str accessor for string methods in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the str accessor in pandas?

The str accessor allows you to apply string methods to each element of a pandas Series that contains text data, making it easy to manipulate strings in a column.

beginner

How do you convert all text in a pandas Series to lowercase using the str accessor?

Use series.str.lower() to convert all strings in the Series to lowercase letters.

beginner

What will series.str.contains('apple') return?

It returns a Series of boolean values indicating whether each string in the Series contains the substring 'apple'.

intermediate

Can you chain multiple string methods using the str accessor? Give an example.

Yes, you can chain methods like series.str.strip().str.upper() to first remove whitespace and then convert text to uppercase.

intermediate

What happens if you use a string method with str accessor on a Series with missing (NaN) values?

The string methods safely handle missing values by returning NaN for those entries without causing errors.

Which pandas code converts all strings in a Series names to uppercase?

A names.upper()

B names.str.upper()

C str.upper(names)

D names.to_upper()

The str accessor is needed to apply string methods element-wise on a Series.

What does series.str.len() return?

A Length of each string in the Series

B Length of the Series

C Number of non-null strings

D Boolean if strings have length

It returns a Series with the length of each string element.

How do you check if strings in a Series start with 'data'?

A series.str.startswith('data')

B series.startswith('data')

C str.startswith(series, 'data')

D series.str.contains('data')

The str.startswith() method checks the start of each string.

If a Series has missing values, what does series.str.lower() do to those?

A Raises an error

B Converts them to empty strings

C Leaves them as NaN

D Removes them

Missing values remain NaN and do not cause errors.

Which method removes whitespace from the start and end of strings in a Series?

A series.trim()

B series.strip()

C series.str.trim()

D series.str.strip()

The str.strip() method removes leading and trailing whitespace.

Explain how the str accessor helps when working with text data in pandas Series.

Think about how you would clean or search text in a list of words.

Describe a real-life example where you might use series.str.contains() in data analysis.

Imagine you have a list of product names and want to find all that mention 'organic'.