#### 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

# Missing Value Analysis

One of the major steps in data analysis is missing value analysis. The primary reason we need to perform missing value analysis is to know how much data is missing in a column and how we are going to handle it.

In general, missing values can be handled in two ways. The first way is to drop the rows with missing values, unless the percentage of missing values is high (for example, 40% missing values in a column) as this will lead to loss of information.

The second method is imputing missing values, which is where we fill in the missing values based on the imputation method employed. For example, in mean imputation, we use the mean value of the particular column to fill in the missing values.

The next step is missing value analysis.

In order to find out how many missing values are present in the DataFrame, we are going to introduce you to a package called `missingno`, which will help you visualize the count of missing values in the DataFrames.