如何在Pandas时间轴散点图中将NaN值显示为间隔

用Pandas DataFrame生成了6个子图控制图，当前按索引绘制，需改为基于时间戳绘制。原始XML数据中的空白值以“-”表示，已通过df[y[0]].replace({'-': np.nan}, inplace=True)转为numpy NaN。因需进行含移动极差计算的统计分析以生成控制限，不能用0替代NaN。按索引绘制结果正常，但改为以非规则时间戳列TIMESTAMP为x轴时，出现x and y must be the same size错误，报错代码行是df.plot.scatter(y = y[0], ax=axes[0], x = 'TIMESTAMP')。此外，各子图的NaN出现时间点不同，需保留完整时间轴，仅将NaN显示为间隔，不能删除整行（同一时间点通常仅单个NaN，其余5个值有效）。

构造DataFrame代码 #

data = {'TIMESTAMP': ['01/07/2023 08:04:11', '01/07/2023 08:04:37', '01/07/2023 08:04:53', '01/07/2023 08:05:06', '01/07/2023 08:05:18', '01/07/2023 08:05:29', '01/07/2023 08:05:40', '01/07/2023 08:05:50', '01/07/2023 08:06:01', '01/07/2023 08:06:12', '01/07/2023 08:06:22', '01/07/2023 08:06:33', '01/07/2023 08:06:43'],
        'y1': ['107.08', '107.54', '107.18', '-', '106.92', '107.16', '107.46', '107.68', '107.84', '107.88', '108.1', '108.06', '108.2'],
        'y2': [107.12, 107.0, 107.92, 107.78, 106.96, 107.36, 107.58, 107.66, 107.92, 107.8, 107.94, 108.2, 108.12],
        'y3': ['107.66', '107.16', '106.92', '108.14', '106.96', '-', '107.54', '107.58', '107.72', '107.82', '107.96', '108.04', '108.12'],
        'y4': ['107.48', '107.6', '107.82', '107.78', '107.02', '-', '107.46', '107.48', '107.76', '107.82', '107.88', '108.02', '108.08'],
        'y5': ['107.38', '107.6', '107.6', '107.72', '107.48', '107.82', '107.9', '108.12', '108.22', '-', '108.7', '107.98', '107.94'],
        'y6': [107.44, 107.62, 107.48, 107.56, 107.46, 107.72, 107.82, 108.08, 108.06, 108.2, 108.18, 108.36, 108.46]}

df = pd.DataFrame(data)

DataFrame展示 #

TIMESTAMP      y1      y2      y3      y4      y5      y6
0   01/07/2023 08:04:11  107.08  107.12  107.66  107.48  107.38  107.44
1   01/07/2023 08:04:37  107.54  107.00  107.16   107.6   107.6  107.62
2   01/07/2023 08:04:53  107.18  107.92  106.92  107.82   107.6  107.48
3   01/07/2023 08:05:06       -  107.78  108.14  107.78  107.72  107.56
4   01/07/2023 08:05:18  106.92  106.96  106.96  107.02  107.48  107.46
5   01/07/2023 08:05:29  107.16  107.36       -       -  107.82  107.72
6   01/07/2023 08:05:40  107.46  107.58  107.54  107.46   107.9  107.82
7   01/07/2023 08:05:50  107.68  107.66  107.58  107.48  108.12  108.08
8   01/07/2023 08:06:01  107.84  107.92  107.72  107.76  108.22  108.06
9   01/07/2023 08:06:12  107.88  107.80  107.82  107.82       -  108.20
10  01/07/2023 08:06:22   108.1  107.94  107.96  107.88   108.7  108.18
11  01/07/2023 08:06:33  108.06  108.20  108.04  108.02  107.98  108.36
12  01/07/2023 08:06:43   108.2  108.12  108.12  108.08  107.94  108.46

内容的提问来源于stack exchange，提问作者ChemEnger