ArrowChart

This example, drawn from the Datawrapper documentation, demonstrates how to create an arrow chart with customized sorting and highlighted elements.

import pandas as pd
import datawrapper as dw

# Load data from GitHub
url = "https://raw.githubusercontent.com/chekos/datawrapper/main/tests/samples/arrow/inequality.csv"
df = pd.read_csv(url, sep="\t")

# Create arrow chart
chart = dw.ArrowChart(
    # Chart title
    title="Many European countries bring income inequality down with taxes. The US and Mexico: Not so much.",
    # The description line with a bit of HTML
    intro="Income inequality (gini index) in selected OECD countries in 2014, before and after taxes. A gini index of 0 means that every household earns exactly the same income, while an index of 1 means that one household in the country makes all the income. <b>The lower the Gini index, the more equal the income is distributed in a country.</b>",
    # Data source attribution
    source_name="OECD",
    # The byline
    byline="Lisa Charlotte Rost, Datawrapper",
    # Pass the DataFrame
    data=df,
    # Start column (Gini before taxes)
    start_column="Gini before taxes",
    # End column (Gini after taxes)
    end_column="Gini after taxes",
    # Custom X-axis range
    range_extent="custom",
    custom_range=[0.15, 0.6],
    # Value label format (three decimal places)
    value_label_format=dw.NumberFormat.THREE_DECIMALS,
    # Sort by the start column
    sort_by="end",
    # Enable sorting
    sort_ranges=True,
    # Show arrow key/legend
    arrow_key=True,
    # Set the default arrow color
    base_color="rgb(196, 148, 67)",
    # Highlight specific countries in red
    color_column="Country",
    label_column="Country",
    color_category={
        "<b>Mexico</b>": "#c71e1d",
        "<b>United States</b>": "#c71e1d"
    }
)

# Create the chart in Datawrapper
chart.create()

Reference

Parameter	Default	Description	Type
`aria_description`	“”	The alternative text for screen readers	str
`arrow_key`	False	Label on the first arrow that shows column names	bool
`auto_dark_mode`	False	Whether the chart should automatically flip to dark mode when the user’s system is in dark mode	bool
`base_color`	0	The base color for the arrows	str or int
`byline`	“”	The byline that appears below the chart	str
`chart_id`	None	The chart ID after creation (populated by create() method)	str or None
`chart_type`	“d3-arrow-plot”	The type of datawrapper chart to create	Literal[d3-arrow-plot]
`color_category`	PydanticUndefined	A mapping of layer names to colors	dict[str, str]
`color_column`	None	The column to color by	str or None
`custom`	PydanticUndefined	A dictionary of custom tags to attach to the chart	dict[str, Any]
`custom_range`	PydanticUndefined	The custom range for the x axis	list[Any] or tuple[Any, Any]
`dark_mode_invert`	True	Whether to invert colors in dark mode	bool
`data`	PydanticUndefined	The data to use for the chart	DataFrame or list[dict]
`download_image`	False	Whether to allow PNG download	bool
`download_pdf`	False	Whether to allow PDF download	bool
`download_svg`	False	Whether to allow SVG download	bool
`embed`	False	Whether to allow embedding	bool
`end_column`	None	The column that arrows should end at	str or None
`force_attribution`	False	Whether to attribute the chart to datawrapper	bool
`forkable`	True	Whether to allow other users to fork this visualization	bool
`get_the_data`	False	Whether to allow data downloads	bool
`groups_column`	None	The column to group arrows by	str or None
`hide_title`	False	Whether or not to hide the title	bool
`intro`	“”	The intro text that appears above the chart	str
`label_column`	None	The column to label by	str or None
`language`	“en-US”	The locale of the chart, which defines decimal and thousand separators as well as translations of month and weekday names.	str
`logo`	False	Whether to show a logo	bool
`logo_id`	“”	The id of the logo to show	str
`notes`	“”	The footnotes that appear below the chart	str
`range_extent`	“nice”	The type of range on the x-axis	Literal[nice, custom, data]
`range_value_labels`	“”	The field you want to use for the value labels	str
`replace_flags`	“off”	Whether to replace country codes with flags. Use ReplaceFlagsType enum for type safety or provide raw strings.	ReplaceFlagsType (OFF, FOUR_BY_THREE, ONE_BY_ONE, …) or str
`reverse_order`	False	Reverse the order of the ranges	bool
`share_buttons`	False	Whether to show social media share buttons	bool
`share_url`	“”	What URL to share	str
`sort_by`	“end”	How to sort the ranges	Literal[end, start, difference, change]
`sort_ranges`	False	Whether to sort the ranges	bool
`source_name`	“”	The source name that appears below the chart	str
`source_url`	“”	The source URL that appears below the chart	str
`start_column`	None	The column that arrows should start at	str or None
`theme`	“datawrapper”	The theme of the chart	str
`thick_arrows`	True	Thicken the arrows	bool
`title`	“”	The headline that appears above the chart	str
`transformations`	PydanticUndefined		Transform or dict[str, Any]
`value_label_format`	“”	The number format for value labels. Use DateFormat for temporal data, NumberFormat for numeric data, or provide custom format strings.	DateFormat (AUTO, YEAR_FULL, YEAR_TWO_DIGIT, …) or NumberFormat (AUTO, THOUSANDS_WITH_OPTIONAL_DECIMALS, INTEGER, …) or str
`y_grid`	“on”	Show the y-axis grid lines	str