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pandasで警察活動を分析する

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更新日 2024/09

Stanford Open Policing Projectのデータを用い、pandasで性別が警察の行動に与える影響を分析します。

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PythonData Manipulation

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コース説明

pandasの基礎を学んだあなたが、実データを使って興味深い問いに答えながら知識を実践できるコースです。Stanford Open Policing Project のデータセットを探索し、交通取り締まりにおける警察の行動に性別が与える影響を分析します。コースを通して、乱れたデータのクリーニング、可視化の作成、データセットの結合と整形、時系列データの操作をさらに練習します。pandasで警察活動を分析することで、データセットを最初から最後まで分析する貴重な経験を積み、データサイエンスのキャリアに備えることができます。

前提条件

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

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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

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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

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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

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pandasで警察活動を分析する

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