Almost every dataset contains categorical information—and often it’s an unexplored goldmine of information. In this chapter, you’ll learn how pandas handles categorical columns using the data type category.  You’ll also discover how to group data by categories to unearth great summary statistics.

Course introduction

Categorical vs. numerical

Exploring a target variable

Ordinal categorical variables

Categorical data in pandas

Setting dtypes and saving memory

Creating a categorical pandas Series

Setting dtype when reading data

Grouping data by category in pandas

Create lots of groups

Setting up a .groupby() statement

Using pandas functions effectively

Introduction to Categorical Data

Now it’s time to learn how to set, add, and remove categories from a Series. You’ll also explore how to update, rename, collapse, and reorder categories, before applying your new skills to clean and access other data within your DataFrame.

Setting category variables

Setting categories

Adding categories

Removing categories

Updating categories

Collapsing categories knowledge check

Renaming categories

Collapsing categories

Reordering categories

Reordering categories in a Series

Using .groupby() after reordering

Cleaning and accessing data

Cleaning variables

Accessing and filtering data

Categorical pandas Series

In this chapter, you’ll use the seaborn Python library to create informative visualizations using categorical data—including categorical plots (cat-plot), box plots, bar plots, point plots, and count plots. You’ll then learn how to visualize categorical columns and split data across categorical columns to visualize summary statistics of numerical columns.

Pendahuluan ke plot kategorikal menggunakan Seaborn

Memahami boxplot

Membuat box plot

Plot batang Seaborn

Membuat bar plot

Mengurutkan kategori

Plot batang menggunakan hue

Plot titik dan plot hitung

Membuat point plot

Membuat count plot

Data Science Process

Meninjau jenis-jenis catplot()

Opsi tambahan catplot()

Satu visualisasi per grup

Memperbarui plot kategorikal

Visualizing Categorical Data

Lastly, you’ll learn how to overcome the common pitfalls of using categorical data. You’ll also grow your data encoding skills as you are introduced to label encoding and one-hot encoding—perfect for helping you prepare your data for use in machine learning algorithms.

Categorical pitfalls

Memory usage knowledge check

Overcoming pitfalls: string issues

Overcoming pitfalls: using NumPy arrays

Label encoding

Create a label encoding and map

Using saved mappings

Creating a Boolean encoding

One-hot encoding

One-hot knowledge check

One-hot encoding specific columns

Wrap-up video

Pitfalls and Encoding

Adult Census Income

Adoptable Dogs

Tripadvisor Reviews

Used Cars

Mampu memahami, menggunakan, dan merangkum data non-numerik—seperti golongan darah atau status perkawinan seseorang—merupakan keterampilan penting bagi seorang data scientist. Dalam kursus ini, Anda akan belajar memanipulasi dan memvisualisasikan data kategorikal menggunakan pandas dan seaborn. Melalui latihan praktik, Anda akan memahami tipe data kategorikal pada pandas, termasuk cara membuat, menghapus, dan memperbarui kolom kategorikal. Anda juga akan bekerja dengan beragam himpunan data, termasuk karakteristik anjing adopsi, ulasan perjalanan Las Vegas, dan data sensus untuk mengembangkan keterampilan Anda dalam mengolah data kategorikal.

Data Manipulation with pandas

Pelajari cara memanipulasi dan memvisualisasikan data kategorikal dengan pandas dan seaborn.

Bekerja dengan Data Kategorikal di Python

Pelajari cara memanipulasi dan memvisualisasikan data kategorikal menggunakan pandas dan seaborn.

Associate Data Scientist  dalam Python

Memahami boxplot

Bekerja dengan Data Kategorikal di Python

Latihan interaktif praktis