![]() ![]() ![]() Inverting axes: Flipping the x-/y-axes and inverting an axisĪxis labels: Setting axis labels using dimensions and optionsĪxis ranges: Controlling axes ranges using dimensions, padding and optionsĪxis ticks: Controlling axis tick locations, labels and formattingĪ plot’s title is usually constructed using a formatter which takes the group and label along with the plots dimensions into consideration. Plot hooks: Using custom hooks to modify plotsĪxes: A set of axes provides scales describing the mapping between data and the space on screenĪxis position: Positioning and hiding axes Legends: Controlling the position and styling of the legend ![]() Titles: Using title formatting and providing custom titlesīackground: Setting the plot background colorįont sizes: Controlling the font sizes on a plot Plot: Refers to the overall plot which can consist of one or more axes Plots have an overall hierarchy and here we will break down the different components: Specifically this guide provides an overview on controlling the various aspects of a plot including titles, axes, legends and colorbars. While different plotting extensions like bokeh, matplotlib and plotly offer different features and the style options may differ, there are a wide array of options and concepts that are shared across the different extensions. The HoloViews options system allows controlling the various attributes of a plot. text ( x =, y =, text =, text_align = "center", text_baseline = "middle" ) p. y_range ), text = "atomic mass", text_font_size = "7px", ** text_props ) p. text ( x = x, y = dodge ( "period", - 0.2, range = p. y_range ), text = "name", text_font_size = "7px", ** text_props ) p. text ( x = x, y = dodge ( "period", - 0.35, range = p. y_range ), text = "atomic number", text_font_size = "11px", ** text_props ) p. text ( x = x, y = dodge ( "period", 0.3, range = p. text ( x = x, y = "period", text = "symbol", text_font_style = "bold", ** text_props ) p. keys ()))) text_props = dict ( source = df, text_align = "left", text_baseline = "middle" ) x = dodge ( "group", - 0.4, range = p. rect ( "group", "period", 0.95, 0.95, source = df, fill_alpha = 0.6, legend_field = "metal", color = factor_cmap ( 'metal', palette = list ( cmap. reset_index () # this is the colormap from the original NYTimes plot colors = TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom" p = figure ( title = f "US Unemployment ( " ), ] p = figure ( title = "Periodic Table (omitting LA and AC Series)", width = 1000, height = 450, x_range = groups, y_range = list ( reversed ( periods )), tools = "hover", toolbar_location = None, tooltips = TOOLTIPS ) r = p. columns )) # reshape to 1D array or rates with a month and year for each row. drop ( 'Annual', axis = 1, inplace = True ) data. Passed to the color bar to provide a visual legend on the right:įrom math import pi import pandas as pd from bokeh.models import BasicTicker, PrintfTickFormatter from otting import figure, show from 1948 import data from ansform import linear_cmap data = data. This example uses the LinearColorMapper to map the colors of the plotīecause the unemployment rate is a continuous variable. Unemployment in a given month of a given year. The color of the rectangle indicates the rate of Each rectangle of the plot corresponds to a The following plot lists years from 1948 to 2016 on its x-axis and months of ![]() Of categories will produce a categorical heatmap. Situation, applying different color shades to rectangles that represent a pair It is possible to have values associated with pairs of categories. range_padding = 0.12 show ( p ) Heatmaps # formatter = PrintfTickFormatter ( format = " %d%% " ) p. ticker = FixedTicker ( ticks = list ( range ( 0, 101, 10 ))) p. patch ( 'x', cat, color = palette, alpha = 0.6, line_color = "black", source = source ) p. keys ())) palette = for i in range ( 17 )] x = linspace ( - 20, 110, 500 ) source = ColumnDataSource ( data = dict ( x = x )) p = figure ( y_range = cats, width = 900, x_range = ( - 5, 105 ), toolbar_location = None ) for i, cat in enumerate ( reversed ( cats )): pdf = gaussian_kde ( probly ) y = ridge ( cat, pdf ( x )) source. Well start by making a scatter plot of adhesive force versus impact. Import colorcet as cc from numpy import linspace from scipy.stats import gaussian_kde from bokeh.models import ColumnDataSource, FixedTicker, PrintfTickFormatter from otting import figure, show from import probly def ridge ( category, data, scale = 20 ): return list ( zip ( * len ( data ), scale * data )) cats = list ( reversed ( probly. import numpy as np import pandas as pd import altair as alt import bootcamputils. ![]()
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