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.com/python/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, pattern_shape=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, pattern_shape_sequence=None, pattern_shape_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, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

In a stacked area plot, each row of data_frame is represented as a 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 transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

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

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

• 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.

• 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.

• 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 (str, or list of str or int, or Series or array-like) – Either name or list of 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 0.07 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.

• 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.

• 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 continuous or both categorical, otherwise 'v'`(‘h’) if `x`(`y) is categorical and y`(`x) is continuous, otherwise 'v'`(‘h’) if only `x`(`y) is provided)

• 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', 'spline', 'hv', 'vh', 'hvh', or 'vhv'

• title (str) – The figure title.

• subtitle (str) – The figure subtitle.

• 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, text_auto=False, title=None, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

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 transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

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

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

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

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

• text (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like appear in the figure as text labels.

• base (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position the base of the bar.

• error_x (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size x-axis error bars. If error_x_minus is None, error bars will be symmetrical, otherwise error_x is used for the positive direction only.

• error_x_minus (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size x-axis error bars in the negative direction. Ignored if error_x is None.

• error_y (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size y-axis error bars. If error_y_minus is None, error bars will be symmetrical, otherwise error_y is used for the positive direction only.

• error_y_minus (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to size y-axis error bars in the negative direction. Ignored if error_y is None.

• animation_frame (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to animation frames.

• animation_group (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to provide object-constancy across animation frames: rows with matching `animation_group`s will be treated as if they describe the same object in each frame.

• category_orders (dict with str keys and list of str values (default {})) – By default, in Python 3.6+, the order of categorical values in axes, legends and facets depends on the order in which these values are first encountered in data_frame (and no order is guaranteed by default in Python below 3.6). This parameter is used to force a specific ordering of values per column. The keys of this dict should correspond to column names, and the values should be lists of strings corresponding to the specific display order desired.

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

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

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

• color_continuous_scale (list of str) – Strings should define valid CSS-colors This list is used to build a continuous color scale when the column denoted by color contains numeric data. Various useful color scales are available in the plotly.express.colors submodules, specifically plotly.express.colors.sequential, plotly.express.colors.diverging and plotly.express.colors.cyclical.

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

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

• range_color (list of two numbers) – If provided, overrides auto-scaling on the continuous color scale.

• color_continuous_midpoint (number (default None)) – If set, computes the bounds of the continuous color scale to have the desired midpoint. Setting this value is recommended when using plotly.express.colors.diverging color scales as the inputs to color_continuous_scale.

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

• orientation (str, one of 'h' for horizontal or 'v' for vertical.) – (default 'v' if x and y are provided and both continuous or both categorical, otherwise 'v'`(‘h’) if `x`(`y) is categorical and y`(`x) is continuous, otherwise 'v'`(‘h’) if only `x`(`y) is provided)

• barmode (str (default 'relative')) – One of 'group', 'overlay' or 'relative' In 'relative' mode, bars are stacked above zero for positive values and below zero for negative values. In 'overlay' mode, bars are drawn on top of one another. In 'group' mode, bars are placed beside each other.

• log_x (boolean (default False)) – If True, the x-axis is log-scaled in cartesian coordinates.

• log_y (boolean (default False)) – If True, the y-axis is log-scaled in cartesian coordinates.

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

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

• text_auto (bool or string (default False)) – If True or a string, the x or y or z values will be displayed as text, depending on the orientation A string like '.2f' will be interpreted as a texttemplate numeric formatting directive.

• title (str) – The figure title.

• subtitle (str) – The figure subtitle.

• 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, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

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 transformed 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 (str, or list of str or int, or Series or array-like) – Either name or list of 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.

• subtitle (str) – The figure subtitle.

• 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, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

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 transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

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

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

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

• 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 0.07 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 (str, or list of str or int, or Series or array-like) – Either name or list of 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 continuous or both categorical, otherwise 'v'`(‘h’) if `x`(`y) is categorical and y`(`x) is continuous, otherwise 'v'`(‘h’) if only `x`(`y) is provided)

• 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.

• subtitle (str) – The figure subtitle.

• 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, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

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 transformed 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 0.07 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 (str, or list of str or int, or Series or array-like) – Either name or list of 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.

• subtitle (str) – The figure subtitle.

• 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_map(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=None, center=None, map_style=None, title=None, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

In a choropleth map, each row of data_frame is represented by a colored region 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 transformed 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 (str, or list of str or int, or Series or array-like) – Either name or list of 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 None)) – Between 0 and 20. Sets initial map zoom level. Higher values are more zoomed-in. If not provided, the map will select its own zoom level.

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

• map_style (str (default 'basic')) – Identifier of base map style. Allowed values are 'basic', 'carto-darkmatter', 'carto-darkmatter-nolabels', 'carto-positron', 'carto-positron-nolabels', 'carto-voyager', 'carto-voyager-nolabels', 'dark', 'light', 'open-street-map', 'outdoors', 'satellite', 'satellite-streets', 'streets', 'white-bg'.

• title (str) – The figure title.

• subtitle (str) – The figure subtitle.

• 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, text_auto=False, title=None, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

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 transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

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

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

• 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 0.07 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 continuous or both categorical, otherwise 'v'`(‘h’) if `x`(`y) is categorical and y`(`x) is continuous, otherwise 'v'`(‘h’) if only `x`(`y) is provided)

• 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.

• text_auto (bool or string (default False)) – If True or a string, the x or y or z values will be displayed as text, depending on the orientation A string like '.2f' will be interpreted as a texttemplate numeric formatting directive.

• title (str) – The figure title.

• subtitle (str) – The figure subtitle.

• 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, text_auto=False, title=None, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

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 transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

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

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

• 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 0.07 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 continuous or both categorical, otherwise 'v'`(‘h’) if `x`(`y) is categorical and y`(`x) is continuous, otherwise 'v'`(‘h’) if only `x`(`y) is provided)

• 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. Also supports 'heatmap', showing a single-row heatmap colored by the aggregate value.

• 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. Also supports 'heatmap', showing a single-column heatmap colored by the aggregate value.

• 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.

• text_auto (bool or string (default False)) – If True or a string, the x or y or z values will be displayed as text, depending on the orientation A string like '.2f' will be interpreted as a texttemplate numeric formatting directive. Also applies to marginal_x/marginal_y='heatmap', in which case the z values are always displayed.

• title (str) – The figure title.

• subtitle (str) – The figure subtitle.

• 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_map(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=None, center=None, map_style=None, radius=None, title=None, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

In a 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 transformed 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 (str, or list of str or int, or Series or array-like) – Either name or list of 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 None)) – Between 0 and 20. Sets initial map zoom level. Higher values are more zoomed-in. If not provided, the map will select its own zoom level.

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

• map_style (str (default 'basic')) – Identifier of base map style. Allowed values are 'basic', 'carto-darkmatter', 'carto-darkmatter-nolabels', 'carto-positron', 'carto-positron-nolabels', 'carto-voyager', 'carto-voyager-nolabels', 'dark', 'light', 'open-street-map', 'outdoors', 'satellite', 'satellite-streets', 'streets', 'white-bg'.

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

• title (str) – The figure title.

• subtitle (str) – The figure subtitle.

• 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, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

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 transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

• x (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the x axis in cartesian coordinates. 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 0.07 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 continuous or both categorical, otherwise 'v'`(‘h’) if `x`(`y) is categorical and y`(`x) is continuous, otherwise 'v'`(‘h’) if only `x`(`y) is provided)

• 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.

• subtitle (str) – The figure subtitle.

• 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, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

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 transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

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

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

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

• 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 0.07 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 (str, or list of str or int, or Series or array-like) – Either name or list of 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 continuous or both categorical, otherwise 'v'`(‘h’) if `x`(`y) is categorical and y`(`x) is continuous, otherwise 'v'`(‘h’) if only `x`(`y) is provided)

• 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.

• subtitle (str) – The figure subtitle.

• 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, subtitle=None, template=None, width=None, height=None, opacity=None) → plotly.graph_objects._figure.Figure¶

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 transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

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

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

• 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 (str, or list of str or int, or Series or array-like) – Either name or list of 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.

• subtitle (str) – The figure subtitle.

• 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, text_auto=False, title=None, subtitle=None, template=None, width=None, height=None) → plotly.graph_objects._figure.Figure¶

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 transformed internally to a pandas DataFrame. Optional: if missing, a DataFrame gets constructed under the hood using the other arguments.

• x (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the x axis in cartesian coordinates. 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 0.07 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 (str, or list of str or int, or Series or array-like, or dict) – Either a name or 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 continuous or both categorical, otherwise 'v'`(‘h’) if `x`(`y) is categorical and y`(`x) is continuous, otherwise 'v'`(‘h’) if only `x`(`y) is provided)

• barmode (str (default 'relative')) – One of 'group', 'overlay' or 'relative' In 'relative' mode, bars are stacked above zero for positive values and below zero for negative values. In 'overlay' mode, bars are drawn on top of one another. In 'group' mode, bars are placed beside each other.

• 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.

• text_auto (bool or string (default False)) – If True or a string, the x or y or z values will be displayed as text, depending on the orientation A string like '.2f' will be interpreted as a texttemplate numeric formatting directive.

• title (str) – The figure title.

• subtitle (str) – The figure subtitle.

• 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