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Area Plots

General

Let’s begin by importing VerticaPy.

import verticapy as vp

Let’s generate a dataset using the following data.

data = vp.vDataFrame({
    "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. VerticaPy, as a versatile tool, provides support for several graphic libraries, such as Matplotlib, Highcharts, 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. VerticaPy 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 VerticaPy 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.

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

In VerticaPy, 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.

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

We load the VerticaPy chart extension.

%load_ext verticapy.chart

We write the SQL query using Jupyter magic cells.

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

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

We load the VerticaPy chart extension.

%load_ext verticapy.chart

We write the SQL query using Jupyter magic cells.

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

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

We load the VerticaPy chart extension.

%load_ext verticapy.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 VerticaPy 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.

We can switch to using the highcharts module.

vp.set_option("plotting_lib", "highcharts")

In VerticaPy, 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.

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

We load the VerticaPy chart extension.

%load_ext verticapy.chart

We write the SQL query using Jupyter magic cells.

%%chart -k area
SELECT date, Asia FROM :data;
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data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "area_stacked")

We load the VerticaPy chart extension.

%load_ext verticapy.chart

We write the SQL query using Jupyter magic cells.

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

Hint

You can achieve the same graphic in VerticaPy 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.

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

We load the VerticaPy chart extension.

%load_ext verticapy.chart

We write the SQL query using Jupyter magic cells.

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

Hint

You can achieve the same graphic in VerticaPy 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.

We can switch to using the matplotlib module.

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

In VerticaPy, 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.

data["Asia"].plot(ts = "date", kind = "area")
Out[2]: <Axes: xlabel='date', ylabel='Asia'>
_images/plotting_matplotlib_area_single.png

We load the VerticaPy chart extension.

%load_ext verticapy.chart

We write the SQL query using Jupyter magic cells.

%%chart -k area
SELECT date, Asia FROM :data;
_images/plotting_matplotlib_area_single1.png 
data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "area_stacked")
Out[3]: <Axes: xlabel='date'>
_images/plotting_matplotlib_area_stacked.png

We load the VerticaPy chart extension.

%load_ext verticapy.chart

We write the SQL query using Jupyter magic cells.

%%chart -k area_stacked
SELECT date, Asia, Africa, Europe, America, Oceania FROM :data;
_images/plotting_matplotlib_area_stacked1.png

Hint

You can achieve the same graphic in VerticaPy 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.

data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "area_percent")
Out[4]: <Axes: xlabel='date'>
_images/plotting_matplotlib_area_percent.png

We load the VerticaPy chart extension.

%load_ext verticapy.chart

We write the SQL query using Jupyter magic cells.

%%chart -k area_percent
SELECT date, Asia, Africa, Europe, America, Oceania FROM :data;
_images/plotting_matplotlib_area_percent1.png

Hint

You can achieve the same graphic in VerticaPy 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.

Chart Customization

VerticaPy 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, VerticaPy’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, Highcharts, and Matplotlib. For a comprehensive list of customization features, please consult the documentation of the respective libraries: plotly, matplotlib and highcharts.

Colors

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"])

Custom colors

data["Asia"].plot(ts = "date", kind = "area", colors = ["green"])
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Custom colors mapping for categories

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

data["Asia"].plot(ts = "date", colors = ["red"], kind = "area", )
Out[5]: <Axes: xlabel='date', ylabel='Asia'>
_images/plotting_matplotlib_area_custom_color_1.png

Custom colors mapping for categories

data.plot(columns = ["Asia", "Africa", "Europe"], ts = "date", kind = "area", colors = ["red", "orange", "green"])
Out[6]: <Axes: xlabel='date'>
_images/plotting_matplotlib_area_custom_color_2.png

Size

Custom Width and Height.

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

Custom Width and Height.

data["Asia"].plot(ts = "date", kind = "area", width = 500, height = 200)
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Custom Width and Height.

data["Asia"].plot(ts = "date", kind = "area", width = 6, height = 3)
Out[7]: <Axes: xlabel='date', ylabel='Asia'>
_images/plotting_matplotlib_area_single_custom_size.png

Text

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")

Custom Title Text

data["Asia"].plot(ts = "date", kind = "area", title = {"text": "Custom Title"})
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Custom Axis Titles

data["Asia"].plot(ts = "date", kind = "area", xAxis = {"title": {"text": "Custom X-Axis Title"}})
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Custom Title Text

data["Asia"].plot(ts = "date", kind = "area").set_title("Custom Title")
Out[8]: Text(0.5, 1.0, 'Custom Title')
_images/plotting_matplotlib_area_custom_title_label.png

Custom Axis Titles

data["Asia"].plot(ts = "date", kind = "area").set_ylabel("Custom Y Axis")
Out[9]: Text(0, 0.5, 'Custom Y Axis')
_images/plotting_matplotlib_area_custom_yaxis_label.png