Creating Strip Plots

Hello and welcome! In this lesson, you'll delve into the creation and customization of a Strip Plot using the Seaborn library in Python. We'll use the diamonds dataset to visualize the distribution of diamond prices based on their clarity. By the end of this lesson, you'll know how to generate and customize a strip plot, and interpret the results for meaningful insights.

A strip plot is a type of plot that represents individual datapoints, and it's specifically useful for showing the distribution of a dataset across different categories. Here, each point corresponds to an observation in the dataset.

Visualizing data with strip plots helps us:

• Identify the spread and density of data points.
• Observe patterns and clusters for different categories.
• Detect anomalies or outliers.

Dots in a strip plot can show you how the values are distributed across unique categories, which makes it an insightful visualization tool.

Let's construct the basic strip plot for diamond prices by clarity.

import seaborn as sns
import matplotlib.pyplot as plt

diamonds = sns.load_dataset('diamonds')

plt.figure(figsize=(10, 6))
sns.stripplot(x='clarity', y='price', data=diamonds)
plt.title('Strip Plot of Price by Clarity')
plt.xlabel('Clarity')
plt.ylabel('Price')
plt.show()

• plt.figure(figsize=(10, 6)): This sets the size of the figure for better visualization.
• sns.stripplot(x='clarity', y='price', data=diamonds): This creates the strip plot with clarity on the x-axis and price on the y-axis.
• Adding the title and axis labels helps in understanding the plot better.

For better readability and presentation, we might need to customize our strip plot. Customizations improve the clarity of the visualization and can highlight important aspects of the dataset.

import seaborn as sns
import matplotlib.pyplot as plt

diamonds = sns.load_dataset('diamonds')

plt.figure(figsize=(10, 6))
sns.stripplot(x='clarity', y='price', hue='clarity', data=diamonds, jitter=True, palette='Set2', size=4, legend=False)
plt.title('Customized Strip Plot of Price by Clarity')
plt.xlabel('Clarity')
plt.ylabel('Price')
plt.show()

• hue='clarity': Assigns the clarity variable to the hue parameter for color differentiation.
• legend=False: Disables the legend, as each category's color is already clear from the x-axis labels.
• jitter=True: Adds some randomness to the placement of the points along the categorical axis to make them more distinguishable.
• palette='Set2': Chooses a color palette for the points, enhancing the visual appearance.
• size=4: Adjusts the size of the dots for better visibility.

The output of the above code will show a more visually appealing strip plot, with adjustments such as jitter, palette, and dot size helping to differentiate the points more clearly and making the plot easier to interpret.