Extracting with str.extract (regex) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to extract the first word from each string in the 'text' column.

Data Analysis Python

df['first_word'] = df['text'].str.[1]('(\\w+)')

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Afindall

Bextract

Csplit

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using str.findall returns a list of all matches, not a single extracted group.

Using str.split splits by delimiter, not regex groups.

Using str.replace changes text instead of extracting.

The str.extract method extracts groups from the first match of the regex pattern. Here, it extracts the first word.

2fill in blank

medium

Complete the code to extract the year (4 digits) from the 'date' column strings.

Data Analysis Python

df['year'] = df['date'].str.[1]('(\\d{4})')

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Acontains

Bmatch

Cextract

Dfindall

Attempts:

3 left

💡 Hint

Common Mistakes

Using str.contains returns True/False, not extracted text.

Using str.match only matches from the start of the string.

Using str.findall returns lists, not single extracted values.

str.extract extracts the first group matching the regex pattern, here the 4-digit year.

3fill in blank

hard

Fix the error in the code to extract the domain name from email addresses in the 'email' column.

Data Analysis Python

df['domain'] = df['email'].str.extract('@(\w+\[1]\w+)')

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A.

B*

C+

D?

Attempts:

3 left

💡 Hint

Common Mistakes

Using '+' or '*' without escaping the dot matches wrong patterns.

Not escaping the dot causes it to match any character.

The dot . matches any character in regex. To match a literal dot, it must be escaped as \.. Here, \. is needed to match the dot in domain names.

4fill in blank

hard

Fill both blanks to extract the area code (3 digits) and the phone number (7 digits) from 'phone' column strings.

Data Analysis Python

df[['area_code', 'number']] = df['phone'].str.extract('\(([1])\) ([2])')

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A\d{3}

B\w{3}

C\d{7}

D\w{7}

Attempts:

3 left

💡 Hint

Common Mistakes

Using \w matches letters and digits, not only digits.

Not matching the exact number of digits causes wrong extraction.

The area code is 3 digits, so \d{3} matches it. The phone number is 7 digits, so \d{7} matches it.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Data Analysis Python

lengths = { [1] : [2] for [3] in words if len([3]) > 3 }

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Aword

Blen(word)

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names for key and loop variable causes errors.

Using the wrong expression for length causes wrong results.

The dictionary comprehension uses word as key, len(word) as value, and iterates over word in words. The condition filters words longer than 3.