5.4 Exploratory Data Analysis with Matplotlib and Seaborn: Univariate Analysis
Exploratory data analysis (AED) is a crucial step in machine learning and deep learning modeling. It allows you to better understand data characteristics, identify patterns and anomalies, and make informed decisions about the preprocessing required before applying learning algorithms. Univariate analysis, which focuses on the distribution of a single variable, is a fundamental component of AED. In this context, the Matplotlib and Seaborn libraries in Python offer powerful tools for visualizing and interpreting data.
Matplotlib: The Foundation of Visualization in Python
Matplotlib is a graph plotting library widely used in the Python community. It offers a low-level interface that allows you to create custom, detailed graphs, and is especially useful for univariate analysis, where visualization needs specific details of a variable.
Histograms with Matplotlib
One of the most common graphs in univariate analysis is the histogram. It allows you to visualize the frequency distribution of a continuous variable. With Matplotlib, a histogram can be created as follows:
import matplotlib.pyplot as plt
# Example data
data = [value1, value2, value3, ...]
# Creating the histogram
plt.hist(data, bins=30, alpha=0.5, color='blue')
# Adding titles and labels
plt.title('Variable Distribution')
plt.xlabel('Value')
plt.ylabel('Frequency')
# Displaying the graph
plt.show()
The bins parameter defines the number of class bins in the histogram and can be adjusted to provide more or less detail about the distribution. The alpha parameter controls the transparency of the barrels, which is useful when overlaying multiple histograms.
Seaborn: Statistical Data Visualization
Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for drawing attractive statistical graphs. It is particularly suitable for visualizations that summarize data in an informative and attractive way.
Distributions with Seaborn
Seaborn simplifies the creation of graphs for univariate analysis, such as distribution plots. For example, the Seaborn distribution plot combines a histogram with a kernel density estimate (KDE) for a smoother, more continuous visualization of the distribution:
import seaborn as sns
# Example data
data = [value1, value2, value3, ...]
# Creating the distribution graph
sns.displot(data, kde=True, color='green')
# Adding titles and labels (using Matplotlib)
plt.title('Variable Distribution with KDE')
plt.xlabel('Value')
plt.ylabel('Density')
# Displaying the graph
plt.show()
The kde=True argument adds the Kernel Density Estimation curve to the graph, providing a smooth view of the distribution. The displot function is one of several high-level functions that Seaborn offers for creating different types of distribution plots.
Boxplots and Violin Plots
Other useful tools for univariate analysis include boxplots and violin plots, which provide information about the median, quartiles, and outliers of a distribution.
# Creating a boxplot with Seaborn
sns.boxplot(x=data)
# Creating a violin plot with Seaborn
sns.violinplot(x=data)
Boxplots provide a visual representation of the five summary numbers of a distribution: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. Violin plots combine the information from a boxplot with the probability density of the variable, offering a richer view of the distribution of the data.
Personalization and Aesthetics
Both Matplotlib and Seaborn allow you to customize graphs to improve aesthetics and clarity. Elements such as color palette, line style, bullets, and annotations can be adjusted to create more informative and engaging visualizations.
Seaborn, in particular, comes with predefined chart styles and color palettes that can be applied with simple commands such as sns.set_style() and sns.set_palette() . These customizations can help highlight important patterns and make charts more understandable for different audiences.
Conclusion
Univariate analysis is an essential part of exploratory data analysis and provides valuable insights into the individual distribution of variables. Using the Matplotlib and Seaborn libraries, you can create a variety of visualizations that facilitate data interpretation and support decision making in machine learning and deep learning modeling. The familiarity withThese tools and techniques are a valuable asset to any data scientist or machine learning engineer.
