BaşlayınÜcretsiz Başlayın

Giriş
Kurslar
pandas ile Polis Faaliyetlerini Analiz Etme

Arama sırasında kimin üstünün arandığını cinsiyet etkiler mi?

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.

Bu egzersiz

pandas ile Polis Faaliyetlerini Analiz Etme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

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: Cinsiyetler farklı ihlaller mi yapıyor?Exercise 2: Trafik ihlallerini inceleme Exercise 3: İhlallerin cinsiyete göre karşılaştırılması Exercise 4: Cinsiyet, hız yaptığı için kimin ceza aldığına etki eder mi?Exercise 5: Birden çok koşula göre filtreleme Exercise 6: Cinsiyete göre hız aşımı sonuçlarını karşılaştırma Exercise 7: Cinsiyet, kimin aracının arandığını etkiler mi?Exercise 8: Arama oranını hesaplama Exercise 9: Cinsiyete göre arama oranlarını karşılaştırma Exercise 10: Analize ikinci bir faktör eklemek Exercise 11: Arama sırasında kimin üstünün arandığını cinsiyet etkiler mi?

Geçerli Egzersiz

Exercise 12: Koruyucu yoklamaları sayma Exercise 13: Cinsiyete göre üst arama oranlarını karşılaştırma

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