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如何比较类似于 Excel VLOOKUP 的两个 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

比较“类型列”后的预期输出，使用这些值创建一个新的 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

到目前为止，代码如下

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()
4

1 回答 1

3

使用pandas，您可以使用merge两个 DataFrame，其中一个包含将在另一个 DataFrame 中使用的相关信息。这是一个例子：

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')

输出将是：

name    type    value
test1   A   1.0
test4   A   1.0
test2   B   2.0
test3   C   3.0
test5   D   NaN
于 2020-05-21T01:35:39.760 回答