#### Overview of this book

Businesses today operate online and generate data almost continuously. While not all data in its raw form may seem useful, if processed and analyzed correctly, it can provide you with valuable hidden insights. The Data Analysis Workshop will help you learn how to discover these hidden patterns in your data, to analyze them, and leverage the results to help transform your business. The book begins by taking you through the use case of a bike rental shop. You'll be shown how to correlate data, plot histograms, and analyze temporal features. As you progress, you’ll learn how to plot data for a hydraulic system using the Seaborn and Matplotlib libraries, and explore a variety of use cases that show you how to join and merge databases, prepare data for analysis, and handle imbalanced data. By the end of the book, you'll have learned different data analysis techniques, including hypothesis testing, correlation, and null-value imputation, and will have become a confident data analyst.
Preface
1. Bike Sharing Analysis
Free Chapter
2. Absenteeism at Work
3. Analyzing Bank Marketing Campaign Data
4. Tackling Company Bankruptcy
5. Analyzing the Online Shopper's Purchasing Intention
6. Analysis of Credit Card Defaulters
7. Analyzing the Heart Disease Dataset
8. Analyzing Online Retail II Dataset
9. Analysis of the Energy Consumed by Appliances
10. Analyzing Air Quality

# Outliers

You should recall that an outlier is a data point that is different from the majority of data points. When visualized, this data point is far away from the rest—hence, the name outlier. For example, if you have a set of 12 numbers, of which 11 are between 1 and 6 and 1 has the value of 37, that data point will be an outlier because it is extremely different and far away from the rest of the data points.

Boxplots are a type of visualization that are great for visualizing outliers. They provide us with a lot of information about our data, such as the median, the first quartile, the third quartile, the minimum and maximum values, as well as the existence of outliers.

Let's do a quick exercise based on the example of 12 numbers to understand how to spot an outlier from a boxplot.

## Exercise 10.02: Identifying Outliers

In this exercise, you will create a small DataFrame with only 12 rows, each consisting of a random number. You will then plot this column...