Pandas子图部分空白或显示异常，折线图绘制场景下的问题求助

子图空白问题修复 #

导致空白的核心问题如下，均已在修正代码中处理：

• 样式配置顺序错误：样式、字体等全局配置需要放在所有绘图代码之前，绘图完成后再修改配置不会生效，还会导致已绘制元素渲染异常
• 子图遍历逻辑错误：df.plot返回的子图对象是shape为(5,2)的二维数组，原代码的遍历逻辑没有正确匹配所有子图与对应数据列，导致部分子图没有绘制折线
• 列名冲突：原代码中两个数据列设置了重复的列名，导致绘图时数据匹配错误
• 冗余代码干扰：末尾的ax.set_title("US Reserves")会覆盖最后一个子图的正确标题，plt.xlabel仅作用于最后一个子图，不符合预期

FRED数据拉取偶发报错修复 #

报错通常由网络波动、FRED接口限流、旧版本库兼容问题导致，修复方案：

• 升级pandas_datareader到最新版本：pip install --upgrade pandas-datareader
• 拉取数据时增加超时参数，避免短时间网络波动导致请求失败

import matplotlib.pyplot as plt 
import seaborn as sns 
import pandas_datareader.data as pdr 
import pandas as pd 
import datetime

# 全局样式配置放在最前面
plt.style.use('seaborn-whitegrid')
plt.rcParams.update({'font.family':'Latin Modern Sans'})

start = datetime.datetime(2000,1,1)
end = datetime.datetime(2021,5,1)
# 拉取数据增加超时参数
df  = pdr.DataReader(['FDHBFRBN', 
                      'FDHBFIN',
                      'FYGFDPUN',
                      'FRDGBSAM',
                      'HBFRGDQ188S',
                      'MTSMFBP133FMS',
                      'GFDEGDQ188S',
                      'GFDEBTN',
                      'FYFSD',
                      'FYFSGDA188S'], 'fred', start, end, timeout=30)
# 修正重复列名，保证每个列名唯一
df.columns = ['Federal Debt Held by Federal Reserve Banks',
              'Federal Debt Held by Foreign and International Investors', 
              'Federal Debt Held by the Public ',
              'Federal Reserve Bank Held Gold Bullion: On Display',
              'Federal Debt Held by Federal Reserve Banks as Percent of Gross Domestic Product',
              'Means of Financing: Borrowing from the Public',
              'Federal Debt: Total Public Debt as Percent of Gross Domestic Product',
              'Federal Debt: Total Public Debt',
              'Federal Surplus or Deficit',
              'Federal Surplus or Deficit [-] as Percent of Gross Domestic Product']

# 绘图返回的axes为5行2列二维数组
axes = df.plot(subplots=True, layout=(5,2), figsize=(15,25), linewidth=2, colormap="summer")

# 用ravel()展平axes后遍历，不破坏原二维索引结构
for ax, col in zip(axes.ravel(), df.columns):
    ax.axvspan('2007-1-12', '2009-6-1', color='teal', 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.set_xlabel('Date') # 给每个子图添加x轴标签

axes[0,0].set_title('Federal Debt Held by Federal Reserve Banks')
axes[0,0].set_ylabel('Billions of Dollars(Quarterly)')
axes[0,0].legend(bbox_to_anchor=(1.05, 1))

axes[0,1].set_title('Federal Debt Held by Foreign and International Investors')
axes[0,1].set_ylabel('Billions of Dollars(Quarterly)')
axes[0,1].legend(bbox_to_anchor=(1.05, 1))

axes[1,0].set_title('Federal Debt Held by the Public ')
axes[1,0].set_ylabel('Millions of Dollars(Quarterly)')
axes[1,0].legend(bbox_to_anchor=(1.05, 1))

axes[1,1].set_title('Federal Reserve Bank Held Gold Bullion: On Display ')
axes[1,1].set_ylabel('Fine Troy Ounces(Monthly)')
axes[1,1].legend(bbox_to_anchor=(1.05, 1))

axes[2,0].set_title('Federal Debt Held by Federal Reserve Banks as Percent of Gross Domestic Product')
axes[2,0].set_ylabel('Percent of GDP(Quarterly)')
axes[2,0].legend(bbox_to_anchor=(1.05, 1))

axes[2,1].set_title('Means of Financing: Borrowing from the Public')
axes[2,1].set_ylabel('Millions of Dollars(Monthly)')
axes[2,1].legend(bbox_to_anchor=(1.05, 1))

axes[3,0].set_title('Federal Debt: Total Public Debt as Percent of Gross Domestic Product ')
axes[3,0].set_ylabel('Percent of GDP(Quarterly)')
axes[3,0].legend(bbox_to_anchor=(1.05, 1))

axes[3,1].set_title('Federal Debt: Total Public Debt')
axes[3,1].set_ylabel('Millions of Dollars(Quarterly)')
axes[3,1].legend(bbox_to_anchor=(1.05, 1))

axes[4,0].set_title('Federal Surplus or Deficit')
axes[4,0].set_ylabel('Billions of Dollars(Annual)')
axes[4,0].legend(bbox_to_anchor=(1.05, 1))

axes[4,1].set_title('Federal Surplus or Deficit [-] as Percent of Gross Domestic Product')
axes[4,1].set_ylabel('Percent of GDP(Annual)')
axes[4,1].legend(bbox_to_anchor=(1.05, 1))

# 布局调整放在所有配置完成后
plt.tight_layout()
plt.show()

内容的提问来源于stack exchange，提问作者user15157590