Python从CSV文件创建嵌套字典 [英] Python Creating A Nested Dictionary From CSV File

问题描述

所以我有一个csv文件input.csv，它有以下数据。

  UID，BID，R 
 U1，B1,4 
 U1，B2,3 
 U2，B1,2 
  

我想让上面看起来像下面的字典;按照UID分组作为他们的键，BID和R作为嵌套字典值。

  {U1：{B1 ：4，B2：3}，U2：{B1：2}} 
  

我有以下代码：

  new_data_dict = defaultdict（str）
 with open（input.csv， 'r'）as data_file：
 data = csv.DictReader（data_file，delimiter =，）
 headers = next（data）
数据行：
 new_data_dict [ row [UID]] + = {row [BID]：int（row [R]）} 
  

b $ b

上述引发了一个明显的错误：

  TypeError：不能连接'str' b $ b  

有办法吗？ 使用常规的 dict（）您可以使用 get（）初始化一个新的子字典，然后填充它。

  import csv 
 
 new_data_dict = {} 
 with open（data.csv，'r'）as data_file：
 data = csv.DictReader（data_file ，delimiter =，）
 for row in data：
 item = new_data_dict.get（row [UID]，dict（））
 item [row [BID]] = int（row [R]）
 
 new_data_dict [row [UID]] = item 
 
 print new_data_dict 
  

此外，您对 next（data）的调用是多余的，因为标头被自动检测和删除从结果。

So I have a a csv file "input.csv" which has the following data.

UID,BID,R
U1,B1,4
U1,B2,3
U2,B1,2

I want the above to look like the following dictionary; group by the UID as they key and BID and R as a nested dictionary value.

{"U1":{"B1":4, "B2": 3}, "U2":{"B1":2}}

I have the below code:

new_data_dict = defaultdict(str)
with open("input.csv", 'r') as data_file:
    data = csv.DictReader(data_file, delimiter=",")
    headers = next(data)
    for row in data:
        new_data_dict[row["UID"]] += {row["BID"]:int(row["R"])}

The above throws an obvious error of

TypeError: cannot concatenate 'str' and 'dict' objects

Is there a way to do this?? Any help will be appreciated.

解决方案

Using the regular dict() you can use get() to initialize a new sub-dict and fill it afterwards.

import csv

new_data_dict = {}
with open("data.csv", 'r') as data_file:
    data = csv.DictReader(data_file, delimiter=",")
    for row in data:
        item = new_data_dict.get(row["UID"], dict())
        item[row["BID"]] = int(row["R"])

        new_data_dict[row["UID"]] = item

print new_data_dict

Also, your call to next(data) was superfluous as the headers were automatically detected and stripped from the result.

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