Extracting with str.extract (regex) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code using str.extract?

Given the pandas Series with mixed strings, what will be the output of extracting the first number using regex?

Data Analysis Python

import pandas as pd
s = pd.Series(['apple 123', 'banana 456', 'cherry 789'])
result = s.str.extract(r'(\d+)')
print(result)

A

       0
0    123
1    456
2    789

B

       0
0  apple
1  banana
2  cherry

C

Empty DataFrame
Columns: []
Index: [0, 1, 2]

D

0    123
1    456
2    789
dtype: object

💡 Hint

Remember str.extract returns a DataFrame with matched groups as columns.

The regex '(\d+)' captures one or more digits. str.extract returns a DataFrame with one column named 0 containing the matched digits as strings.

❓ data_output

intermediate

How many rows have a match in this extraction?

Using str.extract with this regex, how many rows will have a non-null value?

Data Analysis Python

import pandas as pd
s = pd.Series(['cat123', 'dog', 'bird456', 'fish'])
result = s.str.extract(r'(\d+)')
count = result[0].notnull().sum()
print(count)

A 1

B 3

C 4

D 2

💡 Hint

Check which strings contain digits.

Only 'cat123' and 'bird456' contain digits, so 2 rows have matches.

🔧 Debug

advanced

What error does this code raise?

What error will this code raise when trying to extract with an invalid regex pattern?

Data Analysis Python

import pandas as pd
s = pd.Series(['abc123', 'def456'])
result = s.str.extract(r'(\d++')
print(result)

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

B re.error: missing ), unterminated subpattern at position 4

C TypeError: expected string or bytes-like object

D KeyError: 0

💡 Hint

Look carefully at the regex pattern syntax.

The regex '(\d++' is invalid because of the extra '+' and missing closing parenthesis, causing a regex compilation error.

🚀 Application

advanced

Extract date components from strings

Given a Series of date strings like '2023-06-15', which option correctly extracts year, month, and day into separate columns?

Data Analysis Python

import pandas as pd
s = pd.Series(['2023-06-15', '2024-01-30', '2022-12-05'])
result = s.str.extract(r'(\d{4})-(\d{2})-(\d{2})')
print(result)

A

       0
0  2023-06-15
1  2024-01-30
2  2022-12-05

B

0    2023-06-15
1    2024-01-30
2    2022-12-05
dtype: object

C

       0   1   2
0  2023  06  15
1  2024  01  30
2  2022  12  05

D

Empty DataFrame
Columns: []
Index: [0, 1, 2]

💡 Hint

Each pair of parentheses captures one group as a column.

The regex has three groups capturing year, month, and day. str.extract returns a DataFrame with three columns named 0, 1, 2.

🧠 Conceptual

expert

Why does str.extract return a DataFrame instead of a Series?

Choose the best explanation for why pandas str.extract returns a DataFrame even when extracting a single group.

A Because regex groups can be multiple, so str.extract always returns a DataFrame with one column per group for consistency.

B Because str.extract only works on DataFrames, not Series.

C Because pandas Series cannot hold string data, so DataFrame is used instead.

D Because the output must always be numeric, and DataFrame enforces this.

💡 Hint

Think about how many groups regex can capture.

Regex can capture multiple groups, so pandas returns a DataFrame with one column per group to handle all cases uniformly.