str.replace() for substitution in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to replace all occurrences of 'cat' with 'dog' in the 'animal' column.

Pandas

df['animal'] = df['animal'].str.[1]('cat', 'dog')

Drag options to blanks, or click blank then click option'

Aswap

Bsubstitute

Creplace

Dchange

Attempts:

3 left

💡 Hint

Common Mistakes

Using a method name that does not exist like 'substitute' or 'swap'.

Trying to replace without using the str accessor.

The str.replace() method replaces all occurrences of a substring with another substring in a pandas Series.

2fill in blank

medium

Complete the code to replace all digits in the 'code' column with '#' using a regular expression.

Pandas

df['code'] = df['code'].str.[1]('\d', '#', regex=True)

Drag options to blanks, or click blank then click option'

Aswap

Bsub

Ctranslate

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using sub which is not a pandas string method.

Not setting regex=True when using a regex pattern.

The str.replace() method supports regular expressions when regex=True is set, allowing pattern-based replacements.

3fill in blank

hard

Fix the error in the code to replace 'yes' with 'no' in the 'answer' column.

Pandas

df['answer'] = df['answer'].str.[1]('yes', 'no')

Drag options to blanks, or click blank then click option'

Areplace

Bstr.replace()

Cstr.replace

Dreplace()

Attempts:

3 left

💡 Hint

Common Mistakes

Including parentheses in the method name inside the code string.

Using str.replace without calling it as a method.

When using pandas string methods, you call str.replace() on the Series with str. accessor, but here the accessor is already used, so just replace is the method name.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their uppercase form only if the word length is greater than 4.

Pandas

{word: word[1]() for word in words if len(word) [2] 4}

Drag options to blanks, or click blank then click option'

A.upper

B>

C<

D.lower

Attempts:

3 left

💡 Hint

Common Mistakes

Using .lower instead of .upper.

Using less than operator instead of greater than.

The method upper() converts strings to uppercase. The condition len(word) > 4 filters words longer than 4 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps the uppercase word to its length only if the length is less than 6.

Pandas

{ [1]: [2] for word in words if len(word) [3] 6 }

Drag options to blanks, or click blank then click option'

Aword.upper()

Blen(word)

C<

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of word.upper().

Using greater than operator instead of less than.

The key is the uppercase word using word.upper(). The value is the length with len(word). The condition filters words with length less than 6 using <.

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