使用python pandas按名称计算多列 [英] Calculate multiple columns by names using python pandas
问题描述
我有一个类似的数据框,
I have a dataframe similar like this,
cat_A cat_B cat_C cat_D dog_A dog_B dog_C dog_D
3 2 4 1 9 8 10 6
...
...
我知道如何通过使用列名(例如
I knew how to calculate between columns by using column names, like
df['ratio_A'] = df['cat_A']/df['dog_A']
cat_A cat_B cat_C cat_D dog_A dog_B dog_C dog_D ratio_A
3 2 4 1 9 8 10 6 3/9
但是当我尝试通过计算每个列来生成多个列时,还有其他更简便的方法来计算所有列并一次附加新列吗?代替
But when I tried to generate multiple columns by calculate each of those columns, are there any other easier ways to calculate all columns and append new columns by once? Instead of
df['ratio_B'] = df['cat_B']/df['dog_B']
df['ratio_C'] = df['cat_C']/df['dog_C']
df['ratio_D'] = df['cat_D']/df['dog_D']
当列的长度变得很大时,将要复制和粘贴很多冗长的代码. 我是否需要创建2个类似的列表,
When the column length become very large it will be a lot of lengthy code to copy and paste. Do I need to create 2 lists like,
l1 = [cat_A, cat_B, cat_C, cat_D], l2= [dog_A, dog_B, dog_C, dog_D]
然后使用for循环来实现吗?
Then using for loops to implement?
推荐答案
IMO在这里,一个好的做法是使用MultiIndex
es而不是平面列:
IMO a good practice here would be to work with MultiIndex
es instead of flat columns:
df.columns = pd.MultiIndex.from_tuples(map(tuple, df.columns.str.split('_')))
df
cat dog
A B C D A B C D
0 3 2 4 1 9 8 10 6
此时,计算比率是非常简单的礼貌的索引对齐方式.
At this point, computing the ratio is very simple courtesy index alignment.
df['cat'] / df['dog']
A B C D
0 0.333333 0.25 0.4 0.166667
res = df['cat'] / df['dog']
res.columns = pd.MultiIndex.from_product([['ratio'], res.columns])
pd.concat([df, res], axis=1)
cat dog ratio
A B C D A B C D A B C D
0 3 2 4 1 9 8 10 6 0.333333 0.25 0.4 0.166667
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