import pandas as pd
import pandas_read_xml as pdx
from lxml import etree

# The list "all_leaves" includes every node and leaf in the tree

from io import StringIO, BytesIO
#st above is encoded as binary

events = ("start", "end")
# "start" are the xml tags start
# "end" are the xml tags with "</"
parent='root' #root of the xml tree
context = etree.iterparse(BytesIO(st), events=events)
for n, actel in enumerate(context):
  if actel[0]=='end': #this marks the end of a node
    parent = all_leaves[n-1][1] #use previous parent
    #append the action, parent and tag
    if actel[0]=='start': #the current become parent since this
                          #algorithm is depth first
      parent = actel[1].tag

# "leaves" includes only the first row of each table
for entry in all_leaves:
  if (entry not in leaves) and (entry[0]=='start'):

# These are the names of the tables
for entry in leaves[2:]:#ignore the first two:

# The dictionary "all_dic" has keys labeled as the node
# above each set of leaves (i.e. DataFrames) with value equal to the corresponding
# DataFrame or leav
for l in nf_nodes:
    all_dic[l]=pdx.read_xml(url, ['DRFAssessments',l])

I have the following code below that reads and parses xml files. The following code takes about 10:30 seconds to run. I wanted to see if there were any suggestions or any edits that could be made to the code to increase the speed. The code is used to parse the xml file before it is uploaded to a MYSQL database. It also parses 20 tables if that is any useful

  • \$\begingroup\$ Welcome to Code Review! It would be helpful to know the context of the question, since a lot of posts are about parsing XML and increasing its speed. What specifically are you parsing and could you edit the title to match that? Furthermore, it would likely also help to have (a shortened) sample input file so that readers can actually try the code. Hope you get some good answers! \$\endgroup\$
    – ferada
    Mar 2 '21 at 10:45

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