如何获得数据框列值的平均值 [英] how to get the average of dataframe column values

问题描述

                    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

说我有上面的数据框.获得具有相同索引(即列A和B的平均值)的序列的最简单方法是什么?平均需要忽略NaN值.不同之处在于，该解决方案需要灵活地向数据框添加新列.

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.

我最接近的是

df.sum(axis=1) / len(df.columns)

但是，这似乎并没有忽略NaN值

however, this does not seem to ignore the NaN values

(注意:我对熊猫图书馆还是有点陌生​​，所以我猜想有一种很明显的方法可以使我有限的大脑根本看不到)

(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)

推荐答案

仅使用df.mean()就可以正确处理NaN:

Simply using df.mean() will Do The Right Thing(tm) with respect to NaNs:

>>> 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

如果还有其他要忽略的列，则可以使用df[["A", "B"]].mean(axis=1).

You can use df[["A", "B"]].mean(axis=1) if there are other columns to ignore.

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