Python 比較兩個 csv

Question

如何比較類似於 Excel VLOOKUP 的兩個 csv 文件中的列和提取值？

a.csv
name,type
test1,A
test2,B
test3,A
test4,E
test5,C
test6,D


b.csv
type,value
A,1.0
B,0.5
C,0.75
D,0.25

比較“類型列”后預期的 output，使用這些值創建一個新的 csv 文件

newfile.csv
name,type,value
test1,A,1.0
test2,B,0.5
test3,A,1.0
test4,E,N/A
test5,C,0.75
test6,D,0.25

到目前為止，代碼如下

A = 'a.csv'
B = 'b.csv'

df_B = pd.read_csv(B)

with open(A, 'r') as reference:
  with open('newfile.csv', 'w') as results:    
    reader = csv.reader(reference)
    writer = csv.writer(results)

    writer.writerow(next(reader, []) + ['value'])

    for row in reader:
      checkRecords = df_B.loc[df_B['type'] == row[1]]
      #checkRecords_A = df_B[df_B.type == row[1]].iloc[0] # IndexError: index 0 is out of bounds for axis 0 with size 0

      if checkRecords.empty:
        value = 'N/A'
      else:
        value = checkRecords.value
        print(value)
        # This value have name and dtype which is not expected

      writer.writerow(row + [value])
  results.close()

Answer 1

使用pandas ，您可以merge兩個 DataFrame，其中一個包含將在另一個 DataFrame 中使用的相關信息。 這是一個例子：

import pandas as pd

csv1 = pd.DataFrame({"name":["test1","test2","test3","test4","test5"],"type":["A","B","C","A","D"]})

csv2 = pd.DataFrame({"type":["A","B","C"],"value":[1,2,3]})

pd.merge(csv1, csv2, on="type", how='outer')

output 將是：

name    type    value
test1   A   1.0
test4   A   1.0
test2   B   2.0
test3   C   3.0
test5   D   NaN