BarChart

This example, drawn from the Datawrapper documentation, recreates a bar chart showing custom colors and a confidence interval using a bar overlay.

import pandas as pd
import datawrapper as dw

# Load data from GitHub
url = "https://raw.githubusercontent.com/chekos/datawrapper/main/tests/samples/bar/happiness-scores.csv"
df = pd.read_csv(url)

# Create the bar chart
chart = dw.BarChart(
    # Chart title with HTML formatting
    title="Score of Happiness<br>",
    # Source information
    source_name="World Happiness Report 2019",
    source_url="https://worldhappiness.report/ed/2019/changing-world-happiness/",
    # Intro text explaining the ranking methodology
    intro="The ranking is based on life evaluations (the average answer to the question how people evaluate the quality of their current lives on a scale of 0 to 10) for each country, averaged over the years 2016-2018.",
    # Byline
    byline="Daniela Haake",
    # Data
    data=df,
    # Value label format using enum
    value_label_format=dw.NumberFormat.THOUSANDS_WITH_OPTIONAL_DECIMALS,
    # Show value labels
    show_value_labels=True,
    # Value label alignment
    value_label_alignment="left",
    # Don't sort bars (keep original order)
    sort_bars=False,
    # Color by continent with custom mapping
    show_color_key=True,
    color_column="Continent",
    color_category={
        "Asia": "#ec6951",
        "Africa": "#8c1946",
        "Europe": "#ff9f51",
        "Oceania": "#e7d5c2",
        "3. Norway": "#f7d503",
        "7. Sweden": "#f7d503",
        "9. Canada": "#ef7f35",
        "93. China": "#c2c101",
        "1. Finland": "#f7d503",
        "140. India": "#c2c101",
        "147. Haiti": "#ef7f35",
        "149. Syria": "#c2c101",
        "151. Yemen": "#c2c101",
        "2. Denmark": "#f7d503",
        "4. Iceland": "#f7d503",
        "68. Russia": "#c2c101",
        "95. Bhutan": "#c2c101",
        "10. Austria": "#f7d503",
        "150. Malawi": "#2c7f67",
        "152. Rwanda": "#2c7f67",
        "17. Germany": "#f7d503",
        "148. Botswana": "#2c7f67",
        "153. Tanzania": "#2c7f67",
        "North America": "#e7d5c2",
        "5. Netherlands": "#f7d503",
        "6. Switzerland": "#f7d503",
        "8. New Zealand": "#ae5b3e",
        "154. Afghanistan": "#c2c101",
        "156. South Sudan": "#2c7f67",
        "19. United States": "#ef7f35",
        "15. United Kingdom": "#f7d503",
        "155. Central African Republic": "#2c7f67"
    },
    # Category labels for the legend
    label_column="Country",
    category_labels={
        "1. Finland": "Europe",
        "2. Denmark": "Europe",
        "3. Norway": "Europe",
        "4. Iceland": "Europe",
        "5. Netherlands": "Europe",
        "6. Switzerland": "Europe",
        "7. Sweden": "Europe",
        "8. New Zealand": "Oceania",
        "9. Canada": "North America",
        "10. Austria": "Europe",
        "15. United Kingdom": "Europe",
        "17. Germany": "Europe",
        "19. United States": "North America",
        "68. Russia": "Asia",
        "93. China": "Asia",
        "95. Bhutan": "Asia",
        "140. India": "Asia",
        "147. Haiti": "North America",
        "148. Botswana": "Africa",
        "149. Syria": "Asia",
        "150. Malawi": "Africa",
        "151. Yemen": "Asia",
        "152. Rwanda": "Africa",
        "153. Tanzania": "Africa",
        "154. Afghanistan": "Asia",
        "155. Central African Republic": "Africa",
        "156. South Sudan": "Africa"
    },
    # Bar overlay for confidence interval
    overlays=[
        dw.BarOverlay(
            from_column="Whisker-low",
            to_column="Whisker-high",
            color="#999999",
            opacity=0.7,
            pattern="diagonal-up",
            title="Confidence Interval (95%)",
            type="range",
            show_in_color_key=True,
            label_directly=False
        )
    ]
)

# Create the chart
chart.create()

Reference

Parameter	Default	Description	Type
`aria_description`	“”	The alternative text for screen readers	str
`auto_dark_mode`	False	Whether the chart should automatically flip to dark mode when the user’s system is in dark mode	bool
`axis_label_format`	“”	The format of the axis 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
`background`	False	Fill the background of the bar’s full potential with a light color	bool
`bar_column`	“”	The column with the value for the bars	str
`base_color`	0	The default color for the chart (palette index or hex string)	str or int
`block_labels`	False	Whether to move labels to a separate line	bool
`byline`	“”	The byline that appears below the chart	str
`category_labels`	PydanticUndefined	Dictionary mapping category names to their display labels in the color legend	dict[str, str]
`category_order`	PydanticUndefined	List defining the order in which categories appear in the chart and legend	list[str]
`chart_id`	None	The chart ID after creation (populated by create() method)	str or None
`chart_type`	“d3-bars”	The type of datawrapper chart to create	Literal[d3-bars]
`color_category`	PydanticUndefined	A mapping of layer names to colors	dict[str, str]
`color_column`	“”	The name of the column with the color for the bars	str
`custom`	PydanticUndefined	A dictionary of custom tags to attach to the chart	dict[str, Any]
`custom_grid_lines`	PydanticUndefined	Set custom grid lines	list[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
`exclude_from_color_key`	PydanticUndefined	A list of column to exclude from the color key	list[str]
`force_attribution`	False	Whether to attribute the chart to datawrapper	bool
`force_grid`	False	Whether or not to show the x grid	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 use for grouping bars	str or None
`hide_title`	False	Whether or not to hide the title	bool
`highlighted_series`	PydanticUndefined	A list of the highlighted series	list[str]
`intro`	“”	The intro text that appears above the chart	str
`label_alignment`	“left”	On which side are the labels aligned	Literal[left, right]
`label_column`	“”	The column with the labels for the bars	str
`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
`overlays`	PydanticUndefined	A list of bar overlays	list[UnionType[BarOverlay, dict[str, Any]]]
`range_annotations`	PydanticUndefined	A list of range annotations to display on the chart	Sequence[UnionType[RangeAnnotation, dict[Any, Any]]]
`replace_flags`	“off”	Whether to replace country codes with flag	ReplaceFlagsType (OFF, FOUR_BY_THREE, ONE_BY_ONE, …) or str
`reverse_order`	False	Whether to reverse the sort order	bool
`rules`	False	Draw a separating line between bars	bool
`share_buttons`	False	Whether to show social media share buttons	bool
`share_url`	“”	What URL to share	str
`show_category_labels`	True	Whether to show the value labels	bool
`show_color_key`	False	Whether to show the color key	bool
`show_group_labels`	True	Whether to show the group labels	bool
`show_value_labels`	True	Whether or not to show value labels with the bars	bool
`sort_bars`	False	Whether to sort the bars	bool
`source_name`	“”	The source name that appears below the chart	str
`source_url`	“”	The source URL that appears below the chart	str
`stack_color_legend`	False	Whether to stack the color key	bool
`swap_labels`	False	Whether to swap labels and values	bool
`text_annotations`	PydanticUndefined	A list of text annotations to display on the chart	Sequence[UnionType[TextAnnotation, dict[Any, Any]]]
`theme`	“datawrapper”	The theme of the chart	str
`thick_bars`	False	Make the bars thicker	bool
`tick_position`	“top”	The position of the ticks	Literal[top, bottom]
`title`	“”	The headline that appears above the chart	str
`transformations`	PydanticUndefined		Transform or dict[str, Any]
`value_label_alignment`	“left”	The alignment of the value labels	ValueLabelAlignment (LEFT, RIGHT) or str
`value_label_format`	“”	The format of the 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