Selecting rows by condition in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of selecting rows by condition in pandas?

Selecting rows by condition helps you filter data to keep only the rows that meet specific criteria, making it easier to analyze relevant information.

beginner

How do you select rows where a column 'Age' is greater than 30 in a pandas DataFrame named df?

Use df[df['Age'] > 30] to get all rows where the 'Age' column value is greater than 30.

beginner

What does the expression df[df['Score'] >= 80] return?

It returns a new DataFrame containing only the rows where the 'Score' column is 80 or higher.

intermediate

How can you select rows where column 'City' is either 'New York' or 'Chicago'?

Use df[df['City'].isin(['New York', 'Chicago'])] to select rows where 'City' is one of the listed values.

intermediate

What is the difference between using & and | operators in pandas conditions?

& means AND (both conditions must be true), | means OR (at least one condition must be true). Remember to use parentheses around each condition.

Which code selects rows where 'Salary' is less than 50000?

A df.loc['Salary' < 50000]

B df['Salary'] < 50000

C df[df['Salary'] < 50000]

D df['Salary'].filter(<50000)

df[df['Salary'] < 50000] filters the DataFrame to keep rows where 'Salary' is less than 50000.

How do you select rows where 'Age' is between 20 and 30 inclusive?

A df[(df['Age'] >= 20) & (df['Age'] <= 30)]

B df[df['Age'] > 20 and df['Age'] < 30]

C df[df['Age'] >= 20 | df['Age'] <= 30]

D df[df['Age'].between(20, 30)]

Use & with parentheses: df[(df['Age'] >= 20) & (df['Age'] <= 30)] or use df[df['Age'].between(20, 30)] to select rows where Age is between 20 and 30.

What does df[df['Status'] == 'Active'] do?

A Selects rows where 'Status' is 'Active'

B Selects rows where 'Status' is not 'Active'

C Changes 'Status' to 'Active'

D Deletes rows where 'Status' is 'Active'

It filters the DataFrame to keep only rows where the 'Status' column equals 'Active'.

Which method helps select rows where a column matches any value in a list?

A filter()

B isin()

C match()

D contains()

The isin() method checks if values in a column are in a list of values.

How do you combine two conditions to select rows where 'Age' > 25 OR 'Score' > 90?

A df[(df['Age'] > 25) & (df['Score'] > 90)]

B df[df['Age'] > 25 and df['Score'] > 90]

C df[df['Age'] > 25 or df['Score'] > 90]

D df[(df['Age'] > 25) | (df['Score'] > 90)]

Use | for OR with parentheses: df[(df['Age'] > 25) | (df['Score'] > 90)].

Explain how to select rows in a pandas DataFrame based on a condition involving a single column.

Think about how you write conditions like 'Age > 30' inside the brackets.

Describe how to combine multiple conditions to filter rows in pandas.

Remember to use parentheses and the right logical operators.