The exercise
dataset in Seaborn is a built-in dataset that contains data about an experiment measuring the effects of exercise on pulse rates under different conditions. This dataset is often used for demonstrating data visualization techniques, especially for categorical plots, as it has multiple categorical and numerical variables.
Structure of the exercise
Dataset
The dataset typically includes the following columns:
time
: The time of measurement (e.g., “1 min”, “15 min”, “30 min”).kind
: The type of exercise (e.g., “rest”, “walking”, “running”).pulse
: The pulse rate of the individual.diet
: The diet category of the individual (e.g., “low fat”, “no fat”).
Example of Loading the Dataset
You can load and preview the dataset using Seaborn like this:
import seaborn as sns
import pandas as pd
# Load the exercise dataset
exercise = sns.load_dataset("exercise")
# Preview the dataset
print(exercise.head())
Sample Output
The dataset might look like this:
time | kind | pulse | diet | |
---|---|---|---|---|
0 | 1 min | rest | 90 | low fat |
1 | 15 min | walking | 96 | low fat |
2 | 30 min | running | 128 | no fat |
3 | 1 min | rest | 88 | low fat |
4 | 15 min | walking | 97 | no fat |
Usage
The exercise
dataset is useful for visualizing:
- The relationship between exercise type (
kind
) and pulse rate. - Changes in pulse over time (
time
) for different exercise types. - Comparisons across different diet groups.
Example Visualization
Here’s an example of how you might visualize the data:
import seaborn as sns
import matplotlib.pyplot as plt
# Create a boxplot of pulse by kind of exercise
sns.boxplot(data=exercise, x="kind", y="pulse", hue="diet")
plt.title("Pulse Rate by Exercise Type and Diet")
plt.show()
This would generate a grouped boxplot showing how pulse rates vary across exercise types and diet groups.
The target variable in a dataset depends on the context of the analysis or the specific question being addressed. In the case of the Seaborn exercise
dataset, the target variable is usually the one you are trying to predict, analyze, or understand.
Here are some possibilities based on different contexts:
- If you are analyzing the effect of exercise and diet on heart rate, the target variable is likely:
pulse
(since it is a numeric variable that measures the outcome).
- If you are classifying exercise type based on pulse rate and other factors, the target variable could be:
kind
(the type of exercise: “rest”, “walking”, or “running”).
- If you are studying dietary categories, the target variable could be:
diet
(e.g., “low fat” or “no fat”).
In Summary:
- Most commonly,
pulse
is treated as the target variable because the dataset is often used to analyze how factors like exercise type, time, and diet affect the pulse rate. - However, the target variable ultimately depends on your analysis goal.