Welcome! Data analysis often starts with a question, like "Which of our customers are over 30?" To answer this, we need to look at a specific slice of our data. This process of selecting rows based on a rule is called filtering.
Engagement Message
Why might looking at all customers be less useful than looking at customers over 30?
In the Pandas library, data is stored in a table-like structure called a DataFrame. Think of it as a smart spreadsheet. Each row is a record (like a single customer) and each column is an attribute (like age or city).
Engagement Message
What advantage does organizing data in rows and columns give us?
Filtering is how we select rows that meet a specific rule, or "condition." For example, we might want to see only the rows where the 'City' column is 'London'. This helps us zoom in on the exact information we need for our analysis.
Engagement Message
What's a real-world example of filtering you use daily?
To create a filter in Pandas, you write a condition. For instance, to find customers older than 30, the condition is df['Age'] > 30. This checks the 'Age' column for each row and asks, "Is this value greater than 30?"
Engagement Message
What do you think the result of this check would be for each row?
The result of a condition is a series of True or False values—one for each row. This is called a boolean mask. True means the row matches our condition, and means it doesn't. It's like a stencil for our data.
