Are you ready for another captivating session? Today, we are taking a step further into the captivating world of data visualization by learning how to use box plots. Box plots are unique in providing a snapshot of a dataset's distribution and outlier detection, all in one plot!

Box plots are crucial in understanding the Titanic dataset, particularly in discovering relationships between survival rates, passenger classes, and fares. This can answer our central question: How did the passenger class and fare correlate with survival?

A box plot, also known as a whisker plot, is a standardized way of displaying the data distribution based on a five-number summary: the minimum, the maximum, the sample median, and the first and third quartiles. The box shows the quartiles of the dataset while the whiskers extend to show the rest of the distribution, except for points that are determined to be “outliers” using a method that is a function of the interquartile range.

We can create a box plot using the boxplot() function in the Python Seaborn library. First, let's start with pclass (passenger class) against fare:

import seaborn as sns
import matplotlib.pyplot as plt

# Load the dataset
titanic_df = sns.load_dataset('titanic')

# Create a box plot
sns.boxplot(x='pclass', y='fare', data=titanic_df)
plt.title('Fares vs Passenger Classes')
plt.show()

In the box plot:

• The box represents the interquartile range (i.e., 25th to 75th percentile) of the fares in each passenger class.
• The line in the middle of the box is the median fare price in that class.
• The whiskers (lines extending from the box) represent the fare range within 1.5 times the interquartile range above the upper and lower quartile.
• Any points beyond the whiskers can be considered outliers in the fare distribution within each class.

A great feature of box plots in Seaborn is that it allows you to add a hue parameter to add a third dimension of categorical data. For instance, we can differentiate the passengers who survived from those who didn't on the same pclass vs fare plot:

sns.boxplot(x='pclass', y='fare', hue='survived', data=titanic_df)
plt.title('Fares vs Passenger Classes Differentiated by Survival')
plt.show()

This plot visually compares fares among different passenger classes regarding their survival status, enhancing our grasp of the data.