Hello and welcome! In today's lesson, we will focus on using box plots to analyze the prices of diamonds based on their cut quality. Box plots are an effective way to visualize the distribution of a dataset and can help you extract meaningful insights. Our main goal is to create a box plot that illustrates how diamond prices vary according to cut quality and to learn how to interpret this visualization.
Before plotting our data, it's important to ensure it's clean. Although you already understand data cleaning, let's briefly revisit it in context.
To ensure our dataset is clean:
Python1import seaborn as sns 2import matplotlib.pyplot as plt 3 4# Load the diamonds dataset 5diamonds = sns.load_dataset('diamonds') 6 7# Filter out any entries with missing values 8diamonds = diamonds.dropna() 9 10# Check the dataset after cleaning 11print(diamonds.head()) 12print(f"Total number of cleaned entries: {diamonds.shape[0]}") 13print(diamonds.isnull().sum())
The output of the above code will be:
Plain text1 carat cut color clarity depth table price x y z 20 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43 31 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31 42 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31 53 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63 64 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75 7Total number of cleaned entries: 53940 8carat 0 9cut 0 10color 0 11clarity 0 12depth 0 13table 0 14price 0 15x 0 16y 0 17z 0 18dtype: int64
This output confirms that our dataset is now clean, free from missing values, and ready for further analysis.
Now, we will create a box plot to visualize the distribution of diamond prices across different cut categories.
boxplot
function to generate the plot.Python1import seaborn as sns 2import matplotlib.pyplot as plt 3 4# Load the diamonds dataset 5diamonds = sns.load_dataset('diamonds') 6 7# Filter out any entries with missing values 8diamonds = diamonds.dropna() 9 10# Box plot of prices by cut 11plt.figure(figsize=(10,6)) 12sns.boxplot(x='cut', y='price', data=diamonds) 13plt.title('Box Plot of Prices by Cut') 14plt.xlabel('Cut') 15plt.ylabel('Price') 16plt.show()
The output will be an informative visual representation showing the distribution of diamond prices by cut quality, with box plots for each category.
This creates a box plot where:
Seaborn provides various customization parameters to refine the aesthetics and functionality of your box plot. Here are a few useful ones:
Flier size: Adjusts the size of the outlier markers.
Python1sns.boxplot(x='cut', y='price', data=diamonds, fliersize=8)
Order: Specifies the order of categories on the x-axis.
Python1sns.boxplot(x='cut', y='price', data=diamonds, fliersize=8, order=['Fair', 'Good', 'Very Good', 'Premium', 'Ideal'])
Notch: Adds notches to the box plots to give a rough indication of the uncertainty in the median values.
Python1sns.boxplot(x='cut', y='price', data=diamonds, fliersize=8, order=['Fair', 'Good', 'Very Good', 'Premium', 'Ideal'], notch=True)
Hue: Adds a hue dimension to further categorize the data within each box.
Python1sns.boxplot(x='cut', y='price', data=diamonds, fliersize=8, order=['Fair', 'Good', 'Very Good', 'Premium', 'Ideal'], notch=True, hue='clarity')
Palette: Changes the color palette of the plot.
Python1sns.boxplot(x='cut', y='price', data=diamonds, fliersize=8, order=['Fair', 'Good', 'Very Good', 'Premium', 'Ideal'], notch=True, hue='clarity', palette='Set2')
By leveraging these parameters, you can create box plots that are not only informative but also visually appealing and tailored to your specific analysis needs.
Now that we've created the box plot, let's dig into what it tells us:
Observing the plot, it's clear how prices vary with cut quality:
Outliers are individual points that fall outside the whiskers of the box plot. These can provide significant insights:
Outliers are typically marked as individual points above or below the whiskers.
In this lesson, we created and interpreted a box plot to analyze diamond prices based on cut quality. We discussed how to prepare the data, construct the box plot, interpret its components, and identify outliers. Box plots are powerful tools for summarizing and comparing distributions, making them essential in exploratory data analysis.