[英]how to create nested list of dictionaries from XML file in Python
此 XML 样本代表来自HMDB Serum Metabolites
数据集的样本代谢Serum Metabolites
。
<?xml version="1.0" encoding="UTF-8"?>
<hmdb xmlns="http://www.hmdb.ca">
<metabolite>
<version>4.0</version>
<creation_date>2005-11-16 15:48:42 UTC</creation_date>
<update_date>2019-01-11 19:13:56 UTC</update_date>
<accession>HMDB0000001</accession>
<status>quantified</status>
<secondary_accessions>
<accession>HMDB00001</accession>
<accession>HMDB0004935</accession>
<accession>HMDB0006703</accession>
<accession>HMDB0006704</accession>
<accession>HMDB04935</accession>
<accession>HMDB06703</accession>
<accession>HMDB06704</accession>
</secondary_accessions>
<name>1-Methylhistidine</name>
<cs_description>1-Methylhistidine, also known as 1-mhis, belongs to the class of organic compounds known as histidine and derivatives. Histidine and derivatives are compounds containing cysteine or a derivative thereof resulting from reaction of cysteine at the amino group or the carboxy group, or from the replacement of any hydrogen of glycine by a heteroatom. 1-Methylhistidine has been found in human muscle and skeletal muscle tissues, and has also been detected in most biofluids, including cerebrospinal fluid, saliva, blood, and feces. Within the cell, 1-methylhistidine is primarily located in the cytoplasm. 1-Methylhistidine participates in a number of enzymatic reactions. In particular, 1-Methylhistidine and Beta-alanine can be converted into anserine; which is catalyzed by the enzyme carnosine synthase 1. In addition, Beta-Alanine and 1-methylhistidine can be biosynthesized from anserine; which is mediated by the enzyme cytosolic non-specific dipeptidase. In humans, 1-methylhistidine is involved in the histidine metabolism pathway. 1-Methylhistidine is also involved in the metabolic disorder called the histidinemia pathway.</cs_description>
<description>One-methylhistidine (1-MHis) is derived mainly from the anserine of dietary flesh sources, especially poultry. The enzyme, carnosinase, splits anserine into b-alanine and 1-MHis. High levels of 1-MHis tend to inhibit the enzyme carnosinase and increase anserine levels. Conversely, genetic variants with deficient carnosinase activity in plasma show increased 1-MHis excretions when they consume a high meat diet. Reduced serum carnosinase activity is also found in patients with Parkinson's disease and multiple sclerosis and patients following a cerebrovascular accident. Vitamin E deficiency can lead to 1-methylhistidinuria from increased oxidative effects in skeletal muscle. 1-Methylhistidine is a biomarker for the consumption of meat, especially red meat.</description>
<synonyms>
<synonym>(2S)-2-amino-3-(1-Methyl-1H-imidazol-4-yl)propanoic acid</synonym>
<synonym>1-Methylhistidine</synonym>
<synonym>Pi-methylhistidine</synonym>
<synonym>(2S)-2-amino-3-(1-Methyl-1H-imidazol-4-yl)propanoate</synonym>
<synonym>1 Methylhistidine</synonym>
<synonym>1-Methyl histidine</synonym>
</synonyms>
<chemical_formula>C7H11N3O2</chemical_formula>
<smiles>CN1C=NC(C[C@H](N)C(O)=O)=C1</smiles>
<inchikey>BRMWTNUJHUMWMS-LURJTMIESA-N</inchikey>
<diseases>
<disease>
<name>Kidney disease</name>
<omim_id/>
<references>
<reference>
<reference_text>McGregor DO, Dellow WJ, Lever M, George PM, Robson RA, Chambers ST: Dimethylglycine accumulates in uremia and predicts elevated plasma homocysteine concentrations. Kidney Int. 2001 Jun;59(6):2267-72.</reference_text>
<pubmed_id>11380830</pubmed_id>
</reference>
<reference>
<reference_text>Ehrenpreis ED, Salvino M, Craig RM: Improving the serum D-xylose test for the identification of patients with small intestinal malabsorption. J Clin Gastroenterol. 2001 Jul;33(1):36-40.</reference_text>
<pubmed_id>11418788</pubmed_id>
</reference>
</references>
</disease>
</diseases>
我想要做的是运行一个嵌套循环并创建一个字典列表。
每本词典将代表一种代谢物。
字典中的每个键都将被选择节点(按标签名称)。
键的值将是字符串列表或单个字符串。
这是我认为需要的结构(也欢迎更好的想法):
[
{
"accession":"accession.value",
"name": "name.value",
"synonyms":[synonyms.value.1, synonyms.value.2, synonyms.value.3,... ],
"chemical_formula":"chemical_formula.value",
"smiles": "smiles.value",
"inchikey":"inchikey.value",
"biological_properties_pathways":[pathways.value1, pathways.value2, pathways.value3,.. ]
"diseases":[disease.name.1, disease.name.2, disease.name.3,.. ]
"pubmed_id's for disease.name.1":[pubmed_id.value.1, pubmed_id.value.2, pubmed_id.value.3,... ]
"pubmed_id's for disease.name.2":[pubmed_id.value.1, pubmed_id.value.2, pubmed_id.value.3,... ]
.
.
.
},
{"accession":"accession.value",
"name": "name.value",
"synonyms":[synonyms.value.1, synonyms.value.2, synonyms.value.3,... ],
"chemical_formula":"chemical_formula.value",
"smiles": "smiles.value",
"inchikey":"inchikey.value",
"biological_properties_pathways":[pathways.value1, pathways.value2, pathways.value3,.. ]
"diseases":[disease.name.1, disease.name.2, disease.name.3,.. ]
"pubmed_id's for disease.name.1":[pubmed_id.value.1, pubmed_id.value.2, pubmed_id.value.3,... ]
"pubmed_id's for disease.name.2":[pubmed_id.value.1, pubmed_id.value.2, pubmed_id.value.3,... ]
.
.
.
},
.
.
.
]
这是我到目前为止所做的
# Import packges
from xml.dom import minidom
import xml.etree.ElementTree as et
# load data
data1 = et.parse('D:/path/to/my/Projects/HMDB/DataSets/saliva_metabolites/saliva_metabolites.xml')
# create name space
ns = {"h": "http://www.hmdb.ca"}
# extract the first 3 metabolites only for easy work
metabolites = root.findall('./h:metabolite', ns) [0:3]
现在在 3 个代谢物上运行嵌套循环并选择特定节点(我需要的前 2 个)作为字典。
newlist = []
for child in metabolites:
innerlist = []
dicts = {}
for subchild in child:
if subchild.tag=='{http://www.hmdb.ca}accession':
dicts={"accession": subchild.text}
if subchild.tag == '{http://www.hmdb.ca}name':
dicts = {"name": subchild.text}
innerlist.append(subchild.text)
print(innerlist)
newlist.append(dicts)
我收到了这个输出:
>> print(newlist)
[{'name': '1-Methylhistidine'}, {'name': '2-Ketobutyric acid'}, {'name': '2-Hydroxybutyric acid'}]
代替
[{'accession': 'HMDB0000001','name': '1-Methylhistidine' },
{'accession': 'HMDB0000005', 'name': '2-Ketobutyric acid'},
{'accession': 'HMDB0000008', 'name': '2-Hydroxybutyric acid'}]
意味着<name>
超过了<accession>
。
还尝试输入列表作为键的值
newlist = []
for child in metabolites:
innerlist = []
dicts = {}
for subchild in child:
# if subchild.tag=='{http://www.hmdb.ca}accession':
# dicts={"accession": subchild.text}
# if subchild.tag == '{http://www.hmdb.ca}name':
# dicts = {"name": subchild.text}
if subchild.tag == '{http://www.hmdb.ca}synonyms':
for synonym in subchild:
dicts = {"synonyms": synonym.text}
print(synonym.text)
innerlist.append(subchild.text)
print(innerlist)
newlist.append(dicts)
innerlist.append(subchild.text)
newlist.append(innerlist)
输出再次被超越:
>> print(newlist)
[{'synonyms': '1-Methylhistidine dihydrochloride'},
{'synonyms': 'alpha-Ketobutyric acid, sodium salt'},
{'synonyms': '2-Hydroxybutyric acid, monosodium salt, (+-)-isomer'}]
上面 3 个键中的每一个都包含每个列表中的最后一个值,而不是一个值列表。
应该收到类似的东西(但每个同义词都有所有值):
>> print(newlist)
[{'synonyms': ['(2S)-2-amino-3-(1-Methyl-1H-imidazol-4-yl)propanoic acid',
'1-Methylhistidine',
....
'1-Methylhistidine dihydrochloride' ]},
{'synonyms': ['2-Ketobutanoic acid',
'2-Oxobutyric acid',
....
'alpha-Ketobutyric acid, sodium salt']},
{'synonyms': [ '2-Hydroxybutanoic acid',
'alpha-Hydroxybutanoic acid',
....
'2-Hydroxybutyric acid, monosodium salt, (+-)-isomer']}
]
我正在使用这些问题来编写循环:
任何想法、提示、线索或想法将不胜感激
第一个代码片段的问题可能是将新字典重新分配给变量 dict:
newlist = []
for child in metabolites:
innerlist = []
dicts = {}
for subchild in child:
if subchild.tag=='{http://www.hmdb.ca}accession':
dicts={"accession": subchild.text}
if subchild.tag == '{http://www.hmdb.ca}name':
# here the old value of dict is overriden with new value
dicts = {"name": subchild.text}
innerlist.append(subchild.text)
print(innerlist)
newlist.append(dicts)
您可能应该使用 dict[key] = value 形式的赋值:
newlist = []
for child in metabolites:
innerlist = []
dicts = {}
for subchild in child:
if subchild.tag=='{http://www.hmdb.ca}accession':
dicts["accession"] = subchild.text
if subchild.tag == '{http://www.hmdb.ca}name':
dicts["name"] = subchild.text
innerlist.append(subchild.text)
print(innerlist)
newlist.append(dicts)
第二个代码片段似乎也有类似的问题:
newlist = []
for child in metabolites:
dicts = {}
innerlist = []
for subchild in child:
if subchild.tag == '{http://www.hmdb.ca}synonyms':
for synonym in subchild:
innerlist.append(synonym.text)
dicts["synonyms"] = innerlist
newlist.append(dicts)
但是(正如已经指出的那样)您可以使用一些更方便的库,而不是手动解析 XML。
这是合并的脚本:
newlist = []
for child in metabolites:
dicts = {}
innerlist = []
for subchild in child:
if subchild.tag=='{http://www.hmdb.ca}accession':
dicts["accession"] = subchild.text
if subchild.tag == '{http://www.hmdb.ca}name':
dicts["name"] = subchild.text
if subchild.tag == '{http://www.hmdb.ca}synonyms':
for synonym in subchild:
innerlist.append(synonym.text)
dicts["synonyms"] = innerlist
newlist.append(dicts)
print(newlist)
它输出以下结果:
[{'accession': 'HMDB0000001', 'name': '1-Methylhistidine', 'synonyms': ['(2S)-2-amino-3-(1-Methyl-1H-imidazol-4-yl)propanoic acid', '1-Methylhistidine', 'Pi-methylhistidine', '(2S)-2-amino-3-(1-Methyl-1H-imidazol-4-yl)propanoate', '1 Methylhistidine', '1-Methyl histidine']}]
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.