Skip to content

plot module

Module for plotting data using plotly.express.

bar_chart(data=None, x=None, y=None, color=None, descending=True, sort_column=None, max_rows=None, x_label=None, y_label=None, title=None, legend_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.

 True
sort_column str | None

The column to sort the data.

 None
max_rows int | None

Maximum number of rows to display.

 None
x_label str | None

Label for the x axis.

 None
y_label str | None

Label for the y axis.

 None
title str | None

Title for the plot.

 None
legend_title str | None

Title for the legend.

 None
width int | None

Width of the plot in pixels.

 None
height int

Height of the plot in pixels.

 500
layout_args dict | None

Layout arguments for the plot to be passed to fig.update_layout(), such as {'title':'Plot Title', 'title_x':0.5}.

 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 
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
def bar_chart(
    data=None,
    x=None,
    y=None,
    color=None,
    descending: bool = True,
    sort_column: str | None = None,
    max_rows: int | None = None,
    x_label: str | None = None,
    y_label: str | None = None,
    title: str | None = None,
    legend_title: str | None = None,
    width: int | None = None,
    height: int = 500,
    layout_args: dict | None = 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: Whether to sort the data in descending order.
        sort_column: The column to sort the data.
        max_rows: Maximum number of rows to display.
        x_label: Label for the x axis.
        y_label: Label for the y axis.
        title: Title for the plot.
        legend_title: Title for the legend.
        width: Width of the plot in pixels.
        height: Height of the plot in pixels.
        layout_args: Layout arguments for the plot to be passed to fig.update_layout(),
            such as {'title':'Plot Title', 'title_x':0.5}.
        **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://", "https://")):
            data = coreutils.github_raw_url(data)
            data = common.get_direct_url(data)
        data = pd.read_csv(data)

    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:
        if sort_column is None:
            if isinstance(y, str):
                sort_column = y
            elif isinstance(y, list):
                sort_column = y[0]
        data.sort_values([sort_column, x], ascending=not (descending), inplace=True)
        if "barmode" not in kwargs:
            kwargs["barmode"] = "group"

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

    labels = kwargs.pop("labels", {})

    if x_label is not None:
        labels[x] = x_label  # pytype: disable=unsupported-operands
    if y_label is not None:
        if isinstance(y, str):
            labels[y] = y_label  # pytype: disable=unsupported-operands
        elif isinstance(y, list):
            labels[y[0]] = y_label  # pytype: disable=unsupported-operands

    if layout_args is None:
        layout_args = {}

    if isinstance(legend_title, str):
        if "legend" not in layout_args:
            layout_args["legend"] = {}
        layout_args["legend"]["title"] = legend_title

    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

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 | None

Whether to sort the data in descending order.

 None
max_rows int | None

Maximum number of rows to display.

 None
x_label str | None

Label for the x axis.

 None
y_label str | None

Label for the y axis.

 None
title str | None

Title for the plot.

 None
width int | None

Width of the plot in pixels.

 None
height int

Height of the plot in pixels.

 500
layout_args dict | None

Layout arguments for the plot to be passed to fig.update_layout(), such as {'title':'Plot Title', 'title_x':0.5}.

 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 
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
def histogram(
    data=None,
    x=None,
    y=None,
    color=None,
    descending: bool | None = None,
    max_rows: int | None = None,
    x_label: str | None = None,
    y_label: str | None = None,
    title: str | None = None,
    width: int | None = None,
    height: int = 500,
    layout_args: dict | None = 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: Whether to sort the data in descending order.
        max_rows: Maximum number of rows to display.
        x_label: Label for the x axis.
        y_label: Label for the y axis.
        title: Title for the plot.
        width: Width of the plot in pixels.
        height: Height of the plot in pixels.
        layout_args: Layout arguments for the plot to be passed to fig.update_layout(),
            such as {'title':'Plot Title', 'title_x':0.5}.
        **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://", "https://")):
            data = coreutils.github_raw_url(data)
            data = common.get_direct_url(data)
        data = pd.read_csv(data)

    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)

    labels = kwargs.pop("labels", {})

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

    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

line_chart(data=None, x=None, y=None, color=None, descending=None, max_rows=None, x_label=None, y_label=None, title=None, legend_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 | None

Whether to sort the data in descending order.

 None
max_rows int | None

Maximum number of rows to display.

 None
x_label str | None

Label for the x axis.

 None
y_label str | None

Label for the y axis.

 None
title str | None

Title for the plot.

 None
legend_title str | None

Title for the legend.

 None
width int | None

Width of the plot in pixels.

 None
height int

Height of the plot in pixels.

 500
layout_args dict | None

Layout arguments for the plot to be passed to fig.update_layout(), such as {'title':'Plot Title', 'title_x':0.5}. Defaults to no args.

 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 
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
def line_chart(
    data=None,
    x=None,
    y=None,
    color=None,
    descending: bool | None = None,
    max_rows: int | None = None,
    x_label: str | None = None,
    y_label: str | None = None,
    title: str | None = None,
    legend_title: str | None = None,
    width: int | None = None,
    height: int = 500,
    layout_args: dict | None = 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: Whether to sort the data in descending order.
        max_rows: Maximum number of rows to display.
        x_label: Label for the x axis.
        y_label: Label for the y axis.
        title: Title for the plot.
        legend_title: Title for the legend.
        width: Width of the plot in pixels.
        height: Height of the plot in pixels.
        layout_args: Layout arguments for the plot to be passed to fig.update_layout(),
            such as {'title':'Plot Title', 'title_x':0.5}. Defaults to no args.
        **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://", "https://")):
            data = coreutils.github_raw_url(data)
            data = common.get_direct_url(data)
        data = pd.read_csv(data)

    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)

    labels = kwargs.pop("labels", {})

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

    if layout_args is None:
        layout_args = {}

    if isinstance(legend_title, str):
        if "legend" not in layout_args:
            layout_args["legend"] = {}
        layout_args["legend"]["title"] = legend_title

    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

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, legend_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.

 True
max_rows int | None

Maximum number of rows to display.

 None
other_label str | None

Label for the "other" category.

 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 | None

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 | None

The figure width in pixels.

 None
height int | None

The figure height in pixels.

 None
opacity float | None

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

 None
hole float | None

Fraction of the radius to cut out of the pie. Use this to make a donut chart.

 None
layout_args dict | None

TODO

 None

Returns:

Type Description

plotly.graph_objs._figure.Figure: A plotly figure object.
Source code in geemap/plot.py 
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
def pie_chart(
    data,
    names=None,
    values=None,
    descending: bool = True,
    max_rows: int | None = None,
    other_label: str | None = None,
    color=None,
    color_discrete_sequence=None,
    color_discrete_map=None,
    hover_name=None,
    hover_data=None,
    custom_data=None,
    labels=None,
    title: str | None = None,
    legend_title=None,
    template=None,
    width: int | None = None,
    height: int | None = None,
    opacity: float | None = None,
    hole: float | None = None,
    layout_args: dict | None = 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: Whether to sort the data in descending order.
        max_rows: Maximum number of rows to display.
        other_label: Label for the "other" category.
        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: 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: The figure width in pixels.
        height: The figure height in pixels.
        opacity: Value between 0 and 1. Sets the opacity for markers.
        hole: Fraction of the radius to cut out of the pie. Use this to make a donut
            chart.
        layout_args: TODO

    Returns:
        plotly.graph_objs._figure.Figure: A plotly figure object.
    """
    if isinstance(data, str):
        if data.startswith(("http://", "https://")):
            data = coreutils.github_raw_url(data)
            data = common.get_direct_url(data)
        data = pd.read_csv(data)

    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

    if layout_args is None:
        layout_args = {}

    if isinstance(legend_title, str):
        if "legend" not in layout_args:
            layout_args["legend"] = {}
        layout_args["legend"]["title"] = legend_title

    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