注解
本章中的几个例子使用 Pandas ,以便于表示,并因为它是数据操作的常用工具。然而, Pandas 不需要创建此处显示的任何内容。
Pandas
Bokeh使得使用 hbar() 和 vbar() 字形方法。在下面的示例中,我们有以下一系列简单的1级因子:
hbar()
vbar()
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
为了告诉Bokeh x轴是绝对的,我们将这个因素列表作为 x_range 参数 figure() :
x_range
figure()
p = figure(x_range=fruits, ... )
注意,传递因子列表是创建 FactorRange . 等效显式符号为:
FactorRange
p = figure(x_range=FactorRange(factors=fruits), ... )
如果要自定义 FactorRange ,例如更改范围或类别填充。
接下来,我们可以打电话 vbar 以水果名称因素列表作为 x 坐标,条形高度为 top 坐标,可选任何 width 或我们希望设置的其他属性:
vbar
x
top
width
p.vbar(x=fruits, top=[5, 3, 4, 2, 4, 6], width=0.9)
综合起来,我们可以看到输出:
from bokeh.io import output_file, show from bokeh.plotting import figure output_file("bars.html") fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] counts = [5, 3, 4, 2, 4, 6] p = figure(x_range=fruits, plot_height=250, title="Fruit Counts", toolbar_location=None, tools="") p.vbar(x=fruits, top=counts, width=0.9) p.xgrid.grid_line_color = None p.y_range.start = 0 show(p)
像往常一样,数据也可以放入 ColumnDataSource 作为 source 参数到 vbar 而不是直接将数据作为参数传递。后面的例子将证明这一点。
ColumnDataSource
source
由于Bokeh按范围的顺序显示条形图,因此条形图中的“排序”栏与对范围的因子进行排序相同。
在下面的示例中,水果因子根据其相应的计数按递增顺序排序,从而对条进行排序:
from bokeh.io import output_file, show from bokeh.plotting import figure output_file("bar_sorted.html") fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] counts = [5, 3, 4, 2, 4, 6] # sorting the bars means sorting the range factors sorted_fruits = sorted(fruits, key=lambda x: counts[fruits.index(x)]) p = figure(x_range=sorted_fruits, plot_height=350, title="Fruit Counts", toolbar_location=None, tools="") p.vbar(x=fruits, top=counts, width=0.9) p.xgrid.grid_line_color = None p.y_range.start = 0 show(p)
通常,我们可能希望有一些阴影颜色的酒吧。这可以通过不同的方式实现。一种方法是预先提供所有颜色。这可以通过将所有数据(包括每个条的颜色)放入 ColumnDataSource. Then the name of the column containing the colors is passed to vbar as the color (or line_color/fill_color )争论。如下所示:
color
line_color
fill_color
from bokeh.io import output_file, show from bokeh.models import ColumnDataSource from bokeh.palettes import Spectral6 from bokeh.plotting import figure output_file("colormapped_bars.html") fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] counts = [5, 3, 4, 2, 4, 6] source = ColumnDataSource(data=dict(fruits=fruits, counts=counts, color=Spectral6)) p = figure(x_range=fruits, y_range=(0,9), plot_height=250, title="Fruit Counts", toolbar_location=None, tools="") p.vbar(x='fruits', top='counts', width=0.9, color='color', legend_field="fruits", source=source) p.xgrid.grid_line_color = None p.legend.orientation = "horizontal" p.legend.location = "top_center" show(p)
另一种方法是使用 CategoricalColorMapper 颜色映射浏览器中的条形图。有一个函数 factor_cmap() 这样做很简单:
CategoricalColorMapper
factor_cmap()
factor_cmap('fruits', palette=Spectral6, factors=fruits)
这可以传递给 vbar 与上一个示例中的列名相同。把所有的东西放在一起,我们用不同的方式得到同样的情节:
from bokeh.io import output_file, show from bokeh.models import ColumnDataSource from bokeh.palettes import Spectral6 from bokeh.plotting import figure from bokeh.transform import factor_cmap output_file("colormapped_bars.html") fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] counts = [5, 3, 4, 2, 4, 6] source = ColumnDataSource(data=dict(fruits=fruits, counts=counts)) p = figure(x_range=fruits, plot_height=250, toolbar_location=None, title="Fruit Counts") p.vbar(x='fruits', top='counts', width=0.9, source=source, legend_field="fruits", line_color='white', fill_color=factor_cmap('fruits', palette=Spectral6, factors=fruits)) p.xgrid.grid_line_color = None p.y_range.start = 0 p.y_range.end = 9 p.legend.orientation = "horizontal" p.legend.location = "top_center" show(p)
条形图上的另一个常见操作是相互堆叠条形图。Bokeh使这一点很容易与专业人员做 hbar_stack() 和 vbar_stack() 功能。下面的示例显示了上面的水果数据,但每种水果类型的条形图都是堆叠的,而不是分组的:
hbar_stack()
vbar_stack()
from bokeh.io import output_file, show from bokeh.plotting import figure output_file("stacked.html") fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] years = ["2015", "2016", "2017"] colors = ["#c9d9d3", "#718dbf", "#e84d60"] data = {'fruits' : fruits, '2015' : [2, 1, 4, 3, 2, 4], '2016' : [5, 3, 4, 2, 4, 6], '2017' : [3, 2, 4, 4, 5, 3]} p = figure(x_range=fruits, plot_height=250, title="Fruit Counts by Year", toolbar_location=None, tools="") p.vbar_stack(years, x='fruits', width=0.9, color=colors, source=data, legend_label=years) p.y_range.start = 0 p.x_range.range_padding = 0.1 p.xgrid.grid_line_color = None p.axis.minor_tick_line_color = None p.outline_line_color = None p.legend.location = "top_left" p.legend.orientation = "horizontal" show(p)
请注意,在幕后,这些函数的工作原理是将连续的列堆叠在单独的调用中 vbar 或 hbar . 这种操作类似于上面的dodge示例(即本例中的数据是 not 以“整洁”的数据格式)。
hbar
有时我们可能希望堆叠同时具有正范围和负范围的条。下面的示例显示了如何创建按正值和负值拆分的堆积条形图:
from bokeh.io import output_file, show from bokeh.models import ColumnDataSource from bokeh.palettes import GnBu3, OrRd3 from bokeh.plotting import figure output_file("stacked_split.html") fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] years = ["2015", "2016", "2017"] exports = {'fruits' : fruits, '2015' : [2, 1, 4, 3, 2, 4], '2016' : [5, 3, 4, 2, 4, 6], '2017' : [3, 2, 4, 4, 5, 3]} imports = {'fruits' : fruits, '2015' : [-1, 0, -1, -3, -2, -1], '2016' : [-2, -1, -3, -1, -2, -2], '2017' : [-1, -2, -1, 0, -2, -2]} p = figure(y_range=fruits, plot_height=250, x_range=(-16, 16), title="Fruit import/export, by year", toolbar_location=None) p.hbar_stack(years, y='fruits', height=0.9, color=GnBu3, source=ColumnDataSource(exports), legend_label=["%s exports" % x for x in years]) p.hbar_stack(years, y='fruits', height=0.9, color=OrRd3, source=ColumnDataSource(imports), legend_label=["%s imports" % x for x in years]) p.y_range.range_padding = 0.1 p.ygrid.grid_line_color = None p.legend.location = "top_left" p.axis.minor_tick_line_color = None p.outline_line_color = None show(p)
对于堆积条形图,Bokeh提供了一些特殊的悬停变量,这些变量对于常见情况很有用。
堆叠钢筋时,Bokeh会自动设置 name 属性设置为该层的堆栈列的值。悬停工具可以通过 $name 特殊变量。
name
$name
另外,hover变量 @$name 可以用于从堆栈列中查找每个层的值。例如,如果用户悬停在具有名称的堆栈图示符上 "US East" 然后 @$name 等于 @{{US East}} .
@$name
"US East"
@{{US East}}
下面的示例演示了这两个悬停变量:
from bokeh.io import output_file, show from bokeh.plotting import figure output_file("stacked.html") fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] years = ["2015", "2016", "2017"] colors = ["#c9d9d3", "#718dbf", "#e84d60"] data = {'fruits' : fruits, '2015' : [2, 1, 4, 3, 2, 4], '2016' : [5, 3, 4, 2, 4, 6], '2017' : [3, 2, 4, 4, 5, 3]} p = figure(x_range=fruits, plot_height=250, title="Fruit Counts by Year", toolbar_location=None, tools="hover", tooltips="$name @fruits: @$name") p.vbar_stack(years, x='fruits', width=0.9, color=colors, source=data, legend_label=years) p.y_range.start = 0 p.x_range.range_padding = 0.1 p.xgrid.grid_line_color = None p.axis.minor_tick_line_color = None p.outline_line_color = None p.legend.location = "top_left" p.legend.orientation = "horizontal" show(p)
请注意,也可以重写 name 通过手动传递给 vbar_stack 和 hbar_stack . 在这种情况下, $@name 将查找用户提供的列名。
vbar_stack
hbar_stack
$@name
有时还需要为堆栈中的每个层使用不同的悬停工具。对于这种情况 hbar_stack 和 vbar_stack 函数返回创建的所有渲染器的列表(每个堆栈一个)。这些工具可用于为每个层自定义不同的悬停工具:
renderers = p.vbar_stack(years, x='fruits', width=0.9, color=colors, source=source, legend=[value(x) for x in years], name=years) for r in renderers: year = r.name hover = HoverTool(tooltips=[ ("%s total" % year, "@%s" % year), ("index", "$index") ], renderers=[r]) p.add_tools(hover)
创建条形图时,通常需要根据子组直观地显示数据。根据您的用例,可以使用两种基本方法:使用嵌套的分类坐标或应用视觉闪避。
如果绘图区域和数据的坐标有两个或三个级别,Bokeh将自动将因子分组到轴上,包括在组之间使用分隔符的分层刻度标签。在条形图的情况下,这会导致条形图按顶级因素分组在一起。这可能是实现分组条的最常见方法,尤其是当您从“整洁”数据开始时。
下面的示例通过创建一列坐标来展示这种方法,每个列都是表单的两个元组 (fruit, year) . 因此,该图按水果类型对轴进行分组,只调用 vbar :
(fruit, year)
from bokeh.io import output_file, show from bokeh.models import ColumnDataSource, FactorRange from bokeh.plotting import figure output_file("bars.html") fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] years = ['2015', '2016', '2017'] data = {'fruits' : fruits, '2015' : [2, 1, 4, 3, 2, 4], '2016' : [5, 3, 3, 2, 4, 6], '2017' : [3, 2, 4, 4, 5, 3]} # this creates [ ("Apples", "2015"), ("Apples", "2016"), ("Apples", "2017"), ("Pears", "2015), ... ] x = [ (fruit, year) for fruit in fruits for year in years ] counts = sum(zip(data['2015'], data['2016'], data['2017']), ()) # like an hstack source = ColumnDataSource(data=dict(x=x, counts=counts)) p = figure(x_range=FactorRange(*x), plot_height=250, title="Fruit Counts by Year", toolbar_location=None, tools="") p.vbar(x='x', top='counts', width=0.9, source=source) p.y_range.start = 0 p.x_range.range_padding = 0.1 p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None show(p)
我们还可以应用颜色映射,类似于前面的示例。要获得与上述相同的水果分组条形图数据,除了带有年份阴影的条形图,请更改 vbar 要使用的函数调用 factor_cmap 对于 fill_color :
factor_cmap
p.vbar(x='x', top='counts', width=0.9, source=source, line_color="white", # use the palette to colormap based on the the x[1:2] values fill_color=factor_cmap('x', palette=palette, factors=years, start=1, end=2))
回想一下,这些因素是有利的 (fruit, year) . 这个 start=1 和 end=2 在召唤 factor_cmap 选择颜色映射时要使用的数据因子的第二部分。
start=1
end=2
实现成组钢筋的另一种方法是明确指定钢筋的视觉位移。这种视觉偏移也被称为 闪避 .
在这种情况下,我们的数据并不“整洁”。而不是一个按因子索引行的表 (fruit, year) ,我们每年都有单独的系列。我们可以使用单独的调用来绘制所有年份系列 vbar ,但因为每组的每个酒吧都有相同的 fruit 因素是,这些条会在视觉上重叠。我们可以防止这种重叠,并通过使用 dodge() 函数为每个不同的调用提供偏移量 vbar :
fruit
dodge()
from bokeh.io import output_file, show from bokeh.models import ColumnDataSource from bokeh.plotting import figure from bokeh.transform import dodge output_file("dodged_bars.html") fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] years = ['2015', '2016', '2017'] data = {'fruits' : fruits, '2015' : [2, 1, 4, 3, 2, 4], '2016' : [5, 3, 3, 2, 4, 6], '2017' : [3, 2, 4, 4, 5, 3]} source = ColumnDataSource(data=data) p = figure(x_range=fruits, y_range=(0, 10), plot_height=250, title="Fruit Counts by Year", toolbar_location=None, tools="") p.vbar(x=dodge('fruits', -0.25, range=p.x_range), top='2015', width=0.2, source=source, color="#c9d9d3", legend_label="2015") p.vbar(x=dodge('fruits', 0.0, range=p.x_range), top='2016', width=0.2, source=source, color="#718dbf", legend_label="2016") p.vbar(x=dodge('fruits', 0.25, range=p.x_range), top='2017', width=0.2, source=source, color="#e84d60", legend_label="2017") p.x_range.range_padding = 0.1 p.xgrid.grid_line_color = None p.legend.location = "top_left" p.legend.orientation = "horizontal" show(p)
上述堆叠和分组的技术也可一起用于创建堆叠的分组条形图。
继续上面的例子,使用四分之一分组的条,我们可以按区域堆叠每个单独的条。
from bokeh.io import output_file, show from bokeh.models import ColumnDataSource, FactorRange from bokeh.plotting import figure output_file("bar_stacked_grouped.html") factors = [ ("Q1", "jan"), ("Q1", "feb"), ("Q1", "mar"), ("Q2", "apr"), ("Q2", "may"), ("Q2", "jun"), ("Q3", "jul"), ("Q3", "aug"), ("Q3", "sep"), ("Q4", "oct"), ("Q4", "nov"), ("Q4", "dec"), ] regions = ['east', 'west'] source = ColumnDataSource(data=dict( x=factors, east=[ 5, 5, 6, 5, 5, 4, 5, 6, 7, 8, 6, 9 ], west=[ 5, 7, 9, 4, 5, 4, 7, 7, 7, 6, 6, 7 ], )) p = figure(x_range=FactorRange(*factors), plot_height=250, toolbar_location=None, tools="") p.vbar_stack(regions, x='x', width=0.9, alpha=0.5, color=["blue", "red"], source=source, legend_label=regions) p.y_range.start = 0 p.y_range.end = 18 p.x_range.range_padding = 0.1 p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None p.legend.location = "top_center" p.legend.orientation = "horizontal" show(p)
当处理两个或三个级别的层次类别时,可以只使用坐标的“较高级别”部分来定位图示符。例如,如果你有层次因素的范围
factors = [ ("East", "Sales"), ("East", "Marketing"), ("East", "Dev"), ("West", "Sales"), ("West", "Marketing"), ("West", "Dev"), ]
那么就可以使用 "Sales" 和 "Marketing" 等。作为字形的位置。在这种情况下,位置是整个组的中心。下面的示例显示了每个月的条形图,按财务季度分组,并在 Q1 , Q2 等等:
Q1
Q2
from bokeh.io import output_file, show from bokeh.models import FactorRange from bokeh.plotting import figure output_file("mixed.html") factors = [ ("Q1", "jan"), ("Q1", "feb"), ("Q1", "mar"), ("Q2", "apr"), ("Q2", "may"), ("Q2", "jun"), ("Q3", "jul"), ("Q3", "aug"), ("Q3", "sep"), ("Q4", "oct"), ("Q4", "nov"), ("Q4", "dec"), ] p = figure(x_range=FactorRange(*factors), plot_height=250, toolbar_location=None, tools="") x = [ 10, 12, 16, 9, 10, 8, 12, 13, 14, 14, 12, 16 ] p.vbar(x=factors, top=x, width=0.9, alpha=0.5) p.line(x=["Q1", "Q2", "Q3", "Q4"], y=[12, 9, 13, 14], color="red", line_width=2) p.y_range.start = 0 p.x_range.range_padding = 0.1 p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None show(p)
这个例子还演示了其他字形(如直线)也可以使用范畴坐标。
Pandas 是在Python中对表格和timeseries数据进行数据分析的一个强大而通用的工具。虽然不是 必修的 Bokeh,Bokeh试图让你的生活更轻松。
以下是一个情节,展示了在Bokeh使用熊猫时的一些优势:
Pandas GroupBy 对象可用于初始化 ColumnDataSource ,为许多统计指标(如组平均值或计数)自动创建列
GroupBy
GroupBy 对象也可以作为范围参数直接传递给 figure .
figure
from bokeh.io import output_file, show from bokeh.models import ColumnDataSource from bokeh.palettes import Spectral5 from bokeh.plotting import figure from bokeh.sampledata.autompg import autompg as df from bokeh.transform import factor_cmap output_file("groupby.html") df.cyl = df.cyl.astype(str) group = df.groupby('cyl') source = ColumnDataSource(group) cyl_cmap = factor_cmap('cyl', palette=Spectral5, factors=sorted(df.cyl.unique())) p = figure(plot_height=350, x_range=group, title="MPG by # Cylinders", toolbar_location=None, tools="") p.vbar(x='cyl', top='mpg_mean', width=1, source=source, line_color=cyl_cmap, fill_color=cyl_cmap) p.y_range.start = 0 p.xgrid.grid_line_color = None p.xaxis.axis_label = "some stuff" p.xaxis.major_label_orientation = 1.2 p.outline_line_color = None show(p)
注意,在上面的示例中,我们按列分组 'cyl' ,所以我们的CD有一个专栏 'cyl' 对于此索引。此外,其他非分组列,如 'mpg' 有关联的列,如 'mpg_mean' 另外,这给出了每个组的平均MPG值。
'cyl'
'mpg'
'mpg_mean'
当分组是多级分组时,这种用法也适用。下面的示例演示如何按 ('cyl', 'mfr') 结果生成层次嵌套的轴。在本例中,索引列名 'cyl_mfr' 通过将分组列的名称连接在一起而生成。
('cyl', 'mfr')
'cyl_mfr'
from bokeh.io import output_file, show from bokeh.palettes import Spectral5 from bokeh.plotting import figure from bokeh.sampledata.autompg import autompg_clean as df from bokeh.transform import factor_cmap output_file("bar_pandas_groupby_nested.html") df.cyl = df.cyl.astype(str) df.yr = df.yr.astype(str) group = df.groupby(by=['cyl', 'mfr']) index_cmap = factor_cmap('cyl_mfr', palette=Spectral5, factors=sorted(df.cyl.unique()), end=1) p = figure(plot_width=800, plot_height=300, title="Mean MPG by # Cylinders and Manufacturer", x_range=group, toolbar_location=None, tooltips=[("MPG", "@mpg_mean"), ("Cyl, Mfr", "@cyl_mfr")]) p.vbar(x='cyl_mfr', top='mpg_mean', width=1, source=group, line_color="white", fill_color=index_cmap, ) p.y_range.start = 0 p.x_range.range_padding = 0.05 p.xgrid.grid_line_color = None p.xaxis.axis_label = "Manufacturer grouped by # Cylinders" p.xaxis.major_label_orientation = 1.2 p.outline_line_color = None show(p)
我们已经看到了一个常用的条形图,用来表示我们已经绘制了一个条形图。但是,条形图示符也可以用来表示一个范围内的任意间隔。
下面的示例使用 hbar 两者兼而有之 left 和 right 提供物业,以显示多年来奥运会短跑铜牌和金牌得主之间的时间差距:
left
right
from bokeh.io import output_file, show from bokeh.models import ColumnDataSource from bokeh.plotting import figure from bokeh.sampledata.sprint import sprint output_file("sprint.html") sprint.Year = sprint.Year.astype(str) group = sprint.groupby('Year') source = ColumnDataSource(group) p = figure(y_range=group, x_range=(9.5,12.7), plot_width=400, plot_height=550, toolbar_location=None, title="Time Spreads for Sprint Medalists (by Year)") p.hbar(y="Year", left='Time_min', right='Time_max', height=0.4, source=source) p.ygrid.grid_line_color = None p.xaxis.axis_label = "Time (seconds)" p.outline_line_color = None show(p)
当在一个分类类别中绘制多个散点时,点开始在视觉上重叠是很常见的。在这种情况下,Bokeh提供了一个 jitter() 函数可以自动对每个点应用随机闪避。
jitter()
下面的示例显示了2012年至2016年期间GitHub用户每次提交时间的散点图,按星期几分组。对这些数据的一个天真的绘图会导致每天数千个点重叠在一条窄线上。通过使用 jitter 我们可以区分这些点以获得有用的图:
jitter
from bokeh.io import output_file, show from bokeh.models import ColumnDataSource from bokeh.plotting import figure from bokeh.sampledata.commits import data from bokeh.transform import jitter output_file("bars.html") DAYS = ['Sun', 'Sat', 'Fri', 'Thu', 'Wed', 'Tue', 'Mon'] source = ColumnDataSource(data) p = figure(plot_width=800, plot_height=300, y_range=DAYS, x_axis_type='datetime', title="Commits by Time of Day (US/Central) 2012—2016") p.circle(x='time', y=jitter('day', width=0.6, range=p.y_range), source=source, alpha=0.3) p.xaxis[0].formatter.days = ['%Hh'] p.x_range.range_padding = 0 p.ygrid.grid_line_color = None show(p)
我们已经在上面看到了分类位置是如何被操作修改的,比如 闪避 和 抖动 . 也可以显式地为分类位置提供偏移量。这是通过在类别末尾添加一个数值来完成的,例如。 ["Jan", 0.2] 类别“Jan”的偏移量为0.2。对于分层类别,该值将添加到现有列表的末尾,例如。 ["West", "Sales", -0,2] . 类别列表末尾的任何数值总是被解释为偏移量。
["Jan", 0.2]
["West", "Sales", -0,2]
作为一个例子,假设我们从一开始就采用了第一个示例,并对其进行了如下修改:
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] offsets = [-0.5, -0.2, 0.0, 0.3, 0.1, 0.3] # This results in [ ['Apples', -0.5], ['Pears', -0.2], ... ] x = list(zip(fruits, offsets)) p.vbar(x=x, top=[5, 3, 4, 2, 4, 6], width=0.8)
然后,结果图中的条形图会水平移动每个相应偏移量:
下面是一个更复杂的山脊图示例,显示与不同类别相关的时间序列。它使用分类偏移量为每个类别内的时间序列指定面片坐标。
import colorcet as cc from numpy import linspace from scipy.stats.kde import gaussian_kde from bokeh.io import output_file, show from bokeh.models import ColumnDataSource, FixedTicker, PrintfTickFormatter from bokeh.plotting import figure from bokeh.sampledata.perceptions import probly output_file("ridgeplot.html") def ridge(category, data, scale=20): return list(zip([category]*len(data), scale*data)) cats = list(reversed(probly.keys())) palette = [cc.rainbow[i*15] for i in range(17)] x = linspace(-20,110, 500) source = ColumnDataSource(data=dict(x=x)) p = figure(y_range=cats, plot_width=700, x_range=(-5, 105), toolbar_location=None) for i, cat in enumerate(reversed(cats)): pdf = gaussian_kde(probly[cat]) y = ridge(cat, pdf(x)) source.add(y, cat) p.patch('x', cat, color=palette[i], alpha=0.6, line_color="black", source=source) p.outline_line_color = None p.background_fill_color = "#efefef" p.xaxis.ticker = FixedTicker(ticks=list(range(0, 101, 10))) p.xaxis.formatter = PrintfTickFormatter(format="%d%%") p.ygrid.grid_line_color = None p.xgrid.grid_line_color = "#dddddd" p.xgrid.ticker = p.xaxis[0].ticker p.axis.minor_tick_line_color = None p.axis.major_tick_line_color = None p.axis.axis_line_color = None p.y_range.range_padding = 0.12 show(p)
在以上所有的例子中,我们有一个范畴轴和一个连续轴。有可能有两个分类轴的图。如果我们对定义每对类别的矩形进行着色处理,则最终得到一个 范畴热图
下面的图显示了这样一个图,其中x轴类别是1948年至2016年的年份列表,y轴类别是年份中的月份。每个矩形对应于 (year, month) 这个组合是根据当月和当年的失业率绘制的。由于失业率是一个连续变量,因此 LinearColorMapper 颜色条也被用来在地图上提供一个颜色:
(year, month)
LinearColorMapper
import pandas as pd from bokeh.io import output_file, show from bokeh.models import (BasicTicker, ColorBar, ColumnDataSource, LinearColorMapper, PrintfTickFormatter,) from bokeh.plotting import figure from bokeh.sampledata.unemployment1948 import data from bokeh.transform import transform output_file("unemploymemt.html") data.Year = data.Year.astype(str) data = data.set_index('Year') data.drop('Annual', axis=1, inplace=True) data.columns.name = 'Month' # reshape to 1D array or rates with a month and year for each row. df = pd.DataFrame(data.stack(), columns=['rate']).reset_index() source = ColumnDataSource(df) # this is the colormap from the original NYTimes plot colors = ["#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce", "#ddb7b1", "#cc7878", "#933b41", "#550b1d"] mapper = LinearColorMapper(palette=colors, low=df.rate.min(), high=df.rate.max()) p = figure(plot_width=800, plot_height=300, title="US Unemployment 1948—2016", x_range=list(data.index), y_range=list(reversed(data.columns)), toolbar_location=None, tools="", x_axis_location="above") p.rect(x="Year", y="Month", width=1, height=1, source=source, line_color=None, fill_color=transform('rate', mapper)) color_bar = ColorBar(color_mapper=mapper, location=(0, 0), ticker=BasicTicker(desired_num_ticks=len(colors)), formatter=PrintfTickFormatter(format="%d%%")) p.add_layout(color_bar, 'right') p.axis.axis_line_color = None p.axis.major_tick_line_color = None p.axis.major_label_text_font_size = "7px" p.axis.major_label_standoff = 0 p.xaxis.major_label_orientation = 1.0 show(p)
最后一个例子结合了本章中的许多技术:颜色映射器、视觉闪避和熊猫数据帧。这些元素被用来创建一个不同种类的“热图”,形成元素周期表。还添加了一个悬停工具,以便可以检查有关每个元素的附加信息:
from bokeh.io import output_file, show from bokeh.models import ColumnDataSource from bokeh.plotting import figure from bokeh.sampledata.periodic_table import elements from bokeh.transform import dodge, factor_cmap output_file("periodic.html") periods = ["I", "II", "III", "IV", "V", "VI", "VII"] groups = [str(x) for x in range(1, 19)] df = elements.copy() df["atomic mass"] = df["atomic mass"].astype(str) df["group"] = df["group"].astype(str) df["period"] = [periods[x-1] for x in df.period] df = df[df.group != "-"] df = df[df.symbol != "Lr"] df = df[df.symbol != "Lu"] cmap = { "alkali metal" : "#a6cee3", "alkaline earth metal" : "#1f78b4", "metal" : "#d93b43", "halogen" : "#999d9a", "metalloid" : "#e08d49", "noble gas" : "#eaeaea", "nonmetal" : "#f1d4Af", "transition metal" : "#599d7A", } source = ColumnDataSource(df) p = figure(plot_width=900, plot_height=500, title="Periodic Table (omitting LA and AC Series)", x_range=groups, y_range=list(reversed(periods)), toolbar_location=None, tools="hover") p.rect("group", "period", 0.95, 0.95, source=source, fill_alpha=0.6, legend_field="metal", color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys()))) text_props = {"source": source, "text_align": "left", "text_baseline": "middle"} x = dodge("group", -0.4, range=p.x_range) r = p.text(x=x, y="period", text="symbol", **text_props) r.glyph.text_font_style="bold" r = p.text(x=x, y=dodge("period", 0.3, range=p.y_range), text="atomic number", **text_props) r.glyph.text_font_size="11px" r = p.text(x=x, y=dodge("period", -0.35, range=p.y_range), text="name", **text_props) r.glyph.text_font_size="7px" r = p.text(x=x, y=dodge("period", -0.2, range=p.y_range), text="atomic mass", **text_props) r.glyph.text_font_size="7px" p.text(x=["3", "3"], y=["VI", "VII"], text=["LA", "AC"], text_align="center", text_baseline="middle") p.hover.tooltips = [ ("Name", "@name"), ("Atomic number", "@{atomic number}"), ("Atomic mass", "@{atomic mass}"), ("Type", "@metal"), ("CPK color", "$color[hex, swatch]:CPK"), ("Electronic configuration", "@{electronic configuration}"), ] p.outline_line_color = None p.grid.grid_line_color = None p.axis.axis_line_color = None p.axis.major_tick_line_color = None p.axis.major_label_standoff = 0 p.legend.orientation = "horizontal" p.legend.location ="top_center" show(p)