Does gender affect who gets a ticket for speeding?
1. Does gender affect who gets a ticket for speeding?
In the last section, we compared the traffic violations committed by each gender. In this section, we'll narrow our focus to the relationship between gender and stop outcomes for one specific violation, namely speeding.2. Filtering by multiple conditions (1)
We'll need to use one additional technique for this analysis, namely filtering a DataFrame by multiple conditions. You may remember this technique from previous courses, but we'll review it here. In the last exercise, you used a single condition, driver_gender equals F, to create a DataFrame of female drivers. It has 23,774 rows because that's the number of rows in the ri DataFrame that satisfy this condition.3. Filtering by multiple conditions (2)
What if we wanted to create a second DataFrame of female drivers, but only those who were arrested? We simply add a second condition to the filter, namely that the is_arrested column equals True. Notice that each condition is surrounded by parentheses, and there is an ampersand between the two conditions, which represents the logical AND operator. The second DataFrame is much smaller because it only includes rows that satisfy both conditions, meaning that it only includes female drivers who were also arrested.4. Filtering by multiple conditions (3)
When filtering a DataFrame by multiple conditions, another option is to use the vertical pipe character between the two conditions. The pipe represents the logical OR operator, which indicates that a row should be included in the DataFrame if it meets either condition. This DataFrame is larger than the last one because it includes all females regardless of whether they were arrested, as well as all drivers who were arrested, regardless of whether they are female.5. Rules for filtering by multiple conditions
Here's a quick summary of the rules for filtering DataFrames by multiple conditions. Use the ampersand to only include rows that satisfy both conditions. Use the pipe to include rows that satisfy either condition. Each condition must be surrounded by parentheses. Conditions can check for equality, inequality, greater than, less than, and so on. And you can use more than two conditions to create a filter.6. Correlation, not causation
In the upcoming exercises, you'll analyze the relationship between gender and stop outcome when a driver is pulled over for speeding. In other words, you're examining the data to assess whether there is a correlation between these two attributes. However, it's important to note that we're not going to draw any conclusions about causation during this course, since we don't have the data or the expertise required to do so. Instead, we're simply exploring the relationships between different attributes in the dataset.7. Let's practice!
Let's go ahead and get started with the exercises.Create Your Free Account
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