Funnel Plot
A funnel plot is a data visualization that represents a process with various stages and allows multiple stacked categories, showing the quantitative values or counts at each stage in a funnel shape. It is a useful tool for tracking the progression or attrition of data through different stages, providing a visual overview of data distribution within the process. The data must be ordered by the response variable, or the “funnel” shape will not be guaranteed.
Funnel plots differ from funnel area plots in that they display the absolute count of data points in each category, while funnel area plots display the percentage of data points that belong to each category. Funnel plots also count each data point as belonging to at least one category, so the categories are represented as subsets of each other. On the other hand, funnel area plots also count each data point as belonging to exactly one category, and display the categories as mutually exclusive.
Funnel plots are appropriate when the data contain a categorical variable where the frequencies of each category can be computed, and the categories can be ordered. Additionally, funnel plots assume a particular relationship between levels of the categorical variable, where each category is a proper subset of the previous category. If the data contain an unordered categorical variable, or the categories are better conceptualized as parts of a whole, consider a pie plot instead of a funnel plot.
What are funnel plots useful for?
- Visualizing sequential data: Data that are staged or sequential in some way are often visualized with funnel plots, yielding insight on the absolute changes between each stage.
- Comparing categories: Funnel plots can be broken down into categories to produce insights into the distribution of data at each stage within a process. Then
- Evaluating efficiency: Assessing the efficiency and effectiveness of a process or workflow, particularly when evaluating the attrition or conversion at each stage, is easy with funnel plots.
Examples
A basic funnel plot
Visualize the trend in consecutive stages of a categorical variable by passing column names to the x and y arguments.
import deephaven.plot.express as dx
marketing = dx.data.marketing()
# `Count` is the frequency/value column, and `Stage` is the category column
funnel_plot = dx.funnel(marketing, x="Count", y="Stage")
API Reference
Returns a funnel chart
Returns: DeephavenFigure A DeephavenFigure that contains the funnel chart
| Parameters | Type | Default | Description |
|---|---|---|---|
| table | Table | DataFrame | A table to pull data from. | |
| x | str | list[str] | None | None | A column or list of columns that contain x-axis values. |
| y | str | list[str] | None | None | A column or list of columns that contain y-axis values. |
| by | str | list[str] | None | None | A column or list of columns that contain values to plot the figure traces by. All values or combination of values map to a unique design. The variable by_vars specifies which design elements are used. This is overriden if any specialized design variables such as color are specified |
| by_vars | str | list[str] | 'color' | A string or list of string that contain design elements to plot by. Can contain color and pattern_shape. If associated maps or sequences are specified, they are used to map by column values to designs. Otherwise, default values are used. |
| color | str | list[str] | None | None | A column or list of columns that contain color values. If only one column is passed, and it contains numeric values, the value is used as a value on a continuous color scale. Otherwise, the value is used for a plot by on color. See color_discrete_map for additional behaviors. |
| text | str | None | None | A column that contains text annotations. |
| hover_name | str | None | None | A column that contains names to bold in the hover tooltip. |
| labels | dict[str, str] | None | None | A dictionary of labels mapping columns to new labels. |
| color_discrete_sequence | list[str] | None | None | A list of colors to sequentially apply to the series. The colors loop, so if there are more series than colors, colors will be reused. |
| color_discrete_map | dict[str | tuple[str], str] | None | None | If dict, the keys should be strings of the column values (or a tuple of combinations of column values) which map to colors. |
| opacity | float | None | None | Opacity to apply to all markers. 0 is completely transparent and 1 is completely opaque. |
| orientation | str | None | None | "h" for horizontal or "v" for vertical |
| log_x | bool | False | A boolean that specifies if the corresponding axis is a log axis or not. |
| log_y | bool | False | A boolean that specifies if the corresponding axis is a log axis or not. |
| range_x | list[int] | None | None | A list of two numbers that specify the range of the x-axis. |
| range_y | list[int] | None | None | A list of two numbers that specify the range of the y-axis. |
| title | str | None | None | The title of the chart |
| template | str | None | None | The template for the chart. |
| unsafe_update_figure | Callable | <function default_callback> | An update function that takes a plotly figure as an argument and optionally returns a plotly figure. If a figure is not returned, the plotly figure passed will be assumed to be the return value. Used to add any custom changes to the underlying plotly figure. Note that the existing data traces should not be removed. This may lead to unexpected behavior if traces are modified in a way that break data mappings. |