問題描述
我剛開始學習 Python,如果這個問題已經(jīng)在其他地方得到回答,請原諒我.我想創(chuàng)建一個名為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
表格列有點偏,但我只需要 (21.70 + 11.73...+7.95)我只能創(chuàng)建列 Risk_Parity['sum'] =
,但后來我迷路了.
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.
我寧愿不必這樣做 Risk_Parity['sum] = Risk_Parity['VCIT'] + Risk_Parity['VCLT']...
創(chuàng)建總和列后,我想將每一列除以總和列,并將其制成一個新的數(shù)據(jù)框,其中不包括總和列.
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.
如果有人能提供幫助,我將不勝感激.請盡量降低你的答案,哈哈.
If anyone could help, I'd greatly appreciate it. Please try to dumb your answers down as much as possible lol.
謝謝!
湯姆
推薦答案
使用 sum
和參數(shù) axis=1
指定行的總和
Use sum
with the parameter axis=1
to specify summation over rows
Risk_Parity['Sum'] = Risk_Parity.sum(1)
創(chuàng)建 Risk_Parity
的新副本而不向原始列寫入新列
To create a new copy of Risk_Parity
without writing a new column to the original
Risk_Parity.assign(Sum= Risk_Parity.sum(1))
<小時>
還要注意,我將列命名為 Sum
而不是 sum
.我這樣做是為了避免與我用來創(chuàng)建列的名為 sum
的相同方法發(fā)生沖突.
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.
只包含數(shù)字列...但是,sum
無論如何都知道要跳過非數(shù)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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