map() for element-wise transformation in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to apply the function to each element in the 'score' column using map().

Pandas

df['score'] = df['score'].[1](lambda x: x + 10)

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

Amap

Breduce

Cfilter

Dapply

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply instead of map, which works differently on Series.

Using filter or reduce which are not suitable for element-wise transformation.

The map() function applies a given function to each element of the Series, transforming the data element-wise.

2fill in blank

medium

Complete the code to replace values in the 'grade' column using a mapping dictionary with map().

Pandas

grade_map = {'A': 4, 'B': 3, 'C': 2}
df['grade_num'] = df['grade'].[1](grade_map)

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

Amap

Bapply

Creplace

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using replace instead of map, which also works but is different in behavior.

Using apply which does not directly accept a dictionary for mapping.

The map() function can take a dictionary to replace values element-wise in a Series.

3fill in blank

hard

Fix the error in the code to correctly map values in the 'status' column using map().

Pandas

status_map = {1: 'Active', 0: 'Inactive'}
df['status_text'] = df['status'].[1](status_map)

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

Aapply

Bfilter

Creplace

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply which does not accept a dictionary directly.

Using filter which is not for mapping values.

Using map() with a dictionary correctly replaces the numeric codes with text labels element-wise.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths, then apply it with map().

Pandas

length_map = {word: [1] for word in words if len(word) [2] 3}
result = df['text'].map(length_map)

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length in the dictionary values.

Using the wrong comparison operator in the condition.

The dictionary comprehension maps each word to its length if the word length is greater than 3. Then map() applies this mapping.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension mapping uppercase words to their lengths, then apply it with map().

Pandas

length_map = [1]: [2] for word in words if word [3] word.upper()
result = df['text'].map(length_map)

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

Aword.upper()

Blen(word)

C==

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.upper() as the key instead of the original word.

Using incorrect comparison operators.

Using the wrong value for the dictionary.

The dictionary comprehension maps each uppercase word to its length. The condition checks if the word is uppercase by comparing it to its uppercase version.

Practice

1. What does the map() function do when used on a pandas Series?

easy

A. It combines two Series into one.

B. It applies a transformation to each element individually.

C. It filters out elements based on a condition.

D. It sorts the Series in ascending order.

Solution

1. Step 1: Understand the purpose of map()

   map() applies a function or dictionary mapping to each element in a pandas Series one by one.

2. Step 2: Compare with other operations

   Combining, filtering, or sorting are different operations; map() specifically transforms elements individually.

3. Final Answer:

   It applies a transformation to each element individually. -> Option B

4. Quick Check:

   map() = element-wise transform [OK]

Hint: Remember: map changes each item one by one [OK]

Common Mistakes:

• Confusing map() with merge or join
• Thinking map() filters data
• Assuming map() sorts data

2. Which of the following is the correct syntax to replace values in a pandas Series s using a dictionary d with map()?

easy

A. map(s, d)

B. s.map([d])

C. s.map(d)

D. map(d, s)

Solution

1. Step 1: Recall the method call format

   In pandas, map() is a Series method called as s.map(), where s is the Series.

2. Step 2: Check argument type

   The argument to map() can be a dictionary for replacement, so s.map(d) is correct syntax.

3. Final Answer:

   s.map(d) -> Option C

4. Quick Check:

   Series.map(dict) = s.map(d) [OK]

Hint: Use Series.map(dict) syntax, not standalone map() [OK]

Common Mistakes:

• Using map() as a standalone function
• Passing dictionary inside a list
• Swapping argument order

3. Given the code:

import pandas as pd
s = pd.Series(['cat', 'dog', 'bird'])
mapping = {'cat': 'feline', 'dog': 'canine'}
result = s.map(mapping)

What is the value of result?

medium

A. 0 feline
1 canine
2 NaN
dtype: object

B. 0 feline
1 canine
2 canine
dtype: object

C. 0 cat
1 dog
2 bird
dtype: object

D. 0 feline
1 canine
2 bird
dtype: object

Solution

1. Step 1: Apply dictionary mapping with map()

   Values 'cat' and 'dog' are replaced by 'feline' and 'canine' respectively. 'bird' is not in the dictionary, so it becomes NaN.

2. Step 2: Check the output Series

   The resulting Series has 'feline', 'canine', and NaN for the third element.

3. Final Answer:

   0 feline 1 canine 2 NaN dtype: object -> Option A

4. Quick Check:

   Missing keys map to NaN [OK]

Hint: Unmapped values become NaN by default [OK]

Common Mistakes:

• Expecting unmapped values to stay unchanged
• Confusing map() with replace() behavior
• Assuming map fills missing keys automatically

4. What is wrong with this code snippet?

import pandas as pd
s = pd.Series([1, 2, 3])
result = s.map({1: 'one', 2: 'two', 3: 'three'})
print(result)

It raises a KeyError. Why?

medium

A. The dictionary keys match Series values, so no error should occur.

B. The dictionary keys do not match Series values type.

C. The dictionary keys are integers but Series has strings.

D. map() cannot accept dictionaries as arguments.

Solution

1. Step 1: Check Series and dictionary key types

   Series contains integers 1, 2, 3 and dictionary keys are also integers 1, 2, 3.

2. Step 2: Understand map() behavior with matching keys

   Since keys match values, map() replaces all values correctly without error.

3. Final Answer:

   The dictionary keys match Series values, so no error should occur. -> Option A

4. Quick Check:

   Matching keys cause no error [OK]

Hint: Matching keys and values avoid KeyError in map() [OK]

Common Mistakes:

• Assuming map() raises error on missing keys
• Confusing map() with indexing behavior
• Thinking map() cannot use dictionaries

5. You have a pandas Series s = pd.Series(['apple', 'banana', 'cherry', 'date']). You want to replace 'banana' and 'date' with their uppercase versions using map(), but keep other fruits unchanged. Which code achieves this?

hard

A. s.map({'banana': 'BANANA', 'date': 'DATE'})

B. s.map({'banana': 'BANANA', 'date': 'DATE'}, na_action='ignore')

C. s.map(lambda x: x if x in ['banana', 'date'] else x.upper())

D. s.map(lambda x: x.upper() if x in ['banana', 'date'] else x)

Solution

1. Step 1: Understand dictionary map behavior

   Using a dictionary with map replaces only keys found; others become NaN, so s.map({'banana': 'BANANA', 'date': 'DATE'}) loses 'apple' and 'cherry'.

2. Step 2: Check lambda function usage

   s.map(lambda x: x.upper() if x in ['banana', 'date'] else x) uses a lambda to uppercase only 'banana' and 'date', else keeps original, preserving all values correctly.

3. Step 3: Analyze other options

   Options A and B produce NaN for 'apple' and 'cherry' since dictionary map() sets NaN for missing keys even with na_action='ignore'; s.map(lambda x: x if x in ['banana', 'date'] else x.upper()) reverses the condition and uppercases wrong fruits.

4. Final Answer:

   s.map(lambda x: x.upper() if x in ['banana', 'date'] else x) -> Option D

5. Quick Check:

   Use lambda for selective transformation [OK]

Hint: Use lambda in map() for conditional changes [OK]

Common Mistakes:

• Using dictionary map without fillna loses data
• Confusing condition in lambda
• Assuming map() changes all values automatically