在 2 个 csv 文件上使用 Pandas 进行左合并

Question

I have 2 csv sheets:

I am trying to find a way to merge table2 onto table1. Whenever table1 and table2 have the same Name value then replace the corresponding Price in table1 with the one found in table2 otherwise leave table1 as is.

Current code:

table1 = pd.read_csv('path/table1.csv', index_col=0)
table2 = pd.read_csv('path/table2.csv', index_col=0)
print(table1)
print(table2)

new_table = table1[["Name ", "ATT1", "ATT2"]].merge(table2[["Price", "Name "]], on="Name ", how="left")
print(new_table)

However, this leads to the following:

   Price  Name   ATT1  ATT2
0     12   APPL    69    81
1    900  GOOGL   303   392
2     32    INV    39     9
   Price     Name 
0   1231      APPL
1     39  FACEBOOK
   Name   ATT1  ATT2   Price
0   APPL    69    81  1231.0
1  GOOGL   303   392     NaN
2    INV    39     9     NaN

What i want new_table to print is:

   Name   ATT1  ATT2   Price
0   APPL    69    81  1231.0
1  GOOGL   303   392     900
2    INV    39     9     32

Answer 1

drop the "Price" column from the table1 before merging:

new_table = table1.drop("Price", axis=1).merge(table2, on="Name", how="left")

>>> new_table
    Name  ATT1  ATT2   Price
0   APPL    69    81  1231.0
1  GOOGL   303   392     NaN
2    INV    39     9     NaN

As an aside, the "Unnamed: 0" columns in both your tables are likely due to the index column being unnamed in your csv files. You can avoid this by passing index_col=0 to pd.read_csv like so:

table1 = pd.read_csv('path/table1.csv', index_col=0)
table2 = pd.read_csv('path/table2.csv', index_col=0)

Alternatively, only use the columns you need in the merge :

new_table = table1[["Name", "ATT1", "ATT2"]].merge(table2[["Price", "Name"]], on="Name", how="left")