replace() for value substitution in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of replace() with dictionary mapping

What is the output of the following code snippet using pandas replace() method with a dictionary for value substitution?

Pandas

import pandas as pd

df = pd.DataFrame({'Fruit': ['apple', 'banana', 'cherry', 'date']})
result = df.replace({'Fruit': {'banana': 'blueberry', 'date': 'dragonfruit'}})
print(result)

A {'Fruit': ['apple', 'blueberry', 'cherry', 'dragonfruit']}

B {'Fruit': ['apple', 'banana', 'cherry', 'date']}

C KeyError

D {'Fruit': ['blueberry', 'banana', 'dragonfruit', 'date']}

💡 Hint

Think about how replace() uses the dictionary to map old values to new ones only where specified.

The replace() method substitutes 'banana' with 'blueberry' and 'date' with 'dragonfruit' in the 'Fruit' column, leaving other values unchanged.

❓ data_output

intermediate

Number of replaced values in DataFrame

After running the code below, how many values in the DataFrame will be replaced?

Pandas

import pandas as pd

df = pd.DataFrame({'Color': ['red', 'blue', 'green', 'blue', 'yellow']})
new_df = df.replace({'Color': {'blue': 'cyan', 'yellow': 'magenta'}})

A 2

B 3

C 1

D 0

💡 Hint

Count how many times the values to be replaced appear in the column.

The value 'blue' appears twice and 'yellow' once, so total replacements are 3. But since 'yellow' is replaced once and 'blue' twice, total replaced values are 3.

🔧 Debug

advanced

Identify the error in replace() usage

What error will this code raise when trying to replace values in a pandas DataFrame?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})
# Incorrect replace usage
result = df.replace(1, {2: 20})

A KeyError

B ValueError

C TypeError

D No error, runs successfully

💡 Hint

Check the expected argument types for replace() method.

The replace() method expects the first argument to be the value to replace or a dict mapping. Passing 1 and then a dict causes a TypeError.

🚀 Application

advanced

Using replace() to clean categorical data

You have a DataFrame with a column 'Status' containing values 'yes', 'no', 'n/a', and 'unknown'. You want to replace 'n/a' and 'unknown' with NaN for analysis. Which code snippet correctly does this?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({'Status': ['yes', 'no', 'n/a', 'unknown', 'yes']})

A df.replace({'Status': {'n/a': np.nan, 'unknown': np.nan}})

B df.replace({'Status': ['n/a', 'unknown'], np.nan})

C df.replace(['n/a', 'unknown'], np.nan, inplace=True)

D df.replace({'n/a': np.nan, 'unknown': np.nan})

💡 Hint

Use a dictionary mapping column name to another dictionary of old to new values.

Option A correctly uses a nested dictionary to replace specific values in the 'Status' column with np.nan.

🧠 Conceptual

expert

Behavior of replace() with regex=true

Given the DataFrame below, what will be the output after applying replace() with regex=true?

Pandas

import pandas as pd

df = pd.DataFrame({'Code': ['abc123', 'def456', 'ghi789']})
result = df.replace({'Code': r'\d+'}, 'NUM', regex=True)
print(result)

A {'Code': ['NUM123', 'NUM456', 'NUM789']}

B TypeError

C {'Code': ['abc123', 'def456', 'ghi789']}

D {'Code': ['abcNUM', 'defNUM', 'ghiNUM']}

💡 Hint

regex=true allows pattern matching and replacement inside strings.

The regex '\d+' matches digits and replaces them with 'NUM' inside each string in the 'Code' column.