whatever comes your way.