#### 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

# Imputation of Missing Values

In this section, we will be looking at two different methods that we can use to handle the missing values:

• Mean imputation
• Iterative imputation

Let's look at each of these methods in detail.

## Mean Imputation

In mean imputation, the missing values are filled with the mean of each column where the missing values are located. We will be performing mean imputation on the DataFrames in the next exercise.

## Exercise 4.03: Performing Mean Imputation on the DataFrames

In this exercise, you will perform mean imputation on the first DataFrame. This exercise is a continuation of Exercise 4.02, Performing Missing Value Analysis for the DataFrames. Follow these steps to complete this exercise:

1. Import `Imputer` from `sklearn.preprocessing` to perform mean imputation to fill in the missing values:
```from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, \
...```