用 Pandas 数据框中的行填充嵌套字典

Question

我正在尝试通过迭代嵌套字典并使用 Pandas 数据框的一行中的条目填充每个子字典的值，从而在 Python 中使用来自 Pandas 数据框的条目来填充字典字典。

虽然子词典的数量与数据框中的行数一样多，但所有词典都使用数据框最后一行的数据填充，而不是为每个词典使用每一行。

这是一个玩具可重现的例子。

import pandas as pd

# initialize an empty df
data = pd.DataFrame()

# populate data frame with entries
data['name'] = ['Joe Smith', 'Mary James', 'Charles Williams']
data['school'] =  ["Jollywood Secondary", "Northgate Sixth From", "Brompton High"]
data['subjects'] = [['Maths', 'Art', 'Biology'], ['English', 'French', 'History'], ['Chemistry', 'Biology', 'English']]

# use dictionary comprehensions to set up main dictionary and sub-dictionary templates

# sub-dictionary
keys = ['name', 'school', 'subjects']
record = {key: None for key in keys}

# main dictionary
keys2 = ['cand1', 'cand2', 'cand3']
candidates = {key: record for key in keys2}

# as a result i get something like this
# {'cand1': {'name': None, 'school': None, 'subjects': None},
# 'cand2': {'name': None, 'school': None, 'subjects': None},
# 'cand3': {'name': None, 'school': None, 'subjects': None}}

# iterate through main dictionary and populate each sub-dict with row of df
for i, d in enumerate(candidates.items()):

    d[1]['name'] = data['name'].iloc[i]
    d[1]['school'] = data['school'].iloc[i]
    d[1]['subjcts'] = data['subjects'].iloc[i]

# what i end up with is the last row entry in each sub-dictionary
#{'cand1': {'name': 'Charles Williams',
#  'school': 'Brompton High',
#  'subjects': None,
#  'subjcts': ['Chemistry', 'Biology', 'English']},
# 'cand2': {'name': 'Charles Williams',
#  'school': 'Brompton High',
#  'subjects': None,
#  'subjcts': ['Chemistry', 'Biology', 'English']},
# 'cand3': {'name': 'Charles Williams',
#  'school': 'Brompton High',
#  'subjects': None,
#  'subjcts': ['Chemistry', 'Biology', 'English']}}

我需要如何修改我的代码以使每个字典填充我的数据框中的不同行？

Answer 1

我没有通过您的代码来查找错误，因为解决方案是使用方法to_dict 的单行代码。

这是一个包含示例数据的最小工作示例。

import pandas as pd

# initialize an empty df
data = pd.DataFrame()

# populate data frame with entries
data['name'] = ['Joe Smith', 'Mary James', 'Charles Williams']
data['school'] =  ["Jollywood Secondary", "Northgate Sixth From", "Brompton High"]
data['subjects'] = [['Maths', 'Art', 'Biology'], ['English', 'French', 'History'], ['Chemistry', 'Biology', 'English']]

# redefine index to match your keys
data.index = ['cand{}'.format(i) for i in range(1,len(data)+1)]

# convert to dict
data_dict = data.to_dict(orient='index')

print(data_dict)

这看起来像这样

{'cand1': {
     'name': 'Joe Smith', 
     'school': 'Jollywood Secondary', 
     'subjects': ['Maths', 'Art', 'Biology']},
 'cand2': {
     'name': 'Mary James', 
     'school': 'Northgate Sixth From', 
     'subjects': ['English', 'French', 'History']},
 'cand3': {
     'name': 'Charles Williams', 
     'school': 'Brompton High', 
     'subjects': ['Chemistry', 'Biology', 'English']}}

Answer 2

考虑避免绕道而行，因为 Pandas 维护了各种方法来渲染嵌套结构，例如 to_dict 和 to_json。具体来说，考虑添加一个新列 cand 并将其设置为to_dict 输出的索引：

data['cand'] = 'cand' + pd.Series((data.index.astype('int') + 1).astype('str'))

mydict = data.set_index('cand').to_dict(orient='index')

print(mydict)

{'cand1': {'name': 'Joe Smith', 'school': 'Jollywood Secondary', 
           'subjects': ['Maths', 'Art', 'Biology']}, 
 'cand2': {'name': 'Mary James', 'school': 'Northgate Sixth From', 
           'subjects': ['English', 'French', 'History']}, 
 'cand3': {'name': 'Charles Williams', 'school': 'Brompton High', 
           'subjects': ['Chemistry', 'Biology', 'English']}}