Leveraging Seaborn for Advanced Data Visualization: Combining Boxplots and Histograms with the Iris Dataset
Introduction
Exploratory Data Analysis (EDA) often requires various types of visualizations to understand the underlying patterns and characteristics of the data. Seaborn, a Python data visualization library based on Matplotlib, provides a high-level and aesthetically pleasing graphical interface for drawing attractive and informative statistical graphics.
In this article, we will explore an advanced technique of combining a boxplot and a histogram into a single figure using Seaborn and the Iris dataset. This approach allows us to visualize both the distribution and statistical properties of a dataset in one go.
What are Boxplots and Histograms?
– **Boxplot**: A boxplot is a standardized way of displaying the distribution of data based on a five-number summary: minimum, first quartile (Q1), median, third quartile (Q3), and maximum.
– **Histogram**: A histogram is an approximate representation of the distribution of numerical data. It divides the data into bins and shows the frequency of data points within each bin.
Code Explanation
Import Libraries and Dataset
The first step involves importing the essential libraries (Seaborn and Matplotlib) and loading the Iris dataset.
```python
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset("iris")
```Setting the Background Style
We set the background to dark grid lines against a white backdrop for better visibility and contrast.
```python
sns.set(style="darkgrid")
```Creating a Combined Figure
We create a figure composed of two subplots (`ax_box` and `ax_hist`) with different height ratios.
```python
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.15, .85)})
```Assigning Graphs to Each Subplot
We then assign a boxplot and a histogram to these subplots.
```python
sns.boxplot(df["sepal_length"], ax=ax_box)
sns.histplot(data=df, x="sepal_length", ax=ax_hist)
```Customizations
Lastly, we remove the x-axis label for the boxplot to avoid redundancy.
```python
ax_box.set(xlabel='')
```Displaying the Plot
We use `plt.show()` to display the combined figure.
```python
plt.show()
```End-to-End Code Example
Here’s the complete, rewritten code:
```python
# Import necessary libraries and dataset
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset("iris")
# Set the background style
sns.set(style="darkgrid")
# Create a figure with two subplots
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.15, .85)})
# Assign a boxplot and histogram to the subplots
sns.boxplot(x=df["sepal_length"], ax=ax_box)
sns.histplot(data=df, x="sepal_length", ax=ax_hist)
# Remove x-axis label for the boxplot
ax_box.set(xlabel='')
# Show the plot
plt.show()
```Elaborated Prompts for Further Exploration
1. How can you add titles to the individual subplots?
2. How would you customize the colors of the boxplot and the histogram?
3. What other Seaborn themes can you apply to this combined figure?
4. How can you adjust the number of bins in the histogram?
5. Is it possible to add a Kernel Density Estimate (KDE) line to the histogram? How?
6. How would you add y-axis labels to both subplots?
7. Can you add a legend to the histogram?
8. How can you adjust the size and aspect ratio of the combined figure?
9. What happens when you change the height ratios for the subplots?
10. How would you save this combined figure as an image?
11. Can you create a similar combined figure for another numerical feature, like `sepal_width`?
12. Is it possible to stack histograms for different Iris species in the same subplot?
13. How would you annotate statistical measures like mean and median on the plot?
14. Can you use other types of plots, like `violinplot`, instead of a boxplot?
15. What are some real-world scenarios where this combined figure would be useful?
Conclusion
Combining a boxplot and a histogram into a single Seaborn figure offers a comprehensive way to understand both the distribution and statistical properties of a dataset. This advanced technique is particularly useful for EDA, as it provides a fuller picture of the data you are working with. By understanding how to create and customize these combined figures, you can significantly enhance your data visualization capabilities.
Find more … …
End-to-End Machine Learning: model selection in R using boxplot