Area Plots

General

Let’s begin by importing vastorbit.

import vastorbit as vo

Let’s generate a dataset using the following data.

data = vo.VastFrame({
    "date": [1900, 1950, 2000],
    "Asia": [947, 1402, 3634],
    "Africa": [133, 221, 767],
    "Europe": [408, 547, 729],
    "America": [156, 339, 818],
    "Oceania": [6, 13, 30],
})

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, creating one or multiple line charts within a single graphic is a straightforward and flexible process. This feature enables you to efficiently visualize and compare multiple datasets or trends, providing you with a powerful tool for gaining insights from your data.

Single

Python

data["Asia"].plot(ts = "date", kind = "area")

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k area
SELECT date, Asia FROM :data;

Stacked

Python

data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "area_stacked")

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k area_stacked
SELECT date, Asia, Africa, Europe, America, Oceania FROM :data;

Fully Stacked

Python

data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "area_percent")

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k area_percent
SELECT date, Asia, Africa, Europe, America, Oceania FROM :data;

Hint

You can achieve the same graphic in vastorbit by employing the “GROUP BY” functionality, made possible through the “by” parameter. Consider a dataset comprising three columns: a timestamp, a categorical column, and a value. Utilizing the “by” parameter in conjunction with this dataset allows for efficient grouping and visualization. This capability enables you to effectively analyze and present data trends over time, across categories, or based on specific values within a single graph, enhancing your ability to extract meaningful insights from your data.

Matplotlib

We can switch to using the matplotlib module.

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

In vastorbit, creating one or multiple line charts within a single graphic is a straightforward and flexible process. This feature enables you to efficiently visualize and compare multiple datasets or trends, providing you with a powerful tool for gaining insights from your data.

Single

Python

data["Asia"].plot(ts = "date", kind = "area")

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k area
SELECT date, Asia FROM :data;

Stacked

Python

data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "area_stacked")

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k area_stacked
SELECT date, Asia, Africa, Europe, America, Oceania FROM :data;

Hint

You can achieve the same graphic in vastorbit by employing the “GROUP BY” functionality, made possible through the “by” parameter. Consider a dataset comprising three columns: a timestamp, a categorical column, and a value. Utilizing the “by” parameter in conjunction with this dataset allows for efficient grouping and visualization. This capability enables you to effectively analyze and present data trends over time, across categories, or based on specific values within a single graph, enhancing your ability to extract meaningful insights from your data.

Fully Stacked

Python

data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "area_percent")

SQL

We load the vastorbit chart extension.

%load_ext vastorbit.chart

We write the SQL query using Jupyter magic cells.

%%chart -k area_percent
SELECT date, Asia, Africa, Europe, America, Oceania FROM :data;

Hint

You can achieve the same graphic in vastorbit by employing the “GROUP BY” functionality, made possible through the “by” parameter. Consider a dataset comprising three columns: a timestamp, a categorical column, and a value. Utilizing the “by” parameter in conjunction with this dataset allows for efficient grouping and visualization. This capability enables you to effectively analyze and present data trends over time, across categories, or based on specific values within a single graph, enhancing your ability to extract meaningful insights from your data.

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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 colors

fig = data["Asia"].plot(ts = "date", kind = "area", colors = ["red"])

Custom colors mapping for categories

Note

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

fig = data.plot(columns = ["Asia", "Africa", "Europe"], ts = "date", kind = "area_stacked", colors = ["red", "orange","green"])

Matplolib

Custom colors

data["Asia"].plot(ts = "date", colors = ["red"], kind = "area", )

Custom colors mapping for categories

data.plot(columns = ["Asia", "Africa", "Europe"], ts = "date", kind = "area", colors = ["red", "orange", "green"])

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Size

Plotly

Custom Width and Height.

data["Asia"].plot(ts = "date", kind = "area", width = 300, height = 300)

Matplolib

Custom Width and Height.

data["Asia"].plot(ts = "date", kind = "area", width = 6, height = 3)

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Text

Plotly

Custom Title

data["Asia"].plot(ts = "date", kind = "area").update_layout(title_text = "Custom Title")

Custom Legend Title Text

data.plot(columns = ["Asia", "Africa", "Europe"], ts = "date", kind = "area", legend_title_text = "Custom Legend")

Custom Axis Titles

data["Asia"].plot(ts = "date", kind = "area", yaxis_title = "Custom Y-Axis Title")

Matplolib

Custom Title Text

data["Asia"].plot(ts = "date", kind = "area").set_title("Custom Title")

Custom Axis Titles

data["Asia"].plot(ts = "date", kind = "area").set_ylabel("Custom Y Axis")