你如何在Python中创建嵌套的dict？ [英] How do you create nested dict in Python?

问题描述

我有2个csv文件。第一个是数据文件，另一个是映射文件。映射文件有4列 Device_Name GDN Device_Type Device_OS
这些也是数据文件中存在的列，需要处理。

数据文件包含Data_Name列的数据填充&am​​p;休息3列空白。映射文件包含所有列填充。我想要我的Python代码打开这两个文件，并且数据文件中的每个设备名称映射其GDN，Device_Type&来自映射文件的Device_OS值。

我知道只有2列存在时才使用dict（1需要映射），但是我不知道如何完成这需要3列需要映射。

以下是我尝试完成 Device_Type 的映射的代码：

  x = dict（[]）
 with open（Pricing Mapping_2013-04-22.csv rb）as in_file1：
 file_map = csv.reader（in_file1，delimiter ='，'）
 for file_map中的行：
 typemap = [row [0]，row [2] ] 
 x.append（typemap）
 
 with open（Pricing_Updated_Cleaned.csv，rb）as in_file2，open（Data Scraper_GDN.csv，wb）as out_file ：
 writer = csv.writer（out_file，delimiter ='，'）
 for csv.reader中的行（in_file2，delimiter ='，'）：
 try：
 row [27] = x [row [11]] 
除了KeyError：
 row [27] =
 writer.writerow（row）
  

它返回 Atribute Error 。

经过一些研究，我意识到我需要创建一个嵌套的dict，但我不知道如何做到这一点。
请帮助我解决这个问题，或者以正确的方向推动我解决这个问题。

解决方案

嵌套字典是字典中的字典。一个很简单的事情。

 >>> d = {} 
>>> d ['dict1'] = {} 
>>> d ['dict1'] ['innerkey'] ='value'
>>> d 
 {'dict1'：{'innerkey'：'value'}} 
  

你也可以使用 defaultdict 从 集合 包以方便创建嵌套字典。

 >>>导入集合
>>> d = collections.defaultdict（dict）
>>> d ['dict1'] ['innerkey'] ='value'
>>> d＃当前是一个defaultdict类型
 defaultdict（< type'dict'> {'dict1'：{'innerkey'：'value'}}）
>>> dict（d）＃但完全像一个正常的字典。 
 {'dict1'：{'innerkey'：'value'}} 
  

---

你可以填充，但是你想要的。

我会在您的代码中推荐如下：

  d = {}＃可以使用defaultdict（dict）代替
 
在file_map中的行：
＃从某些东西
＃导出行键，当使用defaultdict时，我们可以跳过下一步在row_key上创建一个字典
d [row_key] = {} 
 for idx，col in enumerate（行）：
d [row_key] [idx] = col 
  

---

根据您的评论：

可能上面的代码是混淆的问题。我的问题简而言之：我
有2个文件a.csv b.csv，a.csv有4列i j k l，b.csv也有
这些列。我是这些csvs的关键列。 j k l列
在a.csv中为空，但填充在b.csv中。我想使用'i`作为b.csv的键列映射到a.csv文件的jk
l列的值

我的建议是这样的东西，如这个（不使用defaultdict）：

  a_file =path /到/ a.csv
 b_file =path / to / b.csv
 
＃从文件a.csv 
中打开（a_file）为f：
 $跳过标题
 f.next（）
＃获取第一个列作为键
 keys =（line.split（'，'）[0] for f）
 
＃创建空字典：
d = {} 
 
＃从文件b.csv 
中打开（b_file）为f：
＃标题除了第一个键头
 headers = f.next（）。split（'，'）[1：] 
＃iterate lines 
 for f中的行：
＃ colums 
 cols = line.strip（）。split（'，'）
＃检查以确保应该映射此键。 
如果cols [0]不在键中：
 continue 
＃添加键到dict 
d [cols [0]] = dict（
＃inner keys是标题名称，值为列
（头[idx]，v）为idx，v为枚举（cols [1：]））
  

请注意，对于解析csv文件，有一个 csv模块。

I have 2 csv files. First one is data file and other one is mapping file. Mapping file has 4 columns Device_Name GDN Device_Type Device_OS These are also the columns which are present in data file and need to be worked upon.

Data file contains data with Device_Name column populated & rest 3 columns blank. Mapping file contains all columns populated. I want my Python code to open both files and for each device name in data file map its GDN, Device_Type & Device_OS value from mapping file.

I know how to use dict when only 2 columns are present (1 is needed to be mapped) but I don't know how to accomplish this when 3 columns need to be mapped.

Following is the code using which I tried to accomplish mapping of Device_Type:

x = dict([])
with open("Pricing Mapping_2013-04-22.csv", "rb") as in_file1:
    file_map = csv.reader(in_file1, delimiter=',')
    for row in file_map:
       typemap = [row[0],row[2]]
       x.append(typemap)

with open("Pricing_Updated_Cleaned.csv", "rb") as in_file2, open("Data Scraper_GDN.csv", "wb") as out_file:
    writer = csv.writer(out_file, delimiter=',')
    for row in csv.reader(in_file2, delimiter=','):
         try:
              row[27] = x[row[11]]
         except KeyError:
              row[27] = ""
         writer.writerow(row)

It returns the Atribute Error.

After some researching, I realized that I need to create a nested dict, but I don't have any idea on how to do this. Please help me in resolving this or nudge me in the right direction to resolve this.

解决方案

A nested dict is a dictionary within a dictionary. A very simple thing.

>>> d = {}
>>> d['dict1'] = {}
>>> d['dict1']['innerkey'] = 'value'
>>> d
{'dict1': {'innerkey': 'value'}}

You can also use a defaultdict from the collections package to facilitate creating nested dictionaries.

>>> import collections
>>> d = collections.defaultdict(dict)
>>> d['dict1']['innerkey'] = 'value'
>>> d  # currently a defaultdict type
defaultdict(<type 'dict'>, {'dict1': {'innerkey': 'value'}})
>>> dict(d)  # but is exactly like a normal dictionary.
{'dict1': {'innerkey': 'value'}}

---

You can populate that however you want.

I would recommend in your code something like the following:

d = {}  # can use defaultdict(dict) instead

for row in file_map:
    # derive row key from something 
    # when using defaultdict, we can skip the next step creating a dictionary on row_key
    d[row_key] = {} 
    for idx, col in enumerate(row):
        d[row_key][idx] = col

---

According to your comment:

may be above code is confusing the question. My problem in nutshell: I have 2 files a.csv b.csv, a.csv has 4 columns i j k l, b.csv also has these columns. i is kind of key columns for these csvs'. j k l column is empty in a.csv but populated in b.csv. I want to map values of j k l columns using 'i` as key column from b.csv to a.csv file

My suggestion would be something like this (without using defaultdict):

a_file = "path/to/a.csv"
b_file = "path/to/b.csv"

# read from file a.csv
with open(a_file) as f:
    # skip headers
    f.next()
    # get first colum as keys
    keys = (line.split(',')[0] for line in f) 

# create empty dictionary:
d = {}

# read from file b.csv
with open(b_file) as f:
    # gather headers except first key header
    headers = f.next().split(',')[1:]
    # iterate lines
    for line in f:
        # gather the colums
        cols = line.strip().split(',')
        # check to make sure this key should be mapped.
        if cols[0] not in keys:
            continue
        # add key to dict
        d[cols[0]] = dict(
            # inner keys are the header names, values are columns
            (headers[idx], v) for idx, v in enumerate(cols[1:]))

Please note though, that for parsing csv files there is a csv module.

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