Introduction to Hexbin Plots and Seaborn

Hexbin plots are a useful tool for visualizing the relationship between two numerical variables, especially when dealing with large amounts of data. They represent data using hexagonal bins, which helps in avoiding overplotting and revealing patterns in dense datasets. Seaborn is a powerful Python library that makes it easier to create appealing statistical graphics, including hexbin plots.

In this article, we’ll explore how to create hexbin plots using Seaborn, their properties, and their applications with practical examples.

Properties of Hexbin Plots in Seaborn

Seaborn provides the jointplot() function to create hexbin plots. Some important parameters and properties of this function are:

• x and y: The two numerical variables that you want to visualize.
• data: The dataset to load the variables from.
• kind: Type of plot to draw, use ‘hex’ for hexbin plots.
• color: Color used for the hexbins.
• gridsize: The number of hexagons in the x-direction, which indirectly sets the size of the hexagons.
• cmap: Colormap used for the hexbins colors.

When creating a hexbin plot, you can customize its appearance by altering the parameters mentioned above. Here’s how to use them effectively:

1. Choose relevant numerical variables for the x and y axes to bring out the patterns you’re interested in.
2. Decide the appropriate gridsize, which affects the size of the hexagons and the granularity of the plot.
3. Opt for a visually appealing and informative colormap that shows the density of the data points effectively.

Simplified Real-Life Example

Let’s consider a dataset consisting of house prices and their respective sizes in square feet. We’ll create a hexbin plot to visualize the relationship between these two variables using Seaborn.

import seaborn as sns
import pandas as pd

# Sample data
data = {
    'size': [1500, 2000, 2500, 3100, 3600, 4000, 4600, 4100, 4200, 3700],
    'price': [150000, 180000, 250000, 290000, 320000, 400000, 450000, 430000, 410000, 360000]
}

# Create DataFrame
df = pd.DataFrame(data)

# Create hexbin plot
sns.jointplot(x='size', y='price', data=df, kind='hex', color='blue', gridsize=20)

In this example, a hexbin plot shows the distribution of house sizes and prices, revealing any patterns in their relationship.

Complex Real-Life Example

Now let’s consider a more complex scenario. We’ll use a larger dataset containing information about Uber rides in New York City, including the trip duration in seconds, and the trip distance in miles. We’ll visualize the relationship between these two variables using a hexbin plot.

import seaborn as sns
import pandas as pd

# Load the dataset (you may need to change the file path depending on your setup)
df = pd.read_csv('uber_nyc_rides.csv')

# Convert trip duration from seconds to minutes
df['trip_duration'] = df['trip_duration'] / 60

# Filter out duration and distance outliers
filtered_df = df[(df['trip_duration'] <= 180) & (df['trip_distance'] <= 30)]

# Create a hexbin plot with a custom colormap
cmap = sns.cubehelix_palette(dark=0.1, light=0.8, reverse=True, as_cmap=True)

sns.jointplot(
    x='trip_distance',
    y='trip_duration',
    data=filtered_df,
    kind='hex',
    color='#4CB391',
    gridsize=100,
    cmap=cmap,
)

In this example, the hexbin plot shows the density of trips within various duration and distance ranges, revealing insights about the nature of Uber rides in New York City.

Personal Tips

1. Make sure to filter or preprocess the data to remove outliers or irrelevant points, as they can affect the hexbin plot’s granularity and information value.
2. Experiment with different colormaps and colors to enable easier interpretation of the hexbin plots.
3. Combine the hexbin plots with other plotting techniques, like density estimation, to get more insights from your data.
4. Adjust the gridsize depending on the size of your dataset and your desired level of detail for the visualization.

Now, you are equipped with the knowledge and techniques to create functional and visually appealing hexbin plots using Seaborn in Python. Happy plotting!