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plot module

bar_chart(data=None, x=None, y=None, color=None, descending=True, max_rows=None, x_label=None, y_label=None, title=None, width=None, height=500, layout_args=None, **kwargs)

Create a bar chart with plotly.express,

Parameters:

Name Type Description Default
data

DataFrame | array-like | dict | str (local file path or HTTP URL) This argument needs to be passed for column names (and not keyword names) to be used. Array-like and dict are transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

None
x

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the x axis in cartesian coordinates. Either x or y can optionally be a list of column references or array_likes, in which case the data will be treated as if it were 'wide' rather than 'long'.

None
y

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the y axis in cartesian coordinates. Either x or y can optionally be a list of column references or array_likes, in which case the data will be treated as if it were 'wide' rather than 'long'.

None
color

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign color to marks.

None
descending bool

Whether to sort the data in descending order. Defaults to True.

True
max_rows int

Maximum number of rows to display. Defaults to None.

None
x_label str

Label for the x axis. Defaults to None.

None
y_label str

Label for the y axis. Defaults to None.

None
title str

Title for the plot. Defaults to None.

None
width int

Width of the plot in pixels. Defaults to None.

None
height int

Height of the plot in pixels. Defaults to 500.

500
layout_args dict

Layout arguments for the plot to be passed to fig.update_layout(), such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None.

None
**kwargs

Any additional arguments to pass to plotly.express.bar(), such as:

pattern_shape: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign pattern shapes to marks. facet_row: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to facetted subplots in the vertical direction. facet_col: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to facetted subplots in the horizontal direction. facet_col_wrap: int Maximum number of facet columns. Wraps the column variable at this width, so that the column facets span multiple rows. Ignored if 0, and forced to 0 if facet_row or a marginal is set. facet_row_spacing: float between 0 and 1 Spacing between facet rows, in paper units. Default is 0.03 or 0.0.7 when facet_col_wrap is used. facet_col_spacing: float between 0 and 1 Spacing between facet columns, in paper units Default is 0.02. hover_name: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like appear in bold in the hover tooltip. hover_data: list of str or int, or Series or array-like, or dict Either a list of names of columns in data_frame, or pandas Series, or array_like objects or a dict with column names as keys, with values True (for default formatting) False (in order to remove this column from hover information), or a formatting string, for example ':.3f' or '|%a' or list-like data to appear in the hover tooltip or tuples with a bool or formatting string as first element, and list-like data to appear in hover as second element Values from these columns appear as extra data in the hover tooltip. custom_data: list of str or int, or Series or array-like Either names of columns in data_frame, or pandas Series, or array_like objects Values from these columns are extra data, to be used in widgets or Dash callbacks for example. This data is not user-visible but is included in events emitted by the figure (lasso selection etc.) text: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like appear in the figure as text labels. base: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position the base of the bar. error_x: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size x-axis error bars. If error_x_minus is None, error bars will be symmetrical, otherwise error_x is used for the positive direction only. error_x_minus: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size x-axis error bars in the negative direction. Ignored if error_x is None. error_y: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size y-axis error bars. If error_y_minus is None, error bars will be symmetrical, otherwise error_y is used for the positive direction only. error_y_minus: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size y-axis error bars in the negative direction. Ignored if error_y is None. animation_frame: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to animation frames. animation_group: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to provide object-constancy across animation frames: rows with matching animation_groups will be treated as if they describe the same object in each frame. category_orders: dict with str keys and list of str values (default {}) By default, in Python 3.6+, the order of categorical values in axes, legends and facets depends on the order in which these values are first encountered in data_frame (and no order is guaranteed by default in Python below 3.6). This parameter is used to force a specific ordering of values per column. The keys of this dict should correspond to column names, and the values should be lists of strings corresponding to the specific display order desired. labels: dict with str keys and str values (default {}) By default, column names are used in the figure for axis titles, legend entries and hovers. This parameter allows this to be overridden. The keys of this dict should correspond to column names, and the values should correspond to the desired label to be displayed. color_discrete_sequence: list of str Strings should define valid CSS-colors. When color is set and the values in the corresponding column are not numeric, values in that column are assigned colors by cycling through color_discrete_sequence in the order described in category_orders, unless the value of color is a key in color_discrete_map. Various useful color sequences are available in the plotly.express.colors submodules, specifically plotly.express.colors.qualitative. color_discrete_map: dict with str keys and str values (default {}) String values should define valid CSS-colors Used to override color_discrete_sequence to assign a specific colors to marks corresponding with specific values. Keys in color_discrete_map should be values in the column denoted by color. Alternatively, if the values of color are valid colors, the string 'identity' may be passed to cause them to be used directly. color_continuous_scale: list of str Strings should define valid CSS-colors This list is used to build a continuous color scale when the column denoted by color contains numeric data. Various useful color scales are available in the plotly.express.colors submodules, specifically plotly.express.colors.sequential, plotly.express.colors.diverging and plotly.express.colors.cyclical. pattern_shape_sequence: list of str Strings should define valid plotly.js patterns-shapes. When pattern_shape is set, values in that column are assigned patterns- shapes by cycling through pattern_shape_sequence in the order described in category_orders, unless the value of pattern_shape is a key in pattern_shape_map. pattern_shape_map: dict with str keys and str values (default {}) Strings values define plotly.js patterns-shapes. Used to override pattern_shape_sequences to assign a specific patterns-shapes to lines corresponding with specific values. Keys in pattern_shape_map should be values in the column denoted by pattern_shape. Alternatively, if the values of pattern_shape are valid patterns-shapes names, the string 'identity' may be passed to cause them to be used directly. range_color: list of two numbers If provided, overrides auto-scaling on the continuous color scale. color_continuous_midpoint: number (default None) If set, computes the bounds of the continuous color scale to have the desired midpoint. Setting this value is recommended when using plotly.express.colors.diverging color scales as the inputs to color_continuous_scale. opacity: float Value between 0 and 1. Sets the opacity for markers. orientation: str, one of 'h' for horizontal or 'v' for vertical. (default 'v' if x and y are provided and both continuous or both categorical, otherwise 'v'('h') if x(y) is categorical and y(x) is continuous, otherwise 'v'('h') if only x(y) is provided) barmode: str (default 'relative') One of 'group', 'overlay' or 'relative' In 'relative' mode, bars are stacked above zero for positive values and below zero for negative values. In 'overlay' mode, bars are drawn on top of one another. In 'group' mode, bars are placed beside each other. log_x: boolean (default False) If True, the x-axis is log-scaled in cartesian coordinates. log_y: boolean (default False) If True, the y-axis is log-scaled in cartesian coordinates. range_x: list of two numbers If provided, overrides auto-scaling on the x-axis in cartesian coordinates. range_y: list of two numbers If provided, overrides auto-scaling on the y-axis in cartesian coordinates. text_auto: bool or string (default False) If True or a string, the x or y or z values will be displayed as text, depending on the orientation A string like '.2f' will be interpreted as a texttemplate numeric formatting directive. template: str or dict or plotly.graph_objects.layout.Template instance The figure template name (must be a key in plotly.io.templates) or definition.

{}

Returns:

Type Description
plotly.graph_objs._figure.Figure

A plotly figure object.

Source code in geemap/plot.py
def bar_chart(
    data=None,
    x=None,
    y=None,
    color=None,
    descending=True,
    max_rows=None,
    x_label=None,
    y_label=None,
    title=None,
    width=None,
    height=500,
    layout_args=None,
    **kwargs,
):
    """Create a bar chart with plotly.express,

    Args:
        data: DataFrame | array-like | dict | str (local file path or HTTP URL)
            This argument needs to be passed for column names (and not keyword
            names) to be used. Array-like and dict are transformed internally to a
            pandas DataFrame. Optional: if missing, a DataFrame gets constructed
            under the hood using the other arguments.
        x: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used to
            position marks along the x axis in cartesian coordinates. Either `x` or
            `y` can optionally be a list of column references or array_likes,  in
            which case the data will be treated as if it were 'wide' rather than
            'long'.
        y: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used to
            position marks along the y axis in cartesian coordinates. Either `x` or
            `y` can optionally be a list of column references or array_likes,  in
            which case the data will be treated as if it were 'wide' rather than
            'long'.
        color: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used to
            assign color to marks.
        descending (bool, optional): Whether to sort the data in descending order. Defaults to True.
        max_rows (int, optional): Maximum number of rows to display. Defaults to None.
        x_label (str, optional): Label for the x axis. Defaults to None.
        y_label (str, optional): Label for the y axis. Defaults to None.
        title (str, optional): Title for the plot. Defaults to None.
        width (int, optional): Width of the plot in pixels. Defaults to None.
        height (int, optional): Height of the plot in pixels. Defaults to 500.
        layout_args (dict, optional): Layout arguments for the plot to be passed to fig.update_layout(),
            such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None.
        **kwargs: Any additional arguments to pass to plotly.express.bar(), such as:

            pattern_shape: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign pattern shapes to marks.
            facet_row: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign marks to facetted subplots in the vertical direction.
            facet_col: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign marks to facetted subplots in the horizontal direction.
            facet_col_wrap: int
                Maximum number of facet columns. Wraps the column variable at this
                width, so that the column facets span multiple rows. Ignored if 0, and
                forced to 0 if `facet_row` or a `marginal` is set.
            facet_row_spacing: float between 0 and 1
                Spacing between facet rows, in paper units. Default is 0.03 or 0.0.7
                when facet_col_wrap is used.
            facet_col_spacing: float between 0 and 1
                Spacing between facet columns, in paper units Default is 0.02.
            hover_name: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like appear in bold
                in the hover tooltip.
            hover_data: list of str or int, or Series or array-like, or dict
                Either a list of names of columns in `data_frame`, or pandas Series, or
                array_like objects or a dict with column names as keys, with values
                True (for default formatting) False (in order to remove this column
                from hover information), or a formatting string, for example ':.3f' or
                '|%a' or list-like data to appear in the hover tooltip or tuples with a
                bool or formatting string as first element, and list-like data to
                appear in hover as second element Values from these columns appear as
                extra data in the hover tooltip.
            custom_data: list of str or int, or Series or array-like
                Either names of columns in `data_frame`, or pandas Series, or
                array_like objects Values from these columns are extra data, to be used
                in widgets or Dash callbacks for example. This data is not user-visible
                but is included in events emitted by the figure (lasso selection etc.)
            text: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like appear in the
                figure as text labels.
            base: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                position the base of the bar.
            error_x: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size x-axis error bars. If `error_x_minus` is `None`, error bars will
                be symmetrical, otherwise `error_x` is used for the positive direction
                only.
            error_x_minus: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size x-axis error bars in the negative direction. Ignored if `error_x`
                is `None`.
            error_y: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size y-axis error bars. If `error_y_minus` is `None`, error bars will
                be symmetrical, otherwise `error_y` is used for the positive direction
                only.
            error_y_minus: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size y-axis error bars in the negative direction. Ignored if `error_y`
                is `None`.
            animation_frame: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign marks to animation frames.
            animation_group: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                provide object-constancy across animation frames: rows with matching
                `animation_group`s will be treated as if they describe the same object
                in each frame.
            category_orders: dict with str keys and list of str values (default `{}`)
                By default, in Python 3.6+, the order of categorical values in axes,
                legends and facets depends on the order in which these values are first
                encountered in `data_frame` (and no order is guaranteed by default in
                Python below 3.6). This parameter is used to force a specific ordering
                of values per column. The keys of this dict should correspond to column
                names, and the values should be lists of strings corresponding to the
                specific display order desired.
            labels: dict with str keys and str values (default `{}`)
                By default, column names are used in the figure for axis titles, legend
                entries and hovers. This parameter allows this to be overridden. The
                keys of this dict should correspond to column names, and the values
                should correspond to the desired label to be displayed.
            color_discrete_sequence: list of str
                Strings should define valid CSS-colors. When `color` is set and the
                values in the corresponding column are not numeric, values in that
                column are assigned colors by cycling through `color_discrete_sequence`
                in the order described in `category_orders`, unless the value of
                `color` is a key in `color_discrete_map`. Various useful color
                sequences are available in the `plotly.express.colors` submodules,
                specifically `plotly.express.colors.qualitative`.
            color_discrete_map: dict with str keys and str values (default `{}`)
                String values should define valid CSS-colors Used to override
                `color_discrete_sequence` to assign a specific colors to marks
                corresponding with specific values. Keys in `color_discrete_map` should
                be values in the column denoted by `color`. Alternatively, if the
                values of `color` are valid colors, the string `'identity'` may be
                passed to cause them to be used directly.
            color_continuous_scale: list of str
                Strings should define valid CSS-colors This list is used to build a
                continuous color scale when the column denoted by `color` contains
                numeric data. Various useful color scales are available in the
                `plotly.express.colors` submodules, specifically
                `plotly.express.colors.sequential`, `plotly.express.colors.diverging`
                and `plotly.express.colors.cyclical`.
            pattern_shape_sequence: list of str
                Strings should define valid plotly.js patterns-shapes. When
                `pattern_shape` is set, values in that column are assigned patterns-
                shapes by cycling through `pattern_shape_sequence` in the order
                described in `category_orders`, unless the value of `pattern_shape` is
                a key in `pattern_shape_map`.
            pattern_shape_map: dict with str keys and str values (default `{}`)
                Strings values define plotly.js patterns-shapes. Used to override
                `pattern_shape_sequences` to assign a specific patterns-shapes to lines
                corresponding with specific values. Keys in `pattern_shape_map` should
                be values in the column denoted by `pattern_shape`. Alternatively, if
                the values of `pattern_shape` are valid patterns-shapes names, the
                string `'identity'` may be passed to cause them to be used directly.
            range_color: list of two numbers
                If provided, overrides auto-scaling on the continuous color scale.
            color_continuous_midpoint: number (default `None`)
                If set, computes the bounds of the continuous color scale to have the
                desired midpoint. Setting this value is recommended when using
                `plotly.express.colors.diverging` color scales as the inputs to
                `color_continuous_scale`.
            opacity: float
                Value between 0 and 1. Sets the opacity for markers.
            orientation: str, one of `'h'` for horizontal or `'v'` for vertical.
                (default `'v'` if `x` and `y` are provided and both continuous or both
                categorical,  otherwise `'v'`(`'h'`) if `x`(`y`) is categorical and
                `y`(`x`) is continuous,  otherwise `'v'`(`'h'`) if only `x`(`y`) is
                provided)
            barmode: str (default `'relative'`)
                One of `'group'`, `'overlay'` or `'relative'` In `'relative'` mode,
                bars are stacked above zero for positive values and below zero for
                negative values. In `'overlay'` mode, bars are drawn on top of one
                another. In `'group'` mode, bars are placed beside each other.
            log_x: boolean (default `False`)
                If `True`, the x-axis is log-scaled in cartesian coordinates.
            log_y: boolean (default `False`)
                If `True`, the y-axis is log-scaled in cartesian coordinates.
            range_x: list of two numbers
                If provided, overrides auto-scaling on the x-axis in cartesian
                coordinates.
            range_y: list of two numbers
                If provided, overrides auto-scaling on the y-axis in cartesian
                coordinates.
            text_auto: bool or string (default `False`)
                If `True` or a string, the x or y or z values will be displayed as
                text, depending on the orientation A string like `'.2f'` will be
                interpreted as a `texttemplate` numeric formatting directive.
            template: str or dict or plotly.graph_objects.layout.Template instance
                The figure template name (must be a key in plotly.io.templates) or
                definition.


    Returns:
        plotly.graph_objs._figure.Figure: A plotly figure object.
    """

    if isinstance(data, str):
        if data.startswith("http"):
            data = github_raw_url(data)
            data = get_direct_url(data)

        try:
            data = pd.read_csv(data)
        except Exception as e:
            raise ValueError(f"Could not read data from {data}. {e}")

    if not isinstance(data, pd.DataFrame):
        raise ValueError(
            "data must be a pandas DataFrame, a string or an ee.FeatureCollection."
        )

    if descending is not None:
        data.sort_values([y, x], ascending=not (descending), inplace=True)

    if isinstance(max_rows, int):
        data = data.head(max_rows)

    if "labels" in kwargs:
        labels = kwargs["labels"]
    else:
        labels = {}

    if x_label is not None:
        labels[x] = x_label
    if y_label is not None:
        labels[y] = y_label

    try:
        fig = px.bar(
            data,
            x=x,
            y=y,
            color=color,
            labels=labels,
            title=title,
            width=width,
            height=height,
            **kwargs,
        )

        if isinstance(layout_args, dict):
            fig.update_layout(**layout_args)

        return fig
    except Exception as e:
        raise ValueError(f"Could not create bar plot. {e}")

histogram(data=None, x=None, y=None, color=None, descending=None, max_rows=None, x_label=None, y_label=None, title=None, width=None, height=500, layout_args=None, **kwargs)

Create a line chart with plotly.express,

Parameters:

Name Type Description Default
data

DataFrame | array-like | dict | str (local file path or HTTP URL) This argument needs to be passed for column names (and not keyword names) to be used. Array-like and dict are transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

None
x

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the x axis in cartesian coordinates. Either x or y can optionally be a list of column references or array_likes, in which case the data will be treated as if it were 'wide' rather than 'long'.

None
y

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the y axis in cartesian coordinates. Either x or y can optionally be a list of column references or array_likes, in which case the data will be treated as if it were 'wide' rather than 'long'.

None
color

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign color to marks.

None
descending bool

Whether to sort the data in descending order. Defaults to None.

None
max_rows int

Maximum number of rows to display. Defaults to None.

None
x_label str

Label for the x axis. Defaults to None.

None
y_label str

Label for the y axis. Defaults to None.

None
title str

Title for the plot. Defaults to None.

None
width int

Width of the plot in pixels. Defaults to None.

None
height int

Height of the plot in pixels. Defaults to 500.

500
layout_args dict

Layout arguments for the plot to be passed to fig.update_layout(), such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None.

None
**kwargs

Any additional arguments to pass to plotly.express.bar(), such as:

pattern_shape: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign pattern shapes to marks. facet_row: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to facetted subplots in the vertical direction. facet_col: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to facetted subplots in the horizontal direction. facet_col_wrap: int Maximum number of facet columns. Wraps the column variable at this width, so that the column facets span multiple rows. Ignored if 0, and forced to 0 if facet_row or a marginal is set. facet_row_spacing: float between 0 and 1 Spacing between facet rows, in paper units. Default is 0.03 or 0.0.7 when facet_col_wrap is used. facet_col_spacing: float between 0 and 1 Spacing between facet columns, in paper units Default is 0.02. hover_name: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like appear in bold in the hover tooltip. hover_data: list of str or int, or Series or array-like, or dict Either a list of names of columns in data_frame, or pandas Series, or array_like objects or a dict with column names as keys, with values True (for default formatting) False (in order to remove this column from hover information), or a formatting string, for example ':.3f' or '|%a' or list-like data to appear in the hover tooltip or tuples with a bool or formatting string as first element, and list-like data to appear in hover as second element Values from these columns appear as extra data in the hover tooltip. custom_data: list of str or int, or Series or array-like Either names of columns in data_frame, or pandas Series, or array_like objects Values from these columns are extra data, to be used in widgets or Dash callbacks for example. This data is not user-visible but is included in events emitted by the figure (lasso selection etc.) text: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like appear in the figure as text labels. base: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position the base of the bar. error_x: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size x-axis error bars. If error_x_minus is None, error bars will be symmetrical, otherwise error_x is used for the positive direction only. error_x_minus: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size x-axis error bars in the negative direction. Ignored if error_x is None. error_y: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size y-axis error bars. If error_y_minus is None, error bars will be symmetrical, otherwise error_y is used for the positive direction only. error_y_minus: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size y-axis error bars in the negative direction. Ignored if error_y is None. animation_frame: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to animation frames. animation_group: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to provide object-constancy across animation frames: rows with matching animation_groups will be treated as if they describe the same object in each frame. category_orders: dict with str keys and list of str values (default {}) By default, in Python 3.6+, the order of categorical values in axes, legends and facets depends on the order in which these values are first encountered in data_frame (and no order is guaranteed by default in Python below 3.6). This parameter is used to force a specific ordering of values per column. The keys of this dict should correspond to column names, and the values should be lists of strings corresponding to the specific display order desired. labels: dict with str keys and str values (default {}) By default, column names are used in the figure for axis titles, legend entries and hovers. This parameter allows this to be overridden. The keys of this dict should correspond to column names, and the values should correspond to the desired label to be displayed. color_discrete_sequence: list of str Strings should define valid CSS-colors. When color is set and the values in the corresponding column are not numeric, values in that column are assigned colors by cycling through color_discrete_sequence in the order described in category_orders, unless the value of color is a key in color_discrete_map. Various useful color sequences are available in the plotly.express.colors submodules, specifically plotly.express.colors.qualitative. color_discrete_map: dict with str keys and str values (default {}) String values should define valid CSS-colors Used to override color_discrete_sequence to assign a specific colors to marks corresponding with specific values. Keys in color_discrete_map should be values in the column denoted by color. Alternatively, if the values of color are valid colors, the string 'identity' may be passed to cause them to be used directly. color_continuous_scale: list of str Strings should define valid CSS-colors This list is used to build a continuous color scale when the column denoted by color contains numeric data. Various useful color scales are available in the plotly.express.colors submodules, specifically plotly.express.colors.sequential, plotly.express.colors.diverging and plotly.express.colors.cyclical. pattern_shape_sequence: list of str Strings should define valid plotly.js patterns-shapes. When pattern_shape is set, values in that column are assigned patterns- shapes by cycling through pattern_shape_sequence in the order described in category_orders, unless the value of pattern_shape is a key in pattern_shape_map. pattern_shape_map: dict with str keys and str values (default {}) Strings values define plotly.js patterns-shapes. Used to override pattern_shape_sequences to assign a specific patterns-shapes to lines corresponding with specific values. Keys in pattern_shape_map should be values in the column denoted by pattern_shape. Alternatively, if the values of pattern_shape are valid patterns-shapes names, the string 'identity' may be passed to cause them to be used directly. range_color: list of two numbers If provided, overrides auto-scaling on the continuous color scale. color_continuous_midpoint: number (default None) If set, computes the bounds of the continuous color scale to have the desired midpoint. Setting this value is recommended when using plotly.express.colors.diverging color scales as the inputs to color_continuous_scale. opacity: float Value between 0 and 1. Sets the opacity for markers. orientation: str, one of 'h' for horizontal or 'v' for vertical. (default 'v' if x and y are provided and both continuous or both categorical, otherwise 'v'('h') if x(y) is categorical and y(x) is continuous, otherwise 'v'('h') if only x(y) is provided) barmode: str (default 'relative') One of 'group', 'overlay' or 'relative' In 'relative' mode, bars are stacked above zero for positive values and below zero for negative values. In 'overlay' mode, bars are drawn on top of one another. In 'group' mode, bars are placed beside each other. log_x: boolean (default False) If True, the x-axis is log-scaled in cartesian coordinates. log_y: boolean (default False) If True, the y-axis is log-scaled in cartesian coordinates. range_x: list of two numbers If provided, overrides auto-scaling on the x-axis in cartesian coordinates. range_y: list of two numbers If provided, overrides auto-scaling on the y-axis in cartesian coordinates. text_auto: bool or string (default False) If True or a string, the x or y or z values will be displayed as text, depending on the orientation A string like '.2f' will be interpreted as a texttemplate numeric formatting directive. template: str or dict or plotly.graph_objects.layout.Template instance The figure template name (must be a key in plotly.io.templates) or definition.

{}

Returns:

Type Description
plotly.graph_objs._figure.Figure

A plotly figure object.

Source code in geemap/plot.py
def histogram(
    data=None,
    x=None,
    y=None,
    color=None,
    descending=None,
    max_rows=None,
    x_label=None,
    y_label=None,
    title=None,
    width=None,
    height=500,
    layout_args=None,
    **kwargs,
):
    """Create a line chart with plotly.express,

    Args:
        data: DataFrame | array-like | dict | str (local file path or HTTP URL)
            This argument needs to be passed for column names (and not keyword
            names) to be used. Array-like and dict are transformed internally to a
            pandas DataFrame. Optional: if missing, a DataFrame gets constructed
            under the hood using the other arguments.
        x: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used to
            position marks along the x axis in cartesian coordinates. Either `x` or
            `y` can optionally be a list of column references or array_likes,  in
            which case the data will be treated as if it were 'wide' rather than
            'long'.
        y: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used to
            position marks along the y axis in cartesian coordinates. Either `x` or
            `y` can optionally be a list of column references or array_likes,  in
            which case the data will be treated as if it were 'wide' rather than
            'long'.
        color: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used to
            assign color to marks.
        descending (bool, optional): Whether to sort the data in descending order. Defaults to None.
        max_rows (int, optional): Maximum number of rows to display. Defaults to None.
        x_label (str, optional): Label for the x axis. Defaults to None.
        y_label (str, optional): Label for the y axis. Defaults to None.
        title (str, optional): Title for the plot. Defaults to None.
        width (int, optional): Width of the plot in pixels. Defaults to None.
        height (int, optional): Height of the plot in pixels. Defaults to 500.
        layout_args (dict, optional): Layout arguments for the plot to be passed to fig.update_layout(),
            such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None.
        **kwargs: Any additional arguments to pass to plotly.express.bar(), such as:

            pattern_shape: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign pattern shapes to marks.
            facet_row: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign marks to facetted subplots in the vertical direction.
            facet_col: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign marks to facetted subplots in the horizontal direction.
            facet_col_wrap: int
                Maximum number of facet columns. Wraps the column variable at this
                width, so that the column facets span multiple rows. Ignored if 0, and
                forced to 0 if `facet_row` or a `marginal` is set.
            facet_row_spacing: float between 0 and 1
                Spacing between facet rows, in paper units. Default is 0.03 or 0.0.7
                when facet_col_wrap is used.
            facet_col_spacing: float between 0 and 1
                Spacing between facet columns, in paper units Default is 0.02.
            hover_name: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like appear in bold
                in the hover tooltip.
            hover_data: list of str or int, or Series or array-like, or dict
                Either a list of names of columns in `data_frame`, or pandas Series, or
                array_like objects or a dict with column names as keys, with values
                True (for default formatting) False (in order to remove this column
                from hover information), or a formatting string, for example ':.3f' or
                '|%a' or list-like data to appear in the hover tooltip or tuples with a
                bool or formatting string as first element, and list-like data to
                appear in hover as second element Values from these columns appear as
                extra data in the hover tooltip.
            custom_data: list of str or int, or Series or array-like
                Either names of columns in `data_frame`, or pandas Series, or
                array_like objects Values from these columns are extra data, to be used
                in widgets or Dash callbacks for example. This data is not user-visible
                but is included in events emitted by the figure (lasso selection etc.)
            text: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like appear in the
                figure as text labels.
            base: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                position the base of the bar.
            error_x: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size x-axis error bars. If `error_x_minus` is `None`, error bars will
                be symmetrical, otherwise `error_x` is used for the positive direction
                only.
            error_x_minus: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size x-axis error bars in the negative direction. Ignored if `error_x`
                is `None`.
            error_y: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size y-axis error bars. If `error_y_minus` is `None`, error bars will
                be symmetrical, otherwise `error_y` is used for the positive direction
                only.
            error_y_minus: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size y-axis error bars in the negative direction. Ignored if `error_y`
                is `None`.
            animation_frame: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign marks to animation frames.
            animation_group: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                provide object-constancy across animation frames: rows with matching
                `animation_group`s will be treated as if they describe the same object
                in each frame.
            category_orders: dict with str keys and list of str values (default `{}`)
                By default, in Python 3.6+, the order of categorical values in axes,
                legends and facets depends on the order in which these values are first
                encountered in `data_frame` (and no order is guaranteed by default in
                Python below 3.6). This parameter is used to force a specific ordering
                of values per column. The keys of this dict should correspond to column
                names, and the values should be lists of strings corresponding to the
                specific display order desired.
            labels: dict with str keys and str values (default `{}`)
                By default, column names are used in the figure for axis titles, legend
                entries and hovers. This parameter allows this to be overridden. The
                keys of this dict should correspond to column names, and the values
                should correspond to the desired label to be displayed.
            color_discrete_sequence: list of str
                Strings should define valid CSS-colors. When `color` is set and the
                values in the corresponding column are not numeric, values in that
                column are assigned colors by cycling through `color_discrete_sequence`
                in the order described in `category_orders`, unless the value of
                `color` is a key in `color_discrete_map`. Various useful color
                sequences are available in the `plotly.express.colors` submodules,
                specifically `plotly.express.colors.qualitative`.
            color_discrete_map: dict with str keys and str values (default `{}`)
                String values should define valid CSS-colors Used to override
                `color_discrete_sequence` to assign a specific colors to marks
                corresponding with specific values. Keys in `color_discrete_map` should
                be values in the column denoted by `color`. Alternatively, if the
                values of `color` are valid colors, the string `'identity'` may be
                passed to cause them to be used directly.
            color_continuous_scale: list of str
                Strings should define valid CSS-colors This list is used to build a
                continuous color scale when the column denoted by `color` contains
                numeric data. Various useful color scales are available in the
                `plotly.express.colors` submodules, specifically
                `plotly.express.colors.sequential`, `plotly.express.colors.diverging`
                and `plotly.express.colors.cyclical`.
            pattern_shape_sequence: list of str
                Strings should define valid plotly.js patterns-shapes. When
                `pattern_shape` is set, values in that column are assigned patterns-
                shapes by cycling through `pattern_shape_sequence` in the order
                described in `category_orders`, unless the value of `pattern_shape` is
                a key in `pattern_shape_map`.
            pattern_shape_map: dict with str keys and str values (default `{}`)
                Strings values define plotly.js patterns-shapes. Used to override
                `pattern_shape_sequences` to assign a specific patterns-shapes to lines
                corresponding with specific values. Keys in `pattern_shape_map` should
                be values in the column denoted by `pattern_shape`. Alternatively, if
                the values of `pattern_shape` are valid patterns-shapes names, the
                string `'identity'` may be passed to cause them to be used directly.
            range_color: list of two numbers
                If provided, overrides auto-scaling on the continuous color scale.
            color_continuous_midpoint: number (default `None`)
                If set, computes the bounds of the continuous color scale to have the
                desired midpoint. Setting this value is recommended when using
                `plotly.express.colors.diverging` color scales as the inputs to
                `color_continuous_scale`.
            opacity: float
                Value between 0 and 1. Sets the opacity for markers.
            orientation: str, one of `'h'` for horizontal or `'v'` for vertical.
                (default `'v'` if `x` and `y` are provided and both continuous or both
                categorical,  otherwise `'v'`(`'h'`) if `x`(`y`) is categorical and
                `y`(`x`) is continuous,  otherwise `'v'`(`'h'`) if only `x`(`y`) is
                provided)
            barmode: str (default `'relative'`)
                One of `'group'`, `'overlay'` or `'relative'` In `'relative'` mode,
                bars are stacked above zero for positive values and below zero for
                negative values. In `'overlay'` mode, bars are drawn on top of one
                another. In `'group'` mode, bars are placed beside each other.
            log_x: boolean (default `False`)
                If `True`, the x-axis is log-scaled in cartesian coordinates.
            log_y: boolean (default `False`)
                If `True`, the y-axis is log-scaled in cartesian coordinates.
            range_x: list of two numbers
                If provided, overrides auto-scaling on the x-axis in cartesian
                coordinates.
            range_y: list of two numbers
                If provided, overrides auto-scaling on the y-axis in cartesian
                coordinates.
            text_auto: bool or string (default `False`)
                If `True` or a string, the x or y or z values will be displayed as
                text, depending on the orientation A string like `'.2f'` will be
                interpreted as a `texttemplate` numeric formatting directive.
            template: str or dict or plotly.graph_objects.layout.Template instance
                The figure template name (must be a key in plotly.io.templates) or
                definition.


    Returns:
        plotly.graph_objs._figure.Figure: A plotly figure object.
    """

    if isinstance(data, str):
        if data.startswith("http"):
            data = github_raw_url(data)
            data = get_direct_url(data)

        try:
            data = pd.read_csv(data)
        except Exception as e:
            raise ValueError(f"Could not read data from {data}. {e}")

    if not isinstance(data, pd.DataFrame):
        raise ValueError(
            "data must be a pandas DataFrame, a string or an ee.FeatureCollection."
        )

    if descending is not None:
        data.sort_values([y, x], ascending=not (descending), inplace=True)

    if isinstance(max_rows, int):
        data = data.head(max_rows)

    if "labels" in kwargs:
        labels = kwargs["labels"]
    else:
        labels = {}

    if x_label is not None:
        labels[x] = x_label
    if y_label is not None:
        labels[y] = y_label

    try:
        fig = px.histogram(
            data,
            x=x,
            y=y,
            color=color,
            labels=labels,
            title=title,
            width=width,
            height=height,
            **kwargs,
        )

        if isinstance(layout_args, dict):
            fig.update_layout(**layout_args)

        return fig
    except Exception as e:
        raise ValueError(f"Could not create bar plot. {e}")

line_chart(data=None, x=None, y=None, color=None, descending=None, max_rows=None, x_label=None, y_label=None, title=None, width=None, height=500, layout_args=None, **kwargs)

Create a line chart with plotly.express,

Parameters:

Name Type Description Default
data

DataFrame | array-like | dict | str (local file path or HTTP URL) This argument needs to be passed for column names (and not keyword names) to be used. Array-like and dict are transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

None
x

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the x axis in cartesian coordinates. Either x or y can optionally be a list of column references or array_likes, in which case the data will be treated as if it were 'wide' rather than 'long'.

None
y

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the y axis in cartesian coordinates. Either x or y can optionally be a list of column references or array_likes, in which case the data will be treated as if it were 'wide' rather than 'long'.

None
color

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign color to marks.

None
descending bool

Whether to sort the data in descending order. Defaults to None.

None
max_rows int

Maximum number of rows to display. Defaults to None.

None
x_label str

Label for the x axis. Defaults to None.

None
y_label str

Label for the y axis. Defaults to None.

None
title str

Title for the plot. Defaults to None.

None
width int

Width of the plot in pixels. Defaults to None.

None
height int

Height of the plot in pixels. Defaults to 500.

500
layout_args dict

Layout arguments for the plot to be passed to fig.update_layout(), such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None.

None
**kwargs

Any additional arguments to pass to plotly.express.bar(), such as:

pattern_shape: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign pattern shapes to marks. facet_row: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to facetted subplots in the vertical direction. facet_col: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to facetted subplots in the horizontal direction. facet_col_wrap: int Maximum number of facet columns. Wraps the column variable at this width, so that the column facets span multiple rows. Ignored if 0, and forced to 0 if facet_row or a marginal is set. facet_row_spacing: float between 0 and 1 Spacing between facet rows, in paper units. Default is 0.03 or 0.0.7 when facet_col_wrap is used. facet_col_spacing: float between 0 and 1 Spacing between facet columns, in paper units Default is 0.02. hover_name: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like appear in bold in the hover tooltip. hover_data: list of str or int, or Series or array-like, or dict Either a list of names of columns in data_frame, or pandas Series, or array_like objects or a dict with column names as keys, with values True (for default formatting) False (in order to remove this column from hover information), or a formatting string, for example ':.3f' or '|%a' or list-like data to appear in the hover tooltip or tuples with a bool or formatting string as first element, and list-like data to appear in hover as second element Values from these columns appear as extra data in the hover tooltip. custom_data: list of str or int, or Series or array-like Either names of columns in data_frame, or pandas Series, or array_like objects Values from these columns are extra data, to be used in widgets or Dash callbacks for example. This data is not user-visible but is included in events emitted by the figure (lasso selection etc.) text: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like appear in the figure as text labels. base: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position the base of the bar. error_x: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size x-axis error bars. If error_x_minus is None, error bars will be symmetrical, otherwise error_x is used for the positive direction only. error_x_minus: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size x-axis error bars in the negative direction. Ignored if error_x is None. error_y: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size y-axis error bars. If error_y_minus is None, error bars will be symmetrical, otherwise error_y is used for the positive direction only. error_y_minus: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size y-axis error bars in the negative direction. Ignored if error_y is None. animation_frame: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to animation frames. animation_group: str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to provide object-constancy across animation frames: rows with matching animation_groups will be treated as if they describe the same object in each frame. category_orders: dict with str keys and list of str values (default {}) By default, in Python 3.6+, the order of categorical values in axes, legends and facets depends on the order in which these values are first encountered in data_frame (and no order is guaranteed by default in Python below 3.6). This parameter is used to force a specific ordering of values per column. The keys of this dict should correspond to column names, and the values should be lists of strings corresponding to the specific display order desired. labels: dict with str keys and str values (default {}) By default, column names are used in the figure for axis titles, legend entries and hovers. This parameter allows this to be overridden. The keys of this dict should correspond to column names, and the values should correspond to the desired label to be displayed. color_discrete_sequence: list of str Strings should define valid CSS-colors. When color is set and the values in the corresponding column are not numeric, values in that column are assigned colors by cycling through color_discrete_sequence in the order described in category_orders, unless the value of color is a key in color_discrete_map. Various useful color sequences are available in the plotly.express.colors submodules, specifically plotly.express.colors.qualitative. color_discrete_map: dict with str keys and str values (default {}) String values should define valid CSS-colors Used to override color_discrete_sequence to assign a specific colors to marks corresponding with specific values. Keys in color_discrete_map should be values in the column denoted by color. Alternatively, if the values of color are valid colors, the string 'identity' may be passed to cause them to be used directly. color_continuous_scale: list of str Strings should define valid CSS-colors This list is used to build a continuous color scale when the column denoted by color contains numeric data. Various useful color scales are available in the plotly.express.colors submodules, specifically plotly.express.colors.sequential, plotly.express.colors.diverging and plotly.express.colors.cyclical. pattern_shape_sequence: list of str Strings should define valid plotly.js patterns-shapes. When pattern_shape is set, values in that column are assigned patterns- shapes by cycling through pattern_shape_sequence in the order described in category_orders, unless the value of pattern_shape is a key in pattern_shape_map. pattern_shape_map: dict with str keys and str values (default {}) Strings values define plotly.js patterns-shapes. Used to override pattern_shape_sequences to assign a specific patterns-shapes to lines corresponding with specific values. Keys in pattern_shape_map should be values in the column denoted by pattern_shape. Alternatively, if the values of pattern_shape are valid patterns-shapes names, the string 'identity' may be passed to cause them to be used directly. range_color: list of two numbers If provided, overrides auto-scaling on the continuous color scale. color_continuous_midpoint: number (default None) If set, computes the bounds of the continuous color scale to have the desired midpoint. Setting this value is recommended when using plotly.express.colors.diverging color scales as the inputs to color_continuous_scale. opacity: float Value between 0 and 1. Sets the opacity for markers. orientation: str, one of 'h' for horizontal or 'v' for vertical. (default 'v' if x and y are provided and both continuous or both categorical, otherwise 'v'('h') if x(y) is categorical and y(x) is continuous, otherwise 'v'('h') if only x(y) is provided) barmode: str (default 'relative') One of 'group', 'overlay' or 'relative' In 'relative' mode, bars are stacked above zero for positive values and below zero for negative values. In 'overlay' mode, bars are drawn on top of one another. In 'group' mode, bars are placed beside each other. log_x: boolean (default False) If True, the x-axis is log-scaled in cartesian coordinates. log_y: boolean (default False) If True, the y-axis is log-scaled in cartesian coordinates. range_x: list of two numbers If provided, overrides auto-scaling on the x-axis in cartesian coordinates. range_y: list of two numbers If provided, overrides auto-scaling on the y-axis in cartesian coordinates. text_auto: bool or string (default False) If True or a string, the x or y or z values will be displayed as text, depending on the orientation A string like '.2f' will be interpreted as a texttemplate numeric formatting directive. template: str or dict or plotly.graph_objects.layout.Template instance The figure template name (must be a key in plotly.io.templates) or definition.

{}

Returns:

Type Description
plotly.graph_objs._figure.Figure

A plotly figure object.

Source code in geemap/plot.py
def line_chart(
    data=None,
    x=None,
    y=None,
    color=None,
    descending=None,
    max_rows=None,
    x_label=None,
    y_label=None,
    title=None,
    width=None,
    height=500,
    layout_args=None,
    **kwargs,
):
    """Create a line chart with plotly.express,

    Args:
        data: DataFrame | array-like | dict | str (local file path or HTTP URL)
            This argument needs to be passed for column names (and not keyword
            names) to be used. Array-like and dict are transformed internally to a
            pandas DataFrame. Optional: if missing, a DataFrame gets constructed
            under the hood using the other arguments.
        x: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used to
            position marks along the x axis in cartesian coordinates. Either `x` or
            `y` can optionally be a list of column references or array_likes,  in
            which case the data will be treated as if it were 'wide' rather than
            'long'.
        y: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used to
            position marks along the y axis in cartesian coordinates. Either `x` or
            `y` can optionally be a list of column references or array_likes,  in
            which case the data will be treated as if it were 'wide' rather than
            'long'.
        color: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used to
            assign color to marks.
        descending (bool, optional): Whether to sort the data in descending order. Defaults to None.
        max_rows (int, optional): Maximum number of rows to display. Defaults to None.
        x_label (str, optional): Label for the x axis. Defaults to None.
        y_label (str, optional): Label for the y axis. Defaults to None.
        title (str, optional): Title for the plot. Defaults to None.
        width (int, optional): Width of the plot in pixels. Defaults to None.
        height (int, optional): Height of the plot in pixels. Defaults to 500.
        layout_args (dict, optional): Layout arguments for the plot to be passed to fig.update_layout(),
            such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None.
        **kwargs: Any additional arguments to pass to plotly.express.bar(), such as:

            pattern_shape: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign pattern shapes to marks.
            facet_row: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign marks to facetted subplots in the vertical direction.
            facet_col: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign marks to facetted subplots in the horizontal direction.
            facet_col_wrap: int
                Maximum number of facet columns. Wraps the column variable at this
                width, so that the column facets span multiple rows. Ignored if 0, and
                forced to 0 if `facet_row` or a `marginal` is set.
            facet_row_spacing: float between 0 and 1
                Spacing between facet rows, in paper units. Default is 0.03 or 0.0.7
                when facet_col_wrap is used.
            facet_col_spacing: float between 0 and 1
                Spacing between facet columns, in paper units Default is 0.02.
            hover_name: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like appear in bold
                in the hover tooltip.
            hover_data: list of str or int, or Series or array-like, or dict
                Either a list of names of columns in `data_frame`, or pandas Series, or
                array_like objects or a dict with column names as keys, with values
                True (for default formatting) False (in order to remove this column
                from hover information), or a formatting string, for example ':.3f' or
                '|%a' or list-like data to appear in the hover tooltip or tuples with a
                bool or formatting string as first element, and list-like data to
                appear in hover as second element Values from these columns appear as
                extra data in the hover tooltip.
            custom_data: list of str or int, or Series or array-like
                Either names of columns in `data_frame`, or pandas Series, or
                array_like objects Values from these columns are extra data, to be used
                in widgets or Dash callbacks for example. This data is not user-visible
                but is included in events emitted by the figure (lasso selection etc.)
            text: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like appear in the
                figure as text labels.
            base: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                position the base of the bar.
            error_x: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size x-axis error bars. If `error_x_minus` is `None`, error bars will
                be symmetrical, otherwise `error_x` is used for the positive direction
                only.
            error_x_minus: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size x-axis error bars in the negative direction. Ignored if `error_x`
                is `None`.
            error_y: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size y-axis error bars. If `error_y_minus` is `None`, error bars will
                be symmetrical, otherwise `error_y` is used for the positive direction
                only.
            error_y_minus: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                size y-axis error bars in the negative direction. Ignored if `error_y`
                is `None`.
            animation_frame: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                assign marks to animation frames.
            animation_group: str or int or Series or array-like
                Either a name of a column in `data_frame`, or a pandas Series or
                array_like object. Values from this column or array_like are used to
                provide object-constancy across animation frames: rows with matching
                `animation_group`s will be treated as if they describe the same object
                in each frame.
            category_orders: dict with str keys and list of str values (default `{}`)
                By default, in Python 3.6+, the order of categorical values in axes,
                legends and facets depends on the order in which these values are first
                encountered in `data_frame` (and no order is guaranteed by default in
                Python below 3.6). This parameter is used to force a specific ordering
                of values per column. The keys of this dict should correspond to column
                names, and the values should be lists of strings corresponding to the
                specific display order desired.
            labels: dict with str keys and str values (default `{}`)
                By default, column names are used in the figure for axis titles, legend
                entries and hovers. This parameter allows this to be overridden. The
                keys of this dict should correspond to column names, and the values
                should correspond to the desired label to be displayed.
            color_discrete_sequence: list of str
                Strings should define valid CSS-colors. When `color` is set and the
                values in the corresponding column are not numeric, values in that
                column are assigned colors by cycling through `color_discrete_sequence`
                in the order described in `category_orders`, unless the value of
                `color` is a key in `color_discrete_map`. Various useful color
                sequences are available in the `plotly.express.colors` submodules,
                specifically `plotly.express.colors.qualitative`.
            color_discrete_map: dict with str keys and str values (default `{}`)
                String values should define valid CSS-colors Used to override
                `color_discrete_sequence` to assign a specific colors to marks
                corresponding with specific values. Keys in `color_discrete_map` should
                be values in the column denoted by `color`. Alternatively, if the
                values of `color` are valid colors, the string `'identity'` may be
                passed to cause them to be used directly.
            color_continuous_scale: list of str
                Strings should define valid CSS-colors This list is used to build a
                continuous color scale when the column denoted by `color` contains
                numeric data. Various useful color scales are available in the
                `plotly.express.colors` submodules, specifically
                `plotly.express.colors.sequential`, `plotly.express.colors.diverging`
                and `plotly.express.colors.cyclical`.
            pattern_shape_sequence: list of str
                Strings should define valid plotly.js patterns-shapes. When
                `pattern_shape` is set, values in that column are assigned patterns-
                shapes by cycling through `pattern_shape_sequence` in the order
                described in `category_orders`, unless the value of `pattern_shape` is
                a key in `pattern_shape_map`.
            pattern_shape_map: dict with str keys and str values (default `{}`)
                Strings values define plotly.js patterns-shapes. Used to override
                `pattern_shape_sequences` to assign a specific patterns-shapes to lines
                corresponding with specific values. Keys in `pattern_shape_map` should
                be values in the column denoted by `pattern_shape`. Alternatively, if
                the values of `pattern_shape` are valid patterns-shapes names, the
                string `'identity'` may be passed to cause them to be used directly.
            range_color: list of two numbers
                If provided, overrides auto-scaling on the continuous color scale.
            color_continuous_midpoint: number (default `None`)
                If set, computes the bounds of the continuous color scale to have the
                desired midpoint. Setting this value is recommended when using
                `plotly.express.colors.diverging` color scales as the inputs to
                `color_continuous_scale`.
            opacity: float
                Value between 0 and 1. Sets the opacity for markers.
            orientation: str, one of `'h'` for horizontal or `'v'` for vertical.
                (default `'v'` if `x` and `y` are provided and both continuous or both
                categorical,  otherwise `'v'`(`'h'`) if `x`(`y`) is categorical and
                `y`(`x`) is continuous,  otherwise `'v'`(`'h'`) if only `x`(`y`) is
                provided)
            barmode: str (default `'relative'`)
                One of `'group'`, `'overlay'` or `'relative'` In `'relative'` mode,
                bars are stacked above zero for positive values and below zero for
                negative values. In `'overlay'` mode, bars are drawn on top of one
                another. In `'group'` mode, bars are placed beside each other.
            log_x: boolean (default `False`)
                If `True`, the x-axis is log-scaled in cartesian coordinates.
            log_y: boolean (default `False`)
                If `True`, the y-axis is log-scaled in cartesian coordinates.
            range_x: list of two numbers
                If provided, overrides auto-scaling on the x-axis in cartesian
                coordinates.
            range_y: list of two numbers
                If provided, overrides auto-scaling on the y-axis in cartesian
                coordinates.
            text_auto: bool or string (default `False`)
                If `True` or a string, the x or y or z values will be displayed as
                text, depending on the orientation A string like `'.2f'` will be
                interpreted as a `texttemplate` numeric formatting directive.
            template: str or dict or plotly.graph_objects.layout.Template instance
                The figure template name (must be a key in plotly.io.templates) or
                definition.


    Returns:
        plotly.graph_objs._figure.Figure: A plotly figure object.
    """

    if isinstance(data, str):
        if data.startswith("http"):
            data = github_raw_url(data)
            data = get_direct_url(data)

        try:
            data = pd.read_csv(data)
        except Exception as e:
            raise ValueError(f"Could not read data from {data}. {e}")

    if not isinstance(data, pd.DataFrame):
        raise ValueError(
            "data must be a pandas DataFrame, a string or an ee.FeatureCollection."
        )

    if descending is not None:
        data.sort_values([y, x], ascending=not (descending), inplace=True)

    if isinstance(max_rows, int):
        data = data.head(max_rows)

    if "labels" in kwargs:
        labels = kwargs["labels"]
    else:
        labels = {}

    if x_label is not None:
        labels[x] = x_label
    if y_label is not None:
        labels[y] = y_label

    try:
        fig = px.line(
            data,
            x=x,
            y=y,
            color=color,
            labels=labels,
            title=title,
            width=width,
            height=height,
            **kwargs,
        )

        if isinstance(layout_args, dict):
            fig.update_layout(**layout_args)

        return fig
    except Exception as e:
        raise ValueError(f"Could not create bar plot. {e}")

pie_chart(data, names=None, values=None, descending=True, max_rows=None, other_label=None, color=None, color_discrete_sequence=None, color_discrete_map=None, hover_name=None, hover_data=None, custom_data=None, labels=None, title=None, template=None, width=None, height=None, opacity=None, hole=None, layout_args=None, **kwargs)

Create a plotly pie chart.

Parameters:

Name Type Description Default
data

DataFrame or array-like or dict This argument needs to be passed for column names (and not keyword names) to be used. Array-like and dict are transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

required
names

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used as labels for sectors.

None
values

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to set values associated to sectors.

None
descending bool

Whether to sort the data in descending order. Defaults to True.

True
max_rows int

Maximum number of rows to display. Defaults to None.

None
other_label str

Label for the "other" category. Defaults to None.

None
color

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign color to marks.

None
color_discrete_sequence

list of str Strings should define valid CSS-colors. When color is set and the values in the corresponding column are not numeric, values in that column are assigned colors by cycling through color_discrete_sequence in the order described in category_orders, unless the value of color is a key in color_discrete_map. Various useful color sequences are available in the plotly.express.colors submodules, specifically plotly.express.colors.qualitative.

None
color_discrete_map

dict with str keys and str values (default {}) String values should define valid CSS-colors Used to override color_discrete_sequence to assign a specific colors to marks corresponding with specific values. Keys in color_discrete_map should be values in the column denoted by color. Alternatively, if the values of color are valid colors, the string 'identity' may be passed to cause them to be used directly.

None
hover_name

str or int or Series or array-like Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like appear in bold in the hover tooltip.

None
hover_data

list of str or int, or Series or array-like, or dict Either a list of names of columns in data_frame, or pandas Series, or array_like objects or a dict with column names as keys, with values True (for default formatting) False (in order to remove this column from hover information), or a formatting string, for example ':.3f' or '|%a' or list-like data to appear in the hover tooltip or tuples with a bool or formatting string as first element, and list-like data to appear in hover as second element Values from these columns appear as extra data in the hover tooltip.

None
custom_data

list of str or int, or Series or array-like Either names of columns in data_frame, or pandas Series, or array_like objects Values from these columns are extra data, to be used in widgets or Dash callbacks for example. This data is not user-visible but is included in events emitted by the figure (lasso selection etc.)

None
labels

dict with str keys and str values (default {}) By default, column names are used in the figure for axis titles, legend entries and hovers. This parameter allows this to be overridden. The keys of this dict should correspond to column names, and the values should correspond to the desired label to be displayed.

None
title

str The figure title.

None
template

str or dict or plotly.graph_objects.layout.Template instance The figure template name (must be a key in plotly.io.templates) or definition.

None
width

int (default None) The figure width in pixels.

None
height

int (default None) The figure height in pixels.

None
opacity

float Value between 0 and 1. Sets the opacity for markers.

None
hole

float Sets the fraction of the radius to cut out of the pie.Use this to make a donut chart.

None

Returns:

Type Description
plotly.graph_objs._figure.Figure

A plotly figure object.

Source code in geemap/plot.py
def pie_chart(
    data,
    names=None,
    values=None,
    descending=True,
    max_rows=None,
    other_label=None,
    color=None,
    color_discrete_sequence=None,
    color_discrete_map=None,
    hover_name=None,
    hover_data=None,
    custom_data=None,
    labels=None,
    title=None,
    template=None,
    width=None,
    height=None,
    opacity=None,
    hole=None,
    layout_args=None,
    **kwargs,
):
    """Create a plotly pie chart.

    Args:
        data: DataFrame or array-like or dict
            This argument needs to be passed for column names (and not keyword
            names) to be used. Array-like and dict are transformed internally to a
            pandas DataFrame. Optional: if missing, a DataFrame gets constructed
            under the hood using the other arguments.
        names: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used as
            labels for sectors.
        values: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used to
            set values associated to sectors.
        descending (bool, optional): Whether to sort the data in descending order. Defaults to True.
        max_rows (int, optional): Maximum number of rows to display. Defaults to None.
        other_label (str, optional): Label for the "other" category. Defaults to None.
        color: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like are used to
            assign color to marks.
        color_discrete_sequence: list of str
            Strings should define valid CSS-colors. When `color` is set and the
            values in the corresponding column are not numeric, values in that
            column are assigned colors by cycling through `color_discrete_sequence`
            in the order described in `category_orders`, unless the value of
            `color` is a key in `color_discrete_map`. Various useful color
            sequences are available in the `plotly.express.colors` submodules,
            specifically `plotly.express.colors.qualitative`.
        color_discrete_map: dict with str keys and str values (default `{}`)
            String values should define valid CSS-colors Used to override
            `color_discrete_sequence` to assign a specific colors to marks
            corresponding with specific values. Keys in `color_discrete_map` should
            be values in the column denoted by `color`. Alternatively, if the
            values of `color` are valid colors, the string `'identity'` may be
            passed to cause them to be used directly.
        hover_name: str or int or Series or array-like
            Either a name of a column in `data_frame`, or a pandas Series or
            array_like object. Values from this column or array_like appear in bold
            in the hover tooltip.
        hover_data: list of str or int, or Series or array-like, or dict
            Either a list of names of columns in `data_frame`, or pandas Series, or
            array_like objects or a dict with column names as keys, with values
            True (for default formatting) False (in order to remove this column
            from hover information), or a formatting string, for example ':.3f' or
            '|%a' or list-like data to appear in the hover tooltip or tuples with a
            bool or formatting string as first element, and list-like data to
            appear in hover as second element Values from these columns appear as
            extra data in the hover tooltip.
        custom_data: list of str or int, or Series or array-like
            Either names of columns in `data_frame`, or pandas Series, or
            array_like objects Values from these columns are extra data, to be used
            in widgets or Dash callbacks for example. This data is not user-visible
            but is included in events emitted by the figure (lasso selection etc.)
        labels: dict with str keys and str values (default `{}`)
            By default, column names are used in the figure for axis titles, legend
            entries and hovers. This parameter allows this to be overridden. The
            keys of this dict should correspond to column names, and the values
            should correspond to the desired label to be displayed.
        title: str
            The figure title.
        template: str or dict or plotly.graph_objects.layout.Template instance
            The figure template name (must be a key in plotly.io.templates) or
            definition.
        width: int (default `None`)
            The figure width in pixels.
        height: int (default `None`)
            The figure height in pixels.
        opacity: float
            Value between 0 and 1. Sets the opacity for markers.
        hole: float
            Sets the fraction of the radius to cut out of the pie.Use this to make
            a donut chart.

    Returns:
        plotly.graph_objs._figure.Figure: A plotly figure object.
    """
    if isinstance(data, str):
        if data.startswith("http"):
            data = github_raw_url(data)
            data = get_direct_url(data)

        try:
            data = pd.read_csv(data)
        except Exception as e:
            raise ValueError(f"Could not read data from {data}. {e}")

    if not isinstance(data, pd.DataFrame):
        raise ValueError(
            "data must be a pandas DataFrame, a string or an ee.FeatureCollection."
        )

    if descending is not None and isinstance(values, str):
        data.sort_values([values], ascending=not (descending), inplace=True)

    if other_label is None:
        other_label = "Other"

    if max_rows is not None and isinstance(names, str) and isinstance(values, str):
        max_rows = min(len(data), max_rows) - 2
        value = data.iloc[max_rows][values]
        data.loc[data[values] < value, names] = other_label

    try:
        fig = px.pie(
            data_frame=data,
            names=names,
            values=values,
            color=color,
            color_discrete_sequence=color_discrete_sequence,
            color_discrete_map=color_discrete_map,
            hover_name=hover_name,
            hover_data=hover_data,
            custom_data=custom_data,
            labels=labels,
            title=title,
            template=template,
            width=width,
            height=height,
            opacity=opacity,
            hole=hole,
            **kwargs,
        )

        if isinstance(layout_args, dict):
            fig.update_layout(**layout_args)

        return fig
    except Exception as e:
        raise Exception(f"Could not create pie chart. {e}")

Last update: 2022-08-16
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