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={}, **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 |
None |
|
y |
str or int or Series or array-like
Either a name of a column in |
None |
|
color |
str or int or Series or array-like
Either a name of a column in |
None |
|
descending |
bool |
Whether to sort the data in descending order. Defaults to True. |
True |
sort_column |
str |
The column to sort the data. Defaults to None. |
None |
max_rows |
int |
Maximum number of rows to display. Defaults to None. |
None |
x_label |
str |
Label for the x axis. Defaults to None. |
None |
y_label |
str |
Label for the y axis. Defaults to None. |
None |
title |
str |
Title for the plot. Defaults to None. |
None |
legend_title |
str |
Title for the legend. Defaults to None. |
None |
width |
int |
Width of the plot in pixels. Defaults to None. |
None |
height |
int |
Height of the plot in pixels. Defaults to 500. |
500 |
layout_args |
dict |
Layout arguments for the plot to be passed to fig.update_layout(), such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None. |
{} |
**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 |
{} |
Returns:
| Type | Description |
|---|---|
plotly.graph_objs._figure.Figure |
A plotly figure object. |
Source code in leafmap/plot.py
def 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={},
**kwargs,
):
"""Create a bar chart with plotly.express,
Args:
data: DataFrame | array-like | dict | str (local file path or HTTP URL)
This argument needs to be passed for column names (and not keyword
names) to be used. Array-like and dict are transformed internally to a
pandas DataFrame. Optional: if missing, a DataFrame gets constructed
under the hood using the other arguments.
x: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position marks along the x axis in cartesian coordinates. Either `x` or
`y` can optionally be a list of column references or array_likes, in
which case the data will be treated as if it were 'wide' rather than
'long'.
y: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position marks along the y axis in cartesian coordinates. Either `x` or
`y` can optionally be a list of column references or array_likes, in
which case the data will be treated as if it were 'wide' rather than
'long'.
color: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign color to marks.
descending (bool, optional): Whether to sort the data in descending order. Defaults to True.
sort_column (str, optional): The column to sort the data. Defaults to None.
max_rows (int, optional): Maximum number of rows to display. Defaults to None.
x_label (str, optional): Label for the x axis. Defaults to None.
y_label (str, optional): Label for the y axis. Defaults to None.
title (str, optional): Title for the plot. Defaults to None.
legend_title (str, optional): Title for the legend. Defaults to None.
width (int, optional): Width of the plot in pixels. Defaults to None.
height (int, optional): Height of the plot in pixels. Defaults to 500.
layout_args (dict, optional): Layout arguments for the plot to be passed to fig.update_layout(),
such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None.
**kwargs: Any additional arguments to pass to plotly.express.bar(), such as:
pattern_shape: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign pattern shapes to marks.
facet_row: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to facetted subplots in the vertical direction.
facet_col: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to facetted subplots in the horizontal direction.
facet_col_wrap: int
Maximum number of facet columns. Wraps the column variable at this
width, so that the column facets span multiple rows. Ignored if 0, and
forced to 0 if `facet_row` or a `marginal` is set.
facet_row_spacing: float between 0 and 1
Spacing between facet rows, in paper units. Default is 0.03 or 0.0.7
when facet_col_wrap is used.
facet_col_spacing: float between 0 and 1
Spacing between facet columns, in paper units Default is 0.02.
hover_name: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like appear in bold
in the hover tooltip.
hover_data: list of str or int, or Series or array-like, or dict
Either a list of names of columns in `data_frame`, or pandas Series, or
array_like objects or a dict with column names as keys, with values
True (for default formatting) False (in order to remove this column
from hover information), or a formatting string, for example ':.3f' or
'|%a' or list-like data to appear in the hover tooltip or tuples with a
bool or formatting string as first element, and list-like data to
appear in hover as second element Values from these columns appear as
extra data in the hover tooltip.
custom_data: list of str or int, or Series or array-like
Either names of columns in `data_frame`, or pandas Series, or
array_like objects Values from these columns are extra data, to be used
in widgets or Dash callbacks for example. This data is not user-visible
but is included in events emitted by the figure (lasso selection etc.)
text: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like appear in the
figure as text labels.
base: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position the base of the bar.
error_x: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size x-axis error bars. If `error_x_minus` is `None`, error bars will
be symmetrical, otherwise `error_x` is used for the positive direction
only.
error_x_minus: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size x-axis error bars in the negative direction. Ignored if `error_x`
is `None`.
error_y: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size y-axis error bars. If `error_y_minus` is `None`, error bars will
be symmetrical, otherwise `error_y` is used for the positive direction
only.
error_y_minus: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size y-axis error bars in the negative direction. Ignored if `error_y`
is `None`.
animation_frame: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to animation frames.
animation_group: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
provide object-constancy across animation frames: rows with matching
`animation_group`s will be treated as if they describe the same object
in each frame.
category_orders: dict with str keys and list of str values (default `{}`)
By default, in Python 3.6+, the order of categorical values in axes,
legends and facets depends on the order in which these values are first
encountered in `data_frame` (and no order is guaranteed by default in
Python below 3.6). This parameter is used to force a specific ordering
of values per column. The keys of this dict should correspond to column
names, and the values should be lists of strings corresponding to the
specific display order desired.
labels: dict with str keys and str values (default `{}`)
By default, column names are used in the figure for axis titles, legend
entries and hovers. This parameter allows this to be overridden. The
keys of this dict should correspond to column names, and the values
should correspond to the desired label to be displayed.
color_discrete_sequence: list of str
Strings should define valid CSS-colors. When `color` is set and the
values in the corresponding column are not numeric, values in that
column are assigned colors by cycling through `color_discrete_sequence`
in the order described in `category_orders`, unless the value of
`color` is a key in `color_discrete_map`. Various useful color
sequences are available in the `plotly.express.colors` submodules,
specifically `plotly.express.colors.qualitative`.
color_discrete_map: dict with str keys and str values (default `{}`)
String values should define valid CSS-colors Used to override
`color_discrete_sequence` to assign a specific colors to marks
corresponding with specific values. Keys in `color_discrete_map` should
be values in the column denoted by `color`. Alternatively, if the
values of `color` are valid colors, the string `'identity'` may be
passed to cause them to be used directly.
color_continuous_scale: list of str
Strings should define valid CSS-colors This list is used to build a
continuous color scale when the column denoted by `color` contains
numeric data. Various useful color scales are available in the
`plotly.express.colors` submodules, specifically
`plotly.express.colors.sequential`, `plotly.express.colors.diverging`
and `plotly.express.colors.cyclical`.
pattern_shape_sequence: list of str
Strings should define valid plotly.js patterns-shapes. When
`pattern_shape` is set, values in that column are assigned patterns-
shapes by cycling through `pattern_shape_sequence` in the order
described in `category_orders`, unless the value of `pattern_shape` is
a key in `pattern_shape_map`.
pattern_shape_map: dict with str keys and str values (default `{}`)
Strings values define plotly.js patterns-shapes. Used to override
`pattern_shape_sequences` to assign a specific patterns-shapes to lines
corresponding with specific values. Keys in `pattern_shape_map` should
be values in the column denoted by `pattern_shape`. Alternatively, if
the values of `pattern_shape` are valid patterns-shapes names, the
string `'identity'` may be passed to cause them to be used directly.
range_color: list of two numbers
If provided, overrides auto-scaling on the continuous color scale.
color_continuous_midpoint: number (default `None`)
If set, computes the bounds of the continuous color scale to have the
desired midpoint. Setting this value is recommended when using
`plotly.express.colors.diverging` color scales as the inputs to
`color_continuous_scale`.
opacity: float
Value between 0 and 1. Sets the opacity for markers.
orientation: str, one of `'h'` for horizontal or `'v'` for vertical.
(default `'v'` if `x` and `y` are provided and both continuous or both
categorical, otherwise `'v'`(`'h'`) if `x`(`y`) is categorical and
`y`(`x`) is continuous, otherwise `'v'`(`'h'`) if only `x`(`y`) is
provided)
barmode: str (default `'relative'`)
One of `'group'`, `'overlay'` or `'relative'` In `'relative'` mode,
bars are stacked above zero for positive values and below zero for
negative values. In `'overlay'` mode, bars are drawn on top of one
another. In `'group'` mode, bars are placed beside each other.
log_x: boolean (default `False`)
If `True`, the x-axis is log-scaled in cartesian coordinates.
log_y: boolean (default `False`)
If `True`, the y-axis is log-scaled in cartesian coordinates.
range_x: list of two numbers
If provided, overrides auto-scaling on the x-axis in cartesian
coordinates.
range_y: list of two numbers
If provided, overrides auto-scaling on the y-axis in cartesian
coordinates.
text_auto: bool or string (default `False`)
If `True` or a string, the x or y or z values will be displayed as
text, depending on the orientation A string like `'.2f'` will be
interpreted as a `texttemplate` numeric formatting directive.
template: str or dict or plotly.graph_objects.layout.Template instance
The figure template name (must be a key in plotly.io.templates) or
definition.
Returns:
plotly.graph_objs._figure.Figure: A plotly figure object.
"""
if isinstance(data, str):
if data.startswith("http"):
data = github_raw_url(data)
data = get_direct_url(data)
try:
data = pd.read_csv(data)
except Exception as e:
raise ValueError(f"Could not read data from {data}. {e}")
if not isinstance(data, pd.DataFrame):
raise ValueError(
"data must be a pandas DataFrame, a string or an ee.FeatureCollection."
)
if descending is not None:
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)
if "labels" in kwargs:
labels = kwargs["labels"]
kwargs.pop("labels")
else:
labels = {}
if x_label is not None:
labels[x] = x_label
if y_label is not None:
if isinstance(y, str):
labels[y] = y_label
elif isinstance(y, list):
labels[y[0]] = y_label
if isinstance(legend_title, str):
if "legend" not in layout_args:
layout_args["legend"] = {}
layout_args["legend"]["title"] = legend_title
try:
fig = px.bar(
data,
x=x,
y=y,
color=color,
labels=labels,
title=title,
width=width,
height=height,
**kwargs,
)
if isinstance(layout_args, dict):
fig.update_layout(**layout_args)
return fig
except Exception as e:
raise ValueError(f"Could not create bar plot. {e}")
histogram(data=None, x=None, y=None, color=None, descending=None, max_rows=None, x_label=None, y_label=None, title=None, width=None, height=500, layout_args={}, **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 |
None |
|
y |
str or int or Series or array-like
Either a name of a column in |
None |
|
color |
str or int or Series or array-like
Either a name of a column in |
None |
|
descending |
bool |
Whether to sort the data in descending order. Defaults to None. |
None |
max_rows |
int |
Maximum number of rows to display. Defaults to None. |
None |
x_label |
str |
Label for the x axis. Defaults to None. |
None |
y_label |
str |
Label for the y axis. Defaults to None. |
None |
title |
str |
Title for the plot. Defaults to None. |
None |
width |
int |
Width of the plot in pixels. Defaults to None. |
None |
height |
int |
Height of the plot in pixels. Defaults to 500. |
500 |
layout_args |
dict |
Layout arguments for the plot to be passed to fig.update_layout(), such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None. |
{} |
**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 |
{} |
Returns:
| Type | Description |
|---|---|
plotly.graph_objs._figure.Figure |
A plotly figure object. |
Source code in leafmap/plot.py
def histogram(
data=None,
x=None,
y=None,
color=None,
descending=None,
max_rows=None,
x_label=None,
y_label=None,
title=None,
width=None,
height=500,
layout_args={},
**kwargs,
):
"""Create a line chart with plotly.express,
Args:
data: DataFrame | array-like | dict | str (local file path or HTTP URL)
This argument needs to be passed for column names (and not keyword
names) to be used. Array-like and dict are transformed internally to a
pandas DataFrame. Optional: if missing, a DataFrame gets constructed
under the hood using the other arguments.
x: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position marks along the x axis in cartesian coordinates. Either `x` or
`y` can optionally be a list of column references or array_likes, in
which case the data will be treated as if it were 'wide' rather than
'long'.
y: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position marks along the y axis in cartesian coordinates. Either `x` or
`y` can optionally be a list of column references or array_likes, in
which case the data will be treated as if it were 'wide' rather than
'long'.
color: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign color to marks.
descending (bool, optional): Whether to sort the data in descending order. Defaults to None.
max_rows (int, optional): Maximum number of rows to display. Defaults to None.
x_label (str, optional): Label for the x axis. Defaults to None.
y_label (str, optional): Label for the y axis. Defaults to None.
title (str, optional): Title for the plot. Defaults to None.
width (int, optional): Width of the plot in pixels. Defaults to None.
height (int, optional): Height of the plot in pixels. Defaults to 500.
layout_args (dict, optional): Layout arguments for the plot to be passed to fig.update_layout(),
such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None.
**kwargs: Any additional arguments to pass to plotly.express.bar(), such as:
pattern_shape: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign pattern shapes to marks.
facet_row: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to facetted subplots in the vertical direction.
facet_col: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to facetted subplots in the horizontal direction.
facet_col_wrap: int
Maximum number of facet columns. Wraps the column variable at this
width, so that the column facets span multiple rows. Ignored if 0, and
forced to 0 if `facet_row` or a `marginal` is set.
facet_row_spacing: float between 0 and 1
Spacing between facet rows, in paper units. Default is 0.03 or 0.0.7
when facet_col_wrap is used.
facet_col_spacing: float between 0 and 1
Spacing between facet columns, in paper units Default is 0.02.
hover_name: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like appear in bold
in the hover tooltip.
hover_data: list of str or int, or Series or array-like, or dict
Either a list of names of columns in `data_frame`, or pandas Series, or
array_like objects or a dict with column names as keys, with values
True (for default formatting) False (in order to remove this column
from hover information), or a formatting string, for example ':.3f' or
'|%a' or list-like data to appear in the hover tooltip or tuples with a
bool or formatting string as first element, and list-like data to
appear in hover as second element Values from these columns appear as
extra data in the hover tooltip.
custom_data: list of str or int, or Series or array-like
Either names of columns in `data_frame`, or pandas Series, or
array_like objects Values from these columns are extra data, to be used
in widgets or Dash callbacks for example. This data is not user-visible
but is included in events emitted by the figure (lasso selection etc.)
text: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like appear in the
figure as text labels.
base: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position the base of the bar.
error_x: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size x-axis error bars. If `error_x_minus` is `None`, error bars will
be symmetrical, otherwise `error_x` is used for the positive direction
only.
error_x_minus: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size x-axis error bars in the negative direction. Ignored if `error_x`
is `None`.
error_y: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size y-axis error bars. If `error_y_minus` is `None`, error bars will
be symmetrical, otherwise `error_y` is used for the positive direction
only.
error_y_minus: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size y-axis error bars in the negative direction. Ignored if `error_y`
is `None`.
animation_frame: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to animation frames.
animation_group: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
provide object-constancy across animation frames: rows with matching
`animation_group`s will be treated as if they describe the same object
in each frame.
category_orders: dict with str keys and list of str values (default `{}`)
By default, in Python 3.6+, the order of categorical values in axes,
legends and facets depends on the order in which these values are first
encountered in `data_frame` (and no order is guaranteed by default in
Python below 3.6). This parameter is used to force a specific ordering
of values per column. The keys of this dict should correspond to column
names, and the values should be lists of strings corresponding to the
specific display order desired.
labels: dict with str keys and str values (default `{}`)
By default, column names are used in the figure for axis titles, legend
entries and hovers. This parameter allows this to be overridden. The
keys of this dict should correspond to column names, and the values
should correspond to the desired label to be displayed.
color_discrete_sequence: list of str
Strings should define valid CSS-colors. When `color` is set and the
values in the corresponding column are not numeric, values in that
column are assigned colors by cycling through `color_discrete_sequence`
in the order described in `category_orders`, unless the value of
`color` is a key in `color_discrete_map`. Various useful color
sequences are available in the `plotly.express.colors` submodules,
specifically `plotly.express.colors.qualitative`.
color_discrete_map: dict with str keys and str values (default `{}`)
String values should define valid CSS-colors Used to override
`color_discrete_sequence` to assign a specific colors to marks
corresponding with specific values. Keys in `color_discrete_map` should
be values in the column denoted by `color`. Alternatively, if the
values of `color` are valid colors, the string `'identity'` may be
passed to cause them to be used directly.
color_continuous_scale: list of str
Strings should define valid CSS-colors This list is used to build a
continuous color scale when the column denoted by `color` contains
numeric data. Various useful color scales are available in the
`plotly.express.colors` submodules, specifically
`plotly.express.colors.sequential`, `plotly.express.colors.diverging`
and `plotly.express.colors.cyclical`.
pattern_shape_sequence: list of str
Strings should define valid plotly.js patterns-shapes. When
`pattern_shape` is set, values in that column are assigned patterns-
shapes by cycling through `pattern_shape_sequence` in the order
described in `category_orders`, unless the value of `pattern_shape` is
a key in `pattern_shape_map`.
pattern_shape_map: dict with str keys and str values (default `{}`)
Strings values define plotly.js patterns-shapes. Used to override
`pattern_shape_sequences` to assign a specific patterns-shapes to lines
corresponding with specific values. Keys in `pattern_shape_map` should
be values in the column denoted by `pattern_shape`. Alternatively, if
the values of `pattern_shape` are valid patterns-shapes names, the
string `'identity'` may be passed to cause them to be used directly.
range_color: list of two numbers
If provided, overrides auto-scaling on the continuous color scale.
color_continuous_midpoint: number (default `None`)
If set, computes the bounds of the continuous color scale to have the
desired midpoint. Setting this value is recommended when using
`plotly.express.colors.diverging` color scales as the inputs to
`color_continuous_scale`.
opacity: float
Value between 0 and 1. Sets the opacity for markers.
orientation: str, one of `'h'` for horizontal or `'v'` for vertical.
(default `'v'` if `x` and `y` are provided and both continuous or both
categorical, otherwise `'v'`(`'h'`) if `x`(`y`) is categorical and
`y`(`x`) is continuous, otherwise `'v'`(`'h'`) if only `x`(`y`) is
provided)
barmode: str (default `'relative'`)
One of `'group'`, `'overlay'` or `'relative'` In `'relative'` mode,
bars are stacked above zero for positive values and below zero for
negative values. In `'overlay'` mode, bars are drawn on top of one
another. In `'group'` mode, bars are placed beside each other.
log_x: boolean (default `False`)
If `True`, the x-axis is log-scaled in cartesian coordinates.
log_y: boolean (default `False`)
If `True`, the y-axis is log-scaled in cartesian coordinates.
range_x: list of two numbers
If provided, overrides auto-scaling on the x-axis in cartesian
coordinates.
range_y: list of two numbers
If provided, overrides auto-scaling on the y-axis in cartesian
coordinates.
text_auto: bool or string (default `False`)
If `True` or a string, the x or y or z values will be displayed as
text, depending on the orientation A string like `'.2f'` will be
interpreted as a `texttemplate` numeric formatting directive.
template: str or dict or plotly.graph_objects.layout.Template instance
The figure template name (must be a key in plotly.io.templates) or
definition.
Returns:
plotly.graph_objs._figure.Figure: A plotly figure object.
"""
if isinstance(data, str):
if data.startswith("http"):
data = github_raw_url(data)
data = get_direct_url(data)
try:
data = pd.read_csv(data)
except Exception as e:
raise ValueError(f"Could not read data from {data}. {e}")
if not isinstance(data, pd.DataFrame):
raise ValueError(
"data must be a pandas DataFrame, a string or an ee.FeatureCollection."
)
if descending is not None:
data.sort_values([y, x], ascending=not (descending), inplace=True)
if isinstance(max_rows, int):
data = data.head(max_rows)
if "labels" in kwargs:
labels = kwargs["labels"]
else:
labels = {}
if x_label is not None:
labels[x] = x_label
if y_label is not None:
labels[y] = y_label
try:
fig = px.histogram(
data,
x=x,
y=y,
color=color,
labels=labels,
title=title,
width=width,
height=height,
**kwargs,
)
if isinstance(layout_args, dict):
fig.update_layout(**layout_args)
return fig
except Exception as e:
raise ValueError(f"Could not create bar plot. {e}")
line_chart(data=None, x=None, y=None, color=None, descending=None, max_rows=None, x_label=None, y_label=None, title=None, legend_title=None, width=None, height=500, layout_args={}, **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 |
None |
|
y |
str or int or Series or array-like
Either a name of a column in |
None |
|
color |
str or int or Series or array-like
Either a name of a column in |
None |
|
descending |
bool |
Whether to sort the data in descending order. Defaults to None. |
None |
max_rows |
int |
Maximum number of rows to display. Defaults to None. |
None |
x_label |
str |
Label for the x axis. Defaults to None. |
None |
y_label |
str |
Label for the y axis. Defaults to None. |
None |
title |
str |
Title for the plot. Defaults to None. |
None |
legend_title |
str |
Title for the legend. Defaults to None. |
None |
width |
int |
Width of the plot in pixels. Defaults to None. |
None |
height |
int |
Height of the plot in pixels. Defaults to 500. |
500 |
layout_args |
dict |
Layout arguments for the plot to be passed to fig.update_layout(), such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None. |
{} |
**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 |
{} |
Returns:
| Type | Description |
|---|---|
plotly.graph_objs._figure.Figure |
A plotly figure object. |
Source code in leafmap/plot.py
def line_chart(
data=None,
x=None,
y=None,
color=None,
descending=None,
max_rows=None,
x_label=None,
y_label=None,
title=None,
legend_title=None,
width=None,
height=500,
layout_args={},
**kwargs,
):
"""Create a line chart with plotly.express,
Args:
data: DataFrame | array-like | dict | str (local file path or HTTP URL)
This argument needs to be passed for column names (and not keyword
names) to be used. Array-like and dict are transformed internally to a
pandas DataFrame. Optional: if missing, a DataFrame gets constructed
under the hood using the other arguments.
x: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position marks along the x axis in cartesian coordinates. Either `x` or
`y` can optionally be a list of column references or array_likes, in
which case the data will be treated as if it were 'wide' rather than
'long'.
y: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position marks along the y axis in cartesian coordinates. Either `x` or
`y` can optionally be a list of column references or array_likes, in
which case the data will be treated as if it were 'wide' rather than
'long'.
color: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign color to marks.
descending (bool, optional): Whether to sort the data in descending order. Defaults to None.
max_rows (int, optional): Maximum number of rows to display. Defaults to None.
x_label (str, optional): Label for the x axis. Defaults to None.
y_label (str, optional): Label for the y axis. Defaults to None.
title (str, optional): Title for the plot. Defaults to None.
legend_title (str, optional): Title for the legend. Defaults to None.
width (int, optional): Width of the plot in pixels. Defaults to None.
height (int, optional): Height of the plot in pixels. Defaults to 500.
layout_args (dict, optional): Layout arguments for the plot to be passed to fig.update_layout(),
such as {'title':'Plot Title', 'title_x':0.5}. Defaults to None.
**kwargs: Any additional arguments to pass to plotly.express.bar(), such as:
pattern_shape: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign pattern shapes to marks.
facet_row: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to facetted subplots in the vertical direction.
facet_col: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to facetted subplots in the horizontal direction.
facet_col_wrap: int
Maximum number of facet columns. Wraps the column variable at this
width, so that the column facets span multiple rows. Ignored if 0, and
forced to 0 if `facet_row` or a `marginal` is set.
facet_row_spacing: float between 0 and 1
Spacing between facet rows, in paper units. Default is 0.03 or 0.0.7
when facet_col_wrap is used.
facet_col_spacing: float between 0 and 1
Spacing between facet columns, in paper units Default is 0.02.
hover_name: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like appear in bold
in the hover tooltip.
hover_data: list of str or int, or Series or array-like, or dict
Either a list of names of columns in `data_frame`, or pandas Series, or
array_like objects or a dict with column names as keys, with values
True (for default formatting) False (in order to remove this column
from hover information), or a formatting string, for example ':.3f' or
'|%a' or list-like data to appear in the hover tooltip or tuples with a
bool or formatting string as first element, and list-like data to
appear in hover as second element Values from these columns appear as
extra data in the hover tooltip.
custom_data: list of str or int, or Series or array-like
Either names of columns in `data_frame`, or pandas Series, or
array_like objects Values from these columns are extra data, to be used
in widgets or Dash callbacks for example. This data is not user-visible
but is included in events emitted by the figure (lasso selection etc.)
text: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like appear in the
figure as text labels.
base: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position the base of the bar.
error_x: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size x-axis error bars. If `error_x_minus` is `None`, error bars will
be symmetrical, otherwise `error_x` is used for the positive direction
only.
error_x_minus: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size x-axis error bars in the negative direction. Ignored if `error_x`
is `None`.
error_y: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size y-axis error bars. If `error_y_minus` is `None`, error bars will
be symmetrical, otherwise `error_y` is used for the positive direction
only.
error_y_minus: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size y-axis error bars in the negative direction. Ignored if `error_y`
is `None`.
animation_frame: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to animation frames.
animation_group: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
provide object-constancy across animation frames: rows with matching
`animation_group`s will be treated as if they describe the same object
in each frame.
category_orders: dict with str keys and list of str values (default `{}`)
By default, in Python 3.6+, the order of categorical values in axes,
legends and facets depends on the order in which these values are first
encountered in `data_frame` (and no order is guaranteed by default in
Python below 3.6). This parameter is used to force a specific ordering
of values per column. The keys of this dict should correspond to column
names, and the values should be lists of strings corresponding to the
specific display order desired.
labels: dict with str keys and str values (default `{}`)
By default, column names are used in the figure for axis titles, legend
entries and hovers. This parameter allows this to be overridden. The
keys of this dict should correspond to column names, and the values
should correspond to the desired label to be displayed.
color_discrete_sequence: list of str
Strings should define valid CSS-colors. When `color` is set and the
values in the corresponding column are not numeric, values in that
column are assigned colors by cycling through `color_discrete_sequence`
in the order described in `category_orders`, unless the value of
`color` is a key in `color_discrete_map`. Various useful color
sequences are available in the `plotly.express.colors` submodules,
specifically `plotly.express.colors.qualitative`.
color_discrete_map: dict with str keys and str values (default `{}`)
String values should define valid CSS-colors Used to override
`color_discrete_sequence` to assign a specific colors to marks
corresponding with specific values. Keys in `color_discrete_map` should
be values in the column denoted by `color`. Alternatively, if the
values of `color` are valid colors, the string `'identity'` may be
passed to cause them to be used directly.
color_continuous_scale: list of str
Strings should define valid CSS-colors This list is used to build a
continuous color scale when the column denoted by `color` contains
numeric data. Various useful color scales are available in the
`plotly.express.colors` submodules, specifically
`plotly.express.colors.sequential`, `plotly.express.colors.diverging`
and `plotly.express.colors.cyclical`.
pattern_shape_sequence: list of str
Strings should define valid plotly.js patterns-shapes. When
`pattern_shape` is set, values in that column are assigned patterns-
shapes by cycling through `pattern_shape_sequence` in the order
described in `category_orders`, unless the value of `pattern_shape` is
a key in `pattern_shape_map`.
pattern_shape_map: dict with str keys and str values (default `{}`)
Strings values define plotly.js patterns-shapes. Used to override
`pattern_shape_sequences` to assign a specific patterns-shapes to lines
corresponding with specific values. Keys in `pattern_shape_map` should
be values in the column denoted by `pattern_shape`. Alternatively, if
the values of `pattern_shape` are valid patterns-shapes names, the
string `'identity'` may be passed to cause them to be used directly.
range_color: list of two numbers
If provided, overrides auto-scaling on the continuous color scale.
color_continuous_midpoint: number (default `None`)
If set, computes the bounds of the continuous color scale to have the
desired midpoint. Setting this value is recommended when using
`plotly.express.colors.diverging` color scales as the inputs to
`color_continuous_scale`.
opacity: float
Value between 0 and 1. Sets the opacity for markers.
orientation: str, one of `'h'` for horizontal or `'v'` for vertical.
(default `'v'` if `x` and `y` are provided and both continuous or both
categorical, otherwise `'v'`(`'h'`) if `x`(`y`) is categorical and
`y`(`x`) is continuous, otherwise `'v'`(`'h'`) if only `x`(`y`) is
provided)
barmode: str (default `'relative'`)
One of `'group'`, `'overlay'` or `'relative'` In `'relative'` mode,
bars are stacked above zero for positive values and below zero for
negative values. In `'overlay'` mode, bars are drawn on top of one
another. In `'group'` mode, bars are placed beside each other.
log_x: boolean (default `False`)
If `True`, the x-axis is log-scaled in cartesian coordinates.
log_y: boolean (default `False`)
If `True`, the y-axis is log-scaled in cartesian coordinates.
range_x: list of two numbers
If provided, overrides auto-scaling on the x-axis in cartesian
coordinates.
range_y: list of two numbers
If provided, overrides auto-scaling on the y-axis in cartesian
coordinates.
text_auto: bool or string (default `False`)
If `True` or a string, the x or y or z values will be displayed as
text, depending on the orientation A string like `'.2f'` will be
interpreted as a `texttemplate` numeric formatting directive.
template: str or dict or plotly.graph_objects.layout.Template instance
The figure template name (must be a key in plotly.io.templates) or
definition.
Returns:
plotly.graph_objs._figure.Figure: A plotly figure object.
"""
if isinstance(data, str):
if data.startswith("http"):
data = github_raw_url(data)
data = get_direct_url(data)
try:
data = pd.read_csv(data)
except Exception as e:
raise ValueError(f"Could not read data from {data}. {e}")
if not isinstance(data, pd.DataFrame):
raise ValueError(
"data must be a pandas DataFrame, a string or an ee.FeatureCollection."
)
if descending is not None:
data.sort_values([y, x], ascending=not (descending), inplace=True)
if isinstance(max_rows, int):
data = data.head(max_rows)
if "labels" in kwargs:
labels = kwargs["labels"]
kwargs.pop("labels")
else:
labels = {}
if x_label is not None:
labels[x] = x_label
if y_label is not None:
labels[y] = y_label
if isinstance(legend_title, str):
if "legend" not in layout_args:
layout_args["legend"] = {}
layout_args["legend"]["title"] = legend_title
try:
fig = px.line(
data,
x=x,
y=y,
color=color,
labels=labels,
title=title,
width=width,
height=height,
**kwargs,
)
if isinstance(layout_args, dict):
fig.update_layout(**layout_args)
return fig
except Exception as e:
raise ValueError(f"Could not create bar plot. {e}")
pie_chart(data, names=None, values=None, descending=True, max_rows=None, other_label=None, color=None, color_discrete_sequence=None, color_discrete_map=None, hover_name=None, hover_data=None, custom_data=None, labels=None, title=None, legend_title=None, template=None, width=None, height=None, opacity=None, hole=None, layout_args={}, **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 |
None |
|
values |
str or int or Series or array-like
Either a name of a column in |
None |
|
descending |
bool |
Whether to sort the data in descending order. Defaults to True. |
True |
max_rows |
int |
Maximum number of rows to display. Defaults to None. |
None |
other_label |
str |
Label for the "other" category. Defaults to None. |
None |
color |
str or int or Series or array-like
Either a name of a column in |
None |
|
color_discrete_sequence |
list of str
Strings should define valid CSS-colors. When |
None |
|
color_discrete_map |
dict with str keys and str values (default |
None |
|
hover_name |
str or int or Series or array-like
Either a name of a column in |
None |
|
hover_data |
list of str or int, or Series or array-like, or dict
Either a list of names of columns in |
None |
|
custom_data |
list of str or int, or Series or array-like
Either names of columns in |
None |
|
labels |
dict with str keys and str values (default |
None |
|
title |
str The figure title. |
None |
|
template |
str or dict or plotly.graph_objects.layout.Template instance The figure template name (must be a key in plotly.io.templates) or definition. |
None |
|
width |
int (default |
None |
|
height |
int (default |
None |
|
opacity |
float Value between 0 and 1. Sets the opacity for markers. |
None |
|
hole |
float Sets the fraction of the radius to cut out of the pie.Use this to make a donut chart. |
None |
Returns:
| Type | Description |
|---|---|
plotly.graph_objs._figure.Figure |
A plotly figure object. |
Source code in leafmap/plot.py
def pie_chart(
data,
names=None,
values=None,
descending=True,
max_rows=None,
other_label=None,
color=None,
color_discrete_sequence=None,
color_discrete_map=None,
hover_name=None,
hover_data=None,
custom_data=None,
labels=None,
title=None,
legend_title=None,
template=None,
width=None,
height=None,
opacity=None,
hole=None,
layout_args={},
**kwargs,
):
"""Create a plotly pie chart.
Args:
data: DataFrame or array-like or dict
This argument needs to be passed for column names (and not keyword
names) to be used. Array-like and dict are transformed internally to a
pandas DataFrame. Optional: if missing, a DataFrame gets constructed
under the hood using the other arguments.
names: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used as
labels for sectors.
values: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
set values associated to sectors.
descending (bool, optional): Whether to sort the data in descending order. Defaults to True.
max_rows (int, optional): Maximum number of rows to display. Defaults to None.
other_label (str, optional): Label for the "other" category. Defaults to None.
color: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign color to marks.
color_discrete_sequence: list of str
Strings should define valid CSS-colors. When `color` is set and the
values in the corresponding column are not numeric, values in that
column are assigned colors by cycling through `color_discrete_sequence`
in the order described in `category_orders`, unless the value of
`color` is a key in `color_discrete_map`. Various useful color
sequences are available in the `plotly.express.colors` submodules,
specifically `plotly.express.colors.qualitative`.
color_discrete_map: dict with str keys and str values (default `{}`)
String values should define valid CSS-colors Used to override
`color_discrete_sequence` to assign a specific colors to marks
corresponding with specific values. Keys in `color_discrete_map` should
be values in the column denoted by `color`. Alternatively, if the
values of `color` are valid colors, the string `'identity'` may be
passed to cause them to be used directly.
hover_name: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like appear in bold
in the hover tooltip.
hover_data: list of str or int, or Series or array-like, or dict
Either a list of names of columns in `data_frame`, or pandas Series, or
array_like objects or a dict with column names as keys, with values
True (for default formatting) False (in order to remove this column
from hover information), or a formatting string, for example ':.3f' or
'|%a' or list-like data to appear in the hover tooltip or tuples with a
bool or formatting string as first element, and list-like data to
appear in hover as second element Values from these columns appear as
extra data in the hover tooltip.
custom_data: list of str or int, or Series or array-like
Either names of columns in `data_frame`, or pandas Series, or
array_like objects Values from these columns are extra data, to be used
in widgets or Dash callbacks for example. This data is not user-visible
but is included in events emitted by the figure (lasso selection etc.)
labels: dict with str keys and str values (default `{}`)
By default, column names are used in the figure for axis titles, legend
entries and hovers. This parameter allows this to be overridden. The
keys of this dict should correspond to column names, and the values
should correspond to the desired label to be displayed.
title: str
The figure title.
template: str or dict or plotly.graph_objects.layout.Template instance
The figure template name (must be a key in plotly.io.templates) or
definition.
width: int (default `None`)
The figure width in pixels.
height: int (default `None`)
The figure height in pixels.
opacity: float
Value between 0 and 1. Sets the opacity for markers.
hole: float
Sets the fraction of the radius to cut out of the pie.Use this to make
a donut chart.
Returns:
plotly.graph_objs._figure.Figure: A plotly figure object.
"""
if isinstance(data, str):
if data.startswith("http"):
data = github_raw_url(data)
data = get_direct_url(data)
try:
data = pd.read_csv(data)
except Exception as e:
raise ValueError(f"Could not read data from {data}. {e}")
if not isinstance(data, pd.DataFrame):
raise ValueError(
"data must be a pandas DataFrame, a string or an ee.FeatureCollection."
)
if descending is not None and isinstance(values, str):
data.sort_values([values], ascending=not (descending), inplace=True)
if other_label is None:
other_label = "Other"
if max_rows is not None and isinstance(names, str) and isinstance(values, str):
max_rows = min(len(data), max_rows) - 2
value = data.iloc[max_rows][values]
data.loc[data[values] < value, names] = other_label
if isinstance(legend_title, str):
if "legend" not in layout_args:
layout_args["legend"] = {}
layout_args["legend"]["title"] = legend_title
try:
fig = px.pie(
data_frame=data,
names=names,
values=values,
color=color,
color_discrete_sequence=color_discrete_sequence,
color_discrete_map=color_discrete_map,
hover_name=hover_name,
hover_data=hover_data,
custom_data=custom_data,
labels=labels,
title=title,
template=template,
width=width,
height=height,
opacity=opacity,
hole=hole,
**kwargs,
)
if isinstance(layout_args, dict):
fig.update_layout(**layout_args)
return fig
except Exception as e:
raise Exception(f"Could not create pie chart. {e}")