Python 计算熊猫的行平均值
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/33750326/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Compute row average in pandas
提问by user308827
Y1961 Y1962 Y1963 Y1964 Y1965 Region
0 82.567307 83.104757 83.183700 83.030338 82.831958 US
1 2.699372 2.610110 2.587919 2.696451 2.846247 US
2 14.131355 13.690028 13.599516 13.649176 13.649046 US
3 0.048589 0.046982 0.046583 0.046225 0.051750 US
4 0.553377 0.548123 0.582282 0.577811 0.620999 US
In the above dataframe, I would like to get average of each row. currently, I am doing this:
在上面的数据框中，我想获得每一行的平均值。目前，我正在这样做：
df.mean(axis=0)
However, this does away with the Region column as well. how can I compute mean and also retain Region column
但是，这也取消了 Region 列。我如何计算均值并保留 Region 列
采纳答案by Alexander
You can specify a new column. You also need to compute the mean along the rows, so use axis=1.
您可以指定一个新列。您还需要计算沿行的平均值，因此请使用axis=1.
df['mean'] = df.mean(axis=1)
>>> df
Y1961 Y1962 Y1963 Y1964 Y1965 Region mean
0 82.567307 83.104757 83.183700 83.030338 82.831958 US 82.943612
1 2.699372 2.610110 2.587919 2.696451 2.846247 US 2.688020
2 14.131355 13.690028 13.599516 13.649176 13.649046 US 13.743824
3 0.048589 0.046982 0.046583 0.046225 0.051750 US 0.048026
4 0.553377 0.548123 0.582282 0.577811 0.620999 US 0.576518
回答by Rahul
If you are looking to average column wise. Try this,
如果您希望对列进行平均。尝试这个，
df.drop('Region', axis=1).apply(lambda x: x.mean())
# it drops the Region column
df.drop('Region', axis=1,inplace=True)
回答by pabloverd
I think this is what you are looking for:
我认为这就是你要找的：
df.drop('Region', axis=1).apply(lambda x: x.mean(), axis=1)
