适用于整个数据帧的用户定义函数涉及pandas中的另一个数据帧

Question

I am trying to do some math between two data frames df1 and df2, but I find it difficult to use pd.apply function:

df1:

   number1  number2  number3  … 
0   0         0        0      …
1   0         0.25     0      …
2   0.1       0.1      0      …
3   0         0        0.3    …
4   0         0        0      … 

df2:

   number1  number2  number3    … 
0   2         3.3        6      …
1   2.1       3.4        6      …
2   2.2       3.2      5.8      …
3   2.1       3.4      6.2      …
4   2         4.0      6.4      … 

I want to change each element in df1 by following rules:

1. change every 0 element in df1 to 1
2. each non-zero element in df1, let df.iloc[m,n] = (1-df.iloc[m,n])/df2.shift(1).iloc[m,n], where m,n is the position of the non-zero element

Now I have a code which works:

df1_new=pd.DataFrame(1,index = df1.index,columns = df1.columns)
df2_sft=df2.shift(1)
m,n=np.where(np.array(df1)!=0)
for i in m:
  for j in n:
    df1_new.iloc[i,j]=(1-df1.iloc[i,j])/df2_sft.iloc[i,j]

But as you can see, it is just ugly and incredibly slow if df1 and df2 are large. I believe there must be many other ways to do this simple math very quickly, hope you can give some help.

Also, I am always confused by apply and applymap, what is the difference and when should use one over the other?

Answer 1

You want to vectorize you code, which is to say rather than use for loops do the calculation on the whole DataFrame/array, something like the following will be much much faster:

In [11]: ((1 - df1) / df2_sft).where(df1 != 0, 1)
Out[11]:
    number1   number2  number3
0  1.000000  1.000000  1.00000
1  1.000000  0.227273  1.00000
2  0.428571  0.264706  1.00000
3  1.000000  1.000000  0.12069
4  1.000000  1.000000  1.00000

Note: this doesn't match your code as you ( incorrectly ) don't iterate over just the non-zero elements (as you iterate over all items in m for each element in n, rather than the zipped items).