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pandas로 경찰 활동 분석하기

중급기술 수준

업데이트됨 2024. 9.

Stanford Open Policing Project 데이터셋을 탐색하고, pandas로 성별이 경찰 행동에 미치는 영향을 분석합니다.

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PythonData Manipulation

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16 동영상

50 연습 문제

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이제 pandas의 기초를 배웠으니, 실제 데이터셋으로 흥미로운 질문에 답해 보며 그 지식을 적용해 볼 차례예요! Stanford Open Policing Project 데이터셋을 탐색하고, 교통 단속에서 성별이 경찰의 행동에 어떤 영향을 미치는지 분석해 봅니다. 과정 전반에 걸쳐 지저분한 데이터를 정리하고, 시각화를 만들고, 데이터셋을 결합·재구조화하며, 시계열 데이터를 다루는 연습을 하게 돼요. pandas로 경찰 활동을 분석하기 코스는 데이터셋을 처음부터 끝까지 분석하는 소중한 경험을 제공하여 데이터 사이언스 커리어를 준비하는 데 도움이 될 거예요!

선수 조건

Joining Data with pandas

1

Preparing the data for analysis

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.

Stanford Open Policing Project dataset

Examining the dataset

Dropping columns

Dropping rows

Using proper data types

Finding an incorrect data type

Fixing a data type

Creating a DatetimeIndex

Combining object columns

Setting the index

2

Exploring the relationship between gender and policing

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!

Do the genders commit different violations?

Examining traffic violations

Comparing violations by gender

Does gender affect who gets a ticket for speeding?

Filtering by multiple conditions

Comparing speeding outcomes by gender

Does gender affect whose vehicle is searched?

Calculating the search rate

Comparing search rates by gender

Adding a second factor to the analysis

Does gender affect who is frisked during a search?

Counting protective frisks

Comparing frisk rates by gender

3

Visual exploratory data analysis

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.

Does time of day affect arrest rate?

Calculating the hourly arrest rate

Plotting the hourly arrest rate

Are drug-related stops on the rise?

Plotting drug-related stops

Comparing drug and search rates

What violations are caught in each district?

Tallying violations by district

Plotting violations by district

How long might you be stopped for a violation?

Converting stop durations to numbers

Plotting stop length

4

Analyzing the effect of weather on policing

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.

Exploring the weather dataset

Plotting the temperature

Plotting the temperature difference

Categorizing the weather

Counting bad weather conditions

Rating the weather conditions

Changing the data type to category

Merging datasets

Preparing the DataFrames

Merging the DataFrames

Does weather affect the arrest rate?

Comparing arrest rates by weather rating

Selecting from a multi-indexed Series

Reshaping the arrest rate data

pandas로 경찰 활동 분석하기

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