Matplotlib 两条线之间的阴影

Question

我想使用 axvspan() 函数可视化我使用 Pandas DataReader.But 获得的 DataFrame 当我使用以下代码时，我看到一个错误并且子图中没有阴影.我应该怎么办？谢谢。

import matplotlib.pyplot as plt
import pandas_datareader.data as pdr
import pandas as pd
import datetime
start = datetime.datetime (2000,1,1)
end = datetime.datetime (2021,5,1)
df  = pdr.DataReader(['WFRBSB50215', 'WFRBST01134','WFRBST01122', 'WFRBSN09139', 'WFRBSB50189', 'WFRBST01110','WFRBSB50191'],'fred',start, end)
df.columns = ['Share of Total Net Worth Held by the Bottom 50% (1st to 50th Wealth Percentiles)',
              'Share of Total Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles)', 
              'Share of Corporate Equities and Mutual Fund Shares Helb By Top1%(99th to 100th Wealth Percentiles)', 
              'Share of Financial Assets Held by the 90th to 99th Wealth Percentiles',
              'Share of Total Assets Held by the Bottom 50% (1st to 50th Wealth Percentiles)',
              'Share of Real Estate Held by the Top 1% (99th to 100th Wealth Percentiles)',
              'Share of Real Estate Held by the Bottom %50(1st to 50th Wealth Percentiles)'
              ]
ax = df.plot(subplots=True, layout=(7,1), figsize=(15,15), linewidth=3.5, colormap="summer")
ax.axvspan('2007-1-12', '2009-6-1', color='c', alpha=0.5)
ax.axvspan('2019-12-1', '2020-2-1',color= 'orange', alpha=0.5)
plt.xlabel('Date')
ax[0,].set_title('Share of Total Net Worth Held by the Bottom 50%')
ax[0,].set_ylabel('Percent of Aggregate')
ax[1,].set_title('Share of Total Net Worth Held by the Top 1%')
ax[1,].set_ylabel('Percent of Aggregate')
ax[2,].set_title('Share of Corporate Equities and Mutual Fund Shares Helb By Top 1%')
ax[2,].set_ylabel('Percent of Aggregate')
ax[3,].set_title('Share of Financial Assets Held by the 90th to 99th Wealth Percentiles')
ax[3,].set_ylabel('Percent of Aggregate')
ax[4,].set_title('Share of Total Assets Held by the Bottom 50% ')
ax[4,].set_ylabel('Percent of Aggregate')
ax[5,].set_title('Share of Real Estate Held by the Top 1%')
ax[5,].set_ylabel('Percent of Aggregate')
ax[6,].set_title('Share of Real Estate Held by the Bottom %50')
ax[6,].set_ylabel('Percent of Aggregate')
plt.tight_layout()
plt.style.use('seaborn-white')
plt.show()

Answer 1

尝试遍历所有子图并将 axvspan 添加到特定的 AxesSubplot 中：

axes = df.plot(subplots=True, layout=(7, 1), figsize=(15, 15), linewidth=3.5,
               colormap="summer", ylabel='Percent of Aggregate', xlabel='Date')

for (ax,), col in zip(axes, df.columns):
    ax.axvspan('2007-1-12', '2009-6-1', color='c', alpha=0.5)
    ax.axvspan('2019-12-1', '2020-2-1', color='orange', alpha=0.5)
    ax.set_title(col)

plt.tight_layout()
plt.style.use('seaborn-white')
plt.show()

使用 plot 的 ylabel 和 xlabel kwargs 并从 df.columns 而不是手动设置子图标题，稍微减少了一些代码。

为 axvspan 添加图例。最简单的做法是给每个 axvspan 添加一个标签，并在每次迭代结束时制作图例：

axes = df.plot(subplots=True, layout=(7, 1), figsize=(15, 15), linewidth=3.5,
               colormap="summer", ylabel='Percent of Aggregate', xlabel='Date',
               legend=False)

for (ax,), col in zip(axes, df.columns):
    ax.axvspan('2007-1-12', '2009-6-1', color='c', alpha=0.5,
               label='2008 Crisis')
    ax.axvspan('2019-12-1', '2020-2-1', color='orange', alpha=0.5,
               label='Pandemic')
    ax.set_title(col)
    ax.legend()

plt.style.use('seaborn-white')
plt.tight_layout()
plt.show()

或者，可以只为危机创建一个图例：

fig, axes = plt.subplots(nrows=7, figsize=(15, 15))
df.plot(subplots=True, ax=axes, linewidth=3.5,
        colormap="summer", ylabel='Percent of Aggregate', xlabel='Date')

for ax, col in zip(axes, df.columns):
    ax.axvspan('2007-1-12', '2009-6-1', color='c', alpha=0.5,
               label='2008 Crisis')
    ax.axvspan('2019-12-1', '2020-2-1', color='orange', alpha=0.5,
               label='Pandemic')
    ax.set_title(col)

handles, labels = axes[-1].get_legend_handles_labels()

fig.legend(handles[-2:], labels[-2:], title='Crises',
           loc='lower left', ncol=2)

plt.style.use('seaborn-white')
plt.tight_layout()
plt.show()

^ 单个图例在左下方。

Matplotlib 两条线之间的阴影

Shading between two lines with Matplotlib

python

pandas

matplotlib

pandas-datareader