Python 来自熊猫数据帧的几列的总和
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Sum of several columns from a pandas dataframe
提问by Pauline
So say I have the following table:
所以说我有下表：
In [2]: df = pd.DataFrame({'a': [1,2,3], 'b':[2,4,6], 'c':[1,1,1]})
In [3]: df
Out[3]:
a b c
0 1 2 1
1 2 4 1
2 3 6 1
I can sum a and b that way:
我可以这样对 a 和 b 求和：
In [4]: sum(df['a']) + sum(df['b'])
Out[4]: 18
However this is not very convenient for larger dataframe, where you have to sum multiple columns together.
但是，这对于较大的数据框来说不是很方便，因为您必须将多列相加。
Is there a neater way to sum columns (similar to the below)? What if I want to sum the entire DataFrame without specifying the columns?
有没有更简洁的方法来汇总列（类似于下面的）？如果我想在不指定列的情况下对整个 DataFrame 求和怎么办？
In [4]: sum(df[['a', 'b']]) #that will not work!
Out[4]: 18
In [4]: sum(df) #that will not work!
Out[4]: 21
回答by jezrael
I think you can use double sum- first DataFrame.sumcreate Seriesof sums and second Series.sumget sum of Series:
我认为您可以使用双重sum- 先DataFrame.sum创建Series总和，然后再Series.sum获取总和Series：
print (df[['a','b']].sum())
a 6
b 12
dtype: int64
print (df[['a','b']].sum().sum())
18
You can also use:
您还可以使用：
print (df[['a','b']].sum(axis=1))
0 3
1 6
2 9
dtype: int64
print (df[['a','b']].sum(axis=1).sum())
18
Thank you pirSquaredfor another solution - convert dfto numpy arrayby valuesand then sum:
感谢pirSquared提供另一种解决方案 - 转换df为numpy arraybyvalues然后sum：
print (df[['a','b']].values.sum())
18
print (df.sum().sum())
21
回答by Fermin Pitol
Maybe you are looking something like this:
也许你正在寻找这样的东西：
df["result"] = df.apply(lambda row: row['a' : 'c'].sum(),axis=1)
