plotly.express package

plotly.express is a terse, consistent, high-level wrapper around plotly.graph_objects for rapid data exploration and figure generation. Learn more at https://plotly.express/

class plotly.express.Constant(value, label=None)

Bases: object

Objects of this class can be passed to Plotly Express functions that expect column identifiers or list-like objects to indicate that this attribute should take on a constant value. An optional label can be provided.

class plotly.express.IdentityMap

Bases: object

dict-like object which acts as if the value for any key is the key itself. Objects of this class can be passed in to arguments like color_discrete_map to use the provided data values as colors, rather than mapping them to colors cycled from color_discrete_sequence. This works for any _map argument to Plotly Express functions, such as line_dash_map and symbol_map.

copy()
class plotly.express.Range(label=None)

Bases: object

Objects of this class can be passed to Plotly Express functions that expect column identifiers or list-like objects to indicate that this attribute should be mapped onto integers starting at 0. An optional label can be provided.

plotly.express.area(data_frame=None, x=None, y=None, line_group=None, color=None, symbol=None, hover_name=None, hover_data=None, custom_data=None, text=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, symbol_sequence=None, symbol_map=None, markers=False, orientation=None, groupnorm=None, log_x=False, log_y=False, range_x=None, range_y=None, line_shape=None, title=None, template=None, width=None, height=None)

In a stacked area plot, each row of data_frame is represented as vertex of a polyline mark in 2D space. The area between successive polylines is filled.

Parameters
  • data_frame (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 tranformed 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’.

  • line_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 group rows of data_frame into lines.

  • 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.

  • symbol (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 symbols to marks.

  • 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.

  • 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.

  • 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.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • markers (boolean (default False)) – If True, markers are shown on lines.

  • orientation (str, one of 'h' for horizontal or 'v' for vertical.) – (default 'v' if x and y are provided and both continous 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)

  • groupnorm (str (default None)) – One of 'fraction' or 'percent'. If 'fraction', the value of each point is divided by the sum of all values at that location coordinate. 'percent' is the same but multiplied by 100 to show percentages. None will stack up all values at each location coordinate.

  • 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.

  • line_shape (str (default 'linear')) – One of 'linear' or 'spline'.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.bar(data_frame=None, x=None, y=None, color=None, pattern_shape=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, text=None, base=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, pattern_shape_sequence=None, pattern_shape_map=None, range_color=None, color_continuous_midpoint=None, opacity=None, orientation=None, barmode='relative', log_x=False, log_y=False, range_x=None, range_y=None, title=None, template=None, width=None, height=None)

In a bar plot, each row of data_frame is represented as a rectangular mark.

Parameters
  • data_frame (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 tranformed 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.

  • 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 continous 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.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.bar_polar(data_frame=None, r=None, theta=None, color=None, pattern_shape=None, hover_name=None, hover_data=None, custom_data=None, base=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, pattern_shape_sequence=None, pattern_shape_map=None, range_color=None, color_continuous_midpoint=None, barnorm=None, barmode='relative', direction='clockwise', start_angle=90, range_r=None, range_theta=None, log_r=False, title=None, template=None, width=None, height=None)

In a polar bar plot, each row of data_frame is represented as a wedge mark in polar coordinates.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • r (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 radial axis in polar coordinates.

  • theta (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 angular axis in polar coordinates.

  • 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.

  • 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.

  • 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.)

  • 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.

  • 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.

  • barnorm (str (default None)) – One of 'fraction' or 'percent'. If 'fraction', the value of each bar is divided by the sum of all values at that location coordinate. 'percent' is the same but multiplied by 100 to show percentages. None will stack up all values at each location coordinate.

  • 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.

  • direction (str) – One of ‘counterclockwise' or 'clockwise'. Default is 'clockwise' Sets the direction in which increasing values of the angular axis are drawn.

  • start_angle (int (default 90)) – Sets start angle for the angular axis, with 0 being due east and 90 being due north.

  • range_r (list of two numbers) – If provided, overrides auto-scaling on the radial axis in polar coordinates.

  • range_theta (list of two numbers) – If provided, overrides auto-scaling on the angular axis in polar coordinates.

  • log_r (boolean (default False)) – If True, the radial axis is log-scaled in polar coordinates.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.box(data_frame=None, x=None, y=None, color=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, orientation=None, boxmode=None, log_x=False, log_y=False, range_x=None, range_y=None, points=None, notched=False, title=None, template=None, width=None, height=None)

In a box plot, rows of data_frame are grouped together into a box-and-whisker mark to visualize their distribution.

Each box spans from quartile 1 (Q1) to quartile 3 (Q3). The second quartile (Q2) is marked by a line inside the box. By default, the whiskers correspond to the box’ edges +/- 1.5 times the interquartile range (IQR: Q3-Q1), see “points” for other options.

Parameters
  • data_frame (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 tranformed 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.

  • 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.)

  • 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.

  • orientation (str, one of 'h' for horizontal or 'v' for vertical.) – (default 'v' if x and y are provided and both continous 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)

  • boxmode (str (default 'group')) – One of 'group' or 'overlay' In 'overlay' mode, boxes are on drawn top of one another. In 'group' mode, boxes 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.

  • points (str or boolean (default 'outliers')) – One of 'outliers', 'suspectedoutliers', 'all', or False. If 'outliers', only the sample points lying outside the whiskers are shown. If 'suspectedoutliers', all outlier points are shown and those less than 4*Q1-3*Q3 or greater than 4*Q3-3*Q1 are highlighted with the marker’s 'outliercolor'. If 'outliers', only the sample points lying outside the whiskers are shown. If 'all', all sample points are shown. If False, no sample points are shown and the whiskers extend to the full range of the sample.

  • notched (boolean (default False)) – If True, boxes are drawn with notches.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.choropleth(data_frame=None, lat=None, lon=None, locations=None, locationmode=None, geojson=None, featureidkey=None, color=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, projection=None, scope=None, center=None, fitbounds=None, basemap_visible=None, title=None, template=None, width=None, height=None)

In a choropleth map, each row of data_frame is represented by a colored region mark on a map.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • lat (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 according to latitude on a map.

  • lon (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 according to longitude on a map.

  • locations (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 to be interpreted according to locationmode and mapped to longitude/latitude.

  • locationmode (str) – One of ‘ISO-3’, ‘USA-states’, or ‘country names’ Determines the set of locations used to match entries in locations to regions on the map.

  • geojson (GeoJSON-formatted dict) – Must contain a Polygon feature collection, with IDs, which are references from locations.

  • featureidkey (str (default: 'id')) – Path to field in GeoJSON feature object with which to match the values passed in to locations.The most common alternative to the default is of the form 'properties.<key>.

  • 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.

  • 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.)

  • 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.

  • 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.

  • projection (str) – One of 'equirectangular', 'mercator', 'orthographic', 'natural earth', 'kavrayskiy7', 'miller', 'robinson', 'eckert4', 'azimuthal equal area', 'azimuthal equidistant', 'conic equal area', 'conic conformal', 'conic equidistant', 'gnomonic', 'stereographic', 'mollweide', 'hammer', 'transverse mercator', 'albers usa', 'winkel tripel', 'aitoff', or 'sinusoidal'`Default depends on `scope.

  • scope (str (default 'world').) – One of 'world', 'usa', 'europe', 'asia', 'africa', 'north america', or 'south america'`Default is `'world' unless projection is set to 'albers usa', which forces 'usa'.

  • center (dict) – Dict keys are 'lat' and 'lon' Sets the center point of the map.

  • fitbounds (str (default False).) – One of False, locations or geojson.

  • basemap_visible (bool) – Force the basemap visibility.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.choropleth_mapbox(data_frame=None, geojson=None, featureidkey=None, locations=None, color=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, opacity=None, zoom=8, center=None, mapbox_style=None, title=None, template=None, width=None, height=None)

In a Mapbox choropleth map, each row of data_frame is represented by a colored region on a Mapbox map.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • geojson (GeoJSON-formatted dict) – Must contain a Polygon feature collection, with IDs, which are references from locations.

  • featureidkey (str (default: 'id')) – Path to field in GeoJSON feature object with which to match the values passed in to locations.The most common alternative to the default is of the form 'properties.<key>.

  • locations (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 to be interpreted according to locationmode and mapped to longitude/latitude.

  • 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.

  • 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.)

  • 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.

  • 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.

  • zoom (int (default 8)) – Between 0 and 20. Sets map zoom level.

  • center (dict) – Dict keys are 'lat' and 'lon' Sets the center point of the map.

  • mapbox_style (str (default 'basic', needs Mapbox API token)) – Identifier of base map style, some of which require a Mapbox API token to be set using plotly.express.set_mapbox_access_token(). Allowed values which do not require a Mapbox API token are 'open-street-map', 'white-bg', 'carto-positron', 'carto-darkmatter', 'stamen- terrain', 'stamen-toner', 'stamen-watercolor'. Allowed values which do require a Mapbox API token are 'basic', 'streets', 'outdoors', 'light', 'dark', 'satellite', 'satellite- streets'.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.density_contour(data_frame=None, x=None, y=None, z=None, color=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None, color_discrete_sequence=None, color_discrete_map=None, marginal_x=None, marginal_y=None, trendline=None, trendline_options=None, trendline_color_override=None, trendline_scope='trace', log_x=False, log_y=False, range_x=None, range_y=None, histfunc=None, histnorm=None, nbinsx=None, nbinsy=None, title=None, template=None, width=None, height=None)

In a density contour plot, rows of data_frame are grouped together into contour marks to visualize the 2D distribution of an aggregate function histfunc (e.g. the count or sum) of the value z.

Parameters
  • data_frame (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 tranformed 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’.

  • z (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 z axis in cartesian coordinates. For density_heatmap and density_contour these values are used as the inputs to histfunc.

  • 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.

  • 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.

  • 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.

  • orientation (str, one of 'h' for horizontal or 'v' for vertical.) – (default 'v' if x and y are provided and both continous 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)

  • 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.

  • marginal_x (str) – One of 'rug', 'box', 'violin', or 'histogram'. If set, a horizontal subplot is drawn above the main plot, visualizing the x-distribution.

  • marginal_y (str) – One of 'rug', 'box', 'violin', or 'histogram'. If set, a vertical subplot is drawn to the right of the main plot, visualizing the y-distribution.

  • trendline (str) – One of 'ols', 'lowess', 'rolling', 'expanding' or 'ewm'. If 'ols', an Ordinary Least Squares regression line will be drawn for each discrete-color/symbol group. If 'lowess’, a Locally Weighted Scatterplot Smoothing line will be drawn for each discrete-color/symbol group. If 'rolling’, a Rolling (e.g. rolling average, rolling median) line will be drawn for each discrete-color/symbol group. If 'expanding’, an Expanding (e.g. expanding average, expanding sum) line will be drawn for each discrete-color/symbol group. If 'ewm’, an Exponentially Weighted Moment (e.g. exponentially-weighted moving average) line will be drawn for each discrete-color/symbol group. See the docstrings for the functions in plotly.express.trendline_functions for more details on these functions and how to configure them with the trendline_options argument.

  • trendline_options (dict) – Options passed as the first argument to the function from plotly.express.trendline_functions named in the trendline argument.

  • trendline_color_override (str) – Valid CSS color. If provided, and if trendline is set, all trendlines will be drawn in this color rather than in the same color as the traces from which they draw their inputs.

  • trendline_scope (str (one of 'trace' or 'overall', default 'trace')) – If 'trace', then one trendline is drawn per trace (i.e. per color, symbol, facet, animation frame etc) and if 'overall' then one trendline is computed for the entire dataset, and replicated across all facets.

  • 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.

  • histfunc (str (default 'count' if no arguments are provided, else 'sum')) – One of 'count', 'sum', 'avg', 'min', or 'max'.Function used to aggregate values for summarization (note: can be normalized with histnorm). The arguments to this function are the values of z.

  • histnorm (str (default None)) – One of 'percent', 'probability', 'density', or 'probability density' If None, the output of histfunc is used as is. If 'probability', the output of histfunc for a given bin is divided by the sum of the output of histfunc for all bins. If 'percent', the output of histfunc for a given bin is divided by the sum of the output of histfunc for all bins and multiplied by 100. If 'density', the output of histfunc for a given bin is divided by the size of the bin. If 'probability density', the output of histfunc for a given bin is normalized such that it corresponds to the probability that a random event whose distribution is described by the output of histfunc will fall into that bin.

  • nbinsx (int) – Positive integer. Sets the number of bins along the x axis.

  • nbinsy (int) – Positive integer. Sets the number of bins along the y axis.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.density_heatmap(data_frame=None, x=None, y=None, z=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, marginal_x=None, marginal_y=None, opacity=None, log_x=False, log_y=False, range_x=None, range_y=None, histfunc=None, histnorm=None, nbinsx=None, nbinsy=None, title=None, template=None, width=None, height=None)

In a density heatmap, rows of data_frame are grouped together into colored rectangular tiles to visualize the 2D distribution of an aggregate function histfunc (e.g. the count or sum) of the value z.

Parameters
  • data_frame (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 tranformed 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’.

  • z (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 z axis in cartesian coordinates. For density_heatmap and density_contour these values are used as the inputs to histfunc.

  • 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.

  • 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.

  • orientation (str, one of 'h' for horizontal or 'v' for vertical.) – (default 'v' if x and y are provided and both continous 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)

  • 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.

  • 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.

  • marginal_x (str) – One of 'rug', 'box', 'violin', or 'histogram'. If set, a horizontal subplot is drawn above the main plot, visualizing the x-distribution.

  • marginal_y (str) – One of 'rug', 'box', 'violin', or 'histogram'. If set, a vertical subplot is drawn to the right of the main plot, visualizing the y-distribution.

  • opacity (float) – Value between 0 and 1. Sets the opacity for markers.

  • 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.

  • histfunc (str (default 'count' if no arguments are provided, else 'sum')) – One of 'count', 'sum', 'avg', 'min', or 'max'.Function used to aggregate values for summarization (note: can be normalized with histnorm). The arguments to this function are the values of z.

  • histnorm (str (default None)) – One of 'percent', 'probability', 'density', or 'probability density' If None, the output of histfunc is used as is. If 'probability', the output of histfunc for a given bin is divided by the sum of the output of histfunc for all bins. If 'percent', the output of histfunc for a given bin is divided by the sum of the output of histfunc for all bins and multiplied by 100. If 'density', the output of histfunc for a given bin is divided by the size of the bin. If 'probability density', the output of histfunc for a given bin is normalized such that it corresponds to the probability that a random event whose distribution is described by the output of histfunc will fall into that bin.

  • nbinsx (int) – Positive integer. Sets the number of bins along the x axis.

  • nbinsy (int) – Positive integer. Sets the number of bins along the y axis.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.density_mapbox(data_frame=None, lat=None, lon=None, z=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, opacity=None, zoom=8, center=None, mapbox_style=None, radius=None, title=None, template=None, width=None, height=None)

In a Mapbox density map, each row of data_frame contributes to the intensity of the color of the region around the corresponding point on the map

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • lat (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 according to latitude on a map.

  • lon (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 according to longitude on a map.

  • z (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 z axis in cartesian coordinates.

  • 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.)

  • 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_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.

  • 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.

  • zoom (int (default 8)) – Between 0 and 20. Sets map zoom level.

  • center (dict) – Dict keys are 'lat' and 'lon' Sets the center point of the map.

  • mapbox_style (str (default 'basic', needs Mapbox API token)) – Identifier of base map style, some of which require a Mapbox API token to be set using plotly.express.set_mapbox_access_token(). Allowed values which do not require a Mapbox API token are 'open-street-map', 'white-bg', 'carto-positron', 'carto-darkmatter', 'stamen- terrain', 'stamen-toner', 'stamen-watercolor'. Allowed values which do require a Mapbox API token are 'basic', 'streets', 'outdoors', 'light', 'dark', 'satellite', 'satellite- streets'.

  • radius (int (default is 30)) – Sets the radius of influence of each point.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.ecdf(data_frame=None, x=None, y=None, color=None, text=None, line_dash=None, symbol=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, animation_frame=None, animation_group=None, markers=False, lines=True, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, line_dash_sequence=None, line_dash_map=None, symbol_sequence=None, symbol_map=None, marginal=None, opacity=None, orientation=None, ecdfnorm='probability', ecdfmode='standard', render_mode='auto', log_x=False, log_y=False, range_x=None, range_y=None, title=None, template=None, width=None, height=None)

In a Empirical Cumulative Distribution Function (ECDF) plot, rows of data_frame are sorted by the value x (or y if orientation is 'h') and their cumulative count (or the cumulative sum of y if supplied and orientation is h) is drawn as a line.

Parameters
  • data_frame (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 tranformed 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. If orientation is 'h', the cumulative sum of this argument is plotted rather than the cumulative count. 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. If orientation is 'v', the cumulative sum of this argument is plotted rather than the cumulative count. 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.

  • 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.

  • line_dash (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 dash-patterns to lines.

  • symbol (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 symbols 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.

  • 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.

  • markers (boolean (default False)) – If True, markers are shown on lines.

  • lines (boolean (default True)) – If False, lines are not drawn (forced to True if markers is False).

  • 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.

  • line_dash_sequence (list of str) – Strings should define valid plotly.js dash-patterns. When line_dash is set, values in that column are assigned dash-patterns by cycling through line_dash_sequence in the order described in category_orders, unless the value of line_dash is a key in line_dash_map.

  • line_dash_map (dict with str keys and str values (default {})) – Strings values define plotly.js dash-patterns. Used to override line_dash_sequences to assign a specific dash-patterns to lines corresponding with specific values. Keys in line_dash_map should be values in the column denoted by line_dash. Alternatively, if the values of line_dash are valid line-dash names, the string 'identity' may be passed to cause them to be used directly.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • marginal (str) – One of 'rug', 'box', 'violin', or 'histogram'. If set, a subplot is drawn alongside the main plot, visualizing the distribution.

  • 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 continous 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)

  • ecdfnorm (string or None (default 'probability')) – One of 'probability' or 'percent' If None, values will be raw counts or sums. If `’probability’, values will be probabilities normalized from 0 to 1. If `’percent’, values will be percentages normalized from 0 to 100.

  • ecdfmode (string (default 'standard')) – One of 'standard', 'complementary' or 'reversed' If 'standard', the ECDF is plotted such that values represent data at or below the point. If 'complementary', the CCDF is plotted such that values represent data above the point. If 'reversed', a variant of the CCDF is plotted such that values represent data at or above the point.

  • render_mode (str) – One of 'auto', 'svg' or 'webgl', default 'auto' Controls the browser API used to draw marks. 'svg’ is appropriate for figures of less than 1000 data points, and will allow for fully-vectorized output. 'webgl' is likely necessary for acceptable performance above 1000 points but rasterizes part of the output. 'auto' uses heuristics to choose the mode.

  • 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.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.funnel(data_frame=None, x=None, y=None, color=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, text=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, opacity=None, orientation=None, log_x=False, log_y=False, range_x=None, range_y=None, title=None, template=None, width=None, height=None)

In a funnel plot, each row of data_frame is represented as a rectangular sector of a funnel.

Parameters
  • data_frame (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 tranformed 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.

  • 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.

  • 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.

  • 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 continous 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)

  • 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.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.funnel_area(data_frame=None, names=None, values=None, color=None, color_discrete_sequence=None, color_discrete_map=None, hover_name=None, hover_data=None, custom_data=None, labels=None, title=None, template=None, width=None, height=None, opacity=None)

In a funnel area plot, each row of data_frame is represented as a trapezoidal sector of a funnel.

Parameters
  • data_frame (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 tranformed 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.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.get_trendline_results(fig)

Extracts fit statistics for trendlines (when applied to figures generated with the trendline argument set to "ols").

Parameters

fig – the output of a plotly.express charting call

Returns

A pandas.DataFrame with a column “px_fit_results” containing the statsmodels results objects, along with columns identifying the subset of the data the trendline was fit on.

plotly.express.histogram(data_frame=None, x=None, y=None, color=None, pattern_shape=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, pattern_shape_sequence=None, pattern_shape_map=None, marginal=None, opacity=None, orientation=None, barmode='relative', barnorm=None, histnorm=None, log_x=False, log_y=False, range_x=None, range_y=None, histfunc=None, cumulative=None, nbins=None, title=None, template=None, width=None, height=None)

In a histogram, rows of data_frame are grouped together into a rectangular mark to visualize the 1D distribution of an aggregate function histfunc (e.g. the count or sum) of the value y (or x if orientation is 'h').

Parameters
  • data_frame (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 tranformed 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. If orientation is 'h', these values are used as inputs to histfunc. 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. If orientation is 'v', these values are used as inputs to histfunc. 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.

  • 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.

  • 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.

  • 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.

  • marginal (str) – One of 'rug', 'box', 'violin', or 'histogram'. If set, a subplot is drawn alongside the main plot, visualizing the distribution.

  • 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 continous 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.

  • barnorm (str (default None)) – One of 'fraction' or 'percent'. If 'fraction', the value of each bar is divided by the sum of all values at that location coordinate. 'percent' is the same but multiplied by 100 to show percentages. None will stack up all values at each location coordinate.

  • histnorm (str (default None)) – One of 'percent', 'probability', 'density', or 'probability density' If None, the output of histfunc is used as is. If 'probability', the output of histfunc for a given bin is divided by the sum of the output of histfunc for all bins. If 'percent', the output of histfunc for a given bin is divided by the sum of the output of histfunc for all bins and multiplied by 100. If 'density', the output of histfunc for a given bin is divided by the size of the bin. If 'probability density', the output of histfunc for a given bin is normalized such that it corresponds to the probability that a random event whose distribution is described by the output of histfunc will fall into that bin.

  • 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.

  • histfunc (str (default 'count' if no arguments are provided, else 'sum')) – One of 'count', 'sum', 'avg', 'min', or 'max'.Function used to aggregate values for summarization (note: can be normalized with histnorm). The arguments to this function are the values of y`(`x) if orientation is 'v'`(‘h’`).

  • cumulative (boolean (default False)) – If True, histogram values are cumulative.

  • nbins (int) – Positive integer. Sets the number of bins.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.icicle(data_frame=None, names=None, values=None, parents=None, path=None, ids=None, color=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, color_discrete_sequence=None, color_discrete_map=None, hover_name=None, hover_data=None, custom_data=None, labels=None, title=None, template=None, width=None, height=None, branchvalues=None, maxdepth=None)

An icicle plot represents hierarchial data with adjoined rectangular sectors that all cascade from root down to leaf in one direction.

Parameters
  • data_frame (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 tranformed 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.

  • parents (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 parents in sunburst and treemap charts.

  • path (list of str or int, or Series or array-like) – Either names of columns in data_frame, or pandas Series, or array_like objects List of columns names or columns of a rectangular dataframe defining the hierarchy of sectors, from root to leaves. An error is raised if path AND ids or parents is passed

  • ids (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 ids of sectors

  • 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_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.

  • 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.

  • 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.

  • branchvalues (str) – ‘total’ or ‘remainder’ Determines how the items in values are summed. Whenset to ‘total’, items in values are taken to be valueof all its descendants. When set to ‘remainder’, itemsin values corresponding to the root and the branches:sectors are taken to be the extra part not part of thesum of the values at their leaves.

  • maxdepth (int) – Positive integer Sets the number of rendered sectors from any given level. Set maxdepth to -1 to render all thelevels in the hierarchy.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.imshow(img, zmin=None, zmax=None, origin=None, labels={}, x=None, y=None, animation_frame=None, facet_col=None, facet_col_wrap=None, facet_col_spacing=None, facet_row_spacing=None, color_continuous_scale=None, color_continuous_midpoint=None, range_color=None, title=None, template=None, width=None, height=None, aspect=None, contrast_rescaling=None, binary_string=None, binary_backend='auto', binary_compression_level=4, binary_format='png')

Display an image, i.e. data on a 2D regular raster.

Parameters
  • img (array-like image, or xarray) –

    The image data. Supported array shapes are

    • (M, N): an image with scalar data. The data is visualized using a colormap.

    • (M, N, 3): an image with RGB values.

    • (M, N, 4): an image with RGBA values, i.e. including transparency.

  • zmin (scalar or iterable, optional) – zmin and zmax define the scalar range that the colormap covers. By default, zmin and zmax correspond to the min and max values of the datatype for integer datatypes (ie [0-255] for uint8 images, [0, 65535] for uint16 images, etc.). For a multichannel image of floats, the max of the image is computed and zmax is the smallest power of 256 (1, 255, 65535) greater than this max value, with a 5% tolerance. For a single-channel image, the max of the image is used. Overridden by range_color.

  • zmax (scalar or iterable, optional) – zmin and zmax define the scalar range that the colormap covers. By default, zmin and zmax correspond to the min and max values of the datatype for integer datatypes (ie [0-255] for uint8 images, [0, 65535] for uint16 images, etc.). For a multichannel image of floats, the max of the image is computed and zmax is the smallest power of 256 (1, 255, 65535) greater than this max value, with a 5% tolerance. For a single-channel image, the max of the image is used. Overridden by range_color.

  • origin (str, 'upper' or 'lower' (default 'upper')) – position of the [0, 0] pixel of the image array, in the upper left or lower left corner. The convention ‘upper’ is typically used for matrices and images.

  • labels (dict with str keys and str values (default {})) – Sets names used in the figure for axis titles (keys x and y), colorbar title and hoverlabel (key color). The values should correspond to the desired label to be displayed. If img is an xarray, dimension names are used for axis titles, and long name for the colorbar title (unless overridden in labels). Possible keys are: x, y, and color.

  • x (list-like, optional) – x and y are used to label the axes of single-channel heatmap visualizations and their lengths must match the lengths of the second and first dimensions of the img argument. They are auto-populated if the input is an xarray.

  • y (list-like, optional) – x and y are used to label the axes of single-channel heatmap visualizations and their lengths must match the lengths of the second and first dimensions of the img argument. They are auto-populated if the input is an xarray.

  • animation_frame (int or str, optional (default None)) – axis number along which the image array is sliced to create an animation plot. If img is an xarray, animation_frame can be the name of one the dimensions.

  • facet_col (int or str, optional (default None)) – axis number along which the image array is sliced to create a facetted plot. If img is an xarray, facet_col can be the name of one the dimensions.

  • 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 facet_col is None.

  • facet_col_spacing (float between 0 and 1) – Spacing between facet columns, in paper units. Default is 0.02.

  • facet_row_spacing (float between 0 and 1) – Spacing between facet rows created when facet_col_wrap is used, in paper units. Default is 0.0.7.

  • color_continuous_scale (str or list of str) – colormap used to map scalar data to colors (for a 2D image). This parameter is not used for RGB or RGBA images. If a string is provided, it should be the name of a known color scale, and if a list is provided, it should be a list of CSS- compatible colors.

  • color_continuous_midpoint (number) – If set, computes the bounds of the continuous color scale to have the desired midpoint. Overridden by range_color or zmin and zmax.

  • range_color (list of two numbers) – If provided, overrides auto-scaling on the continuous color scale, including overriding color_continuous_midpoint. Also overrides zmin and zmax. Used only for single-channel images.

  • title (str) – The figure title.

  • template (str or dict or plotly.graph_objects.layout.Template instance) – The figure template name or definition.

  • width (number) – The figure width in pixels.

  • height (number) – The figure height in pixels.

  • aspect ('equal', 'auto', or None) –

    • ‘equal’: Ensures an aspect ratio of 1 or pixels (square pixels)

    • ’auto’: The axes is kept fixed and the aspect ratio of pixels is adjusted so that the data fit in the axes. In general, this will result in non-square pixels.

    • if None, ‘equal’ is used for numpy arrays and ‘auto’ for xarrays (which have typically heterogeneous coordinates)

  • contrast_rescaling ('minmax', 'infer', or None) – how to determine data values corresponding to the bounds of the color range, when zmin or zmax are not passed. If minmax, the min and max values of the image are used. If infer, a heuristic based on the image data type is used.

  • binary_string (bool, default None) – if True, the image data are first rescaled and encoded as uint8 and then passed to plotly.js as a b64 PNG string. If False, data are passed unchanged as a numerical array. Setting to True may lead to performance gains, at the cost of a loss of precision depending on the original data type. If None, use_binary_string is set to True for multichannel (eg) RGB arrays, and to False for single-channel (2D) arrays. 2D arrays are represented as grayscale and with no colorbar if use_binary_string is True.

  • binary_backend (str, 'auto' (default), 'pil' or 'pypng') – Third-party package for the transformation of numpy arrays to png b64 strings. If ‘auto’, Pillow is used if installed, otherwise pypng.

  • binary_compression_level (int, between 0 and 9 (default 4)) – png compression level to be passed to the backend when transforming an array to a png b64 string. Increasing binary_compression decreases the size of the png string, but the compression step takes more time. For most images it is not worth using levels greater than 5, but it’s possible to test len(fig.data[0].source) and to time the execution of imshow to tune the level of compression. 0 means no compression (not recommended).

  • binary_format (str, 'png' (default) or 'jpg') – compression format used to generate b64 string. ‘png’ is recommended since it uses lossless compression, but ‘jpg’ (lossy) compression can result if smaller binary strings for natural images.

Returns

fig

Return type

graph_objects.Figure containing the displayed image

See also

plotly.graph_objects.Image

image trace

plotly.graph_objects.Heatmap

heatmap trace

Notes

In order to update and customize the returned figure, use go.Figure.update_traces or go.Figure.update_layout.

If an xarray is passed, dimensions names and coordinates are used for axes labels and ticks.

plotly.express.line(data_frame=None, x=None, y=None, line_group=None, color=None, line_dash=None, symbol=None, hover_name=None, hover_data=None, custom_data=None, text=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None, color_discrete_sequence=None, color_discrete_map=None, line_dash_sequence=None, line_dash_map=None, symbol_sequence=None, symbol_map=None, markers=False, log_x=False, log_y=False, range_x=None, range_y=None, line_shape=None, render_mode='auto', title=None, template=None, width=None, height=None)

In a 2D line plot, each row of data_frame is represented as vertex of a polyline mark in 2D space.

Parameters
  • data_frame (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 tranformed 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’.

  • line_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 group rows of data_frame into lines.

  • 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.

  • line_dash (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 dash-patterns to lines.

  • symbol (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 symbols to marks.

  • 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.

  • 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.

  • 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.

  • orientation (str, one of 'h' for horizontal or 'v' for vertical.) – (default 'v' if x and y are provided and both continous 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)

  • 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.

  • line_dash_sequence (list of str) – Strings should define valid plotly.js dash-patterns. When line_dash is set, values in that column are assigned dash-patterns by cycling through line_dash_sequence in the order described in category_orders, unless the value of line_dash is a key in line_dash_map.

  • line_dash_map (dict with str keys and str values (default {})) – Strings values define plotly.js dash-patterns. Used to override line_dash_sequences to assign a specific dash-patterns to lines corresponding with specific values. Keys in line_dash_map should be values in the column denoted by line_dash. Alternatively, if the values of line_dash are valid line-dash names, the string 'identity' may be passed to cause them to be used directly.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • markers (boolean (default False)) – If True, markers are shown on lines.

  • 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.

  • line_shape (str (default 'linear')) – One of 'linear' or 'spline'.

  • render_mode (str) – One of 'auto', 'svg' or 'webgl', default 'auto' Controls the browser API used to draw marks. 'svg’ is appropriate for figures of less than 1000 data points, and will allow for fully-vectorized output. 'webgl' is likely necessary for acceptable performance above 1000 points but rasterizes part of the output. 'auto' uses heuristics to choose the mode.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.line_3d(data_frame=None, x=None, y=None, z=None, color=None, line_dash=None, text=None, line_group=None, symbol=None, hover_name=None, hover_data=None, custom_data=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, error_z=None, error_z_minus=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, line_dash_sequence=None, line_dash_map=None, symbol_sequence=None, symbol_map=None, markers=False, log_x=False, log_y=False, log_z=False, range_x=None, range_y=None, range_z=None, title=None, template=None, width=None, height=None)

In a 3D line plot, each row of data_frame is represented as vertex of a polyline mark in 3D space.

Parameters
  • data_frame (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 tranformed 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.

  • 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.

  • z (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 z axis in cartesian coordinates.

  • 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.

  • line_dash (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 dash-patterns to lines.

  • 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.

  • line_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 group rows of data_frame into lines.

  • symbol (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 symbols to marks.

  • 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.)

  • 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.

  • error_z (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 z-axis error bars. If error_z_minus is None, error bars will be symmetrical, otherwise error_z is used for the positive direction only.

  • error_z_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 z-axis error bars in the negative direction. Ignored if error_z 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.

  • line_dash_sequence (list of str) – Strings should define valid plotly.js dash-patterns. When line_dash is set, values in that column are assigned dash-patterns by cycling through line_dash_sequence in the order described in category_orders, unless the value of line_dash is a key in line_dash_map.

  • line_dash_map (dict with str keys and str values (default {})) – Strings values define plotly.js dash-patterns. Used to override line_dash_sequences to assign a specific dash-patterns to lines corresponding with specific values. Keys in line_dash_map should be values in the column denoted by line_dash. Alternatively, if the values of line_dash are valid line-dash names, the string 'identity' may be passed to cause them to be used directly.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • markers (boolean (default False)) – If True, markers are shown on lines.

  • 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.

  • log_z (boolean (default False)) – If True, the z-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.

  • range_z (list of two numbers) – If provided, overrides auto-scaling on the z-axis in cartesian coordinates.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.line_geo(data_frame=None, lat=None, lon=None, locations=None, locationmode=None, geojson=None, featureidkey=None, color=None, line_dash=None, text=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, line_group=None, symbol=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, line_dash_sequence=None, line_dash_map=None, symbol_sequence=None, symbol_map=None, markers=False, projection=None, scope=None, center=None, fitbounds=None, basemap_visible=None, title=None, template=None, width=None, height=None)

In a geographic line plot, each row of data_frame is represented as vertex of a polyline mark on a map.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • lat (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 according to latitude on a map.

  • lon (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 according to longitude on a map.

  • locations (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 to be interpreted according to locationmode and mapped to longitude/latitude.

  • locationmode (str) – One of ‘ISO-3’, ‘USA-states’, or ‘country names’ Determines the set of locations used to match entries in locations to regions on the map.

  • geojson (GeoJSON-formatted dict) – Must contain a Polygon feature collection, with IDs, which are references from locations.

  • featureidkey (str (default: 'id')) – Path to field in GeoJSON feature object with which to match the values passed in to locations.The most common alternative to the default is of the form 'properties.<key>.

  • 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.

  • line_dash (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 dash-patterns to lines.

  • 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.

  • 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.)

  • line_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 group rows of data_frame into lines.

  • symbol (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 symbols to marks.

  • 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.

  • line_dash_sequence (list of str) – Strings should define valid plotly.js dash-patterns. When line_dash is set, values in that column are assigned dash-patterns by cycling through line_dash_sequence in the order described in category_orders, unless the value of line_dash is a key in line_dash_map.

  • line_dash_map (dict with str keys and str values (default {})) – Strings values define plotly.js dash-patterns. Used to override line_dash_sequences to assign a specific dash-patterns to lines corresponding with specific values. Keys in line_dash_map should be values in the column denoted by line_dash. Alternatively, if the values of line_dash are valid line-dash names, the string 'identity' may be passed to cause them to be used directly.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • markers (boolean (default False)) – If True, markers are shown on lines.

  • projection (str) – One of 'equirectangular', 'mercator', 'orthographic', 'natural earth', 'kavrayskiy7', 'miller', 'robinson', 'eckert4', 'azimuthal equal area', 'azimuthal equidistant', 'conic equal area', 'conic conformal', 'conic equidistant', 'gnomonic', 'stereographic', 'mollweide', 'hammer', 'transverse mercator', 'albers usa', 'winkel tripel', 'aitoff', or 'sinusoidal'`Default depends on `scope.

  • scope (str (default 'world').) – One of 'world', 'usa', 'europe', 'asia', 'africa', 'north america', or 'south america'`Default is `'world' unless projection is set to 'albers usa', which forces 'usa'.

  • center (dict) – Dict keys are 'lat' and 'lon' Sets the center point of the map.

  • fitbounds (str (default False).) – One of False, locations or geojson.

  • basemap_visible (bool) – Force the basemap visibility.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.line_mapbox(data_frame=None, lat=None, lon=None, color=None, text=None, hover_name=None, hover_data=None, custom_data=None, line_group=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, zoom=8, center=None, mapbox_style=None, title=None, template=None, width=None, height=None)

In a Mapbox line plot, each row of data_frame is represented as vertex of a polyline mark on a Mapbox map.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • lat (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 according to latitude on a map.

  • lon (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 according to longitude on a map.

  • 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.

  • 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.

  • 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.)

  • line_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 group rows of data_frame into lines.

  • 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.

  • zoom (int (default 8)) – Between 0 and 20. Sets map zoom level.

  • center (dict) – Dict keys are 'lat' and 'lon' Sets the center point of the map.

  • mapbox_style (str (default 'basic', needs Mapbox API token)) – Identifier of base map style, some of which require a Mapbox API token to be set using plotly.express.set_mapbox_access_token(). Allowed values which do not require a Mapbox API token are 'open-street-map', 'white-bg', 'carto-positron', 'carto-darkmatter', 'stamen- terrain', 'stamen-toner', 'stamen-watercolor'. Allowed values which do require a Mapbox API token are 'basic', 'streets', 'outdoors', 'light', 'dark', 'satellite', 'satellite- streets'.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.line_polar(data_frame=None, r=None, theta=None, color=None, line_dash=None, hover_name=None, hover_data=None, custom_data=None, line_group=None, text=None, symbol=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, line_dash_sequence=None, line_dash_map=None, symbol_sequence=None, symbol_map=None, markers=False, direction='clockwise', start_angle=90, line_close=False, line_shape=None, render_mode='auto', range_r=None, range_theta=None, log_r=False, title=None, template=None, width=None, height=None)

In a polar line plot, each row of data_frame is represented as vertex of a polyline mark in polar coordinates.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • r (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 radial axis in polar coordinates.

  • theta (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 angular axis in polar coordinates.

  • 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.

  • line_dash (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 dash-patterns to lines.

  • 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.)

  • line_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 group rows of data_frame into lines.

  • 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.

  • symbol (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 symbols to marks.

  • 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.

  • line_dash_sequence (list of str) – Strings should define valid plotly.js dash-patterns. When line_dash is set, values in that column are assigned dash-patterns by cycling through line_dash_sequence in the order described in category_orders, unless the value of line_dash is a key in line_dash_map.

  • line_dash_map (dict with str keys and str values (default {})) – Strings values define plotly.js dash-patterns. Used to override line_dash_sequences to assign a specific dash-patterns to lines corresponding with specific values. Keys in line_dash_map should be values in the column denoted by line_dash. Alternatively, if the values of line_dash are valid line-dash names, the string 'identity' may be passed to cause them to be used directly.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • markers (boolean (default False)) – If True, markers are shown on lines.

  • direction (str) – One of ‘counterclockwise' or 'clockwise'. Default is 'clockwise' Sets the direction in which increasing values of the angular axis are drawn.

  • start_angle (int (default 90)) – Sets start angle for the angular axis, with 0 being due east and 90 being due north.

  • line_close (boolean (default False)) – If True, an extra line segment is drawn between the first and last point.

  • line_shape (str (default 'linear')) – One of 'linear' or 'spline'.

  • render_mode (str) – One of 'auto', 'svg' or 'webgl', default 'auto' Controls the browser API used to draw marks. 'svg’ is appropriate for figures of less than 1000 data points, and will allow for fully-vectorized output. 'webgl' is likely necessary for acceptable performance above 1000 points but rasterizes part of the output. 'auto' uses heuristics to choose the mode.

  • range_r (list of two numbers) – If provided, overrides auto-scaling on the radial axis in polar coordinates.

  • range_theta (list of two numbers) – If provided, overrides auto-scaling on the angular axis in polar coordinates.

  • log_r (boolean (default False)) – If True, the radial axis is log-scaled in polar coordinates.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.line_ternary(data_frame=None, a=None, b=None, c=None, color=None, line_dash=None, line_group=None, symbol=None, hover_name=None, hover_data=None, custom_data=None, text=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, line_dash_sequence=None, line_dash_map=None, symbol_sequence=None, symbol_map=None, markers=False, line_shape=None, title=None, template=None, width=None, height=None)

In a ternary line plot, each row of data_frame is represented as vertex of a polyline mark in ternary coordinates.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • a (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 a axis in ternary coordinates.

  • b (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 b axis in ternary coordinates.

  • c (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 c axis in ternary coordinates.

  • 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.

  • line_dash (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 dash-patterns to lines.

  • line_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 group rows of data_frame into lines.

  • symbol (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 symbols to marks.

  • 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.

  • 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.

  • line_dash_sequence (list of str) – Strings should define valid plotly.js dash-patterns. When line_dash is set, values in that column are assigned dash-patterns by cycling through line_dash_sequence in the order described in category_orders, unless the value of line_dash is a key in line_dash_map.

  • line_dash_map (dict with str keys and str values (default {})) – Strings values define plotly.js dash-patterns. Used to override line_dash_sequences to assign a specific dash-patterns to lines corresponding with specific values. Keys in line_dash_map should be values in the column denoted by line_dash. Alternatively, if the values of line_dash are valid line-dash names, the string 'identity' may be passed to cause them to be used directly.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • markers (boolean (default False)) – If True, markers are shown on lines.

  • line_shape (str (default 'linear')) – One of 'linear' or 'spline'.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.parallel_categories(data_frame=None, dimensions=None, color=None, labels=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, title=None, template=None, width=None, height=None, dimensions_max_cardinality=50)

In a parallel categories (or parallel sets) plot, each row of data_frame is grouped with other rows that share the same values of dimensions and then plotted as a polyline mark through a set of parallel axes, one for each of the dimensions.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • dimensions (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 used for multidimensional visualization.

  • 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.

  • 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_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.

  • 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.

  • 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.

  • dimensions_max_cardinality (int (default 50)) – When dimensions is None and data_frame is provided, columns with more than this number of unique values are excluded from the output. Not used when dimensions is passed.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.parallel_coordinates(data_frame=None, dimensions=None, color=None, labels=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, title=None, template=None, width=None, height=None)

In a parallel coordinates plot, each row of data_frame is represented by a polyline mark which traverses a set of parallel axes, one for each of the dimensions.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • dimensions (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 used for multidimensional visualization.

  • 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.

  • 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_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.

  • 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.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.pie(data_frame=None, names=None, values=None, color=None, color_discrete_sequence=None, color_discrete_map=None, hover_name=None, hover_data=None, custom_data=None, labels=None, title=None, template=None, width=None, height=None, opacity=None, hole=None)

In a pie plot, each row of data_frame is represented as a sector of a pie.

Parameters
  • data_frame (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 tranformed 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.

  • 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

Return type

plotly.graph_objects.Figure

plotly.express.scatter(data_frame=None, x=None, y=None, color=None, symbol=None, size=None, hover_name=None, hover_data=None, custom_data=None, text=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None, marginal_x=None, marginal_y=None, trendline=None, trendline_options=None, trendline_color_override=None, trendline_scope='trace', log_x=False, log_y=False, range_x=None, range_y=None, render_mode='auto', title=None, template=None, width=None, height=None)

In a scatter plot, each row of data_frame is represented by a symbol mark in 2D space.

Parameters
  • data_frame (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 tranformed 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.

  • symbol (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 symbols to marks.

  • size (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 mark sizes.

  • 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.

  • 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.

  • 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.

  • orientation (str, one of 'h' for horizontal or 'v' for vertical.) – (default 'v' if x and y are provided and both continous 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)

  • 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.

  • 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.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • opacity (float) – Value between 0 and 1. Sets the opacity for markers.

  • size_max (int (default 20)) – Set the maximum mark size when using size.

  • marginal_x (str) – One of 'rug', 'box', 'violin', or 'histogram'. If set, a horizontal subplot is drawn above the main plot, visualizing the x-distribution.

  • marginal_y (str) – One of 'rug', 'box', 'violin', or 'histogram'. If set, a vertical subplot is drawn to the right of the main plot, visualizing the y-distribution.

  • trendline (str) – One of 'ols', 'lowess', 'rolling', 'expanding' or 'ewm'. If 'ols', an Ordinary Least Squares regression line will be drawn for each discrete-color/symbol group. If 'lowess’, a Locally Weighted Scatterplot Smoothing line will be drawn for each discrete-color/symbol group. If 'rolling’, a Rolling (e.g. rolling average, rolling median) line will be drawn for each discrete-color/symbol group. If 'expanding’, an Expanding (e.g. expanding average, expanding sum) line will be drawn for each discrete-color/symbol group. If 'ewm’, an Exponentially Weighted Moment (e.g. exponentially-weighted moving average) line will be drawn for each discrete-color/symbol group. See the docstrings for the functions in plotly.express.trendline_functions for more details on these functions and how to configure them with the trendline_options argument.

  • trendline_options (dict) – Options passed as the first argument to the function from plotly.express.trendline_functions named in the trendline argument.

  • trendline_color_override (str) – Valid CSS color. If provided, and if trendline is set, all trendlines will be drawn in this color rather than in the same color as the traces from which they draw their inputs.

  • trendline_scope (str (one of 'trace' or 'overall', default 'trace')) – If 'trace', then one trendline is drawn per trace (i.e. per color, symbol, facet, animation frame etc) and if 'overall' then one trendline is computed for the entire dataset, and replicated across all facets.

  • 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.

  • render_mode (str) – One of 'auto', 'svg' or 'webgl', default 'auto' Controls the browser API used to draw marks. 'svg’ is appropriate for figures of less than 1000 data points, and will allow for fully-vectorized output. 'webgl' is likely necessary for acceptable performance above 1000 points but rasterizes part of the output. 'auto' uses heuristics to choose the mode.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.scatter_3d(data_frame=None, x=None, y=None, z=None, color=None, symbol=None, size=None, text=None, hover_name=None, hover_data=None, custom_data=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, error_z=None, error_z_minus=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, size_max=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, log_x=False, log_y=False, log_z=False, range_x=None, range_y=None, range_z=None, title=None, template=None, width=None, height=None)

In a 3D scatter plot, each row of data_frame is represented by a symbol mark in 3D space.

Parameters
  • data_frame (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 tranformed 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.

  • 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.

  • z (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 z axis in cartesian coordinates.

  • 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.

  • symbol (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 symbols to marks.

  • size (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 mark sizes.

  • 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.

  • 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.)

  • 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.

  • error_z (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 z-axis error bars. If error_z_minus is None, error bars will be symmetrical, otherwise error_z is used for the positive direction only.

  • error_z_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 z-axis error bars in the negative direction. Ignored if error_z 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.

  • size_max (int (default 20)) – Set the maximum mark size when using size.

  • 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.

  • 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.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • opacity (float) – Value between 0 and 1. Sets the opacity for markers.

  • 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.

  • log_z (boolean (default False)) – If True, the z-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.

  • range_z (list of two numbers) – If provided, overrides auto-scaling on the z-axis in cartesian coordinates.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.scatter_geo(data_frame=None, lat=None, lon=None, locations=None, locationmode=None, geojson=None, featureidkey=None, color=None, text=None, symbol=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, size=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None, projection=None, scope=None, center=None, fitbounds=None, basemap_visible=None, title=None, template=None, width=None, height=None)

In a geographic scatter plot, each row of data_frame is represented by a symbol mark on a map.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • lat (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 according to latitude on a map.

  • lon (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 according to longitude on a map.

  • locations (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 to be interpreted according to locationmode and mapped to longitude/latitude.

  • locationmode (str) – One of ‘ISO-3’, ‘USA-states’, or ‘country names’ Determines the set of locations used to match entries in locations to regions on the map.

  • geojson (GeoJSON-formatted dict) – Must contain a Polygon feature collection, with IDs, which are references from locations.

  • featureidkey (str (default: 'id')) – Path to field in GeoJSON feature object with which to match the values passed in to locations.The most common alternative to the default is of the form 'properties.<key>.

  • 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.

  • 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.

  • symbol (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 symbols 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.)

  • size (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 mark sizes.

  • 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.

  • 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.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • opacity (float) – Value between 0 and 1. Sets the opacity for markers.

  • size_max (int (default 20)) – Set the maximum mark size when using size.

  • projection (str) – One of 'equirectangular', 'mercator', 'orthographic', 'natural earth', 'kavrayskiy7', 'miller', 'robinson', 'eckert4', 'azimuthal equal area', 'azimuthal equidistant', 'conic equal area', 'conic conformal', 'conic equidistant', 'gnomonic', 'stereographic', 'mollweide', 'hammer', 'transverse mercator', 'albers usa', 'winkel tripel', 'aitoff', or 'sinusoidal'`Default depends on `scope.

  • scope (str (default 'world').) – One of 'world', 'usa', 'europe', 'asia', 'africa', 'north america', or 'south america'`Default is `'world' unless projection is set to 'albers usa', which forces 'usa'.

  • center (dict) – Dict keys are 'lat' and 'lon' Sets the center point of the map.

  • fitbounds (str (default False).) – One of False, locations or geojson.

  • basemap_visible (bool) – Force the basemap visibility.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.scatter_mapbox(data_frame=None, lat=None, lon=None, color=None, text=None, hover_name=None, hover_data=None, custom_data=None, size=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, opacity=None, size_max=None, zoom=8, center=None, mapbox_style=None, title=None, template=None, width=None, height=None)

In a Mapbox scatter plot, each row of data_frame is represented by a symbol mark on a Mapbox map.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • lat (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 according to latitude on a map.

  • lon (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 according to longitude on a map.

  • 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.

  • 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.

  • 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.)

  • size (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 mark sizes.

  • 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.

  • 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.

  • size_max (int (default 20)) – Set the maximum mark size when using size.

  • zoom (int (default 8)) – Between 0 and 20. Sets map zoom level.

  • center (dict) – Dict keys are 'lat' and 'lon' Sets the center point of the map.

  • mapbox_style (str (default 'basic', needs Mapbox API token)) – Identifier of base map style, some of which require a Mapbox API token to be set using plotly.express.set_mapbox_access_token(). Allowed values which do not require a Mapbox API token are 'open-street-map', 'white-bg', 'carto-positron', 'carto-darkmatter', 'stamen- terrain', 'stamen-toner', 'stamen-watercolor'. Allowed values which do require a Mapbox API token are 'basic', 'streets', 'outdoors', 'light', 'dark', 'satellite', 'satellite- streets'.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.scatter_matrix(data_frame=None, dimensions=None, color=None, symbol=None, size=None, hover_name=None, hover_data=None, custom_data=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None, title=None, template=None, width=None, height=None)

In a scatter plot matrix (or SPLOM), each row of data_frame is represented by a multiple symbol marks, one in each cell of a grid of 2D scatter plots, which plot each pair of dimensions against each other.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • dimensions (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 used for multidimensional visualization.

  • 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.

  • symbol (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 symbols to marks.

  • size (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 mark sizes.

  • 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.)

  • 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.

  • 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.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • opacity (float) – Value between 0 and 1. Sets the opacity for markers.

  • size_max (int (default 20)) – Set the maximum mark size when using size.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.scatter_polar(data_frame=None, r=None, theta=None, color=None, symbol=None, size=None, hover_name=None, hover_data=None, custom_data=None, text=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, direction='clockwise', start_angle=90, size_max=None, range_r=None, range_theta=None, log_r=False, render_mode='auto', title=None, template=None, width=None, height=None)

In a polar scatter plot, each row of data_frame is represented by a symbol mark in polar coordinates.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • r (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 radial axis in polar coordinates.

  • theta (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 angular axis in polar coordinates.

  • 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.

  • symbol (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 symbols to marks.

  • size (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 mark sizes.

  • 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.

  • 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.

  • 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.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • opacity (float) – Value between 0 and 1. Sets the opacity for markers.

  • direction (str) – One of ‘counterclockwise' or 'clockwise'. Default is 'clockwise' Sets the direction in which increasing values of the angular axis are drawn.

  • start_angle (int (default 90)) – Sets start angle for the angular axis, with 0 being due east and 90 being due north.

  • size_max (int (default 20)) – Set the maximum mark size when using size.

  • range_r (list of two numbers) – If provided, overrides auto-scaling on the radial axis in polar coordinates.

  • range_theta (list of two numbers) – If provided, overrides auto-scaling on the angular axis in polar coordinates.

  • log_r (boolean (default False)) – If True, the radial axis is log-scaled in polar coordinates.

  • render_mode (str) – One of 'auto', 'svg' or 'webgl', default 'auto' Controls the browser API used to draw marks. 'svg’ is appropriate for figures of less than 1000 data points, and will allow for fully-vectorized output. 'webgl' is likely necessary for acceptable performance above 1000 points but rasterizes part of the output. 'auto' uses heuristics to choose the mode.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.scatter_ternary(data_frame=None, a=None, b=None, c=None, color=None, symbol=None, size=None, text=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None, title=None, template=None, width=None, height=None)

In a ternary scatter plot, each row of data_frame is represented by a symbol mark in ternary coordinates.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • a (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 a axis in ternary coordinates.

  • b (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 b axis in ternary coordinates.

  • c (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 c axis in ternary coordinates.

  • 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.

  • symbol (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 symbols to marks.

  • size (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 mark sizes.

  • 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.

  • 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.)

  • 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.

  • 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.

  • symbol_sequence (list of str) – Strings should define valid plotly.js symbols. When symbol is set, values in that column are assigned symbols by cycling through symbol_sequence in the order described in category_orders, unless the value of symbol is a key in symbol_map.

  • symbol_map (dict with str keys and str values (default {})) – String values should define plotly.js symbols Used to override symbol_sequence to assign a specific symbols to marks corresponding with specific values. Keys in symbol_map should be values in the column denoted by symbol. Alternatively, if the values of symbol are valid symbol names, the string 'identity' may be passed to cause them to be used directly.

  • opacity (float) – Value between 0 and 1. Sets the opacity for markers.

  • size_max (int (default 20)) – Set the maximum mark size when using size.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.set_mapbox_access_token(token)
Parameters

token – A Mapbox token to be used in plotly.express.scatter_mapbox and plotly.express.line_mapbox figures. See https://docs.mapbox.com/help/how-mapbox-works/access-tokens/ for more details

plotly.express.strip(data_frame=None, x=None, y=None, color=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, orientation=None, stripmode=None, log_x=False, log_y=False, range_x=None, range_y=None, title=None, template=None, width=None, height=None)

In a strip plot each row of data_frame is represented as a jittered mark within categories.

Parameters
  • data_frame (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 tranformed 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.

  • 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.)

  • 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.

  • orientation (str, one of 'h' for horizontal or 'v' for vertical.) – (default 'v' if x and y are provided and both continous 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)

  • stripmode (str (default 'group')) – One of 'group' or 'overlay' In 'overlay' mode, strips are on drawn top of one another. In 'group' mode, strips 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.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.sunburst(data_frame=None, names=None, values=None, parents=None, path=None, ids=None, color=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, color_discrete_sequence=None, color_discrete_map=None, hover_name=None, hover_data=None, custom_data=None, labels=None, title=None, template=None, width=None, height=None, branchvalues=None, maxdepth=None)

A sunburst plot represents hierarchial data as sectors laid out over several levels of concentric rings.

Parameters
  • data_frame (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 tranformed 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.

  • parents (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 parents in sunburst and treemap charts.

  • path (list of str or int, or Series or array-like) – Either names of columns in data_frame, or pandas Series, or array_like objects List of columns names or columns of a rectangular dataframe defining the hierarchy of sectors, from root to leaves. An error is raised if path AND ids or parents is passed

  • ids (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 ids of sectors

  • 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_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.

  • 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.

  • 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.

  • branchvalues (str) – ‘total’ or ‘remainder’ Determines how the items in values are summed. Whenset to ‘total’, items in values are taken to be valueof all its descendants. When set to ‘remainder’, itemsin values corresponding to the root and the branches:sectors are taken to be the extra part not part of thesum of the values at their leaves.

  • maxdepth (int) – Positive integer Sets the number of rendered sectors from any given level. Set maxdepth to -1 to render all thelevels in the hierarchy.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.timeline(data_frame=None, x_start=None, x_end=None, y=None, color=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, text=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, opacity=None, range_x=None, range_y=None, title=None, template=None, width=None, height=None)

In a timeline plot, each row of data_frame is represented as a rectangular mark on an x axis of type date, spanning from x_start to x_end.

Parameters
  • data_frame (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 tranformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

  • x_start (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. (required) Values from this column or array_like are used to position marks along the x axis in cartesian coordinates.

  • x_end (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. (required) Values from this column or array_like are used to position marks along the x axis in cartesian coordinates.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.treemap(data_frame=None, names=None, values=None, parents=None, ids=None, path=None, color=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, color_discrete_sequence=None, color_discrete_map=None, hover_name=None, hover_data=None, custom_data=None, labels=None, title=None, template=None, width=None, height=None, branchvalues=None, maxdepth=None)

A treemap plot represents hierarchial data as nested rectangular sectors.

Parameters
  • data_frame (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 tranformed 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.

  • parents (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 parents in sunburst and treemap charts.

  • ids (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 ids of sectors

  • path (list of str or int, or Series or array-like) – Either names of columns in data_frame, or pandas Series, or array_like objects List of columns names or columns of a rectangular dataframe defining the hierarchy of sectors, from root to leaves. An error is raised if path AND ids or parents is passed

  • 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_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.

  • 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.

  • 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.

  • branchvalues (str) – ‘total’ or ‘remainder’ Determines how the items in values are summed. Whenset to ‘total’, items in values are taken to be valueof all its descendants. When set to ‘remainder’, itemsin values corresponding to the root and the branches:sectors are taken to be the extra part not part of thesum of the values at their leaves.

  • maxdepth (int) – Positive integer Sets the number of rendered sectors from any given level. Set maxdepth to -1 to render all thelevels in the hierarchy.

Returns

Return type

plotly.graph_objects.Figure

plotly.express.violin(data_frame=None, x=None, y=None, color=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, orientation=None, violinmode=None, log_x=False, log_y=False, range_x=None, range_y=None, points=None, box=False, title=None, template=None, width=None, height=None)

In a violin plot, rows of data_frame are grouped together into a curved mark to visualize their distribution.

Parameters
  • data_frame (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 tranformed 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.

  • 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.)

  • 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.

  • orientation (str, one of 'h' for horizontal or 'v' for vertical.) – (default 'v' if x and y are provided and both continous 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)

  • violinmode (str (default 'group')) – One of 'group' or 'overlay' In 'overlay' mode, violins are on drawn top of one another. In 'group' mode, violins 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.

  • points (str or boolean (default 'outliers')) – One of 'outliers', 'suspectedoutliers', 'all', or False. If 'outliers', only the sample points lying outside the whiskers are shown. If 'suspectedoutliers', all outlier points are shown and those less than 4*Q1-3*Q3 or greater than 4*Q3-3*Q1 are highlighted with the marker’s 'outliercolor'. If 'outliers', only the sample points lying outside the whiskers are shown. If 'all', all sample points are shown. If False, no sample points are shown and the whiskers extend to the full range of the sample.

  • box (boolean (default False)) – If True, boxes are drawn inside the violins.

  • 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.

Returns

Return type

plotly.graph_objects.Figure

Submodules

plotly.express.imshow_utils module

Vendored code from scikit-image in order to limit the number of dependencies Extracted from scikit-image/skimage/exposure/exposure.py

plotly.express.imshow_utils.intensity_range(image, range_values='image', clip_negative=False)

Return image intensity range (min, max) based on desired value type.

Parameters
  • image (array) – Input image.

  • range_values (str or 2-tuple, optional) –

    The image intensity range is configured by this parameter. The possible values for this parameter are enumerated below.

    ’image’

    Return image min/max as the range.

    ’dtype’

    Return min/max of the image’s dtype as the range.

    dtype-name

    Return intensity range based on desired dtype. Must be valid key in DTYPE_RANGE. Note: image is ignored for this range type.

    2-tuple

    Return range_values as min/max intensities. Note that there’s no reason to use this function if you just want to specify the intensity range explicitly. This option is included for functions that use intensity_range to support all desired range types.

  • clip_negative (bool, optional) – If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values.

plotly.express.imshow_utils.rescale_intensity(image, in_range='image', out_range='dtype')

Return image after stretching or shrinking its intensity levels.

The desired intensity range of the input and output, in_range and out_range respectively, are used to stretch or shrink the intensity range of the input image. See examples below.

Parameters
  • image (array) – Image array.

  • in_range (str or 2-tuple, optional) –

    Min and max intensity values of input and output image. The possible values for this parameter are enumerated below.

    ’image’

    Use image min/max as the intensity range.

    ’dtype’

    Use min/max of the image’s dtype as the intensity range.

    dtype-name

    Use intensity range based on desired dtype. Must be valid key in DTYPE_RANGE.

    2-tuple

    Use range_values as explicit min/max intensities.

  • out_range (str or 2-tuple, optional) –

    Min and max intensity values of input and output image. The possible values for this parameter are enumerated below.

    ’image’

    Use image min/max as the intensity range.

    ’dtype’

    Use min/max of the image’s dtype as the intensity range.

    dtype-name

    Use intensity range based on desired dtype. Must be valid key in DTYPE_RANGE.

    2-tuple

    Use range_values as explicit min/max intensities.

Returns

out – Image array after rescaling its intensity. This image is the same dtype as the input image.

Return type

array

Notes

Changed in version 0.17: The dtype of the output array has changed to match the output dtype, or float if the output range is specified by a pair of floats.

See also

equalize_hist

Examples

By default, the min/max intensities of the input image are stretched to the limits allowed by the image’s dtype, since in_range defaults to ‘image’ and out_range defaults to ‘dtype’:

>>> image = np.array([51, 102, 153], dtype=np.uint8)
>>> rescale_intensity(image)
array([  0, 127, 255], dtype=uint8)

It’s easy to accidentally convert an image dtype from uint8 to float:

>>> 1.0 * image
array([ 51., 102., 153.])

Use rescale_intensity to rescale to the proper range for float dtypes:

>>> image_float = 1.0 * image
>>> rescale_intensity(image_float)
array([0. , 0.5, 1. ])

To maintain the low contrast of the original, use the in_range parameter:

>>> rescale_intensity(image_float, in_range=(0, 255))
array([0.2, 0.4, 0.6])

If the min/max value of in_range is more/less than the min/max image intensity, then the intensity levels are clipped:

>>> rescale_intensity(image_float, in_range=(0, 102))
array([0.5, 1. , 1. ])

If you have an image with signed integers but want to rescale the image to just the positive range, use the out_range parameter. In that case, the output dtype will be float:

>>> image = np.array([-10, 0, 10], dtype=np.int8)
>>> rescale_intensity(image, out_range=(0, 127))
array([  0. ,  63.5, 127. ])

To get the desired range with a specific dtype, use .astype():

>>> rescale_intensity(image, out_range=(0, 127)).astype(np.int8)
array([  0,  63, 127], dtype=int8)

If the input image is constant, the output will be clipped directly to the output range: >>> image = np.array([130, 130, 130], dtype=np.int32) >>> rescale_intensity(image, out_range=(0, 127)).astype(np.int32) array([127, 127, 127], dtype=int32)