由于您正在使用pandas,因此可以这样做:
import pandas as pd
import matplotlib.pyplot as plt
pd.np.random.seed(1234)
idx = pd.date_range(end=datetime.today().date(), periods=10, freq='D')
vals = pd.Series(pd.np.random.randint(1, 10, size=idx.size), index=idx)
vals.iloc[4:8] = pd.np.nan
print vals
DatetimeIndex
的DataFrame列的示例。2016-03-29 4.0
2016-03-30 7.0
2016-03-31 6.0
2016-04-01 5.0
2016-04-02 NaN
2016-04-03 NaN
2016-04-04 NaN
2016-04-05 NaN
2016-04-06 9.0
2016-04-07 1.0
Freq: D, dtype: float64
NaN
的图表,您可以像这样操作:fig, ax = plt.subplots()
ax.plot(range(vals.dropna().size), vals.dropna())
ax.set_xticklabels(vals.dropna().index.date.tolist());
fig.autofmt_xdate()
.plot
方法时触发matplotlib的内部日期处理。.autofmt_xdate()
使标签更易读。