用年度数据绘制大 pandas 数据框 [英] Plot pandas data frame with year over year data

问题描述

我有一个格式的数据框

              value
2000-01-01    1
2000-03-01    2
2000-06-01    15
2000-09-01    3
2000-12-01    7
2001-01-01    1
2001-03-01    3
2001-06-01    8
2001-09-01    5
2001-12-01    3
2002-01-01    1
2002-03-01    1
2002-06-01    8
2002-09-01    5
2002-12-01    19

(索引是日期时间)，我需要逐年绘制所有结果以比较每3个月的结果(数据也可以是每月)，加上所有年份的平均值.

(index is datetime) and I need to plot all results year over year to compare the results each 3 months (The data can be monthly, too), plus the average of all years.

我可以轻松地分别绘制它们，但是由于有索引，它将根据索引来移动图:

I can easily plot they separately, but because of the index, it will shift the plots according with the index:

fig, axes = plt.subplots()
df['2000'].plot(ax=axes, label='2000')
df['2001'].plot(ax=axes, label='2001')
df['2002'].plot(ax=axes, label='2002')
axes.plot(df["2000":'2002'].groupby(df["2000":'2002'].index.month).mean())

所以这不是期望的结果.我在这里似乎已经找到了一些答案，但是您必须合并，创建多索引并进行绘图.如果其中一个数据帧具有NaN或缺少值，则可能非常麻烦.有熊猫可以做到吗?

So it's not the desired result. I've seem some answers here, but you have to concat, create a multiindex and plot. If one of the data frames has NaNs or missing values, it can be very cumbersome. Is there a pandas way to do it?

推荐答案

这是您想要的吗?您可以在转换后添加均值.

Is this what you want? You can add means after transformation.

df = pd.DataFrame({'value': [1, 2, 15, 3, 7, 1, 3, 8, 5, 3, 1, 1, 8, 5, 19]},
              index=pd.DatetimeIndex(['2000-01-01', '2000-03-01', '2000-06-01', '2000-09-01',
                                      '2000-12-01', '2001-01-01', '2001-03-01', '2001-06-01',
                                      '2001-09-01', '2001-12-01', '2002-01-01', '2002-03-01',
                                      '2002-06-01', '2002-09-01', '2002-12-01']))


pv = pd.pivot_table(df, index=df.index.month, columns=df.index.year,
                    values='value', aggfunc='sum')
pv
#     2000  2001  2002
# 1      1     1     1
# 3      2     3     1
# 6     15     8     8
# 9      3     5     5
# 12     7     3    19

pv.plot()

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