Exploring EDA techniques

We can perform EDA through different techniques. In this chapter, we focus on two techniques:

• Non-visual EDA – We calculate some statistics or metrics to extract insights from data.
• Visual EDA – We use graphs to extract insights from data.

You will see the main concepts behind the two techniques through a practical example in Python.

This section is organized as follows:

• Load and prepare the dataset.
• Non-visual EDA.
• Visual EDA.

Let's start from the first step: loading and preparing the dataset.

Loading and preparing the dataset

Let's consider the Hotel Booking dataset available at https://www.kaggle.com/jessemostipak/hotel-booking-demand?select=hotel_bookings.csv under the CC-BY 4.0 license. Let's proceed as follows:

1. Firstly, we import all the Python packages we will use in this example:

   import pandas as pd
   import matplotlib.pyplot as plt
   import seaborn as sns
   from datetime...