5.8 Exploratory Data Analysis with Matplotlib and Seaborn
Exploratory data analysis (AED) is a fundamental step in the Machine Learning and Deep Learning process. It allows data scientists to better understand the structure, distribution, and relationships between variables in a data set. Visual tools like Matplotlib and Seaborn are essential to accomplish this task effectively, offering a wide range of graphs and visualizations that make data interpretation easier. In this chapter, we will explore the use of histograms, boxplots, and scatter plots to perform efficient exploratory data analysis using Python.
Histograms
Histograms are graphs that show the frequency distribution of a set of continuous data. They are essential for understanding the shape of data distribution, identifying modes, asymmetries and possible outliers. In Python, the Matplotlib library is commonly used to create histograms through the hist() function.
import matplotlib.pyplot as plt
# Example data
data = [numeric_values]
# Creating the histogram
plt.hist(data, bins='auto') # 'bins' defines the number of bars in the histogram
plt.title('Data Histogram')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()
With Seaborn, the process is equally simple, using the distplot() function, which in addition to the histogram, can also include a kernel density line (KDE) to estimate the distribution of the data.
import seaborn as sns
# Creating the histogram with Seaborn
sns.distplot(data, bins=30, kde=True)
plt.title('Histogram with KDE')
plt.xlabel('Value')
plt.ylabel('Density')
plt.show()
Boxplots
Boxplots are another powerful tool for exploratory data analysis. They provide a visual representation of the data distribution, highlighting the median, quartiles and outliers. Boxplots are particularly useful for comparing distributions across multiple groups or categories of data.
With Matplotlib, a boxplot can be created using the boxplot() function:
import matplotlib.pyplot as plt
# Example data
data = [group1, group2, group3]
# Creating the boxplot
plt.boxplot(data)
plt.title('Data Groups Boxplot')
plt.xlabel('Group')
plt.ylabel('Value')
plt.show()
Seaborn further simplifies the creation of boxplots with the boxplot() function, which allows direct integration with pandas DataFrames and automatic data categorization.
import seaborn as sns
# Creating the boxplot with Seaborn
sns.boxplot(x='category', y='value', data=DataFrame)
plt.title('Boxplot by Category')
plt.xlabel('Category')
plt.ylabel('Value')
plt.show()
Scatter Plots
Scatter plots, or dispersion graphs, are essential for visualizing the relationship between two quantitative variables. They help identify correlations, patterns, and groupings in data. Both Matplotlib and Seaborn offer functions to create scatter plots efficiently.
Using Matplotlib, a scatter plot can be generated with the scatter():
import matplotlib.pyplot as plt
# Example data
x = [value_x1, value_x2, value_x3]
y = [y_value1, y_value2, y_value3]
# Creating the scatter plot
plt.scatter(x, y)
plt.title('Scatter Plot of Variables X and Y')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
With Seaborn, the scatterplot() function allows you to create scatter plots with additional features such as color coding by category and inclusion of regression lines.
import seaborn as sns
# Creating the scatter plot with Seaborn
sns.scatterplot(x='x', y='y', hue='category', data=DataFrame)
plt.title('Scatter Plot with Categories')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
Conclusion
Exploratory data analysis is a crucial step in the process of developing Machine Learning and Deep Learning models. Using histograms, boxplots and scatter plots makes data easier to understand and helps identify patterns, correlations and anomalies. The Matplotlib and Seaborn libraries are powerful tools that offer a wide range of functionality for data visualization in Python. By mastering these techniques, data scientists can extract valuable insights and more effectively prepare data for subsequent modeling.
