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Analyzing Police Activity with pandas

Does gender affect who gets a ticket for speeding?

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Analyzing Police Activity with pandas

SkillTag.level.intermediateSkillTag.label

4.8+

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Before beginning your analysis, it is critical that you first examine and clean the dataset, to make working with it a more efficient process. In this chapter, you will practice fixing data types, handling missing values, and dropping columns and rows while learning about the Stanford Open Policing Project dataset.

Exercise 1: Stanford Open Policing Project dataset Exercise 2: Examining the dataset Exercise 3: Dropping columns Exercise 4: Dropping rows Exercise 5: Using proper data types Exercise 6: Finding an incorrect data type Exercise 7: Fixing a data type Exercise 8: Creating a DatetimeIndex Exercise 9: Combining object columns Exercise 10: Setting the index

Does the gender of a driver have an impact on police behavior during a traffic stop? In this chapter, you will explore that question while practicing filtering, grouping, method chaining, Boolean math, string methods, and more!

Exercise 1: Do the genders commit different violations?Exercise 2: Examining traffic violations Exercise 3: Comparing violations by gender Exercise 4: Does gender affect who gets a ticket for speeding?

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Exercise 5: Filtering by multiple conditions Exercise 6: Comparing speeding outcomes by gender Exercise 7: Does gender affect whose vehicle is searched?Exercise 8: Calculating the search rate Exercise 9: Comparing search rates by gender Exercise 10: Adding a second factor to the analysis Exercise 11: Does gender affect who is frisked during a search?Exercise 12: Counting protective frisks Exercise 13: Comparing frisk rates by gender

Are you more likely to get arrested at a certain time of day? Are drug-related stops on the rise? In this chapter, you will answer these and other questions by analyzing the dataset visually, since plots can help you to understand trends in a way that examining the raw data cannot.

Exercise 1: Does time of day affect arrest rate?Exercise 2: Calculating the hourly arrest rate Exercise 3: Plotting the hourly arrest rate Exercise 4: Are drug-related stops on the rise?Exercise 5: Plotting drug-related stops Exercise 6: Comparing drug and search rates Exercise 7: What violations are caught in each district?Exercise 8: Tallying violations by district Exercise 9: Plotting violations by district Exercise 10: How long might you be stopped for a violation?Exercise 11: Converting stop durations to numbers Exercise 12: Plotting stop length

In this chapter, you will use a second dataset to explore the impact of weather conditions on police behavior during traffic stops. You will practice merging and reshaping datasets, assessing whether a data source is trustworthy, working with categorical data, and other advanced skills.

Exercise 1: Exploring the weather dataset Exercise 2: Plotting the temperature Exercise 3: Plotting the temperature difference Exercise 4: Categorizing the weather Exercise 5: Counting bad weather conditions Exercise 6: Rating the weather conditions Exercise 7: Changing the data type to category Exercise 8: Merging datasets Exercise 9: Preparing the DataFrames Exercise 10: Merging the DataFrames Exercise 11: Does weather affect the arrest rate?Exercise 12: Comparing arrest rates by weather rating Exercise 13: Selecting from a multi-indexed Series Exercise 14: Reshaping the arrest rate data Exercise 15: Conclusion