在 Python Dataframe 中对行求和
问题描述
我刚开始学习 Python，如果这个问题已经在其他地方得到回答，请原谅我.我想创建一个名为Sum"的新列，它只是之前添加的列.
I just started learning Python so forgive me if this question has already been answered somewhere else. I want to create a new column called "Sum", which will simply be the previous columns added up.
Risk_Parity.tail()
VCIT VCLT PCY RWR IJR XLU EWL
Date
2017-01-31 21.704155 11.733716 9.588649 8.278629 5.061788 7.010918 7.951747
2017-02-28 19.839319 10.748690 9.582891 7.548530 5.066478 7.453951 7.950232
2017-03-31 19.986782 10.754507 9.593623 7.370828 5.024079 7.402774 7.654366
2017-04-30 18.897307 11.102380 10.021139 9.666693 5.901137 7.398604 11.284331
2017-05-31 63.962659 23.670240 46.018698 9.917160 15.234977 12.344524 20.405587
表格列有点偏，但我只需要 (21.70 + 11.73...+7.95)我只能创建列 Risk_Parity['sum'] = ，但后来我迷路了.
The table columns are a little off but all I need is (21.70 + 11.73...+7.95)
I can only get as far as creating the column Risk_Parity['sum'] = , but then I'm lost.
我宁愿不必这样做 Risk_Parity['sum] = Risk_Parity['VCIT'] + Risk_Parity['VCLT']...
创建总和列后，我想将每一列除以总和列，并将其制成一个新的数据框，其中不包括总和列.
After creating the sum column, I want to divide each column by the sum column and make that into a new dataframe, which wouldn't include the sum column.
如果有人能提供帮助，我将不胜感激.请尽量降低你的答案，哈哈.
If anyone could help, I'd greatly appreciate it. Please try to dumb your answers down as much as possible lol.
谢谢！
汤姆
解决方案
使用 sum 和参数 axis=1 指定行的总和
Use sum with the parameter axis=1 to specify summation over rows
Risk_Parity['Sum'] = Risk_Parity.sum(1)
创建 Risk_Parity 的新副本而不向原始列写入新列
To create a new copy of Risk_Parity without writing a new column to the original
Risk_Parity.assign(Sum= Risk_Parity.sum(1))
<小时>
还要注意，我将列命名为 Sum 而不是 sum.我这样做是为了避免与我用来创建列的名为 sum 的相同方法发生冲突.
Notice also, that I named the column Sum and not sum. I did this to avoid colliding with the very same method named sum I used to create the column.
只包含数字列...但是，sum 无论如何都知道要跳过非数字列.
To only include numeric columns... however, sum knows to skip non-numeric columns anyway.
RiskParity.assign(Sum=RiskParity.select_dtypes(['number']).sum(1))
# same as
# RiskParity.assign(Sum=RiskParity.sum(1))
VCIT VCLT PCY RWR IJR XLU EWL Sum
Date
2017-01-31 21.70 11.73 9.59 8.28 5.06 7.01 7.95 71.33
2017-02-28 19.84 10.75 9.58 7.55 5.07 7.45 7.95 68.19
2017-03-31 19.99 10.75 9.59 7.37 5.02 7.40 7.65 67.79
2017-04-30 18.90 11.10 10.02 9.67 5.90 7.40 11.28 74.27
2017-05-31 63.96 23.67 46.02 9.92 15.23 12.34 20.41 191.55
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