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

Preparing the data for analysis

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

Exploring the relationship between gender and policing

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

Visual exploratory data analysis

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

Conclusion

Analyzing the effect of weather on policing

Traffic stops in Rhode Island

Weather in Providence, Rhode Island

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!

Joining Data with pandas

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

Analyzing Police Activity with pandas

Founder, Data School

Analyzing datetime data

Accessing datetime attributes (1)

Accessing datetime attributes (2)

Calculating the monthly mean price

Plotting the monthly mean price

Line plot

Let's practice!

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Visual exploratory data analysis - Does time of day affect arrest rate?

Does time of day affect arrest rate?

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