Boxplot

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 = 100

data = vo.VastFrame({
  "category": [np.random.choice(['A','B','C']) for _ in range(N)],
  "score1": np.random.normal(5, 1, N),
  "score2": np.random.normal(8, 1.5, N),
  "score3": np.random.normal(10, 2, 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 can create a single boxplot or multiple boxplots within the same graphic.

Single

Python

data["score1"].boxplot()

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k boxplot
SELECT score1 FROM :data;

Multi

Python

data.boxplot(columns = ["score1", "score2", "score3"])

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k boxplot
SELECT score1, score2, score3 FROM :data;

Matplotlib

We can switch to using the matplotlib module.

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

In vastorbit, you can create a single boxplot or multiple boxplots within the same graphic.

Single

Python

data["score1"].boxplot()

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k boxplot
SELECT score1 FROM :data;

Multi

Python

data.boxplot(columns = ["score1", "score2", "score3"])

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k boxplot
SELECT score1, score2, score3 FROM :data;

---

Custom Parameters

In vastorbit, you have a range of options for customizing your boxplots. You can select custom quantiles for both the lower and upper bounds, allowing you to precisely define the range of data displayed in your boxplot. Additionally, you can specify the maximum number of outliers, or “flyers,” that you wish to be displayed in your plot, giving you fine-grained control over the visualization of your data.

Plotly

Quantiles

data["score1"].boxplot(q = (0.1, 0.9))

Note

By selecting the tuple (0.1, 0.9), we are effectively utilizing the values corresponding to the first and ninth deciles of the data distribution.

Fliers

Note

“Flyers” in this context represents the outliers within the distribution, and the process of selecting them can be time-consuming. If you wish to expedite the computation of your box plot, you can set the “flyers” parameter to 0. This will result in a faster box plot generation without displaying outliers.

data["score1"].boxplot(q= (0.4, 0.6), max_nb_fliers = 5)

Matplolib

Quantiles

data["score1"].boxplot(q = (0.1, 0.9))

Note

By selecting the tuple (0.1, 0.9), we are effectively utilizing the values corresponding to the first and ninth deciles of the data distribution.

Fliers

Note

“Flyers” in this context represents the outliers within the distribution, and the process of selecting them can be time-consuming. If you wish to expedite the computation of your box plot, you can set the “flyers” parameter to 0. This will result in a faster box plot generation without displaying outliers.

data["score1"].boxplot(q = (0.4, 0.6), max_nb_fliers = 5)

---

Grouping

Plotly

Group by categories.

Python

data["score1"].boxplot(by = "category")

SQL

%%chart -k boxplot
SELECT score1, category FROM :data;

Matplolib

Python

data["score1"].boxplot(by = "category")

SQL

%%chart -k boxplot
SELECT score1, category FROM :data;

---

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 colors for 1D

fig = data["score1"].boxplot()
fig.update_traces(marker = dict(color="red"))

Custom colors mapping for categories

Note

You can leverage all the capabilities of the Plotly object, including functions like update_trace.

fig = data.boxplot(columns = ["score1", "score2", "score3"])
new_colors = ["red", "orange","green"]
for trace_index, new_color in enumerate(new_colors):
    if trace_index < len(fig.data):
        fig.data[trace_index].marker.color = new_color

Matplolib

Custom colors for 1D

data["score1"].boxplot(colors = ["red"])

Custom colors mapping for categories

data.boxplot(columns = ["score1", "score2", "score3"], color = ["red", "orange", "green"])

---

Size

Plotly

Custom Width and Height.

data.boxplot(columns = ["score1", "score2", "score3"], width = 300, height = 300)

Matplolib

Custom Width and Height.

data["score1"].boxplot(width = 6, height = 3)

---

Text

Plotly

Custom Title

data["score1"].boxplot().update_layout(title_text = "Custom Title")

Custom Axis Titles

data.boxplot(columns = ["score1", "score2", "score3"], yaxis_title = "Custom Y-Axis Title")

Matplolib

Custom Title Text

data["score1"].boxplot().set_title("Custom Title")

Custom Axis Titles

data["score1"].boxplot().set_ylabel("Custom Y Axis")