str.len() for string length in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to get the length of each string in the 'Name' column.

Pandas

df['Name_length'] = df['Name'].str.[1]()

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

Alen

Blength

Ccount

Dsize

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'length' instead of 'len' causes an AttributeError.

Using 'count' counts occurrences of a substring, not string length.

The str.len() function returns the length of each string in the Series.

2fill in blank

medium

Complete the code to filter rows where the 'City' name length is greater than 5.

Pandas

filtered = df[df['City'].str.len() [1] 5]

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

A<

B==

C<=

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' selects shorter or equal length strings, not longer ones.

Using '==' selects only strings exactly length 5.

The condition str.len() > 5 selects rows where the city name has more than 5 characters.

3fill in blank

hard

Fix the error in the code to correctly calculate string lengths in the 'Country' column.

Pandas

lengths = df['Country'].str.[1]()

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

Alen

Blen()

Clength()

Dlength

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'len' without parentheses returns the method object instead of the lengths.

Using 'length' or 'length()' causes AttributeError.

The str.len() method is used with parentheses to get a Series of lengths.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 4 characters.

Pandas

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

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

A__len__()

Blen()

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'len()' in the first blank causes an error because 'len' is a function, not a method of string.

Using '<=' in the second blank selects shorter words, not longer.

Use word.__len__() to get the length of each word, and filter words with length greater than 4 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values for words shorter than 6 characters.

Pandas

result = { [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()' for keys.

Using '>' instead of '<' in the filter condition.

Using 'word' instead of 'len(word)' for values.

The dictionary keys are uppercase words (word.upper()), values are their lengths (len(word)), and the filter selects words shorter than 6 (<).

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