河流图能够动态的直观的反映出多个指标随着时序的变化而变化。其实在pyecharts中也提供了ThemeRiver图表，后文会继续讲解；seaborn中也提供了类似的river图，不过效果不是很理想；matplotlib中提供了stackplot图表，baseline要指定为“wiggle”，不过是点与点的直线，比较生硬；后查询了很多材料，需要通过scipy的spline进行插值法处理，经过几天的反复测试，今天终于完全搞定了。
代码示例
# coding:utf-8import pylabimport numpy as npfrom matplotlib import pyplot as pltfrom matplotlib import cmfrom numpy import matrixfrom sklearn.feature_extraction.text import CountVectorizerfrom sklearn.feature_extraction.text import TfidfTransformerfrom scipy.interpolate import spline#文本词频可视化图表stackplot风格# streamgraph风格的在beaborn上也有，不过不太符合要求# streamgraph风格的在pyechart上也有，可以直接使用，下次再讲用法# streamgraph风格的在matplotlib上只有类stackplot，不够圆滑def draw_river(data,xlabels,ylabels,title='',step=300):# X标签 行，即章节# Y标签 列，即词汇# 数据 即词频，需要转置后才能应用#获取y轴数量ylen=len(ylabels)#初始化一个X轴的序列numpy数组，默认为[0 1 2 len(xlabel)]initX = np.array(range(len(xlabels)))#linspace用于创建一个是等差数列的一维数组，最小值是0，最大值是X轴长度，xnew=np.linspace(initX.min(), initX.max(), step)#创建一个numpy空的二维数组newdata，以便存储转换后的data值newdata=np.empty(shape=[0,step])#spline只能应用于一维数组，所需需要分行读取for datarow in range(ylen):power_smooth = spline(initX, data[datarow], xnew)#将一维numpy数组变为二维数据middata = power_smooth[np.newaxis, :]#将二维数组添加到最终的数组中newdata=np.append(newdata,middata,axis=0)pylab.mpl.rcParams['font.sans-serif'] = ['SimHei'] # 防止中文乱码pylab.mpl.rcParams['axes.unicode_minus'] = False # 防止中文乱码fig, ax = plt.subplots()#用stackplot绘制新的图形ax.stackplot(xnew, newdata,labels=ylabels, baseline='wiggle')ax.axes.set_yticks(range(len(ylabels)))ax.axes.set_yticklabels(ylabels)ax.axes.set_xticks(range(len(xlabels)))ax.axes.set_xticklabels(xlabels)ax.legend(loc='best')ax.set_title(title)plt.show()def draw_stackplot(data,xlabels,ylabels):# X标签 行，即章节# Y标签 列，即词汇# 数据 即词频，需要转置后才能应用#data= [[0, 3, 3, 3, 0, 0, 3, 0, 3], [0, 3, 0, 3, 0, 6, 3, 0, 3], [3, 0, 0, 0, 3, 0, 3, 3, 0], [0, 3, 3, 3, 0, 0, 3, 0, 3]]#xlablels= range(0, 4)#ylablels= ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']pylab.mpl.rcParams['font.sans-serif'] = ['SimHei'] # 防止中文乱码pylab.mpl.rcParams['axes.unicode_minus'] = False # 防止中文乱码fig, ax = plt.subplots()ax.stackplot(xlabels, data, labels=ylabels, baseline='wiggle')ax.axes.set_yticks(range(len(ylabels)))ax.axes.set_yticklabels(ylabels)ax.axes.set_xticks(range(len(xlabels)))ax.axes.set_xticklabels(xlabels)ax.legend(loc='best')ax.set_title('Interesting Graph\nCheck it out')plt.show()#文本词频可视化图表heatmap风格def draw_heatmap(data, xlabels, ylabels):pylab.mpl.rcParams['font.sans-serif'] = ['SimHei'] # 防止中文乱码pylab.mpl.rcParams['axes.unicode_minus'] = False # 防止中文乱码vmin=np.amin(matrix(data))vmax = np.amax(matrix(data))cmap = cm.Bluesfigure = plt.figure(facecolor='w')ax = figure.add_subplot(2, 1, 1, position=[0.1, 0.15, 0.8, 0.8])ax.set_yticks(range(len(ylabels)))ax.set_yticklabels(ylabels)ax.set_xticks(range(len(xlabels)))ax.set_xticklabels(xlabels)map = ax.imshow(data, interpolation='nearest', cmap=cmap, aspect='auto', vmin=vmin, vmax=vmax)cb = plt.colorbar(mappable=map, cax=None, ax=None, shrink=0.5)plt.xticks(rotation=90) # 将字体进行旋转plt.yticks(rotation=360)plt.show()#------------------------------英文tf-idf-------------------------------#英文文本corpus = ['This is the first document.'*3,'This is the second second document.'*3,'And the third one.'*3,'Is this the first document?'*3,]# -------------------------词频分析---------------------------#将文本中的词语转换为词频矩阵vectorizer = CountVectorizer()#计算个词语出现的次数X = vectorizer.fit_transform(corpus)#X格式如下，主要包括（行 词）词频#(0, 1) 1 (0, 2) 1 (0, 6) 1 (0, 3) 1 (0, 8) 1 (1, 5) 2 (1, 1) 1#获取语句中所有文本关键词word = vectorizer.get_feature_names()# word格式如下，是个英文词汇的数组列表# ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']#查看词频结果，转置为Numpy 2维数组后的输出print('X.toarray()=',X.toarray())#and document first is one second the third this#0 1 1 1 0 0 1 0 1#0 1 0 1 0 2 1 0 1#1 0 0 0 1 0 1 1 0#0 1 1 1 0 0 1 0 1# ---------------------------可视化----------------------------#热力图方式xlabels=wordylabels=list(range(len(corpus)))data=X.toarray().tolist()draw_heatmap(data, xlabels, ylabels)#转置维stackflow的格式要求，y轴为字符，x轴为章节#stackplt方式data=X.T.toarray().tolist()draw_stackplot(data, ylabels, xlabels)draw_river(data,ylabels,xlabels,title='词云河流图',step=300)
