Crash Course in Data — Uncovering Relationships: Visualizing Multivariate Data with Scatter Plot Matrices
Scatter plot matrix is a matrix (or grid) of scatter plots where each scatter plot in the grid is created between different combinations of variables.
In other words, scatter plot matrix represents bi-variate or pairwise relationship between different combinations of variables while laying them in grid form.
A scatterplot matrix, also known as a pair plot or SPLOM, is a visualization tool that allows you to explore relationships between multiple variables. Here are some of the more common uses of the scatter matrix:
- Exploratory Data Analysis:
The scatter matrix is often used in exploratory data analysis to visualize relationships between multiple variables in a data set. By looking at scatter plots, you can quickly identify interesting patterns or trends in the data, as well as any outliers or outliers.
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="ticks")
df_penguins = sns.load_dataset("penguins")
sns.pairplot(df_penguins, hue="species")<seaborn.axisgrid.PairGrid at 0x7f9080964be0>
2. Correlation analysis:
A covariance matrix can be used to analyze the correlation between variables. By looking at scatterplots, you can see whether there is a linear or non-linear relationship between each pair of variables, and whether the relationship is positive or negative.
sns.pairplot(df_penguins, hue='species')
plt.show()
3. Multivariate Analysis:
A covariance matrix can be used to visualize the relationship between multiple variables in a data set. This can be useful if you have many variables and want to see how they relate to each other.
df_tips = sns.load_dataset('tips')
sns.pairplot(df_tips, hue='sex')
plt.show()
4. Feature Selection:
A disambiguation matrix can be used to select the most important features for a predictive model. By looking at scatterplots, you can see which variables are most strongly correlated with the target variable and which are not.
sns.pairplot(df_tips, vars=['total_bill', 'tip'])
plt.show()
5. Outlier Detection:
A scatter matrix can be used to detect outliers in a dataset. Outliers are data points that are significantly different from other data points and can be identified by looking at scatterplots.
import plotly.express as px
df_iris = px.data.iris()sns.pairplot(df_iris, diag_kind='hist')
plt.show()
In conclusion, the disc matrix is a powerful tool for exploring the relationships between multiple variables in a dataset and can be used for a variety of data analysis tasks, from exploratory data analysis to feature selection and outlier detection.
