String methods on Series in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a Series in pandas?

A Series is a one-dimensional labeled array in pandas that can hold any data type, like numbers or strings. Think of it like a list with labels for each item.

beginner

How do you access string methods on a pandas Series?

You use the .str accessor before the string method. For example, series.str.lower() converts all strings in the Series to lowercase.

beginner

What does series.str.contains('abc') do?

It checks each string in the Series to see if it contains the substring 'abc'. It returns a Series of True or False values.

intermediate

How can you handle missing values when using string methods on a Series?

Most string methods on Series automatically skip missing values (NaN). You can also use na='' or na=False in some methods to control behavior.

beginner

Give an example of using series.str.replace().

If you have a Series with strings and want to replace 'cat' with 'dog', you can use series.str.replace('cat', 'dog'). This changes all 'cat' words to 'dog' in the Series.

Which pandas accessor allows you to use string methods on a Series?

A .s

B .str

C .text

D .string

The correct accessor for string methods on a Series is .str.

What does series.str.upper() do?

A Removes uppercase letters from strings

B Checks if strings contain uppercase letters

C Converts all strings in the Series to uppercase

D Splits strings at uppercase letters

series.str.upper() converts all strings in the Series to uppercase.

If a Series has missing values (NaN), what happens when you use series.str.len()?

A Returns NaN for missing values

B Raises an error

C Ignores missing values and returns length for others

D Replaces missing values with zero

String methods return NaN for missing values by default.

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

A series.str.startswith('data')

B series.str.contains('data')

C series.str.find('data')

D series.str.match('data')

series.str.startswith('data') checks if strings start with 'data'.

What does series.str.split(',') return?

A A single string with commas removed

B An error because split is not supported

C A boolean Series if commas exist

D A Series of lists splitting strings at commas

series.str.split(',') splits each string at commas and returns a Series of lists.

Explain how to use string methods on a pandas Series and give two examples.

Think about how you call string functions on each item in the Series.

Describe how missing values are handled when applying string methods on a Series.

Consider what happens if a string method meets a NaN value.

Practice

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

easy

A. Converts all characters in each string of the Series to lowercase.

B. Removes all whitespace from each string in the Series.

C. Counts the number of characters in each string of the Series.

D. Replaces all digits in each string with a special character.

Solution

1. Step 1: Understand the .str accessor

   The .str accessor allows string methods to be applied element-wise on a pandas Series.

2. Step 2: Identify the effect of .lower()

   The .lower() method converts all uppercase letters in each string to lowercase.

3. Final Answer:

   Converts all characters in each string of the Series to lowercase. -> Option A

4. Quick Check:

   .str.lower() = lowercase conversion [OK]

Hint: Remember .str.lower() makes all letters small [OK]

Common Mistakes:

• Confusing .str.lower() with .str.strip()
• Thinking it removes whitespace
• Assuming it counts characters

2. Which of the following is the correct syntax to check if each string in a pandas Series names contains the substring 'data'?

easy

A. names.contains(str('data'))

B. names.contains('data')

C. names.str.contains(data)

D. names.str.contains('data')

Solution

1. Step 1: Recall the correct usage of string methods on Series

   String methods must be accessed via the .str accessor on a pandas Series.

2. Step 2: Check the correct method call syntax

   The method .contains() requires the substring as a string argument inside quotes.

3. Final Answer:

   names.str.contains('data') -> Option D

4. Quick Check:

   Use .str.contains('substring') for substring check [OK]

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

Common Mistakes:

• Omitting .str accessor
• Passing variable without quotes
• Using .contains() directly on Series

3. Given the pandas Series fruits = pd.Series(['Apple', 'banana', 'Cherry', 'date']), what is the output of fruits.str.upper()?

medium

A. ['apple', 'banana', 'cherry', 'date']

B. ['APPLE', 'banana', 'CHERRY', 'DATE']

C. ['APPLE', 'BANANA', 'CHERRY', 'DATE']

D. ['Apple', 'banana', 'Cherry', 'date']

Solution

1. Step 1: Understand .str.upper() effect on Series

   The .str.upper() method converts all characters in each string to uppercase.

2. Step 2: Apply .str.upper() to each element

   Each fruit name is converted fully to uppercase: 'Apple' -> 'APPLE', 'banana' -> 'BANANA', etc.

3. Final Answer:

   ['APPLE', 'BANANA', 'CHERRY', 'DATE'] -> Option C

4. Quick Check:

   .str.upper() = all uppercase strings [OK]

Hint: Uppercase method makes all letters capital [OK]

Common Mistakes:

• Thinking it only affects first letter
• Confusing with .str.capitalize()
• Expecting original case unchanged

4. What is wrong with this code snippet?

import pandas as pd
s = pd.Series(['cat', 'dog', 'bird'])
s.str.replace('d', '')

medium

A. It raises a FutureWarning and requires regex=false to avoid it.

B. It works correctly and removes 'd' from each string.

C. It raises a TypeError because 'replace' is not a string method.

D. It returns null instead of a Series.

Solution

1. Step 1: Understand .str.replace() default behavior

   By default, .str.replace() treats the first argument as a regex pattern.

2. Step 2: Identify warning about regex default change

   Recent pandas versions raise a FutureWarning suggesting to specify regex=false when replacing literal strings.

3. Final Answer:

   It raises a FutureWarning and requires regex=false to avoid it. -> Option A

4. Quick Check:

   .str.replace() warns about regex=true default [OK]

Hint: Add regex=false to .str.replace() for literal replace [OK]

Common Mistakes:

• Ignoring FutureWarning
• Assuming no warning occurs
• Expecting error instead of warning

5. You have a pandas Series emails = pd.Series(['[e-mail]', '[e-mail]', '[e-mail]']). How can you extract the domain names (like 'example.com') from each email using string methods?

hard

A. emails.str.extract('(@.*)')

B. emails.str.split('@').str[1]

C. emails.str.replace('.*@', '')

D. emails.str.slice(start=emails.str.find('@')+1)

Solution

1. Step 1: Understand the goal to get text after '@'

   We want the part of each string after the '@' symbol, which is the domain.

2. Step 2: Use .str.split('@') and select the second part

   Splitting each string by '@' creates a list; index 1 is the domain part.

3. Final Answer:

   emails.str.split('@').str[1] -> Option B

4. Quick Check:

   Split by '@' and take second element [OK]

Hint: Split by '@' and pick index 1 for domain [OK]

Common Mistakes:

• Using incorrect regex in .str.extract()
• Forgetting to select index after split
• Using .str.replace() incorrectly