数据可视化｜用气泡图进行对比分析

# 导入所需的库import numpy as npimport pandas as pdimport matplotlib as mplimport matplotlib.pyplot as pltimport matplotlib.image as image# 正常显示中文标签mpl.rcParams["font.sans-serif"] = ["SimHei"]# 自动适应布局mpl.rcParams.update({"figure.autolayout": True})# 正常显示负号mpl.rcParams["axes.unicode_minus"] = False# 禁用科学计数法pd.set_option("display.float_format", lambda x: "%.2f" % x)# 定义颜色，主色：蓝色，辅助色：灰色，互补色：橙色colors = {"蓝色":"#00589F", "深蓝色":"#003867", "浅蓝色":"#5D9BCF",          "灰色":"#999999", "深灰色":"#666666", "浅灰色":"#CCCCCC",          "橙色":"#F68F00", "深橙色":"#A05D00", "浅橙色":"#FBC171"}

# 数据源路径filepath="./data/气泡图数据源.xlsx"# 读取 Excel文件df = pd.read_excel(filepath)# 定义画图用的数据x = df.点赞率y = df.分享率s = df.阅读量labels = df.文章

# 使用「面向对象」的方法画图，定义图片的大小fig, ax = plt.subplots(figsize=(8, 8))# 设置标题ax.text(-0.0035, y.max()+0.015, "
文章的阅读量、点赞率与分享率
", fontsize=26, color=colors["深灰色"])# 定义气泡的基础颜色color = [colors["灰色"]]*6# 突出显示点赞率最高的气泡color[x.idxmax()] = colors["蓝色"]# 突出显示分享率最高的气泡color[y.idxmax()] = colors["橙色"]# 画气泡图ax.scatter(x, y, s, c=color)# 设置刻度范围ax.set_xlim(0, x.max()+0.005)ax.set_ylim(0.01, y.max()+0.01)# 用百分比格式显示坐标轴def to_percent(temp, position=1):    return "%.1f"%(100 * temp) + "%"formatter = mpl.ticker.FuncFormatter(to_percent)ax.yaxis.set_major_formatter(formatter)ax.xaxis.set_major_formatter(formatter)# 设置 X、Y 轴的标题，适当留白ax.text(-0.001, 0, "点赞率", ha="left", fontsize=16, color=colors["深灰色"])ax.text(-0.0035, y.max()+0.01, "分
享
率", va="top", fontsize=16, color=colors["深灰色"])# 平均值线ax.hlines(y.mean(), 0, 1, color=colors["浅灰色"], lw=1)ax.vlines(x.mean(), 0, 1, color=colors["浅灰色"], lw=1)# 显示标签for i, a, b, c, d in zip(np.arange(len(x)), x, y, labels, s):    ax.text(a-0.001, b, c+"("+str(d)+")", ha="right", va="center", fontsize=15, color=color[i])# 设置边框颜色ax.spines["top"].set_color(colors["灰色"])ax.spines["bottom"].set_color(colors["灰色"])ax.spines["left"].set_color(colors["灰色"])ax.spines["right"].set_color(colors["灰色"])# 隐藏刻度线ax.tick_params(axis="x", which="major", length=0)ax.tick_params(axis="y", which="major", length=0)# 设置坐标标签字体大小和颜色ax.tick_params(labelsize=16, colors=colors["深灰色"])plt.show()