Hello and welcome! In today's lesson, we will focus on visualizing the distribution of diamond prices using histograms and Kernel Density Estimates (KDE). This visualization is a crucial part of Exploratory Data Analysis (EDA) and helps us uncover patterns in our data.
By the end of this lesson, you will be able to create a histogram, overlay it with a KDE, and interpret the resulting visualization effectively.
A histogram is a type of bar plot that groups data points into specified ranges (bins) and then displays the number of points that fall into each bin. This makes histograms useful for understanding the distribution, central tendency, and variability of your data. Here is a simple example, with the corresponding figure below:
Python1import matplotlib.pyplot as plt 2 3data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4] 4plt.hist(data, bins=4) 5plt.show()
The hist()
function in Matplotlib takes several parameters:
Kernel Density Estimate (KDE) is a method used to estimate the probability density function of a continuous variable. Unlike histograms, KDEs provide a smooth curve representing the data distribution, as presented below. This can offer a clearer picture of the data.
Python1import seaborn as sns 2 3data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4] 4 5sns.kdeplot(data)
The kdeplot()
function takes several parameters:
Now that we've loaded the dataset, it's time to plot our first histogram for the 'price' column. We will use sns.histplot()
from the seaborn
library to create the histogram. This function allows us to easily plot a histogram and customize it as needed.
Python1import seaborn as sns 2import matplotlib.pyplot as plt 3 4diamonds = sns.load_dataset('diamonds') 5 6plt.figure(figsize=(10, 6)) 7sns.histplot(diamonds['price'], bins=50) 8plt.title('Distribution of Diamond Prices') 9plt.xlabel('Price') 10plt.ylabel('Frequency') 11plt.show()
figsize
for better readability.To enhance our histogram, we can overlay it with a KDE to provide a smoother density estimate. KDE will help us visualize the data distribution more smoothly, providing clearer insight into the data's density patterns.
Python1plt.figure(figsize=(10, 6)) 2sns.histplot(diamonds['price'], kde=True, bins=50) 3plt.title('Distribution of Diamond Prices with KDE') 4plt.xlabel('Price') 5plt.ylabel('Frequency') 6plt.show()
Customization is key to making our plots not only informative but also presentable. We can modify colors, edge colors, grid lines, and more to improve the plot's readability and aesthetics.
Python1plt.figure(figsize=(10, 6)) 2sns.histplot(diamonds['price'], kde=True, bins=50, color='blue', edgecolor='black') 3plt.title('Customized Distribution of Diamond Prices with KDE') 4plt.xlabel('Diamond Price ($)') 5plt.ylabel('Frequency') 6plt.grid(True) 7plt.show()
Finally, let's discuss how to interpret our histogram and KDE plot. In our histogram, if we observe more bars on the lower end with a gradual decline as prices increase, it suggests that lower-priced diamonds are more common. The KDE line will provide a smoother interpretation of this pattern.
Great job! Today, you've learned how to visualize the distribution of diamond prices using histograms and KDE. You can now load datasets, plot histograms, add KDE overlays, customize your plots, and interpret the visualizations effectively.
These skills are vital for exploratory data analysis, allowing you to uncover hidden patterns and distributions in your data. Practice these techniques with other dataset columns to master the art of data visualization, and let's continue our journey in data science!