Correlation Matrix

General

Let’s begin by importing vastorbit.

import vastorbit as vo

Let’s also import numpy to create a random dataset.

import numpy as np

Let’s generate a dataset using the following data.

N = 30 # Number of records

data = vo.VastFrame({
    "score1": np.random.normal(5, 1, N),
    "score2": np.random.normal(8, 1.5, N),
    "score3": np.random.normal(10, 2, N),
    "score4": np.random.normal(14, 3, N),
})

In the context of data visualization, we have the flexibility to harness multiple plotting libraries to craft a wide range of graphical representations. vastorbit, as a versatile tool, provides support for several graphic libraries, such as Matplotlib and Plotly. Each of these libraries offers unique features and capabilities, allowing us to choose the most suitable one for our specific data visualization needs.

Note

To select the desired plotting library, we simply need to use the set_option() function. vastorbit offers the flexibility to smoothly transition between different plotting libraries. In instances where a particular graphic is not supported by the chosen library or is not supported within the vastorbit framework, the tool will automatically generate a warning and then switch to an alternative library where the graphic can be created.

Please click on the tabs to view the various graphics generated by the different plotting libraries.

Plotly

We can switch to using the plotly module.

vo.set_option("plotting_lib", "plotly")

In vastorbit, you have the flexibility to generate either the complete correlation matrix or a more resource-efficient correlation vector for a specific feature, reducing computational costs. This choice allows you to tailor your analysis to your specific needs while optimizing performance.

Hint

In vastorbit, you have access to a variety of correlation techniques, including Pearson for linear relationships, Spearman for monotonic relationships, Cramer’s V for categorical data, and more. It’s important to note that each of these techniques involves SQL generation and may vary in computational cost. You can choose the most suitable technique based on your analysis requirements, considering the potential computational overhead.

Matrix

Python

data.corr(method = "pearson")

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k pearson
SELECT * FROM :data;

Vector

data.corr(method = "pearson", focus = "score1")

Matplotlib

We can switch to using the matplotlib module.

vo.set_option("plotting_lib", "matplotlib")

In vastorbit, you have the flexibility to generate either the complete correlation matrix or a more resource-efficient correlation vector for a specific feature, reducing computational costs. This choice allows you to tailor your analysis to your specific needs while optimizing performance.

Hint

In vastorbit, you have access to a variety of correlation techniques, including Pearson for linear relationships, Spearman for monotonic relationships, Cramer’s V for categorical data, and more. It’s important to note that each of these techniques involves SQL generation and may vary in computational cost. You can choose the most suitable technique based on your analysis requirements, considering the potential computational overhead.

Matrix

Python

data.corr(method = "pearson")

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k pearson
SELECT * FROM :data;

Vector

data.corr(method = "pearson", focus = "score1")

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Chart Customization

vastorbit empowers users with a high degree of flexibility when it comes to tailoring the visual aspects of their plots. This customization extends to essential elements such as color schemes, text labels, and plot sizes, as well as a wide range of other attributes that can be fine-tuned to align with specific design preferences and analytical requirements. Whether you want to make your visualizations more visually appealing or need to convey specific insights with precision, vastorbit’s customization options enable you to craft graphics that suit your exact needs.

Hint

For SQL users who use Jupyter Magic cells, chart customization must be done in Python. They can then export the graphic using the last magic cell result.

chart = _

Now, the chart variable includes the graphic. Depending on the library you are using, you will obtain a different object.

Important

Different customization parameters are available for Plotly and Matplotlib. For a comprehensive list of customization features, please consult the documentation of the respective libraries: plotly, matplotlib.

Colors

Plotly

Custom CMAP

data.corr(method = "pearson", color_continuous_scale = [[0, "white"], [1, "red"]])

Matplolib

Custom CMAP

data.corr(method = "pearson", cmap = "Reds")

---

Size

Plotly

Custom Width and Height

data.corr(method = "pearson", width = 300, height = 300)

Matplolib

Custom Width and Height

data.corr(method = "pearson", width = 6, height = 3)

---

Text

Plotly

Custom Title

data.corr(method = "pearson").update_layout(title_text = "Custom Title")

Custom Axis Titles

data.corr(method = "pearson", yaxis_title = "Custom Y-Axis Title")

Matplolib

Custom Title Text

data.corr(method = "pearson").set_title("Custom Title")

Custom Axis Titles

data.corr(method = "pearson").set_xlabel("Custom X Axis")