Spider¶
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 # Number of records
data = vo.VastFrame({
"category": [np.random.choice(['A','B','C']) for _ in range(N)],
"score1": np.random.normal(5, 1, 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.
We can switch to using the plotly module.
vo.set_option("plotting_lib", "plotly")
In vastorbit, you can create a single spider chart or multiple spider charts within the same graphic.
data["score1"].spider()
data["score1"].spider(by = "category")
We can switch to using the matplotlib module.
vo.set_option("plotting_lib", "matplotlib")
In vastorbit, you can create a single spider chart or multiple spider charts within the same graphic.
data["score1"].spider()
data["score1"].spider(by = "category")
Custom Aggregations¶
Within the vastorbit framework, you have the flexibility to apply a wide array of aggregation techniques according to your specific analytical needs. This extends to the option of utilizing SQL statements, allowing you to craft custom aggregations that precisely match your data summarization requirements. vastorbit empowers you with the versatility to aggregate data in the manner that best serves your analytical objectives.
General Options
data["score1"].spider(by = "category", method = "mean", of = "score2")
Note
vastorbit simplifies the usage of aggregations, such as percentiles. You only need to specify the percentile number without a decimal point to compute it. For instance, 50% for the median, 75% for the third quartile, and 99% for the last percentile.
Direct SQL statement
Note
You are free to utilize any SQL statement as long as it is compatible with the supported features of vastorbit.
data["score1"].spider(by = "category", method = "MIN(score2) AS min_score2")
General Options
data["score1"].spider(by = "category", method = "mean", of = "score2")
Note
vastorbit simplifies the usage of aggregations, such as percentiles. You only need to specify the percentile number without a decimal point to compute it. For instance, 50% for the median, 75% for the third quartile, and 99% for the last percentile.
Direct SQL statement
Note
You are free to utilize any SQL statement as long as it is compatible with the supported features of vastorbit.
data["score1"].spider(by = "category", method = "MIN(score2) AS min_score2")
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 for 1D
fig = data["score1"].spider()
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["score1"].spider(by = "category")
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
Custom colors for 1D
data["score1"].spider(color = ["red"])
Custom colors mapping for categories
data["score1"].spider(by = "category", color = ["red", "orange", "green"])
Size¶
Custom Width and Height.
data["score1"].spider(by = "category", width = 300, height = 300)
Custom Width and Height.
data["score1"].spider(by = "category", width = 6, height = 3)
Text¶
Custom Title
data["score1"].spider().update_layout(title_text = "Custom Title")
Custom Legend Title Text
data["score1"].spider(by = "category", legend_title_text = "Custom Legend")