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.

Menjelajahi himpunan data cuaca

Memplot suhu

Memplot selisih suhu

Mengategorikan cuaca

Menghitung kondisi cuaca buruk

Memberi peringkat kondisi cuaca

Mengubah tipe data menjadi category

Menggabungkan himpunan data

Menyiapkan DataFrame

Menggabungkan DataFrame

Apakah cuaca memengaruhi tingkat penangkapan?

Membandingkan tingkat penangkapan berdasarkan penilaian cuaca

Memilih dari Series dengan MultiIndex

Mengubah bentuk data tingkat penangkapan

Kesimpulan

Analyzing the effect of weather on policing

Traffic stops in Rhode Island

Weather in Providence, Rhode Island

Setelah mempelajari dasar-dasar pandas, kursus ini memberi Anda kesempatan untuk menerapkan pengetahuan tersebut dengan menjawab pertanyaan menarik tentang himpunan data nyata! Anda akan mengeksplorasi himpunan data Stanford Open Policing Project dan menganalisis dampak gender terhadap perilaku polisi. Sepanjang kursus, Anda akan berlatih kembali membersihkan data yang berantakan, membuat visualisasi, menggabungkan dan membentuk ulang himpunan data, serta memanipulasi data deret waktu. Menganalisis Aktivitas Polisi dengan pandas akan memberi Anda pengalaman berharga menganalisis himpunan data dari awal hingga akhir, mempersiapkan Anda untuk karier data science!

Joining Data with pandas

Jelajahi dataset Stanford Open Policing Project dan analisis dampak gender pada perilaku polisi dengan pandas.

Menganalisis Aktivitas Polisi dengan pandas

Jelajahi dataset Stanford Open Policing Project dan analisis dampak gender terhadap perilaku polisi menggunakan pandas.

Menjelajahi himpunan data cuaca

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