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Analyzing Police Activity with pandas

Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.

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4 Hours16 Videos50 Exercises60,360 Learners4100 XPData Manipulation with Python TrackData Scientist with Python Track

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Course Description

Now that you have learned the foundations of pandas, this course will give you the chance to apply that knowledge by answering interesting questions about a real dataset! You will explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior. During the course, you will gain more practice cleaning messy data, creating visualizations, combining and reshaping datasets, and manipulating time series data. Analyzing Police Activity with pandas will give you valuable experience analyzing a dataset from start to finish, preparing you for your data science career!
  1. 1

    Preparing the data for analysis

    Free

    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.

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    Stanford Open Policing Project dataset
    50 xp
    Examining the dataset
    100 xp
    Dropping columns
    100 xp
    Dropping rows
    100 xp
    Using proper data types
    50 xp
    Finding an incorrect data type
    50 xp
    Fixing a data type
    100 xp
    Creating a DatetimeIndex
    50 xp
    Combining object columns
    100 xp
    Setting the index
    100 xp
  2. 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!

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    Do the genders commit different violations?
    50 xp
    Examining traffic violations
    100 xp
    Comparing violations by gender
    100 xp
    Does gender affect who gets a ticket for speeding?
    50 xp
    Filtering by multiple conditions
    50 xp
    Comparing speeding outcomes by gender
    100 xp
    Does gender affect whose vehicle is searched?
    50 xp
    Calculating the search rate
    100 xp
    Comparing search rates by gender
    100 xp
    Adding a second factor to the analysis
    100 xp
    Does gender affect who is frisked during a search?
    50 xp
    Counting protective frisks
    100 xp
    Comparing frisk rates by gender
    100 xp
  3. 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.

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    Does time of day affect arrest rate?
    50 xp
    Calculating the hourly arrest rate
    100 xp
    Plotting the hourly arrest rate
    100 xp
    Are drug-related stops on the rise?
    50 xp
    Plotting drug-related stops
    100 xp
    Comparing drug and search rates
    100 xp
    What violations are caught in each district?
    50 xp
    Tallying violations by district
    100 xp
    Plotting violations by district
    100 xp
    How long might you be stopped for a violation?
    50 xp
    Converting stop durations to numbers
    100 xp
    Plotting stop length
    100 xp
  4. 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.

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    Exploring the weather dataset
    50 xp
    Plotting the temperature
    100 xp
    Plotting the temperature difference
    100 xp
    Categorizing the weather
    50 xp
    Counting bad weather conditions
    100 xp
    Rating the weather conditions
    100 xp
    Changing the data type to category
    100 xp
    Merging datasets
    50 xp
    Preparing the DataFrames
    100 xp
    Merging the DataFrames
    100 xp
    Does weather affect the arrest rate?
    50 xp
    Comparing arrest rates by weather rating
    100 xp
    Selecting from a multi-indexed Series
    100 xp
    Reshaping the arrest rate data
    100 xp
    Conclusion
    50 xp

In the following tracks

Data Manipulation with PythonData Scientist with Python

Datasets

Traffic stops in Rhode IslandWeather in Providence, Rhode Island

Collaborators

Becca Robins
Sara Snell

Prerequisites

Joining Data with pandas
Kevin Markham Headshot

Kevin Markham

Founder of Data School

Kevin is the founder of Data School, an online school for learning data science with Python. He is passionate about teaching people who are new to the field, regardless of their educational and professional backgrounds. Currently, he teaches machine learning and data analysis to over 10,000 students each month through the Data School YouTube channel. He has a degree in Computer Engineering from Vanderbilt University.
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