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Correlation Matrix

General

Let’s begin by importing VerticaPy.

import verticapy as vp

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 = vp.vDataFrame({
    "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. VerticaPy, as a versatile tool, provides support for several graphic libraries, such as Matplotlib, Highcharts, 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.

_images/plotting_libs.png

Note

To select the desired plotting library, we simply need to use the set_option function. VerticaPy 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 VerticaPy 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.

We can switch to using the plotly module.

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

In VerticaPy, 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 VerticaPy, 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.

data.corr(method = "pearson")

We load the VerticaPy chart extension.

%load_ext verticapy.chart

We write the SQL query using Jupyter magic cells.

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

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

We can switch to using the highcharts module.

vp.set_option("plotting_lib", "highcharts")

In VerticaPy, 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 VerticaPy, 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.

data.corr(method = "pearson")

We load the VerticaPy chart extension.

%load_ext verticapy.chart

We write the SQL query using Jupyter magic cells.

%%chart -k pearson
SELECT * FROM :data;
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data.corr(method = "pearson", focus = "score1")
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We can switch to using the matplotlib module.

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

In VerticaPy, 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 VerticaPy, 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.

data.corr(method = "pearson")
Out[3]: <Axes: >
_images/plotting_matplotlib_corr_matrix.png

We load the VerticaPy chart extension.

%load_ext verticapy.chart

We write the SQL query using Jupyter magic cells.

%%chart -k pearson
SELECT * FROM :data;
_images/plotting_matplotlib_corr_matrix1.png 
data.corr(method = "pearson", focus = "score1")
Out[4]: <Axes: >
_images/plotting_matplotlib_corr_vector.png

Chart Customization

VerticaPy 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, VerticaPy’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, Highcharts, and Matplotlib. For a comprehensive list of customization features, please consult the documentation of the respective libraries: plotly, matplotlib and highcharts.

Colors

Custom CMAP

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

Custom CMAP

fig = data.corr(method = "pearson")
fig.set_options(
    "colorAxis",
    {
        "stops": [
            [0, "white"],
            [0.45, "yellow"],
            [0.55, "pink"],
            [1, "red"],
        ],
        "min": -1,
        "max": 1,
    },
)
fig
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Custom CMAP

data.corr(method = "pearson", cmap = "Reds")
Out[5]: <Axes: >
_images/plotting_matplotlib_corr_custom_color_1.png

Size

Custom Width and Height

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

Custom Width and Height

data.corr(method = "pearson", width = 500, height = 200)
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Custom Width and Height

data.corr(method = "pearson", width = 6, height = 3)
Out[6]: <Axes: >
_images/plotting_matplotlib_corr_matrix_custom_size.png

Text

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")

Custom Title Text

data.corr(method = "pearson", title = {"text": "Custom Title"})
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Custom Axis Titles

data.corr(method = "pearson", xAxis = {"title": {"text": "Custom X-Axis Title"}})
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Custom Title Text

data.corr(method = "pearson").set_title("Custom Title")
Out[7]: Text(0.5, 1.0, 'Custom Title')
_images/plotting_matplotlib_corr_custom_title_label.png

Custom Axis Titles

data.corr(method = "pearson").set_xlabel("Custom X Axis")
Out[8]: Text(0.5, 0, 'Custom X Axis')
_images/plotting_matplotlib_corr_custom_xaxis_label.png