Question

How do you select rows from a DataFrame 'df' where the 'Salary' column is greater than 50000?

• A. df.select_rows("Salary$>$50000")
• B. df[df['Salary'] $>$ 50000]
• C. df["Salary$>$50000"]
• D. df.rows("Salary$>$50000")

Hint:

Boolean indexing is formatted as: df[df['column_name'] condition].
Remember to write the dataframe name twice to filter rows properly.

Answer 1

The Correct Option is B

Step 1: Understanding the Question:
The question asks for the correct syntax to filter or select rows from a Pandas DataFrame named df based on a condition applied to the 'Salary' column.

Step 2: Key Formula or Approach:
In Pandas, boolean indexing is the standard method for filtering rows.
The syntax is df[condition], where condition is a boolean series created by applying a comparison operator to a column, such as df['column_name'] > value.

Step 3: Detailed Explanation:
Let's analyze the syntax:
- df['Salary'] > 50000 evaluates to a series of boolean values (True or False) for every row in the DataFrame, indicating whether the 'Salary' is greater than 50000.
- Placing this boolean series inside the square brackets of the DataFrame, i.e., df[df['Salary'] > 50000], filters the rows. Pandas retains only those rows where the boolean condition is True.
- Let's check other options:
- df.select_rows() is not a valid Pandas DataFrame method.
- df["Salary>50000"] attempts to access a column named exactly "Salary>50000", which does not exist and will raise a KeyError.
- df.rows() is also not a valid Pandas method.
Therefore, the correct and standard Pandas syntax is option (B).

Step 4: Final Answer:
The correct option is (B).