Seaborn catplot(kind='count') 条形图转饼图

3回答

慕森卡

我最终使用 matplotlib 库从底层构建它：plt.style.use('seaborn')IAP = df_original_small['Information and awareness purposes'].value_counts().to_frame().TQE = df_original_small['Quarantine Enforcement'].value_counts().to_frame().TCTCR = df_original_small['Contact Tracing and Cross-Referencing'].value_counts().to_frame().TVPID = df_original_small['Voluntary provision of infection data'].value_counts().to_frame().TQMA = df_original_small['Quarantine Monitoring App'].value_counts().to_frame().TQRCode = df_original_small['QR code provided registration tracking'].value_counts().to_frame().Ttotal = pd.concat([IAP, QE, CTCR, VPID, QMA, QRCode])fig, ax = plt.subplots(nrows=3, ncols=2)labels = 'acceptable', 'unacceptable'colors = ['#008fd5', '#fc4f30']explode = (0, 0.1)explode2 = (0.2, 0)plt.title('Pie chart per CTQ-tool')plt.tight_layout()ax[0,0].pie(total.iloc[[0]], startangle=90, colors=colors, wedgeprops={'edgecolor': 'black'}, autopct='%1.f%%', explode=explode, shadow=True)ax[0,0].set_title('Information and awareness purposes', fontweight='bold')ax[0,1].pie(total.iloc[[1]],  startangle=90, colors=colors, wedgeprops={'edgecolor': 'black'}, autopct='%1.f%%', explode=explode, shadow=True)ax[0,1].set_title('Quarantine Enforcement', fontweight='bold')ax[1,0].pie(total.iloc[[2]],  startangle=90, colors=colors, wedgeprops={'edgecolor': 'black'}, autopct='%1.f%%', explode=explode2, shadow=True)ax[1,0].set_title('Contact Tracing and Cross-Referencing', fontweight='bold')ax[1,1].pie(total.iloc[[3]], startangle=90, colors=colors, wedgeprops={'edgecolor': 'black'}, autopct='%1.f%%', explode=explode, shadow=True)ax[1,1].set_title('Voluntary provision of infection data', fontweight='bold')ax[2,0].pie(total.iloc[[4]], startangle=90, colors=colors, wedgeprops={'edgecolor': 'black'}, autopct='%1.f%%', explode=explode2, shadow=True)ax[2,0].set_title('Quarantine Monitoring App', fontweight='bold')ax[2,1].pie(total.iloc[[5]], startangle=90, colors=colors, wedgeprops={'edgecolor': 'black'}, autopct='%1.f%%', explode=explode, shadow=True)ax[2,1].set_title('QR code provided registration tracking', fontweight='bold')fig.suptitle('Public Opinion on CTQ-measures', fontsize=20, y=1.07, fontweight='bold', x=0.37)fig.set_figheight(10)fig.set_figwidth(7)fig.legend(loc='best', labels=labels, fontsize='medium')fig.tight_layout()fig.savefig('Opinions_ctq')plt.show()

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慕婉清6462132

如果你想要快速的东西，你也可以试试这个：import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsdf = pd.DataFrame({'CTQ-tool':np.random.choice(['a','b','c','d'],50),                  'opinion':np.random.choice(['acceptable','unacceptable'],50)})fig, ax = plt.subplots(2,2)ax = ax.flatten()tab = pd.crosstab(df['CTQ-tool'],df['opinion'])for i,cat in enumerate(tab.index):    tab.loc[cat].plot.pie(ax=ax[i],startangle=90)    ax[i].set_ylabel('')    ax[i].set_title(cat, fontweight='bold')

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慕标5832272

问题是关于创建饼图，python所以我认为你可以使用另一个可视化库，比如Plotly，除了作为一个可视化库之外，Plotly它还是一个交互式可视化库，所以你所有的图表都是交互式的！快速浏览一下饼图文档。现在，对于你的问题，我创建了一个小数据集并创建了两个饼图来说明代码的样子。首先，导入所需的库：import pandas as pdimport plotly.graph_objects as gofrom plotly.subplots import make_subplotsfrom kaleido.scopes.plotly import PlotlyScope # this will be used to export the chart as static image玩具数据集：df = pd.DataFrame(    {        "CTQ-tool": [            "Information and awareness purposes",            "Information and awareness purposes",            "Information and awareness purposes",            "Information and awareness purposes",            "Information and awareness purposes",            "Information and awareness purposes",            "Quarantine Enforcement",            "Quarantine Enforcement",            "Quarantine Enforcement",            "Quarantine Enforcement",            "Quarantine Enforcement",            "Quarantine Enforcement",        ],        "opinion": [            "unacceptable",            "unacceptable",            "unacceptable",            "unacceptable",            "acceptable",            "unacceptable",            "acceptable",            "unacceptable",            "acceptable",            "unacceptable",            "unacceptable",            "unacceptable",        ],    })保存独特的不同工具：tools = df["CTQ-tool"].unique()创建聚合数据：以下代码将按工具类型和意见类型分组，然后counts为每个工具创建一个新列，用于存储每种意见类型的计数。df_agg = df.groupby(by=["CTQ-tool", "opinion"]).size().reset_index(name="counts")新的数据框df_agg将是：|      | CTQ-tool                           | opinion      | counts || ---: | :--------------------------------- | :----------- | -----: ||    0 | Information and awareness purposes | acceptable   |      1 ||    1 | Information and awareness purposes | unacceptable |      5 ||    2 | Quarantine Enforcement             | acceptable   |      2 ||    3 | Quarantine Enforcement             | unacceptable |      4 |可视化数据（有趣的部分）：由于这个玩具数据只有两个不同的工具，我创建了一个sub-plot只有一行和两列的工具，但您可以将其扩展为任意多的行/列。fig = make_subplots(rows=1, cols=2, specs=[[{"type": "domain"}, {"type": "domain"}]])然后分别添加每个图表（您可以使用 for 循环来完成）：fig = make_subplots(rows=1, cols=2, specs=[[{"type": "domain"}, {"type": "domain"}]])# Information and awareness purposes toolfig.add_trace(    go.Pie(        values=df_agg[df_agg["CTQ-tool"] == tools[0]]["counts"],        labels=df_agg[df_agg["CTQ-tool"] == tools[0]]["opinion"],        pull=[0.2, 0.0],        title=tools[0],    ),    1,    1,)# Quarantine Enforcement toolfig.add_trace(    go.Pie(        values=df_agg[df_agg["CTQ-tool"] == tools[1]]["counts"],        labels=df_agg[df_agg["CTQ-tool"] == tools[1]]["opinion"],        pull=[0.2, 0.0],        title=tools[1],    ),    1,    2,)更新图表布局：fig.update_layout(title_text="Public Opinion on CTQ-measures")fig.show()最后，导出为静态图像：现在您已经准备好数据并对其进行可视化，是时候将其保存为图像了。Plotly 的创作者为此构建了一个工具：Kaleido。您可以简单地使用它如下：scope = PlotlyScope()fig_name = "Public-Opinion-on-CTQ-measures"with open(f"{fig_name}.png", "wb") as f:    f.write(scope.transform(fig, "png"))这个数字是：

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