关于matplotlib：如何在python中绘制显示置信区间的时间序列数组？

How to plot a time series array, with confidence intervals displayed, in python?

我有一个逐渐增加的时间序列，但是在很短的时间内，它们非常波动。例如，时间序列可能如下所示：

1	[10 + np.random.rand() for i in range(100)] + [12 + np.random.rand() for i in range(100)] + [14 + np.random.rand() for i in range(100)]

我想绘制时间序列，重点放在总体趋势上，而不是在小浪上。有没有办法绘制一段时间内的平均值，并用表示波浪的条纹包围(条纹应代表置信区间，此时数据点可能在该区间)？

一个简单的情节看起来像这样：

enter

对于置信区间的阴影(用标准偏差之间的间隔表示)，可以使用matplotlib.pyplot中的函数fill_between()。有关更多信息，您可以在这里看看，从中启发了以下代码。

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

#Declare the array containing the series you want to plot.
#For example:
time_series_array = np.sin(np.linspace(-np.pi, np.pi, 400)) + np.random.rand((400))
n_steps = 15 #number of rolling steps for the mean/std.

#Compute curves of interest:
time_series_df = pd.DataFrame(time_series_array)
smooth_path = time_series_df.rolling(n_steps).mean()
path_deviation = 2 * time_series_df.rolling(n_steps).std()

under_line = (smooth_path-path_deviation)[0]
over_line = (smooth_path+path_deviation)[0]

#Plotting:
plt.plot(smooth_path, linewidth=2) #mean curve.
plt.fill_between(path_deviation.index, under_line, over_line, color='b', alpha=.1) #std curves.

使用上面的代码，您将获得如下内容：
enter

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time_series_df = pd.DataFrame(time_series_array)
smooth_path = time_series_df.rolling(20).mean()
path_deviation = time_series_df.rolling(20).std()
plt.plot(smooth_path, linewidth=2)
plt.fill_between(path_deviation.index, (smooth_path-2*path_deviation)[0], (smooth_path+2*path_deviation)[0], color='b', alpha=.1)