Bokeh是一个专门针对Web浏览器的交互式可视化Python库

　　1 说明：
　　=====
　　1.1 Bokeh是专门针对Web浏览器的交互式、可视化Python绘图库。
　　1.2 Bokeh，可以做出像D3.js简洁漂亮的交互可视化效果，但是使用难度低于D3.js。
　　1.3 不需要使用Javascript。
　　2 官网：
　　======https://docs.bokeh.org/en/latest/ https://github.com/bokeh/bokeh
　　3 安装：
　　=====pip install bokeh #本机安装 sudo pip3.8 install bokeh
　　4 环境：
　　=====
　　华为笔记本电脑、深度deepin-linux操作系统、python3.8和微软vscode编辑器。
　　5 静态基本作图：
　　============
　　5.1 柱状图：
　　5.1.1 代码：from bokeh.io import output_file, show from bokeh.plotting import figure #数据，支持中文 fruits = [＂苹果＂, ＂Pears＂, ＂Nectarines＂, ＂Plums＂, ＂Grapes＂, ＂Strawberries＂] counts = [5, 3, 4, 2, 4, 6] #绘图 p = figure(x_range=fruits, plot_height=350, title=＂Fruit Counts＂,            toolbar_location=None, tools=＂＂) #柱状图，vbar是指垂直柱状图 p.vbar(x=fruits, top=counts, width=0.9) #导出文件：文件名和指定路径， #注意没有这一行，也会自动在代码所在的生成同名的html文件 #output_file(＂/home/xgj/Desktop/bokeh/bar_basic.html＂) #展示图 show(p)
　　5.1.2 图：
　　5.2 折线图
　　5.2.1 代码：from bokeh.io import output_file, show from bokeh.plotting import figure #数据，支持中文 fruits = [＂苹果＂, ＂Pears＂, ＂Nectarines＂, ＂Plums＂, ＂Grapes＂, ＂Strawberries＂] counts = [5, 3, 4, 2, 4, 6] #绘图 p = figure(x_range=fruits, plot_height=350, title=＂Fruit Counts＂,            toolbar_location=None, tools=＂＂) #柱状图 p.line(x=fruits, y=counts) #展示图 show(p)
　　5.2.2 图：
　　5.3 散点图：
　　5.3.1 代码：#from bokeh.io import output_file, show from bokeh.plotting import figure,output_file, show  #同上 #数据，支持中文 fruits = [＂苹果＂, ＂Pears＂, ＂Nectarines＂, ＂Plums＂, ＂Grapes＂, ＂Strawberries＂] counts = [5, 3, 4, 2, 4, 6] #绘图 p = figure(x_range=fruits, plot_height=350, title=＂Fruit Counts＂,            toolbar_location=None, tools=＂＂) #柱状图 p.scatter(x=fruits, y=counts,size=20, fill_color=＂#74add1＂) #展示图 show(p)
　　5.3.2 图：
　　===基本作图方便，优美；比matplotlib简单，暂时介绍到这里===
　　6 高级作图：
　　=========
　　6.1 js_events：调用js事件
　　6.2 代码：import numpy as np from bokeh import events from bokeh.io import output_file, show from bokeh.layouts import column, row from bokeh.models import Button, CustomJS, Div from bokeh.plotting import figure #定义js和事件 def display_event(p, attributes=[]):     style = ＂float: left; clear: left; font-size: 13px＂     return CustomJS(args=dict(p=p), code=＂＂＂         var attrs = %s;         var args = [];         for (var i = 0; i < attrs.length; i++) {             var val = JSON.stringify(cb_obj[attrs[i]], function(key, val) {                 return val.toFixed ? Number(val.toFixed(2)) : val;             })             args.push(attrs[i] + ＂=＂ + val)         }         var line = ＂＂ + cb_obj.event_name + ＂(＂ + args.join(＂, ＂) + ＂)n＂;         var text = p.text.concat(line);         var lines = text.split(＂n＂)         if (lines.length > 35)             lines.shift();         p.text = lines.join(＂n＂);     ＂＂＂ % (attributes, style)) #数据 N = 4000 x = np.random.random(size=N) * 100 y = np.random.random(size=N) * 100 radii = np.random.random(size=N) * 1.5 colors = [     ＂#%02x%02x%02x＂ % (int(r), int(g), 150) for r, g in zip(50+2*x, 30+2*y) ]  p = figure(tools=＂pan,wheel_zoom,zoom_in,zoom_out,reset,tap,lasso_select,box_select＂) #调用散点图 p.scatter(x, y, radius=radii,           fill_color=colors, fill_alpha=0.6,           line_color=None) #容器实例化，宽 p = Div(width=1000) button = Button(label=＂Button＂, button_type=＂success＂, width=300) layout = column(button, row(p, p)) #注册事件回调 #按钮事件 button.js_on_event(events.ButtonClick, display_event(p)) # LOD事件 p.js_on_event(events.LODStart, display_event(p)) p.js_on_event(events.LODEnd, display_event(p)) # Point events点事件 point_attributes = [＂x＂,＂y＂,＂sx＂,＂sy＂] p.js_on_event(events.Tap,       display_event(p, attributes=point_attributes)) p.js_on_event(events.DoubleTap, display_event(p, attributes=point_attributes)) p.js_on_event(events.Press,     display_event(p, attributes=point_attributes)) p.js_on_event(events.PressUp,   display_event(p, attributes=point_attributes)) # Mouse wheel event p.js_on_event(events.MouseWheel, display_event(p,attributes=point_attributes+[＂delta＂])) # Mouse move, enter and leave p.js_on_event(events.MouseMove,  display_event(p, attributes=point_attributes)) p.js_on_event(events.MouseEnter, display_event(p, attributes=point_attributes)) p.js_on_event(events.MouseLeave, display_event(p, attributes=point_attributes)) # Pan events pan_attributes = point_attributes + [＂delta_x＂, ＂delta_y＂] p.js_on_event(events.Pan,      display_event(p, attributes=pan_attributes)) p.js_on_event(events.PanStart, display_event(p, attributes=point_attributes)) p.js_on_event(events.PanEnd,   display_event(p, attributes=point_attributes)) # Pinch events pinch_attributes = point_attributes + [＂scale＂] p.js_on_event(events.Pinch,      display_event(p, attributes=pinch_attributes)) p.js_on_event(events.PinchStart, display_event(p, attributes=point_attributes)) p.js_on_event(events.PinchEnd,   display_event(p, attributes=point_attributes)) # Selection events p.js_on_event(events.SelectionGeometry, display_event(p, attributes=[＂geometry＂, ＂final＂])) show(layout)
　　6.3 效果图：
　　6.4 图形总体
　　6.4.1 代码：from bokeh.core.enums import MarkerType from bokeh.layouts import row from bokeh.models import ColumnDataSource, Panel, Tabs from bokeh.plotting import figure, output_file, show from bokeh.sampledata.iris import flowers  source = ColumnDataSource(flowers) def make_plot(title, marker, backend):     p = figure(title=title, plot_width=350, plot_height=350, output_backend=backend)     p.scatter(＂petal_length＂, ＂petal_width＂, source=source,               color=＂blue＂, fill_alpha=0.2, size=12, marker=marker)     return p tabs = [] for marker in MarkerType:     p1 = make_plot(marker, marker, ＂canvas＂)     p2 = make_plot(marker + ＂ SVG＂, marker, ＂svg＂)     p3 = make_plot(marker + ＂ GL＂, marker, ＂webgl＂)     tabs.append(Panel(child=row(p1, p2, p3), title=marker)) #output_file(＂marker_compare.html＂, title=＂Compare regular, SVG, and WebGL markers＂) show(Tabs(tabs=tabs))
　　6.4.2 效果图
　　===一般基本作图是小白和普通人需要的掌握的，下次重点讲===
　　7 机器学习：scikit-learn project
　　========================
　　7.1 代码：import numpy as np from sklearn import cluster, datasets from sklearn.preprocessing import StandardScaler from bokeh.layouts import column, row from bokeh.plotting import figure, output_file, show print(＂  *** This example may take several seconds to run before displaying. ***  ＂) print(＂  *** 该示例展示前需要等待几秒. ***  ＂) N = 50000 PLOT_SIZE = 400 # generate datasets. np.random.seed(0) noisy_circles = datasets.make_circles(n_samples=N, factor=.5, noise=.04) noisy_moons = datasets.make_moons(n_samples=N, noise=.05) centers = [(-2, 3), (2, 3), (-2, -3), (2, -3)] blobs1 = datasets.make_blobs(centers=centers, n_samples=N, cluster_std=0.4, random_state=8) blobs2 = datasets.make_blobs(centers=centers, n_samples=N, cluster_std=0.7, random_state=8)  colors = np.array([x for x in (＂#00f＂, ＂#0f0＂, ＂#f00＂, ＂#0ff＂, ＂#f0f＂, ＂#ff0＂)]) colors = np.hstack([colors] * 20) # create clustering algorithms dbscan   = cluster.DBSCAN(eps=.2) birch    = cluster.Birch(n_clusters=2) means    = cluster.MiniBatchKMeans(n_clusters=2) spectral = cluster.SpectralClustering(n_clusters=2, eigen_solver=＂arpack＂, affinity=＂nearest_neighbors＂) affinity = cluster.AffinityPropagation(damping=.9, preference=-200) # change here, to select clustering algorithm (note: spectral is slow) algorithm = dbscan  # <- SELECT ALG plots =[] for dataset in (noisy_circles, noisy_moons, blobs1, blobs2):     X, y = dataset     X = StandardScaler().fit_transform(X)     # predict cluster memberships     algorithm.fit(X)     if hasattr(algorithm, ＂labels_＂):         y_pred = algorithm.labels_.astype(np.int)     else:         y_pred = algorithm.predict(X)      p = figure(output_backend=＂webgl＂, title=algorithm.__class__.__name__,                plot_width=PLOT_SIZE, plot_height=PLOT_SIZE)      p.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), alpha=0.1,)     plots.append(p) # generate layout for the plots layout = column(row(plots[:2]), row(plots[2:])) output_file(＂clustering.html＂, title=＂clustering with sklearn＂) show(layout)
　　7.2 效果图：
　　===自己整理并分享出来===
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