从两个 csv 文件创建嵌套字典

Question

我有两个 csv 文件
file1.csv:

ID,map1,map2  
a,x1,x2  
b,y1,  
c,z1,z2

file2.csv:

ID,map1Val1,map1Val2,map2Val1
a,a1,a2,l1
b,b1,b2,
c,c1,c2,n1

我希望输出如下所示：

{'ID': {'map1':['map1Val1','map1Val2'], 'map2':'map2Val1'},'a': {'x1':['a1','a2'], 'x2':'l1'},'b': {'y1':['b1','b2']},'c': {'z1':['c1','c2'], 'z2':'n1'},}

我想不出任何方法来创建它。到目前为止，我只有一个代码可以从一个 csv 文件创建字典：

import csv
new_data_dict = {}
with open("file1.csv", 'r') as map_file:
    mapping = csv.DictReader(map_file, delimiter=",")
    for row in mapping:
        new_data_dict= {row[0]:{row[1],row[2]}}
print new_data_dict

输出：

{"ID":{map1,map2}, "a":{x1,x2}, "b":{y1}, "a":{z1,z2}}

Answer 1

您可以使用 zip 聚合来自两个 csv 文件的行：

>>> list(zip([1,2,3], [4,5,6]))   # assume 1, 2, 3 /  4, 5, 6 as row values
[(1, 4), (2, 5), (3, 6)]

import csv

new_data_dict = {}
with open('file1.csv') as f1, open('file2.csv') as f2:
    reader1, reader2 = csv.reader(f1), csv.reader(f2)
    for row1, row2 in zip(reader1, reader2):
        id_, map1, map2 = row1
        new_data_dict[id_] = {map1: row2[1:3]}
        map2 = map2.strip()
        if map2:  # put map2 only if map2 key exists
            new_data_dict[id_][map2] = row2[3]

new_data_dict 变为：

{'ID': {'map1': ['map1Val1', 'map1Val2'], 'map2': 'map2Val2'},
 'a': {'x1': ['a1', 'a2'], 'x2': 'l1'},
 'b': {'y1': ['b1', 'b2']},
 'c': {'z1': ['c1', 'c2'], 'z2  ': 'n1'}}

Answer 2

这是一个更动态的解决方案，允许您 pre-configure file1 中的哪些列映射到 file2 中的哪些列：

import csv

 = {'map1': ['map1Val1', 'map1Val2'],
              'map2': ['map2Val1']
              }

joined_data = dict()
joined_data['ID'] = column_map

with open("file1.txt") as f1, open("file2.txt") as f2:
    key_list = list(csv.DictReader(f1))
    value_list = list(csv.DictReader(f2))

for kl, vl in zip(key_list, value_list):
    inner = {}
    for key, value_list in column_map.items():
        if kl[key]:
            inner[kl[key]] = [vl[el] for el in value_list]

    joined_data[kl['ID']] = inner

csv.DictReader 的使用让我们可以将每一行的数据映射到 dict，其键（默认情况下）由文件的第一行给出。两个 DictReader objects 被转换为列表并使用 zip 进行迭代。使用 column_map 作为我们的指南，我们创建了一个新的 inner 字典，将来自 key_list 的键与来自 value_list 的值相关联。

编辑

对于 fully-dynamic 解决方案，您可以通过比较 file1 中的列 headers 与 file2[= 中的列来即时创建 column_map 25=]

import csv
from collections import defaultdict

joined_data = dict()
column_map = defaultdict(list)

with open("file1.txt") as f1, open("file2.txt") as f2:
    kh = next(f1).strip()
    vh = next(f2).strip()
    key_headers = kh.split(',')
    value_headers = vh.split(',')

    [column_map[k].append(v) for k in key_headers[1:] for v in value_headers[1:] if v.startswith(k)]
    joined_data['ID'] = dict(column_map)

    key_list = list(csv.DictReader(f1, fieldnames=key_headers))
    value_list = list(csv.DictReader(f2, fieldnames=value_headers))

for kl, vl in zip(key_list, value_list):
    inner = {}
    for key, value_list in column_map.items():
        if kl[key]:
            inner[kl[key]] = [vl[el] for el in value_list]

    joined_data[kl['ID']] = inner

从两个 csv 文件创建嵌套字典

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