前言
对于很多NLP类型的标注任务，往往在标注人员标注完数据后都会把对应的数据保存到一个csv文件中，这个时候，标注的内容一般都是在一个比较复杂的多层嵌套字典中的，这篇博客就跟大家分享一下如何去获取csv文件中多层嵌套字典中的内容
任务背景介绍
csv文件的字段以及部分内容如上，我们具体看标注人员标注结果对应的字段“答案1”
下面是前面三行“答案1”的内容
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表面上看起来确实是十分复杂
代码实现
我们先来打印一下每行的“答案1”字段的内容
import pandas as pd
data_path = r"csv文件的路径"
data = pd.read_csv(data_path)
for i,row in data.iterrows():
answer = row['答案1']
print(answer)
这些都是字符串数据，我们把对应的符号转化成python的格式数据，就能对嵌套字典进行遍历了
先来看看最外层的字典的主键
for i,row in data.iterrows():
answer = row['答案1']
answer = eval(answer)
print(answer.keys())
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可见最外层的主键有两个，‘nodes’和‘relations’，那我们来赋值一下
for i,row in data.iterrows():
answer = row['答案1']
answer = eval(answer)
nodes = answer['nodes']
relations = answer['relations']
print('nodes:',nodes)
print('relation:',relations)
可以直观地看到这两个主键对应的是一个列表数据，列表中的每一个元素又是一个字典，到这一步，我们可以一次打印出列表中的每个字典
for i,row in data.iterrows():
answer = row['答案1']
answer = eval(answer)
nodes = answer['nodes']
relations = answer['relations']
print("nodes:")
for node in nodes:
print(node)
print("relations:")
for relation in relations:
print(relation)
标注人员标注的结果就变得很清晰了
欢迎大家讨论交流~