I am using the following sets of generators to parse XML in to CSV:
import xml.etree.cElementTree as ElementTree
from xml.etree.ElementTree import XMLParser
import csv
def flatten_list(aList, prefix=''):
for i, element in enumerate(aList, 1):
eprefix = "{}{}".format(prefix, i)
if element:
# treat like dict
if len(element) == 1 or element[0].tag != element[1].tag:
yield from flatten_dict(element, eprefix)
# treat like list
elif element[0].tag == element[1].tag:
yield from flatten_list(element, eprefix)
elif element.text:
text = element.text.strip()
if text:
yield eprefix[:].rstrip('.'), element.text
def flatten_dict(parent_element, prefix=''):
prefix = prefix + parent_element.tag
if parent_element.items():
for k, v in parent_element.items():
yield prefix + k, v
for element in parent_element:
eprefix = element.tag
if element:
# treat like dict - we assume that if the first two tags
# in a series are different, then they are all different.
if len(element) == 1 or element[0].tag != element[1].tag:
yield from flatten_dict(element, prefix=prefix)
# treat like list - we assume that if the first two tags
# in a series are the same, then the rest are the same.
else:
# here, we put the list in dictionary; the key is the
# tag name the list elements all share in common, and
# the value is the list itself
yield from flatten_list(element, prefix=eprefix)
# if the tag has attributes, add those to the dict
if element.items():
for k, v in element.items():
yield eprefix+k
# this assumes that if you've got an attribute in a tag,
# you won't be having any text. This may or may not be a
# good idea -- time will tell. It works for the way we are
# currently doing XML configuration files...
elif element.items():
for k, v in element.items():
yield eprefix+k
# finally, if there are no child tags and no attributes, extract
# the text
else:
yield eprefix, element.text
def makerows(pairs):
headers = []
columns = {}
for k, v in pairs:
if k in columns:
columns[k].extend((v,))
else:
headers.append(k)
columns[k] = [k, v]
m = max(len(c) for c in columns.values())
for c in columns.values():
c.extend(' ' for i in range(len(c), m))
L = [columns[k] for k in headers]
rows = list(zip(*L))
return rows
def main():
with open('2-Response_duplicate.xml', 'r', encoding='utf-8') as f:
xml_string = f.read()
xml_string= xml_string.replace('�', '') #optional to remove ampersands.
root = ElementTree.XML(xml_string)
# for key, value in flatten_dict(root):
# key = key.rstrip('.').rsplit('.', 1)[-1]
# print(key,value)
writer = csv.writer(open("try5.csv", 'wt'))
writer.writerows(makerows(flatten_dict(root)))
if __name__ == "__main__":
main()
One column of the CSV, when opened in Excel, looks like this:
ObjectGuid
2adeb916-cc43-4d73-8c90-579dd4aa050a
2e77c588-56e5-4f3f-b990-548b89c09acb
c8743bdd-04a6-4635-aedd-684a153f02f0
1cdc3d86-f9f4-4a22-81e1-2ecc20f5e558
2c19d69b-26d3-4df0-8df4-8e293201656f
6d235c85-6a3e-4cb3-9a28-9c37355c02db
c34e05de-0b0c-44ee-8572-c8efaea4a5ee
9b0fe8f5-8ec4-4f13-b797-961036f92f19
1d43d35f-61ef-4df2-bbd9-30bf014f7e10
9cb132e8-bc69-4e4f-8f29-c1f503b50018
24fd77da-030c-4cb7-94f7-040b165191ce
0a949d4f-4f4c-467e-b0a0-40c16fc95a79
801d3091-c28e-44d2-b9bd-3bad99b32547
7f355633-426d-464b-bab9-6a294e95c5d5
This is due to the fact that there are 14 tags with name ObjectGuid. For example, one of these tags looks like this:
<ObjectGuid>2adeb916-cc43-4d73-8c90-579dd4aa050a</ObjectGuid>
My question: is there an efficient method to enumerate the headers (the keys) such that each key is enumerated like so with it's corresponding value (text in the XML data structure):
It would be displayed in Excel as follows:
ObjectGuid_1 ObjectGuid_2 ObejectGuid3 etc.....
Please let me know if there is any other information that you need from me (such as sample XML). Thank you for your help.
答案 0 :(得分：1)
为了身份的目的，将元素，属性或注释描述符添加到数据集本身是错误的...只有拥有该数据并且100％保证这样做才能正常化数据。不 对其他消费者有任何负面影响（依赖于属性顺序来操纵DOM）。但是，如果通过对此属性新属性进行0（n）检查来获取散列表查找的效率，那么使用dict或嵌套dicts（我不太理解）的重点是什么？这个散列的重点是随机查找..
如果它只是结构化的（键，值）对，这在这里是有意义的。为什么不只是使用一些其他连续的数据结构，但把它当作字典对待..说一个命名的元组......
第二个解决方案是，如果你想添加额外的状态就是把你的生成器抛给一个类。
班级订单：
def__init__(self, lines, order):
self.lines = lines
self.order - python(max)
def __iter__(self):
for l, line in enumerate(self.lines, 1);
self.order.append( l, line))
yield line
when open (some file.csv) as f:
lines = oder( f);
将数据与无害转换相混淆？例如，如果要创建转换字典（见下文）
那很好，直到其中一个值为空......
types = [（'x'，float'）， （'y'，漂浮'）
with open(‘some.csv’) as f:
for row in cvs.DictReader(f):
row.update((key, conversion (row [ key]))
for key, conversion in field_types)
[x: ,y: 2. 2] — > that is until there is an empty data point.. Kaboom.
所以我的建议不是改变或添加数据，而是更改处理此类数据的算法。如果问题是顺序，为什么不简单地将元组称为类似于字典的命名元组，但需要注意的是数据的一致性......
*我不明白嵌套字典...那对于y标头值是吗？
值和订单键 - ＆gt;键 - ＆gt; （ 核心价值 ） ？或者你可以跳过
first row :p..
So just skip the first row..
for line in {header: list, header: list }
line.next() # problem solved.. or print(line , end = ‘’)
*** Notables
-To iterator over multiple sequences in parallel
h = [a,b,c]
x = [1,2,3]
for i in zip(a,b):
print(i)
(a, 1)
(b, 2)
-Chaining
a = [1,2 , 3]
b= [a, b , c ]enter code here
for x in chain(a, b):
//remove white space