Python 如何获得数据框列值的平均值
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how to get the average of dataframe column values
提问by badideas
A B
DATE
2013-05-01 473077 71333
2013-05-02 35131 62441
2013-05-03 727 27381
2013-05-04 481 1206
2013-05-05 226 1733
2013-05-06 NaN 4064
2013-05-07 NaN 41151
2013-05-08 NaN 8144
2013-05-09 NaN 23
2013-05-10 NaN 10
say i have the dataframe above. what is the easiest way to get a series with the same index which is the average of the columns A and B? the average needs to ignore NaN values. the twist is that this solution needs to be flexible to the addition of new columns to the dataframe.
说我有上面的数据框。获得具有相同索引（即 A 列和 B 列的平均值）的系列的最简单方法是什么？平均值需要忽略 NaN 值。扭曲的是，该解决方案需要灵活地向数据帧添加新列。
the closest i have come was
我最接近的是
df.sum(axis=1) / len(df.columns)
however, this does not seem to ignore the NaN values
然而，这似乎并没有忽略 NaN 值
(note: i am still a bit new to the pandas library, so i'm guessing there's an obvious way to do this that my limited brain is simply not seeing)
（注意：我对 Pandas 库还是有点陌生，所以我猜有一种明显的方法可以做到这一点，而我有限的大脑根本看不到）
采纳答案by DSM
Simply using df.mean()will Do The Right Thing(tm) with respect to NaNs:
简单地使用df.mean()will Do The Right Thing(tm) 就 NaN 而言：
>>> df
A B
DATE
2013-05-01 473077 71333
2013-05-02 35131 62441
2013-05-03 727 27381
2013-05-04 481 1206
2013-05-05 226 1733
2013-05-06 NaN 4064
2013-05-07 NaN 41151
2013-05-08 NaN 8144
2013-05-09 NaN 23
2013-05-10 NaN 10
>>> df.mean(axis=1)
DATE
2013-05-01 272205.0
2013-05-02 48786.0
2013-05-03 14054.0
2013-05-04 843.5
2013-05-05 979.5
2013-05-06 4064.0
2013-05-07 41151.0
2013-05-08 8144.0
2013-05-09 23.0
2013-05-10 10.0
dtype: float64
You can use df[["A", "B"]].mean(axis=1)if there are other columns to ignore.
df[["A", "B"]].mean(axis=1)如果还有其他要忽略的列，您可以使用。
