:orphan:
.. _chart_gallery.corr:
==================
Correlation Matrix
==================
.. Necessary Code Elements
.. ipython:: python
:suppress:
import vastorbit as vo
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),
})
General
-------
Let's begin by importing ``vastorbit``.
.. ipython:: python
import vastorbit as vo
Let's also import ``numpy`` to create a random dataset.
.. ipython:: python
import numpy as np
Let's generate a dataset using the following data.
.. code-block:: python
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.
.. image:: ../../docs/source/_static/plotting_libs.png
:width: 80%
:align: center
.. note::
To select the desired plotting library, we simply need to use the :py:func:`~vastorbit.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.
.. ipython:: python
:suppress:
import vastorbit as vo
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
We can switch to using the ``plotly`` module.
.. code-block:: python
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.
.. tab:: Matrix
.. tab:: Python
.. code-block:: python
data.corr(method = "pearson")
.. tab:: SQL
We load the vastorbit `chart` extension.
.. code-block:: python
%load_ext vastorbit.chart
We write the SQL query using Jupyter magic cells.
.. code-block:: sql
%%chart -k pearson
SELECT * FROM :data;
.. ipython:: python
:suppress:
fig = data.corr(method = "pearson")
fig.write_html("figures/plotting_plotly_corr_matrix.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_corr_matrix.html
.. tab:: Vector
.. code-block:: python
data.corr(method = "pearson", focus = "score1")
.. ipython:: python
:suppress:
fig = data.corr(method = "pearson", focus = "score1")
fig.write_html("figures/plotting_plotly_corr_vector.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_corr_vector.html
.. tab:: Matplotlib
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "matplotlib")
We can switch to using the ``matplotlib`` module.
.. code-block:: python
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.
.. tab:: Matrix
.. tab:: Python
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_corr_matrix.png
data.corr(method = "pearson")
.. tab:: SQL
We load the vastorbit `chart` extension.
.. code-block:: python
%load_ext vastorbit.chart
We write the SQL query using Jupyter magic cells.
.. code-block:: sql
%%chart -k pearson
SELECT * FROM :data;
.. image:: ../../docs/source/savefig/plotting_matplotlib_corr_matrix.png
:width: 100%
:align: center
.. tab:: Vector
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_corr_vector.png
data.corr(method = "pearson", focus = "score1")
___________________
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.
.. code-block:: python
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
~~~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom CMAP**
.. code-block:: python
data.corr(method = "pearson", color_continuous_scale = [[0, "white"], [1, "red"]])
.. ipython:: python
:suppress:
fig = data.corr(method = "pearson", color_continuous_scale = [[0, "white"], [1, "red"]])
fig.write_html("figures/plotting_plotly_corr_custom_color_1.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_corr_custom_color_1.html
.. tab:: Matplolib
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "matplotlib")
**Custom CMAP**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_corr_custom_color_1.png
data.corr(method = "pearson", cmap = "Reds")
____
Size
~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom Width and Height**
.. code-block:: python
data.corr(method = "pearson", width = 300, height = 300)
.. ipython:: python
:suppress:
fig = data.corr(method = "pearson", width = 300, height = 300)
fig.write_html("figures/plotting_plotly_corr_custom_size.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_corr_custom_size.html
.. tab:: Matplolib
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "matplotlib")
**Custom Width and Height**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_corr_matrix_custom_size.png
data.corr(method = "pearson", width = 6, height = 3)
_____
Text
~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom Title**
.. code-block:: python
data.corr(method = "pearson").update_layout(title_text = "Custom Title")
.. ipython:: python
:suppress:
fig = data.corr(method = "pearson").update_layout(title_text = "Custom Title")
fig.write_html("figures/plotting_plotly_corr_custom_main_title.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_corr_custom_main_title.html
**Custom Axis Titles**
.. code-block:: python
data.corr(method = "pearson", yaxis_title = "Custom Y-Axis Title")
.. ipython:: python
:suppress:
fig = data.corr(method = "pearson", yaxis_title = "Custom Y-Axis Title")
fig.write_html("figures/plotting_plotly_corr_custom_y_title.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_corr_custom_y_title.html
.. tab:: Matplolib
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "matplotlib")
**Custom Title Text**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_corr_custom_title_label.png
data.corr(method = "pearson").set_title("Custom Title")
**Custom Axis Titles**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_corr_custom_xaxis_label.png
data.corr(method = "pearson").set_xlabel("Custom X Axis")
.. ipython:: python
:suppress:
from vastorbit._utils._sql._sys import purge_memory
purge_memory()