map() for element-wise transformation in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of map() with dictionary mapping

What is the output of the following code snippet?

Pandas

import pandas as pd

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

A

0    feline
1    canine
2    None
3    feline
dtype: object

B

0    cat
1    dog
2    bird
3    cat
dtype: object

C

0    feline
1    canine
2    bird
3    feline
dtype: object

D

0    feline
1    canine
2      NaN
3    feline
dtype: object

💡 Hint

Think about what happens when a value is not found in the mapping dictionary.

The map() function replaces values found in the dictionary. Values not found become NaN (missing). Here, 'bird' is not in the dictionary, so it becomes NaN.

❓ data_output

intermediate

Result of map() with a function

Given the code below, what is the resulting Series after applying map()?

Pandas

import pandas as pd

s = pd.Series([1, 2, 3, 4])
result = s.map(lambda x: x**2)
print(result)

A

0     1
1     4
2     9
3    16
dtype: int64

B

0     2
1     3
2     4
3     5
dtype: int64

C

0     1
1     8
2    27
3    64
dtype: int64

D

0     1
1     2
2     3
3     4
dtype: int64

💡 Hint

The lambda function squares each element.

map() applies the lambda function to each element, squaring the numbers: 1²=1, 2²=4, 3²=9, 4²=16.

🔧 Debug

advanced

Identify the error in map() usage

What error does the following code raise?

Pandas

import pandas as pd

s = pd.Series(['apple', 'banana', 'cherry'])
result = s.map(['fruit', 'yellow', 'red'])
print(result)

A TypeError: mapping must be a callable, dict, or Series

B TypeError: unhashable type: 'list'

C ValueError: Mapping correspondence is not valid

D No error, outputs a Series with mapped values

💡 Hint

Check what types map() accepts as its argument.

map() expects a function, dictionary, or Series. Passing a list causes a TypeError.

🚀 Application

advanced

Using map() to replace values with a default

You want to replace values in a Series using a dictionary but keep original values if not found in the dictionary. Which code achieves this?

Pandas

import pandas as pd

s = pd.Series(['red', 'blue', 'green', 'yellow'])
mapping = {'red': '#FF0000', 'blue': '#0000FF'}

A s.map(mapping).fillna('unknown')

B s.map(lambda x: mapping.get(x))

C s.map(mapping).fillna(s)

D s.map(mapping).replace({None: s})

💡 Hint

Think about how to keep original values when mapping returns NaN.

map() replaces known values and returns NaN for unknown. fillna(s) replaces NaN with original values.

🧠 Conceptual

expert

Understanding map() behavior with mixed types

Consider a Series with mixed types. What will be the output of map() when mapping integers to strings but the Series contains strings and integers?

Pandas

import pandas as pd

s = pd.Series([1, '2', 3, '4'])
mapping = {1: 'one', 2: 'two', 3: 'three', 4: 'four'}
result = s.map(mapping)
print(result)

A

0      one
1      two
2    three
3      four
dtype: object

B

0      one
1      NaN
2    three
3      NaN
dtype: object

C

0      NaN
1      NaN
2      NaN
3      NaN
dtype: object

D

0      one
1      2
2    three
3      4
dtype: object

💡 Hint

map() matches keys exactly, including type.

The keys in mapping are integers. The Series has strings '2' and '4' which do not match integer keys, so those map to NaN.