Pandas Interview Questions for 2025

How can you create a pandas DataFrame from a dictionary?

import pandas as pd
# Create a dictionary
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
}

# Create DataFrame from the dictionary
df = pd.DataFrame(data)

# Display the DataFrame
print(df)

output

 Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago

What are the methods for adding a new column to a pandas DataFrame?

There are different methods for adding a column:

1. Direct assignment

We can directly assign a new column by specifying the column name and the values in forms like list, series etc

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35]
})

# Add a new column by direct assignment
df['City'] = ['New York', 'Los Angeles', 'Chicago']

1. Using assign method

df = df.assign(Country=['USA', 'USA', 'USA'])

print(df)

Output

Name  Age Country
0    Alice   25     USA
1      Bob   30     USA
2  Charlie   35     USA

Using insert method

The insert() method allows us to insert a new column at a particular position in the DataFrame

# Insert a new column at the second position (index 1)
df.insert(1, 'Gender', ['Female', 'Male', 'Male'])
print(df)

output

     Name  Gender  Age Country
0    Alice  Female   25     USA
1      Bob    Male   30     USA
2  Charlie    Male   35     USA

2. Using loc[] or iloc[]

Loc[] method in pandas can be handy for adding a column in dataframe

df.loc[:, 'Salary'] = [50000, 60000, 70000]
print(df)

Output

     Name  Gender  Age Country  Salary
0    Alice  Female   25     USA   50000
1      Bob    Male   30     USA   60000
2  Charlie    Male   35     USA   70000

How do you set a column as the index of a pandas DataFrame?

We have a function called set_index() which takes two parameters i.e. column name and inplace.

df.set_index('column_name', inplace=False)

• “column_name” is the name of the column you want to set as the index.
• “inplace=True” modifies the original DataFrame, while inplace=False (default) returns a new DataFrame with the updated index.

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
})

# Set 'Name' column as the index
df_new = df.set_index('Name')

Above code set Name column as index.

How do you reset the index of a pandas DataFrame?

The syntax for dropping the index

df.reset_index(drop=False, inplace=False)

• “drop=False”: This keeps the current index as a column in the DataFrame. If you set it to True, the index will be removed and not added as a column.
• “inplace=False”: By default, reset_index() returns a new DataFrame. If you want to modify the DataFrame in place, you can set inplace=True.

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35]
}, index=['a', 'b', 'c'])

# Reset the index without dropping the index column
df_reset = df.reset_index()

What are the ways to delete a column from a pandas DataFrame?

We can use drop(), del, loc[], and pop() methods to delete a column in Pandas DataFrame.

# Sample DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
})

Using drop function

# Drop 'Age' column
df_dropped = df.drop(columns=['Age'])

Using del function

del df['Age']

Using pop function

city_column = df.pop('City')

Using loc function

While loc and iloc are generally used for indexing, you can use them to select specific columns that you want to keep, effectively removing the ones you don’t want.

# Select all columns except 'City' using `loc`
df = df.loc[:, df.columns != 'City']
print(df)

How can you transpose a pandas DataFrame?

The .T attribute is a shorthand for transposing the DataFrame.

import pandas as pd

# Sample DataFrame df = pd.DataFrame({ ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’], ‘Age’: [25, 30, 35], ‘City’: [‘New York’, ‘Los Angeles’, ‘Chicago’] }) #before transpose print(df)

output

    Name  Age          City
0  Alice   25      New York
1    Bob   30  Los Angeles
2 Charlie   35       Chicago

# Transpose the DataFrame
df_transposed = df.T
print(df_transposed)

output

            0            1          2
Name     Alice        Bob    Charlie
Age         25          30          35
City  New York  Los Angeles    Chicago