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

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of str.replace() with regex in pandas

What is the output of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({'text': ['apple123', 'banana456', 'cherry789']})
df['cleaned'] = df['text'].str.replace(r'\d+', '', regex=True)
print(df['cleaned'].tolist())

A ['apple', 'banana', 'cherry']

B ['apple123', 'banana456', 'cherry789']

C ['123', '456', '789']

D ['apple ', 'banana ', 'cherry ']

💡 Hint

The regex '\d+' matches digits and str.replace removes them.

The code removes all digits from each string in the 'text' column using regex '\d+'. The result is the list without numbers.

❓ data_output

intermediate

Resulting DataFrame after str.replace() substitution

Given the DataFrame below, what is the resulting DataFrame after applying the str.replace() method?

Pandas

import pandas as pd

df = pd.DataFrame({'city': ['New York', 'Los Angeles', 'San Francisco']})
df['city'] = df['city'].str.replace(' ', '_')
print(df)

A

          city
0      NewYork
1   LosAngeles
2  SanFrancisco

B

          city
0      New_York
1   Los_Angeles
2  San_Francisco

C

          city
0      New-York
1   Los-Angeles
2  San-Francisco

D

          city
0      New York
1   Los Angeles
2  San Francisco

💡 Hint

str.replace(' ', '_') replaces spaces with underscores.

The spaces in city names are replaced by underscores, so 'New York' becomes 'New_York', etc.

🔧 Debug

advanced

Identify the error in str.replace() usage

What error will this code raise and why?

Pandas

import pandas as pd

df = pd.DataFrame({'code': ['A1', 'B2', 'C3']})
df['code'] = df['code'].str.replace('\d+', '')
print(df)

A TypeError: replace() missing 1 required positional argument: 'repl'

B ValueError: regex pattern not provided

C SyntaxError due to missing raw string prefix

D No error, output is ['A', 'B', 'C']

💡 Hint

Check the parameters passed to str.replace() in pandas.

In pandas, str.replace() requires both 'pat' and 'repl' arguments. Here, repl is missing, causing TypeError.

🚀 Application

advanced

Using str.replace() to clean phone numbers

You have a DataFrame column with phone numbers in various formats. Which code snippet correctly removes all non-digit characters to keep only numbers?

Pandas

import pandas as pd

df = pd.DataFrame({'phone': ['[phone]', '987.654.3210', '[phone]']})

A df['phone_clean'] = df['phone'].str.replace(r'[^0-9]', '')

B df['phone_clean'] = df['phone'].str.replace(r'\d', '', regex=True)

C df['phone_clean'] = df['phone'].str.replace(r'\D', '', regex=True)

D df['phone_clean'] = df['phone'].str.replace(r'\s', '')

💡 Hint

Use regex to remove all non-digit characters.

The pattern '\D' matches any non-digit character. Replacing them with '' keeps only digits.

🧠 Conceptual

expert

Behavior of str.replace() with overlapping patterns

Consider the string 'aaaa'. What is the result of applying str.replace('aa', 'b') once in pandas with regex=False?

Pandas

import pandas as pd

s = pd.Series(['aaaa'])
result = s.str.replace('aa', 'b', regex=False)
print(result[0])

A 'bb'

B 'aaaa'

C 'ab'

D 'ba'

💡 Hint

str.replace replaces non-overlapping occurrences from left to right.

The first 'aa' is replaced by 'b', then the next 'aa' is replaced by 'b', resulting in 'bb'.

Practice

1. What does the pandas str.replace() method do when applied to a DataFrame column?

easy

A. It sorts the column values in ascending order.

B. It deletes the entire column from the DataFrame.

C. It converts all strings in the column to uppercase.

D. It replaces specified substrings in each string of the column with a new substring.

Solution

1. Step 1: Understand the purpose of str.replace()

   This method targets each string in a pandas column and looks for a substring to replace.

2. Step 2: Identify the effect on the column

   It creates a new column or series where the specified substring is replaced by the new substring in every string.

3. Final Answer:

   It replaces specified substrings in each string of the column with a new substring. -> Option D

4. Quick Check:

   str.replace() = substring substitution [OK]

Hint: Think: change parts of text in each cell [OK]

Common Mistakes:

• Confusing replace with deleting columns
• Thinking it changes case of strings
• Assuming it sorts data

2. Which of the following is the correct syntax to replace all occurrences of 'cat' with 'dog' in a pandas column named pets?

easy

A. df['pets'].str.replace('cat', 'dog')

B. df['pets'].replace('cat', 'dog')

C. df.str.replace('cat', 'dog')['pets']

D. df['pets'].replace_all('cat', 'dog')

Solution

1. Step 1: Identify the correct method for string replacement

   In pandas, str.replace() is used on a Series of strings to replace substrings.

2. Step 2: Check the syntax for applying str.replace() on a column

   The correct syntax is df['column'].str.replace(old, new). df['pets'].str.replace('cat', 'dog') matches this.

3. Final Answer:

   df['pets'].str.replace('cat', 'dog') -> Option A

4. Quick Check:

   Use .str.replace() on Series for string substitution [OK]

Hint: Use .str.replace() on the column, not .replace() alone [OK]

Common Mistakes:

• Using .replace() directly on the DataFrame column
• Trying to call .str.replace() on the whole DataFrame
• Using non-existent methods like replace_all

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'fruit': ['apple', 'banana', 'grape', 'pineapple']})
df['fruit_new'] = df['fruit'].str.replace('apple', 'orange')

What is the content of df['fruit_new']?

medium

A. ['orange', 'banana', 'grape', 'pineapple']

B. ['apple', 'banana', 'grape', 'pineapple']

C. ['orange', 'banana', 'grape', 'pineorange']

D. ['orange', 'orange', 'orange', 'orange']

Solution

1. Step 1: Understand what str.replace('apple', 'orange') does

   It replaces every occurrence of 'apple' in each string with 'orange'.

2. Step 2: Apply replacement to each value

   'apple' becomes 'orange', 'banana' unchanged, 'grape' unchanged, 'pineapple' contains 'apple' at the end, so it becomes 'pineorange'.

3. Final Answer:

   ['orange', 'banana', 'grape', 'pineorange'] -> Option C

4. Quick Check:

   Substring 'apple' replaced wherever found [OK]

Hint: Replace changes substrings inside words too [OK]

Common Mistakes:

• Assuming only exact matches replaced, not substrings
• Thinking 'pineapple' stays unchanged
• Confusing replacement with full string swap

4. What is wrong with this code snippet?

df['col'].str.replace('a', 'b')

It does not change df['col'] as expected.

medium

A. The result is not assigned back to df['col'] to save changes.

B. The method str.replace() does not exist in pandas.

C. The arguments 'a' and 'b' must be integers, not strings.

D. You must use df.replace() instead of str.replace().

Solution

1. Step 1: Understand that str.replace() returns a new Series

   The method does not change the original column in place; it returns a modified copy.

2. Step 2: Identify the missing assignment

   To keep changes, assign the result back: df['col'] = df['col'].str.replace('a', 'b').

3. Final Answer:

   The result is not assigned back to df['col'] to save changes. -> Option A

4. Quick Check:

   Assign result to keep changes [OK]

Hint: Always assign back to save string replacements [OK]

Common Mistakes:

• Expecting in-place change without assignment
• Confusing str.replace() with DataFrame.replace()
• Thinking str.replace() modifies original data silently

5. You have a DataFrame with a column emails containing email addresses. You want to anonymize the domain part by replacing everything after '@' with 'example.com'. Which code correctly does this?

hard

A. df['emails'].replace(r'@.*', '@example.com')

B. df['emails'].str.replace(r'@.*', '@example.com', regex=True)

C. df['emails'].str.replace('example.com', '@')

D. df['emails'].str.replace('@', '@example.com')

Solution

1. Step 1: Identify the pattern to replace

   The domain part starts with '@' and continues to the end, so the regex '@.*' matches it.

2. Step 2: Use str.replace() with regex=True to replace domain

   df['emails'].str.replace(r'@.*', '@example.com', regex=True) uses regex to replace everything after '@' with '@example.com'.

3. Step 3: Check other options

   A uses Series.replace() which defaults to regex=False, treating '@.*' literally. B replaces only '@' leaving the domain. C replaces in the wrong direction.

4. Final Answer:

   df['emails'].str.replace(r'@.*', '@example.com', regex=True) -> Option B

5. Quick Check:

   Use regex in str.replace() for domain substitution [OK]

Hint: Use regex '@.*' with regex=True to replace domain [OK]

Common Mistakes:

• Not using regex=True for pattern matching
• Replacing only '@' without domain
• Using DataFrame.replace instead of str.replace