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Version: Python

Create treemap charts

This guide shows you how to use the plot_treemap method to create treemap charts, which show data in a hierarchical view where each element takes up a proportional amount based on its value.

Data sourcing​

From a formatted table​

Your table may already be formatted in the structure plot_treemap requires. At a minimum, you need to include the item IDs and parents for the structure of the treemap. When data is already structured in the source table (i.e. all parents are defined with a specific root) sourced from a table, the following syntax can be used to create a treemap plot:

from deephaven import read_csv
from deephaven.plot.figure import Figure

# source the data
source = read_csv(
    "https://media.githubusercontent.com/media/deephaven/examples/main/StandardAndPoors500Financials/csv/s_and_p_500_market_cap.csv"
)

# plot the data
s_and_p = (
    Figure()
    .plot_treemap(
        series_name="S&P 500 Companies by Sector", t=source, id="Label", parent="Parent"
    )
    .show()
)

You can interact with this plot by clicking on the group headers and cells. Clicking on a header will expand that group; clicking again reverts to the full view. Clicking on an individual cell expands its contents.

  • s_and_p is a treemap plot created with the plot_treemap method.
  • Figure is the class in which treemap plotting functionalities live.
  • series_name is the name that will be displayed on the plot.
  • t is the table in which data will be sourced from.
  • id is the name of the column which holds the item IDs to use in structuring the treemap chart.
  • parent is the name of the column which holds the parent IDs each item is under to use in the treemap chart. There should only be one root.
  • show tells Deephaven to draw the plot in the console.

By default, the colors selected for each section are selected from a default theme, and the label is simply the ID displayed.

You can provide other parameters to display more information:

from deephaven import read_csv
from deephaven.plot.figure import Figure

# source the data
source = read_csv(
    "https://media.githubusercontent.com/media/deephaven/examples/main/StandardAndPoors500Financials/csv/s_and_p_500_market_cap.csv"
)

# plot the data
s_and_p_marketcap = (
    Figure()
    .plot_treemap(
        series_name="S&P 500 Market Cap",
        t=source,
        id="Label",
        parent="Parent",
        label="Label",
        value="Value",
        hover_text="HoverText",
        color="Color",
    )
    .show()
)
  • s_and_p_marketcap is a treemap plot created with the plot_treemap method.
  • Figure is the class in which treemap plotting functionalities live.
  • series_name is the name that will be displayed on the plot.
  • t is the table from which data will be sourced.
  • id is the name of the column that holds the item IDs to use in structuring the treemap chart.
  • parent is the name of the column that holds the parent IDs each item is under to use in the treemap chart. There should only be one root.
  • label is the label to use as display text instead of the raw ID.
  • value is how large the sector should be compared to other sectors.
  • hover_text is the text to display when hovering over a sector.
  • color is the color to use for the sector.
  • show tells Deephaven to draw the plot in the console.

From raw data table​

Sometimes when you pull data, it may not have the parent/child relationships defined. You can use Deephaven functions to format the table correctly:

from deephaven import read_csv, merge
from deephaven.plot.figure import Figure

# Source the data
source = read_csv(
    "https://media.githubusercontent.com/media/deephaven/examples/main/StandardAndPoors500Financials/csv/s_and_p_500_companies_financials.csv"
)

# Get a view of just the data we need
financials = source.view(["Symbol", "Name", "Sector", "MarketCap=Market_Cap"])

# Find the distinct sectors defined in the data
sectors = financials.select_distinct(["Sector"]).sort(["Sector"])

# Define `root` as the top parent, use sectors as the first level of children
sector_items = sectors.view(
    [
        "Parent=`root`",
        "Label=Sector",
        "Value=(long)0",
        "Color=`#373438`",
        "HoverText=Sector",
    ]
)


# Define our heat map function that will provide a color based on the percent change provided
def heatmap_daily_perc(perc):
    if perc < -0.02:
        return "#f95d84"
    if perc < -0.01:
        return "#f37e3f"
    if perc > 0.01:
        return "#9edc6f"
    return "#fcd65b"


# Update our data with random percent changes for display purposes
stock_items = financials.update(
    ["Perc=randomGaussian(0, 2)/100.0", "PercLabel=String.format(`%.1f%%`, Perc*100)"]
).view(
    [
        "Parent=Sector",
        "Label=Symbol",
        "Value=(long)MarketCap",
        "Color=(String)heatmap_daily_perc(Perc)",
        "HoverText=Name + `: ` + PercLabel",
    ]
)

# Merge the topmost sectors with the rest of the stock items
items = merge([sector_items, stock_items])

# Plot the data
s_and_p_treemap = (
    Figure()
    .plot_treemap(
        series_name="S&P 500 Daily Change",
        t=items,
        id="Label",
        parent="Parent",
        label="Label",
        value="Value",
        hover_text="HoverText",
        color="Color",
    )
    .show()
)
  • financials is the source table with the raw data.
  • sectors is a table of the top-level distinct sectors in the raw data.
  • sector_items is a table of the top-level sectors with label and color information associated with them.
  • heatmap_daily_perc is a function that returns a color based on the percent change provided.
  • stock_items is a table with all the stock items and their associated labels.
  • s_and_p_treemap is a treemap plot created with the plot_treemap method.
  • Figure is the class in which treemap plotting functionalities live.
  • series_name is the name that will be displayed on the plot.
  • t is the table from which data will be sourced.
  • id is the name of the column which holds the item IDs to use in structuring the treemap chart.
  • parent is the name of the column which holds the parent IDs each item is under to use in the treemap chart. There should only be one root.
  • label is the label to use as display text instead of the raw ID.
  • value is how large the sector should be compared to other sectors.
  • hover_text is the text to display when hovering over a sector.
  • color is the color to use for the sector.
  • show tells Deephaven to draw the plot in the console.