如何将 dataframe 中的每一行与另一个 dataframe 中的每一行进行比较，并查看值之间的差异？

Question

I have two dataframes:

df1

     Code     Number
0   ABC123      1
1   DEF456      2
2   GHI789      3
3   DEA456      4

df2

     Code 
0   ABD123
1   DEA458
2   GHI789

df1 acts like a dictionary, from which I can get the respective number for each item by checking their code. There are, however, unregistered codes, and in case I find an unregistered code, I'm supposed to look for the codes that look the most like them. So, the outcome should to be:

ABD123 = 1 (because it has 1 different character from ABC123)

DEA456 = 4 (because it has 1 different character from DEA456, and 2 from DEF456, so it chooses the closest one)

GHI789 = 3 (because it has an equivalent at df1)

I know how to check for the differences of each code individually and save the "length" of characters that differ, but I don't know how to apply this code as I don't know how to compare each row from df2 against all rows from df1. Is there a way?

Answer 1

don't know how to compare each row from df2 against all rows from df1.

Nested loops will work. If you had a function named compare it would look like this...

for index2, row2 in df2.iterrows():
    for index1, row1 in df1.iterrows():
        difference = compare(row2,row1)
        #do something with the difference.

---

Nested loops are usually not ideal when working with Pandas or Numpy but they do work. There may be better solutions.

---

DataFrame.iterrows()

Answer 2

This should work too:

df['Code_e'] = df['Code'].str.extract(r'(\d+)').astype(int)
df2['Code_e'] = df2['Code'].str.extract(r'(\d+)').astype(int)
final_df = pd.merge_asof(df2,df.sort_values(by='Code_e'),on='Code_e',suffixes=('','_right')).drop(['Code_e','Code_right'],axis=1)