python - matplotlib 中每日时间序列数据的每月阴影误差/标准图

Question

我有一个包含 5 年每日时间序列数据的 pandas 数据框。我想从整个数据集制作每月图，以便该图应显示每月数据内的变化(标准或其他)。我尝试创建类似的图，但没有找到方法:

例如我有一个sudo每日降水量数据:

date = pd.to_datetime("1st of Dec, 1999")
dates = date+pd.to_timedelta(np.arange(1900), 'D')
ppt = np.random.normal(loc=0.0, scale=1.0, size=1900).cumsum()
df = pd.DataFrame({'pre':ppt},index=dates)

手动我可以这样做:

one   = df['pre']['1999-12-01':'2000-11-29'].values
two   = df['pre']['2000-12-01':'2001-11-30'].values
three = df['pre']['2001-12-01':'2002-11-30'].values
four  = df['pre']['2002-12-01':'2003-11-30'].values
five  = df['pre']['2003-12-01':'2004-11-29'].values
df = pd.DataFrame({'2000':one,'2001':two,'2002':three,'2003':four,'2004':five})
std = df.std(axis=1)
lw = df.mean(axis=1)-std
up = df.mean(axis=1)+std

plt.fill_between(np.arange(365), up, lw, alpha=.4)

我正在寻找更Pythonic的方法来做到这一点，而不是手动执行!

任何帮助将不胜感激

Answer 1

如果我没理解错的话，您想根据每月定期平均值 +/- 1 标准差绘制每日观察结果。这就是您在下面的屏幕截图中看到的内容。不要介意平淡的设计和颜色选择。如果这是您可以使用的东西，我们会解决这个问题。请注意，我已将您的 ppt = np.random.rand(1900) 替换为 ppt = np.random.normal(loc=0.0, scale=1.0, size=1900) .cumsum()只是为了让数据看起来更像你的屏幕截图。

在这里，我按月汇总了每日数据，并检索了每个月的平均值和标准差。然后，我将该数据与原始数据框合并，以便您能够绘制源数据和分组数据，如下所示:

# imports
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.dates as mdates
import numpy as np

# Data that matches your setup, but with a random
# seed to make it reproducible
np.random.seed(42)
date = pd.to_datetime("1st of Dec, 1999")
dates = date+pd.to_timedelta(np.arange(1900), 'D')
#ppt = np.random.rand(1900)
ppt = np.random.normal(loc=0.0, scale=1.0, size=1900).cumsum()

df = pd.DataFrame({'ppt':ppt},index=dates)

# A subset
df = df.tail(200)

# Add a yearmonth column
df['YearMonth'] = df.index.map(lambda x: 100*x.year + x.month)

# Create aggregated dataframe
df2 = df.groupby('YearMonth').agg(['mean', 'std']).reset_index()
df2.columns = ['YearMonth', 'mean', 'std']

# Merge original data and aggregated data
df3 = pd.merge(df,df2,how='left',on=['YearMonth'])
df3 = df3.set_index(df.index)
df3 = df3[['ppt', 'mean', 'std']]

# Function to make your plot
def monthplot():
    fig, ax = plt.subplots(1)
    ax.set_facecolor('white')

    # Define upper and lower bounds for shaded variation
    lower_bound = df3['mean'] + df3['std']*-1
    upper_bound = df3['mean'] + df3['std']

    fig, ax = plt.subplots(1)
    ax.set_facecolor('white')

    # Source data and mean
    ax.plot(df3.index,df3['mean'], lw=0.5, color = 'red')
    ax.plot(df3.index, df3['ppt'], lw=0.1, color = 'blue')

    # Variation and shaded area
    ax.fill_between(df3.index, lower_bound, upper_bound, facecolor='grey', alpha=0.5)

    fig = ax.get_figure()

    # Assign months to X axis
    locator = mdates.MonthLocator()  # every month
    # Specify the format - %b gives us Jan, Feb...
    fmt = mdates.DateFormatter('%b')

    X = plt.gca().xaxis
    X.set_major_locator(locator)
    X.set_major_formatter(fmt)

    fig.show()

monthplot()

查看this post有关轴格式的更多信息和 this post关于如何添加 YearMonth 列。