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Range Plots

General

Let’s begin by importing vastorbit.

import vastorbit as vo

Let’s generate a dataset using the following data.

N = 20 # Number of records

data = vo.VastFrame({
    "date": [1990 + i for i in range(N)] * 5,
    "population1": [100 + i for i in range(N)] + [300 + i * 2 for i in range(N)] + [200 + i ** 2 - 3 * i for i in range(N)] + [50 + i ** 2 - 6 * i for i in range(N)] + [700 + i ** 2 - 10 * i for i in range(N)],
    "population2": [200 + i ** 2 - i for i in range(N)] + [1000 + i * 2 for i in range(N)] + [500 + i ** 2 - 5 * i for i in range(N)] + [900 + i ** 2 + 3 * i for i in range(N)] + [100 + i ** 2 - 0.5 * i for i in range(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.

_images/plotting_libs.png

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.

We can switch to using the plotly module.

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

In vastorbit, creating one or multiple range plots 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.

data["population1"].range_plot(ts = "date")

data.range_plot(columns = ["population1", "population2"], ts = "date")

We can switch to using the matplotlib module.

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

In vastorbit, creating one or multiple range plots 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.

data["population1"].range_plot(ts = "date")

data.range_plot(columns = ["population1", "population2"], ts = "date")

Custom Parameters

Hint

In vastorbit, you have a range of options for customizing your range plots. You can select custom quantiles for both the lower and upper bounds, allowing you to precisely define the range of data displayed in your range plot.

Quantiles

data["population1"].range_plot(ts = "date", 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.

Quantiles

data["population1"].range_plot(ts = "date", 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.

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.

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

Custom colors

fig = data["population1"].range_plot(ts = "date", 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.range_plot(columns = ["population1", "population2"], ts = "date", colors = ["red", "orange","green"])

Custom colors

data["population1"].range_plot(ts = "date", colors = ["red"])

Custom colors mapping for categories

data.range_plot(columns = ["population1", "population2"], ts = "date", colors = ["red", "orange", "green"])

Size

Custom Width and Height

data["population1"].range_plot(ts = "date", width = 300, height = 300)

Custom Width and Height

data["population1"].range_plot(ts = "date", width = 6, height = 3)

Text

Custom Title

data["population1"].range_plot(ts = "date").update_layout(title_text = "Custom Title")

Custom Legend Title Text

data.range_plot(columns = ["population1", "population2"], ts = "date", legend_title_text = "Custom Legend")

Custom Axis Titles

data["population1"].range_plot(ts = "date", yaxis_title = "Custom Y-Axis Title")

Custom Title Text

data["population1"].range_plot(ts = "date").set_title("Custom Title")

Custom Axis Titles

data["population1"].range_plot(ts = "date").set_ylabel("Custom Y Axis")