Does gender affect who is frisked during a search?
1. Does gender affect who is frisked during a search?
In the last section, you investigated whether the gender of a driver affects the likelihood that their vehicle will be searched. In this section, we'll take a look at what happens during a search.2. Examining the search types
As you've seen previously, the search_conducted field is True if there's a search during a traffic stop, and False otherwise. There's also a related field, search_type, that contains additional information about the search. Notice that the search_type field has 83,229 missing values, which is identical to the number of False values in the search_conducted field. That's because any time a search is not conducted, there's no information to record about a search, and thus the search_type will be missing. Note that the value_counts() method excludes missing values by default, and so we specified dropna equals False in order to see the missing values.3. Examining the search types
There are only five possible values for search_type, which you can see at the top of the value_counts() output: Incident to Arrest, Probable Cause, Inventory, Reasonable Suspicion, and Protective Frisk. But sometimes, multiple values are relevant for a single traffic stop, in which case they're separated by commas. Let's focus on Inventory, meaning searches in which the police took an inventory of the vehicle. Looking at the third line of the value_counts() output, we see 219, which is the number of searches in which Inventory was the only search type. But what if we wanted to know the total number of times in which an inventory was done during a search? We'd also have to include any stops in which Inventory was one of multiple search types. To do this, we'll use a string method.4. Searching for a string (1)
Back in chapter 1, you used a string method to concatenate two columns. This time, we'll use a string method called contains() that checks whether a string is present in each element of a given column. It returns True if the string is found, and False if it's not found. We also specify na equals False, which tells the contains() method to return False when it finds a missing value in the search_type column. We'll save the results in a new column called inventory.5. Searching for a string (2)
As expected, the data type of the column is Boolean. To be clear, a True value in this column means that an inventory was done during a search, and a False value means it was not. We can take the sum() of the inventory column to see that an inventory was done during 441 searches. This includes the 219 stops in which Inventory was the only search type, plus additional stops in which Inventory was one of multiple search types.6. Calculating the inventory rate
What if we wanted to calculate the percentage of searches which included an inventory? You might think this would be as simple as taking the mean() of the inventory column, and the answer would be about 0.5%. But what's wrong with this calculation? 0.5% is the percentage of all traffic stops which resulted in an inventory, including those stops in which a search was not even done. Instead, we first need to filter the DataFrame to only include those rows in which a search was done, and then take the mean() of the inventory column. The correct answer is that 13.3% of searches included an inventory. This is a vastly different result, and it highlights the importance of carefully choosing which rows are relevant before doing a calculation.7. Let's practice!
Let's get started with the exercises, during which you'll use the search_type data to investigate protective frisks.Create Your Free Account
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