5.1 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 us to better understand the structure, patterns and anomalies present in the data before applying more complex techniques. To perform effective AED, we often turn to data visualization libraries like Matplotlib and Seaborn, which are powerful tools for creating a wide variety of graphs and visualizations.
Library Import
The first step is to import the necessary libraries. Matplotlib is a plotting library for the Python programming language and its numerical mathematics package, NumPy. Seaborn is a data visualization library based on Matplotlib that offers a high-level interface for drawing attractive and informative statistical graphs.
import matplotlib.pyplot as plt
import seaborn as sns
With these imports, we have access to the functionalities of these powerful tools. Let's explore how we can use them to perform efficient exploratory data analysis.
Exploring Data with Matplotlib
Matplotlib is one of the most popular and widely used libraries for data visualization in Python. It gives you full control over the style and format elements of your charts, making it very flexible.
To start, we can create a simple graph to understand the distribution of a single variable. For example, if we have a set of data on the heights of individuals, we can use a histogram to visualize this distribution:
plt.hist(data['height'], bins=30, edgecolor='black')
plt.title('Height Distribution')
plt.xlabel('Height (cm)')
plt.ylabel('Frequency')
plt.show()
This code will produce a histogram with 30 bars (or "bins"), allowing us to see where the most heights are concentrated.
We can also compare two variables using a scatterplot. If we wanted to examine the relationship between height and weight, for example, we could do:
plt.scatter(date['height'], date['weight'])
plt.title('Height and Weight Ratio')
plt.xlabel('Height (cm)')
plt.ylabel('Weight (kg)')
plt.show()
This chart will help us visualize whether there is any apparent correlation between height and weight in the data we have.
Exploring Data with Seaborn
Seaborn is built on top of Matplotlib and offers a more user-friendly and stylized interface for creating plots. Additionally, it has built-in functions to create plots that would be more complex to do with Matplotlib.
For example, we can create a boxplot, which is useful for visualizing the distribution of a variable and identifying outliers:
sns.boxplot(x='category', y='value', date=date)
plt.title('Boxplot of Values by Category')
plt.xlabel('Category')
plt.ylabel('Value')
plt.show()
This graph shows us the median, quartiles and outliers for the variable "value", grouped by "category".
Seaborn also makes it easier to visualize pairs of variables with pairplots, which create a matrix of plots for each pair of variables:
sns.pairplot(data)
plt.show()
This code will generate a grid of graphs that shows the relationship between each pair of variables in the dataset, which is extremely useful for quickly identifying relationships between multiple variables.
Customizing Graphics
With both Matplotlib and Seaborn, we have the ability to customize our plots to make them more informative and aesthetically pleasing. We can change colors, line styles, add annotations, and much more.
For example, to define a specific style in Seaborn and increase the font size of titles, we can do:
sns.set_style('whitegrid')
sns.set_context('talk', font_scale=1.2)
With these settings, all charts created next will have a white grid background and larger titles, which may be better suited for presentations or reports.
Conclusion
Exploratory data analysis is a crucial step in machine learning and deep learning, and data visualization plays a key role in this process. The Matplotlib and Seaborn libraries are essential tools for any data scientist, enabling deep understanding of data through graphs and visualizations. Through visual exploration, we can identify patterns, trends and anomalies that are key to subsequent modeling and extracting insights. With practice, these tools become even more powerful, allowing you to create complex visualizations that can reveal the story behind a project.the data.
