Aan de slagGa gratis aan de slag

Thuis
Cursussen
Politie-activiteit analyseren met pandas

Beïnvloedt het tijdstip van de dag het arrestatiepercentage?

Create Your Free Account

or

Email Address

Password

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Deze oefening maakt deel uit van de cursus

Politie-activiteit analyseren met pandas

SkillTag.level.intermediateSkillTag.label

4.9+

Begin de cursus gratis

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?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: Beïnvloedt het tijdstip van de dag het arrestatiepercentage?

Huidige oefening

Exercise 2: Het uurlijkse arrestatiepercentage berekenen Exercise 3: De uurlijkse arrestatiegraad plotten Exercise 4: Nemen druggerelateerde staandehoudingen toe?Exercise 5: Stops met drugs-gerelateerde vondsten plotten Exercise 6: Drugs- en fouilleerpercentages vergelijken Exercise 7: Welke overtredingen worden in elk district geconstateerd?Exercise 8: Overtredingen per district turven Exercise 9: Overtredingen per district plotten Exercise 10: Hoe lang kun je worden aangehouden voor een overtreding?Exercise 11: Stopduren omzetten naar getallen Exercise 12: Stoptijd plotten

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