str.len() for string length in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of str.len() on a pandas Series

What is the output of the following code snippet?

Pandas

import pandas as pd
s = pd.Series(['apple', 'banana', 'cherry', None])
result = s.str.len()
print(result)

A

0    5
1    6
2    6
3    None
dtype: object

B

0    5
1    6
2    6
3    0
dtype: int64

C

0    5.0
1    6.0
2    6.0
3    NaN
dtype: float64

D

0    5
1    6
2    6
3    NaN
dtype: float64

💡 Hint

Remember that missing values in pandas string operations usually result in NaN, not zero or None.

The str.len() method returns the length of each string in the Series. For missing values (None), it returns NaN. The dtype is float64 because NaN is a float.

❓ data_output

intermediate

Count of strings with length greater than 4

Given the Series below, what is the count of strings with length greater than 4?

Pandas

import pandas as pd
s = pd.Series(['dog', 'elephant', 'cat', 'lion', 'tiger', None])
count = (s.str.len() > 4).sum()
print(count)

A 2

B 3

C 4

D 5

💡 Hint

Check which strings have length more than 4 and remember None values are ignored in comparison.

'elephant' (8) and 'tiger' (5) have length > 4. 'lion' (4) does not. 'dog' (3), 'cat' (3), and None (NaN, False in comparison) do not. Count is 2.

❓ visualization

advanced

Plot string length distribution

You have a pandas Series of strings. Which code snippet correctly plots a histogram of string lengths?

Pandas

import pandas as pd
import matplotlib.pyplot as plt
s = pd.Series(['red', 'blue', 'green', 'yellow', 'purple', 'orange'])
# Fill in the missing code to plot histogram of string lengths

A

plt.hist(s.str.length())
plt.show()

B

plt.hist(s.len())
plt.show()

C

plt.hist(len(s))
plt.show()

D

plt.hist(s.str.len())
plt.show()

💡 Hint

Use the pandas string accessor to get lengths before plotting.

s.str.len() returns a Series of string lengths. plt.hist() can plot this numeric data. Other options are invalid because len() is not a pandas method or attribute.

🔧 Debug

advanced

Identify the error in string length calculation

What error will the following code raise?

Pandas

import pandas as pd
s = pd.Series(['a', 'bb', 'ccc'])
lengths = s.len()
print(lengths)

A AttributeError: 'Series' object has no attribute 'len'

B TypeError: 'Series' object is not callable

C No error, prints lengths

D ValueError: Length mismatch

💡 Hint

Check if 'len' is a direct method of Series or accessed differently.

The Series object does not have a 'len()' method. The correct way is to use s.str.len() to get string lengths.

🚀 Application

expert

Filter DataFrame rows by string length in a column

Given a DataFrame df with a column 'Name', which code correctly filters rows where the 'Name' length is exactly 5 characters?

Pandas

import pandas as pd
df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve']})
# Fill in the code to filter rows with Name length 5

A

filtered = df[df['Name'].apply(len) == 5]
print(filtered)

B

filtered = df[df['Name'].str.len() == 5]
print(filtered)

C

filtered = df[df['Name'].len() == 5]
print(filtered)

D

filtered = df[df['Name'].str.length() == 5]
print(filtered)

💡 Hint

Use the pandas string accessor for vectorized string length operations.

Option B uses the correct pandas string accessor str.len() to get lengths. Option B works but is slower because it uses apply with Python len. Options B and D are invalid methods.

Practice

1. What does the pandas method str.len() do when applied to a Series of strings?

easy

A. It returns the length of each string in the Series.

B. It counts the number of words in each string.

C. It converts all strings to lowercase.

D. It removes spaces from each string.

Solution

1. Step 1: Understand the method purpose

   The str.len() method is designed to count characters in each string element of a pandas Series.

2. Step 2: Compare with other options

   Counting words, converting case, or removing spaces are different string operations, not what str.len() does.

3. Final Answer:

   It returns the length of each string in the Series. -> Option A

4. Quick Check:

   str.len() = length count [OK]

Hint: Remember: str.len() counts characters, not words or changes text [OK]

Common Mistakes:

• Confusing length with word count
• Thinking it changes string case
• Assuming it removes spaces

2. Which of the following is the correct syntax to get the length of each string in a pandas Series named names?

easy

A. names.str.length()

B. names.len()

C. len(names)

D. names.str.len()

Solution

1. Step 1: Recall pandas string accessor usage

   To use string methods on a Series, you must use the .str accessor followed by the method name.

2. Step 2: Identify the correct method name

   The method to get string length is len(), so the correct syntax is names.str.len().

3. Final Answer:

   names.str.len() -> Option D

4. Quick Check:

   Use .str.len() on Series for string lengths [OK]

Hint: Use .str before string methods on Series [OK]

Common Mistakes:

• Calling len() directly on Series
• Using .len() without .str
• Using incorrect method name like .length()

3. Given the pandas Series data = pd.Series(['apple', 'banana', '', None]), what is the output of data.str.len()?

medium

A. [5, 6, 0, 0]

B. [5, 6, NaN, 0]

C. [5, 6, 0, NaN]

D. [5, 6, 1, NaN]

Solution

1. Step 1: Calculate length for each string

   'apple' has 5 characters, 'banana' has 6, '' is empty so length 0, and None is missing so length is NaN.

2. Step 2: Understand handling of None values

   pandas returns NaN for None or missing values when using str.len().

3. Final Answer:

   [5, 6, 0, NaN] -> Option C

4. Quick Check:

   Empty string = 0, None = NaN in str.len() [OK]

Hint: Empty string length is 0; None becomes NaN [OK]

Common Mistakes:

• Counting None as 0 instead of NaN
• Assuming empty string length is NaN
• Mixing order of output values

4. What is wrong with this code snippet?

import pandas as pd
data = pd.Series(['cat', 'dog', 'bird'])
lengths = data.len()

medium

A. The method len() should be called as data.str.len()

B. The Series object does not support len() method

C. The code is correct and will run without errors

D. You must import numpy to use len() on Series

Solution

1. Step 1: Identify method usage on Series

   The len() function is a Python built-in and cannot be called directly on a pandas Series to get string lengths.

2. Step 2: Correct method to get string lengths

   To get length of strings in a Series, use data.str.len() instead of data.len().

3. Final Answer:

   The method len() should be called as data.str.len() -> Option A

4. Quick Check:

   Use .str.len() for string lengths on Series [OK]

Hint: Use .str before string methods on Series [OK]

Common Mistakes:

• Calling len() directly on Series
• Forgetting .str accessor
• Assuming Series has len() method for strings

5. You have a pandas DataFrame df with a column 'comments' containing text data. You want to create a new column 'length' that stores the length of each comment, but some comments are missing (NaN). Which code correctly creates the 'length' column with lengths and keeps NaN for missing comments?

hard

A. df['length'] = df['comments'].apply(len)

B. df['length'] = df['comments'].str.len()

C. df['length'] = df['comments'].map(len)

D. df['length'] = df['comments'].fillna('').str.len()

Solution

1. Step 1: Understand handling of missing values

   The str.len() method returns NaN for missing values, preserving them in the new column.

2. Step 2: Compare with other options

   Using apply(len) or map(len) will cause errors on NaN values because Python's len() cannot handle None/NaN. Filling NaN with empty string changes missing data to length 0, which is incorrect.

3. Final Answer:

   df['length'] = df['comments'].str.len() -> Option B

4. Quick Check:

   str.len() keeps NaN for missing data [OK]

Hint: Use str.len() to keep NaN for missing strings [OK]

Common Mistakes:

• Using apply(len) causing errors on NaN
• Replacing NaN with empty string losing missing info
• Using map(len) without handling NaN