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:

1. We need to filter out any entries with missing values.
2. We will then confirm that the dataset is clean by inspecting the first few rows and the total number of entries after cleaning.

Python
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1import 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:

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1   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.

1. Objective: To compare the distribution of diamond prices based on their cut quality.
2. Setup: We will use Seaborn's boxplot function to generate the plot.
3. Customization: Set the figure size for better readability and add titles and labels for clarity.

Python
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1import 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:

• The x-axis represents the different cut categories (Fair, Good, Very Good, Premium, Ideal).
• The y-axis represents the price of diamonds.
• Each box plot shows the distribution of prices for a specific cut category by representing the spread and central tendency.

Seaborn provides various customization parameters to refine the aesthetics and functionality of your box plot. Here are a few useful ones:

1. Flier size: Adjusts the size of the outlier markers.

   Python
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   1sns.boxplot(x='cut', y='price', data=diamonds, fliersize=8)

   

2. Order: Specifies the order of categories on the x-axis.

   Python
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   1sns.boxplot(x='cut', y='price', data=diamonds, fliersize=8, order=['Fair', 'Good', 'Very Good', 'Premium', 'Ideal'])

   

3. Notch: Adds notches to the box plots to give a rough indication of the uncertainty in the median values.

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   1sns.boxplot(x='cut', y='price', data=diamonds, fliersize=8, order=['Fair', 'Good', 'Very Good', 'Premium', 'Ideal'], notch=True)

   

4. Hue: Adds a hue dimension to further categorize the data within each box.

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   1sns.boxplot(x='cut', y='price', data=diamonds, fliersize=8, order=['Fair', 'Good', 'Very Good', 'Premium', 'Ideal'], notch=True, hue='clarity')

   

5. Palette: Changes the color palette of the plot.

   Python
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   1sns.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:

1. Median (Q2): The line inside the box represents the median price of diamonds for each cut category.
2. Quartiles (Q1 and Q3): The box itself spans from the first quartile (25th percentile) to the third quartile (75th percentile), capturing the middle 50% of the data.
3. Whiskers: These extend from the box to show the range of the data within 1.5 times the interquartile range (IQR) from the quartiles.
4. Outliers: Points beyond the whiskers are considered outliers.

Observing the plot, it's clear how prices vary with cut quality:

• Median Prices: Premium cuts generally have higher median prices compared to Very Good cuts.
• Spread: Ideal cut diamonds have a wider spread in prices compared to Fair cut diamonds.

Outliers are individual points that fall outside the whiskers of the box plot. These can provide significant insights:

1. Outliers in diamond prices might indicate diamonds with exceptional characteristics or errors in data entry.
2. Identifying these outliers helps in understanding the range and variability in diamond prices for each cut category.

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.