tessl/pypi-plotly
An open-source interactive data visualization library for Python
—
Pending
express-plotting.mddocs/
Express Plotting Interface
High-level plotting functions for rapid visualization creation. Plotly Express provides over 40 chart types with sensible defaults and automatic styling, making it ideal for exploratory data analysis and quick visualization prototyping.
Capabilities
Basic Charts
Core chart types for fundamental data visualization needs.
def scatter(data_frame, x=None, y=None, color=None, size=None, hover_name=None,
hover_data=None, custom_data=None, text=None, facet_row=None,
facet_col=None, facet_col_wrap=None, facet_row_spacing=None,
facet_col_spacing=None, error_x=None, error_x_minus=None,
error_y=None, error_y_minus=None, animation_frame=None,
animation_group=None, category_orders=None, labels=None,
orientation=None, color_discrete_sequence=None,
color_discrete_map=None, color_continuous_scale=None,
range_color=None, color_continuous_midpoint=None,
symbol=None, symbol_sequence=None, symbol_map=None,
opacity=None, size_max=None, marginal_x=None, marginal_y=None,
trendline=None, trendline_options=None, trendline_color_override=None,
trendline_scope=None, log_x=False, log_y=False, range_x=None,
range_y=None, render_mode='auto', title=None, template=None,
width=None, height=None):
"""
Create a scatter plot.
Parameters:
- data_frame: DataFrame, data to plot
- x: str or int or Series, column for x-axis
- y: str or int or Series, column for y-axis
- color: str or int or Series, column for color encoding
- size: str or int or Series, column for size encoding
- hover_name: str or int or Series, column for hover tooltip names
- hover_data: list of str, columns to show in hover tooltip
- text: str or int or Series, column for text annotations
- facet_row: str or int or Series, column for subplot rows
- facet_col: str or int or Series, column for subplot columns
- animation_frame: str or int or Series, column for animation frames
- trendline: str, trendline type ('ols', 'lowess', etc.)
- log_x: bool, use log scale for x-axis
- log_y: bool, use log scale for y-axis
- title: str, figure title
Returns:
Figure: Plotly figure object
"""
def line(data_frame, x=None, y=None, line_group=None, color=None,
line_dash=None, hover_name=None, hover_data=None, custom_data=None,
text=None, facet_row=None, facet_col=None, facet_col_wrap=None,
facet_row_spacing=None, facet_col_spacing=None, error_x=None,
error_x_minus=None, error_y=None, error_y_minus=None,
animation_frame=None, animation_group=None, category_orders=None,
labels=None, orientation=None, color_discrete_sequence=None,
color_discrete_map=None, line_dash_sequence=None, line_dash_map=None,
log_x=False, log_y=False, range_x=None, range_y=None,
line_shape=None, render_mode='auto', title=None, template=None,
width=None, height=None):
"""
Create a line chart.
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for x-axis
- y: str, column for y-axis
- line_group: str, column to group lines by
- color: str, column for color encoding
- line_dash: str, column for line dash pattern encoding
- line_shape: str, line interpolation ('linear', 'spline', 'hv', 'vh', 'hvh', 'vhv')
Returns:
Figure: Plotly figure object
"""
def bar(data_frame, x=None, y=None, color=None, pattern_shape=None,
facet_row=None, facet_col=None, facet_col_wrap=None,
facet_row_spacing=None, facet_col_spacing=None, hover_name=None,
hover_data=None, custom_data=None, text=None, base=None,
error_x=None, error_x_minus=None, error_y=None, error_y_minus=None,
animation_frame=None, animation_group=None, category_orders=None,
labels=None, color_discrete_sequence=None, color_discrete_map=None,
color_continuous_scale=None, pattern_shape_sequence=None,
pattern_shape_map=None, range_color=None, color_continuous_midpoint=None,
opacity=None, orientation=None, barmode='relative', log_x=False,
log_y=False, range_x=None, range_y=None, title=None, template=None,
width=None, height=None):
"""
Create a bar chart.
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for x-axis (categories)
- y: str, column for y-axis (values)
- color: str, column for color encoding
- orientation: str, bar orientation ('v' for vertical, 'h' for horizontal)
- barmode: str, bar grouping mode ('group', 'overlay', 'relative')
- text: str, column for bar text annotations
Returns:
Figure: Plotly figure object
"""
def histogram(data_frame, x=None, y=None, color=None, pattern_shape=None,
facet_row=None, facet_col=None, facet_col_wrap=None,
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, template=None, width=None, height=None):
"""
Create a histogram.
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for histogram data
- y: str, alternative column for histogram data
- color: str, column for color encoding
- nbins: int, number of bins
- histnorm: str, normalization mode ('', 'percent', 'probability', 'density', 'probability density')
- cumulative: bool, whether to show cumulative histogram
Returns:
Figure: Plotly figure object
"""
def box(data_frame, x=None, y=None, color=None, facet_row=None, facet_col=None,
facet_col_wrap=None, 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='group', log_x=False, log_y=False, range_x=None, range_y=None,
points=None, notched=None, title=None, template=None, width=None, height=None):
"""
Create a box plot.
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for categories
- y: str, column for continuous values
- color: str, column for color encoding
- points: str, point display mode ('outliers', 'suspectedoutliers', 'all', False)
- notched: bool, whether to show notched boxes
Returns:
Figure: Plotly figure object
"""Geographic Charts
Visualization functions for geospatial data with support for coordinate mapping and choropleth regions.
def scatter_geo(data_frame, lat=None, lon=None, locations=None, locationmode=None,
geojson=None, featureidkey=None, color=None, text=None,
facet_row=None, facet_col=None, facet_col_wrap=None,
facet_row_spacing=None, facet_col_spacing=None, hover_name=None,
hover_data=None, custom_data=None, size=None, animation_frame=None,
animation_group=None, category_orders=None, labels=None,
color_discrete_sequence=None, color_discrete_map=None,
color_continuous_scale=None, range_color=None,
color_continuous_midpoint=None, opacity=None, size_max=None,
projection=None, scope=None, center=None, title=None,
template=None, width=None, height=None):
"""
Create a geographic scatter plot.
Parameters:
- data_frame: DataFrame, data to plot
- lat: str, column for latitude coordinates
- lon: str, column for longitude coordinates
- locations: str, column for location identifiers
- color: str, column for color encoding
- size: str, column for marker size encoding
- projection: str, map projection type
- scope: str, geographic scope ('world', 'usa', 'europe', etc.)
Returns:
Figure: Plotly figure object
"""
def choropleth(data_frame, geojson=None, featureidkey=None, locations=None,
color=None, facet_row=None, facet_col=None, facet_col_wrap=None,
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, locationmode=None, scope=None,
projection=None, center=None, title=None, template=None,
width=None, height=None):
"""
Create a choropleth map.
Parameters:
- data_frame: DataFrame, data to plot
- locations: str, column for region identifiers
- color: str, column for color encoding
- geojson: dict, GeoJSON data for custom regions
- locationmode: str, location matching mode ('ISO-3', 'USA-states', etc.)
- scope: str, geographic scope
Returns:
Figure: Plotly figure object
"""
def scatter_map(data_frame, lat=None, lon=None, locations=None, locationmode=None,
color=None, text=None, hover_name=None, hover_data=None,
custom_data=None, size=None, animation_frame=None,
animation_group=None, category_orders=None, labels=None,
color_discrete_sequence=None, color_discrete_map=None,
color_continuous_scale=None, range_color=None,
color_continuous_midpoint=None, opacity=None, size_max=None,
projection=None, scope=None, center=None, title=None,
template=None, width=None, height=None):
"""
Create a scatter plot on geographic map.
Parameters:
- data_frame: DataFrame, data to plot
- lat: str, column for latitude coordinates
- lon: str, column for longitude coordinates
- locations: str, column for location identifiers
- color: str, column for color encoding
- size: str, column for marker size encoding
- projection: str, map projection type
- scope: str, geographic scope ('world', 'usa', 'europe', etc.)
Returns:
Figure: Plotly figure object
"""
def line_map(data_frame, lat=None, lon=None, locations=None, locationmode=None,
color=None, text=None, hover_name=None, hover_data=None,
custom_data=None, line_group=None, animation_frame=None,
animation_group=None, category_orders=None, labels=None,
color_discrete_sequence=None, color_discrete_map=None,
projection=None, scope=None, center=None, title=None,
template=None, width=None, height=None):
"""
Create line traces on geographic map.
Parameters:
- data_frame: DataFrame, data to plot
- lat: str, column for latitude coordinates
- lon: str, column for longitude coordinates
- color: str, column for color encoding
- line_group: str, column to group lines by
- projection: str, map projection type
Returns:
Figure: Plotly figure object
"""
def line_geo(data_frame, lat=None, lon=None, locations=None, locationmode=None,
geojson=None, featureidkey=None, color=None, line_dash=None,
text=None, facet_row=None, facet_col=None, facet_col_wrap=None,
facet_row_spacing=None, facet_col_spacing=None, hover_name=None,
hover_data=None, custom_data=None, line_group=None,
animation_frame=None, animation_group=None, category_orders=None,
labels=None, color_discrete_sequence=None, color_discrete_map=None,
line_dash_sequence=None, line_dash_map=None, projection=None,
scope=None, center=None, title=None, template=None,
width=None, height=None):
"""
Create geographic line plot with advanced styling.
Parameters:
- data_frame: DataFrame, data to plot
- lat: str, column for latitude coordinates
- lon: str, column for longitude coordinates
- locations: str, column for location identifiers
- color: str, column for color encoding
- line_group: str, column to group lines by
- line_dash: str, column for line dash pattern encoding
Returns:
Figure: Plotly figure object
"""
def choropleth_map(data_frame, geojson=None, featureidkey=None, locations=None,
color=None, facet_row=None, facet_col=None, facet_col_wrap=None,
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, locationmode=None, scope=None,
projection=None, center=None, title=None, template=None,
width=None, height=None):
"""
Create choropleth map with geographic projections.
Parameters:
- data_frame: DataFrame, data to plot
- locations: str, column for region identifiers
- color: str, column for color encoding
- geojson: dict, GeoJSON data for custom regions
- locationmode: str, location matching mode
- projection: str, map projection type
Returns:
Figure: Plotly figure object
"""
def density_map(data_frame, lat=None, lon=None, z=None, radius=None,
hover_name=None, hover_data=None, custom_data=None,
animation_frame=None, animation_group=None, labels=None,
color_continuous_scale=None, range_color=None,
color_continuous_midpoint=None, opacity=None,
projection=None, scope=None, center=None, title=None,
template=None, width=None, height=None):
"""
Create density heatmap on geographic map.
Parameters:
- data_frame: DataFrame, data to plot
- lat: str, column for latitude coordinates
- lon: str, column for longitude coordinates
- z: str, column for density values
- radius: float, radius for density calculation
- projection: str, map projection type
Returns:
Figure: Plotly figure object
"""3D Visualization
Three-dimensional plotting capabilities for spatial data visualization.
def scatter_3d(data_frame, x=None, y=None, z=None, color=None, symbol=None,
size=None, text=None, hover_name=None, hover_data=None,
custom_data=None, error_x=None, error_x_minus=None, error_y=None,
error_y_minus=None, error_z=None, error_z_minus=None,
animation_frame=None, animation_group=None, category_orders=None,
labels=None, size_max=None, color_discrete_sequence=None,
color_discrete_map=None, color_continuous_scale=None,
range_color=None, color_continuous_midpoint=None, symbol_sequence=None,
symbol_map=None, opacity=None, log_x=False, log_y=False, log_z=False,
range_x=None, range_y=None, range_z=None, title=None, template=None,
width=None, height=None):
"""
Create a 3D scatter plot.
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for x-axis
- y: str, column for y-axis
- z: str, column for z-axis
- color: str, column for color encoding
- size: str, column for marker size encoding
- symbol: str, column for symbol encoding
Returns:
Figure: Plotly figure object
"""
def line_3d(data_frame, x=None, y=None, z=None, color=None, line_dash=None,
text=None, line_group=None, hover_name=None, hover_data=None,
custom_data=None, error_x=None, error_x_minus=None, error_y=None,
error_y_minus=None, error_z=None, error_z_minus=None,
animation_frame=None, animation_group=None, category_orders=None,
labels=None, color_discrete_sequence=None, color_discrete_map=None,
line_dash_sequence=None, line_dash_map=None, log_x=False,
log_y=False, log_z=False, range_x=None, range_y=None, range_z=None,
title=None, template=None, width=None, height=None):
"""
Create a 3D line plot.
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for x-axis
- y: str, column for y-axis
- z: str, column for z-axis
- color: str, column for color encoding
- line_group: str, column to group lines by
Returns:
Figure: Plotly figure object
"""Statistical Charts
Specialized functions for statistical data visualization and distribution analysis.
def violin(data_frame, x=None, y=None, color=None, facet_row=None, facet_col=None,
facet_col_wrap=None, facet_row_spacing=None, facet_col_spacing=None,
hover_name=None, hover_data=None, custom_data=None, animation_frame=None,
animation_group=None, category_orders=None, labels=None,
color_discrete_sequence=None, color_discrete_map=None, orientation=None,
violinmode='group', log_x=False, log_y=False, range_x=None,
range_y=None, points=None, box=None, title=None, template=None,
width=None, height=None):
"""
Create a violin plot.
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for categories
- y: str, column for continuous values
- color: str, column for color encoding
- points: str, point display mode ('outliers', 'suspectedoutliers', 'all', False)
- box: bool, whether to show box plot inside violin
Returns:
Figure: Plotly figure object
"""
def strip(data_frame, x=None, y=None, color=None, facet_row=None, facet_col=None,
facet_col_wrap=None, facet_row_spacing=None, facet_col_spacing=None,
hover_name=None, hover_data=None, custom_data=None, animation_frame=None,
animation_group=None, category_orders=None, labels=None,
color_discrete_sequence=None, color_discrete_map=None, orientation=None,
stripmode='group', log_x=False, log_y=False, range_x=None, range_y=None,
title=None, template=None, width=None, height=None):
"""
Create a strip chart (1D scatter plot).
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for categories
- y: str, column for continuous values
- color: str, column for color encoding
- stripmode: str, point grouping mode ('group', 'overlay')
Returns:
Figure: Plotly figure object
"""
def density_heatmap(data_frame, x=None, y=None, z=None, facet_row=None,
facet_col=None, facet_col_wrap=None, 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,
template=None, width=None, height=None):
"""
Create a 2D histogram heatmap.
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for x-axis
- y: str, column for y-axis
- z: str, optional column for aggregation values
- nbinsx: int, number of bins on x-axis
- nbinsy: int, number of bins on y-axis
- histfunc: str, aggregation function ('count', 'sum', 'avg', 'min', 'max')
Returns:
Figure: Plotly figure object
"""Specialized Charts
Unique chart types for specific visualization needs and hierarchical data.
def pie(data_frame, names=None, values=None, color=None, facet_row=None,
facet_col=None, facet_col_wrap=None, facet_row_spacing=None,
facet_col_spacing=None, color_discrete_sequence=None,
color_discrete_map=None, hover_name=None, hover_data=None,
custom_data=None, labels=None, title=None, template=None,
width=None, height=None):
"""
Create a pie chart.
Parameters:
- data_frame: DataFrame, data to plot
- names: str, column for pie slice labels
- values: str, column for pie slice values
- color: str, column for color encoding
Returns:
Figure: Plotly figure object
"""
def sunburst(data_frame, names=None, values=None, parents=None, ids=None,
path=None, color=None, color_continuous_scale=None,
range_color=None, color_continuous_midpoint=None,
color_discrete_sequence=None, color_discrete_map=None,
hover_name=None, hover_data=None, custom_data=None,
labels=None, title=None, template=None, width=None, height=None,
branchvalues=None, maxdepth=None):
"""
Create a sunburst chart for hierarchical data.
Parameters:
- data_frame: DataFrame, data to plot
- names: str, column for node names
- values: str, column for node values
- parents: str, column for parent node names
- path: list of str, columns defining hierarchy path
- ids: str, column for unique node identifiers
Returns:
Figure: Plotly figure object
"""
def treemap(data_frame, names=None, values=None, parents=None, ids=None,
path=None, color=None, color_continuous_scale=None,
range_color=None, color_continuous_midpoint=None,
color_discrete_sequence=None, color_discrete_map=None,
hover_name=None, hover_data=None, custom_data=None,
labels=None, title=None, template=None, width=None, height=None,
branchvalues=None, maxdepth=None):
"""
Create a treemap chart for hierarchical data.
Parameters:
- data_frame: DataFrame, data to plot
- names: str, column for rectangle names
- values: str, column for rectangle sizes
- parents: str, column for parent names
- path: list of str, columns defining hierarchy path
Returns:
Figure: Plotly figure object
"""
def icicle(data_frame, names=None, values=None, parents=None, ids=None,
path=None, color=None, color_continuous_scale=None,
range_color=None, color_continuous_midpoint=None,
color_discrete_sequence=None, color_discrete_map=None,
hover_name=None, hover_data=None, custom_data=None,
labels=None, title=None, template=None, width=None, height=None,
branchvalues=None, maxdepth=None):
"""
Create an icicle chart for hierarchical data.
Parameters:
- data_frame: DataFrame, data to plot
- names: str, column for node names
- values: str, column for node values
- parents: str, column for parent node names
- path: list of str, columns defining hierarchy path
- ids: str, column for unique node identifiers
Returns:
Figure: Plotly figure object
"""
def funnel_area(data_frame, names=None, values=None, color=None,
color_discrete_sequence=None, color_discrete_map=None,
hover_name=None, hover_data=None, custom_data=None,
labels=None, title=None, template=None, width=None, height=None):
"""
Create a funnel area chart.
Parameters:
- data_frame: DataFrame, data to plot
- names: str, column for funnel stage names
- values: str, column for funnel stage values
- color: str, column for color encoding
Returns:
Figure: Plotly figure object
"""Additional Chart Types
Extended chart types for specialized visualization needs.
def area(data_frame, x=None, y=None, line_group=None, color=None,
pattern_shape=None, facet_row=None, facet_col=None,
facet_col_wrap=None, 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,
pattern_shape_sequence=None, pattern_shape_map=None,
line_shape=None, groupnorm=None, log_x=False, log_y=False,
range_x=None, range_y=None, title=None, template=None,
width=None, height=None):
"""
Create an area chart.
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for x-axis
- y: str, column for y-axis
- line_group: str, column to group areas by
- color: str, column for color encoding
- groupnorm: str, normalization mode ('fraction', 'percent')
Returns:
Figure: Plotly figure object
"""
def timeline(data_frame, x_start=None, x_end=None, y=None, color=None,
pattern_shape=None, facet_row=None, facet_col=None,
facet_col_wrap=None, 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,
pattern_shape_sequence=None, pattern_shape_map=None,
opacity=None, range_x=None, range_y=None, title=None,
template=None, width=None, height=None):
"""
Create a timeline (Gantt) chart.
Parameters:
- data_frame: DataFrame, data to plot
- x_start: str, column for start times
- x_end: str, column for end times
- y: str, column for task names/categories
- color: str, column for color encoding
Returns:
Figure: Plotly figure object
"""
def ecdf(data_frame, x=None, y=None, color=None, text=None, line_dash=None,
symbol=None, facet_row=None, facet_col=None, facet_col_wrap=None,
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,
line_dash_sequence=None, line_dash_map=None, symbol_sequence=None,
symbol_map=None, margainal_x=None, marginal_y=None,
log_x=False, log_y=False, range_x=None, range_y=None,
title=None, template=None, width=None, height=None):
"""
Create an empirical cumulative distribution function (ECDF) plot.
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for continuous data
- y: str, alternative column for continuous data
- color: str, column for color encoding
- orientation: str, plot orientation ('v' or 'h')
Returns:
Figure: Plotly figure object
"""
def imshow(img, zmin=None, zmax=None, origin=None, labels=None, x=None, y=None,
animation_frame=None, facet_col=None, facet_col_wrap=None,
facet_col_spacing=None, facet_row_spacing=None, color_continuous_scale=None,
color_continuous_midpoint=None, range_color=None, title=None,
template=None, width=None, height=None, aspect=None,
contrast_rescaling=None, binary_string=None, binary_backend='auto',
binary_compression_level=None, binary_format='png', text_auto=False):
"""
Display an image using heatmap-style visualization.
Parameters:
- img: array-like, image data (2D or 3D array)
- zmin: float, minimum value for color scale
- zmax: float, maximum value for color scale
- origin: str, image origin ('lower' or 'upper')
- labels: dict, axis labels
- x: array-like, x-axis values
- y: array-like, y-axis values
Returns:
Figure: Plotly figure object
"""Mapbox Visualizations
Interactive map visualizations using Mapbox for geographic data with satellite imagery and street map backgrounds.
def scatter_mapbox(data_frame, lat=None, lon=None, color=None, text=None,
hover_name=None, hover_data=None, custom_data=None, size=None,
animation_frame=None, animation_group=None, category_orders=None,
labels=None, color_discrete_sequence=None, color_discrete_map=None,
color_continuous_scale=None, range_color=None,
color_continuous_midpoint=None, opacity=None, size_max=None,
zoom=None, center=None, mapbox_style=None, title=None,
template=None, width=None, height=None):
"""
Create a scatter plot on a Mapbox map.
Parameters:
- data_frame: DataFrame, data to plot
- lat: str, column for latitude coordinates
- lon: str, column for longitude coordinates
- color: str, column for color encoding
- size: str, column for marker size encoding
- mapbox_style: str, map style ('open-street-map', 'satellite', etc.)
- zoom: float, initial zoom level
- center: dict, map center coordinates {'lat': float, 'lon': float}
Returns:
Figure: Plotly figure object
"""
def line_mapbox(data_frame, lat=None, lon=None, color=None, text=None,
hover_name=None, hover_data=None, custom_data=None,
line_group=None, animation_frame=None, animation_group=None,
category_orders=None, labels=None, color_discrete_sequence=None,
color_discrete_map=None, zoom=None, center=None,
mapbox_style=None, title=None, template=None, width=None, height=None):
"""
Create line traces on a Mapbox map.
Parameters:
- data_frame: DataFrame, data to plot
- lat: str, column for latitude coordinates
- lon: str, column for longitude coordinates
- color: str, column for color encoding
- line_group: str, column to group lines by
- mapbox_style: str, map style
Returns:
Figure: Plotly figure object
"""
def choropleth_mapbox(data_frame, 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, mapbox_style=None, title=None, template=None,
width=None, height=None):
"""
Create a choropleth map on Mapbox.
Parameters:
- data_frame: DataFrame, data to plot
- geojson: dict, GeoJSON data for regions
- locations: str, column for location identifiers
- color: str, column for color encoding
- featureidkey: str, GeoJSON property for matching locations
Returns:
Figure: Plotly figure object
"""
def density_mapbox(data_frame, lat=None, lon=None, z=None, radius=None,
hover_name=None, hover_data=None, custom_data=None,
animation_frame=None, animation_group=None, labels=None,
color_continuous_scale=None, range_color=None,
color_continuous_midpoint=None, opacity=None,
zoom=None, center=None, mapbox_style=None, title=None,
template=None, width=None, height=None):
"""
Create a density heatmap on Mapbox.
Parameters:
- data_frame: DataFrame, data to plot
- lat: str, column for latitude coordinates
- lon: str, column for longitude coordinates
- z: str, column for density values
- radius: float, radius for density calculation
Returns:
Figure: Plotly figure object
"""Polar and Ternary Charts
Specialized coordinate system visualizations for polar and ternary data.
def scatter_polar(data_frame, r=None, theta=None, color=None, symbol=None,
size=None, hover_name=None, hover_data=None, custom_data=None,
text=None, animation_frame=None, animation_group=None,
category_orders=None, labels=None, color_discrete_sequence=None,
color_discrete_map=None, color_continuous_scale=None,
range_color=None, color_continuous_midpoint=None,
symbol_sequence=None, symbol_map=None, opacity=None,
direction='clockwise', start_angle=90, size_max=None,
range_r=None, range_theta=None, log_r=False,
title=None, template=None, width=None, height=None):
"""
Create a scatter plot in polar coordinates.
Parameters:
- data_frame: DataFrame, data to plot
- r: str, column for radial coordinates
- theta: str, column for angular coordinates
- color: str, column for color encoding
- size: str, column for marker size encoding
- direction: str, angular direction ('clockwise' or 'counterclockwise')
- start_angle: float, starting angle in degrees
Returns:
Figure: Plotly figure object
"""
def line_polar(data_frame, r=None, theta=None, color=None, line_dash=None,
hover_name=None, hover_data=None, custom_data=None, text=None,
line_group=None, animation_frame=None, animation_group=None,
category_orders=None, labels=None, color_discrete_sequence=None,
color_discrete_map=None, line_dash_sequence=None,
line_dash_map=None, direction='clockwise', start_angle=90,
line_close=False, line_shape=None, range_r=None,
range_theta=None, log_r=False, title=None, template=None,
width=None, height=None):
"""
Create line traces in polar coordinates.
Parameters:
- data_frame: DataFrame, data to plot
- r: str, column for radial coordinates
- theta: str, column for angular coordinates
- color: str, column for color encoding
- line_close: bool, whether to close the line
Returns:
Figure: Plotly figure object
"""
def bar_polar(data_frame, r=None, theta=None, color=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, range_color=None,
color_continuous_midpoint=None, barnorm=None, barmode='relative',
direction='clockwise', start_angle=90, range_r=None,
range_theta=None, log_r=False, title=None, template=None,
width=None, height=None):
"""
Create bar traces in polar coordinates.
Parameters:
- data_frame: DataFrame, data to plot
- r: str, column for radial values
- theta: str, column for angular categories
- color: str, column for color encoding
- barmode: str, bar grouping mode
Returns:
Figure: Plotly figure object
"""
def scatter_ternary(data_frame, a=None, b=None, c=None, color=None, symbol=None,
size=None, text=None, hover_name=None, hover_data=None,
custom_data=None, animation_frame=None, animation_group=None,
category_orders=None, labels=None, color_discrete_sequence=None,
color_discrete_map=None, color_continuous_scale=None,
range_color=None, color_continuous_midpoint=None,
symbol_sequence=None, symbol_map=None, opacity=None,
size_max=None, title=None, template=None, width=None, height=None):
"""
Create a scatter plot in ternary coordinates.
Parameters:
- data_frame: DataFrame, data to plot
- a: str, column for first ternary coordinate
- b: str, column for second ternary coordinate
- c: str, column for third ternary coordinate
- color: str, column for color encoding
- size: str, column for marker size encoding
Returns:
Figure: Plotly figure object
"""
def line_ternary(data_frame, a=None, b=None, c=None, color=None, line_dash=None,
hover_name=None, hover_data=None, custom_data=None, text=None,
line_group=None, animation_frame=None, animation_group=None,
category_orders=None, labels=None, color_discrete_sequence=None,
color_discrete_map=None, line_dash_sequence=None,
line_dash_map=None, line_shape=None, title=None,
template=None, width=None, height=None):
"""
Create line traces in ternary coordinates.
Parameters:
- data_frame: DataFrame, data to plot
- a: str, column for first ternary coordinate
- b: str, column for second ternary coordinate
- c: str, column for third ternary coordinate
- color: str, column for color encoding
- line_group: str, column to group lines by
Returns:
Figure: Plotly figure object
"""Matrix and Parallel Coordinate Charts
Specialized visualizations for high-dimensional data analysis and correlation exploration.
def scatter_matrix(data_frame, dimensions=None, color=None, symbol=None,
size=None, hover_name=None, hover_data=None, custom_data=None,
category_orders=None, labels=None, color_discrete_sequence=None,
color_discrete_map=None, color_continuous_scale=None,
range_color=None, color_continuous_midpoint=None,
symbol_sequence=None, symbol_map=None, opacity=None,
size_max=None, title=None, template=None, width=None, height=None):
"""
Create a scatter plot matrix for exploring relationships between multiple variables.
Parameters:
- data_frame: DataFrame, data to plot
- dimensions: list of str, columns to include in the matrix
- color: str, column for color encoding
- size: str, column for marker size encoding
- symbol: str, column for symbol encoding
Returns:
Figure: Plotly figure object
"""
def parallel_coordinates(data_frame, dimensions=None, color=None,
labels=None, color_continuous_scale=None,
range_color=None, color_continuous_midpoint=None,
title=None, template=None, width=None, height=None):
"""
Create a parallel coordinates plot for multivariate data visualization.
Parameters:
- data_frame: DataFrame, data to plot
- dimensions: list of str, columns to include as parallel axes
- color: str, column for color encoding lines
- color_continuous_scale: str or list, color scale for continuous data
Returns:
Figure: Plotly figure object
"""
def parallel_categories(data_frame, dimensions=None, color=None,
color_continuous_scale=None, labels=None,
dimensions_max_cardinality=50, title=None,
template=None, width=None, height=None):
"""
Create a parallel categories (parallel sets) plot for categorical data.
Parameters:
- data_frame: DataFrame, data to plot
- dimensions: list of str, categorical columns to include
- color: str, column for color encoding
- dimensions_max_cardinality: int, maximum number of unique values per dimension
Returns:
Figure: Plotly figure object
"""
def density_contour(data_frame, x=None, y=None, z=None, color=None,
facet_row=None, facet_col=None, facet_col_wrap=None,
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=None,
log_x=False, log_y=False, range_x=None, range_y=None,
histfunc=None, histnorm=None, nbinsx=None, nbinsy=None,
title=None, template=None, width=None, height=None):
"""
Create a 2D density contour plot.
Parameters:
- data_frame: DataFrame, data to plot
- x: str, column for x-axis
- y: str, column for y-axis
- z: str, optional column for contour values
- nbinsx: int, number of bins on x-axis
- nbinsy: int, number of bins on y-axis
- histfunc: str, aggregation function for z values
Returns:
Figure: Plotly figure object
"""Usage Examples
import plotly.express as px
import pandas as pd
# Basic scatter plot with color encoding
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length",
color="species", title="Iris Dataset")
fig.show()
# Geographic visualization
df_geo = px.data.gapminder().query("year==2007")
fig = px.choropleth(df_geo, locations="iso_alpha",
color="lifeExp", hover_name="country",
color_continuous_scale=px.colors.sequential.Plasma)
fig.show()
# 3D scatter plot with multiple encodings
df_3d = px.data.tips()
fig = px.scatter_3d(df_3d, x="total_bill", y="tip", z="size",
color="day", symbol="time")
fig.show()
# Animated scatter plot
df_anim = px.data.gapminder()
fig = px.scatter(df_anim, x="gdpPercap", y="lifeExp",
animation_frame="year", animation_group="country",
size="pop", color="continent", hover_name="country",
log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90])
fig.show()Install with Tessl CLI
npx tessl i tessl/pypi-plotly