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Politie-activiteit analyseren met pandas

Heeft gender invloed op wie een bekeuring krijgt voor te hard rijden?

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Politie-activiteit analyseren met pandas

SkillTag.level.intermediateSkillTag.label

4.9+

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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: Maken mannen en vrouwen verschillende overtredingen?Exercise 2: Verkeersovertredingen onderzoeken Exercise 3: Overtredingen vergelijken naar gender Exercise 4: Heeft gender invloed op wie een bekeuring krijgt voor te hard rijden?

Huidige oefening

Exercise 5: Filteren met meerdere voorwaarden Exercise 6: Snelheidsovertredingen vergelijken naar gender Exercise 7: Beïnvloedt gender van wie het voertuig wordt doorzocht?Exercise 8: Het zoekpercentage berekenen Exercise 9: Zoekpercentages vergelijken op basis van gender Exercise 10: Een tweede factor toevoegen aan de analyse Exercise 11: Heeft gender invloed op wie wordt gefouilleerd tijdens een doorzoeking?Exercise 12: Protective frisks tellen Exercise 13: Fouilleerpercentages vergelijken per geslacht

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