name = 'Month' # reshape to 1D array or rates with a month and year for each row. drop ( 'Annual', axis = 1, inplace = True ) data. Import pandas as pd from bokeh.io import output_file, show from bokeh.models import ( BasicTicker, ColorBar, ColumnDataSource, LinearColorMapper, PrintfTickFormatter ) from otting import figure from 1948 import data from ansform import transform output_file ( "unemploymemt.html" ) data. Passed to the color bar to provide a visual legend on the right: 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 = 700, x_range = ( - 5, 105 ), toolbar_location = None ) for i, cat in enumerate ( reversed ( cats )): pdf = gaussian_kde ( probly ) y = ridge ( cat, pdf ( x )) source. Import colorcet as cc from numpy import linspace from import gaussian_kde from bokeh.io import output_file, show from bokeh.models import ColumnDataSource, FixedTicker, PrintfTickFormatter from otting import figure from import probly output_file ( "ridgeplot.html" ) def ridge ( category, data, scale = 20 ): return list ( zip ( * len ( data ), scale * data )) cats = list ( reversed ( probly. axis_label = "Manufacturer grouped by # Cylinders" p. vbar ( x = 'cyl_mfr', top = 'mpg_mean', width = 1, source = group, line_color = "white", fill_color = index_cmap, ) p. unique ()), end = 1 ) p = figure ( width = 800, height = 300, title = "Mean MPG by # cylinders and manufacturer", x_range = group, toolbar_location = None, tooltips = ) p. groupby ( by = ) index_cmap = factor_cmap ( 'cyl_mfr', palette = Spectral5, factors = sorted ( df. orientation = "horizontal" show ( p )įrom bokeh.io import output_file, show from bokeh.palettes import Spectral5 from otting import figure from import autompg_clean as df from ansform import factor_cmap output_file ( "bar_pandas_groupby_nested.html" ) df. vbar_stack ( regions, x = 'x', width = 0.9, alpha = 0.5, color =, source = source, legend_label = regions ) p. orientation = "horizontal" show ( p )įrom bokeh.io import output_file, show from bokeh.models import ColumnDataSource, FactorRange from otting import figure output_file ( "bar_stacked_grouped.html" ) factors = regions = source = ColumnDataSource ( data = dict ( x = factors, east =, west =, )) p = figure ( x_range = FactorRange ( * factors ), height = 250, toolbar_location = None, tools = "" ) p. vbar ( x = dodge ( 'fruits', 0.25, range = p. vbar ( x = dodge ( 'fruits', 0.0, range = p. vbar ( x = dodge ( 'fruits', - 0.25, range = p. The example below shows a sequence of simpleįrom bokeh.io import output_file, show from bokeh.models import ColumnDataSource from bokeh.palettes import GnBu3, OrRd3 from otting import figure output_file ( "stacked_split.html" ) fruits = years = exports = source = ColumnDataSource ( data = data ) p = figure ( x_range = fruits, y_range = ( 0, 10 ), height = 250, title = "Fruit counts by year", toolbar_location = None, tools = "" ) p. To create a basic bar chart, use the hbar() (horizontal bars) or vbar() This section will demonstrate how to draw a variety ofĭifferent categorical bar charts. The length of this bar along the continuous axis corresponds toīar charts may also be stacked or grouped together according to hierarchical The values associated with each category are represented by drawing a bar for BarĬharts are useful when there is one value to plot for each category. Bar charts have one categorical axis and one continuous axis. One of the most common ways to handle categorical data is to present it in aīar chart. Present several kinds of common plot types for categorical data. Months_by_quarter = ĭepending on the structure of your data, you can use different kinds of charts:īar charts, categorical heatmaps, jitter plots, and others.
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